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How to Write a Discussion Section | Tips & Examples

Published on August 21, 2022 by Shona McCombes . Revised on July 18, 2023.

The discussion section is where you delve into the meaning, importance, and relevance of your results .

It should focus on explaining and evaluating what you found, showing how it relates to your literature review and paper or dissertation topic , and making an argument in support of your overall conclusion. It should not be a second results section.

There are different ways to write this section, but you can focus your writing around these key elements:

• Summary : A brief recap of your key results
• Interpretations: What do your results mean?
• Implications: Why do your results matter?
• Limitations: What can’t your results tell us?
• Recommendations: Avenues for further studies or analyses

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Table of contents

What not to include in your discussion section, step 1: summarize your key findings, step 2: give your interpretations, step 3: discuss the implications, step 4: acknowledge the limitations, step 5: share your recommendations, discussion section example, other interesting articles, frequently asked questions about discussion sections.

There are a few common mistakes to avoid when writing the discussion section of your paper.

• Don’t introduce new results: You should only discuss the data that you have already reported in your results section .
• Don’t make inflated claims: Avoid overinterpretation and speculation that isn’t directly supported by your data.
• Don’t undermine your research: The discussion of limitations should aim to strengthen your credibility, not emphasize weaknesses or failures.

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Start this section by reiterating your research problem and concisely summarizing your major findings. To speed up the process you can use a summarizer to quickly get an overview of all important findings. Don’t just repeat all the data you have already reported—aim for a clear statement of the overall result that directly answers your main research question . This should be no more than one paragraph.

Many students struggle with the differences between a discussion section and a results section . The crux of the matter is that your results sections should present your results, and your discussion section should subjectively evaluate them. Try not to blend elements of these two sections, in order to keep your paper sharp.

• The results indicate that…
• The study demonstrates a correlation between…
• This analysis supports the theory that…
• The data suggest that…

The meaning of your results may seem obvious to you, but it’s important to spell out their significance for your reader, showing exactly how they answer your research question.

The form of your interpretations will depend on the type of research, but some typical approaches to interpreting the data include:

• Identifying correlations , patterns, and relationships among the data
• Discussing whether the results met your expectations or supported your hypotheses
• Contextualizing your findings within previous research and theory
• Explaining unexpected results and evaluating their significance
• Considering possible alternative explanations and making an argument for your position

You can organize your discussion around key themes, hypotheses, or research questions, following the same structure as your results section. Alternatively, you can also begin by highlighting the most significant or unexpected results.

• In line with the hypothesis…
• Contrary to the hypothesized association…
• The results contradict the claims of Smith (2022) that…
• The results might suggest that x . However, based on the findings of similar studies, a more plausible explanation is y .

As well as giving your own interpretations, make sure to relate your results back to the scholarly work that you surveyed in the literature review . The discussion should show how your findings fit with existing knowledge, what new insights they contribute, and what consequences they have for theory or practice.

Ask yourself these questions:

• Do your results support or challenge existing theories? If they support existing theories, what new information do they contribute? If they challenge existing theories, why do you think that is?
• Are there any practical implications?

Your overall aim is to show the reader exactly what your research has contributed, and why they should care.

• These results build on existing evidence of…
• The results do not fit with the theory that…
• The experiment provides a new insight into the relationship between…
• These results should be taken into account when considering how to…
• The data contribute a clearer understanding of…
• While previous research has focused on  x , these results demonstrate that y .

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Even the best research has its limitations. Acknowledging these is important to demonstrate your credibility. Limitations aren’t about listing your errors, but about providing an accurate picture of what can and cannot be concluded from your study.

Limitations might be due to your overall research design, specific methodological choices , or unanticipated obstacles that emerged during your research process.

Here are a few common possibilities:

• If your sample size was small or limited to a specific group of people, explain how generalizability is limited.
• If you encountered problems when gathering or analyzing data, explain how these influenced the results.
• If there are potential confounding variables that you were unable to control, acknowledge the effect these may have had.

After noting the limitations, you can reiterate why the results are nonetheless valid for the purpose of answering your research question.

• The generalizability of the results is limited by…
• The reliability of these data is impacted by…
• Due to the lack of data on x , the results cannot confirm…
• The methodological choices were constrained by…
• It is beyond the scope of this study to…

Based on the discussion of your results, you can make recommendations for practical implementation or further research. Sometimes, the recommendations are saved for the conclusion .

Suggestions for further research can lead directly from the limitations. Don’t just state that more studies should be done—give concrete ideas for how future work can build on areas that your own research was unable to address.

• Further research is needed to establish…
• Future studies should take into account…
• Avenues for future research include…

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In the discussion , you explore the meaning and relevance of your research results , explaining how they fit with existing research and theory. Discuss:

• Your  interpretations : what do the results tell us?
• The  implications : why do the results matter?
• The  limitation s : what can’t the results tell us?

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.

In a thesis or dissertation, the discussion is an in-depth exploration of the results, going into detail about the meaning of your findings and citing relevant sources to put them in context.

The conclusion is more shorter and more general: it concisely answers your main research question and makes recommendations based on your overall findings.

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How to Write the Discussion Section of a Research Paper

The discussion section of a research paper analyzes and interprets the findings, provides context, compares them with previous studies, identifies limitations, and suggests future research directions.

Updated on September 15, 2023

Structure your discussion section right, and you’ll be cited more often while doing a greater service to the scientific community. So, what actually goes into the discussion section? And how do you write it?

The discussion section of your research paper is where you let the reader know how your study is positioned in the literature, what to take away from your paper, and how your work helps them. It can also include your conclusions and suggestions for future studies.

First, we’ll define all the parts of your discussion paper, and then look into how to write a strong, effective discussion section for your paper or manuscript.

Discussion section: what is it, what it does

The discussion section comes later in your paper, following the introduction, methods, and results. The discussion sets up your study’s conclusions. Its main goals are to present, interpret, and provide a context for your results.

What is it?

The discussion section provides an analysis and interpretation of the findings, compares them with previous studies, identifies limitations, and suggests future directions for research.

This section combines information from the preceding parts of your paper into a coherent story. By this point, the reader already knows why you did your study (introduction), how you did it (methods), and what happened (results). In the discussion, you’ll help the reader connect the ideas from these sections.

Why is it necessary?

The discussion provides context and interpretations for the results. It also answers the questions posed in the introduction. While the results section describes your findings, the discussion explains what they say. This is also where you can describe the impact or implications of your research.

Adds context for your results

Most research studies aim to answer a question, replicate a finding, or address limitations in the literature. These goals are first described in the introduction. However, in the discussion section, the author can refer back to them to explain how the study's objective was achieved. 

Shows what your results actually mean and real-world implications

The discussion can also describe the effect of your findings on research or practice. How are your results significant for readers, other researchers, or policymakers?

What to include in your discussion (in the correct order)

A complete and effective discussion section should at least touch on the points described below.

Summary of key findings

The discussion should begin with a brief factual summary of the results. Concisely overview the main results you obtained.

Begin with key findings with supporting evidence

Your results section described a list of findings, but what message do they send when you look at them all together?

Your findings were detailed in the results section, so there’s no need to repeat them here, but do provide at least a few highlights. This will help refresh the reader’s memory and help them focus on the big picture.

Read the first paragraph of the discussion section in this article (PDF) for an example of how to start this part of your paper. Notice how the authors break down their results and follow each description sentence with an explanation of why each finding is relevant. 

State clearly and concisely

Following a clear and direct writing style is especially important in the discussion section. After all, this is where you will make some of the most impactful points in your paper. While the results section often contains technical vocabulary, such as statistical terms, the discussion section lets you describe your findings more clearly. 

Interpretation of results

Once you’ve given your reader an overview of your results, you need to interpret those results. In other words, what do your results mean? Discuss the findings’ implications and significance in relation to your research question or hypothesis.

Analyze and interpret your findings

Look into your findings and explore what’s behind them or what may have caused them. If your introduction cited theories or studies that could explain your findings, use these sources as a basis to discuss your results.

For example, look at the second paragraph in the discussion section of this article on waggling honey bees. Here, the authors explore their results based on information from the literature.

Unexpected or contradictory results

Sometimes, your findings are not what you expect. Here’s where you describe this and try to find a reason for it. Could it be because of the method you used? Does it have something to do with the variables analyzed? Comparing your methods with those of other similar studies can help with this task.

Context and comparison with previous work

Refer to related studies to place your research in a larger context and the literature. Compare and contrast your findings with existing literature, highlighting similarities, differences, and/or contradictions.

How your work compares or contrasts with previous work

Studies with similar findings to yours can be cited to show the strength of your findings. Information from these studies can also be used to help explain your results. Differences between your findings and others in the literature can also be discussed here. 

How to divide this section into subsections

If you have more than one objective in your study or many key findings, you can dedicate a separate section to each of these. Here’s an example of this approach. You can see that the discussion section is divided into topics and even has a separate heading for each of them. 

Limitations

Many journals require you to include the limitations of your study in the discussion. Even if they don’t, there are good reasons to mention these in your paper.

Why limitations don’t have a negative connotation

A study’s limitations are points to be improved upon in future research. While some of these may be flaws in your method, many may be due to factors you couldn’t predict.

Examples include time constraints or small sample sizes. Pointing this out will help future researchers avoid or address these issues. This part of the discussion can also include any attempts you have made to reduce the impact of these limitations, as in this study .

How limitations add to a researcher's credibility

Pointing out the limitations of your study demonstrates transparency. It also shows that you know your methods well and can conduct a critical assessment of them.  

Implications and significance

The final paragraph of the discussion section should contain the take-home messages for your study. It can also cite the “strong points” of your study, to contrast with the limitations section.

Restate your hypothesis

Remind the reader what your hypothesis was before you conducted the study. 

How was it proven or disproven?

Identify your main findings and describe how they relate to your hypothesis.

How your results contribute to the literature

Were you able to answer your research question? Or address a gap in the literature?

Future implications of your research

Describe the impact that your results may have on the topic of study. Your results may show, for instance, that there are still limitations in the literature for future studies to address. There may be a need for studies that extend your findings in a specific way. You also may need additional research to corroborate your findings. 

Sample discussion section

This fictitious example covers all the aspects discussed above. Your actual discussion section will probably be much longer, but you can read this to get an idea of everything your discussion should cover.

Our results showed that the presence of cats in a household is associated with higher levels of perceived happiness by its human occupants. These findings support our hypothesis and demonstrate the association between pet ownership and well-being. 

The present findings align with those of Bao and Schreer (2016) and Hardie et al. (2023), who observed greater life satisfaction in pet owners relative to non-owners. Although the present study did not directly evaluate life satisfaction, this factor may explain the association between happiness and cat ownership observed in our sample.

Our findings must be interpreted in light of some limitations, such as the focus on cat ownership only rather than pets as a whole. This may limit the generalizability of our results.

Nevertheless, this study had several strengths. These include its strict exclusion criteria and use of a standardized assessment instrument to investigate the relationships between pets and owners. These attributes bolster the accuracy of our results and reduce the influence of confounding factors, increasing the strength of our conclusions. Future studies may examine the factors that mediate the association between pet ownership and happiness to better comprehend this phenomenon.

This brief discussion begins with a quick summary of the results and hypothesis. The next paragraph cites previous research and compares its findings to those of this study. Information from previous studies is also used to help interpret the findings. After discussing the results of the study, some limitations are pointed out. The paper also explains why these limitations may influence the interpretation of results. Then, final conclusions are drawn based on the study, and directions for future research are suggested.

How to make your discussion flow naturally

If you find writing in scientific English challenging, the discussion and conclusions are often the hardest parts of the paper to write. That’s because you’re not just listing up studies, methods, and outcomes. You’re actually expressing your thoughts and interpretations in words.

• How formal should it be?
• What words should you use, or not use?
• How do you meet strict word limits, or make it longer and more informative?

Always give it your best, but sometimes a helping hand can, well, help. Getting a professional edit can help clarify your work’s importance while improving the English used to explain it. When readers know the value of your work, they’ll cite it. We’ll assign your study to an expert editor knowledgeable in your area of research. Their work will clarify your discussion, helping it to tell your story. Find out more about AJE Editing.

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6 Steps to Write an Excellent Discussion in Your Manuscript

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Table of Contents

The discussion section in scientific manuscripts might be the last few paragraphs, but its role goes far beyond wrapping up. It’s the part of an article where scientists talk about what they found and what it means, where raw data turns into meaningful insights. Therefore, discussion is a vital component of the article.  

An excellent discussion is well-organized. We bring to you authors a classic 6-step method for writing discussion sections, with examples to illustrate the functions and specific writing logic of each step. Take a look at how you can impress journal reviewers with a concise and focused discussion section!  

Discussion frame structure   

Conventionally, a discussion section has three parts: an introductory paragraph, a few intermediate paragraphs, and a conclusion¹.  Please follow the steps below:  

1.Introduction—mention gaps in previous research¹⁻ ²

Here, you orient the reader to your study. In the first paragraph, it is advisable to mention the research gap your paper addresses.  

Example: This study investigated the cognitive effects of a meat-only diet on adults. While earlier studies have explored the impact of a carnivorous diet on physical attributes and agility, they have not explicitly addressed its influence on cognitively intense tasks involving memory and reasoning.  

2. Summarizing key findings—let your data speak ¹⁻ ²

After you have laid out the context for your study, recapitulate some of its key findings. Also, highlight key data and evidence supporting these findings.  

Example: We found that risk-taking behavior among teenagers correlates with their tendency to invest in cryptocurrencies. Risk takers in this study, as measured by the Cambridge Gambling Task, tended to have an inordinately higher proportion of their savings invested as crypto coins.  

3. Interpreting results—compare with other papers¹⁻²    

Here, you must analyze and interpret any results concerning the research question or hypothesis. How do the key findings of your study help verify or disprove the hypothesis? What practical relevance does your discovery have?  

Example: Our study suggests that higher daily caffeine intake is not associated with poor performance in major sporting events. Athletes may benefit from the cardiovascular benefits of daily caffeine intake without adversely impacting performance.    

Remember, unlike the results section, the discussion ideally focuses on locating your findings in the larger body of existing research. Hence, compare your results with those of other peer-reviewed papers.  

Example: Although Miller et al. (2020) found evidence of such political bias in a multicultural population, our findings suggest that the bias is weak or virtually non-existent among politically active citizens.  

4. Addressing limitations—their potential impact on the results¹⁻²    

Discuss the potential impact of limitations on the results. Most studies have limitations, and it is crucial to acknowledge them in the intermediary paragraphs of the discussion section. Limitations may include low sample size, suspected interference or noise in data, low effect size, etc.  

Example: This study explored a comprehensive list of adverse effects associated with the novel drug ‘X’. However, long-term studies may be needed to confirm its safety, especially regarding major cardiac events.  

5. Implications for future research—how to explore further¹⁻²    

Locate areas of your research where more investigation is needed. Concluding paragraphs of the discussion can explain what research will likely confirm your results or identify knowledge gaps your study left unaddressed.  

Example: Our study demonstrates that roads paved with the plastic-infused compound ‘Y’ are more resilient than asphalt. Future studies may explore economically feasible ways of producing compound Y in bulk.  

6. Conclusion—summarize content¹⁻²    

A good way to wind up the discussion section is by revisiting the research question mentioned in your introduction. Sign off by expressing the main findings of your study.  

Example: Recent observations suggest that the fish ‘Z’ is moving upriver in many parts of the Amazon basin. Our findings provide conclusive evidence that this phenomenon is associated with rising sea levels and climate change, not due to elevated numbers of invasive predators.  

A rigorous and concise discussion section is one of the keys to achieving an excellent paper. It serves as a critical platform for researchers to interpret and connect their findings with the broader scientific context. By detailing the results, carefully comparing them with existing research, and explaining the limitations of this study, you can effectively help reviewers and readers understand the entire research article more comprehensively and deeply¹⁻² , thereby helping your manuscript to be successfully published and gain wider dissemination.  

In addition to keeping this writing guide, you can also use Elsevier Language Services to improve the quality of your paper more deeply and comprehensively. We have a professional editing team covering multiple disciplines. With our profound disciplinary background and rich polishing experience, we can significantly optimize all paper modules including the discussion, effectively improve the fluency and rigor of your articles, and make your scientific research results consistent, with its value reflected more clearly. We are always committed to ensuring the quality of papers according to the standards of top journals, improving the publishing efficiency of scientific researchers, and helping you on the road to academic success. Check us out here !  

Type in wordcount for Standard Total: USD EUR JPY Follow this link if your manuscript is longer than 12,000 words. Upload  

References:   

• Masic, I. (2018). How to write an efficient discussion? Medical Archives , 72(3), 306. https://doi.org/10.5455/medarh.2018.72.306-307  
• Şanlı, Ö., Erdem, S., & Tefik, T. (2014). How to write a discussion section? Urology Research & Practice , 39(1), 20–24. https://doi.org/10.5152/tud.2013.049  

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Study Skills

Writing a discussion section

In the discussion section, you will draw connections between your findings, existing theory and other research. You will have an opportunity to tell the story arising from your findings. 

This page will help you to: 

understand the purpose of the discussion section 

follow the steps required to plan your discussion section 

structure your discussion  

enhance the depth of your discussion 

use appropriate language to discuss your findings.  

Introduction to the discussion section

When you have reached this stage, you might be thinking “All I have to do now is to sum up what I have done, and then make a few remarks about what I did” (as cited in Swales & Feak, 2012, p.263). However, writing a discussion section is not that simple. Read on to learn more.

  Before you continue, reflect on your earlier writing experiences and the feedback you have received. How would you rate your ability in the following skills? Rate your ability from ‘good’ to ‘needs development’. 

Reflect on your answers. Congratulations if you feel confident about your skills. You may find it helpful to review the materials on this page to confirm your knowledge and possibly learn more. Don't worry if you don't feel confident. Work through these materials to build your skills. 

A discussion critically analyses and interprets the results of a scientific study, placing the results in the context of published literature and explaining how they affect the field . 

In this section, you will relate the specific findings of your research to the wider scientific field. This is the opposite of the introduction section, which starts with the broader context and narrows to focus on your specific research topic.  

The discussion will: 

review the findings  

put the findings into the context of the overall research  

tell readers why the research results are important and where they fit in with the current literature 

acknowledge the limitations of the study 

make recommendations for future research.

Let's review your understanding of the discussion section by identifying what makes a strong discussion.

Planning for a discussion section

Planning for a discussion section starts with analysing your data. For some kinds of research, the analysis cannot be done until your data has been collected. For others, analysing data can happen early as the data already exists in literary texts, archival documents or similar.  

Before starting to write the discussion section, it is important to:  

analyse your data (usually reported in the Results or Findings section) 

select the key issues that are the substance of your research  

relate the findings to the literature and 

plan for the process of going from your specific findings to the broader scientific field.  

Your analysis of the results will inform the Findings or Results section of your thesis or publication. It is the stage where you organise and visualise your data, and identify trends, patterns and causal relationships in the themes.

As the section discusses the key findings without restating the results, it is important to identify the key issues. For example, you should focus on four or five issues that agree or do not agree with your hypothesis or with previously published work. It is also important to include and discuss any unexpected results.

You refer to previous research in your discussion section for explaining your results, confirming how your results support the theories and previous studies, comparing your results with similar studies, or showing how your results contradict similar studies. 

Therefore, papers that you are likely to refer to in your discussion are those that led to: 

your hypothesis  

your experimental design 

your results.

In writing the discussion section, you will start with your research and then broaden your focus to the field or scientific community. This means you will go from narrowest (your specific findings) to broadest (the wider scientific community). You do this by following the six moves: 

As you can see, your discussion may follow six moves (stages) which broadens the scope of your discussion section. Watch this video to learn how to apply these moves.

Structuring your discussion

This section reviews how a discussion section can be organised.

A discussion section usually includes five parts or steps, which are illustrated in the image below. 

In some disciplines, the researcher's argument determines the structure of the presentation and discussion of findings. In other disciplines, the structure follows established conventions. Therefore, it is important for you to investigate the conventions of your own discipline, by looking at theses in your discipline and articles published in your target journals. The discussion section may be: 

in a combined section called Results and Discussion 

in a combined section called Discussion and Conclusion 

in a separate section. 

Your discussion section may be an independent chapter or it might be combined with the Findings chapter. Common chapter headings include:  

Discussion chapter 

Findings and Discussion chapter  

Discussion, Recommendations and Conclusion chapter

Discussion and Conclusion chapter 

It is important to have a good understanding of the expected content of each chapter.  Below is an example of a chapter in which discussion, recommendations and conclusion are combined.

Click on the hotspots to learn more.

This section focuses on useful language for writing your discussion.

Boosters and hedges should be used to demonstrate your confidence in your interpretation of the results. They help you to distinguish between clear and strong results and those that you feel less confident about or that may be open to different interpretations.

 Read both sentences. Which one shows more confidence in the results? 

The Dutch supervisors reported using different types of questions more frequently and deliberately than the Chinese supervisors. This difference may have its roots in the underlying educational philosophies. (Adapted from Hu, Rijst, Veen, & Verloop, 2016)  

The findings clearly demonstrate that psychological capital had considerable influence on the 10 employability skills included in the study, and especially on those related to teamwork, self-knowledge and self-management (Adapted from Harper, Bregta & Rundle, 2021) 

The writers of sentence two are more confident in the interpretation of their results.  

Test your knowledge of hedges and boosters by doing the task below. 

It is important to make it clear in your discussion: 

which research has been done by you 

which research has been done by other people 

how they complement each other.

Image 2: Note that present perfect is also used to refer to other studies when you want to emphasise that an area of research is still current and ongoing. Take a look at the example below which uses present perfect to refer to other studies 

Like other studies (e.g., Larcombe et al., 2021; Naylor, 2020) that have shown a strong connection between course experience and wellbeing, our study shows that a significant portion of international students believe that aspects of their immediate environment could be improved to better support their wellbeing.  

More information on tenses in the Discussion section is presented in Language Tip 4 below.  

Below are some useful discussion phrases that were adapted from Paltridge & Starfield (2020) and the APA Discussion phrases guide (7th edition).

You can download this APA discussion phrase guide here and visit the Academic Phrasebank for further phrases and examples. 

Let's look at these extracts and identify the functions of the paragraphs.  

Past, present and present perfect tenses are commonly used in the discussion section.  

• Past tense is used to summarise the key findings and to refer to the work of previous researchers  
• Present perfect is used to refer to the work of previous researchers (usually an area of research that is current and on-going rather than one single study) 
• Present tense is used to interpret the results or describe the significance of the findings  
• Future  is used to make recommendations for further research or providing future direction 

Below is an example of some paragraphs in a discussion section in which different tenses are used.

Test your knowledge of using the right tenses in the discussion section by doing the task below. 

Use this template to plan your discussion.  

The template is an example of a planning tool that will help you develop an overview of the key content that you are going to include in your section. You can download the draft and save it as a Word document once you have finished. 

You may have more or less than 3 key findings that you would like to discuss in your section.  

If you would like more support, visit the Language and Learning Advisors page. 

Butler, K. (2020, 7 April). Breakdown of an ideal discussion of scientific research paper. Scientific Communications . https://butlerscicomm.com/breakdown-of-ideal-discussion-section-research-paper  

Calvo, J. C. A & García, G. M. (2021). The influence of psychological capital on graduates’ perception of employability: the mediating role of employability skills. Higher Education Research & Development , 40(2), 293-308, DOI: 10.1080/07294360.2020.1738350   

Cenamor, J. (2022) To teach or not to teach? Junior academics and the teaching-research relationship. Higher Education Research & Development , 41(5), 1417-1435. DOI: 10.1080/07294360.2021.1933395  

Harper, R.,  Bretag, T & Rundle, K. (2021) Detecting contract cheating: examining the role of assessment type. Higher Education Research & Development, 40(2), 263-278, DOI: 10.1080/07294360.2020.1724899   

Hu, Y., Rijst, R. M., Veen, K & N Verloop, N. (2016) The purposes and processes of master's thesis supervision: a comparison of Chinese and Dutch supervisors. Higher Education Research & Development , 35(5), 910-924, DOI: 10.1080/07294360.2016.1139550  

Humphrey, P. (2015). English language proficiency in higher education: student conceptualisations and outcomes . [Doctoral dissertation, Griffith University]  

Marangell, S., & Baik, C. (2022). International students’ suggestions for what universities can do to better support their mental wellbeing. Journal of International Students, 12(4), 933-954.  

Merga, M., & Mason, S. (2021) Early career researchers’ perceptions of the benefits and challenges of sharing research with academic and non-academic end-users, Higher Education Research & Development , 40(7), 1482-1496, DOI: 10.1080/07294360.2020.1815662  

Paltridge, B., & Starfield, S. (2019). Thesis and Dissertation Writing in a Second Language: A Handbook for Students and their Supervisors (2nd ed.). Routledge.  

Rendle-Short, J. (2009). The Address Term Mate in Australian English: Is it Still a Masculine Term?. Australian Journal of Linguistics, 29(2), 245-268, DOI: 10.1080/07268600902823110  

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Guide to Writing the Results and Discussion Sections of a Scientific Article

A quality research paper has both the qualities of in-depth research and good writing ( Bordage, 2001 ). In addition, a research paper must be clear, concise, and effective when presenting the information in an organized structure with a logical manner ( Sandercock, 2013 ).

In this article, we will take a closer look at the results and discussion section. Composing each of these carefully with sufficient data and well-constructed arguments can help improve your paper overall.

The results section of your research paper contains a description about the main findings of your research, whereas the discussion section interprets the results for readers and provides the significance of the findings. The discussion should not repeat the results.

Let’s dive in a little deeper about how to properly, and clearly organize each part.

How to Organize the Results Section

Since your results follow your methods, you’ll want to provide information about what you discovered from the methods you used, such as your research data. In other words, what were the outcomes of the methods you used?

You may also include information about the measurement of your data, variables, treatments, and statistical analyses.

To start, organize your research data based on how important those are in relation to your research questions. This section should focus on showing major results that support or reject your research hypothesis. Include your least important data as supplemental materials when submitting to the journal.

The next step is to prioritize your research data based on importance – focusing heavily on the information that directly relates to your research questions using the subheadings.

The organization of the subheadings for the results section usually mirrors the methods section. It should follow a logical and chronological order.

Subheading organization

Subheadings within your results section are primarily going to detail major findings within each important experiment. And the first paragraph of your results section should be dedicated to your main findings (findings that answer your overall research question and lead to your conclusion) (Hofmann, 2013).

In the book “Writing in the Biological Sciences,” author Angelika Hofmann recommends you structure your results subsection paragraphs as follows:

• Experimental purpose
• Interpretation

Each subheading may contain a combination of ( Bahadoran, 2019 ; Hofmann, 2013, pg. 62-63):

• Text: to explain about the research data
• Figures: to display the research data and to show trends or relationships, for examples using graphs or gel pictures.
• Tables: to represent a large data and exact value

Decide on the best way to present your data — in the form of text, figures or tables (Hofmann, 2013).

Data or Results?

Sometimes we get confused about how to differentiate between data and results . Data are information (facts or numbers) that you collected from your research ( Bahadoran, 2019 ).

Whereas, results are the texts presenting the meaning of your research data ( Bahadoran, 2019 ).

One mistake that some authors often make is to use text to direct the reader to find a specific table or figure without further explanation. This can confuse readers when they interpret data completely different from what the authors had in mind. So, you should briefly explain your data to make your information clear for the readers.

Common Elements in Figures and Tables

Figures and tables present information about your research data visually. The use of these visual elements is necessary so readers can summarize, compare, and interpret large data at a glance. You can use graphs or figures to compare groups or patterns. Whereas, tables are ideal to present large quantities of data and exact values.

Several components are needed to create your figures and tables. These elements are important to sort your data based on groups (or treatments). It will be easier for the readers to see the similarities and differences among the groups.

When presenting your research data in the form of figures and tables, organize your data based on the steps of the research leading you into a conclusion.

Common elements of the figures (Bahadoran, 2019):

• Figure number
• Figure title
• Figure legend (for example a brief title, experimental/statistical information, or definition of symbols).

