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How to Write About Negative (Or Null) Results in Academic Research

Researchers are often disappointed when their work yields "negative" results, meaning that the null hypothesis cannot be rejected. However, negative results are essential for research to progress. Negative results tell researchers that they are on the wrong path, or that their current techniques are ineffective. This is a natural and necessary part of discovering something that was previously unknown. Solving problems that lead to negative results is an integral part of being an effective researcher. Publishing negative results that are the result of rigorous research contributes to scientific progress.

There are three main reasons for negative results:

• The original hypothesis was incorrect
• The findings of a published report cannot be replicated
• Technical problems

Here, we will discuss how to write about negative results, first focusing on the most common reason: technical problems.

Writing about technical problems

Technical problems might include faulty reagents, inappropriate study design, and insufficient statistical power. Most researchers would prefer to resolve technical problems before presenting their work, and focus instead on their convincing results. In reality, researchers often need to present their work at a conference or to a thesis committee before some problems can be resolved.

When presenting at a conference, the objective should be to clearly describe your overall research goal and why it is important, your preliminary results, the current problem, and how previously published work is informing the steps you are taking to resolve the problem. Here, you want to take advantage of the collective expertise at the conference. By being straightforward about your difficulties, you increase the chance that someone can help you find a solution.

When presenting to a thesis committee, much of what you discuss will be the same (overall research goal and why it is important, results, problem(s) and possible solutions). Your primarily goal is to show that you are well prepared to move forward in your research career, despite the recent difficulties. The thesis defense is a defined stopping point, so most thesis students should write about solutions they would pursue if they were to continue the work. For example, "To resolve this problem, it would be advisable to increase the survey area by a factor of 4, and then…" In contrast, researchers who will be continuing their work should write about possible solutions using present and future tense. For example, "To resolve this problem, we are currently testing a wider variety of standards, and will then conduct preliminary experiments to determine…"

Putting the "re" in "research"

Whether you are presenting at a conference, defending a thesis, applying for funding, or simply trying to make progress in your research, you will often need to search through the academic literature to determine the best path forward. This is especially true when you get unexpected results—either positive or negative. When trying to resolve a technical problem, you should often find yourself carefully reading the materials and methods sections of papers that address similar research questions, or that used similar techniques to explore very different problems. For example, a single computer algorithm might be adapted to address research questions in many different fields.

In searching through published papers and less formal methods of communication—such as conference abstracts—you may come to appreciate the important details that good researchers will include when discussing technical problems or other negative results. For example, "We found that participants were more likely to complete the process when light refreshments were provided between the two sessions." By including this information, the authors may help other researchers save time and resources.

Thus, you are advised to be as thorough as possible in reviewing the relevant literature, to find the most promising solutions for technical problems. When presenting your work, show that you have carefully considered the possibilities, and have developed a realistic plan for moving forward. This will help a thesis committee view your efforts favorably, and can also convince possible collaborators or advisors to invest time in helping you.

Publishing negative results

Negative results due to technical problems may be acceptable for a conference presentation or a thesis at the undergraduate or master's degree level. Negative results due to technical problems are not sufficient for publication, a Ph.D. dissertation, or tenure. In those situations, you will need to resolve the technical problem and generate high quality results (either positive or negative) that stand up to rigorous analysis. Depending on the research field, high quality negative results might include multiple readouts and narrow confidence intervals.

Researchers are often reluctant to publish negative results, especially if their data don't support an interesting alternative hypothesis. Traditionally, journals have been reluctant to publish negative results that are not paired with positive results, even if the study is well designed and the results have sufficient statistical power. This is starting to change— especially for medical research —but publishing negative results can still be an uphill battle.

Not publishing high quality negative results is a disservice to the scientific community and the people who support it (including tax payers), since other scientists may need to repeat the work. For studies involving animal research or human tissue samples, not publishing would squander significant sacrifices. For research involving medical treatments—especially studies that contradict a published report—not publishing negative results leads to an inaccurate understanding of treatment efficacy.

So how can researchers write about negative results in a way that reflects its importance? Let's consider a common reason for negative results: the original hypothesis was incorrect.

Writing about negative results when the original hypothesis was incorrect

Researchers should be comfortable with being wrong some of the time, such as when results don't support an initial hypothesis. After all, research wouldn't be necessary if we already knew the answer to every possible question. The next step is usually to revise the hypothesis after reconsidering the available data, reading through the relevant literature, and consulting with colleagues.

Ideally, a revised hypothesis will lead to results that allow you to reject a (revised) null hypothesis. The negative results can then be reported alongside the positive results, possibly bolstering the significance of both. For example, "The DNA mutations in region A had a significant effect on gene expression, while the mutations outside of domain A had no effect. Don't forget to include important details about how you overcame technical problems, so that other researchers don't need to reinvent the wheel.

Unfortunately, it isn't always possible to pair negative results with related positive results. For example, imagine a year-long study on the effect of COVID-19 shelter-in-place orders on the mental health of avid video game players compared to people who don't play video games. Despite using well-established tools for measuring mental health, having a large sample size, and comparing multiple subpopulations (e.g. gamers who live alone vs. gamers who live with others), no significant differences were identified. There is no way to modify and repeat this study because the same shelter-in-place conditions no longer exist. So how can this research be presented effectively?

Writing when you only have negative results

When you write a scientific paper to report negative results, the sections will be the same as for any other paper: Introduction, Materials and Methods, Results and Discussion. In the introduction, you should prepare your reader for the possibility of negative results. You can highlight gaps or inconsistencies in past research, and point to data that could indicate an incomplete understanding of the situation.

In the example about video game players, you might highlight data showing that gamers are statistically very similar to large chunks of the population in terms of age, education, marital status, etc. You might discuss how the stigma associated with playing video games might be unfair and harmful to people in certain situations. You could discuss research showing the benefits of playing video games, and contrast gaming with engaging in social media, which is another modern hobby. Putting a positive spin on negative results can make the difference between a published manuscript and rejection.

In a paper that focuses on negative results—especially one that contradicts published findings—the research design and data analysis must be impeccable. You may need to collaborate with other researchers to ensure that your methods are sound, and apply multiple methods of data analysis.

As long as the research is rigorous, negative results should be used to inform and guide future experiments. This is how science improves our understanding of the world.

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Filling in the Scientific Record: The Importance of Negative and Null Results

PLOS strives to publish scientific research with transparency, openness, and integrity. Whether that means giving authors the choice to preregister their study, publish peer review comments, or diversifying publishing outputs; we’re here to support researchers as they work to uncover and communicate discoveries that advance scientific progress. Negative and null results are an important part of this process. 

This is something we agree on across our journal portfolio — the most recent updates from PLOS Biology being one example– and it’s something we care about especially on PLOS ONE . Our journal’s mission is to provide researchers with a quality, peer-reviewed and Open Access venue for all rigorously conducted research, regardless of novelty or impact. Our role in the publishing ecosystem is to provide a complete, transparent view of scientific literature to enable discovery. While negative and null results can often be overlooked — by authors and publishers alike — their publication is equally as important as positive outcomes and can help fill in critical gaps in the scientific record. 

We encourage researchers to share their negative and null results.

To provide checks and balances for emerging research 

Positive results are often viewed as more impactful. From authors, editors, and publishers alike, there is a tendency to favor the publication of positive results over negative ones and, yes, there is evidence to suggest that positive results are more frequently cited by other researchers. 

Negative results, however, are crucial to providing a system of checks and balances against similar positive findings. Studies have attempted to determine to what extent the lack of negative results in scientific literature has inflated the efficacy of certain treatments or allowed false positives to remain unchecked. 

The effect is particularly dramatic in meta-analyses which are typically undertaken with the assumption that the sample of retrieved studies is representative of all conducted studies:

“ However, it is clear that a positive bias is introduced when studies with negative results remain unreported, thereby jeopardizing the validity of meta-analysis ( 25 , 26 ). This is potentially harmful as the false positive outcome of meta-analysis misinforms researchers, doctors, policymakers and greater scientific community, specifically when the wrong conclusions are drawn on the benefit of the treatment.” — Mlinarić, et al (2017). Dealing with publication bias: why you should really publish your negative results . Biochem Med (Zagreb) 27(3): 030201

As important as it is to report on studies that show a positive effect, it is equally vital to document instances where the same processes were not effective. We should be actively reporting, evaluating, and sharing negative and null results with the same emphasis we give to positive outcomes.

To reduce time and resources needed for researchers to continue investigation

Regardless of the outcomes, new research requires time and financial resources to complete. At the end of the process, something is learned — even if the answer is unexpected or less clear than you had hoped for. Nevertheless, these efforts can provide valuable insights to other research groups.

If you’re seeking the answer to a particular scientific question, chances are that another research group is looking for that answer as well: either as a main focus or to provide additional background for a different study. Independent verification of the results through replication studies are also an important piece of solidifying the foundation of future research. This also can only happen when researchers have a complete record of previous results to work from. 

By making more findings available, we can help increase efficiencies and advance scientific discovery faster. 

To fill in the scientific record and increase reproducibility

It’s difficult to draw reliable conclusions from a set of data that we know is incomplete. This lack of information affects the entire scientific ecosystem. Readers are often unaware that negative results for a particular study may even exist, and it may even be more difficult for researchers to replicate studies where pieces of the data have been left out of the published record.

Some researchers opt to obtain specific null and negative results from outside the published literature, from non peer-reviewed depositories, or by requesting data directly from the authors. The inclusions of this “ grey literature ” can improve accuracy, but the additional time and effort that goes into obtaining and verifying this information would be prohibitive for many to include.

This is where publishers can play a pivotal role in ensuring that authors not only feel welcome to submit and publish negative results, but to make sure those efforts are properly recognized and credited. Published, peer-reviewed results allow for a more complete analysis of all available data and increased trust in the scientific record.  

We know it’s difficult to get into the lab right now and many researchers are having to rethink the way that they work or focus on other projects. We encourage anyone with previously unpublished negative and null results to submit their work to PLOS ONE and help fill in the gaps of the scientific record, or consider doing so in the future. 

This is a great initiative. I have a number of study manuscripts sitting in the draw.

I think that the idea for publishing of negative and null results is very good. Most scientists reject data which do not have a possitive effect on expected end results. Publishing of negative and null results will make research credible.

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Positives in negative results: when finding ‘nothing’ means something

Doctoral Candidate, University of Wollongong

Associate Research Fellow, CNS disorders, University of Wollongong

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The authors do not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.

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UNDERSTANDING RESEARCH: What do we actually mean by research and how does it help inform our understanding of things? It’s important to publish all results – both positive and negative – if researchers are to avoid repeating old mistakes. But where is the glory in negative results?

Scientists usually communicate their latest findings by publishing results as scientific papers in journals that are almost always accessible online (albeit often at a price), ensuring fast sharing of latest knowledge.

But negative findings – those that do not agree with what the researchers hypothesised – are often overlooked, discouraged or simply not put forward for publication.

Yet negative findings can save scientists valuable time and resources by not repeating already performed experiments, so it is important that all results, regardless of the outcome, are published.

Adding human nature to the mix

Despite devoting their lives to logic and facts, scientists are still human. Their decisions are influenced by emotions and opinions. They are, at times, unlikely to trust conflicting results due to a pre-existing belief that something else is true.

This phenomenon is known as cognitive bias . If presented with evidence that disproves an old theory, scientists may simply attribute the discrepancy to experimental error.

In extreme cases, reporting a negative result, particularly when it refutes previous research, is to some extent considered a form of discreditation.

At other times, human error and the fact that science cannot always be reproduced has led to the belief that negative results are associated with flawed or poor science.