Tables in the result section may contain several elements (Bahadoran, 2019):

• Table number
• Table title
• Row headings (for example groups)
• Column headings
• Row subheadings (for example categories or groups)
• Column subheadings (for example categories or variables)
• Footnotes (for example statistical analyses)

Tips to Write the Results Section

• Direct the reader to the research data and explain the meaning of the data.
• Avoid using a repetitive sentence structure to explain a new set of data.
• Write and highlight important findings in your results.
• Use the same order as the subheadings of the methods section.
• Match the results with the research questions from the introduction. Your results should answer your research questions.
• Be sure to mention the figures and tables in the body of your text.
• Make sure there is no mismatch between the table number or the figure number in text and in figure/tables.
• Only present data that support the significance of your study. You can provide additional data in tables and figures as supplementary material.

How to Organize the Discussion Section

It’s not enough to use figures and tables in your results section to convince your readers about the importance of your findings. You need to support your results section by providing more explanation in the discussion section about what you found.

In the discussion section, based on your findings, you defend the answers to your research questions and create arguments to support your conclusions.

Below is a list of questions to guide you when organizing the structure of your discussion section ( Viera et al ., 2018 ):

• What experiments did you conduct and what were the results?
• What do the results mean?
• What were the important results from your study?
• How did the results answer your research questions?
• Did your results support your hypothesis or reject your hypothesis?
• What are the variables or factors that might affect your results?
• What were the strengths and limitations of your study?
• What other published works support your findings?
• What other published works contradict your findings?
• What possible factors might cause your findings different from other findings?
• What is the significance of your research?
• What are new research questions to explore based on your findings?

Organizing the Discussion Section

The structure of the discussion section may be different from one paper to another, but it commonly has a beginning, middle-, and end- to the section.

One way to organize the structure of the discussion section is by dividing it into three parts (Ghasemi, 2019):

• The beginning: The first sentence of the first paragraph should state the importance and the new findings of your research. The first paragraph may also include answers to your research questions mentioned in your introduction section.
• The middle: The middle should contain the interpretations of the results to defend your answers, the strength of the study, the limitations of the study, and an update literature review that validates your findings.
• The end: The end concludes the study and the significance of your research.

Another possible way to organize the discussion section was proposed by Michael Docherty in British Medical Journal: is by using this structure ( Docherty, 1999 ):

• Discussion of important findings
• Comparison of your results with other published works
• Include the strengths and limitations of the study
• Conclusion and possible implications of your study, including the significance of your study – address why and how is it meaningful
• Future research questions based on your findings

Finally, a last option is structuring your discussion this way (Hofmann, 2013, pg. 104):

• First Paragraph: Provide an interpretation based on your key findings. Then support your interpretation with evidence.
• Secondary results
• Limitations
• Unexpected findings
• Comparisons to previous publications
• Last Paragraph: The last paragraph should provide a summarization (conclusion) along with detailing the significance, implications and potential next steps.

Remember, at the heart of the discussion section is presenting an interpretation of your major findings.

Tips to Write the Discussion Section

• Highlight the significance of your findings
• Mention how the study will fill a gap in knowledge.
• Indicate the implication of your research.
• Avoid generalizing, misinterpreting your results, drawing a conclusion with no supportive findings from your results.

Aggarwal, R., & Sahni, P. (2018). The Results Section. In Reporting and Publishing Research in the Biomedical Sciences (pp. 21-38): Springer.

Bahadoran, Z., Mirmiran, P., Zadeh-Vakili, A., Hosseinpanah, F., & Ghasemi, A. (2019). The principles of biomedical scientific writing: Results. International journal of endocrinology and metabolism, 17(2).

Bordage, G. (2001). Reasons reviewers reject and accept manuscripts: the strengths and weaknesses in medical education reports. Academic medicine, 76(9), 889-896.

Cals, J. W., & Kotz, D. (2013). Effective writing and publishing scientific papers, part VI: discussion. Journal of clinical epidemiology, 66(10), 1064.

Docherty, M., & Smith, R. (1999). The case for structuring the discussion of scientific papers: Much the same as that for structuring abstracts. In: British Medical Journal Publishing Group.

Faber, J. (2017). Writing scientific manuscripts: most common mistakes. Dental press journal of orthodontics, 22(5), 113-117.

Fletcher, R. H., & Fletcher, S. W. (2018). The discussion section. In Reporting and Publishing Research in the Biomedical Sciences (pp. 39-48): Springer.

Ghasemi, A., Bahadoran, Z., Mirmiran, P., Hosseinpanah, F., Shiva, N., & Zadeh-Vakili, A. (2019). The Principles of Biomedical Scientific Writing: Discussion. International journal of endocrinology and metabolism, 17(3).

Hofmann, A. H. (2013). Writing in the biological sciences: a comprehensive resource for scientific communication . New York: Oxford University Press.

Kotz, D., & Cals, J. W. (2013). Effective writing and publishing scientific papers, part V: results. Journal of clinical epidemiology, 66(9), 945.

Mack, C. (2014). How to Write a Good Scientific Paper: Structure and Organization. Journal of Micro/ Nanolithography, MEMS, and MOEMS, 13. doi:10.1117/1.JMM.13.4.040101

Moore, A. (2016). What's in a Discussion section? Exploiting 2‐dimensionality in the online world…. Bioessays, 38(12), 1185-1185.

Peat, J., Elliott, E., Baur, L., & Keena, V. (2013). Scientific writing: easy when you know how: John Wiley & Sons.

Sandercock, P. M. L. (2012). How to write and publish a scientific article. Canadian Society of Forensic Science Journal, 45(1), 1-5.

Teo, E. K. (2016). Effective Medical Writing: The Write Way to Get Published. Singapore Medical Journal, 57(9), 523-523. doi:10.11622/smedj.2016156

Van Way III, C. W. (2007). Writing a scientific paper. Nutrition in Clinical Practice, 22(6), 636-640.

Vieira, R. F., Lima, R. C. d., & Mizubuti, E. S. G. (2019). How to write the discussion section of a scientific article. Acta Scientiarum. Agronomy, 41.

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How to Start a Discussion Section in Research? [with Examples]

The examples below are from 72,017 full-text PubMed research papers that I analyzed in order to explore common ways to start writing the Discussion section.

Research papers included in this analysis were selected at random from those uploaded to PubMed Central between the years 2016 and 2021. Note that I used the BioC API to download the data (see the References section below).

Examples of how to start writing the Discussion section

In the Discussion section, you should explain the meaning of your results, their importance, and implications. [for more information, see: How to Write & Publish a Research Paper: Step-by-Step Guide ]

The Discussion section can:

1. Start by restating the study objective

“ The purpose of this study was to investigate the relationship between muscle synergies and motion primitives of the upper limb motions.” Taken from the Discussion section of this article on PubMed

“ The main objective of this study was to identify trajectories of autonomy.” Taken from the Discussion section of this article on PubMed

“ In the present study, we investigated the whole brain regional homogeneity in patients with melancholic MDD and non-melancholic MDD at rest . “ Taken from the Discussion section of this article on PubMed

2. Start by mentioning the main finding

“ We found that autocracy and democracy have acted as peaks in an evolutionary landscape of possible modes of institutional arrangements.” Taken from the Discussion section of this article on PubMed

“ In this study, we demonstrated that the neural mechanisms of rhythmic movements and skilled movements are similar.” Taken from the Discussion section of this article on PubMed

“ The results of this study show that older adults are a diverse group concerning their activities on the Internet.” Taken from the Discussion section of this article on PubMed

3. Start by pointing out the strength of the study

“ To our knowledge, this investigation is by far the largest epidemiological study employing real-time PCR to study periodontal pathogens in subgingival plaque.” Taken from the Discussion section of this article on PubMed

“ This is the first human subject research using the endoscopic hemoglobin oxygen saturation imaging technology for patients with aero-digestive tract cancers or adenomas.” Taken from the Discussion section of this article on PubMed

“ In this work, we introduced a new real-time flow imaging method and systematically demonstrated its effectiveness with both flow phantom experiments and in vivo experiments.” Taken from the Discussion section of this article on PubMed

Most used words at the start of the Discussion

Here are the top 10 phrases used to start a discussion section in our dataset:

• Comeau DC, Wei CH, Islamaj Doğan R, and Lu Z. PMC text mining subset in BioC: about 3 million full text articles and growing,  Bioinformatics , btz070, 2019.

Further reading

• How Long Should the Discussion Section Be? Data from 61,517 Examples
• How to Write & Publish a Research Paper: Step-by-Step Guide
• “I” & “We” in Academic Writing: Examples from 9,830 Studies

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How to Write a Discussion Section for a Research Paper

We’ve talked about several useful writing tips that authors should consider while drafting or editing their research papers. In particular, we’ve focused on  figures and legends , as well as the Introduction ,  Methods , and  Results . Now that we’ve addressed the more technical portions of your journal manuscript, let’s turn to the analytical segments of your research article. In this article, we’ll provide tips on how to write a strong Discussion section that best portrays the significance of your research contributions.

What is the Discussion section of a research paper?

In a nutshell,  your Discussion fulfills the promise you made to readers in your Introduction . At the beginning of your paper, you tell us why we should care about your research. You then guide us through a series of intricate images and graphs that capture all the relevant data you collected during your research. We may be dazzled and impressed at first, but none of that matters if you deliver an anti-climactic conclusion in the Discussion section!

Are you feeling pressured? Don’t worry. To be honest, you will edit the Discussion section of your manuscript numerous times. After all, in as little as one to two paragraphs ( Nature ‘s suggestion  based on their 3,000-word main body text limit), you have to explain how your research moves us from point A (issues you raise in the Introduction) to point B (our new understanding of these matters). You must also recommend how we might get to point C (i.e., identify what you think is the next direction for research in this field). That’s a lot to say in two paragraphs!

So, how do you do that? Let’s take a closer look.

What should I include in the Discussion section?

As we stated above, the goal of your Discussion section is to  answer the questions you raise in your Introduction by using the results you collected during your research . The content you include in the Discussions segment should include the following information:

• Remind us why we should be interested in this research project.
• Describe the nature of the knowledge gap you were trying to fill using the results of your study.
• Don’t repeat your Introduction. Instead, focus on why  this  particular study was needed to fill the gap you noticed and why that gap needed filling in the first place.
• Mainly, you want to remind us of how your research will increase our knowledge base and inspire others to conduct further research.
• Clearly tell us what that piece of missing knowledge was.
• Answer each of the questions you asked in your Introduction and explain how your results support those conclusions.
• Make sure to factor in all results relevant to the questions (even if those results were not statistically significant).
• Focus on the significance of the most noteworthy results.
• If conflicting inferences can be drawn from your results, evaluate the merits of all of them.
• Don’t rehash what you said earlier in the Results section. Rather, discuss your findings in the context of answering your hypothesis. Instead of making statements like “[The first result] was this…,” say, “[The first result] suggests [conclusion].”
• Do your conclusions line up with existing literature?
• Discuss whether your findings agree with current knowledge and expectations.
• Keep in mind good persuasive argument skills, such as explaining the strengths of your arguments and highlighting the weaknesses of contrary opinions.
• If you discovered something unexpected, offer reasons. If your conclusions aren’t aligned with current literature, explain.
• Address any limitations of your study and how relevant they are to interpreting your results and validating your findings.
• Make sure to acknowledge any weaknesses in your conclusions and suggest room for further research concerning that aspect of your analysis.
• Make sure your suggestions aren’t ones that should have been conducted during your research! Doing so might raise questions about your initial research design and protocols.
• Similarly, maintain a critical but unapologetic tone. You want to instill confidence in your readers that you have thoroughly examined your results and have objectively assessed them in a way that would benefit the scientific community’s desire to expand our knowledge base.
• Recommend next steps.
• Your suggestions should inspire other researchers to conduct follow-up studies to build upon the knowledge you have shared with them.
• Keep the list short (no more than two).

How to Write the Discussion Section

The above list of what to include in the Discussion section gives an overall idea of what you need to focus on throughout the section. Below are some tips and general suggestions about the technical aspects of writing and organization that you might find useful as you draft or revise the contents we’ve outlined above.

Technical writing elements

• Embrace active voice because it eliminates the awkward phrasing and wordiness that accompanies passive voice.
• Use the present tense, which should also be employed in the Introduction.
• Sprinkle with first person pronouns if needed, but generally, avoid it. We want to focus on your findings.
• Maintain an objective and analytical tone.

Discussion section organization

• Keep the same flow across the Results, Methods, and Discussion sections.
• We develop a rhythm as we read and parallel structures facilitate our comprehension. When you organize information the same way in each of these related parts of your journal manuscript, we can quickly see how a certain result was interpreted and quickly verify the particular methods used to produce that result.
• Notice how using parallel structure will eliminate extra narration in the Discussion part since we can anticipate the flow of your ideas based on what we read in the Results segment. Reducing wordiness is important when you only have a few paragraphs to devote to the Discussion section!
• Within each subpart of a Discussion, the information should flow as follows: (A) conclusion first, (B) relevant results and how they relate to that conclusion and (C) relevant literature.
• End with a concise summary explaining the big-picture impact of your study on our understanding of the subject matter. At the beginning of your Discussion section, you stated why  this  particular study was needed to fill the gap you noticed and why that gap needed filling in the first place. Now, it is time to end with “how your research filled that gap.”

Discussion Part 1: Summarizing Key Findings

Begin the Discussion section by restating your  statement of the problem  and briefly summarizing the major results. Do not simply repeat your findings. Rather, try to create a concise statement of the main results that directly answer the central research question that you stated in the Introduction section . This content should not be longer than one paragraph in length.

Many researchers struggle with understanding the precise differences between a Discussion section and a Results section . The most important thing to remember here is that your Discussion section should subjectively evaluate the findings presented in the Results section, and in relatively the same order. Keep these sections distinct by making sure that you do not repeat the findings without providing an interpretation.

Phrase examples: Summarizing the results

• The findings indicate that …
• These results suggest a correlation between A and B …
• The data present here suggest that …
• An interpretation of the findings reveals a connection between…

Discussion Part 2: Interpreting the Findings

What do the results mean? It may seem obvious to you, but simply looking at the figures in the Results section will not necessarily convey to readers the importance of the findings in answering your research questions.

The exact structure of interpretations depends on the type of research being conducted. Here are some common approaches to interpreting data:

• Identifying correlations and relationships in the findings
• Explaining whether the results confirm or undermine your research hypothesis
• Giving the findings context within the history of similar research studies
• Discussing unexpected results and analyzing their significance to your study or general research
• Offering alternative explanations and arguing for your position

Organize the Discussion section around key arguments, themes, hypotheses, or research questions or problems. Again, make sure to follow the same order as you did in the Results section.

Discussion Part 3: Discussing the Implications

In addition to providing your own interpretations, show how your results fit into the wider scholarly literature you surveyed in the  literature review section. This section is called the implications of the study . Show where and how these results fit into existing knowledge, what additional insights they contribute, and any possible consequences that might arise from this knowledge, both in the specific research topic and in the wider scientific domain.

Questions to ask yourself when dealing with potential implications:

• Do your findings fall in line with existing theories, or do they challenge these theories or findings? What new information do they contribute to the literature, if any? How exactly do these findings impact or conflict with existing theories or models?
• What are the practical implications on actual subjects or demographics?
• What are the methodological implications for similar studies conducted either in the past or future?

Your purpose in giving the implications is to spell out exactly what your study has contributed and why researchers and other readers should be interested.

Phrase examples: Discussing the implications of the research

• These results confirm the existing evidence in X studies…
• The results are not in line with the foregoing theory that…
• This experiment provides new insights into the connection between…
• These findings present a more nuanced understanding of…
• While previous studies have focused on X, these results demonstrate that Y.

Step 4: Acknowledging the limitations

All research has study limitations of one sort or another. Acknowledging limitations in methodology or approach helps strengthen your credibility as a researcher. Study limitations are not simply a list of mistakes made in the study. Rather, limitations help provide a more detailed picture of what can or cannot be concluded from your findings. In essence, they help temper and qualify the study implications you listed previously.

Study limitations can relate to research design, specific methodological or material choices, or unexpected issues that emerged while you conducted the research. Mention only those limitations directly relate to your research questions, and explain what impact these limitations had on how your study was conducted and the validity of any interpretations.

Possible types of study limitations:

• Insufficient sample size for statistical measurements
• Lack of previous research studies on the topic
• Methods/instruments/techniques used to collect the data
• Limited access to data
• Time constraints in properly preparing and executing the study

After discussing the study limitations, you can also stress that your results are still valid. Give some specific reasons why the limitations do not necessarily handicap your study or narrow its scope.

Phrase examples: Limitations sentence beginners

• “There may be some possible limitations in this study.”
• “The findings of this study have to be seen in light of some limitations.”
•  “The first limitation is the…The second limitation concerns the…”
•  “The empirical results reported herein should be considered in the light of some limitations.”
• “This research, however, is subject to several limitations.”
• “The primary limitation to the generalization of these results is…”
• “Nonetheless, these results must be interpreted with caution and a number of limitations should be borne in mind.”

Discussion Part 5: Giving Recommendations for Further Research

Based on your interpretation and discussion of the findings, your recommendations can include practical changes to the study or specific further research to be conducted to clarify the research questions. Recommendations are often listed in a separate Conclusion section , but often this is just the final paragraph of the Discussion section.

Suggestions for further research often stem directly from the limitations outlined. Rather than simply stating that “further research should be conducted,” provide concrete specifics for how future can help answer questions that your research could not.

Phrase examples: Recommendation sentence beginners

• Further research is needed to establish …
• There is abundant space for further progress in analyzing…
• A further study with more focus on X should be done to investigate…
• Further studies of X that account for these variables must be undertaken.

Consider Receiving Professional Language Editing

As you edit or draft your research manuscript, we hope that you implement these guidelines to produce a more effective Discussion section. And after completing your draft, don’t forget to submit your work to a professional proofreading and English editing service like Wordvice, including our manuscript editing service for  paper editing , cover letter editing , SOP editing , and personal statement proofreading services. Language editors not only proofread and correct errors in grammar, punctuation, mechanics, and formatting but also improve terms and revise phrases so they read more naturally. Wordvice is an industry leader in providing high-quality revision for all types of academic documents.

For additional information about how to write a strong research paper, make sure to check out our full  research writing series !

Wordvice Writing Resources

• How to Write a Research Paper Introduction 
• Which Verb Tenses to Use in a Research Paper
• How to Write an Abstract for a Research Paper
• How to Write a Research Paper Title
• Useful Phrases for Academic Writing
• Common Transition Terms in Academic Papers
• Active and Passive Voice in Research Papers
• 100+ Verbs That Will Make Your Research Writing Amazing
• Tips for Paraphrasing in Research Papers

Additional Academic Resources

•   Guide for Authors.  (Elsevier)
•  How to Write the Results Section of a Research Paper.  (Bates College)
•   Structure of a Research Paper.  (University of Minnesota Biomedical Library)
•   How to Choose a Target Journal  (Springer)
•   How to Write Figures and Tables  (UNC Writing Center)

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Writing a scientific paper.

• Writing a lab report
• INTRODUCTION

Writing a "good" discussion section

"discussion and conclusions checklist" from: how to write a good scientific paper. chris a. mack. spie. 2018., peer review.

• LITERATURE CITED
• Bibliography of guides to scientific writing and presenting
• Presentations
• Lab Report Writing Guides on the Web

This is is usually the hardest section to write. You are trying to bring out the true meaning of your data without being too long. Do not use words to conceal your facts or reasoning. Also do not repeat your results, this is a discussion.

• Present principles, relationships and generalizations shown by the results
• Point out exceptions or lack of correlations. Define why you think this is so.
• Show how your results agree or disagree with previously published works
• Discuss the theoretical implications of your work as well as practical applications
• State your conclusions clearly. Summarize your evidence for each conclusion.
• Discuss the significance of the results
•  Evidence does not explain itself; the results must be presented and then explained.
• Typical stages in the discussion: summarizing the results, discussing whether results are expected or unexpected, comparing these results to previous work, interpreting and explaining the results (often by comparison to a theory or model), and hypothesizing about their generality.
• Discuss any problems or shortcomings encountered during the course of the work.
• Discuss possible alternate explanations for the results.
• Avoid: presenting results that are never discussed; presenting discussion that does not relate to any of the results; presenting results and discussion in chronological order rather than logical order; ignoring results that do not support the conclusions; drawing conclusions from results without logical arguments to back them up. 

CONCLUSIONS

• Provide a very brief summary of the Results and Discussion.
• Emphasize the implications of the findings, explaining how the work is significant and providing the key message(s) the author wishes to convey.
• Provide the most general claims that can be supported by the evidence.
• Provide a future perspective on the work.
• Avoid: repeating the abstract; repeating background information from the Introduction; introducing new evidence or new arguments not found in the Results and Discussion; repeating the arguments made in the Results and Discussion; failing to address all of the research questions set out in the Introduction. 

WHAT HAPPENS AFTER I COMPLETE MY PAPER?

 The peer review process is the quality control step in the publication of ideas.  Papers that are submitted to a journal for publication are sent out to several scientists (peers) who look carefully at the paper to see if it is "good science".  These reviewers then recommend to the editor of a journal whether or not a paper should be published. Most journals have publication guidelines. Ask for them and follow them exactly.    Peer reviewers examine the soundness of the materials and methods section.  Are the materials and methods used written clearly enough for another scientist to reproduce the experiment?  Other areas they look at are: originality of research, significance of research question studied, soundness of the discussion and interpretation, correct spelling and use of technical terms, and length of the article.

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General Research Paper Guidelines: Discussion

Discussion section.

The overall purpose of a research paper’s discussion section is to evaluate and interpret results, while explaining both the implications and limitations of your findings. Per APA (2020) guidelines, this section requires you to “examine, interpret, and qualify the results and draw inferences and conclusions from them” (p. 89). Discussion sections also require you to detail any new insights, think through areas for future research, highlight the work that still needs to be done to further your topic, and provide a clear conclusion to your research paper. In a good discussion section, you should do the following:

• Clearly connect the discussion of your results to your introduction, including your central argument, thesis, or problem statement.
• Provide readers with a critical thinking through of your results, answering the “so what?” question about each of your findings. In other words, why is this finding important?
• Detail how your research findings might address critical gaps or problems in your field
• Compare your results to similar studies’ findings
• Provide the possibility of alternative interpretations, as your goal as a researcher is to “discover” and “examine” and not to “prove” or “disprove.” Instead of trying to fit your results into your hypothesis, critically engage with alternative interpretations to your results.

For more specific details on your Discussion section, be sure to review Sections 3.8 (pp. 89-90) and 3.16 (pp. 103-104) of your 7 th edition APA manual

*Box content adapted from:

University of Southern California (n.d.). Organizing your social sciences research paper: 8 the discussion . https://libguides.usc.edu/writingguide/discussion

Limitations

Limitations of generalizability or utility of findings, often over which the researcher has no control, should be detailed in your Discussion section. Including limitations for your reader allows you to demonstrate you have thought critically about your given topic, understood relevant literature addressing your topic, and chosen the methodology most appropriate for your research. It also allows you an opportunity to suggest avenues for future research on your topic. An effective limitations section will include the following:

• Detail (a) sources of potential bias, (b) possible imprecision of measures, (c) other limitations or weaknesses of the study, including any methodological or researcher limitations.
• Sample size: In quantitative research, if a sample size is too small, it is more difficult to generalize results.
• Lack of available/reliable data : In some cases, data might not be available or reliable, which will ultimately affect the overall scope of your research. Use this as an opportunity to explain areas for future study.
• Lack of prior research on your study topic: In some cases, you might find that there is very little or no similar research on your study topic, which hinders the credibility and scope of your own research. If this is the case, use this limitation as an opportunity to call for future research. However, make sure you have done a thorough search of the available literature before making this claim.
• Flaws in measurement of data: Hindsight is 20/20, and you might realize after you have completed your research that the data tool you used actually limited the scope or results of your study in some way. Again, acknowledge the weakness and use it as an opportunity to highlight areas for future study.
• Limits of self-reported data: In your research, you are assuming that any participants will be honest and forthcoming with responses or information they provide to you. Simply acknowledging this assumption as a possible limitation is important in your research.
• Access: Most research requires that you have access to people, documents, organizations, etc.. However, for various reasons, access is sometimes limited or denied altogether. If this is the case, you will want to acknowledge access as a limitation to your research.
• Time: Choosing a research focus that is narrow enough in scope to finish in a given time period is important. If such limitations of time prevent you from certain forms of research, access, or study designs, acknowledging this time restraint is important. Acknowledging such limitations is important, as they can point other researchers to areas that require future study.
• Potential Bias: All researchers have some biases, so when reading and revising your draft, pay special attention to the possibilities for bias in your own work. Such bias could be in the form you organized people, places, participants, or events. They might also exist in the method you selected or the interpretation of your results. Acknowledging such bias is an important part of the research process.
• Language Fluency: On occasion, researchers or research participants might have language fluency issues, which could potentially hinder results or how effectively you interpret results. If this is an issue in your research, make sure to acknowledge it in your limitations section.

University of Southern California (n.d.). Organizing your social sciences research paper: Limitations of the study . https://libguides.usc.edu/writingguide/limitations

In many research papers, the conclusion, like the limitations section, is folded into the larger discussion section. If you are unsure whether to include the conclusion as part of your discussion or as a separate section, be sure to defer to the assignment instructions or ask your instructor.

The conclusion is important, as it is specifically designed to highlight your research’s larger importance outside of the specific results of your study. Your conclusion section allows you to reiterate the main findings of your study, highlight their importance, and point out areas for future research. Based on the scope of your paper, your conclusion could be anywhere from one to three paragraphs long. An effective conclusion section should include the following:

• Describe the possibilities for continued research on your topic, including what might be improved, adapted, or added to ensure useful and informed future research.
• Provide a detailed account of the importance of your findings
• Reiterate why your problem is important, detail how your interpretation of results impacts the subfield of study, and what larger issues both within and outside of your field might be affected from such results

University of Southern California (n.d.). Organizing your social sciences research paper: 9. the conclusion . https://libguides.usc.edu/writingguide/conclusion

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How to Write a Dissertation Discussion Chapter: Guide & Examples

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Dissertation discussion section is a chapter that interprets the results obtained from research and offers an in-depth analysis of findings. In this section, students need to analyze the outcomes, evaluate their significance, and compare them to previous research. The discussion section may also explore the limitations of the study and suggest further research perspectives.

If you are stuck with your thesis or dissertation discussion chapter, you are in the right place to complete this section successfully. This article will outline our best solutions and methods on how to write the discussion of a dissertation or thesis. We also will share advanced dissertation discussion examples to help you finalize your PhD work.  Feel like academic writing gives you hassles? Remember that you can always rely on academic experts qualified in your field to get professional dissertation help online .

What Is a Dissertation Discussion?

First and foremost, students need to have a clear understanding of what dissertation discussion is. This is not the same as your results section , where you share data from your research. You are going deeper into the explanation of the existing data in your thesis or dissertation discussion section. In other words, you illustrate practical implications of your research and how the data can be used, researched further, or limited.  What will make your discussion section of a dissertation excellent:

• clear structure
• practical implication
• elaboration on future work on this topic.

This section should go after research methodology and before the dissertation conclusion . It should be directly relevant to questions posed in your introduction.  The biggest mistake you can make is to rewrite your result chapter with other words and add some limitations and recommendation paragraphs. However, this is an entirely different type of writing you need to complete.

Purpose of a Dissertation Discussion Chapter

A dissertation discussion section is critical to explaining students’ findings and the application of data to real-life cases. As we mentioned before, this section will often be read right after the dissertation methods . It evaluates and elaborates on findings and helps to understand the importance of your performed thesis research.  A dissertation discussion opens a new perspective on further research on the same field or topic. It also outlines critical data to consider in subsequent studies. In a nutshell, this is the section where you explain your work to a broad audience.

Structure of a Dissertation Discussion Section

Let’s start your writing journey of this research part with a clear delineation of what it should include and then briefly discuss each component. Here are some basic things you need to consider for an excellent discussion chapter of dissertation :

• Brief summary It does not mean copying an introduction section. However, the first few paragraphs will make an overview of your findings and topic.
• Interpretations This is a critical component of your work — elaborate on your results and explain possible ways of using them.
• Implication Research work is not just 100+ pages of text. Students should explain and illustrate how it could be used for solving practical problems.
• Constraints This is where you outline your limitations. For instance, your research was done only on students, and it may have different results with elderly people.
• Recommendations You can also define possible ways of future research on the exact topic when writing a discussion for your thesis or dissertation. Tell readers, for example, that it would be helpful to run similar research in other specific circumstances.

How to Write a Dissertation Discussion Chapter?

One of the most commonly asked questions for our experts is how to write the discussion section of a dissertation or thesis. We understand why it can be complicated to get a clear answer. Students often think that this section is similar to the result chapter and just retells it in other words. But it is not so. Let’s go through all steps to writing a discussion in a dissertation, and share our best examples from academic papers.

1. Remind Your Research Questions & Objectives

Writing the discussion chapter of a dissertation is not a big deal if you understand its aim and each component in a text structure. First of all, you need to evaluate how your results help to answer research questions you defined in the beginning. It is not about repeating the result, you did it in previous paragraphs.  However, dissertation or thesis discussion should underline how your findings help to answer the research problem. Start writing from a brief intro by recalling research questions or hypotheses . Then, show how your results answer them or support a hypothesis in your work.

2. Sum Up Key Findings

Next part of your discussion for dissertation is to provide a short summary of previous data. But do not respite the same summary paragraphs from results or introduction of a dissertation . Here researchers should be more thoughtful and go deeper into the work’s aims.  Try to explain in a few sentences what you get from running research. For instance, starters usually write the statement that “our data proves that…” or “survey results illustrate a clear correlation between a and b that is critical for proving our working hypothesis…”.  A discussion chapter of your dissertation is not just a fixation on results but a more profound summary connected to research goals and purpose. Here is an example: Summary of Findings Example

According to the data, implementing the co-orientation theory was successful and can be used for the same circumstances in the future. As we found, most participants agreed with the importance of those theses on the five fundamental reforms. It means that the results identified a successful government work in choosing the messages to communicate about examined reforms. At the same time, the situation is not so favorable with implementing the principles of two-way symmetrical communications. According to the results, people did not feel that the government had a mutual, open, and equal dialogue with the public about the reforms.

3. Interpret the Results

The most critical part of a discussion section is to explain and enact the results you’ve got. It is the most significant part of any text. Students should be clear about what to include in these paragraphs. Here is some advice to make this elaboration structured:

• Identify correlations or patterns in the data for dissertation discussion.
• Underline how results can answer research questions or prove your hypothesis.
• Emphasize how your findings are connected to the previous topic studies.
• Point out essential statements you can use in future research.
• Evaluate the significance of your results and any unexpected data you have.
• What others can learn from your research and how this work contributes to the field.
• Consider any possible additional or unique explanation of your findings.
• Go deeper with options of how results can be applied in practice.

Writing a dissertation discussion chapter can be tough, but here is a great sample to learn from. Example of Interpretations in Disssertation Discussion

Our study underlines the importance of future research on using TikTok for political communication. As discussed above, TikTok is the most commonly used social media platform for many young voters. This means that political discussion will also move to this platform. Our research and typology of political communication content can be used in the future planning of effective political campaigns. For example, we can assume that “play videos” have enormous potential to facilitate complicated topics and provide specific agenda settings. We also identified additional affordances of TikTok used for political communication, such as built-in video editors, playlists for specific topics, a green screen for news explainers, and duets for reflection on news and discussion. It means that these features make TikTok suitable for efficient political communications.

4. Discuss How Your Findings Relate to the Literature

Here we came to the implications of your findings for the dissertation discussion. In other words, this is a few sentences on how your work is connected to other studies on the same research topic or what literature gap you are going to fill with the data and research you launched. Remember to mention how your study address the limitations you have discovered while writing a literature review .  First, outline how your hypothesis relates to theories or previous works in the field. Maybe, you challenged some theories or tried to define your own. Be specific in this section. Second, define a practical implementation of your work. Maybe, it can support recommendations or change legislation.  Discussion chapter of a thesis is a place where you explain your work, make it valuable, and incorporate additional meaning for some specific data.  Example of Implications in Disssertation Discussion

As we pointed out in the literature review, there are few works on using TikTok affordances for political communications, and this topic can be expanded in the future. Government institutions have already understood the importance of this platform for efficient communication with younger audiences, and we will see more political projects on TikTok. That is why expanding research on using TikTok for political communication will be enormous in the following years. Our work is one of the first research on the role of emerging media in war communication and can be used as a practical guide for government's strategic planning in times of emergencies.

5. Mention Possible Limitations

It is pretty tricky to conduct research without limitations. You will always have some, which does not mean that your work is not good. When you write a discussion chapter in a thesis or dissertation, focus on what may influence your results and how changing independent variables can affect your data collection methods and final outcomes.  Here are some points to consider when you structure your dissertation discussion limitation part:

• If results can change in case you change the reference group?
• What will happen with data if it changes circumstances?
• What could influence results?

Critical thinking and analysis can help you to outline possible limitations. It can be the age of the reference group, change of questionnaire in a survey, or specific use of data extraction equipment. Be transparent about what could affect your results.  Example of Complications

Although this study has provided critical first insights into the effects of multimodal disinformation and rebuttals, there are some limitations. First and most importantly, the effects of multimodal disinformation and rebuttals partially depend on the topic of the message. Although fact-checkers reduce credibility of disinformation in both settings, and attitudinal congruence plays a consistent role in conditioning responses to multimodal disinformation, visuals do not have the same impact on affecting the credibility of news on school shootings and refugees.

6. Provide Recommendations for Further Research

Writing a dissertation discussion also makes a connection to possible future research. So, other scientists may complete that. While elaborating on possible implementations of your study, you may also estimate future approaches in topic research.  Here are some points to consider while your discussion in thesis writing:

• Outline questions related to your topic that you did not answer in defined study or did not outline as research questions. There are other possible gaps to research.
• Suggest future research based on limitations. For example, if you define surveyed people’s age as a limitation, recommend running another survey for older or younger recipients.

Example of Recommendations

As we mentioned before, our study has some limitations, as the research was conducted based on data from United State citizens. However, for a better understanding of government communication practices, it would be productive to implement the same research in other countries. Some cultural differences can influence the communication strategies the government uses in times of emergency. Another possible way to examine this topic is to conduct research using a specific period of time. For future studies, it will be beneficial to expand the number of survey recipients. 

7. Conclude Your Thesis/ Dissertation Discussion

You are almost done, the last step is to provide a brief summary of a section. It is not the same as a conclusion for whole research. However, you need to briefly outline key points from the dissertation discussion.  To finalize writing the discussion section of a dissertation, go through the text and check if there is no unimportant information. Do not overload the text with relevant data you did not present in the result section. Be specific in your summary paragraphs. It is a holistic view of everything you pointed out. Provide a few sentences to systemize all you outlined in the text. Example of a Concluding Summary in a Dissertation Discussion Section

To summarize, Airbnb has expertise in communicating CSR and CSA campaigns. We defined their communication strategy about the program for Ukrainian refugees as quite successful. They applied all the principles of CSR communication best practices, used dialogic theory to engage with the public on social media, and created clear messaging on applying for the program. Airbnb examples of CSR communication can be used by other businesses to create a communication strategy for unplanned CSR campaigns. Moreover, it can be further researched how Airbnb's CSR campaign influenced the organizational reputation in the future. 

Dissertation Discussion Example

If we need to share one piece of practical advice, it would be to use thesis or dissertation discussion examples when writing your own copy. StudyCrumb provides the best samples from real students' work to help you understand the stylistic and possible structure of this part. It does not mean you need to copy and paste them into your work.  However, you can use a  dissertation discussion example for inspiration and brainstorming ideas for breaking writing blocks. Here’s a doctoral thesis discussion chapter example.

Dissertation Discussion Writing Tips

Before reading this blog, you should already know how to write a thesis discussion. However, we would share some essential tips you need to have in mind while working on the document. 

• Be consistent Your dissertation discussion chapter is a part of bigger research, and it should be in line with your whole work.
• Understand your reader You are writing an academic text that will be analyzed by professionals and experts in the same field. Be sure that you are not trying to simplify your discussion.
• Be logical Do not jump into a new line of discussion if you did not delineate it as a research question at the beginning.
• Be clear Do not include any data that was not presented in the result section.
• Consider word choice Use such terms as “our data indicate…” or “our data suggests…” instead of “the data proves.”
• Use proper format Follow the formatting rules specified by a specific paper style (e.g., APA style format , MLA format , or Chicago format ) or provided by your instructor.

Bottom Line on Writing a Dissertation Discussion Chapter

At this stage, it should not be a question for you on how to write a discussion chapter in a PhD thesis or dissertation. Let’s make it clear. It is not a result section but still a place to elaborate on data and go deeper with explanations. Dissertation discussion section includes some intro, result interpretations, limitations, and recommendations for future research. Our team encourages you to use examples before starting your own piece of writing. It will help you to realize the purpose and structure of this chapter and inspire better texts! If you have other questions regarding the PhD writing process, check our blog for more insights. From detailed instruction on how to write a dissertation or guide on formatting a dissertation appendix , we’ve got you covered.

Order dissertation discussion from our proficient writers. They will take a significant burden off of you. Instead, they will carry out high-level academic work in a short time.

FAQ About Dissertation Discussion Chapter

1. where does a discussion section go in a dissertation.

Dissertation discussion section is used to go right after the result chapter. The logic is simple — you share your data and then go to the elaboration and explanation of it. Check the sample thesis we provide to students for details on structure.

2. How long should a dissertation discussion chapter be?

It is not a surprise that dissertation discussion chapter is extremely significant for the research. Here you will go into the details of your study and interpret results to prove or not your hypothesis. It should take almost 25% of your work. 

3. What tense should I use in a dissertation discussion?

Thesis or dissertation discussion used to have some rules on using tenses. You need to use the present tense when referring to established facts and use the past tense when referring to previous studies. And check your text before submission to ensure that you did not miss something.

4. What not to include in a dissertation discussion section?

The answer is easy. Discussion section of a dissertation should not include any new findings or describe some unsupported claims. Also, do not try to feel all possible gaps with one research. It may be better to outline your ideas for future studies in recommendations.

Joe Eckel is an expert on Dissertations writing. He makes sure that each student gets precious insights on composing A-grade academic writing.

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Discover How to Write a Discussion Section of a Research Paper

Updated 13 Jun 2024

When working on a research paper, one of the most important parts you must include is the discussion or the analytical section where you outline your findings. While it is almost the final part of an academic research paper, learning how to use it correctly without keeping things cluttered is essential. This article will help you understand the specifics regarding the discussion section of a research paper. You will learn why this section must be implemented, what to consider, and what things to avoid. This guide shall provide you with the structure and the basic rules. 

What is the discussion section of a research paper?

Speaking of an academic definition, a good discussion section in a research paper should provide a comprehensive analysis of any findings that you have encountered , especially when it's something unexpected. Since the talk is about the final part of your research, you must keep things condensed and realize that your target audience already knows the subject. It means that you have to analyze and interpret your findings. Once done, you must provide a brief significance of what you have discussed by bringing things back to the primary research objectives. In a certain sense, you must show the importance of what you have studied or an issue that has been brought up. 

What is the purpose of a discussion section? 

Your discussion must represent a brief review, so you must analyze and evaluate your research paper first. If you are not finished with your exploration yet, do not start with the discussion section because the purpose is to provide a clear context by discussing what’s already done. The purpose is to take each part of the preceding sections and make it easier for your target audience to connect the dots.

You have to provide at least three parts in your discussion section of a research paper, where you include an interpretation , an analytical part , and an explanation . It means you have to talk to your readers and provide a review by showing why your outcome is important and how it fits within the science. At the same time, you must remain self-critical and mention the limitations that you have encountered. If you require assistance, consider getting professional  research paper help for valuable guidance.

How long should the discussion section of a research paper be? 

The length aspect is tricky because your general requirements may differ depending on your subject and the total length of your research paper. The common rule is that your "Discussion" part should not be longer than the sum of other sections. You have to keep it within 6-7 paragraphs. The median discussion length is usually between 1100 and 2200 words. 

In a certain sense, your length will always relate to the number of your findings. Do not repeat your findings word after word because the purpose is to include a concise statement by dividing your total amount of words for three vital sections. 

Structure of discussion section of a research paper 

The most challenging part for college and university students is the structure of the discussion part of the research paper. As we have already learned, three sub-sections must be included. As an author, starting with a discussion in a research paper, you have to provide three main objectives, or contact someone to write my discussion post for you. 

• Discussion of your research paper. You have to start with the main objectives and answers that explain your research and the brief background of the methodology you have taken. It should be up to five sentences. Include your main thesis and remind your target audience about the key elements of your study. 
• Analysis of your findings. The middle part of your "Discussion" section should provide analytical writing. It should include a brief comparison with the previous research papers with references. The purpose is to support your study and the methods you have taken. This is where you start with interpreting your results and defend the answers as the introduction to your findings. 
• Interpretation of the research results. In the final section of your discussion, you must discuss your study's strengths and limitations by interpreting the results in greater depth. Discuss how your findings relate to the objectives that you have taken before. The tone of your structure must show that the research has been completed. Talk about the conclusion of your study and discuss the significance of your research paper. If it fills the gap in a certain field of study, mention it as well. 

When you are done with your structure and the notes, you have to understand that it is not the conclusion section because you have to talk and do a short review. It means that you have to learn how to write a research summary and always take all the key points as you explain things in writing. It is exactly what you must do when you think over your structure, where you go step by step to make your research paper or essay sound clear. 

How to write the discussion section of a research paper: 7 steps 

Step 1:  Read your research and always take notes. Start by reading through the previous sections of your research by taking notes of all the important elements. 

For example, if your research paper focuses on children’s literature and the interest among the kids, set your objectives to remind what you will discuss. It may start with: “The importance of studying young authors and the classics of children’s literature has been supported by the children’s book shows and social initiatives. The statistical data and research surveys have shown that the social campaigns boost the interest among the young readers”. 

Step 2:  Outline your main thesis objectives. Feel free to provide your thesis in a different, more condensed way. Once again, if we are dealing with a certain subject, it has to be related to the main problem. 

Suppose we are dealing with Digital Marketing and logistics in the post-Covid world. In that case, it has to be something like that: “The digital marketing in 2023 has been based on the lessons that we have learned during the pandemic times, which shows that the future of digital sales will relate to what we already know”. 

Step 3: Introduce your findings. Once you have introduced your main research subject, you should discuss what you could learn. It should not be a carbon copy of the results because you have to interpret them clearly, as if you are talking to a good friend. 

If you are studying autistic children, your example of discussion in the research paper can be like that: “The methodology used has shown that the use of technology apps helps the autistic children learn with the help of AI-based tools and seek the safe methods of communication. Based on statistical data, we can see that virtual classrooms have increased the learning time by at least 67%”. 

Step 4: Provide an analysis of the process. You should offer an analytical tone next as you discuss your research paper. It is one of the most challenging aspects that must be done. 

Suppose you are studying ER Nursing and the burnout problem. In that case, your discussion part of a research paper will be this way: “While the research methods used have been mostly limited to surveys and questionnaires, the majority of the nurses have also kept to journaling and the diaries. It has shown that most ER nursing personnel wish to join free Psychology courses to manage the stress”. 

Step 5:  Interpret the pros and cons of research. Do not pick only the good or the bad parts as you provide an interpretation. It means that you must include all sides of your research as you look at the findings. 

An example of this part in the field of Education would be this way: “ The Learning Management Systems that have been researched have shown that while they are flexible and accessible, there are still mental aspects like an emotional bonding. It shows that the physical presence of a teacher will never replace the virtual assistants”.  

Step 6:  Limitations and the results. It is where you should talk about the limitations and the challenges you have faced. 

For example, when you research Modern Fashion, your limitations may look this way: “Since there was a small sample of the customers that could visit the shops in person, it creates a specific group that differs from the online customers who could see the items differently. It shows that the sample group used has been quite biased”. 

Step 7: The place of your research in the scientific realm. In other words, it should show how your results fit within the local scientific community and/or the world. 

A discussion section of a research paper example for the field of Legal Studies will look this way: “It shows that the Freedom of Speech in practice is not the same aspect as the theoretical paradigm one studies during the academic course. The practical side of freedom done in this research shows why it is important to conduct field sessions based on the actual case studies”. 

What to avoid in a discussion section? 

The majority of modern students often need help understanding what must be done. When you explore your notes and look back at the previous sections, it is important to ensure that you are not missing something important or do not repeat aspects that have already been discussed. Keeping all the challenges on how to write the discussion section of a research paper in mind, our experts developed a basic checklist regarding things you must avoid as you work on your discussion section. Furthermore, you can find cheap research papers here, ensuring you can overcome any problems while staying within your budget.

• Don't copy your research results section! Although you should provide information about your research results, there are other objectives than this! You have to interpret the results and not only state them repeatedly. Your tone must be analytical, as you do not have to keep things up to a simple summary. Explain your results and discuss as you evaluate the pros and cons of the findings by staying unbiased and honest. 
• Make arguments with at least one piece of evidence! When you talk about your findings, you have to offer support and examples that relate to your research. It can be a case study or a research reference. A quote or statistical data should back up your explanations. At the same time, do just what is necessary and ensure you stay within your scientific field. Only make assumptions or start with an interdisciplinary approach if it is absolutely necessary! 
• Connect the dots with your target audience! Remember to provide information in a special way that would connect all of the previous sections. If there is some important statistical data, mention it again and point out why it’s essential for your research results. 
• Only introduce information that has been included before! Your discussion should talk about what has been mentioned in the previous sections. Only add information that could make things clear. The purpose of discussion in a research paper is to base things on what you already have! If you have a new idea, incorporate it in the past parts and discuss it in your following section. Do not forget to include anything new in the "Results" section to ensure that everything remains equal. 
• Only pick some of the essential and positive aspects. Since you have to interpret your findings, pick some crucial facts that make your research sound good. Even if your work could not address the problem or an issue that has been researched, say so! It is absolutely fine to provide an honest answer as you have to discuss your limitations. Discuss and explain why something has happened or why your research did not work by explaining. 

Making your discussion section inspiring 

Now, what is the discussion section of a research paper to sum things up? It is a piece of writing that should motivate your readers and even make a call to action as you talk about your take's advantages and the limitations you have faced. When you discuss something, it should briefly explain your research. If a person starts reading your research from this section, it should represent an executive plan that instantly explains every single bit. Always start with a research paper thesis statement because it will remind the readers about your main goals and show how it has been achieved and what barriers have been encountered. The golden rule of research writing will help you narrow things down and keep your writing condensed, inspiring, and clear! 

What is the main focus of the discussion section? 

It is to provide your readers with a clear explanation, analysis, and interpretation of what you could find as a researcher. Create a small review of your research to showcase all the advantages achieved. If a certain section provides the summary, it should be included in your discussion. Do not explain your methodology at greater length or repeat your thesis alone because you have to discuss and explain why your research is helpful. Most importantly, always show how your research findings fit within the main scientific field. 

What to include in the discussion section of a research paper? 

Let us assume a simple example: when you are writing a research paper on domestic abuse in Chile. Your discussion section should pose a thesis statement in the beginning and discuss why it is important and what objectives have been set. In the next part, you should explain your findings as to the reasons why domestic abuse happens and what solutions have been used. It must be based on the thesis and methodology without getting too deep. Once done, talk about the strong and the weak parts by analyzing your take on the problem. Offer an interpretation of how your research paper will help to address the problem not only in Chile but globally as well.

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Written by Meredith Anderson

Meredith, a dedicated editor at EduBirdie, specializes in academic writing. Her keen eye for grammar and structure ensures flawless papers, while her insightful feedback helps students improve their writing skills and achieve higher grades.

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Academic Phrases for Writing Results & Discussion Sections of a Research Paper

Overview |   Abstract   | Introduction | Literature Review | Materials & Methods | Results & Discussion | Conclusion & Future Work | Acknowledgements & Appendix

The results and discussion sections are one of the challenging sections to write. It is important to plan this section carefully as it may contain a large amount of scientific data that needs to be presented in a clear and concise fashion. The purpose of a Results section is to present the key results of your research. Results and discussions can either be combined into one section or organized as separate sections depending on the requirements of the journal to which you are submitting your research paper. Use subsections and subheadings to improve readability and clarity. Number all tables and figures with descriptive titles. Present your results as figures and tables and point the reader to relevant items while discussing the results. This section should highlight significant or interesting findings along with P values for statistical tests. Be sure to include negative results and highlight potential limitations of the paper. You will be criticised by the reviewers if you don’t discuss the shortcomings of your research. This often makes up for a great discussion section, so do not be afraid to highlight them.

The results and discussion section of your research paper should include the following:

• Comparison with prior studies
• Limitations of your work
• Casual arguments
• Speculations
• Deductive arguments

1. Findings

From the short review above, key findings emerge: __ We describe the results of __, which show __ This suggests that __ We showed that __ Our findings on __ at least hint that __ This is an important finding in the understanding of the __ The present study confirmed the findings about __ Another promising finding was that __ Our results demonstrated that __ This result highlights that little is known about the __ A further novel finding is that __ Together, the present findings confirm __ The implications of these findings are discussed in __ The results demonstrate two things.  First, __. Second,  __ The results of the experiment found clear support for the __ This analysis found evidence for __ Planned comparisons revealed that __ Our results casts a new light on __ This section summarises the findings and contributions made. It performs well, giving good results. This gives clearly better results than __ The results confirm that this a good choice for __ From the results, it is clear that __ In this section, we will illustrate some experimental results. This delivers significantly better results due to __ The result now provides evidence to __ It leads to good results, even if the improvement is negligible. This yields increasingly good results on data. The result of this analysis is then compared with the  __ The applicability of these new results are then tested on __ This is important to correctly interpret the results. The results are substantially better than __ The results lead to similar conclusion where __ Superior results are seen for __ From these results it is clear that __ Extensive results carried out show that this method improves __ We obtain good results with this simple method. However, even better results are achieved when using our algorithm. It is worth discussing these interesting facts revealed by the results of  __ Overall, our method was the one that obtained the most robust results. Slightly superior results are achieved with our algorithm. The result is equal to or better than a result that is currently accepted.

2. Comparison with prior studies

The results demonstrated in this chapter match state of the art methods. Here we compare the results of the proposed method with those of the traditional methods. These results go beyond previous reports, showing that __ In line with previous studies __ This result ties well with previous studies wherein __ Contrary to the findings of __ we did not find __ They have demonstrated that __ Others have shown that __ improves __ By comparing the results from __, we hope to determine __ However, in line with the ideas of __, it can be concluded that __ When comparing our results to those of older studies, it must be pointed out that __ We have verified that using __ produces similar results Overall these findings are in accordance with findings reported by __ Even though we did not replicate the previously reported __, our results suggest that __ A similar conclusion was reached by __ However, when comparing our results to those of older studies, it must be pointed out __ This is consistent with what has been found in previous __ A similar pattern of results was obtained in __ The findings are directly in line with previous findings These basic findings are consistent with research showing that __ Other results were broadly in line with __

3. Limitations of your work

Because of the lack of __ we decided to not investigate __ One concern about the findings of __ was that __ Because of this potential limitation, we treat __ The limitations of the present studies naturally include __ Regarding the limitations of __, it could be argued that __ Another limitation of this __ This limitation is apparent in many __ Another limitation in __ involves the issue of __ The main limitation is the lack of __ One limitation is found in this case. One limitation of these methods however is that they __ It presents some limitations such as __ Although widely accepted, it suffers from some limitations due to __ An apparent limitation of the method is __ There are several limitations to this approach. One limitation of our implementation is that it is __ A major source of limitation is due to  __ The approach utilised suffers from the limitation that __ The limitations are becoming clear __ It suffers from the same limitations associated with a __

4. Casual arguments

A popular explanation of __ is that __ It is by now generally accepted that __ A popular explanation is that __ As it is not generally agreed that __ These are very small and difficult to observe. It is important to highlight the fact that __ It is notable that __ An important question associated with __ is __ This did not impair the __ This is important because there is __ This implies that __ is associated with __ This is indicative for lack of __ This will not be biased by __ There were also some important differences in __ It is interesting to note that, __ It is unlikely that __ This may alter or improve aspects of __ In contrast, this makes it possible to __ This is particularly important when investigating __ This has been used to successfully account for __ This introduces a possible confound in __ This was included to verify that __

5. Speculations

However, we acknowledge that there are considerable discussions among researchers as to __ We speculate that this might be due to __ There are reasons to doubt this explanation of __ It remains unclear to which degree __ are attributed to __ However, __ does seem to improve __ This does seem to depend on __ It is important to note, that the present evidence relies on __ The results show that __ does not seem to impact the __ However, the extent to which it is possible to __ is unknown Alternatively, it could simply mean that __ It is difficult to explain such results within the context of __ It is unclear whether this is a suitable for __ This appears to be a case of __ From this standpoint, __ can be considered as __ To date, __remain unknown Under certain assumptions, this can be construed as __ Because of this potential limitation, we treat __ In addition, several questions remain unanswered. At this stage of understanding, we believe__ Therefore, it remains unclear whether __ This may explain why __

6. Deductive arguments

A difference between these __ can only be attributable to __ Nonetheless, we believe that it is well justified to __ This may raise concerns about __ which can be addressed by __ As discussed, this is due to the fact that __ Results demonstrate that this is not necessarily true. These findings support the notion that __ is not influenced by __ This may be the reason why we did not find __ In order to test whether this is equivalent across __, we __ Therefore, __ can be considered to be equivalent for __

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Dissertation findings and discussion sections

(Last updated: 2 March 2020)

Since 2006, Oxbridge Essays has been the UK’s leading paid essay-writing and dissertation service

We have helped 10,000s of undergraduate, Masters and PhD students to maximise their grades in essays, dissertations, model-exam answers, applications and other materials. If you would like a free chat about your project with one of our UK staff, then please just reach out on one of the methods below.

Granted that at some point in the discussion you are going to have to link back to this previous research. But you still have the opportunity to demonstrate how you have met that coveted gap in the research and generally made a useful contribution to knowledge.

There are many ways to write up both your findings and discussion. In shorter dissertations, it might make sense to have both of these comprise one section. In longer pieces of work, these chapters are usually separate.

Preparing to write

We also assume that you have used some sort of software program to help you with the organisation of your findings. If you have not completed this process, you must do so before beginning to write. If not, your findings chapter may end up a confusing and unorganised mess of random information. If you need help in this area, make sure to seek it out before beginning to put your findings down on paper.

One of the main issues that students tend to encounter when writing up their findings is the amount of data to include. By the end of the research process, you've probably collected very large amounts of data . Not all of this can possibly appear in your dissertation without completely overwhelming the reader. As a result, you need to be able to make smart decisions about what to include and what to leave out.

One of the easiest ways to approach this task is to create an outline. In approaching the outline, it is in your best interest to focus on two key points. Firstly, you need to focus on answering your research questions. Secondly, you must include any particularly interesting findings that have cropped up as you completed your research.

An outline will give you the structure you need, and should make the whole process of presenting your findings easier. We realise that it is going to be a difficult process to pick and choose pieces of data to include. But you must be diligent in the work that you cut out. A findings chapter that is long and confusing is going to put the reader off reading the rest of your work.

Introducing your findings

It can be up to 40% of the total word count within your dissertation writing . This is a huge chunk of information, so it's essential that it is clearly organised and that the reader knows what is supposed to be happening. One of the ways you can achieve this is through a logical and organised introduction.

There are four main components that your introduction should include:

Reminding the reader of what you set out to do

A brief description of how you intend approaching the write up of the results

Placing the research in context

Letting the reader know where they can find the research instruments (i.e. the Appendix)

With a findings chapter, there should be no suspense for the reader. You need to tell them what they need to know right from the beginning. This way, they'll have a clear idea about what is still to come. A good introduction will start by telling the reader where you have come from in the research process and what the outcome was (in a couple of paragraphs or less).

You need to highlight the structure of the chapter (as you generally will do with all chapters) and where the reader might find any further information (e.g. in the appendices).

Organisation of data

This is really going to depend on the type of project you have created .

For example, if you have completed a qualitative research project, you might have identified some key themes within the software program you used to organise your data. In this case, highlighting these themes in your findings chapter may be the most appropriate way to proceed. Not only are you using information that you have already documented, you are telling a story in each of your sections (which can be useful in qualitative research).

But what if you undertook a more quantitative type study? You might be better off structuring your findings chapter in relation to your research questions or your hypotheses. This assumes, of course, that you have more than one research question or hypothesis. Otherwise you would end up just having one really long section.

This brings us to our next student mistake – trying to do too much within one section.