Revolt against the negative-finding culture

The stigma surrounding negative findings means that they are a low priority for publication. High-quality journals are less likely to accept negative findings because they are associated with a lower citation rate, lower impact knowledge and are often controversial.

This raises a major issue: if results are not reported (positive or negative) then other scientists may waste time and resources needlessly repeating experiments.

Or, in some situations, theories that are untrue or incomplete are never corrected, despite their potentially dire consequences (as in the case of the measles, mumps and rubella MMR vaccine despite the original research linking it to autism being retracted by The Lancet ).

A scientist’s success depends largely on the impact of their research. Higher-impact findings published in prominent journals tend to attract more funding grants.

As citations are a measure of a scientist’s worth, and negative results attract fewer citations , many scientists simply choose not to spend the time publishing negative results.

Dissemination of negative results has traditionally been one of the hardest battles faced by scientists. It is particularly difficult when these negative findings contradict previously published research, even though many reputable journals have policies to publish such work.

It was a problem Australian researcher David Vaux wrote about in a Retraction Watch blog on his attempts to publish contradictory results.

In recent years, open-access and broad-scope journals such as PLOS One , Frontiers and the Biomed Central journal series are increasingly publishing papers with negative findings.

Additionally, a number of journals have surfaced whose primary objective is to disseminate negative findings, such as Journal of Articles in Support of the Null Hypothesis , Journal of Negative Results in Biomedicine and The All Results Journal .

The purpose of these journals is to give negative findings a home, where they can still be accessed widely by the international science community without facing prejudice in the review process.

But these journals have lower publication rates, reflective of a scientific culture that deems negative results less valuable.

How to turn a negative into a positive

The issues surrounding the negative finding culture are certainly gaining traction. Many reputable journals such as Disease Models & Mechanisms and Nature have covered the topic recently.

Nonetheless, publication bias is still an issue, indicating that a shift in the scientific culture is required.

Some journals have suggested that negative findings be published open access and free of charge, while others have suggested that scientists be encouraged to submit corrections as well as new results.

Additionally, a push by funding agencies for scientists to make available all data gathered (such as via Open Science ) from their support may reduce the stigma attached to negative findings.

As proposed by American physicist and philosopher Thomas Kuhn , a shift in scientific thinking will occur when the amount of evidence in support of the new paradigm overtakes the old one.

Following this logic, perhaps the answer to reversing the anti-negative-finding culture lies in educating young scientists about the importance of disseminating all results.

This way, the next generation of scientists may experience improved scientific communication and more efficient science.

This article is part of a series on Understanding Research .

Further reading: Why research beats anecdote in our search for knowledge Clearing up confusion between correlation and causation Where’s the proof in science? There is none The risks of blowing your own trumpet too soon on research How to find the knowns and unknowns in any research How myths and tabloids feed on anomalies in science The 10 stuff-ups we all make when interpreting research

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Not junk: Negative results in research present an opportunity to learn

In science, positive findings conforming with established hypotheses are celebrated via publication—the coin of the realm in academia—whereas nonconforming or negative results are often frowned upon and discarded by the researcher. This is surely also true for optics and photonics.

Many scientists do not proceed further with negative findings because the related value in the scientific community tends to be much lower than for so-called positive data. However, null outcomes sometimes need to be demonstrated and, in some cases, scrutinizing negative results from an alternative perspective can help to understand a larger problem.

In the early 1900s, Lord Rayleigh derived the famous λ –4 formula to predict the spectral radiance of electromagnetic radiation from a blackbody—a derivation that led to the Rayleigh-Jeans law. This derivation relied on the classical physics theories and on empirical observations for low frequencies. However, that derivation implied that the emitted energy was infinite at high frequencies. This idea was at odds with the then-known fact that total emitted energy is finite, and the prediction significantly diverged from observations above 100 THz.

From its first derivation in 1900, it took approximately five years for the idea of the “ultraviolet catastrophe” to catch on, mostly thanks to the contributions of Lord Rayleigh himself and Albert Einstein, who managed to convince the scientific community that classical electromagnetism had to be rejected in favor of the quanta of light theory derived in 1900 by Max Planck (who, ironically, wasn’t initially convinced of the reality of the theory, and thought of it just as a mathematical trick). This negative result was one of several that led to the birth of quantum mechanics.

So, disregarding negative results not only represents an obstacle for the development of science, but also encourages researchers to dismiss results contradicting existing literature, regardless of potential for leading to significant breakthroughs. Validating negative results by further experimentation or analyses, ensuring reproducibility and statistical significance, is sometimes necessary.

Researchers, editors, publishers, and funding institutions  should be aware of the significance of negative findings and support their dissemination. We need a change of mindset to transfer in-depth knowledge gleaned from negative results to next-generation researchers. In other words, it’s time to be positive about negative results.

Negative findings can be defined as results that contradict research hypotheses, established scientific knowledge, and previous evidence or predictions. They are typically characterized by a different, opposite, or absent correlation between observed phenomena. Of course, these results must still rely on sound theories and carefully performed experiments. Erroneous results cannot be considered negative results per se.

Negative results are usually rejected or even regarded as failures by researchers and their peers. As a consequence, positive results in published articles sometimes lead to conclusions different from those arising from unpublished negative results. When this is systemic, we say there is a publication bias.

Lord Rayleigh (right) and Albert Einstein. Photo credits: The Nobel Foundation archive

Published negative results also tend to be less cited than positive results, or only cited by a small group of researchers, leading to exclusion from meta-analyses or literature reviews. This so-called citation bias also has indirect consequences: In academia, well-cited articles often lead to a higher probability of continued research funding.

So, it is crucial for the advancement of knowledge to publish both positive and negative findings. The publication of well-documented, well-designed, and well-executed “failures” could add important perspectives and records to the scientific literature.

For example, results considered negative are often statistically more reliable since they are reproduced at multiple occurrences. They are valuable elements in the scientific literature that can help to evaluate data, reveal undiscovered relationships, or point out wrong assumptions and flaws in theories. They can also be used to steer research strategies, and provide inspiration for innovative development of theories, simulations, or experiments. What’s more, their dissemination avoids duplication of effort by other groups.

Instead of rejecting negative results, we might allow greater room for nature’s complexity by placing greater trust in our data, rather than outright rejecting that which does not immediately fit our preconceptions. This practice may mean acquiring more data and discussing it with peers in our own research groups and institutions, as well as with colleagues from other institutions.

When negative results are encountered, the path forward for the research team can vary. If obtained in an early stage of a project, decide whether to investigate further depending upon, for example, significance of the data, the project timeframe, project objective, and researchers’ instincts. If secured in a late stage of investigation, negative results should be disseminated.

This practice is true for both early-career and established researchers. The former should take advantage of such publications to practice technical writing, which is a valuable skill. The latter typically have the time to address time-consuming questions that arise from negative results and enough credibility to question widespread assumptions in their fields.

Fortunately, an increasing number of peer-reviewed scientific journals aiming to reduce publication bias have emerged in recent years. All reinforce the idea that, in science, failure can be as important as success. In addition, open-access broad-topic journals such as PeerJ, PLOS ONE, Scientific Reports, and F1000Research also allow the publication of negative results. Still, the number of articles reporting negative findings remains a small percentage of the overall literature.

Ideally, results should be published regardless of negative or positive conclusions. Even if journals favoring novel, impactful, and positive results cannot be avoided, publishers could give negative findings more room. Conference proceedings, like SPIE’s, are one such venue for publications of this type.

Publishing scientific articles is not an end unto itself. As researchers, we should not be disappointed or frustrated by negative results but strive to reach unbiased conclusions driven solely by the data. Such research has value.

Sébastien R. Mouchet, Priyanka Dey, Michele Gintoli, Jayakrupakar Nallala, and Bob-Dan Lechner  are researchers in the Department of Physics and Astronomy, University of Exeter, UK. Contact the authors at [email protected] ; or [email protected]

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• Published: 01 May 2019

Learning from negative findings

• Mark I. Taragin   ORCID: orcid.org/0000-0002-8649-7630 1  

Israel Journal of Health Policy Research volume  8 , Article number:  38 ( 2019 ) Cite this article

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The Original Article was published on 21 December 2018

A recent IJHPR article by Azulay et al. found no association between the patient activation measure (PAM) and adherence to colonoscopy after a positive fecal occult blood test result. This commentary will use that article as a jumping-off point to discuss why studies sometimes get negative results and how one should interpret such results. It will explore why the Azulay study had negative findings and describe what can be learnt from this study, despite the negative findings.

It is important to publish studies with negative findings to know which interventions do not have an effect, avoid publication bias, allow robust meta-analyses, and to encourage sub-analyses to generate new hypotheses.

To support these goals authors must submit articles with negative findings with sufficient detail to support the above aims and perform sub-analyses to identify additional relationships that merit study.

The commentary will discuss the importance of publishing articles in which the hypothesis is not proven and demonstrate how such articles should be written to maximize learning from their negative findings.

In a recent IJHPR article, Azulay et al. explored the factors associated with whether a patient underwent a recommended colonoscopy after having an abnormal result when screened with a fecal occult blood test (FOBT). They were specifically interested in whether “patient empowerment” as measured by a well-tested scale (the patient activation measure, or PAM) was associated with greater adherence to testing recommendations. Surprisingly, their study found no association ( p value 0.774). Why did that happen? Was their study flawed? What can other researchers learn from their experience? Did they “fail” or can we learn something from this study?

In general, the publication of negative studies has been called for, primarily to overcome publication bias when performing a meta-analysis. This commentary will suggest that beyond that reason, by analyzing the various aspects of a study’s methodology, one can glean additional insights from a study with negative findings. By performing this analysis, one can also determine whether a negative study is a false negative study or a true negative study. The Azulay study will be used to illustrate these points.

The majority of researchers set forth a hypothesis and then attempt to test this hypothesis with the most rigorous study methodology possible. However, all researchers must struggle with limited resources. The “gold-standard” of a double-blind or even a triple-blind randomized study is often not feasible and frequently not even doable. Thus, researchers settle for less. The challenge a researcher faces is to find the balance between performing a meaningful study and optimizing their “investment”. One must publish or perish using limited resources. What challenges faced the designers of the Azulay study and how well did this study perform?

The authors targeted the issue of compliance with screening guidelines. This is an important health care issue which could save many lives. The ideal way to study this question would have been a prospective cohort study where patient awareness was measured at baseline, and perhaps at subsequent decision points, and then the outcome of interest, in this case a colonoscopy after screening, would be objectively measured. In addition, information would be collected on all known and suspected factors that could impact on the outcome of interest – the potential confounders.

However, a retrospective case-control study was done instead. This approach was undertaken presumably to take advantage of known screening results. Further, this method is significantly cheaper and produces results much more quickly. But, alongside these advantages, this approach introduces a number of potential problems, discussed below, some of which are discussed by the authors.

Study population determines the generalizability of a study. The population is determined by what population is targeted and what exclusion criteria are applied. The study population was limited to a single health fund, which has demographic characteristics that are different from the other funds. These differences need to be described and their implications discussed. Regarding the exclusion criteria, the authors assessed medication adherence in the health fund and could have easily assessed this in the excluded population. Similarly, the authors should have described the number and characteristics of the excluded group. Without addressing these issues one cannot be certain of the generalizability of this study. On the positive side, the authors indicate that the distribution of PAM levels in their population is similar to that found in other studies.

Selection bias is a potential critical problem. Who participated and who did not, and especially what caused this discrepancy, can critically affect one’s results. In this study, 54% of the target population could not be reached and 13% refused to participate. These are huge numbers and many would consider this a fatal flaw. The target for success is an 80% response rate. While the authors try to address these problems with some demographic comparisons, they could have done more. They could have compared medical factors between study participants and non-participants, including diagnoses, medications and health care utilization.