Subheadings are ultimately going to be your friend throughout your dissertation writing . Not only do they organise your information into logical pieces, they give the reader guidelines for where your research might be going. This is also a break for the reader. Looking at pages and pages of text without any breaks can be daunting and overwhelming for a reader. You don't want to overwhelm someone who is going to mark your work and who is responsible for your success (or failure).

When writing your introduction, be clear, organised and methodical. Tell the reader what they need to know and try to organise the information in a way that makes the most sense to you and your project. If in doubt, discuss this with your supervisor before you start writing.

Presentation of qualitative data

If you have conducted things like interviews or observations, you are likely to have transcripts that encompass pages and pages of work.

Putting this all together cohesively within one chapter can be particularly challenging. This is true for two reasons. First, it is always difficult to determine what you are going to cut and/or include. Secondly, unlike quantitative data, it can often be difficult to represent qualitative data through figures and tables, so condensing the information into a visual representation is simply not possible. As a writer, it is important to address both these challenges.

When considering how to present your qualitative data, it may be helpful to begin with the initial outline you have created (and the one described above). Within each of your subsections, you are going to have themes or headings that represent impactful talking points that you want to focus on.

Once you have these headings, it might be helpful to go back to your data and highlight specific lines that can/might be used as examples in your writing. If you have used multiple different instruments to collect data (e.g. interviews and observations), you are going to want to ensure that you are using both examples within each section (if possible). This is so that you can demonstrate to more well-rounded perspective of the points you are trying to make. Once you have identified some key examples for each section, you might still have to do some further cutting/editing.

Once you have your examples firmly selected for each subsection, you want to ensure that you are including enough information. This way, the reader will understand the context and circumstances around what you are trying to ‘prove’. You must set up the examples you have chosen in a clear and coherent way.

Students often make the mistake of including quotations without any other information. It is important that you embed your quotes/examples within your own thoughts. Usually this means writing about the example both before and after. So you might say something like, “One of the main topics that my participants highlighted was the need for more teachers in elementary schools. This was a focal point for 7 of my 12 participants, and examples of their responses included: [insert example] by participant 3 and [insert example] by participant 9. The reoccurring focus by participants on the need for more teachers demonstrates [insert critical thought here]. By embedding your examples in the context, you are essentially highlighting to the reader what you want them to remember.

Aside from determining what to include, the presentation of such data is also essential. Participants, when speaking in an interview might not do so in a linear way. Instead they might jump from one thought to another and might go off topic here and there.

It is your job to present the reader with information on your theme/heading without including all the extra information. So the quotes need to be paired down to incorporate enough information for the reader to be able to understand, while removing the excess.

Finding this balance can be challenging. You have likely worked with the data for a long time and so it might make sense to you. Try to see your writing through the eyes of someone else, which should help you write more clearly.

Presentation of quantitative data

Something to consider first with numeric data is that presentation style depends what department you are submitting to. In the hard sciences, there is likely an expectation of heavy numeric input and corresponding statistics to accompany the findings. In the arts and humanities, however, such a detailed analysis might not be as common. Therefore as you write out your quantitative findings, take your audience into consideration.

Just like with the qualitative data, you must ensure that your data is appropriately organised. Again, you've likely used a software program to run your statistical analysis, and you have an outline and subheadings where you can focus your findings. There are many software programs available and it is important that you have used one that is most relevant to your field of study.

For some, Microsoft Excel may be sufficient for basic analysis. Others may rely on SPSS, Stata, R, or any of the other programs available through your institution or online. Whatever program you have used, make sure that you document what you have done and the variables that have affected your analysis.

One common mistake found in student writing is the presentation of the statistical analysis. During your analysis of the data, you are likely to have run multiple different analyses from regressions to correlations. Often, we see students presenting multiple different statistical analyses without any real understanding of what the tests mean.

Presentation of quantitative data is more than just about numbers and tables. You must explain your findings and justify why you have run/presented the tests that you have. You could also explain how they relate to the research question. However, depending on how you have organised your work, this might end up in the discussion section.

Students who are not confident with statistical analysis often have a tendency to revert back to their secondary school mathematics skills. They commonly document the mean, median, and mode for all of their results. Now, these three outcomes can be important. But having a good understanding of why you are proceeding with this strategy of analysis is going to be essential in a primarily quantitative study.

That noted, there are different expectations for an undergraduate dissertation and a PhD thesis, so knowing what these expectations are can be really helpful before you begin.

Presentation of graphs, tables, and figures

The first is the use of colour and/or variables. Depending on the presentation of your dissertation, you may be required to print out a final copy for the marker(s). In many cases, this final copy must be printed in black and white. This means that any figures or graphs that you create must be readable in a black and white (or greyscale) format.

This can be challenging because there are only so many distinct shades of grey. In a pie chart, you might show one section as purple and the other as green. Yet when printed, both the purple and the green translate to approximately the same shade of grey, making your graph suddenly unreadable.

Another common error is overwhelming the reader with graphs and tables. Let's think about your outline and subheadings. If you're including a table under each subheadings, it needs to be relevant to the information that is being discussed in that chapter. There is no correct or incorrect number of graphs that should exist within the section, but you should use your judgement about what looks appropriate.

The final mistake we see is the duplication of writing (or absence of writing) when presenting a graph. Some students will present their findings in a graph or table and then write out this information again below the graph. This defeats the entire purpose of using the graph in the first place. So avoid this at all times.

Conversely, other students sometimes include a graph or figure but nothing else. Doing this denies the reader of context or purpose of said graph or figure. At some point, a balance needs to be struck where the reader has the information they require to really understand the point being made within the section.

Analysis and synthesis in a discussion

The purpose of a discussion chapter.

The structure of your discussion chapter is really going to depend on what you are trying to do and how you have structured your findings. If you chose to structure your findings by theme, it might make sense to continue this into the analysis chapter.

Other people might structure it according to the research questions. This clearly indicates to the reader how you have addressed your study. Marking a dissertation usually requires the marker to comment on the extent to which the research questions have been addressed. So by structuring a dissertation that lays out each research question for the marker, you are making their job easier. Needless to say, this a great thing.

Like any other chapter in your thesis, an introduction is an essential component of your discussion. By this point, the reader has gone through your findings and is now looking for your interpretation. Therefore, at the end of your discussion introduction you should highlight the content that each of the subsections will cover.

A conclusion to your discussion section (or a chapter summary) is also going to be beneficial. The length of the analysis chapter is usually quite long, so a wrap up of the key points at the end can help the reader digest your work. It can also help ensure that the reader actually understands the points you are trying to highlight within your project.

Critical thinking

Without any critical thinking, you are really doing yourself a disservice. It will affect the mark that you obtain on your overall dissertation. This is why the analysis chapter is usually weighted quite heavily on the marking rubric.

We tell students about critical thinking and the importance of it on a daily basis. And yet, there does seem to be a general confusion about what critical thinking entails, i.e. what constitutes critical thinking versus what is a simple description.

Critical thinking asks you to provide your own opinion on your topic, which can be daunting at first. For much of your academic career, you've likely been asked to use research to justify a position that has already been set. Unlike critical thinking, this requires you to use other people’s ideas. But even if you're new to it, try and get to grips with what critical thinking entails and use it in your work.

Creating sub-sections

Subheadings need to be informative but not too long. It is possible to layer your subheadings, so you might have a Chapter 2, a Section 2.1 and then a 2.1.1 and 2.2.2. Usually anything after 3 numerical points does not get a number and would not appear in your table of contents.

When creating titles for your subheadings, consider how they are going to look in the table of contents. They need to fit on one line, ideally, so putting your research question as the subheading might end up being too long. Conversely, one- or two-word subheadings usually doesn't give enough information about the purpose of the section.

Finding this balance is important. But remember you can always edit your subheadings retrospectively.

Linking to previous chapters

Ideally, you will be able to concisely and effectively link your research to what has been researched previously. But this can be a challenge. You don't want to repeat what has been said in your literature review or the findings . But you need to pull examples from both of these sections in order to make the points that you need to.

So, how do you tackle this?

One way is by referring the reader back to previous chapters, sections, or subsections. This process can generally be done at the end. You can put in a place holder until you know how your sections will be numbered. For example you might write: “In Section XYZ, the theme of … was discussed. Findings from this study indicate…. (see Section XYZ for details)”. While ‘XYZ’ is obviously not going to be the same section, by using the same abbreviation, you can then search ‘XYZ’ after you have completed writing and replace each term with the appropriate number. This also makes the proofreading process easier.

If you are submitting an electronic version of this document, you may also consider hyperlinks to take the reader to the different sections. But be aware that this can be considerably more work, so you should allow for this in your timescale if it's something you wish to implement.

Let's outline the main takeaway points:

It is essential that you keep in mind the ‘describe, analyse, synthesise’ model.

The findings chapter is essentially the describe part. You need to ensure that you have clearly identified data that relates to your research questions, hypotheses, or themes of your study.

For the ‘describe’ component, you are not looking to support your work with other research, but rather to present your contribution. It is also important to consider your data in the ‘describe’ section. If you have qualitative data, ensure that you have edited the quotes and examples to a reasonable length. Pick quotes that accurately represent your theme. Try not to focus solely on one or two participants (if possible). Ensure that you are demonstrating links between multiple instruments, if you used them.

If you are using quantitative data, be careful about how many statistical tests you run. Make sure you can justify why you chose one particular test over another. When presenting graphs, use a colour scheme that's appropriate for the reader when printing in black and white. Ensure that graphs and tables are appropriately explained, but that the information provided is not duplicated.

From the ‘describe’ element, you move into the 'analysis' and 'synthesis'. These parts usually appear in the discussion and ask you to employ your critical thinking skills to demonstrate how your research fits into the bigger picture. It is often the case that your analysis holds the most weight in the marking scheme. So you should spend considerable time ensuring this section is appropriate. It needs to demonstrate how you have attempted to answer your research questions.

Finally, create an outline before you begin. While this might seem tedious at first, filling in the sections with the appropriate information will mean that you are not writing things over and over again. It'll also make sure you do not go wildly off topic. It is always beneficial to have a second set of eyes assess your work for any errors or omissions. Many students choose to contact professional editors to help with this as they hold the relevant expertise to guide you on the correct path to creating a perfect discussion section that is ready for submission.

In terms of presentation, both the findings and discussion chapters will benefit from a clear and logical introduction and chapter summary. Remember that both of these chapters are meant to inform. You are leading the reader on a journey, so make sure they stay on the path and arrive at the final destination with you!

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What’s Included: Discussion Template

This template covers all the core components required in the discussion/analysis chapter of a typical dissertation or thesis, including:

• The opening/overview section
• Overview of key findings
• Interpretation of the findings
• Concluding summary

The purpose of each section is explained in plain language, followed by an overview of the key elements that you need to cover. The template also includes practical examples to help you understand exactly what’s required, along with links to additional free resources (articles, videos, etc.) to help you along your research journey.

The cleanly formatted Google Doc can be downloaded as a fully editable MS Word Document (DOCX format), so you can use it as-is or convert it to LaTeX.

PS – if you’d like a high-level template for the entire thesis, you can we’ve got that too .

FAQ: Thesis Discussion Template

What types of dissertations/theses can this template be used for.

The discussion chapter template follows the standard format for academic research projects, which means it will be suitable for the majority of dissertations, theses and research projects (especially those within the sciences).

Keep in mind that the exact requirements for the discussion chapter/section will vary between universities and degree programs. For example, your university may require that the discussion chapter and conclusion chapter are merged into one, or that the results and discussion are covered together (this is often the case with qualitative research). So, be sure to double-check your university’s requirements before you finalise your structure.

Is this template for an undergrad, Master or PhD-level thesis?

This template can be used for a dissertation, thesis or research project at any level of study. Doctoral-level projects typically require the discussion chapter to be more extensive/comprehensive, but the structure will typically remain the same. Again, be sure to check your university’s requirements and norms in terms of document structure.

How long should the discussion chapter be?

This can vary a fair deal, depending on the level of study (undergrad, Master or Doctoral), the field of research, as well as your university’s specific requirements. Therefore, it’s best to check with your university or review past dissertations from your program to get an accurate estimate.

Can I share this template with my friends/colleagues?

Yes, you’re welcome to share this template in its original format (no editing allowed). If you want to post about it on your blog or social media, please reference this page as your source.

What format is the template (DOC, PDF, PPT, etc.)?

The dissertation discussion chapter template is provided as a Google Doc. You can download it in MS Word format or make a copy to your Google Drive. You’re also welcome to convert it to whatever format works best for you, such as LaTeX or PDF.

Do you have templates for the other chapters?

Yes, we do. We are constantly developing our collection of free resources to help students complete their dissertations and theses. You can view all of our template resources here .

Can Grad Coach help me with my discussion/analysis?

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• How to Write a Discussion Section | Tips & Examples

How to Write a Discussion Section | Tips & Examples

Published on 21 August 2022 by Shona McCombes . Revised on 25 October 2022.

The discussion section is where you delve into the meaning, importance, and relevance of your results .

It should focus on explaining and evaluating what you found, showing how it relates to your literature review , and making an argument in support of your overall conclusion . It should not be a second results section .

There are different ways to write this section, but you can focus your writing around these key elements:

• Summary: A brief recap of your key results
• Interpretations: What do your results mean?
• Implications: Why do your results matter?
• Limitations: What can’t your results tell us?
• Recommendations: Avenues for further studies or analyses

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Table of contents

What not to include in your discussion section, step 1: summarise your key findings, step 2: give your interpretations, step 3: discuss the implications, step 4: acknowledge the limitations, step 5: share your recommendations, discussion section example.

There are a few common mistakes to avoid when writing the discussion section of your paper.

• Don’t introduce new results: You should only discuss the data that you have already reported in your results section .
• Don’t make inflated claims: Avoid overinterpretation and speculation that isn’t directly supported by your data.
• Don’t undermine your research: The discussion of limitations should aim to strengthen your credibility, not emphasise weaknesses or failures.

Prevent plagiarism, run a free check.

Start this section by reiterating your research problem  and concisely summarising your major findings. Don’t just repeat all the data you have already reported – aim for a clear statement of the overall result that directly answers your main  research question . This should be no more than one paragraph.

Many students struggle with the differences between a discussion section and a results section . The crux of the matter is that your results sections should present your results, and your discussion section should subjectively evaluate them. Try not to blend elements of these two sections, in order to keep your paper sharp.

• The results indicate that …
• The study demonstrates a correlation between …
• This analysis supports the theory that …
• The data suggest  that …

The meaning of your results may seem obvious to you, but it’s important to spell out their significance for your reader, showing exactly how they answer your research question.

The form of your interpretations will depend on the type of research, but some typical approaches to interpreting the data include:

• Identifying correlations , patterns, and relationships among the data
• Discussing whether the results met your expectations or supported your hypotheses
• Contextualising your findings within previous research and theory
• Explaining unexpected results and evaluating their significance
• Considering possible alternative explanations and making an argument for your position

You can organise your discussion around key themes, hypotheses, or research questions, following the same structure as your results section. Alternatively, you can also begin by highlighting the most significant or unexpected results.

• In line with the hypothesis …
• Contrary to the hypothesised association …
• The results contradict the claims of Smith (2007) that …
• The results might suggest that x . However, based on the findings of similar studies, a more plausible explanation is x .

As well as giving your own interpretations, make sure to relate your results back to the scholarly work that you surveyed in the literature review . The discussion should show how your findings fit with existing knowledge, what new insights they contribute, and what consequences they have for theory or practice.

Ask yourself these questions:

• Do your results support or challenge existing theories? If they support existing theories, what new information do they contribute? If they challenge existing theories, why do you think that is?
• Are there any practical implications?

Your overall aim is to show the reader exactly what your research has contributed, and why they should care.

• These results build on existing evidence of …
• The results do not fit with the theory that …
• The experiment provides a new insight into the relationship between …
• These results should be taken into account when considering how to …
• The data contribute a clearer understanding of …
• While previous research has focused on  x , these results demonstrate that y .

Even the best research has its limitations. Acknowledging these is important to demonstrate your credibility. Limitations aren’t about listing your errors, but about providing an accurate picture of what can and cannot be concluded from your study.

Limitations might be due to your overall research design, specific methodological choices , or unanticipated obstacles that emerged during your research process.

Here are a few common possibilities:

• If your sample size was small or limited to a specific group of people, explain how generalisability is limited.
• If you encountered problems when gathering or analysing data, explain how these influenced the results.
• If there are potential confounding variables that you were unable to control, acknowledge the effect these may have had.

After noting the limitations, you can reiterate why the results are nonetheless valid for the purpose of answering your research question.

• The generalisability of the results is limited by …
• The reliability of these data is impacted by …
• Due to the lack of data on x , the results cannot confirm …
• The methodological choices were constrained by …
• It is beyond the scope of this study to …

Based on the discussion of your results, you can make recommendations for practical implementation or further research. Sometimes, the recommendations are saved for the conclusion .

Suggestions for further research can lead directly from the limitations. Don’t just state that more studies should be done – give concrete ideas for how future work can build on areas that your own research was unable to address.

• Further research is needed to establish …
• Future studies should take into account …
• Avenues for future research include …

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Open Access

Peer-reviewed

Research Article

Intersectional analysis of the experiences of women who fail to conceive in low and middle income neighbourhoods of Delhi, India: Findings from a qualitative study

Roles Formal analysis, Investigation, Project administration, Writing – original draft, Writing – review & editing

Affiliation Society for Applied Studies, New Delhi, India

Roles Conceptualization, Writing – review & editing

Affiliation UNDP-UNFPA-UNICEF-WHO-World Bank Special Programme of Research, Development and Research Training in Human Reproduction (HRP), Department of Sexual and Reproductive Health and Research, World Health Organization, Geneva, Switzerland

Roles Conceptualization

Roles Conceptualization, Supervision, Writing – review & editing

* E-mail: [email protected]

• Priyanka Adhikary, 
• Gitau Mburu, 
• Rita Kabra, 
• Ndema Abu Habib, 
• James Kiarie, 
• Neeta Dhabhai, 
• Ranadip Chowdhury, 
• Sarmila Mazumder

• Published: July 3, 2024
• https://doi.org/10.1371/journal.pone.0304029
• Reader Comments

Experiences of delayed conception and infertility have been reported among women. However, the concept of intersectionality is rarely utilised in studies of infertility, and it is particularly uncommon in research from low- and middle- income countries.

Research question

What are the lived experiences of women with delayed conception in low to -middle income neighbourhoods of Delhi, India?

This was a qualitative study (n = 35) that recruited women who had failed to conceive after 18 months of regular unprotected sexual intercourse. Data were collected between February and July 2021. Data were collected through focus group discussions in low income to middle income neighbourhoods of Delhi, India. Analysis identified themes related to intersecting axes of inequality.

The results showed that gender intersected with economics, masculinity, patriarchal norms and class to influence the experiences of women. The intersection of gender, economics and patriarchal norms compromised women’s agency to be active generators of family income, and this dynamic was exacerbated by patrilocal residence. In addition, masculinity contributed to stigmatisation and blaming of women, due to the inaccurate perception that men did not contribute to a couple’s infertility. The intersection of gender and social class in medical settings created barriers to women’s access to medical information.

Findings from this study provide representative examples of the variety of axes of inequality that shape women’s experiences in the study setting. Although these findings may not be generalisable to all women who are experiencing delayed conception, they highlight a need for improved awareness and education on infertility, as well as a need to ensure the availability and accessibility of fertility care for couples in need.

Citation: Adhikary P, Mburu G, Kabra R, Habib NA, Kiarie J, Dhabhai N, et al. (2024) Intersectional analysis of the experiences of women who fail to conceive in low and middle income neighbourhoods of Delhi, India: Findings from a qualitative study. PLoS ONE 19(7): e0304029. https://doi.org/10.1371/journal.pone.0304029

Editor: Martin Mbonye, Makerere University, UGANDA

Received: November 3, 2023; Accepted: May 5, 2024; Published: July 3, 2024

Copyright: © 2024 Adhikary et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Our dataset includes the transcripts of focus group discussions with a group of individuals in low and middle income neighbourhoods of Delhi. We have extracted and included relevant quotes from participants that support our findings. The entire dataset cannot be shared publicly because of the sensitivity of this qualitative data which concerns as stigmatised condition (infertility), and the risk that participants could be identified from the information divulged during the interaction with participants. This would contradict the agreements with participants that our study would not publish data that can potentially identify them as outlined in the informed consent and study information sheet. Please contact Ethics Review Committee (ERC), Society for Applied Studies via email ( [email protected] ) or via phone number (+91-7838350052) that is the focal point for data restriction and access requests for researchers who meet the criteria for access to confidential data. Please note that we cannot upload this on publicly accessible platform and therefore we are not in a position to provide the citation of where the full data set can be found.

Funding: UNDP-UNFPA-UNICEF-WHO-World Bank Special Programme of Research, Development and Research Training in Human Reproduction (HRP), a cosponsored programme executed by the World Health Organization (WHO) Funder-World Health Organization URL of the funder website- https://www.who.int/ The funders did not have a role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Infertility is an important public health issue globally [ 1 ]. It is defined as a disease of the male or female reproductive system, characterised by the failure to achieve pregnancy after 12 months or more of unprotected sexual intercourse [ 2 ]. Infertility as a public health issue is closely linked to biological sex because it is concerned with bodily reproductive function [ 3 ], as well as gender and gender roles [ 4 ]. As a social construct, gender is what people are expected to do in their everyday lives, based on their biological attributes [ 5 ]. Consequently, gender is shaped by social interactions [ 4 , 5 ] within a wider structural environment [ 6 ]. As such, an exploration of how infertility is experienced by women or men can reveal how gender is linked with structural drivers of gender-based inequalities [ 6 ].

In several low and middle income countries (LMICs), infertility has significant implications for women. A woman’s ability to beget a child determines her identity and womanhood. The kinship system, together with patriarchal descent, patrilocal residence, and property inheritance, all contribute to the value of fertility intertwined with women’s status in such societies [ 7 , 8 ].The normative constructs of femininity and masculinity reinforce man’s role as a protector and breadwinner, while the woman is expected to be the keeper of the house and raise children. In these contexts gender roles endorse a woman’s primary role as a mother [ 9 ]. Only in becoming a mother does she fulfil her role in society. Deviating from normative gender expectations surrounding motherhood is discouraged by mainstream society, and often results in stigmatisation [ 10 – 13 ].

In this context, we posit that intersectionality is a useful concept for elaborating women’s experiences of infertility. Such experiences may reflect diverse intersecting factors such as age, class, ethnicity, education and financial capacity, since these and other factors affect women’s social positions and influence the multiple interactions of their social identities [ 14 , 15 ]. An intersectional approach therefore has the potential to illuminate multiple dimensions of women’s lives that co-exist and interact to shape their lives [ 16 , 17 ].

Although the concept of intersectionality is increasingly used to understand inequalities [ 16 ], it is rarely employed in infertility studies [ 15 ]. An intersectional analysis of infertility experience is useful as it provides an overarching view of how various social facets act as instruments in shaping women’s experiential journey of failed conception, and consequently, how these facets contribute to gender inequality [ 15 ]. This approach is particularly useful in elaborating context-specific interactions of social categories and the power dynamics that produce or uphold inequalities [ 18 ]. In this paper, we draw on an intersectional approach to provide insight into the lived experiences of women who failed to conceive in low to middle income neighbourhoods of Delhi, India. The paper explores the drivers of inequality among these women using an intersectionality approach. Findings from this study will provide useful information that can be used to improve health services and social support for women who failed to conceive a child.

Materials and methods

Study aims and design.

This was an exploratory qualitative study conducted among 35 women who had failed to achieve pregnancy with their husbands/partners after 18 months of regular unprotected sexual intercourse, as reported by them. Qualitative methods are suited for exploration of people’s experiences [ 19 ]. The study was nested in a larger cross-sectional mixed methods study that aimed to understand (a) the baseline characteristics of women who experience delayed conception; (b) their perspectives, actions and experiences related to the delay in conceiving; (c) their quality of life and mental health; and (d) costs of any interventions obtained. The study was carried out in urban and peri-urban low-to-middle socio-economic neighbourhoods of South Delhi, India [ 20 ]. Study sites were selected based on feasibility, given that investigators had previously been running a randomised controlled trial in the area [ 21 ] and had built a rapport with community leaders. Data were collected over a period of 6 months between February and July 2021. Reporting in this paper follows COREQ guidelines for qualitative research [ 22 ] as shown in S1 Checklist .

Participant recruitment

A detailed protocol for this study has been published elsewhere [ 23 ]. In brief, participants were selected from the population of women who had participated in the Women and Infants Integrated Interventions for Growth Study (WINGS) [ 21 ] and had failed to become pregnant during the 18-month study follow-up period. WINGS was an individually randomised trial with factorial design that assessed the impact of an intervention package comprising nutrition, health, psychosocial, and water, sanitation and hygiene (WaSH) interventions spanning across the preconception, pregnancy and early childhood period [ 21 ].

At the end of the 18-month study follow-up period, women who had failed to achieve pregnancy with their husbands/ partners were invited to participate in this study on infertility. A total of 1530 women had failed to achieve pregnancy with their husbands/ partners in WINGS despite being sexually active, not using contraception, and not lactating over a period of 18 months. Of these, 775 had primary infertility (i.e., had never conceived), while 755 had secondary infertility (i.e., had conceived previously). Among these 1530 women, 35 were purposively sampled to participate in qualitative focus group discussions (FGDs), until saturation of data. Through this sampling method, researchers identified individuals who had never been pregnant (i.e., had primary infertility) as well as those who had a child and wanted one more child (i.e., likely to have had secondary infertility), in order to ensure the inclusion of women with diverse experiences. Participants were contacted by researchers via a combination of phone calls and in-person by visiting their homes.

Study sample

Thirty five women who had failed to conceive over a period of 18 months constituted the sample for this study. Table 1 presents the demographic characteristics of the participants, who were identified using a numerical code to protect their identities. Of the 35 women, nine already had a child and wished for a second child. Most of the participants were Hindu (n = 28), while others were Muslim (n = 4), Christian (n = 2) and Jain (n = 1). The mean age of participants was 27.68 (± 3.25 SD) years, with an age range of 21 to 33 years. All were married and identified as heterosexual; 33 were homemakers, one participant was working in the private sector and one was self-employed, managing a grocery store. The average annual household income was 220,000 Indian Rupees (2674 USD). Only two of 35 had health insurance.

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https://doi.org/10.1371/journal.pone.0304029.t001

Informed consent procedures

Ethical approval for this study was obtained from the Ethics Review Committees of the WHO (A-ID: A65998) and CHRD-SAS (SAS/ERC/RHR-Infertility/2020). Participants were informed about the aim of the study, study procedures, voluntary participation denoting that they were entitled to decline participation or withdraw from the FGDs, or decline to respond to questions at any time, even after initially consenting to participate. Those who consented to participate were enrolled. The FGDs were recorded if the women consented for the same. Participants were provided with a unique number, with which they could be identified, and their personal information was anonymized and their identity was kept confidential. FGDs were conducted by a female researcher (PA) who is trained in qualitative research. Before each FGD, the researcher confirmed that participants understood the purpose, benefits, and risks of participating, and then obtained their written consent to participate. This was done to ensure informed decision-making to participate.

Data collection procedures

To guide the FGDs, topic guides were prepared. The topic guide questions focused on the experience of delayed conception, coping mechanisms, treatment-seeking behaviour, and the type of help sought, including from family, friends and medical professionals, and other actions taken by the participants to try to increase the likelihood of conception. The topic guide was designed to explore areas where the participants struggled with during delayed conception, and about decisions that participants made in their efforts to conceive.

Prior to data collection, the topic guide was translated into Hindi script, the local vernacular, and pilot tested on a small sample of women (n = 6). The topic guide was then refined with the aims of making questions more context-specific, ensuring that questions would be relevant to participants, and would elicit meaningful responses, while maintaining sensitivity to the challenges associated with discussing infertility, such as stigma.

Following this, four FGDs were conducted with 12 participants (First FGD), 8 participants (Second FGD), 8 participants (Third FGD) and 7 participants (Fourth FGD). The FGDs were conducted in Hindi.