Sample size is the next consideration. After we have identified our study population, do we have enough respondents to reach a meaningful conclusion? For a positive study this can be assessed by examining statistical significance. A negative study may be defined as a study showing a result that goes against the investigated hypothesis of an increased (or prevented) risk [ 1 ]. However, rejecting the investigated hypothesis (which is typically the opposite of the null hypothesis) requires a narrow confidence interval, which in turn is driven by sample size. This study had 429 participants and generated reasonable p values. For example, for the main question of interest, patient activation, the PAM means for the adherent and non-adherent group were 62.77 and 61.59 with a p value of 0.472. The p -value suggests that there is little difference between the groups and one would need a very large study to find a statistically significant difference. One could do a power analysis to determine how large a study would be needed to find this difference to be statistically significant. However, while statistical significance might be obtained with a larger sample size, it is unlikely that this difference would be clinically significant.

Clinical significance is a qualitative determination which is primarily driven by the importance of the outcome, the difference between the outcomes for the various alternatives, and the cost of achieving this difference. Cost includes process differences and associated side effects. For example, in this study, the outcome of importance would be preventing cancer deaths by screening. The alternatives would include the different methods to achieve better screening participation. The cost would assess the financial impact, including the ancillary results (good and bad) for each alternative method. Of note, there is a possibility of a PAM effect in the categorical analysis, where for the highest PAM score, 44.3% were in the adherent group and 39.6% were in the non-adherent group. Thus, while the analysis was not statistically significant, if one wanted to pursue this relationship it might be worth focusing on the highest PAM score group.

Exposure and Outcome Measures refers to how one measures the outcome of interest and assesses potential factors that can influence this outcome. The presence of confounding (discussed below) and bias must be addressed. Patient activation, the primary exposure of interest, may be important when taking the decision to screen as well as when making the decision to follow-up on screening results. Thus, regarding PAM, the screened population may already be a select population, a form of bias. Furthermore, because of the retrospective assessment of PAM, patient activation might have been influenced by the test results themselves, another form of bias.

The authors acknowledge that “patient activation may vary with time and context” yet do not provide us with any literature describing the stability of this measure over time. For patient activation to be useful one must know if it is stable over time and if it can be modified.

Even if PAM cannot be modified, it could be used to optimize different strategies for targeting different populations. The authors’ primary hypothesis is that there is an association between patient activation and the decision to follow-through on a screening result. However, the importance of patient activation may vary with disease, screening approaches, and interventions. A perusal of the PAM website [ 2 ] reveals that a number of studies did not find an association between patient activation and the outcome being studied. The authors should have described this and whether any similar factors were present in their study. Their study could contribute to the PAM literature by exploring in which populations PAM is significant and why.

Potential confounders must be properly assessed. The authors provide a review of what factors are associated with non-adherence, including many that they did not assess, e.g., health status, patient knowledge, fear of undergoing CRC (colo-rectal cancer) screening, high self-efficacy, risk perception, and perception of the chance of developing CRC. Furthermore, the authors identified a local study which demonstrated that higher educational attainment and higher self-efficacy were important factors associated with non-adherence. However, despite doing a phone interview, the authors did not report results on any of these known factors. One can surmise that either they did not evaluate these factors, or that they plan an additional paper with those results. Nevertheless, in Table 1, the “Characteristics of study population by colonoscopy adherence”, the authors present findings on additional potential confounders. Although no differences were statistically significant, this could be a sample size issue. For example, country of birth, ethnicity, BMI, and smoking with p values of 0.15, 0.264, 0.118, and 0.066 respectively, may have reached significance with a greater sample size. This is especially important when planning further studies.

Statistical methods are generally not a problematic issue, especially for articles published in serious peer-reviewed journals. Yet, it is worth noting that there are statistical methods to deal with negative results. The basic ones, described above, are p values and confidence intervals. In addition, there are methods to quickly estimate a maximum effect, such as described in the paper “If nothing goes wrong is everything all right?” [ 3 ]. In that paper the authors describe a rule of thumb “3/N” where N is the sample size where no effect was found. Thus, for example, if 20 patients were reported to have no outcome, then the upper confidence interval can be estimated as being 3/20, 15%. In general, despite our attraction to numbers, typically, study quality is much more important.

All of the parameters discussed above will determine the quality of the study. In recent years, the importance of study quality has been increasingly recognized. For example, many meta-analysis papers perform sub-analyses that evaluate the effect of study quality on conclusions. These papers often find large differences in results when stratifying by quality. Primarily driven by the need to do meta-analysis, an alphabet of tools has been developed to evaluate the quality of studies, e.g., AMSTAR, PRISMA, and STROBE [ 4 , 5 , 6 , 7 ]. While these tools are ultimately subjective, their structured format ensures a more complete and transparent process that can be reproduced by others – and allows evaluations to be compared. The need for, and the development of, these tools stresses the fact that there is a wide spectrum of quality among studies. This variance in study quality can partially explain why different studies of the same issue get different results and why some studies have significant findings and others don’t.

The public as well as physicians are frustrated by scientific “flip-flops” with changing recommendations over the years, e.g., hormonal therapy for post-menopausal women, cut-offs for treating hypertension in the elderly, and PSA screening, to name a few. The evaluation of study quality and the publication of negative results have the potential to generate more transparent results and better explain the variation between study results. Not only will this enable researchers to reach a better understanding of what they are studying, but this will also allow more robust models of what factors drive specific outcomes. Understanding the impact of study quality on results should also facilitate the scientific community’s ability to explain conflicting results to the public and regain the trust the public has lost in the scientific literature [ 8 ].

The quality of a study should be evident, in part, in the completeness of the discussion section. The authors of the Azulay paper did a very nice job of evaluating their results and comparing them to other relevant studies in the field of screening and PAM. In general, authors have the best knowledge of the strengths and limitations of their study. A thorough discussion not only allows better understanding of the value of a study and what future work needs to be done, but it also reflects on the knowledge and skill set of the authors. Sharing and discussing study flaws and study limitations displays the authors’ knowledge of the field they are studying and their understanding of study methodology. The discussion section should describes how reality differed from what was planned and generates the approaches needed to further develop this area of study. As noted above, the ideal prospective study is expensive and time-intensive. Lower quality studies form the basis for creating better future studies and are appropriate when beginning to study a new area.

Study results can be categorized into “positive” and “negative” but really should be more often labeled “mixed” or “I don’t know”. As described above, study quality can render a positive study’s results fatally flawed and misleading. Alternatively, despite sample size issues, a negative study can be informative. Indeed, the examples noted above illustrate how the Azulay study contributes to better understanding of potential confounders and PAM. Thus, labeling a study as negative is deceptive and should be avoided.

The need for multiple studies to form a basis for understanding is clear. Conflicting results should not be surprising and should form the basis for a more comprehensive understanding of the area being investigated. Researchers need to discuss the limitations of their studies and be more willing to admit that their results are unclear. Overconfidence is dangerous [ 9 , 10 ] . As I learned in medical school, the best physicians know when to say “I don’t know” and are not afraid to say so. Only thus can “truth” be sought, and hopefully, revealed.

Conclusions

Azulay and colleagues targeted an important health care decision with a reasonable hypothesis. As is the case with most studies, their study design and methodology had several flaws. Despite these flaws and the lack of a finding of an association, much can be learned from the Azulay study. It contributes to the knowledge of the importance of patient activation and may help us better understand when patient activation plays an important role. If Azulay and colleagues still believe that patient activation is associated with CRC/FOBT they will need to invest more resources to assess this potential relationship, and better assess the relevant confounders.

This commentary has used the Azulay paper to demonstrate the importance of publishing studies with negative findings. Perhaps, in this clinical setting, patient activation is an intervention which does not have an effect. Publishing this result can help others avoid investing in this intervention and encourage looking for better alternative interventions. By publishing a negative finding on patient activation other authors can use these results in a meta-analysis on the effectiveness of patient activation. Finally, as discussed above, some of the sub-analyses in the Azulay paper suggest additional hypotheses that can be pursued.

I hope that this commentary on the Azulay study will encourage the IJHPR and other journals to publish negative studies more often. Perhaps journals should also publish a statistic of how many negative studies they publish. This statistic would enable the creation of a benchmark for what percentage of studies are expected to be negative, encourage the publication of negative studies, and help identify which journals are promoting better quality research results by diminishing publication bias.

Abbreviations

Colo-rectal cancer

Fecal occult blood test

Patient activation measure

Prostate specific antigen

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Taragin, M.I. Learning from negative findings. Isr J Health Policy Res 8 , 38 (2019). https://doi.org/10.1186/s13584-019-0309-5

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Seeing the Positives in Negative Results: Why it is Important to Publish ‘Negative’ Research Findings

• December 13, 2018
• Jemimah Kim
• News , Read

The weekly news cycle is routinely scattered with announcements of new scientific research that promises to change our understanding of health and disease. Unsurprisingly, the research that ends up as news headlines tends to be the more exciting information–scientists located a new gene for Alzheimer’s, researchers cloned an animal, et cetera. What often gets left out of the popular media and even from scientific journals, however, is some less exciting, but equally important research–negative findings, or null findings.

Negative findings refer to results that do not support the researcher’s initial hypothesis.[1]  Usually , a researcher predicts that their results will support the alternative hypothesis–that there  is  a difference between the two variables they are investigating–rather than the null hypothesis–that there is no difference between the variables. These results are often called non-significant, because the researcher did not find a statistically significant difference between the control group and the intervention group.[2] 

Despite the importance of negative findings for advancing research, ‘negative’ research results are significantly less likely to be published than positive results.[2] There are several reasons that negative findings are not published as often as positive findings. In the research world, there is a negative value or  stigma  attached to null findings, and there is a positive value attached to significant findings. We place a cultural value on getting positive results and correctly proving your original hypothesis. The very naming of the terms themselves– negative, null,  or  insignificant  findings–frames them as something bad.

There are several reasons that negative findings are not published as often as positive findings. In the research world, there is a negative value or stigma  attached to null findings, and there is a positive value attached to significant findings. We place a cultural value on getting positive results and correctly proving your original hypothesis. The very naming of the terms themselves– negative, null, or  insignificant  findings–frames them as something bad.

The resulting stigma attached to null findings makes them a low priority for publication. Many reputable journals are  less likely to accept and publish negative findings  because they generally have a lower citation rate, and thus a  lower impact factor , and are often controversial. Also due to this stigma, many scientists who get negative results deem their work to be useless and a waste of time. As a result, they may choose not to publish them.[1] Many scientists– particularly young ones –even fear publishing negative results could negatively impact their career, even forcing them out of research altogether. This fear is not unfounded. Researchers who spend a lot of time and money on the ‘wrong’ project will likely find that their research is published with a low impact factor. As a result they could receive less funding for future research.  One site  even warns (in bold), “Do not publish negative results as a young scientist. Leave it to the senior scientists who already have a successful career and can afford it to publish negative findings for the sake of good science!” Even well-established scientists experience this pressure to publish only their significant findings. Because scientists are involved in research as a career, they naturally find themselves having to compete for positions and funding for their research.[2] Publishing negative findings could frame a scientist as ‘unsuccessful’ and make it harder for him or her to secure funding for future projects.

References: [1] Miller-Halegoua, Suzanne M. (2017). Why null results do not mean no results: negative findings have implications for policy, practice, and research. Translational Behavioral Medicine , 7(2): 137.