Although the FGD moderator i.e. the researcher and the participants shared a common socio-cultural background i.e. nationality, language, and gender, the moderator was cognizant of her socio-cultural privilege including education, status, and class. She was aware of her situatedness in the study and therefore constantly negotiated her positionality as a researcher and as an individual who has her own subjective stance, shaped by her personal experiences, socio-cultural status, and surroundings. During the discussions, the researcher reflected on her objectivity as a researcher, and her subjectivity as a human being sharing a social niche that was different to that of the participants. What was paramount for the researcher was to make the women feel comfortable. She attempted to remain empathetic and sensitive towards the participants, while accepting her positionality. In order to make participants comfortable and not self-conscious, the researcher dressed similarly to the women and went along with the conversation, switching languages between Hindi and English when participants did so, and with humour, attempted to put them at ease.

The FGDs were conducted at a neutral place outside the home environment, not in any clinic that can be identified with infertility care. This was to avoid apprehensions and safeguard privacy and confidentiality, while enabling participants to feel comfortable while interacting with the researcher. The FGDs were carried out in the recommended seating arrangement, in a circle with a distance that was appropriate for each one to be clearly visible and heard, which facilitated engaging discussions. Multiple dictaphones were placed at various positions to capture the voice of all participants. Along with field notes, participants’ non-verbal communications were documented to obtain deeper insights into their responses. The FGD sessions lasted for an average of 60 to 90 minutes. At the end of each FGD session, the topic guide was cross-checked to confirm that all questions had been asked. The key points were shared with the participants for their final comments and to ensure that their perspectives were appropriately captured.

Data analysis

The lead researcher who conducted the FGDs (PA) transcribed the recordings and was involved in data coding. By performing each transcription shortly after the FGD took place, the researcher was able to detect and recall nuances in the responses and observations, thus maximising the accuracy, reliability and completeness of the data. Given the complexity of translation [ 24 ], the research group selected a qualitative researcher (PA) who was fluent in writing, reading and speaking Hindi language, which preserved participants’ meanings and trustworthiness of the translation. Where Hindi words could be translated into several closely linked English words, the researcher strived to take into account the context of the discussion, participants’ words and gestures in order to mitigate the risk of loss of meaning during translation.

The transcripts were analysed using NVIVO software, version 1.7.1. First, similar responses were coded and grouped in nodes, which were then used to generate sub-themes and themes through an inductive thematic process [ 25 ]. Patterns and relationships among the themes and sub-themes were identified and conceptualized iteratively to ensure that the potential significance of all the data was fully explored. A code book was generated by PA text by text, which identified themes and sub-themes, and codes, corroborating the theory of intersectionality. The generation of the codebook was an iterative process in which each text was read repeatedly, followed by generation of themes through a combination of both inductive (data-driven) and deductive (theory of intersectionality) approach [ 26 ]. The lead researcher (PA), who is a social scientist with doctoral level training in anthropology, and was unknown to the participants prior to the study, was aware of the potential for her own background, values and beliefs to influence data collection and interpretation of research findings [ 27 ]. The researcher (PA) initially coded the data, and discussed the preliminary findings with the larger research team comprising all the authors, and modified codes following discussions. To further mitigate researcher bias, the analysis hinged on verbatim and bodily expressions of the study participants to capture the richness and depth of participants’ experiences.

The results provided insight into how gender intersected with economics, masculinity, patrilocal norms and class to influence the experiences of women, as shown in Table 2 .

https://doi.org/10.1371/journal.pone.0304029.t002

Intersection of gender, finances, and labour participation

The socioeconomic status of many study participants and their families meant that the cost of infertility treatment imposed a major burden. In many cases, couples made financial sacrifices or were forced to forego other needs in order to pay for it:

We , my husband and I , have prioritised the treatment and medicines required to have a child rather than spending money on food . We have cut down our budget just for the treatment . ( FGD participant # 28 )

Having experienced high treatment costs, many women understood the financial difficulties they and their husbands faced. The women shared that they performed their duties in the household as per the gender norms with all dedication. They believed that being committed to the desired gender specific duties in the household will project their image as a good and responsible daughter-in-law who contributes her labour efficiently in the household. Therefore, they felt by doing so, they would not be taken as a burden to the family members. Nevertheless, despite all their efforts their failure to conceive a child devalued their role and duties in the family and they were perceived as an economic liability by their in-laws:

My mother-in-law’s behaviour fills me with immense pain because of her disdained comments [such as] “What good is she for if she cannot bear a child , but never forgets to eat food on time . ” It has been harrowing for me to experience such negativity from her . Even though I try to be engaged in household chores and stay in my room , her words eat me up from inside . ( FGD participant # 7 )

The link was commonly made between economic consumption and women’s inability to contribute to the household by bearing children. This link was a catalyst for feelings of shame and internalised stigma:

Since my husband’s family is spending money on me with the hope of me conceiving a child , I hesitate to ask for anything extra if I am in need of it , or if I want to hang out with my cousins . I struggle with feelings of guilt for using up their money for my treatment . ( FGD participant # 4 )

The idea that women were economically draining their families without producing an offspring was a common reason for overt stigmatisation by family members, particularly in the context of patrilocal residence, where women were observed by other family members on an ongoing basis. Women’s consumption of food and other household necessities was considered a waste of money since these women did not reciprocate by bringing a child into the family:

I admit that my husband’s family has spent way more than I could imagine . I sought treatment from both private and public health facilities . I have visited 15 doctors to date . Whatever we earned , we used it on my treatment . My mother-in-law often taunts that I brought bad luck to the family with my unsuccessful pregnancy attempts . She also taunts me with the thought that I earn just enough to pay for the medicine and treatment . She provokes my husband by saying , “ Your wife will never get pregnant no matter how much money you waste on her .” To date , about 5–7 lakhs [equivalent to USD 6100–8600] have been spent on my treatment . ( FGD participant # 22 )

Faced with high treatment costs and perceptions of only being consumers in the household economy, many women were keen to contribute to paying for the cost of their infertility treatment by earning money. However, they reported being discouraged from working and told that they should stay in the homemaker role:

I am a housewife and I do not think I will be allowed [by the husband’s family] to get a job anywhere . Neither my parents nor my husband’s parents will like this idea . If I had this opportunity to work , I would not have to be disdained by my husband , who thinks he is wasting his money as he sees no positive outcome of pregnancy . ( FGD participant # 18 )

Other women reported being actively discouraged from seeking employment or other forms of labour participation. Financial freedom seemed to have been confiscated for these women. It was not unusual to hear that “ my mother-in-law disallowed me from finding a job ,” as stated by one participant (FGD participant # 9). It was clear that these women’s financial ability was greatly limited as their families sought to conform to social expectations. FGD participants supported the idea that in accordance with the prevailing gender roles, a woman was to stay at home and be a homemaker; she was expected to allow the husband to provide economically, and to depend on him:

I have reached that stage of my life that I have spent all we have on treatment . But my in-laws have a problem with me taking up a job outside . I have to ask people to lend me money for treatment . (FGD participant # 2 )

When women could not access treatment because their husbands were unemployed and were unable to fulfil the prescribed role of provider, this contributed to tension between the expected gender roles and the perceived economic implications of infertility:

My husband was jobless for some time , and I could not seek medical assistance due to financial strain . After he found a job after months , my mother-in-law and sister-in-law did not like him giving me money to see a doctor . I heard her say , “ Do not spend your hard-earned money on your wife . She will not bring a new member into the family .” ( FGD participant # 11 )

According to the FGDs, it was considered that a woman would bring dishonour to the family by working. As one participant noted, being a daughter-in-law , I am responsible for keeping the family’s honour . (FGD participant # 6) Consequently, the husbands’ family members disapproved of the idea of a daughter-in-law seeking work outside of the home. By seeking employment, it was perceived that women would be exposing their husbands as unable to meet the needs of their families. Participants explained that the husband’s kin, i.e., the woman’s brothers-in-law and mother-in-law, found it shameful for a family to send a daughter-in-law to work. Doing so was thought to damage the pride of the family in the eyes of the husband’s kin, neighbours and society at large:

Due to societal pressure , my mother-in-law always talks about losing the dignity of the house if a daughter-in-law goes out for work . She thinks our family will be criticised by the neighbours for letting me take up a job . She feels that she will be the face of embarrassment and will be seen as someone who uses her daughter-in-law as a financial provider of the family . ( FGD participant # 30 )

As a result, most study participants did not pursue the opportunity to work. Two women in the study sample worked and contributed to their treatment, but it was explained that this was frowned upon:

My husband did contribute towards treatment costs as well , but that is not the point . I too have spent so much . But despite giving all I have got , my mother-in-law’s outrage never stops . She berates her son; my husband , for allowing me to pay for my medical expenditure . Can you fathom that [it is expected that] I should not spend money on my treatment , treatment that can allegedly help restore family dignity in the eyes of outsiders . ( FGD participant # 33 )

Thus, study participants’ autonomy in the labour market was curtailed in order to uphold the gender roles of women as homemakers and husbands as breadwinners, even though this undermined their ability to pay for medical services that could potentially enable them to fulfil expectations about childbearing. Challenging their prescribed roles led to negative consequences:

Initially , my husband did not allow me to take up the job as it was against their family values where the daughter-in-law is supposed to stay inside the home and look after her husband and his family . He continued to object to my decision for about a month . In fact , he got too far to raise his hand on me . He slapped me . But I was determined and did not give up on my decision to take up a job . I got a job . ( FGD participant # 34 )

It should be noted that as opposed to the woman quoted here, who pressed on and acquired a job, almost all the other study participants were dependent on their husbands. Not surprisingly, many ended up borrowing money from their own parents or friends:

My brother-in-law and my husband’s parents never liked that my husband spent on doctors , check-ups , and medicines . So , I tried to find an alternative way to cover the medical expenses and asked my mother to lend me money . ( FGD participant # 23 )

However, borrowing money for treatment, sometimes in secret, imposed the burden of indebtedness:

My parents sent me money for treatment . They are still sending me money , hoping for me to become pregnant . I owe them a huge debt . Sometimes my father makes me feel that I was being born a burden to my parents . He does not talk much with me . It is only my mother who has been supporting me all along . ( FGD participant # 10 )

Across the study sample, findings showed that finances were a problem, even when study participants sought assistance from parents and friends. One woman said, She [my mother] did [lend me money] at first , but later could not as she herself was in dire straits . Her hands were tied financially . (FGD participant # 15) . Thus, some of these women were left in a dilemma whereby they could no longer borrow, nor work to pay for treatment.

Intersection of gender, economics and masculinity

In this study population, the role of gender was also apparent with regard to how men’s potential contribution to infertility was understood. In nearly all cases, women were blamed for failing to conceive, even in cases in which men had not undergone fertility assessments:

Everybody points at the woman . Nobody realises that one can’t clap with just one hand (i.e. it takes two to tango). People behave as if men are born fertile . ( FGD participant # 20 )

A consistent finding was that men did not visit physicians to have fertility assessments performed:

He would not go to any clinic no matter how many times he is told . I know if he did , then his manliness would be put under question . He is so firm that the problem lies with me . ( FGD participant # 5 )

This is particularly notable given that the initial tests for men were thought by the participants to be relatively cheaper compared to infertility evaluations for women:

My husband and I both went through diagnoses . Surprisingly , his test was less expensive than what I had anticipated and not unaffordable in comparison to ours as in women’s . In my case , I had to go through multiple tests while only one test was prescribed for him , which was [a] test of his semen . We ended up paying more for my test than for his . ( FGD participant # 29 )

In several cases, husbands either backed away from taking their wives to health clinics or reneged from doing so. A participant mentioned that what is ironic is that when it comes to partaking in escorting me to the hospital or a clinic , I see none of my family members are ready to take me . (FGD participant # 25) In addition, in-laws were unhappy that husbands spent money on what was essentially perceived to be a woman’s fault. As a result, husbands often were reluctant to pay for women’s treatment costs, and often, they were explicitly discouraged from paying by their families:

My husband’s parents have forbidden my husband to pay for my treatment . His parents pick at me and hold me responsible . ( FGD participant # 17 )

In several cases, apart from having their husbands being actively discouraged from paying for their treatments, women were also blamed for “wasting” their husbands’ earnings, given that they had failed to conceive despite visits to medical practitioners:

I have been married for about 15 years now . The reason my mother-in-law used to beat me up was because I could not give them what I was supposed to give them : a child . Moreover , she could not put up with the fact that I am the reason my husband frittered away his earnings and yet I remained childless for so long . Now we are not on talking terms . Yet , I am responsible for keeping the family’s honour . I often wonder how righteous it is to beat the one who is the family’s face of dignity . ( FGD participant # 21 )

It was clear that the patrilocal organisation of families was not generally conducive to or supportive of these women, as explained by one participant who stated that she ended up convincing her husband to stop living with her in-laws:

I was emotionally drained and hemmed in by my husband initially when I tried getting him to move us out of the home , [and we eventually] started living on our own . ( FGD participant # 32 )

Intersection of gender and social economic class in the context of medical treatment

The intersection of socio-economic class and gender during consultations with physicians shaped the experiences of study participants in multiple ways. Findings illustrated how differences in power and status were underlined by class and gender:

The doctor said that because I am 33 , I am not getting pregnant . He was like , “Why are you so late to visit my clinic ? Do you women not understand the effect of ageing on fertility ? Now it is difficult for you to get pregnant .” ( FGD participant # 1 )

Interactions of this nature, which were commonly reported, demonstrated how physicians serve as having gender bias and lack professional ethics in this study community, yet they are the gatekeepers to information that can facilitate or limit access to infertility treatment. Several accounts indicated that study participants, keen to pursue treatment that would redeem their identity and self-esteem, encountered stigmatization by some doctors in the study community, who generally attend to middle-class families. Many of the women in our sample did not have medical insurance, and they held multiple subordinate group identities as unemployed low-income women, mostly from scheduled castes and other backward castes. They did not seem to be aware of the schemes such as Pradhan Mantri Jan Arogya Yojana (PM-JAY) and other Ayushman Bharat Schemes. Some women reported experiences of being negatively judged in medical settings:

I asked about the reason for my failed pregnancy when the doctor checked my test report at the hospital . He replied with a condescending voice that there is a lump in my uterus . I did not understand and asked him for an explanation . To which he responded , “ You would not understand even if I explained it to you .” I later learned this is called a fibroid . ( FGD participant # 27 )

As the above verbatim indicates, doctors made assumptions about the participants’ ability to grasp basic information about their reproductive organs or their reproductive potential, which ultimately exacerbated their experiences of marginalisation in medical settings.

While these experiences were not always negative, the only positive experiences that were reported involved female physicians, as illustrated by the following excerpt from one response, a participant who had visited several physicians:

I have gone to about five doctors so far . All of them prescribed ultrasound and other tests . One of them was nice to me . She would listen to me and console me because I happened to break into tears as I was mentally exhausted with drifting door to door and changing doctors . I felt that she understood what I was going through and gave me hope , which otherwise is rare these days . Not only did she give me hope but listened to me which never really happens with the [other] doctors . They are always in a hurry . I remember once a doctor did not give me time to listen to me and not even five minutes he interacted . He just prescribed a few medicines and a test and was done with me . ( FGD participant # 8 )

A similarly positive experience was reported by another study participant who was attended by a female physician:

The doctor I had gone to assured me that I could get pregnant once my thyroid treatment is done and resolved . When I was getting a negative attitude from everybody from family to friends , she was the one who was the beacon of hope . ( FGD participant # 14 )

In sum, participants’ experiences within medical settings may have been impacted by the intersections of gender and class and its attendant social privilege.

This paper demonstrates how infertility reinforces gender inequality, through the intersection of gender norms, economics of treatment, and labour participation; produce experiences of marginalisation and dependency among women of low to middle socio-economic status in our study community. This is an important contribution given that despite the potential for the convergence of many determinants of inequality, the concept of intersectionality is rarely utilised in studies of infertility [ 15 ], particularly in low- and middle-income countries [ 16 ].

We demonstrate how among women who were trying to access costly fertility treatment, the intersection of gender, economics, and class excluded them from labour participation, marginalized them financially, and limited their access to medical information. These experiences were further exacerbated by the gendered role and masculinity of husbands. In this context, failing to enact motherhood as per societal expectations reinforced gender-based discrimination, which was further exacerbated by the economics of paying the high cost of fertility treatment.

Patrilocal family structure has long been ingrained in Indian society [ 28 ]. Women inhabiting patrilocal systems are less likely to have agency in areas including mobility and healthcare decisions than those living in nuclear households [ 28 ]. Furthermore, the interaction between family structure and participants’ experience of infertility was made more apparent by the patrilocal residence of participants, whereby newly married women live with their husbands’ families. In this situation, a woman’s identity automatically shifts from being a daughter of her natal family to the daughter-in-law in another household [ 29 ]. This habitation provides the grounds for the perpetuation of the dominant social discourse that promotes the idea that women’s primary role is motherhood. The intersection of economics and gender within patrilocal residence also provides the grounds for the paradoxical observation that while men were expected to be providers, women’s responsibility for producing children was not expected to impose any extra economic burden on their husbands. The influence of in-laws in upholding these expectations is not surprising given that while the study participants shared the same household with their-in-laws, they occupied a subordinate position [ 30 ], which undermined their ability to act on their choices and decisions [ 31 , 32 ].

Feminist scholars theorise that the basis of women’s subordination is largely economic, given that women who are confined to the domestic space become economically dependent on men, which thereby, provides the opportunity for oppression. All except two of the study participants were homemakers and were entirely financially dependent on their husbands to access healthcare. Participants’ decision-making autonomy in relation to their reproductive health was therefore subjected to hegemonic family norms that excluded them from obtaining the financial means to access medical services.

In several communities, the rationale for discouraging married women from taking up work is to protect the family honour, which places the onus of giving the family a good name upon married women [ 33 ]. Women’s mobility and activity are therefore controlled to some extent [ 30 , 34 ]. Any deviation from this norm may disparage a woman’s moral character, and have implications on economics and their ability to redeem their identity as women in the face of infertility and costly infertility treatment. Our study findings suggest that family prestige is prioritised over addressing women’s needs in relation to infertility. The participants in our study were unable to obtain financial means to pay for medical assistance.

Findings also suggest that gender socialisation shapes the expectations that traditionally assign women to the role of having a biological obligation to bear children in the study setting. When these gendered expectations were not met in our study population, infertility was perceived as a woman’s problem, similar to observations made elsewhere [ 35 ]. Thus, women were blamed on account of failing to mother a child, while men were not equally held responsible. Furthermore, this socialisation promotes the notion that motherhood is a defining factor of womanhood and that it is the only way to be fulfilled as a woman, as perceived in some communities. Other scholars posit that Indian women acquire respect, honour and power in a household by virtue of being mothers and homemakers [ 36 ]. Yet the economic marginalisation of women who are unable to attain pregnancy is a deterrent to women’s ability to achieve this status.

Examining the intersection of gender, economics and patriarchal norms enabled us to identify how patrilocal residence was associated with women’s experiences, as this played a part in compromising women’s agency as generators of family income. Although economic reasons have been identified as a motivating reason for patrilocal residency [ 37 ], we also show that it can have a negative impact on women labor participation. We noted that some of our participants’ family members discouraged them from entering the labour market, a finding that is consistent with the expectation of women being subservient, compliant, passive, and docile and attached to the home [ 38 ]. Such gender doctrine validates traditional gender roles, which set a precedent for female subordination in which women are denied autonomy, which subsequently leaves them with limited control over their lives. Not only did patriarchal structures obstruct women from enjoying the right to make decisions, but the exertion of patriarchal values by mothers-in-law exacerbated their subordinate position in the family. Along with various forms of domination, controlling women’s financial autonomy in the family unit was one of them. Male household members and in most cases participants’ husbands were the earners, while women were hamstrung by the supremacy of mothers-in-law. Similar observations, including the impact of work participation on women’s agency, as well as the detrimental effect of patrilocal residence on the women’s share of the labour market, have been made by other researchers [ 33 , 39 ].

The intersection of gender and masculine norms allowed us to acquire an understanding of the ways in which masculinity can contribute to women’s experiences in relation to infertility, including through the perceived notion that men do not contribute to a couple’s infertility. Studies suggest that fear of being emasculated at the hand of society leads men to avoid being assessed for their fertility [ 3 ]. This shapes how women are solely held responsible for conceiving. Participants in our study reported that most of the male partners who were asked to undergo fertility assessment refused to do so. As described by the women, their husbands equated attending a fertility clinic with experiencing a direct attack on their masculinity, which is also consistent with observations that both virility and fertility are often linked to manhood [ 40 ].

Our study findings indicate that the intersection of gender and social class in medical settings has the potential to further shape women’s experiences in relation to infertility. Although the impact of class has been explored among women with infertility, for example in Bangladesh [ 14 ], the exploration within medical settings is rare in low- and middle-income countries. In our study two participants perceived that their status may have been a barrier in receiving appropriate care. Although this was not observed commonly in our study setting, discomfort and mistreatment in medical settings has been reported in high income settings [ 15 ].

Implications for service and policy

These findings have several implications. First, findings suggest that there is a need for comprehensive information in the study setting to raise awareness about infertility, increase understanding about its causes, enhance knowledge about the various government insurance schemes, and ensure that the population understands that there are infertility treatments that can be accessed by couples. It is important for the general population to know that infertility can result from female factors, male factors, a combination of female and male factors, and in some cases, unexplained factors. In the study context for example, Accredited Social Health Activists (ASHAs), who are a cadre of community health workers, can be trained to include infertility as part of the information they routinely provide to communities.

From a social perspective, there is a need to find ways to provide women with access to peer support groups that can mitigate the negative psychological experiences of infertility. This proposal is consistent with other researchers’ calls for interventions to address the stigmatisation of infertility [ 9 , 41 ]. Here, Anganwadi Centres (a type of child care centre in India) could play a role, and community health workers including ASHAs could be trained to facilitate such peer support groups for people with infertility. Additionally, given that stigma and discrimination is linked to gender norms, roles and expectations, it is critical to implement gender transformative interventions through behaviour change communication to sensitise communities on harmful gender norms and de-stigmatise infertility.

In terms of health systems, health care providers in medical facilities should provide counselling to women and couples who are dealing with infertility. Most counselling interventions are often low-cost and can have a significant impact on how infertility is experienced in the short term [ 42 ]. Furthermore, healthcare providers need training and sensitisation regarding infertility, and the need to listen to, respect and communicate in a non-judgmental way to patients with infertility. In the longer term, however, it is critical for governments and other stakeholders to aim for universal access to fertility care, including assisted reproduction technology (ART), as part of universal health coverage. This is particularly important given that private sector provision, and lack of regulations have been associated with higher costs of fertility treatments in low and middle income countries [ 43 ].

Implications for future research

Our study adds to the rare literature on the gendered nature of how infertility is experienced and shows that individual experiences are shaped by an intersection of multiple social factors. In response to the need to explore circumstances that may exacerbate some experiences and identities more than others, we have demonstrated how the patrilocal residence can exacerbate women’s struggles relating to infertility. We suggest that women’s experiences might differ if living arrangements were separate, and this hypothesis could inform further research in this area. Further research could explore intersections of other axes including education, income levels, migrant status, and religion among others. In addition, more in-depth research is needed on how to scale up the presently available fertility care within the government health systems in order to further improve women’s positive experiences and empower couples to receive these services.

Study limitations

This study had several limitations. Women who participated in the study were part of a pre-conception randomized (WINGS) study where they had been followed up to 18 months and failed to achieve pregnancy during this period. Recruitment was based on women’s history of failing to conceive over an 18 months period, despite intentions to achieve pregnancy with their partners. The study intended to understand the perspectives and experiences of women, but many of them did not go through any clinical diagnosis to confirm infertility. Therefore, while the study captured the experiences of these women it does not confirm a diagnosis of that infertility. Delay or failure to conceive affects a couple; however, only women were recruited in this study and they were asked to report what partner’s attitude and reactions were. Perspectives of male partners would have been useful, particularly because women tend to be blamed when male factors can also contribute to infertility. Future studies will need to include men.

Although intersectionality was applied to gender and economic axes of inequality, other factors not identified in this analysis may be at play among the study sample. Failing to identify complex dimensions during analysis is common even when multiple axes are explicitly examined together [ 44 ]. However, we chose to interrogate the dynamics of gender and financial status given the gendered view of biological reproduction and the high cost of infertility treatment in most low- and middle-income settings [ 43 ].

Our sample entirely comprised women of low to middle economic status, we therefore applied an intra-categorical approach to intersectionality analysis [ 45 ], and showed how economics and masculinity affected women’s experiences. Our intention was not to compare men’s and women’s negative experiences, which is postulated to be milder among men [ 46 ], but to explore how masculine norms affected women’s experiences. Expanding the analytical focus to encompass different genders, economic levels, castes and other identities could be useful in future research. Although extensive thematic analysis was conducted through the aid of software, it is possible that some relevant themes may remain unidentified [ 25 ]. Although reflexivity seemingly neutralised the emic (insider) and etic (outsider) perspectives of the lead researcher (PA), it is possible that her positionality could not be entirely discounted [ 47 ]. Although attempts were made to limit the extent to which the research team’s backgrounds, values and beliefs impacted on the data collection, interpretation and reporting of results it is impossible to entirely eliminate these influences [ 27 ].

Infertility is often an invisible problem and poor unemployed women are often an invisible population. This study provides needed attention to a neglected topic in global health by examining the experiences of a marginalized group of women who have been unable to conceive children with their husbands. Through the application of intersectional analysis, this study shows how gender, economics and masculinity intersect to influence women’s experiences of this problem. Findings demonstrate the need for improved awareness and education on infertility and highlight the need for additional studies applying intersectionality to better understand perspectives of women, and the need to improve counselling and other infertility interventions for women and their spouses. These findings support calls to ensure that infertility is given more attention and that fertility care is available for all.

Supporting information

S1 checklist. coreq (consolidated criteria for reporting qualitative research) checklist..

https://doi.org/10.1371/journal.pone.0304029.s001

Acknowledgments

We are grateful to all the women who participated in the study and shared their time and cooperation. We extend our thanks to Ms. Nivedita Roy for support with focus-group discussions. We would also like to thank Prof. Asha George for her review and suggestions in a prior version of this paper.

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• Open access
• Published: 01 July 2024

Gestational exposure to organochlorine compounds and metals and infant birth weight: effect modification by maternal hardships

• Janice M. Y. Hu 1 , 2 ,
• Tye E. Arbuckle 1 ,
• Patricia A. Janssen 3 ,
• Bruce P. Lanphear 2 ,
• Joshua D. Alampi 2 ,
• Joseph M. Braun 4 ,
• Amanda J. MacFarlane 5 ,
• Aimin Chen 6 &
• Lawrence C. McCandless 2  

Environmental Health volume  23 , Article number:  60 ( 2024 ) Cite this article

Metrics details

Gestational exposure to toxic environmental chemicals and maternal social hardships are individually associated with impaired fetal growth, but it is unclear whether the effects of environmental chemical exposure on infant birth weight are modified by maternal hardships.

We used data from the Maternal-Infant Research on Environmental Chemicals (MIREC) Study, a pan-Canadian cohort of 1982 pregnant females enrolled between 2008 and 2011. We quantified eleven environmental chemical concentrations from two chemical classes – six organochlorine compounds (OCs) and five metals – that were detected in ≥ 70% of blood samples collected during the first trimester. We examined fetal growth using birth weight adjusted for gestational age and assessed nine maternal hardships by questionnaire. Each maternal hardship variable was dichotomized to indicate whether the females experienced the hardship. In our analysis, we used elastic net to select the environmental chemicals, maternal hardships, and 2-way interactions between maternal hardships and environmental chemicals that were most predictive of birth weight. Next, we obtained effect estimates using multiple linear regression, and plotted the relationships by hardship status for visual interpretation.

Elastic net selected trans -nonachlor, lead, low educational status, racially minoritized background, and low supplemental folic acid intake. All were inversely associated with birth weight. Elastic net also selected interaction terms. Among those with increasing environmental chemical exposures and reported hardships, we observed stronger negative associations and a few positive associations. For example, every two-fold increase in lead concentrations was more strongly associated with reduced infant birth weight among participants with low educational status ( β  = -100 g (g); 95% confidence interval (CI): -215, 16), than those with higher educational status ( β  = -34 g; 95% CI: -63, -3). In contrast, every two-fold increase in mercury concentrations was associated with slightly higher birth weight among participants with low educational status ( β  = 23 g; 95% CI: -25, 71) compared to those with higher educational status ( β  = -9 g; 95% CI: -24, 6).