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Negative Results: The Data are the Data

The data are the data. We have all used this line repeatedly while conducting research and while teaching others how to do research. Generally these words are expressed with a frustrated grimace, a shaking of the head, a pulling of the hair or an exasperated sigh. Negative data are a regular result from good research questions, solid experimental designs, and weeks/months of reliable data collection. The results are not what we wanted, we often initially cannot explain them, but as we have all been trained, "the data are the data". As frustrating as it is to obtain negative results after weeks/months of work, experienced researchers are aware that negative results are a pretty common occurrence in research. However, for an inexperienced researcher or a junior scientist, negative results can evoke negative emotions such as fear of failure or fear of disappointing their mentor.

The phrase "negative results" is actually somewhat ambiguous. The phrase is generally thought to describe results that are inconclusive due to a failure to reach statistical significance. However, unexpected results or results contrary to the reported literature are also often described as negative. Furthermore, regarding inconclusive results, there can be multiple reasons that data fails to reveal a clear trend or effect. As stated above, it could be that a researcher's hypothesis is incorrect and thus, their experimental manipulations fail to reveal an effect. However, it could also be that the experiment failed to produce clear positive results because there are experimental factors (i.e., drug side effects) that were not considered and thus not controlled by the experimenter. These extraneous factors can contribute notable variability to the data. Finally, and sadly, unexpected negative data can be a reflection of poor or sloppy research practices due to inexperience and/or poor training or supervision of the research team.

Therefore, upon the frustrating revelation that one's data is negative, it is critical that a researcher takes a deep breath and assesses both their team and their data. Assessing the team for awareness of and adhering to experimental protocol can be relatively easy. Simply sit down the members of the team and ask them to describe their procedures. Attentively listen to their description and then note any inconsistencies between their report and the defined protocol and then work to correct any mistakes or omissions. It is frustrating to realize after the completion of an experiment that mistakes have resulted in wasted time and resources but these problems can generally be easily corrected and a valuable lesson will be learned. Furthermore, the team needs to realize that experimental protocol shift is not uncommon, especially when there is regular personnel turnover combined with poor direction or supervision. Learn the lesson, correct the problem and clean up experimental protocols.

However, if you assess your team and cannot find any procedural inconsistencies, the data should also be assessed for both expected and unexpected observations and trends, even if these trends are somewhat variable. Researchers often detect highly variable, but repeatable trends or observations in their data. These puzzles often require researchers to consider alternate factors that may be contributing to data variability so that they can refine their experimental questions and/or protocols. Delving into these puzzles is often the fun part of research. However, it can also be a very costly phase of discovery. It takes time, resources and considerable risk to discover what these undetected factors may be. And a great deal of effort may never see the reward of publication due to the large volume of inconclusive data that is produced in the process of discovery due to the bias against publishing negative data.

During my undergraduate and graduate training, there were research teams in our department focusing on fetal alcohol syndrome (FAS). Through coursework and journal clubs, I learned a bit about the difficult experimental history surrounding fetal alcohol syndrome research. Although researchers, clinicians and even teachers recognized commonalities and consistent observations that were suggestive of a disorder, it took a great deal of time and effort for the experimental data to reveal conclusive FAS data and a clinical profile. The reason that this early work was so difficult is that there were many factors that needed to be worked out in the research. The frequency of binge drinking during pregnancy, the volume of alcohol consumed, the developmental phase of fetal exposure to binge drinking as well as the genetic susceptibility to alcohol exposure all impacted the type and magnitude of impairment produced by alcohol exposure during fetal development. All of these factors needed to be experimentally addressed before researchers successfully and reliably revealed a reliable and reproducible link between fetal alcohol exposure and developmental problems in children. Each of these factors contributed to variability in the data and for a long time, the variation or "noise" in the data hindered recognition of the syndrome. However, throughout these difficulties with the research, scientists persevered through file cabinets full of negative results while recognizing that there was meaning hidden in their observations and inconclusive data trends.

Difficult success stories, such as that experienced by early FAS researchers, serve as a reminder that the agenda of original research is to conduct experiments to explore and explain the unknown. As such, our data often reveal that there are many more unknowns that we have not yet considered. These are critical phases of research and therefore, it is unfortunate that science often fails to reward the effort that goes into acquiring these interim negative or inconclusive results. As you peruse the literature, it is quite uncommon to come across publications that report negative results. Positive results are much more likely to be published as compared to negative results. Most researchers recognize the ethical and scientific importance of sharing and publishing negative results. However, like many issues in responsible research, recognizing that there is a flaw in research practices does not readily or rapidly elicit a change in research practices or resources.

Furthermore, although many researchers agree that there should be increased opportunities to publish negative results, there are arguments against these practices. As stated above, one of the explanations for negative or contrary research results can be due to inexperienced researchers conducting poor research. If this is the case, it is arguable that ready availability to publishing opportunities may fail to reveal and "weed out" poor research practices. However, most researchers do not want wish to publish poor work. Rather, researchers are very particular about work that they want to publish and thus, only want the opportunity to publish negative results when they are collected through well designed and reliably conducted research. Researchers can be so particular about work that they choose to publish that I have had colleagues express reluctance to publish even positive results when they did not trust the reliability or integrity of their team members that conducted the work.

An additional reservation regarding the opportunities to publish negative results addresses the potential for untrained readers to misinterpret data that is consistent with the null hypothesis to be "proof" of the null hypothesis. Scientists are trained that the goal of research is to disprove hypotheses and that data can never prove a hypothesis to be true. However, when untrained readers peruse the scientific literature, they often misinterpret a single research report as proof of a causal or correlative relationship rather than a piece of evidence that fails to disprove a hypothesis. These types of untrained or premature interpretations can cause a great deal of harm if they reach a popular audience. An example is the discredited and retracted Wakefield (1998) report of a link between MMR vaccine and the development of autism. Even though this single study was proven to be fraudulent and that follow-up studies have failed to reveal any link between vaccines and autism (Taylor et al., 1999; Madsen et al, 2002) the popular myth asserting a link between vaccination and autism persists. Scientists are trained to treat even positive results with skepticism. However, public or popular misinterpretation of negative or conflicting results could potentially impact public opinion and/or funding opportunities, especially during difficult phases of discovery as was described above for fetal alcohol syndrome researchers. Scientists are trained to expect conflicting reports and critically assess the methodology and results of conflicting reports to find a seed of truth and as that seed grows, the scientific literature self-corrects. However, the untrained audience can misinterpret these conflicts as wasteful, fail to recognize that discovery is a process and public opinion will often fail to recognize that large amounts of new data are a critical part of the correction process.

The Wakefield fraud case is a good example of an inaccurate positive result being proven false (false positives). In contrast, negative results can also be proven false (false negatives). However to prove any results to be false, one must have access to the published results. I clearly remember during my graduate training, during a late night study session, the night before a statistics exam, a friend of mine sounded off in frustration regarding everyone's focus on using statistics to avoid false-positive results. She expressed that this concern was ridiculous because science is self-correcting, thus a published false-positive would be replicated and failure to replicate would correct the literature. She expressed that our concern should be focused on false-negative results because once published data failed to support a hypothesis, the hypothesis would be abandoned because researchers would not waste time and resources working on negative findings. Therefore, she asserted, good ideas could rapidly and mistakenly be abandoned once a negative result was reported, even if that negative result was false.

In a way she was correct. However, her rant was based on a few assumptions. First, she assumed that a false-negative result would be reported in the literature. As stated above, there are file cabinets full of negative results that have never been submitted for publication and even more that have been submitted for publication only to have been rejected. Second, although the ideal model is that the literature is self-correcting, that assertion is based on the assumption that follow-up studies, failing to replicate false-positive results, are actually submitted for publication and that these negative or contrary results are actually published. However, due to the tendency of researchers to hesitate wasting time and resources constructing manuscripts that largely contain negative or contrary results and the publication bias toward rejecting publications containing negative or contrary results it is hard to readily assert that our system of scientific reporting is in fact, self-correcting. Failing to publish negative results impairs the self-correcting design of research.

Independent of the above controversies, most researchers agree that "the data are the data" and as such, negative results are as important as positive results. We need those negative results to alter our hypotheses and redesign our experiments. We need access to negative results to guide us on the path to positive results. Therefore, we need to address the bias against publishing negative results and come up with a system that enables us to share those valuable negative results that were produced from solid research questions and reliable data collection techniques. Historically, much of this data has been shared unofficially. We meet at conferences and discuss our ideas and frustrations and we often find that our colleagues are addressing similar questions and encountering similar frustrations. However, those casual conversations often do not reveal all aspects of their methodology so that they can be utilized to modify experimental design. Thus, there needs to be a focus on increased resources that enable researchers to share their negative results so that wasted time and resources are minimized.

There are valid concerns regarding an agenda to increase opportunities to publish negative or inconclusive research results, but many of these concerns can be addressed through the system of peer review that currently exists in academics. In contrast, maintaining practices that are biased against publication of inconclusive or contrary results has too much potential to negatively impact scientific progress. These practices could impact research decisions made by junior scientists, working to build a career, because they may perceive positive results as more valuable than maintaining objectivity. Furthermore, publication bias against reporting negative results also limits researcher access to evolving data and methodology and potentially biases the information that is available in the literature. Finally, bias against publishing negative results slows the self-correcting virtue of science. It is great to encounter increased dialogue regarding the importance of negative data and discussion on how best to share the methods and results associated with inconclusive yet intriguing data. These discussions are a first step toward improving our system of reporting and acknowledgement for the efforts of investigators that struggle with the difficult phases of discovery.

Marianne Evola is senior administrator in the Responsible Research area of the Office of the Vice President for Research. She is a monthly contributor to Scholarly Messenger.

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Negative Findings / Negative Evidence

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• Evidence Analysis Issues

My Genealogy Question Is:

Did James A Wake serve in the Civil War?

Here is a link to an Ancestry.com Image of an 1863 Congressonal Registration Record:

http://interactive.ancestry.com/1666/32178_620305173_0106-00258/899095?backurl=http%3a%2f%2fsearch.ancestry.com%2fcgi-bin%2fsse.dll%3findiv%3d1%26db%3dconsolidatedlistsofcivilwarreg%26rank%3d1%26new%3d1%26MSAV%3d1%26msT%3d1%26gss%3dangs-d%26gsfn%3djames%26gsfn_x%3dNP_NN_NIC%26gsln%3dwake%26gsln_x%3dNS_NP_NN%26msbdy%3d1828%26msrpn__ftp%3dNew%2bYork%2bCity%2b%28All%2bBoroughs%29%252c%2bNew%2bYork%252c%2bUSA%26msrpn%3d1652382%26msrpn_PInfo%3d6-&ssrc=&backlabel=ReturnRecord

According to Ancestry.com:

Source Citation: National Archives and Records Administration (NARA); Washington, D.C.;  Consolidated Lists of Civil War Draft Registration Records (Provost Marshal General's Bureau; Consolidated Enrollment Lists, 1863-1865) ; Record Group:  110, Records of the Provost Marshal General's Bureau (Civil War) ; Collection Name:  Consolidated Enrollment Lists, 1863-1865 (Civil War Union Draft Records) ; ARC Identifier:  4213514 ; Archive Volume Number:  3 of 6 .

Source Information:

Consolidated Lists of Civil War Draft Registrations, 1863-1865 . NM-65, entry 172, 620 volumes. ARC ID: 4213514 . Records of the Provost Marshal General’s Bureau (Civil War), Record Group 110. National Archives, Washington D.C.

Description: This is a collection of Civil War Registrations from 1863-1865. There were four drafts that included 776,000 individuals in that time

In Evaluating this Source, I entered my information into Evidentia, where I generated a Source Analysis Report. You can find a link to it here:

http://worthy2be.wordpress.com/2013/08/09/evidentiasource-analysis-report-for-comments/

I have developed the family of James A Wake, starting with the 1850 US Census and have identified him through to his death. I have found no Civil War records for this gentlemen on Ancestry.com, Fold3.com, nor FamilySearch.org and not mention of any civil war activity.