Conclusions

Our findings suggest that maternal hardships can modify the associations of gestational exposure to some OCs and metals with infant birth weight.

Peer Review reports

Fetal growth is a complex process impacted by various factors including psychosocial stressors, toxic chemicals, nutrition, placental function, and the actions of many intrauterine hormones and growth factors [ 1 ]. Fetal growth restriction, commonly assessed by infant birth weight for a given gestational age [ 2 ], occurs when the fetus does not reach its intrauterine potential for growth and development [ 3 ]. It is an important determinant for the risk of infant mortality and the development of chronic diseases [ 2 ].

Recent studies have found that impaired fetal growth is associated with gestational exposure to toxic environmental chemicals, which are ubiquitous in our environment, food sources and personal care products [ 1 , 4 , 5 ]. However, few have reported contradictory results [ 1 , 6 ]. In our previous work, we reported associations of two classes of environmental chemicals – metals and organochlorine compounds (OCs) – with reduced infant birth weight [ 7 ]. We found that first trimester blood concentrations of trans -nonachlor, an organochlorine compound (OC), and lead (Pb), a toxic metal, were inversely associated with birth weight. Every twofold increase in trans -nonachlor and Pb concentrations was associated with reduced birth weight by an average of 38 g (g) (95% confidence interval (CI): -67, -10) and 39 g (95% CI: -69, -9), respectively. Our study, however, did not consider the effects of non-chemical stressors such as maternal hardships [ 8 , 9 , 10 ].

Recent research by Goin et al. (2021) reported that maternal hardships such as stressful life events, food insecurity, living alone and facing unplanned pregnancy can interact in pairwise fashion to adversely affect fetal growth [ 11 ], potentially via behavioral, physiological or immunological mechanisms [ 11 , 12 , 13 , 14 ]. These 2-way interactions observed are supported by Knudson’s “two-hit” hypothesis, where it was found that two gene mutations are needed for retinoblastoma, a childhood cancer of the retina, to develop [ 13 ]. The major implication of this “two-hit” hypothesis is that two distinct gestational stressors are needed to affect an outcome. Thus, gestational exposure to environmental chemicals and maternal hardships may interact and jointly influence fetal growth.

While there is robust evidence of an association between either chemical exposures or maternal hardships and fetal growth, there is a paucity of research on the joint associations of chemical exposures and maternal hardship on fetal growth. Aker et al. (2020) found evidence, among the 752 Puerto Rican females from the Puerto Rico Testsite for Exploring Contamination Threats (PROTECT) cohort, that life event score or stress interacted with bisphenol-S and triclocarban exposures to modify their associations with gestational length [ 15 ]. Although Aker et al. (2020) did not examine metals and OCs specifically, their observation supports further exploration of maternal hardships as an effect modifier in the associations between environmental chemicals and fetal growth.

We assessed the independent associations of nine maternal hardships including self-reported belonging to a racially minoritized group, immigrant status, financial strain, low supplemental folic acid intake, low educational status, living status, lone parenthood, experiencing chronic diseases, and being a student, on birth weight and the potential modifying effects of these maternal hardships on the associations between OCs and metals and birth weight among a cohort of 1982 Canadian females and their infants who participated in the Maternal-Infant Research on Environmental Chemicals (MIREC) Study.

Study participants

We analyzed data from the MIREC Study, a cohort study of 1982 pregnant females recruited between 2008 and 2011 from ten Canadian cities. The goal of the MIREC Study was to obtain Canadian biomonitoring data on pregnant females and examine associations between prenatal exposure to environmental chemicals with pregnancy and child health outcomes [ 16 ]. Detailed information on demographic and lifestyle factors were collected from questionnaires administered at recruitment in the first trimester. Eligibility criteria and exclusions are described in Arbuckle et al. (2013) [ 16 ]. For the present study, we included females who had complete socio-demographic information, provided biological samples during the first trimester of the pregnancy, and delivered singleton live births. Participants with missing information were excluded from the analysis (Supplemental Figure S1).

Fetal growth measurement

To examine fetal growth, we considered infant birth weight adjusted for gestational age (GA) [ 17 , 18 ]. We used cubic splines to allow infant birth weight curves to vary across gestation in a smooth manner [ 18 ]. Both infant birth weight, measured in grams (g), and GA, measured in weeks, were abstracted from the medical records and examined as continuous variables.

Biomarkers of prenatal environmental chemical exposure

We measured environmental chemical exposures using biomarkers in blood samples collected from MIREC participants during the first trimester of pregnancy. Biomarker analysis was conducted at the Toxicology Laboratory of the Institut national de santé publique du Québec (INSPQ) using previously described methodology [ 19 , 20 , 21 ]. A brief description of the laboratory analysis method can be found in Supplemental Appendix A.

In our previous analysis, we reported associations between two classes of environmental chemicals – metals and OCs – and reduced infant birth weight [ 7 ]. Hence, in this study, we limited our analysis to OCs and metals only. We retained biomarkers that were detected in at least 70% of the samples [ 22 ]. These included six plasma OCs (polychlorinated biphenyl (PCB) 118, PCB 180, Aroclor 1260 (consists of PCB 138 and 153), dichlorodiphenyldichloroethylene (DDE), oxychlordane, and trans -nonachlor) and five whole blood metals or metalloids (arsenic (As), cadmium (Cd), mercury (Hg), manganese (Mn), and lead (Pb)).

We imputed measurements below the limit of detection (LOD) using the single imputation “fill-in” approach where the log 2 chemical concentrations lower than the LOD were randomly sampled from a truncated lognormal distribution with mean and standard deviation (SD) estimated from the observed data [ 22 ]. To account for individual-level variability in lipid levels, we standardized OC concentrations by total plasma lipid concentrations and expressed them in units of ng/g lipids [ 23 ]. Standardization was only applied when presenting the descriptive statistics on the environmental chemical concentrations. Otherwise, total lipids was included as a covariate in regression models [ 23 , 24 ]. Due to the highly correlated nature of PCB 153 and PCB 138, INSPQ summed and multiplied them by a factor of 5.2 to create Aroclor 1260 [ 25 ]. To reduce the potential influence of outliers due to the right skewed distributions of chemical concentrations, the concentrations were log 2 -transformed before inclusion in the models. The transformed unit indicates a two-fold increase in concentrations. Furthermore, before creating the interaction terms with maternal hardships to examine their joint associations, the log 2 -transformed chemical concentrations were median-centered to improve interpretation and decrease the multicollinearity effects, which would affect model convergence and inflate the standard errors [ 26 ].

Maternal social hardships

We selected maternal social hardship variables based on factors related to social determinants of health [ 27 ]. We used a questionnaire administered at baseline [ 16 ] to capture responses that signal indicators of hardships directly affecting the study participants. The eight maternal hardships included self-reported belonging to a racial and ethnic minoritized group [ 8 , 28 ], immigrant status [ 28 , 29 ], financial strain [ 9 , 28 ], low supplemental folic acid intake [ 30 , 31 ], low educational status [ 8 , 9 ], living status [ 32 , 33 ], lone parenthood [ 34 , 35 ], and experiencing chronic diseases [ 36 , 37 ]. Furthermore, we also examined whether being a student is a potential maternal hardship. Each hardship variable was dichotomized to indicate whether a study participant experienced the hardship or not.

Race has definitions ranging from biological to social and its operationalization as a variable in research has been debated [ 38 ]. Race or having a racially minoritized background, as reported here, is considered a social construct that reflects lived experiences of systematic discrimination [ 39 ]. We classified participants who reported their racial or ethnicity status as non-white as belonging in a racially minoritized group [ 40 ].

Participants who reported their birth country as countries other than Canada were considered immigrants and faced hardship associated with being immigrants. Participants were classified as having financial strain if their reported annual household income was below $20,000, which is approximately the 2008 Canada poverty line for a two-person household [ 41 ]. Folate is a key pregnancy nutrient and pregnant individuals with hardship of food insecurity are more likely to have inadequate folic acid supplementation [ 30 , 31 ]. We classified participants with daily folic acid supplementation levels below 400 µg per day as low supplemental folic acid intake given that they did not meet the recommended intakes for females at low-risk for a neural tube defect-affected pregnancy [ 42 ]. Supplemental folic acid intake was assessed via a structured survey conducted at 16-weeks’ gestation where participants were asked to list all the supplements they took in the past 30 days. The product information and intake frequency were used to calculate daily total folic acid consumption from supplements [ 43 ]. However, when supplement information was missing from this 30-day recall form, we then estimated the supplement information using the 24-h recall form (also completed at 16-weeks’ gestation) ( n  = 41; 2%) or if necessary, the baseline questionnaire, completed between 6- and 13-weeks’ gestation, where participants were asked to list all the supplements they had taken within the past 3 months ( n  = 462; 23%).

Participants who reported their highest educational level achieved as high school diploma or less were classified as having low educational status. For living status, participants who reported not living with a roommate, spouse, partner, or parents were classified as living alone. Participants with marital status as single (i.e., not married or not with the same partner for 1 year or more) were classified as having a hardship of lone parenthood. Participants who responded affirmatively to “Do you have any chronic medical condition(s)?” were classified as having chronic disease(s). Here, chronic medical condition was defined as any condition that treatment can manage but not cure. Examples of such conditions include high blood pressure, diabetes, and other pre-existing conditions prior to pregnancy such as asthma, depression, arthritis, heart conditions, and psoriasis. Lastly, we also examined whether being a student is a potential maternal hardship. Participants who answered yes to the question “are you currently attending school?” were classified as being current students.

We created a directed acyclic graph (Figure S2) to identify predictors of infant birth weight and factors associated with both environmental chemical exposure and infant birth weight. Additionally, we identified predictors of maternal hardships and infant birth weight. The following, derived from the baseline questionnaire, were examined: maternal age (under 25, ≥ 25 to < 30, ≥ 30 to < 35, ≥ 35 to < 40, and ≥ 40), race and ethnicity (white, non-white), education (high school diploma or less, college or trade school diploma, undergraduate university degree, and graduate university degree), cigarette smoking status (never, current, former, and quit during pregnancy), parity (0, 1, 2, ≥ 3), infant sex (male and female) and pre-pregnancy body mass index (BMI) (underweight, normal, overweight, and obese). We adjusted for the non-linear effect of gestational age (GA) as a covariate on birth weight using the cubic spline approach [ 18 ]. For OC models, we additionally adjusted for plasma lipid concentrations. For models assessing maternal hardship belonging to a racially minoritized group, we did not adjust for maternal race and ethnicity. For models assessing maternal hardship low educational status or low income, we did not adjust for maternal education.

Analytical approach

We first tabulated participant characteristics, examined the relations between maternal hardship variables using Cramer’s V correlation coefficients, and calculated geometric means and percentiles of the environmental chemicals. We then proceeded to examine the moderating influence of maternal hardships on the relationship between OC and metal exposures and birth weight by using elastic net regression to choose which variables to include in our models.

Elastic net is a machine learning method that is built on conventional regression with an added penalty term [ 44 , 45 ]. The penalty term biased the estimates to reduce overfitting and improve prediction on data not used in the model fitting procedure. There are two penalty parameters (i.e., ɑ and λ) that can be tuned to produce the best performing model. The quantity ɑ adjusts the balance between lasso and ridge penalty and has a range of 0 to 1. When ɑ is 1, the model is a lasso model and when ɑ is 0, the model is a ridge regression model. Furthermore, when ɑ is closer to 1, the elastic net model will focus more on selection and when the ɑ is closer to 0, the model can better handle multicollinearity. The second tuning parameter λ controls the magnitude of the penalty where smaller λ leads to estimates that are closer to those of conventional regression method. To determine the optimal degrees of penalization, we used cross-validation and tested the models over a grid of ɑ and λ sequences.

We used elastic net for variable selection purposes in order to reduce multicollinearity and to ensure that only important variables and interaction terms are included in the model, thus potentially reducing the probability of type I errors. Eventhough elastic net can account for multicollinearity, we created separate models for the OC compounds and the metals to avoid the curse of dimensionality, which will result in decreased statistical power to detect a true effect [ 44 ]. In each model, the dependent variable was birth weight adjusted for gestational age, and the predictor variables included all environmental chemicals in the same chemical class, all the maternal hardships, and the interaction terms between the environmental chemicals and the hardships (e.g., Pb x low education). The covariates and the cubic spline gestational age were adjusted in the elastic net regression model as unpenalized variables.

For parameter estimation and calculating 95% CIs, it has been recommended that penalized regression, such as elastic net regression, be used to select the model first and then the selected model be used in an unpenalized regression (i.e., ordinary least squares (OLS) regression models) to quantify the associations of gestational exposures to individual environmental chemicals with infant birth weight, while adjusting for covariates [ 45 , 46 ]. This utilizes the predictive power of the overall modeling procedure while maintaining the interpretability of the individual estimates. We therefore fitted the subset of variables and the interaction terms selected and inferred to be important by elastic net in OLS regression models. The selected environmental chemicals and maternal hardships were entered into the OLS regression models individually and adjusted for covariates. The selected interaction terms were also entered into OLS regression models that included both the main effects and the interaction terms and adjusted for covariates. We examined the associations based on hardship levels, screened for suggestive associations with p -values of 0.1 and 0.05, and visualized the relationships graphically using interaction plots with 95% CI bands.

Furthermore, using Cook’s distance [ 47 ], we identified and removed one influential outlier that negatively affected the models. All analyses were conducted using Microsoft R Open version 3.5.1 and elastic net models were fitted using the glmnet package [ 48 ].

Supplemental analyses

We conducted additional analyses using first trimester plasma total folate concentrations instead of supplemental folic acid intake. Plasma total folate, a biomarker of folate status, specifically of recent folate intake, was measured by LC–MS/MS, as previously described [ 43 ]. Currently, no cutoff values for either low or high concentrations or a reference range for plasma total folate have been identified for fetal growth [ 49 ] but 25.5 nmol/L was estimated as a potential cut-off for neural tube defects (NTD) risk reduction [ 50 ]. Among the MIREC participants, less than 0.5% ( n  = 8) had a plasma total folate concentration < 25.5 nmol/L, while approximately 5% ( n  = 104) reported a low total daily supplemental folic acid intake of < 400 μg/day. Therefore, to allow for easier comparison of the low plasma total folate and the low supplemental folic acid intake effects and to mitigate issues with low sample size, we selected the 5th percentile of plasma total folate (51.5 nmol/L) as the cutoff value to indicate low folate status.

GA is a potential mediator in the relationship between environmental chemical exposures and birth weight [ 51 ]. To rule out any bias or over-adjustment in our models, we conducted additional analyses that excluded GA as a covariate in the models. Lastly, we summed the number of hardships to examine the association between cumulative hardship and birth weight.

Descriptive statistics

We included a total of 1982 MIREC participants in the analysis (Figure S1). Most of the MIREC participants were white (82%), over 30 years of age (70%) with at least an undergraduate degree or higher (63%), never smoked (61%) and had normal BMI (61%). Forty-four percent were nulliparous and 53% had male infants. Maternal characteristics associated with lower birth weight included being non-white, older age, lower educational status, smoking, underweight BMI, and nulliparity. Female infants weighed on average 100 g lower at birth compared to males (Table 1 ).

From the Cramer’s V correlation coefficient plot (Figure S3), we observed low to moderate correlations (0 to 0.5) between the maternal hardship variables. The most commonly experienced maternal hardships were having a chronic disease (25%), being an immigrant (19%) and belonging to a racially minoritized group (18%). The least common maternal hardship was living alone (2%). All hardships, except for being a student, were associated with lower birth weight (Table 2 ).

More than half of the participants (57%) reported having at least one maternal hardship(s) (Table S1). The most common hardships occurring together were immigrants with racially minoritized background at 10%, followed by low education with racially minoritized background, and immigrants who were students, both at 3% (Table S2). Furthermore, we detected most chemicals in over 90% of study participants, with the exception of PCB 118 and trans -nonachlor with 74% and 84% detection rates, respectively (Table S3).

Variables and interaction terms that were selected in the elastic net models of infant birth weight

In the OC model, trans -nonachlor and three maternal hardship variables (i.e., low educational status, racially minoritized background, and low supplemental folic acid intake) were selected and inferred as important contributors in the elastic net regression of infant birth weight model. Elastic net identified sixteen interaction terms as important (Fig. 1 ).

Interaction plots as selected by elastic net showing the differences in mean birth weight (grams) and 95% confidence intervals (shaded bands) associated with exposures to organochlorine compounds during the first trimester across different levels of maternal hardships. A Interaction plots for trans -nonachlor. B Interaction plots for PCB 118. C  Interaction plots for PCB 180. D Interaction plots for Aroclor 1260. E Interaction plots for Oxychlordane. F Interaction plot for DDE

In the metal model, Pb and the same three maternal hardship variables (i.e., low educational status, racially minoritized background, and low supplemental folic acid intake) were selected. Elastic net identified eleven interaction terms as important (Fig. 2 ).

Interaction plots as selected by elastic net showing the differences in mean birth weight (grams) and 95% confidence intervals (shaded bands) associated with exposures to metals during the first trimester across different levels of maternal hardships. A Interaction plots for Pb. B Interaction plots for As. C Interaction plot for Hg. D Interaction plots for Mn. E Interaction plot for Cd

Independent associations between environmental chemical exposure and infant birth weight

Our elastic net variable selection results agreed with those of Bayesian Kernel Machine Regression method from our previous study [ 7 ]. Both mixture methods indicated the importance of trans -nonachlor and Pb exposures, which were negatively associated with infant birth weight (Table 3 ). Every doubling of trans -nonachlor and Pb concentrations corresponded to a 37 g (95% CI: -65, -8) and a 38 g (95% CI: -68, -8) reduction in birth weight, respectively.

Independent associations between maternal hardships and infant birth weight

We found that all three hardships selected by elastic net (i.e., low educational status, racially minoritized background and low supplemental folic acid intake) had negative associations with birth weight (Table 3 ). Infants born to females with a high school diploma or less had lower birth weight ( \(\beta\) = -103 g; 95% CI: -185, -21) compared to those born to more educated females. Infants born to females with racially minoritized background had lower birth weight ( \(\beta\) = -78 g; 95% CI: -130, -26) compared to those born to white females. Lastly, infants born to females with low supplemental folic acid intake had lower birth weight ( \(\beta\) = -61 g; 95% CI: -152, 31) compared to those born to females who met or exceeded recommended folic acid intakes.

Regression analysis of the modifying effect of maternal social hardships on the relationship between environmental chemical exposure and infant birth weight

We found that maternal hardships can modify the strength of the relationships (i.e., steeper negative slope and lower birth weight) or the direction of the relationships (i.e., positive slope and higher birth weight) between biomarker of exposure to OCs and metals and birth weight. Although the 95% CIs were imprecise, some associations showed statistical significance at the 0.05 level (Tables 4 and 5 ).

Among the OCs, we observed negative (e.g., trans -nonachlor) to no associations (e.g., PCB 118) with birth weight for females who did not report any of the hardships examined. For trans -nonachlor and Aroclor 1260, we observed larger than expected negative associations on mean birth weight among females with hardships (Fig. 1 A and D). For example, each twofold increase in trans -nonachlor concentration was associated with a lower mean birth weight of -128 g (95% CI: -238, -19) among those with low income and -44 g (95% CI: -90, 2) among immigrants (Table 4 ). Meanwhile, infants born to higher income females or Canadian-born females had smaller birth weight reductions of 39 g (95% CI: -69, -10) and 33 g (95% CI: -67, 1), respectively. Similarly, we observed steeper negative slopes for those with increasing PCB 118 concentrations and belonging to a racially minoritized group (Fig. 1 B-i), increasing PCB 118 concentrations and inadequate supplemental folic acid intake (Fig. 1 B-ii), increasing PCB 180 concentrations and immigrant status (Fig. 1 C-i), and increasing oxychlordane concentrations and low income (Fig. 1 E-iii). Furthermore, students with increasing exposure to environmental chemicals also showed steeper curves and greater birth weight reduction, compared to non-students. However, the dose response curves crossed over indicating that students had initial gains in birth weight at low exposure compared to non-students but as exposure increased, greater reduction in birth weight was observed (Fig. 1 D-ii and E-iv).

We observed a change in direction for the following associations: PCB 118 × low income (Fig. 1 B-iii), PCB 180 × lone parenthood (Fig. 1 B-iv), oxychlordane x low education (Fig. 1 E-ii), and DDE x low supplemental folic acid intake (Fig. 1 F). Among those who reported hardships, we saw a gain, on average, in birth weight of at least 21 g (95% CI: -69, 111) for infants born to females with increasing oxychlordane concentrations and low education to a maximum of 76 g (95% CI: -70, 221) for infants born to females with increasing PCB 118 concentrations and low income (Table 4 ). Conversely, among those who did not report a hardship, we saw reductions in mean birth weight of between 15 g (95: CI: -36, 5) for infants born to females with increasing DDE concentrations but had adequate supplemental folic acid intake and 30 g (95: CI: -60, -0) for infants born to females with increasing PCB 118 concentrations but had higher income (Table 4 ).

Among the metals, we also observed negative (e.g., Pb) to no associations (e.g., As) with birth weight for those who did not report hardships. For Pb exposure, we observed both a strengthening of association and change in direction when combined with hardships. Among infants born to females with increasing Pb concentrations and low educational status, we observed a larger reduction in mean birth weight ( \(\beta\) = -100 g; 95% CI: -215, 16) compared to those with higher educational status ( \(\beta\) = -34 g; 95% CI: -64, -3) (Fig. 2 A-i and Table 5 ); while for infants born to females with increasing Pb concentrations and belonging to a racially minoritized group, we observed a positive association ( \(\beta\) = 18 g; 95% CI: -54, 89) and a gain of 68 g, on average compared to infants born to white females (Fig. 2 A-ii and Table 5 ). For As and Cd exposures, among those with hardships, we observed stronger associations (i.e., steeper slope) with birth weight compared to those who did not report hardships (Fig. 2 B and E). Similarly to that of Pb, the associations between As and birth weight were stronger (steeper slope) among infants born to females with low educational status ( \(\beta\) = -58 g; 95% CI: -130, 13), low income ( \(\beta\) = -87 g; 95% CI: -189, 15), and lone parenthood ( \(\beta\) = -100 g; 95% CI: -201, 1) compared to their counterparts (Fig. 2 B and Table 5 ). Furthermore, for Mn and Hg exposures, we observed positive associations among those who experienced hardships. For instance, infants born to females with low educational status, compared to those with higher educational status, had lower birth weight at low Mn or Hg concentration . As Mn or Hg level increased, the dose response curve trended upward leading to higher birth weights at high Mn or Hg concentrations (Fig. 2 C and D). One exception to this pattern is lone parents with increasing Mn concentrations. A 242 g (95% CI: -454, -29) reduction in mean birth weight was observed among infants born to single females compared to females who were not single ( \(\beta\) = 20 g; 95% CI: -25, 64) (Fig. 2 D-iii and Table 5 ).

Supplemental analyses results

Low first trimester plasma total folate concentrations (i.e., < 5th percentile or < 51.5 nmol/L) (Table S4) was associated with a 124 g decrease in birth weight (95% CI: -223, -26) in the adjusted model (Table S5). From the Cramer’s V correlation coefficient plot (Figure S4), we observed low correlations (0 to 0.2) between low plasma total folate and other maternal hardship variables.

When we included low plasma total folate (instead of low supplemental folic acid intake) in our elastic net model, the selections of variables by elastic net for both the OC and the metal models were similar except that in the metal models, Hg and immigrant status were additionally selected. In the adjusted models, we observed that every doubling of Hg concentration corresponded to a 6 g (95% CI: -21, 8) decrease in birth weight and infants born to immigrants faced a 21 g (95% CI: -78, 35) reduction in birth weight compared to those born to non-immigrants (Table S5). Elastic net also identified three interactions associated with Hg as important: Hg x low income, Hg x low plasma total folate and Hg x chronic diseases (Table S6). The interaction plot of Hg x chronic diseases showed a stronger association (steeper slope) indicating greater detriments in birth weight at higher Hg concentration for those who reported having one or more chronic diseases compared to those who did not report any. Meanwhile, the interaction plots of Hg x low income and Hg x low plasma total folate showed positive associations where the dose response curves for those who reported hardship had positive slopes (Figure S5). Additionally, we found that adjustment for GA as a covariate attenuated the relationships between exposure to chemical concentrations and birth weight (Table S5).

Our cumulative hardship analysis found that the proportions of participants who reported no hardship, one hardship, and two or more hardships were 43%, 33%, and 24% respectively (Table S1). We observed a negative relationship where a higher number of hardships corresponded to lower mean birth weight (Table S1). In the adjusted model, the presence of one hardship or two or more hardships corresponded to lower birth weight of 58 g (95% CI: -106, -11) and 93 g (95% CI: -148, -39), respectively, compared to no hardship (Table S7).

Our findings suggest that maternal hardships that co-occur with gestational exposure to some OCs and metals, may interact and produce greater detrimental effects on fetal growth than either exposure alone. For instance, trans -nonachlor and oxychlordane were more strongly associated with decreased infant birth weight among females with lower income compared to those with higher income. This finding is not unexpected as it has long been recognized that females with lower income have disproportionately higher exposure for environmental stressors as well as higher risk for impaired fetal growth [ 52 ]. For instance, Borders et al. (2007) found, among their population of 1,363 pregnant American females with low income, that maternal social hardships and low birth weight were strongly related. They also reported that pregnant females with lower income are more likely to face food insecurity and consume inadequate supplemental folic acid compared to those with higher income [ 53 ]. This relationship among poverty, food insecurity, and folic acid supplementation is well supported [ 30 , 31 , 54 ]. In our study, 104 (5%) participants had low supplemental folic acid intake; only 10 (1%) experienced both low income and low supplemental folic acid intake. Among those infants born to females with increasing PCB 118 concentrations and low supplemental folic acid intake, we observed that every twofold increase in PCB 118 concentration was associated with an 87 g reduction in birth weight among females with low supplemental folic acid intake, compared to a 7 g reduction among females with the recommended supplemental folic acid intake level.

Beside maternal income, maternal education also had a strong and positive association with birth weight [ 55 ]. In our study, Pb x low education was one of the selected interaction terms by elastic net. Pb exposure is unequally distributed across populations where higher exposure to Pb is typically found in communities of lower socioeconomic status and among individuals with less access to resources including financial, educational, social, and health [ 56 ]. Furthermore, both Pb exposure and low maternal education can independently contribute to lower birth weight [ 6 , 7 , 55 , 57 , 58 ]. In combination, we found that Pb was associated with a greater reduction in birth weight among females with low educational status ( \(\beta\) = -100 g; 95% CI: -215, 16) compared to those who had higher education ( \(\beta\) = -34 g; 95% CI: -64, -3) (Table 5 ).

Another hardship that has been known to affect birth weight is immigration status. When we considered the females’ immigration status along with their exposure to OCs (i.e., trans -nonachlor and PCB 180), immigrants showed a lower mean birth weight compared with Canadian-born participants (Fig. 2 A,C and Table 4 ). This birth weight reduction is supported by an increasing number of studies that found that immigration status affects fetal growth [ 59 ] and that the birth weights of babies born to immigrants are generally lower than those of babies born to Canadian-born females [ 60 , 61 ]. For example, South Asian born females tend to give birth to smaller babies than non-migrant females [ 62 ]. However, in a systematic review where Gagnon et al. (2009) explored whether immigrants have poorer infant outcomes compared to Canadian-born females, the authors found that being an immigrant was not a consistent marker for poorer infant outcomes [ 60 ]. Nevertheless, among our MIREC population, immigrants had lower mean birth weights and when considering the effects of environmental chemical exposures, they experienced a slight increase in vulnerability compared with Canadian-born females. Interestingly, we also observed a greater reduction in birth weight among infants born to students with increasing concentrations of OCs (i.e., Aroclor 1260 and PCB 180), compared to those of non-students. There is a dearth of studies on environmental chemical exposures in the context of pregnant students. Available studies typically examined behaviours in reducing exposure [ 63 ], knowledge and awareness of exposures to environmental chemicals [ 64 ], or exposure assessment on campus or in laboratories [ 65 ]. Future work, therefore, should include pregnant students as the intersection of their experiences and exposure status may generate differential impacts on their pregnancy outcomes.