The only CLUE that I have is the Classification of him, in this Registration (Class II), in that he was 36 years of age as of 1 July 1863 and that he had 2 young children as of the registration. That has been continued to be true information. I can only GUESS that he did not serve. That does not mean that I have completed my research.

I am posting the question here for your guidenance. Also, I posted on my Blog, the request for peer review, as I have not done anything like this, especially not being able to answer my genealogical question.

Russ, it's a good thing you

Russ, it's a good thing you didn't ask EE to comment upon Ancestry's totally unfathomable bifurcation of its source data between "Source Citation" and "Source Information." :)

Beyond that, and just for clarification, EE does not comment on the workings of specific software programs. The focus of this forum is the principles of evidence analysis. Having made this caveat, EE will offer that "peer review" of the evidentiary conclusions you've made in your blog posting.

You link to a Civil War draft registration book which offers the following for your person of interest:

• Residence: Christopher Street
• Name: Wake, James A.
• Race: White
• Occupation: Foreman
• Place of Birth: New York

You then set out to evaluate each piece of data according to the Evidence Analysis Process Map.  EE agrees with you that the informant is unidentifiable. We might presume it to be James A. Wake, but that presumption could be wrong. EE would, therefore, consider the quality of the information to be indeterminable. Past this point, EE sees two problems:

• For each information statement, you conclude that it offers only indirect evidence. Why? In each case, the register entry makes an explicit statement about the "fact" or "claim" that you question. That explicit, direct statement would be direct evidence about the fact. That does not mean the claim is correct, of course. But it is direct evidence.
• For some evaluations of your information, you say "It is indeterminable whether the information being considered is Primary, and must be treated as Secondary." EE would argue that if we cannot determine the identity of the informant for this information, then the information's quality cannot be deemed either primary or secondary. Rather, it is undetermined. The issue is in limbo.

As for your major question (Did James A. Wake serve in the war?), the information in this record is indirect evidence for that question. It does not provide an explicit answer. It provides information that is relevant to your question, but that information was created prior to any potential service. It could not possibly predict or attest what has not yet happened. It makes no assertion at all as to whether he served.

To quote EE's QuickLesson 17: "Indirect Evidence: information that does not directly address our question, although our experience suggests a way to use the information to discover direct evidence or to help build a case."  This is what you have: information that doesn't answer your question but does suggest to you ways you can use it to discover direct evidence. 

You also report negative results from those further searches—which, of course, constitutes negative findings , but not negative evidence. On the other hand, as you say, you have not completed your research. With negative findings, there is always the possibility that further research will yield further evidence. 

© Evidence Explained  2011-2024.

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Home » Research Findings – Types Examples and Writing Guide

Research Findings – Types Examples and Writing Guide

Table of Contents

Research Findings

Definition:

Research findings refer to the results obtained from a study or investigation conducted through a systematic and scientific approach. These findings are the outcomes of the data analysis, interpretation, and evaluation carried out during the research process.

Types of Research Findings

There are two main types of research findings:

Qualitative Findings

Qualitative research is an exploratory research method used to understand the complexities of human behavior and experiences. Qualitative findings are non-numerical and descriptive data that describe the meaning and interpretation of the data collected. Examples of qualitative findings include quotes from participants, themes that emerge from the data, and descriptions of experiences and phenomena.

Quantitative Findings

Quantitative research is a research method that uses numerical data and statistical analysis to measure and quantify a phenomenon or behavior. Quantitative findings include numerical data such as mean, median, and mode, as well as statistical analyses such as t-tests, ANOVA, and regression analysis. These findings are often presented in tables, graphs, or charts.

Both qualitative and quantitative findings are important in research and can provide different insights into a research question or problem. Combining both types of findings can provide a more comprehensive understanding of a phenomenon and improve the validity and reliability of research results.

Parts of Research Findings

Research findings typically consist of several parts, including:

• Introduction: This section provides an overview of the research topic and the purpose of the study.
• Literature Review: This section summarizes previous research studies and findings that are relevant to the current study.
• Methodology : This section describes the research design, methods, and procedures used in the study, including details on the sample, data collection, and data analysis.
• Results : This section presents the findings of the study, including statistical analyses and data visualizations.
• Discussion : This section interprets the results and explains what they mean in relation to the research question(s) and hypotheses. It may also compare and contrast the current findings with previous research studies and explore any implications or limitations of the study.
• Conclusion : This section provides a summary of the key findings and the main conclusions of the study.
• Recommendations: This section suggests areas for further research and potential applications or implications of the study’s findings.

How to Write Research Findings

Writing research findings requires careful planning and attention to detail. Here are some general steps to follow when writing research findings:

• Organize your findings: Before you begin writing, it’s essential to organize your findings logically. Consider creating an outline or a flowchart that outlines the main points you want to make and how they relate to one another.
• Use clear and concise language : When presenting your findings, be sure to use clear and concise language that is easy to understand. Avoid using jargon or technical terms unless they are necessary to convey your meaning.
• Use visual aids : Visual aids such as tables, charts, and graphs can be helpful in presenting your findings. Be sure to label and title your visual aids clearly, and make sure they are easy to read.
• Use headings and subheadings: Using headings and subheadings can help organize your findings and make them easier to read. Make sure your headings and subheadings are clear and descriptive.
• Interpret your findings : When presenting your findings, it’s important to provide some interpretation of what the results mean. This can include discussing how your findings relate to the existing literature, identifying any limitations of your study, and suggesting areas for future research.
• Be precise and accurate : When presenting your findings, be sure to use precise and accurate language. Avoid making generalizations or overstatements and be careful not to misrepresent your data.
• Edit and revise: Once you have written your research findings, be sure to edit and revise them carefully. Check for grammar and spelling errors, make sure your formatting is consistent, and ensure that your writing is clear and concise.

Research Findings Example

Following is a Research Findings Example sample for students:

Title: The Effects of Exercise on Mental Health

Sample : 500 participants, both men and women, between the ages of 18-45.

Methodology : Participants were divided into two groups. The first group engaged in 30 minutes of moderate intensity exercise five times a week for eight weeks. The second group did not exercise during the study period. Participants in both groups completed a questionnaire that assessed their mental health before and after the study period.

Findings : The group that engaged in regular exercise reported a significant improvement in mental health compared to the control group. Specifically, they reported lower levels of anxiety and depression, improved mood, and increased self-esteem.

Conclusion : Regular exercise can have a positive impact on mental health and may be an effective intervention for individuals experiencing symptoms of anxiety or depression.

Applications of Research Findings

Research findings can be applied in various fields to improve processes, products, services, and outcomes. Here are some examples:

• Healthcare : Research findings in medicine and healthcare can be applied to improve patient outcomes, reduce morbidity and mortality rates, and develop new treatments for various diseases.
• Education : Research findings in education can be used to develop effective teaching methods, improve learning outcomes, and design new educational programs.
• Technology : Research findings in technology can be applied to develop new products, improve existing products, and enhance user experiences.
• Business : Research findings in business can be applied to develop new strategies, improve operations, and increase profitability.
• Public Policy: Research findings can be used to inform public policy decisions on issues such as environmental protection, social welfare, and economic development.
• Social Sciences: Research findings in social sciences can be used to improve understanding of human behavior and social phenomena, inform public policy decisions, and develop interventions to address social issues.
• Agriculture: Research findings in agriculture can be applied to improve crop yields, develop new farming techniques, and enhance food security.
• Sports : Research findings in sports can be applied to improve athlete performance, reduce injuries, and develop new training programs.

When to use Research Findings

Research findings can be used in a variety of situations, depending on the context and the purpose. Here are some examples of when research findings may be useful:

• Decision-making : Research findings can be used to inform decisions in various fields, such as business, education, healthcare, and public policy. For example, a business may use market research findings to make decisions about new product development or marketing strategies.
• Problem-solving : Research findings can be used to solve problems or challenges in various fields, such as healthcare, engineering, and social sciences. For example, medical researchers may use findings from clinical trials to develop new treatments for diseases.
• Policy development : Research findings can be used to inform the development of policies in various fields, such as environmental protection, social welfare, and economic development. For example, policymakers may use research findings to develop policies aimed at reducing greenhouse gas emissions.
• Program evaluation: Research findings can be used to evaluate the effectiveness of programs or interventions in various fields, such as education, healthcare, and social services. For example, educational researchers may use findings from evaluations of educational programs to improve teaching and learning outcomes.
• Innovation: Research findings can be used to inspire or guide innovation in various fields, such as technology and engineering. For example, engineers may use research findings on materials science to develop new and innovative products.

Purpose of Research Findings

The purpose of research findings is to contribute to the knowledge and understanding of a particular topic or issue. Research findings are the result of a systematic and rigorous investigation of a research question or hypothesis, using appropriate research methods and techniques.

The main purposes of research findings are:

• To generate new knowledge : Research findings contribute to the body of knowledge on a particular topic, by adding new information, insights, and understanding to the existing knowledge base.
• To test hypotheses or theories : Research findings can be used to test hypotheses or theories that have been proposed in a particular field or discipline. This helps to determine the validity and reliability of the hypotheses or theories, and to refine or develop new ones.
• To inform practice: Research findings can be used to inform practice in various fields, such as healthcare, education, and business. By identifying best practices and evidence-based interventions, research findings can help practitioners to make informed decisions and improve outcomes.
• To identify gaps in knowledge: Research findings can help to identify gaps in knowledge and understanding of a particular topic, which can then be addressed by further research.
• To contribute to policy development: Research findings can be used to inform policy development in various fields, such as environmental protection, social welfare, and economic development. By providing evidence-based recommendations, research findings can help policymakers to develop effective policies that address societal challenges.

Characteristics of Research Findings

Research findings have several key characteristics that distinguish them from other types of information or knowledge. Here are some of the main characteristics of research findings:

• Objective : Research findings are based on a systematic and rigorous investigation of a research question or hypothesis, using appropriate research methods and techniques. As such, they are generally considered to be more objective and reliable than other types of information.
• Empirical : Research findings are based on empirical evidence, which means that they are derived from observations or measurements of the real world. This gives them a high degree of credibility and validity.
• Generalizable : Research findings are often intended to be generalizable to a larger population or context beyond the specific study. This means that the findings can be applied to other situations or populations with similar characteristics.
• Transparent : Research findings are typically reported in a transparent manner, with a clear description of the research methods and data analysis techniques used. This allows others to assess the credibility and reliability of the findings.
• Peer-reviewed: Research findings are often subject to a rigorous peer-review process, in which experts in the field review the research methods, data analysis, and conclusions of the study. This helps to ensure the validity and reliability of the findings.
• Reproducible : Research findings are often designed to be reproducible, meaning that other researchers can replicate the study using the same methods and obtain similar results. This helps to ensure the validity and reliability of the findings.

Advantages of Research Findings

Research findings have many advantages, which make them valuable sources of knowledge and information. Here are some of the main advantages of research findings:

• Evidence-based: Research findings are based on empirical evidence, which means that they are grounded in data and observations from the real world. This makes them a reliable and credible source of information.
• Inform decision-making: Research findings can be used to inform decision-making in various fields, such as healthcare, education, and business. By identifying best practices and evidence-based interventions, research findings can help practitioners and policymakers to make informed decisions and improve outcomes.
• Identify gaps in knowledge: Research findings can help to identify gaps in knowledge and understanding of a particular topic, which can then be addressed by further research. This contributes to the ongoing development of knowledge in various fields.
• Improve outcomes : Research findings can be used to develop and implement evidence-based practices and interventions, which have been shown to improve outcomes in various fields, such as healthcare, education, and social services.
• Foster innovation: Research findings can inspire or guide innovation in various fields, such as technology and engineering. By providing new information and understanding of a particular topic, research findings can stimulate new ideas and approaches to problem-solving.
• Enhance credibility: Research findings are generally considered to be more credible and reliable than other types of information, as they are based on rigorous research methods and are subject to peer-review processes.