The stronger relationships we observed for females with both higher environmental chemical concentrations and a maternal hardship is consistent with Knudson’s “two-hit” hypothesis [ 66 ] where two distinct gestational stressors (e.g., gestational exposures to environmental chemical and maternal hardship) showed different associations with birth weight when combined compared to either “hit” alone. To further explore the impact of cumulative effects, we summed the number of hardships each participant faced and found that as the number of hardships increased, the mean birth weight decreased (Table S1). Specifically, the presence of one hardship was associated with a 59 g (95% CI: -105, -12) lower birth weight while the presence of two or more hardships was associated with an 87 g (95% CI: -139, -34) lower birth weight, compared to no hardship (Table S7). While the findings of the present study support the hypothesis that exposure to two stressors can have different effects compared to exposure to a single stressor, these findings may depend on the nature of the stressor(s). For instance, infants born to MIREC participants with increasing Mn or Hg concentrations and reported maternal hardship had higher birth weights compared to those born to participants who did not report hardship.

Studies had shown that Mn exhibits a nonlinear inverse U-shaped relationship with birth weight where lower birth weight was observed at both low and high concentrations of blood Mn level [ 67 , 68 ]. Despite that, most studies, including the present one, assumed linear functions. In our previous study, using a flexible Bayesian Kernel Machine Regression method that can accommodate non-linearity, we found that Mn showed a positive and linear relationship with infant birth weight among MIREC participants [ 7 ]. It is possible that we have captured the ascending segment of the inverted U-shaped Mn-birth weight relationship, which showed an association with higher birth weight in our adjusted regression analysis. As a result, when examined by maternal hardship status, we found evidence that infants born to MIREC participants with both higher Mn concentration and low income had a higher birth weight, while those with no reported hardship showed no association with birth weight. However, the pattern doesn't hold true for lone parenthood as infants born to single females experienced approximately 200 g greater birth weight reduction compared to those born to married parents. The limited research on lone parenthood (or single mother families) and environmental chemical exposures has focused on exposure to air pollutants. Two US-based environmental inequality studies reported that single-mother families are more likely to live in neighborhoods with higher pollution compared to other family types (e.g., married or single father families) [ 69 , 70 ]. Another US air pollution study found that living in a poor neighbourhood may increase the risk of exposure to OCs and metals in the air [ 71 ]. We are not aware of any study that has examined the combined effects of lone parenthood and Mn exposure on pregnancy outcomes. Research that assesses the relationship between wider ranges in Mn concentrations and fetal growth is highly recommended to consider the shape of the Mn-birth weight function as well as the impact of lone parenthood.

Like Mn, total Hg also showed a positive relationship with birth weight among those with maternal hardships. Although Hg is a toxic chemical that can freely cross the placenta and potentially disrupt a range of important pregnancy processes [ 72 ], in a recent systematic review, Hg was reported to have a minimal to null association with birth weight [ 73 ]. However, one particular study reported that the lowest tertile hair Hg concentration was associated with a higher risk of having infants with low birth weight (adjusted odds ratio (aOR) = 7.2; 95% CI: 1.5, 35.6) compared to those with higher Hg concentration (aOR = 0.52; 95% CI: 0.17, 1.55) [ 74 ]. As Hg exposure generally results from maternal fish consumption, the authors hypothesized that the consumption of contaminated fish with high levels of Hg may have coincided with an increased intake of selenium (Se), an essential element found in fish. Se, in this case, may have moderated the toxic effects of Hg [ 75 , 76 ] and thus resulted in a decreased OR for low-birth-weight infants among those with higher Hg exposure. Other studies have also reported that Hg levels have a positive association with socioeconomic status among females of childbearing age [ 77 , 78 ]. Specifically, pregnant participants with low income and low education consumed more fish per week compared to pregnant participants with higher income and higher educational status, but they were found to have lower blood Hg levels [ 78 ]. This is because the type of fish consumed by different demographic groups can also affect Hg exposure levels [ 78 ]. Therefore, future studies should include Se, the types of fish consumed, and if possible, the whole diet when assessing the potential toxic effects of Hg.

Among the MIREC study participants, we observed that maternal hardships can modify the strength of the relationships (i.e., steeper negative slope and lower birth weight) as well as the direction of the relationships (i.e., positive slope and higher birth weight) between biomarkers of exposures to OCs and metals during pregnancy and infant birth weight. To our knowledge, this study is the largest single cohort study conducted on the modifying effect of maternal hardship on the relationship between gestational environmental chemical concentrations and birth weight. With the benefits of a large sample size and the use of elastic net regularization technique, we had higher statistical power to detect associations and identify key variables. However, our findings should be interpreted with caution due to the following limitations. First, we used multivariable linear regression to assess the independent effects of each maternal hardship and environmental chemical on birth weight and did not account for non-linear relationships. Second, we performed post-selection inference by first using elastic net for variable selection, then using OLS to derive statistical inference from the best-fitting model. A limitation of variable selection methods such as elastic net is their instability where any change in observations may change the model selected [ 79 ]. As OLS assumes that we have obtained a perfect model, which may not be the case, the resulting 95% CIs may be inaccurate and too narrow. Accounting for the effects of variable selection is a challenging task and work is underway to develop tools for selective inference so that we can properly assess the strength of the relationships [ 79 ]. Third, we used Lubin’s single imputation approach for measurements below the LOD and the standard errors may be biased. However, as the majority of our environmental chemicals were detected in over 90% of the participants, the impact of bias, if any exists, should be minimal [ 22 ]. Fourth, maternal hardships were based on a positive–negative dichotomy (i.e., yes–no hardship), which produced easy to interpret findings but essentially weighted all hardships equally. This assumption may have overlooked important and differential aspects of the potential effect of each hardship on the outcome . Furthermore, low maternal hardship proportions of 2 to 25% and missing maternal hardship values may bias the effect estimates, reduce our study power to detect a statistically significant association, and induce spurious interactions. Regardless, our use of dichotomized hardships served as an important starting point for the conceptualization of maternal hardships and highlighted the importance for future research to include parental hardships when examining the effects of environmental chemical exposure. Additionally, our use of objective maternal hardships may not fully represent a woman’s experience and well-being [ 80 ]. Obtaining qualitative data and additional quantitative data such as food security, stressful life events, and resilience will enhance our understanding of maternal hardships and enrich our current findings. Fifth, we examined only OCs and metals and did not consider co-exposures among the environmental chemicals or hardships. Therefore, we may have missed other environmental chemicals-birth weight associations modified by maternal hardships or any potential 2-way interactions between environmental chemicals or between hardships. Sixth, blood may not be an adequate biomarker of exposure to all metals as each medium may represent a different window of exposure [ 81 ]. For example, blood Cd, As and Hg reflect recent exposures, while urinary Cd, hair or nail As and Hg may better assess chronic exposures [ 81 , 82 , 83 ]. Lastly, MIREC is not a representative sample of all Canadian females because it is not population-based [ 16 ]. As a result, the generalizability of our results may be limited.

While it is widely agreed that fetal growth is determined by multiple factors encompassing genetic, environmental, social and maternal factors, little is known about how these factors work together to maintain the persistent disparities in abnormal fetal growth. We present supporting evidence that maternal hardships may modify the relationship between gestational environmental chemical exposures and infant birth weight and that two “hits” of stressors have different effects compared to a single “hit”. Future research should examine the mixture or cumulative effects of environmental chemicals and non-chemical stressors such as maternal hardships to further advance our knowledge of the mechanisms of impaired fetal growth. Greater knowledge can address the adverse fetal growth outcomes where vulnerable populations are at increased risk for impaired fetal growth. These hardships or social factors that co-occur with gestational environmental chemical exposure can become a source of inequality and have downstream effects on infant health [ 13 ].

Availability of data and materials

Due to data privacy issues, we are not able to make these data publicly available. Individuals may apply to access the data through the MIREC Biobank ( www.mirec-canada.ca/en/research ).

Abbreviations

Adjusted odds ratio

Confidence interval

Dichlorodiphenyldichloroethylene

Gestational age

Institut national de santé publique du Québec

Limit of detection

Maternal-Infant Research on Environmental Chemicals

Neural tube defects

Organochlorines

Ordinary least squares

Polychlorinated biphenyl

Puerto Rico Testsite for Exploring Contamination Threats

Standard deviation

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Acknowledgements

We would like to thank the participants of the MIREC study, study coordinators, nurses, and research assistants who made this work possible.

This work was supported by a Catalyst Grant from the Canadian Institutes of Health Research (L-CIP-150736). The original MIREC study was supported by the Chemicals Management Plan of the Government of Canada, the Ontario Ministry of the Environment., and a research grant from the Canadian Institutes for Health Research (MOP–81285).

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Janice M. Y. Hu & Tye E. Arbuckle

Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada

Janice M. Y. Hu, Bruce P. Lanphear, Joshua D. Alampi & Lawrence C. McCandless

School of Population and Public Health, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada

Patricia A. Janssen

Department of Epidemiology, Brown University, Providence, RI, USA

Joseph M. Braun

Texas A&M Agriculture, Food and Nutrition Evidence Center, Fort Worth, TX, USA

Amanda J. MacFarlane

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JH: Conceptualization, Methodology, Formal analysis, Writing - original draft. TA: Writing – review & editing. PJ: Writing – review & editing. BL: Writing – review & editing. JA: Writing – review & editing. JB: Writing – review & editing. AM: Writing – review & editing. AC: Writing – review & editing. LM: Supervision, Funding acquisition, Writing – review & editing. All authors reviewed the manuscript.

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Brown University was compensated for Dr. Braun’s services as an expert witness for plaintiffs in litigation related to PFAS-contaminated drinking water; these funds were not paid to Dr. Braun directly. Dr. Braun also reports a relationship with Quest Diagnostics that includes consulting or advisory. Dr. Lanphear serves as an expert witness in cases of lead poisoning and other toxic chemicals, but he receives no financial payment.

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Hu, J.M.Y., Arbuckle, T.E., Janssen, P.A. et al. Gestational exposure to organochlorine compounds and metals and infant birth weight: effect modification by maternal hardships. Environ Health 23 , 60 (2024). https://doi.org/10.1186/s12940-024-01095-x

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Metformin use correlated with lower risk of cardiometabolic diseases and related mortality among US cancer survivors: evidence from a nationally representative cohort study

• Yukun Li   ORCID: orcid.org/0000-0003-0516-5480 1 , 2 ,
• Xiaoying Liu 1 , 2 ,
• Wenhe Lv 1 , 2 ,
• Xuesi Wang 1 , 2 ,
• Zhuohang Du 1 , 2 ,
• Xinmeng Liu 1 , 2 ,
• Fanchao Meng 1 , 2 ,
• Shuqi Jin 1 , 2 ,
• Songnan Wen 4 ,
• Rong Bai 3 ,
• Nian Liu 1 , 2 &
• Ribo Tang 1 , 2  

BMC Medicine volume  22 , Article number:  269 ( 2024 ) Cite this article

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In the USA, the prolonged effective survival of cancer population has brought significant attention to the rising risk of cardiometabolic morbidity and mortality in this population. This heightened risk underscores the urgent need for research into effective pharmacological interventions for cancer survivors. Notably, metformin, a well-known metabolic regulator with pleiotropic effects, has shown protective effects against cardiometabolic disorders in diabetic individuals. Despite these promising indications, evidence supporting its efficacy in improving cardiometabolic outcomes in cancer survivors remains scarce.

A prospective cohort was established using a nationally representative sample of cancer survivors enrolled in the US National Health and Nutrition Examination Survey (NHANES), spanning 2003 to 2018. Outcomes were derived from patient interviews, physical examinations, and public-access linked mortality archives up to 2019. The Oxidative Balance Score was utilized to assess participants’ levels of oxidative stress. To evaluate the correlations between metformin use and the risk of cardiometabolic diseases and related mortality, survival analysis of cardiometabolic mortality was performed by Cox proportional hazards model, and cross-sectional analysis of cardiometabolic diseases outcomes was performed using logistic regression models. Interaction analyses were conducted to explore the specific pharmacological mechanism of metformin.

Among 3995 cancer survivors (weighted population, 21,671,061, weighted mean [SE] age, 62.62 [0.33] years; 2119 [53.04%] females; 2727 [68.26%] Non-Hispanic White individuals), 448 reported metformin usage. During the follow-up period of up to 17 years (median, 6.42 years), there were 1233 recorded deaths, including 481 deaths from cardiometabolic causes. Multivariable models indicated that metformin use was associated with a lower risk of all-cause (hazard ratio [HR], 0.62; 95% confidence interval [CI], 0.47–0.81) and cardiometabolic (HR, 0.65; 95% CI, 0.44–0.97) mortality compared with metformin nonusers. Metformin use was also correlated with a lower risk of total cardiovascular disease (odds ratio [OR], 0.41; 95% CI, 0.28–0.59), stroke (OR, 0.44; 95% CI, 0.26–0.74), hypertension (OR, 0.27; 95% CI, 0.14–0.52), and coronary heart disease (OR, 0.41; 95% CI, 0.21–0.78). The observed inverse associations were consistent across subgroup analyses in four specific cancer populations identified as cardiometabolic high-risk groups. Interaction analyses suggested that metformin use as compared to non-use may counter-balance oxidative stress.

Conclusions

In this cohort study involving a nationally representative population of US cancer survivors, metformin use was significantly correlated with a lower risk of cardiometabolic diseases, all-cause mortality, and cardiometabolic mortality.

Is metformin use associated with diminished risk of cardiometabolic diseases and related mortality in cancer survivors? If so, what mechanisms contribute to this inverse association with cardiometabolic risk?

In this cohort study of 3995 cancer survivors over a median period of 6.42 years, metformin use was correlated with decreased risks of cardiometabolic diseases, all-cause and cardiometabolic mortality, likely due to its oxidative stress antagonism.

These study findings indicated that metformin use was associated with improved cardiometabolic health, leading to enhanced overall survival and quality of life in cancer survivors. Moreover, targeting oxidative stress may be crucial in developing cardiometabolic protective drugs for patients with cancer in the future.

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Cardiometabolic disease (CMD) and cancer are two major global public health concerns [ 1 , 2 ]. Recent advancements in cancer therapies including chemotherapy, radiotherapy, targeted therapy, and immunotherapy, have led to an expanding population of cancer survivors (CS). Two-thirds of patients diagnosed with cancer survive beyond 5 years post-diagnosis. However, the extended lifespan of CS presents new challenges for long-term care and comorbidity management. CMD has emerged as the primary comorbidity in patients with cancer, ranking as the leading cause of noncancer deaths in the CS population [ 3 , 4 , 5 ]. This increased risk of CMD and related mortality arose from various factors, including direct effects of cancers, anticancer treatments (including radiation and chemotherapy), pre-existing cardiometabolic risk factors (such as dysglycemia, dyslipidemia, and obesity), and physical deconditioning [ 3 ].

Apart from the previously mentioned risk factors, cancer is hypothesized as a type of metabolic disease. The link between cancer and CMD can possibly be explained by the abnormal metabolic phenotype of cancer cells (known as the Warburg effect) and elevated levels of oxidative stress [ 6 , 7 ], particularly considering that the cardiovascular system is highly energy-consuming and sensitive to altered metabolic patterns. Currently, there are no specific guidelines for managing and preventing cardiometabolic complications in this highly metabolically challenged group, relying instead on recommendations extrapolated from general populations. In this context, urgent attention is required for the development of prevention strategies aimed at alleviating the CMD burden in cancer survivors.

Both experimental and clinical data indicated that metformin, the primary oral antihyperglycemic agent with pharmacological adenosine 5’ monophosphate-activated protein kinase (AMPK) activation, could improve the cardiometabolic status in populations with obesity, diabetes, or psychiatric disorders [ 8 ]. This improvement was attributed to mechanisms including oxidative stress inhibition and redox rebalance [ 9 , 10 , 11 ]. To date, scarce studies have investigated the correlations of metformin use with CMD risk and CMD-related mortality in patients with cancer. Therefore, this study aimed to investigate the correlations between metformin use and the risk of CMD / CMD-related mortality, as well as the role of the antioxidative stress mechanism in a nationally representative sample of US cancer survivors with sufficient follow-up time. Our study findings offered crucial insights into the clinical application, mechanistic understanding, and future development of effective interventions to mitigate the increasing trend of cardiometabolic dysfunction among cancer survivors.

Study sample and population

This cohort study utilized a nationally representative population of cancer survivors from the National Health and Nutrition Examination Survey (NHANES) [ 12 , 13 ]. This study’s analysis adhered to the analytical guidelines of NHANES, which adopted a multi-stage stratified systematic sampling design. The sampling and testing processes in NHANES have been thoroughly documented in previously published articles. Briefly, the survey conducted health-related interviews and examinations, encompassing participants from diverse geographical locations and racial/ethnic backgrounds to ensure its nationwide representativeness. The NHANES protocols received approval from the National Center for Health Statistics research ethics review board. Written informed consent was obtained from all participants involved in the survey.

NHANES is an ongoing series of cross-sectional studies conducted in 2-year cycles. Except for mortality data, which was obtained from the 2019 Public-Use Linked Mortality Files, all other participant data, including the exposure of interest (metformin use) and covariates of our study, were collected during the survey cycle in which the participants were enrolled. Specifically, NHANES gathered health-related information through a combination of health interviews, medical measurements, and laboratory assessments. One or more individuals per household were selected to participate. Data was collected from each participant via face-to-face interviews conducted in the respondents’ homes. Medical measurements were performed in specially equipped mobile centers that travel to various locations throughout the country. Subsequently, participants were invited to provide biological samples and undergo laboratory assessments. In the Cox proportional hazards regression analysis, follow-up was calculated using person-months from the date of the interview to either the date of death or the deadline of the 2019 Public-Use Linked Mortality Files (December 31, 2019). Comprehensive methodology and protocols can be found on the NHANES website.

In this study, we analyzed data of sociodemographic variables, lifestyle factors, and medical and medication history from adult cancer survivors with follow-up records spanning eight cycles of NHANES from 2003 to 2018. Cancer diagnosis and type information were obtained through in-person interviews, encompassing details regarding specific cancer type(s), recodes of up to three cancer diagnoses, and the age at each diagnosis. Participants were asked, “Have you ever been told by a doctor or other health professional that you had cancer or a malignancy of any kind?” Those answering “yes” were identified as cancer survivors and further asked “What kind of cancer was it?” and “How old were you when this cancer was first diagnosed?”. The value of variable “years since first cancer diagnosis” was calculated by subtracting the age at the first cancer diagnosis from the participant’s current age. During household interviews, participants were questioned about their prescription medication use in the past month. If their response was affirmative, the participants were further asked to present medication containers for recording. Medication names, upon entry, were systematically aligned with an existing prescription drug database. In cases where medication containers were unavailable, participants were requested to report the medication name verbally. Metformin use was obtained from participants’ self-reports during the in-home questionnaire. Figure  1 illustrates the participant enrollment process in a flowchart.

Flowchart diagram of the screening and enrollment of study participants

Oxidative balance score

This study computed the Overall Oxidative Balance Score (OBS) by totaling the points allocated to each component. A lower OBS score indicated a higher individual oxidative stress level. Sixteen nutrients and four lifestyle-related components were selected for OBS calculation, including fifteen antioxidants and five prooxidants, based on previous studies examining their relationships with oxidative stress (OS) [ 14 , 15 ].

The scoring allocation scheme for OBS components is detailed in Supplementary Table 1 (Additional file 1: Table S1). For alcohol consumption, the scoring was as follows: nondrinkers received 2 points, nonheavy drinkers (0–15 g/day for women and 0–30 g/day for men) received 1 point, and heavy drinkers (≥ 15 g/day for women and ≥ 30 g/day for men) received 0 points. Notably, smoking exposure was quantified using serum cotinine levels, reflecting both direct tobacco use and environmental tobacco smoke exposure. Other components were categorized into three groups based on gender-specific tertiles. Antioxidants were scored from 0 to 2, progressively assigned from the first to the third tertile. However, prooxidants were scored inversely, with the highest tertile receiving 0 points and the lowest tertile receiving 2 points. Patients with cancer were classified into high OS and low OS groups based on OS level, defined by the lower and higher 50% of OBS values, respectively.

Outcome definition

In this study, mortality data were obtained from the 2019 public-access linked mortality archives up to December 31, 2019. All-cause and cardiometabolic mortality statuses were acquired from the publicly accessible dataset mentioned above. For participants identified as “deceased,” death cases were coded according to the Tenth Revision of the International Classification of Diseases (ICD-10). The underlying causes of death were categorized into the following 10 types: diseases of the heart; malignant neoplasms; chronic lower respiratory diseases; accidents (unintentional injuries); cerebrovascular diseases; Alzheimer’s disease; diabetes mellitus; influenza and pneumonia; nephritis, nephrotic syndrome, and nephrosis; and all other causes (residual).

Cardiometabolic mortality outcome was defined as the combination of death events resulting from three primary causes: diseases of the heart, cerebrovascular diseases, and diabetes mellitus. Death from accidents (unintentional injuries) was defined as a negative control outcome, which was unlikely to be influenced by the metformin treatment.

Although there is no consensus on the exact scope of CMD, prior studies typically encompassed coronary heart disease, stroke, hypertension, dyslipidemia, and diabetes mellitus within this category. Notably, all metformin users in our study had a history of diabetes comorbidity. Based on previous NHANES-related research on CMD and considering the sample sizes of certain diseases in the NHANES database, along with clinical practice, four common CMDs were included to assess the cardiometabolic diseases risk of cancer survivors: total cardiovascular disease, stroke, hypertension, and coronary heart disease (CHD). Hypertension diagnosis was based on the response “yes” to self-reported hypertension questions (“Have you ever been told by a doctor or other health professional that you had hypertension, also called high blood pressure?”), antihypertension drug usage (“Because of your high blood pressure/hypertension, have you ever been told to take prescribed medicine?”), or abnormal average values in three blood pressure measurements (systolic blood pressure greater than 130 mmHg or diastolic blood pressure greater than 80 mmHg). Stroke was diagnosed among those who responded “yes” to the self-reported stroke question (“Has a doctor or other health professional ever told you that you had a stroke?”). CHD was diagnosed among individuals responding “yes” to the self-reported CHD question (“Has a doctor or other health professional ever told you that you had coronary heart disease?”). The diagnosis of total cardiovascular disease included individuals diagnosed with any or a combination of the following conditions: coronary heart disease, congestive heart failure, heart attack, stroke, or angina.

Study covariates

We constructed a directed acyclic graph (DAG) to visualize the hypothesized associations of the primary exposure (metformin treatment) with the outcomes of interest (cardiometabolic outcomes of cancer survivors), and potential covariates. The selection of clinical and biochemical covariates incorporated into the DAG was guided by pragmatic considerations and prior mechanistic insights into the pathophysiology of cardiometabolic diseases. The resulting DAG is presented in Supplementary Fig. 1 (Additional file 1: Figure S1).

In our study, covariates including age, gender, ethnicity/race (categorized as non-Hispanic white, non-Hispanic black, Mexican American, other Hispanic, and others), educational level (categorized as less than high school, high school or equivalent, and college or above), and socioeconomic status measured by the poverty to income ratio (PIR = Family income / Poverty threshold for family size and composition) were extracted from interviews and physical examinations. The PIR index was stratified into three levels: < 1.30, 1.30–3.49, and ≥ 3.5. Body mass index (BMI) was calculated as weight (kg) divided by the square of height (m 2 ) and categorized into three subgroups based on the cutoff values of 25 and 30, with BMI ≥ 30 indicating obesity. Smoking status was assessed and classified as “never” for individuals who smoked less than 100 cigarettes in their lifetime, “former” for those who had smoked more than 100 cigarettes but currently do not smoke, and “now” for individuals who smoked more than 100 cigarettes in their lifetime and smoke some days or every day. Alcohol consumption status was grouped into five categories: (1) never (< 12 drinks in a lifetime), (2) former (≥ 12 drinks in 1 year and did not drink last year, or did not drink last year but drank ≥ 12 drinks in a lifetime), (3) current mild alcohol use (< two drinks per day for women, < three drinks per day for men), (4) current moderate alcohol use (≥ two drinks per day for women, ≥ three drinks per day for men, or binge drinking ≥ two days per month), and (5) current heavy alcohol use (≥ three drinks per day for women, > four drinks per day for men, or binge drinking on ≥ 5 days per month).

The metabolic equivalent (MET) measured energy metabolism during various daily activities. Physical activity was assessed through the Physical Activity Questionnaire (PAQ) section and quantified as PA(MET-h/week) = MET * weekly frequency * duration of each of physical activity. Distinct MET values were assigned for diverse physical activities by NHANES guidelines, including vigorous work-related activity (MET = 8.0), vigorous leisure-time physical activity (MET = 8.0), moderate work-related activity (MET = 4.0), walking or bicycling for transportation (MET = 4.0), and moderate leisure-time physical activity (MET = 4.0). Participants were categorized into two subgroups based on their PA scores: low-intensity physical activity (PA < 48 MET-h/week) and high-intensity physical activity (PA > 48 MET-h/week). The history of hyperlipidemia, diabetes, and depression (Patient Health Questionnaire-9 [PHQ9] ≥ 10) was identified through questionnaires section. The use of antihypertensive and antihyperlipidemic agents was ascertained based on participants’ self-reported medication usage in the in-home questionnaire. Multiple imputation methods were employed for missing covariate values.

Statistical analysis

All data analyses incorporated the complex stratified survey design and NHANES sampling weights to ensure national representativeness. Continuous variables in this study were reported as means and standard error of mean (SE), while categorical variables were presented as weighted percentages. Statistical tests were conducted two-sided, and a significance threshold of p  < 0.05 was applied. Data analysis was conducted from May 1 to August 1, 2023, using R and R Studio (R Foundation for Statistical Computing, Version 4.2.0). Multiple logistic regression models were employed in this study to evaluate the correlation between metformin use and the risk of CMD. Furthermore, multivariable Cox proportional hazards regression models were utilized to assess the impact of metformin use on risks of all-cause mortality and cardiometabolic mortality. The fully adjusted models were adjusted for a range of covariates, including age, gender, race and ethnicity, educational level, PIR, BMI, smoking status, alcohol use, intensity of physical activity, health conditions (including histories of hyperlipidemia, depression, and diabetes), and medication history (antihypertensive agents usage and antihyperlipidemic agents usage) and years since first cancer diagnosis. The final reported outcomes from these analyses were the adjusted odds ratios / hazard ratios and their corresponding 95% confidence intervals (95% CI). The robustness of all logistic regression and Cox proportional-hazards models was further evaluated by calculating the E-value [ 16 ], which represents the minimum strength of relationship, on the OR/HR scale, that an unmeasured confounding variable would need to have with both metformin use and cardiometabolic outcomes to entirely suppress the observed correlations, after adjusting for the measured covariates.

To further investigate whether metformin exerted its cardiometabolic protective effect by counteracting oxidative stress, the distinctive nature of interaction analyses was considered in logistic regression and Cox proportional hazards regression models. Notably, Rothman et al. have highlighted that the interaction terms (a*b) in these models only reflected the multiplicative interaction from a statistical perspective, rather than translating into a biologically interpretable additive interaction effect [ 17 ]. Accordingly, new variables were created, including four exclusive categories based on combinations of metformin use and oxidative stress levels: (1) metformin users in the low OS group, (2) metformin nonusers in the low OS group, (3) metformin users in the high OS group and (4) metformin nonusers in the high OS group. This categorization allowed us to quantify the additive interaction effect between metformin use and OS levels using the indicator of Relative Excess Risk due to Interaction (RERI), as recommended by the STROBE statement [ 18 ]. The RERI was computed using the formula RERI = RR 11  − RR 10  − RR 01  + 1, where RR 11 represents the relative risk for those exposed to both factors (metformin nonuser + high OS), RR 10 for exposure to one factor (metformin user + high OS), and RR01 for exposure to the other factor. The 95% CI for these estimations was derived using the delta method described by Hosmer and Lemeshow [ 19 ]. This analysis was adjusted for the same covariates as in the fully adjusted multivariable model above.