Limitations of Research Findings

While research findings have many advantages, they also have some limitations. Here are some of the main limitations of research findings:

• Limited scope: Research findings are typically based on a particular study or set of studies, which may have a limited scope or focus. This means that they may not be applicable to other contexts or populations.
• Potential for bias : Research findings can be influenced by various sources of bias, such as researcher bias, selection bias, or measurement bias. This can affect the validity and reliability of the findings.
• Ethical considerations: Research findings can raise ethical considerations, particularly in studies involving human subjects. Researchers must ensure that their studies are conducted in an ethical and responsible manner, with appropriate measures to protect the welfare and privacy of participants.
• Time and resource constraints : Research studies can be time-consuming and require significant resources, which can limit the number and scope of studies that are conducted. This can lead to gaps in knowledge or a lack of research on certain topics.
• Complexity: Some research findings can be complex and difficult to interpret, particularly in fields such as science or medicine. This can make it challenging for practitioners and policymakers to apply the findings to their work.
• Lack of generalizability : While research findings are intended to be generalizable to larger populations or contexts, there may be factors that limit their generalizability. For example, cultural or environmental factors may influence how a particular intervention or treatment works in different populations or contexts.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• Multiple adverse...

Multiple adverse outcomes associated with antipsychotic use in people with dementia: population based matched cohort study

Linked editorial.

Use of antipsychotics in adults with dementia

• Related content
• Peer review
• Pearl L H Mok , research fellow 1 2 ,
• Matthew J Carr , research fellow 1 2 3 ,
• Bruce Guthrie , professor 4 ,
• Daniel R Morales , Wellcome Trust clinical research fellow 5 ,
• Aziz Sheikh , professor 6 7 ,
• Rachel A Elliott , professor 3 8 ,
• Elizabeth M Camacho , senior research fellow 8 ,
• Tjeerd van Staa , professor 9 ,
• Anthony J Avery , professor 3 10 ,
• Darren M Ashcroft , professor 1 2 3
• 1 Centre for Pharmacoepidemiology and Drug Safety, Division of Pharmacy and Optometry, University of Manchester, Manchester, M13 9PT, UK
• 2 Manchester Academic Health Science Centre, Manchester, UK
• 3 NIHR Greater Manchester Patient Safety Research Collaboration, University of Manchester, Manchester, UK
• 4 Advanced Care Research Centre, Usher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK
• 5 Population Health and Genomics, University of Dundee, Dundee, UK
• 6 Usher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK
• 7 Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
• 8 Manchester Centre for Health Economics, Division of Population Health, Manchester, UK
• 9 Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK
• 10 Centre for Primary Care, School of Medicine, University of Nottingham, Nottingham, UK
• Correspondence to: P L H Mok pearl.mok{at}manchester.ac.uk
• Accepted 29 February 2024

Objective To investigate risks of multiple adverse outcomes associated with use of antipsychotics in people with dementia.

Design Population based matched cohort study.

Setting Linked primary care, hospital and mortality data from Clinical Practice Research Datalink (CPRD), England.

Population Adults (≥50 years) with a diagnosis of dementia between 1 January 1998 and 31 May 2018 (n=173 910, 63.0% women). Each new antipsychotic user (n=35 339, 62.5% women) was matched with up to 15 non-users using incidence density sampling.

Main outcome measures The main outcomes were stroke, venous thromboembolism, myocardial infarction, heart failure, ventricular arrhythmia, fracture, pneumonia, and acute kidney injury, stratified by periods of antipsychotic use, with absolute risks calculated using cumulative incidence in antipsychotic users versus matched comparators. An unrelated (negative control) outcome of appendicitis and cholecystitis combined was also investigated to detect potential unmeasured confounding.

Results Compared with non-use, any antipsychotic use was associated with increased risks of all outcomes, except ventricular arrhythmia. Current use (90 days after a prescription) was associated with elevated risks of pneumonia (hazard ratio 2.19, 95% confidence interval (CI) 2.10 to 2.28), acute kidney injury (1.72, 1.61 to 1.84), venous thromboembolism (1.62, 1.46 to 1.80), stroke (1.61, 1.52 to 1.71), fracture (1.43, 1.35 to 1.52), myocardial infarction (1.28, 1.15 to 1.42), and heart failure (1.27, 1.18 to 1.37). No increased risks were observed for the negative control outcome (appendicitis and cholecystitis). In the 90 days after drug initiation, the cumulative incidence of pneumonia among antipsychotic users was 4.48% (4.26% to 4.71%) versus 1.49% (1.45% to 1.53%) in the matched cohort of non-users (difference 2.99%, 95% CI 2.77% to 3.22%).

Conclusions Antipsychotic use compared with non-use in adults with dementia was associated with increased risks of stroke, venous thromboembolism, myocardial infarction, heart failure, fracture, pneumonia, and acute kidney injury, but not ventricular arrhythmia. The range of adverse outcomes was wider than previously highlighted in regulatory alerts, with the highest risks soon after initiation of treatment.

Introduction

Dementia is a clinical syndrome characterised by progressive cognitive decline and functional disability, with estimates suggesting that by 2050 around 152.8 million people globally will be affected. 1 Behavioural and psychological symptoms of dementia are common aspects of the disease and include features such as apathy, depression, aggression, anxiety, irritability, delirium, and psychosis. Such symptoms can negatively impact the quality of life of patients and their carers and are associated with early admission to care. 2 3 Antipsychotics are commonly prescribed for the management of behavioural and psychological symptoms of dementia, despite longstanding concerns about their safety. 4 5 6 During the covid-19 pandemic, the proportion of people with dementia prescribed antipsychotics increased, possibly owing to worsened behavioural and psychological symptoms of dementia linked to lockdown measures or reduced availability of non-pharmaceutical treatment options. 7 According to guidelines from the UK’s National Institute for Health and Care Excellence, antipsychotics should only be prescribed for the treatment of behavioural and psychological symptoms of dementia if non-drug interventions have been ineffective, if patients are at risk of harming themselves or others or are experiencing agitation, hallucinations, or delusions causing them severe distress. 8 Antipsychotics should at most be prescribed at the lowest effective dose and for the shortest possible time. Only two antipsychotics, risperidone (an atypical, or second generation, antipsychotic) and haloperidol (a typical, or first generation, antipsychotic), are licensed in the UK for the treatment of behavioural and psychological symptoms of dementia, 9 although others have been commonly prescribed off-label. 5 10

Based on evidence from clinical trials of risperidone, the US Food and Drug Administration (FDA) first issued a warning in 2003 about the increased risks of cerebrovascular adverse events (eg, stroke, transient ischaemic attack) associated with use of atypical antipsychotics in older adults with dementia. 11 A meta-analysis of 17 trials among such patients subsequently found a 1.6-1.7-fold increased risk of mortality with atypical antipsychotics compared with placebo, which led the FDA to issue a “black box” warning in 2005 for all atypical antipsychotics. 11 This warning was extended to typical antipsychotics in 2008, after two observational studies reported that the risk of death associated with their use among older people might be even greater than for atypical antipsychotics. 12 13 14 The increased risks for stroke and mortality have been consistently reported by many observational studies and meta-analyses since, 11 15 16 17 18 19 20 21 and they have led to regulatory safety warnings and national interventions in the UK, US, and Europe, aiming to reduce inappropriate prescribing of these drugs for the treatment of behavioural and psychological symptoms of dementia. 8 11 22 23 24 25 26 Other adverse outcomes have also been investigated in observational studies, 27 28 29 although, with the exception of pneumonia, 14 30 31 32 the evidence is less conclusive or is more limited among people with dementia. For example, inconsistent or limited evidence has been found for risks of myocardial infarction, 33 34 ventricular arrhythmia, 35 36 venous thromboembolism, 37 38 39 40 fracture, 41 42 43 and acute kidney injury. 44 45 46 Most studies also reported only one outcome or type of outcomes. Examining multiple adverse events in a single cohort is needed to give a more comprehensive estimate of the total potential harm associated with use of antipsychotics in people with dementia.

Using linked primary and secondary care data in England, we investigated the risks of a range of adverse outcomes potentially associated with antipsychotic use in a large cohort of adults with dementia—namely, stroke, venous thromboembolism, myocardial infarction, heart failure, ventricular arrhythmia, fracture, pneumonia, and acute kidney injury. We report both relative and absolute risks.

Data sources

The study used anonymised electronic health records from Clinical Practice Research Datalink (CPRD). In the UK, residents are required to be registered with a primary care general practice to receive care from the NHS. The NHS is a publicly funded healthcare service, free at the point of use. More than 98% of the UK population are registered with a general practice, and their electronic health records are transferred when they change practice. 47 48 Community prescribing is most often done by the general practitioner, including antipsychotic treatment recommended by specialists. CPRD data are sourced from more than 2000 general practices covering around 20% of the UK population, and include information on diagnoses, primary healthcare contacts, prescribed drugs, laboratory test results, and referrals to secondary healthcare services. 47 48 CPRD contains two databases: Aurum and GOLD. CPRD Aurum includes data from contributing general practices in England that use the EMIS Web patient management software, and CPRD GOLD consists of patient data from practices across all four UK nations that use the Vision system. Both datasets are broadly representative of the UK population. 47 48 49 Primary care data from general practices in England can be linked to other datasets, including hospital admissions in Hospital Episode Statistics, and mortality and index of multiple deprivation data from the Office for National Statistics (ONS). Individual patients can opt-out of sharing their records with CPRD, and individual patient consent was not required as all data were deidentified.

Study population

We delineated two cohorts, one each from Aurum and GOLD. For the latter, we included patients from English practices only because linkage to hospital admission and mortality data were required in our analyses. To ensure that the study dataset would not contain any duplicate patient records, we used the bridging file provided by CPRD to identify English practices that have migrated from the GOLD to the Aurum dataset, and removed such practices from the GOLD dataset. For both cohorts, we included patients who had a first dementia diagnosis code between 1 January 1998 and 31 May 2018. Dementia was identified from Read, SNOMED, or EMIS codes used in the databases (see supplementary appendix). We defined the date of first dementia diagnosis as the date of first dementia code. Patients needed to be aged 50 years or over at the time of dementia diagnosis, have been registered with the CPRD practice for at least a year, not be prescribed an antipsychotic in the 365 days before their first dementia code, and have records that were eligible for linkage to Hospital Episodes Statistics, mortality, and index of multiple deprivation data. In addition, because anticholinesterases (such as donepezil, rivastigmine, and galantamine) may sometimes be prescribed to patients showing signs of dementia before their first dementia code, we excluded patients with an anticholinesterase prescription before their first dementia code. Supplementary figures S1 and S2 show how the two cohorts for Aurum and GOLD, respectively, were delineated.

Study design

Matched cohort design —We implemented a matched cohort design. Supplementary figure S3 shows the study design graphically. 50 For the Aurum and GOLD cohorts separately, patients who used antipsychotics were defined as patients in each cohort issued with an antipsychotic prescription after (or on the same day as) the date of their first dementia diagnosis, with the date of first antipsychotic prescription being the index date after which outcomes were measured. For each outcome, follow-up began from the date of the first antipsychotic prescription (the index date) and ended on the earliest of date of first diagnosis of outcome (ie, the earliest recording of the outcome whether it was from the patient’s primary or secondary care or mortality records), death, transfer out of the general practice, last data collection date of the general practice, two years from the date of antipsychotics initiation, or 31 May 2018. Because patients who have experienced an outcome were potentially at higher risk of subsequently experiencing the same event, which could confound any risks associated with antipsychotic use, we excluded those with a history of the specific outcome under investigation before the index date from the analysis of that outcome. For example, we excluded patients with a record of stroke before the index date from the analysis of stroke, but they would still be eligible for the study of other outcomes. For the analysis of acute kidney injury, patients with a diagnosis of end stage kidney disease before the index date were also excluded, and a diagnosis of end stage kidney disease after the index date was an additional condition for end of follow-up. 44

Matched comparators —Each patient who used antipsychotics on or after the date of their first dementia diagnosis was matched using incidence density sampling with up to 15 randomly selected patients who had the same date of first dementia diagnosis (or up to 56 days after) and who had not been prescribed an antipsychotic before diagnosis. Incidence density sampling involves matching on sampling time, with each antipsychotic user in our study being matched to one or more comparators who were eligible for an antipsychotic but had not become a user at the time of matching. 51 The selection of comparators was done with replacement—that is, an individual could be used as a comparator in multiple matched sets. In our study, this meant that patients were eligible to be a non-user matched comparator up to the date of their first antipsychotic prescription. We excluded matched comparators with a history of the specific outcome under investigation before the index date from the analysis of that event. For each outcome, follow-up of matched comparators began on the same day as the patient to whom they were matched (the index date) and ended on the earliest of date of their first antipsychotic prescription (if any), or date of one of the end of follow-up events described earlier for the antipsychotic users.

Use of antipsychotics

We included both typical and atypical antipsychotics, identified by product codes in Aurum and GOLD (see supplementary appendix for list of drugs included). Senior author DMA (pharmacist) reviewed the code lists. As previous studies have shown a temporal association between antipsychotic use and development of adverse outcomes, 30 31 52 we treated use of antipsychotics as a time varying variable, classified as current, recent, and past use. Current use was defined as the first 90 days from the date of an antipsychotic prescription, recent use as up to 180 days after current use ended, and past use as the time after the recent use period had ended. If a patient was issued another prescription during the 90 days after their last prescription, their current use period would be extended by 90 days from the date of their latest prescription. For example, if a patient had two prescriptions and the second was issued 60 days after the first, their current use period would be a total of 150 days: 60 days after the first prescription plus 90 days after the second. At the end of the 150 days current use period, the next 180 days would be the recent use period, and the time after this recent use period would be past use. As patients could have multiple prescriptions over time, they could move between the three antipsychotic use categories during follow-up, and they could therefore be defined as current, recent, or past users more than once. See the supplementary appendix for further information on how this definition is applied.

In post hoc analyses, we also investigated typical versus atypical antipsychotics, and specific drug substances: haloperidol, risperidone, quetiapine, and other antipsychotics (as a combined category).

Outcomes were stroke, venous thromboembolism (including deep vein thrombosis and pulmonary embolism), myocardial infarction, heart failure, ventricular arrhythmia, fracture, pneumonia, and acute kidney injury. With the exceptions of pneumonia and acute kidney injury, outcomes were identified by Read, SNOMED, or EMIS codes in the primary care records, and by ICD-10 (international classification of diseases, 10th revision) codes from linked secondary care data from Hospital Episodes Statistics, and cause of death data from the ONS mortality records. For pneumonia and acute kidney injury, we only included those that were diagnosed in hospitals or as a cause of death, ascertained from Hospital Episodes Statistics and ONS data.

We also investigated appendicitis and cholecystitis combined as an unrelated (negative control) outcome to detect potential unmeasured confounding. 53 These outcomes were chosen because evidence of an association with antipsychotic use is lacking from the literature. We identified appendicitis and cholecystitis from Read, SNOMED, EMIS, and ICD-10 codes. Clinicians (BG, AJA, DRM) checked all code lists (see supplementary appendix).

We used propensity score methods to control for imbalances in measurable patient characteristics between antipsychotic users and their matched non-users, with personal characteristics, lifestyle, comorbidities, and prescribed drugs included in the propensity score models. A counterfactual framework for causal inference was applied to estimate the average treatment effect adjusting for inverse probability of treatment weights generated from the propensity score models. 54 55 Selection of covariates was informed by the literature, based on their potential associations with antipsychotic initiation and study outcomes. 31 34 44 56 57 All variables were assessed before the index date (see supplementary figure S3). Variables for personal characteristics included sex, age at dementia diagnosis, age at start of follow-up, ethnicity, and index of multiple deprivation fifths based on the location of the general practice. Comorbidities were derived as dichotomous variables and included a history of hypertension, types 1 and 2 diabetes mellitus, chronic obstructive pulmonary disease, rheumatoid arthritis, moderate or severe renal disease, moderate or severe liver disease, atrial fibrillation, cancer, and serious mental illness (bipolar disorders, schizophrenia, schizoaffective disorders, and other psychotic disorders). Lifestyle factors included smoking status and alcohol use. Medication covariates were represented as dichotomous indicators, defined by at least two prescriptions for each of the following drugs in the 12 months before the index date: antiplatelets, oral anticoagulants, angiotensin converting enzyme inhibitors or angiotensin II receptor blockers, alpha blockers, beta blockers, calcium channel blockers, diuretics, lipid lowering drugs, insulin and antidiabetic drugs, non-steroidal anti-inflammatory drugs, antidepressants, benzodiazepines, and lithium. We also included the following potential confounders for the investigations of venous thromboembolism and fracture: prescriptions for hormone replacement therapy and selective oestrogen receptor modulators (for venous thromboembolism), 58 59 a history of inflammatory bowel disease (for pneumonia and fracture), 60 61 and prescriptions for immunosuppressants, oral corticosteroids, and inhaled corticosteroids (for pneumonia). 62 63

Statistical analysis

For each patient included in the study, we derived a propensity score representing the patient’s probability of receiving antipsychotic treatment. Propensity scores were estimated using multivariable logistic regression, with antipsychotic use as the dependent variable. Predictors included personal characteristics, lifestyle, comorbidities, and prescribed drugs. Patients with missing information on ethnicity, index of multiple deprivation, smoking, or alcohol use were grouped into an unknown category for each of these variables and included in the propensity score models. We used the Hosmer-Lemeshow test and likelihood ratio test to test the fit of the models, and interaction terms were included to improve the model fit. 64 The derived scores were used as inverse probability of treatment weights to reweigh the data, balancing the distribution of baseline covariates between antipsychotic users and non-users (matched comparators)—that is, standardised differences <0.1 after weighting. 65 Propensity score models were run for each outcome, and for the Aurum and GOLD cohorts separately. For further information, see the supplementary appendix section on propensity score methods to control for potential confounding.

Analyses for estimating harms were then conducted after combining (appending) the Aurum and GOLD datasets. We used Cox regression survival analyses to estimate the risks of each outcome associated with antipsychotic use relative to the comparator cohort, and we report the results as hazard ratios. Use of an antipsychotic was treated as a time varying variable. To account for the matched design, we fitted stratified models according to the matched sets and used robust variance estimation. In all models, we also included a covariate indicating whether the patient was from the Aurum or GOLD cohort and calculated hazard ratios with adjustments for inverse probability of treatment weights. Cox regression assumes proportional hazards—that is, the relative hazard of the outcome remains constant during the follow-up period. 66 We assessed this assumption using the Grambsch-Therneau test based on the Schoenfeld residuals. 67 Because this assumption did not hold for all outcomes examined, in addition to reporting the hazard ratios pertaining to the whole follow-up period, we estimated hazard ratios separately for the several time windows: the first seven days, 8-30 days, 31-180 days, 181-365 days, and 366 days to two years (see supplementary appendix for an illustration of stratification of follow-up time). For each outcome, we calculated the incidence rate and the number needed to harm (NNH) over the first 180 days as well as two years after start of follow-up. The NNH represents the number of patients needed to be treated with an antipsychotic for one additional patient to experience the outcome compared with no treatment. We also calculated cumulative incidence percentages (absolute risks) for each outcome accounting for competing mortality risks based on previous recommendations. 68 These were calculated at 90 days, 180 days, 365 days, and two years after start of follow-up for antipsychotic users and their matched comparators separately. We also reported the difference in cumulative incidence between antipsychotic users and their matched comparators at these time points. Analyses were conducted using Stata/MP v16.1.

Sensitivity analyses

We investigated two other definitions of antipsychotic use as sensitivity analyses: the first 60 days as current use followed by 120 days of recent use, and a current use period of 30 days followed by a recent use period of 60 days. We also conducted the following post hoc sensitivity analyses. Firstly, as levomepromazine is often prescribed in palliative care to treat distressing symptoms in the last days of life, 69 we censored individuals at the time of their first levomepromazine prescription. Secondly, we used Fine-Gray subdistribution hazard regression models to estimate the hazard of each adverse outcome, accounting for the competing risks of death. 70 These results were reported as subhazard ratios. Thirdly, we compared the incidence rates and hazards of adverse outcomes for male versus female individuals. For these sex specific analyses, we modified the existing matched cohort by excluding non-user comparators who were of a different sex from the antipsychotic user to whom they were matched. We then derived a new propensity score for each individual by excluding sex as a covariate in the propensity score models. Incidence rate ratios and corresponding 95% confidence intervals (CIs) for male versus female individuals were calculated using the ‘iri’ command in Stata. To investigate whether hazards of each adverse outcome associated with antipsychotic use differed by sex, we fitted Cox regression models with sex, antipsychotic use, and their interaction as covariates. Sex specific hazard ratios and ratios of male to female hazard ratios were reported.

Patient and public involvement

This study is part of a National Institute of Health and Care Research funded programme (RP-PG-1214-20012): Avoiding patient harm through the application of prescribing safety indicators in English general practices (PRoTeCT). Two patient and public involvement members in the project team contributed to the study design and protocol of this study. Our study was not, however, coproduced with people with dementia or their carers.

Characteristics of study population

A total of 173 910 adults (63.0% women) with dementia were eligible for inclusion in the study: 139 772 (62.9% women) in the Aurum dataset and 34 138 (63.4% women) in GOLD. The mean age at dementia diagnosis for individuals in both cohorts was 82.1 years (standard deviation (SD) 7.9 years), and the median age was 83 years (interquartile range (IQR) 78-88 years in Aurum and 78-87 years in GOLD). A total of 35 339 individuals (62.5% women; 28 187 in Aurum, 62.6% women; 7152 in GOLD, 62.5% women) were prescribed an antipsychotic during the study period, and a matched set was generated for each of these individuals. The mean number of days between first dementia diagnosis and date of a first antipsychotic prescription was 693.8 ((SD 771.1), median 443 days) in Aurum and 576.6 ((SD 670.0), median 342 days) in GOLD. A total of 544 203 antipsychotic prescriptions (433 694 in Aurum, 110 509 in GOLD) were issued, of which 25.3% were for a typical antipsychotic and 74.7% for an atypical antipsychotic. The most prescribed antipsychotics were risperidone (29.8% of all prescriptions), quetiapine (28.7%), haloperidol (10.5%), and olanzapine (8.8%), which together accounted for almost 80% of all prescriptions (see supplementary table S1).

Since we excluded people with a history of the event before the start of follow-up, the number of individuals and matched sets included in analysis varies by outcome. Table 1 shows the baseline characteristics of patients for the analysis of stroke, before and after inverse probability of treatment weighting. Antipsychotic users were more likely than their matched comparators to have a history of serious mental illness and to be prescribed antidepressants or benzodiazepines in the 12 months before start of follow-up. After inverse probability of treatment weighting, standardised differences were <0.1 for all covariates. Baseline characteristics of individuals included in the analyses of other outcomes were similar to those reported for stroke (see supplementary tables S2-S9).