A series of sensitivity analyses were conducted to assess the robustness of the findings. First, we excluded deaths occurring in the first year of follow-up to minimize the potential for reverse causation. Second, accidental death was applied as a negative control outcome (deaths from “Accidents (unintentional injuries) (V01-X59, Y85-Y86)”). Third, considering that patients taking metformin all had comorbid diabetes, we examined the impact of sulfonylureas, another commonly used medication for cancer survivors with type 2 diabetes mellitus (T2DM) in our study cohort, on cardiometabolic outcomes to rule out the confounding effect of the presence of T2DM. Fourth, we further adjusted for the following covariates: variates reflecting the severity of diabetes (HbA1c, diabetic retinopathy) and the use of glucose-lowering medications with potential cardiometabolic benefits including GLP-1 receptor agonists and SGLT-2 inhibitors. Fifth, patients who underwent renal dialysis within the past 12 months were excluded from the analysis. Lastly, our study was conducted across the overall population and within specific subgroups stratified by age, gender, BMI, and race.

In this study involving 3995 cancer survivors (representative of a national weighted population of 21,671,061 individuals, weighted mean [SE] age, 62.62 [0.33] years, 53.04% women), the ethnic composition was diverse. The majority, 2727 participants (68.26%), identified as Non-Hispanic White. Five hundred eighty five individuals (14.64%) were Non-Hispanic Black, 260 (6.51%) were Mexican American, 220 (5.51%) were of other Hispanic backgrounds, and 203 (5.08%) belonged to other racial groups, including American Indian/Native Alaskan/Pacific Islander, Asian, and multiracial categories. There were a total of 3547 metformin nonusers and 448 metformin users. Table  1 presents the baseline profile of these participants, categorized based on their usage of metformin.

Among 3995 cancer survivors, a total of 1233 deaths occurred during a median follow-up of 6.42 years (77 months), including 481 deaths from cardiometabolic diseases. Table 2 illustrates the notable reduction in both all-cause and cardiometabolic mortality among cancer survivors undergoing metformin therapy. In the minimally adjusted model, hazard ratios (HRs) with 95% CIs for all-cause and cardiometabolic mortality were 0.60 (95% CI, 0.45–0.81) and 0.62 (95% CI, 0.42–0.93), respectively. Upon further adjustments in the fully adjusted model for covariates, the HRs for all-cause and cardiometabolic mortality were 0.62 (95% CI, 0.47–0.81, E-value = 2.61) and 0.65 (95% CI, 0.44–0.97, E-value = 2.45), respectively, among cancer survivors receiving metformin treatment compared to nonusers.

In our study cohort, 981 participants experienced cardiovascular diseases (CVDs), representing a weighted prevalence of 19.66% (95% CI, 17.64–21.69%). Furthermore, stroke occurred in 356 participants (weighted prevalence: 6.54% [95% CI, 5.51–7.56%]), hypertension in 2885 (weighted prevalence: 67.96% [95% CI, 63.49–72.43%]), and coronary heart disease in 390 (weighted prevalence: 8.01% [95% CI, 6.81–9.22%]). Among these cancer survivors, a total of 448 individuals were identified as metformin users. Survey-weighted logistic regression analysis on the cross-sectional data in Table  3 showed an inverse association between metformin use and cardiometabolic diseases in cancer survivors. The odds ratio (OR) for the incidence of total CVD in those undergoing metformin use, compared to those not receiving it, was 0.41 (95% CI, 0.28–0.59) after full adjustment. The ORs for the outcomes of stroke, hypertension, and coronary heart disease were 0.44 (95% CI, 0.26–0.74), 0.27 (95% CI, 0.14–0.52), and 0.41 (95% CI, 0.21–0.78), respectively, in the fully adjusted model. The E-values ranged from 2.49 to 4.31, which indicated that an unmeasured confounder would need to have at least an OR of 2.49 to explain the observed associations.

Previous research has indicated an elevated risk of cardiometabolic diseases in patients with certain types of tumors. In our study involving a nationally representative cohort of cancer survivors, the inverse association between cardiometabolic risk and metformin use was validated in specific cancer types. Fully adjusted analyses, as illustrated in Fig.  2 , show a reduction in all-cause mortality risk in survivors of hematologic (HR, 0.58; 95% CI, 0.40–0.82), breast (HR, 0.63; 95% CI, 0.45–0.89), and colorectal (HR, 0.57; 95% CI, 0.38–0.85) cancers treated with metformin. Decreased risk of cardiometabolic mortality was also noted in survivors of hematologic (HR, 0.56; 95% CI, 0.33–0.97) and breast (HR, 0.58; 95% CI, 0.36–0.94) cancers, but not among those with colorectal (HR, 0.70; 95% CI, 0.38–1.29) and prostate (HR, 0.78; 95% CI, 0.41–1.51) cancers when compared to individuals without metformin use. Regarding the risk of cardiometabolic diseases, a significant reduction in total CVD risk was observed in patients with all four specific cancer types treated with metformin. In breast, colorectal, and prostate cancer survivor subgroups, metformin demonstrated significantly beneficial effects on the risks of stroke and hypertension, with the protective effect of metformin against coronary heart disease primarily observed in survivors of hematologic and breast cancers.

Association of metformin use with the risk of all-cause mortality ( A ), cardiometabolic mortality ( B ), and cardiometabolic diseases ( C - F ) in the overall cohort of cancer survivors and four specific cancer subgroups with high cardiometabolic risk. Metformin nonusers group was defined as the reference. Hazard ratios (depicted by solid symbols) with corresponding 95% CIs (represented by error bars) of metformin use for all-cause mortality ( A ) and cardiometabolic mortality ( B ) were estimated using weighted multivariable Cox regression models. Odds ratios (indicated by solid symbols) with corresponding 95% CIs (represented by error bars) of metformin use for the total cardiovascular diseases ( C ), stroke ( D ), hypertension ( E ), and coronary heart disease ( F ) were estimated using weighted multivariable logistic regression models. Both the multivariable Cox and logistic regression models were adjusted for age, gender, race/ethnicity, educational level, family poverty income ratio, BMI, smoking status, alcohol use, physical activity, hyperlipidemia, diabetes, depression, antihyperlipidemic drug use, antihypertensive drug use, and years since the first cancer diagnosis. HR, Hazard Ratio; OR, Odds Ratio; CI, Confidence interval; CVD, Cardiovascular disease; BMI, body mass index

Oxidative stress is a known hallmark of cardio-oncology, and metformin plays an important role in regulating the oxidant-antioxidant system. We further investigated whether antioxidant properties could explain the inverse relationship between metformin use and cardiometabolic risk in cancer survivors. Participants’ exposure to OS-related damage was evaluated by oxidative balance scores, and the interaction analysis was conducted to examine the antagonistic effect of metformin on OS. The interaction effects between metformin use and oxidative stress levels on all the cardiometabolic outcomes are shown in the Table  4 . Compared to cancer survivors with metformin use and low OS level (reference), low-OS survivors without metformin use (“single-hit”), and high-OS survivors with metformin use (“single-hit”), those with no metformin usage but in high OS level (“double-hit”) exhibited the highest risks of all-cause/cardiometabolic mortality and cardiometabolic diseases (Table  4 ). A significant additive interaction was observed in the outcomes of all-cause mortality (RERI, 0.47; 95% CI, 0.21 to 0.73), cardiometabolic mortality (RERI, 0.53; 95% CI, 0.24 to 0.82), total CVD (RERI, 0.29; 95% CI, 0.06 to 0.52), stroke (RERI, 0.79; 95% CI, 0.28 to 1.30), and hypertension (RERI, 0.66; 95% CI, 0.09 to 1.23).

In the stratified analyses detailed in Supplementary Table 2 (Additional file 1: Table S2), based on gender, race, and baseline BMI, the cardiometabolic protective influence of metformin on the risk of total CVD, hypertension, and CHD was more significant among older individuals. The study revealed no significant variations in the negative relationship of metformin use with cardiometabolic outcomes across diverse genders, races, or obesity categories. The robustness of our findings was further validated by performing a series of sensitivity analyses. In order to minimize the potential reverse causation, we excluded deaths that occurred within the initial 1-year follow-up period and the results remained significant (Additional file 1: Table S3, 4). The analysis of the negative control outcome of accidental death revealed the absence of significant association of metformin use and the risk of accidental death ( N  = 34; HR, 2.59; 95% CI, 0.27–24.78). When sulfonylureas use, instead of metformin use, was considered as the exposure, the correlations with all-cause mortality and cardiometabolic outcomes were non-significant (Additional file 1: Table S5, 6). Further adjustments for HbA1c, diabetic retinopathy, GLP-1 receptor agonist use, and SGLT-2 inhibitor use did not substantially alter the results (Additional file 1: Table S7, 8). After excluding participants who underwent renal dialysis within the past 12 months, the robust inverse correlation between cardiometabolic risk and metformin use remained evident (Additional file 1: Table S9, 10).

In this study, conducted within a nationally representative cohort of cancer survivors in the USA, with a median follow-up duration of 6.4 years, metformin use as compared to non-use was inversely associated with the risk of cardiometabolic diseases, all-cause mortality, and cardiometabolic mortality in cancer survivors. This inverse association was observed not only in the overall population of cancer survivors, but also in patients with specific cancer types associated with a higher cardiometabolic risk. The potential mechanisms underlying this inverse association of metformin were further explored. Considering the pivotal role of oxidative stress in cardio-oncology and the widely reported antioxidative capacity of metformin, the OBS system was implemented for a quantitative assessment of oxidative stress levels within our cohort. The findings suggested t hat metformin use might exert cardiometabolic protection in patients with cancer by antagonizing oxidative stress. These findings remained consistent across diverse clinical subgroups and were corroborated by sensitivity analyses.

Challenges in managing cardiometabolic risks among cancer survivors

In the current landscape of oncological advancements, the USA has witnessed an increase in the number of cancer survivors, reaching nearly 17 million, many of whom continued to receive long-term cancer treatment post-diagnosis. Despite extended survival and reduced mortality due to cancer treatment innovations, the growing burden of CMD and associated mortality risks among cancer survivors is gaining attention. Early screening and treatment approaches in breast cancer cohort have elevated the 5-year survival rate above 90% [ 20 ]. However, this is contrasted sharply by an increased risk of heart disease, diabetes, and cardiovascular-related deaths in these patients [ 21 , 22 ]. A common reason is the reduced adherence to cardiometabolic medications following cancer diagnosis. Research indicated a notable decline in statin adherence in patients with breast cancer, from 67% pre-diagnosis to just 35% 2 years post-diagnosis, which was also evident in antihypertensive and antidiabetic medications [ 23 , 24 ]. Furthermore, treatment regimens for breast cancer, such as anthracycline therapy and endocrine therapy, have been associated with heightened adverse effects on cardiac well-being and metabolic diseases [ 22 , 25 ]. These factors, coupled with reduced physical activity during cancer treatment contributed to a detrimental cycle that heightens CMD risk in patients with breast cancer [ 26 , 27 ]. This cycle is recognized as prevalent across various genders and cancer types. Addressing and actively preventing the potential CMD risk in patients with cancer is crucial, not only for cancer cure or chronic management but also for maximizing long-term health and productivity.

Interplay of cancer and cardiometabolic diseases: the key role of oxidative stress

Cancer and cardiometabolic diseases are intricately linked and mutually exacerbating. Specifically, the treatment pattern and lifestyle factors in patients with cancer can elevate the risk of CMD, as discussed above. And vice versa, an elevated risk of cancer incidence and cancer-related mortality has also been observed in populations with cardiometabolic diseases [ 28 ]. These two conditions appear to share common mechanisms related to metabolic disorders [ 29 ]. The “Warburg effect” underscores that cancer cells display a distinct metabolic phenotype, marked by augmented glucose uptake compared to normal cells. Advances in sequencing technology have also unveiled the significant role of metabolic dysregulation in tumor growth and metastasis. The observed metabolic abnormalities, such as fumarate in renal cell carcinoma, glycine in breast cancer suggested that somatic mutations may emerge as downstream effects of disruptions in cellular energy metabolism [ 30 , 31 ].

Existing research has highlighted the pivotal role of oxidative stress in the field of Cardio-oncology. The cancer metabolic theory posited that the crux of metabolic dysregulation in cancer centers on defects in mitochondrial oxidative phosphorylation (OXPHOS) [ 32 ]. Impaired OXPHOS complements glycolysis in tumor metabolism. Concurrently, decreased respiratory efficiency in the OXPHOS pathway produces heightened reactive oxygen species (ROS). This resulting oxidative stress, characterized by elevated ROS levels, possesses notable mutagenic and carcinogenic properties. Oxidative stress increases the mutation rate in cells with for its DNA-damaging capacity [ 33 ]. Furthermore, chemotherapeutic agents like doxorubicin have also been reported to exhibit substantial cardiotoxicity via oxidative stress [ 34 ]. Shifting the focus to cardiometabolic diseases, an imbalance between oxidants and antioxidants is also a common characteristic. Markers of redox imbalance were found to be elevated in models of hypertension [ 35 , 36 ]. Moreover, Niemann et al. observed increased OS markers in cardiomyocytes of patients undergoing coronary artery bypass graft surgery [ 37 ].

In summary, oxidative stress emerges as a significant potential comorbid mechanism in the relationship between cancer and cardiometabolic diseases. Interventions targeting oxidative stress might play a key role in breaking the vicious cycle of “cancer-cardiometabolic disease” interplay.

Metformin: a potential game-changer in cardiometabolic disease management among cancer survivors

Metformin, acknowledged as a crucial first-line agent in managing T2DM, primarily functions by activating AMPK pathway in cells and curtailing hepatic glucose production. Beyond its conventional hypoglycemic efficacy, the pharmacological versatility of metformin across various human systems has garnered significant clinical interest. Apart from regulating glucose and lipid metabolism in cardiomyocytes, metformin lowered advanced glycation end products and ROS levels in the endothelium, offering substantial protection for cardiometabolic health [ 38 ]. Ongoing clinical trials affirmed metformin’s beneficial impact on diverse cardiometabolic outcomes in diabetic population, including coronary death, primary cardiovascular disease, body weight, and so on [ 39 ]. Furthermore, Zheng et al., by assessing the genetically proxied effects of metformin targets on a comprehensive array of cardiometabolic outcomes, have demonstrated its effectiveness in improving cardiometabolic conditions such as CHD, BMI, and blood pressure in the general population [ 40 ].

Apart from its prominent regulatory role in CMD, metformin may be promising in combating malignancies in organs such as the breast, kidney, and endometrium. Although its anti-carcinogenic effect has yet to be proven clinically [ 41 ], properties found in preclinical studies, including inhibiting growth and inducing cell death of cancerous cells, supported its anticancer potential [ 42 ]. With the increasing risk of CMD in the cancer survivors, it is regrettable that medication targeting the underlying mechanisms has yet gained approval for treatment. Our research findings suggested that metformin might be crucial in disrupting the detrimental “cancer-cardiometabolic disease” cycle. Findings from our research revealed that patients with cancer using metformin experience significant reductions in the risk of all-cause mortality and those associated with CMD and related mortality, when compared to those not using metformin.

To further evaluate the robustness of all regression models, E-value was also applied to explore the impact of unmeasured confounding variables on our findings. The E-values of all-cause and cardiometabolic mortality outcomes were 2.61 and 2.45, respectively. These findings implied that an unobserved confounder need to exhibit stronger associations with both metformin use and all-cause/cardiometabolic mortality than the measured confounder (HR of the confounder diabetes were 1.56 and 2.38, respectively), in order to fully explain away the observed HR of metformin use. The same was true for E-values of specific cardiometabolic diseases. The existence of an unobserved confounder that would have a stronger association with both metformin use and cardiometabolic outcomes than the confounder diabetes seems unlikely.

Oxidative stress mechanism and robust cardiometabolic protection across cancer subgroups

The mechanisms through which metformin intervenes to modulate cardiometabolic risk among cancer survivors were further investigated. As mentioned earlier, oxidative stress is a key shared pathogenic mechanism in both cancer and cardiometabolic diseases. Previous studies have already confirmed the potential of metformin in counteracting oxidative stress [ 43 , 44 , 45 ]. Expanding on our research affirming the protective impact of metformin on cardiometabolic health of cancer survivors, the OBS was utilized to assess OS levels in these individuals quantitatively. Subsequent interaction analysis revealed that metformin enhances cardiometabolic outcomes by exerting an antagonistic effect on the pathological process of oxidative stress. This observation shed light on the potential mechanism underlying metformin’s cardiometabolic protective properties.

In our comprehensive cancer-patient cohort, metformin was found to significantly reduce the risks of all-cause mortality, cardiometabolic mortality, and the specific risk of cardiometabolic diseases. However, the risk of subsequent CMD varied among patients with different cancer types. A recent cohort study involving 126,120 cancer survivors indicated an increased risk of cardiovascular events, such as CHD and stroke. Subgroup analysis further revealed elevated cardiovascular event risks in patients with hematologic malignancies and increased stroke risks in patients with breast cancer [ 46 ]. Another study of cancer survivors in UK Biobank suggested the highest hypertension comorbidity risk in prostate and colorectal cancer subgroups [ 47 ]. A subgroup analysis was conducted on patients with hematologic cancer, breast cancer, colorectal cancer, and prostate cancer who are at high risk for the CMD as mentioned above. The results of subgroup analysis similarly supported that metformin effectively reduces the risk of CMD and associated mortality risks in these specific cancer subgroups.

Strengths and limitation

As our understanding of the pathogenesis and treatment principles of cancer deepens, the therapeutic objectives for cancer survivors are evolving beyond merely “curing” cancer, aiming instead to foster a prolonged and productive lifestyle. With increasing oncologic survival, cancer survivors are at growing risk for various chronic conditions. Effective management of cancer survivors with concurrent CMD is critically essential. However, no medication has been widely recognized as effective in treating CMD among cancer survivors. Our current study addressed gaps in existing literature concerning the pharmacological management of cardiometabolic comorbidities in cancer survivors. It offered concrete evidence linking metformin use to enhanced cardiometabolic health post-cancer. One key strength of our study lies in the novel finding that metformin significantly reduces the risk of cardiometabolic diseases, all-cause mortality, and cardiometabolic mortality in cancer survivors. Moreover, even within subgroups of patients with four cancer types at higher cardiometabolic risk, the robust protective effect of metformin on cardiometabolic outcomes persisted. Crucially, our analysis utilized a large, nationally representative sample of cancer survivors in the USA, encompassing cancers with relatively high 5-year survival rates, such as breast, colorectal, and prostate cancers, and those with less favorable outcomes, including hematologic and ovarian cancers. This diverse representation enhances the clinical applicability of our findings, offering promising aspects for CMD treatment in cancer survivors with metabolic vulnerability and suggesting a new avenue for expanding the therapeutic spectrum of the classic drug metformin. Lastly, by systematically assessing oxidative stress levels in patients and conducting interaction analyses with metformin use, our results elucidated the potential pharmacological mechanism of metformin’s cardiometabolic protective effect by antagonizing oxidative stress in cancer survivors. This insight holds significance for future interventional target of oxidative stress and the development of CMD treatments among patients with cancer.

Despite these strengths, our study has several limitations that merit consideration. First, due to the observational nature of the NHANES database, it was challenging to incorporate all residual covariates into the adjusted model. However, we determined E-values to illustrate that the influence of unmeasured confounding factors is unlikely to be sufficient to nullify the observed correlations. Second, although adjustments for tumor-related factors, such as “years since the first cancer diagnosis,” were incorporated, NHANES lacked detailed data on specific cancer staging and treatment.

Third, information on cardiometabolic disease outcomes in this study was derived from cross-sectional data. Therefore, we could not definitively ascertain the temporal relationship between metformin use and reported cardiometabolic diseases. We also concur that reliance on self-reported data for cardiometabolic disease outcomes and cancer status might lead to misclassification, which possibly resulted in non-differential or differential bias. Non-differential misclassification, which occurs when the misclassification of cardiometabolic outcome is independent of the exposure status, could potentially attenuate the observed correlations towards the null. In certain cases, this bias may lead to false positive results, especially when the misclassification interacts with other variables. Although the NHANES survey employed a stringent data collection protocol to minimize misclassification, we cannot fully rule out the possibility of misclassification. To mitigate this concern, future studies could incorporate medical records or clinical assessments to validate the self-reported diagnoses, thereby reducing the likelihood of misclassification and providing more precise correlation estimation.

Fourth, another limitation of this study was that we only had binary information on whether participants used metformin or not, but lacked data on the duration of medication use, dosage, and other relevant details. Fifth, information on metformin use (exposure) was obtained by reviewing participants’ medication use in the past month during the household interview. However, the NHANES database lacked survival data of cardiometabolic outcomes for the period between metformin treatment initiation and the start of the follow-up (household interview). Consequently, our findings were possibly influenced by immortal time bias. Although we conducted a sensitivity analysis by excluding cancer survivors who died early in the follow-up period and found that the results remained robust, the potential impact of immortal time bias should be considered when applying our findings.

Sixth, due to the strong association of the exposure with the negative control outcome (i.e., accidental deaths), we cannot exclude the possibility of a certain degree of bias in our main analyses. Lastly, the oxidative balance score was computed based on the assumption that all prooxidants and antioxidants linearly correlated with oxidative stress levels, without considering the threshold effect of antioxidants. Studies have indicated that specific antioxidants, such as carotenoids and copper, might demonstrate prooxidative effects when administered in elevated concentrations. Future research should consider these factors or introduce more robust biomarkers related to oxidative stress to provide more reliable validation of the cardiometabolic protective mechanisms of metformin.

In this prospective cohort study encompassing a nationally representative sample of US cancer survivors, metformin use as compared to non-use was inversely associated with the risks of all-cause mortality, cardiometabolic disease, and associated mortality. This study provided novel evidences and perspectives on the pharmaceutical management of cancer survivors in the field of cardio-oncology, highlighting potential directions for the design and development of cardiometabolic protective drugs specifically beneficial for cancer populations. Considering the lack of detailed data on specific cancer staging and treatment as well as the cross-sectional nature of cardiometabolic disease outcomes, subsequent longitudinal and more comprehensive studies are urgently needed to further elucidate the practical application and pharmacological mechanisms of metformin in the management of cardiometabolic health among cancer survivors.

Availability of data and materials

The NHANES data supporting the results of this study are available online through https://wwwn.cdc.gov/nchs/nhanes/Default.aspx .

Abbreviations

95% Confidence intervals

Adenosine 5’ monophosphate-activated protein kinase

Body mass index

Coronary heart disease

• Cardiometabolic disease

Cancer survivors

Cardiovascular diseases

Directed acyclic graph

Hazard ratio

The tenth revision of the international classification of diseases

Metabolic equivalent

National health and nutrition examination survey

• Oxidative stress

Oxidative phosphorylation

Physical activity questionnaire

Patient health questionnaire-9

Poverty to income ratio

Relative excess risk due to interaction

Reactive oxygen species

Standard error of mean

Type 2 diabetes mellitus

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Acknowledgements

We thank our colleagues for helpful discussions related to the study. We thank Home for Researchers and Bullet Edits Limited for providing linguistic support for editing and proofreading the manuscript.

This work was supported by the National Natural Science Foundation of China [grant numbers 81870243, 82170318, 82170310].

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Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, 100012, China

Yukun Li, Xiaoying Liu, Wenhe Lv, Xuesi Wang, Zhuohang Du, Xinmeng Liu, Fanchao Meng, Shuqi Jin, Nian Liu & Ribo Tang

National Clinical Research Center for Cardiovascular Diseases, Beijing, 100012, China

Banner University Medical Center Phoenix, College of Medicine University of Arizona, Phoenix, AZ, 85123, USA

Department of Cardiovascular Medicine, Mayo Clinic, Scottsdale, AZ, 85259, USA

Songnan Wen

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XL1, WL, and XW collected data and related articles. YL analyzed data and wrote this manuscript. ZD, SJ, XL2, and FM contributed to the revision of the manuscript. SW, RB, NL, and RT designed the study and interpreted results. All authors read and approved of the final manuscript.

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Correspondence to Rong Bai , Nian Liu or Ribo Tang .

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The National Center for Health Statistics Ethics Review Committee granted ethics approval of NHANES (Protocol No. 98–12 for the 2003–2004 cycle, Protocol No. 2005–06 for the 2005–2010 cycles, Protocol No. 2011–17 for the 2011–2016 cycles, Protocol No. 2018–01 for the 2017–2018 cycle), as detailed on their official site ( https://www.cdc.gov/nchs/nhanes/irba98.htm ). NHANES provides open-access data for public use. The analysis in our study is a secondary analysis of NHANES data. Secondary analyses of publicly accessible de-identified datasets are exempt from review by the Research Ethics Committee at our institution (human research ethics committees of Beijing Anzhen Hospital). All individuals signed written informed consent before participating in the NHANES study.

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Additional file 1: Figure S1. The hypothetical directed acyclic graph used to select potential covariates. Table S1. Components of the oxidative balance score. Table S2. Association of Metformin Use with All-Cause Mortality and Cardiometabolic Outcomes Among US Cancer Survivors by Age, Gender, BMI, and Race, NHANES 2003 to 2018. Table S3. Association of Metformin Use with All-Cause and Cardiometabolic Mortality Risk Among US Cancer Survivors, NHANES 2003 to 2018. Table S4. Relative excess risk of all-cause mortality, cardiometabolic mortality due to antagonistic interaction effect of metformin use and oxidative stress levels in cancer survivors. Table S5. Association of Sulfonylurea Use with All-Cause and Cardiometabolic Mortality Risk Among US Cancer Survivors, NHANES 2003 to 2018. Table S6. Correlations of Sulfonylurea Use with Four Specific Cardiometabolic Diseases Risk Among US Cancer Survivors, NHANES 2003 to 2018. Table S7. Association between Metformin Use and All-Cause/Cardiometabolic Mortality Risk with further adjustment of HbA1c, Diabetic Retinopathy, GLP-1 Receptor Agonists Use and SGLT-2 Inhibitors Use. Table S8. Correlations between Metformin Use and Four Specific Cardiometabolic Diseases Risk with further adjustment of HbA1c, Diabetic Retinopathy, GLP-1 Receptor Agonists Use and SGLT-2 Inhibitors Use. Table S9. Association between Metformin Use and All-Cause/Cardiometabolic Mortality Risk Among US Cancer Survivors after Excluding Patients Receiving Dialysis in Past 12 Months, NHANES 2003 to 2018. Table S10. Correlations between Metformin Use and Four Specific Cardiometabolic Diseases Risk Among US Cancer Survivors after Excluding Patients Receiving Dialysis in Past 12 Months, NHANES 2003 to 2018.

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Li, Y., Liu, X., Lv, W. et al. Metformin use correlated with lower risk of cardiometabolic diseases and related mortality among US cancer survivors: evidence from a nationally representative cohort study. BMC Med 22 , 269 (2024). https://doi.org/10.1186/s12916-024-03484-y

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DOI : https://doi.org/10.1186/s12916-024-03484-y

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Main article content, the effect of budget formulation on the public sector effectiveness moderated by participative budgeting in addis ababa city administration, mehari haileselassie, deresse marsha, biniam berhe.

Budget formulation, the process of creating and developing a government's budget, plays a crucial role in determining the effectiveness of public sector organizations. The budget serves as a financial plan that allocates resources and guides the implementation of government policies and programs. The effectiveness of budget formulation can be influenced by various factors, including the moderating variable of participative budgeting. The study conducted in the Addis Ababa City Administration aimed to assess the effect of budget formulation on public sector effectiveness, with the moderating variable of participative budgeting. The research employed a pragmatic research paradigm and a mixed research approach, combining qualitative and quantitative data collection methods. Quantitative data were collected through a 7-point Likert scale questionnaire administered to employees working in selected public sectors. The research utilized a combination of probabilistic and non-probabilistic sampling methods. The population size of the study was 4580, and a sample of 368 employees was selected, with 346 questionnaires returned. Once the data was gathered, it was analyzed using SPSS version-26 and AMOS-23. The analysis focused on factors related to budget formulation, including strategic planning, budget preparation, the competence of human resources, and budget approval. The study also examined the influence of participative budgeting as a moderating variable on the effectiveness of public sectors. The findings of the study were presented using descriptive and explanatory research techniques, leading to a discussion of the results. The study revealed a direct and significant positive relationship between the independent variables (strategic planning, budget preparation, budget approval, competence of human resources) and the dependent variable (organizational effectiveness). Additionally, the study found a significant effect of the moderating variable, the participative budget. Based on the results, the study suggests that to achieve effectiveness in the public sector, the city administration's public sectors should consider strategic planning, participative budgeting, and the revision of the existing line-item budget system during budget formulation. These factors contribute to improving organizational effectiveness in the public sectors examined in the study.

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