Baseline characteristics of antipsychotic users and matched comparators included in the analysis of stroke (CPRD Aurum and GOLD combined data). Values are number (percentage) unless stated otherwise

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Incidence rates and relative hazards of adverse outcomes

All antipsychotics.

In the two years after initiation of antipsychotics, the highest incidence rates of adverse outcomes were for pneumonia, fracture, and stroke, and ventricular arrhythmias were rare ( table 2 ). Figure 1 shows the hazard ratios of adverse outcomes associated with current, recent, past, and any use of antipsychotics versus non-use (ie, matched comparators). Except for ventricular arrhythmia, any use of antipsychotics was associated with increased risks for all adverse outcomes, ranging from a hazard ratio of 2.03 (95% CI 1.96 to 2.10) for pneumonia to 1.16 (1.09 to 1.24) for heart failure. Current use (ie, prescribed in the previous 90 days) was associated with high risks for pneumonia (2.19, 2.10 to 2.28), acute kidney injury (1.72, 1.61 to 1.84), venous thromboembolism (1.62, 1.46 to 1.80), and stroke (1.61, 1.52 to 1.71). Recent antipsychotic use (ie, in the 180 days after current use ended) was also associated with increased risk for these outcomes, as well as for fracture, but past use of antipsychotics (ie, after recent use ended) was not associated with increased risks of the adverse outcomes examined, except for pneumonia. For the negative control outcome (appendicitis and cholecystitis), no significant associations were found with current, recent, or any antipsychotic use, but a statistically significant association was observed with past use (1.90, 1.01 to 3.56).

Incidence rate (per 10 000 person years) and number needed to harm of adverse outcomes associated with antipsychotic use during the first 180 days and two years of follow-up period

Hazard ratios (adjusted for inverse probability of treatment weights) of adverse outcomes associated with current, recent, and past antipsychotic use; with current use being defined as the first 90 days from the date of an antipsychotic prescription, recent use as up to 180 days after current use ended, and past use as after recent use. CI=confidence interval

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Table 2 shows that the NNH ranged from 9 (95% CI 9 to 10) for pneumonia to 167 (116 to 301) for myocardial infarction during the first 180 days after initiation of antipsychotics, and from 15 (14 to 16) for pneumonia to 254 (183 to 413) for myocardial infarction after two years. These figures suggest that over the 180 days after drug initiation, use of antipsychotics might be associated with one additional case of pneumonia for every nine patients treated, and one additional case of myocardial infarction for every 167 patients treated. At two years, there might be one additional case of pneumonia for every 15 patients treated, and one additional case of myocardial infarction for every 254 patients treated.

Table 3 shows hazard ratios stratified by follow-up time (except for ventricular arrhythmia and the negative control where the number of patients was very low). For almost all outcomes, relative hazards were highest in the first seven days after initiation of antipsychotic treatment. Risks for pneumonia were particularly increased in the first seven days (9.99, 8.78 to 11.40) and remained substantial afterwards (3.39, 3.04 to 3.77, 8-30 days). No increased risks for heart failure were found for current users after 180 days from start of treatment, nor for myocardial infarction one year after drug initiation. However, risks for stroke, venous thromboembolism, fracture, pneumonia, and acute kidney injury remained increased among continuous antipsychotic users up to two years after initiation of treatment.

Hazard ratios (adjusted for IPT weights) of adverse outcomes associated with current, recent, and past antipsychotic use stratified by follow-up period

Types of antipsychotics

During the current use period of 90 days after a prescription, both typical and atypical antipsychotics were associated with increased risks of all adverse outcomes compared with non-use, except for ventricular arrhythmia and the negative control (see supplementary table S10). Hazards were higher when current use of typical antipsychotics was directly compared with atypical antipsychotics for stroke (1.23, 1.09 to 1.40), heart failure (1.18, 1.01 to 1.39), fracture (1.22, 1.08 to 1.38), pneumonia (1.92, 1.77 to 2.08), and acute kidney injury (1.22, 1.05 to 1.42), but no significant differences between the two types of drug were found for the risks of venous thromboembolism or myocardial infarction.

Supplementary table S11 shows the risks of adverse outcomes associated with haloperidol (the most prescribed typical antipsychotic) and with risperidone and quetiapine (the two most prescribed atypical antipsychotics). Current use of risperidone and haloperidol compared with non-use was associated with increased risks of all adverse outcomes except for ventricular arrhythmia and the negative control. Current use of quetiapine compared with non-use was associated with increased risks for fracture, pneumonia, and acute kidney injury. Among current users of haloperidol or risperidone, risks for fracture, pneumonia, and acute kidney injury were higher for haloperidol versus risperidone, but risks for stroke, venous thromboembolism, myocardial infarction, and heart failure were similar for both drugs. With the exceptions of myocardial infarction, ventricular arrhythmia, and the negative control, risks of all adverse outcomes were higher for haloperidol than for quetiapine, especially for pneumonia (2.53, 2.21 to 2.89) and venous thromboembolism (1.99, 1.33 to 2.97). Among current users of quetiapine compared with risperidone, there were no significant differences in risks for myocardial infarction, heart failure, or fracture. However, risks for stroke (0.64, 0.53 to 0.78), venous thromboembolism (0.49, 0.36 to 0.68), pneumonia (0.72, 0.63 to 0.81), and acute kidney injury (0.81, 0.67 to 0.96) were lower for quetiapine than for risperidone.

Absolute risks of adverse outcomes

Cumulative incidence for all outcomes examined was higher for antipsychotic users versus matched comparators, except for ventricular arrhythmia and the negative control ( table 4 ). The absolute risk, as well as risk difference, was particularly large for pneumonia. In the 90 days after initiation of an antipsychotic, the cumulative incidence of pneumonia among antipsychotic users was 4.48% (95% CI 4.26% to 4.71%) v 1.49% (1.45% to 1.53%) in the matched cohort of non-users (difference 2.99%, 95% CI 2.77% to 3.22%). At one year, this increased to 10.41% (10.05% to 10.78%) for antipsychotic users compared with 5.63% (5.55% to 5.70%) for non-users (difference 4.78%, 4.41% to 5.16%).

Cumulative incidence of adverse outcomes associated with antipsychotic use at 90, 180, and 365 days and at two years after start of follow-up

Similar results were found in sensitivity analysis using two other definitions of antipsychotic use (see supplementary figures S4 and S5). Of the 544 203 antipsychotic prescriptions issued, 1.3% were for levomepromazine (see supplementary table S1). Results remained similar when patients were censored at the time of their first levomepromazine prescription (see supplementary figure S6). Results of the Fine-Gray models accounting for the competing risks of death also showed broadly similar patterns of hazards to those from the Cox models (see supplementary table S12 and figure S7). Sex specific analyses showed that male patients had higher incidence rates of all adverse outcomes than female patients, except for fracture and venous thromboembolism where incidence was higher for female patients than for male patients (see supplementary table S13). Compared with female antipsychotic users, male users had increased hazards for pneumonia and acute kidney injury (male to female hazard ratio 1.16, 95% CI 1.08 to 1.25 for pneumonia and 1.22, 1.08 to 1.37 for acute kidney injury), but lower hazards for stroke (0.81, 0.73 to 0.91). No significant differences were found by sex in the hazards for venous thromboembolism, myocardial infarction, heart failure, ventricular arrhythmia, or fracture (see supplementary table S14).

In this population based cohort study of adults (≥50 years) with dementia, use of antipsychotics compared with non-use was associated with increased risks for stroke, venous thromboembolism, myocardial infarction, heart failure, fracture, pneumonia, and acute kidney injury. Increased risks were observed among current and recent users and were highest in the first week after initiation of treatment. In the 90 days after a prescription, relative hazards were highest for pneumonia, acute kidney injury, stroke, and venous thromboembolism, with increased risks ranging from 1.5-fold (for venous thromboembolism) to twofold (for pneumonia) compared with non-use. No increased risk was found for ventricular arrhythmia or the negative control outcome (appendicitis and cholecystitis). Absolute risk differences between antipsychotic users and their matched comparators were substantial for most adverse events, and largest for pneumonia. In the 90 days after a prescription, risks of stroke, heart failure, fracture, pneumonia, and acute kidney injury were higher for typical antipsychotics versus atypical antipsychotics, whereas no significant differences between these two drug classes were found for risks of venous thromboembolism or myocardial infarction. Haloperidol was associated with higher risks for fracture, pneumonia, and acute kidney injury than risperidone, but no significant differences between the two drugs were found for the other outcomes. Risks of almost all adverse outcomes were higher for haloperidol than for quetiapine. No significant differences were found between risperidone and quetiapine for risks of myocardial infarction, heart failure, or fracture, but risks for stroke, venous thromboembolism, pneumonia, and acute kidney injury were lower for quetiapine versus risperidone.

Comparison with other studies

A population based study in Wales reported no increased risks for non-fatal acute cardiac events associated with antipsychotic use in patients with all cause dementia, although those with Alzheimer’s disease showed increased risks. 37 Systematic reviews and meta-analyses of studies not limited to patients with dementia have also reported inconsistent evidence for myocardial infarction, or lack of robustness of these data. 33 34 71 Our findings for myocardial infarction were similar to those in a study that first documented a modest and time limited increase in risk of this outcome associated with antipsychotic use among patients with dementia. 56 In a study of nursing home residents in the US, users of typical, but not atypical, antipsychotics were more likely than non-users to be admitted to hospital for ventricular arrhythmia or cardiac arrest, 35 and a study not limited to older people reported increased risks for ventricular arrhythmia or sudden cardiac death associated with both typical and atypical antipsychotics. 36 We did not find any association with ventricular arrhythmia, but the number of events was low and we did not examine cardiac arrest or sudden death.

Increased risks of venous thromboembolism associated with antipsychotic use have been reported in the general population, 38 but meta-analyses found increased risks of venous thromboembolism only among younger users. 39 40 Our findings are consistent with those of the Welsh study, which reported increased risks of venous thromboembolism in the 12 months after drug initiation (prior event rate ratio 1.95, 95% CI 1.83 to 2.0). 37 In absolute terms, however, these risks were relatively low compared with other outcomes examined in this study.

We found that both the relative and the absolute risks for pneumonia were highest among all outcomes examined. Current users of antipsychotics had a twofold increased risk compared with non-users ( fig 1 ), and although this magnitude of increased risk was comparable to previous reports, 14 31 32 we additionally observed that risks were greater in the first week after drug initiation. One study also reported a particularly high risk for patients with hospital diagnosed pneumonia in the first week, but the magnitude of increase (odds ratio 4.5, 95% CI 2.8 to 7.3) was much lower than our observation. 30 The mechanisms linking antipsychotic use and development of pneumonia is not well understood, and substantial heterogeneity exists among the drug substances, but antipsychotic induced extrapyramidal symptoms, sedation, xerostomia (dry mouth), and dyskinesia or impaired swallowing are commonly considered as potential risk factors. 72 In addition, because elderly people with pneumonia may be less likely than younger patients to present with respiratory symptoms but more likely to show signs of delirium, 73 it is possible that reverse causality might have contributed to the high risks observed in the early days after drug initiation, as delirium from the onset of pneumonia might have been treated with antipsychotics before pneumonia was diagnosed. 30 However, although causality cannot be inferred, the particularly high increased risks observed for a range of outcomes and not only for pneumonia in the early days after drug initiation are consistent with other studies. 28 This could be partly explained by further prescriptions being given only to patients who tolerated the first days of drug use.

The use of atypical antipsychotics in older adults (≥65 years) has been shown to be associated with increased risk of acute kidney injury. 44 45 46 Two studies reported significantly increased risks in users compared with non-users in the 90 days after initiation of atypical antipsychotics. 44 45 In contrast, another study observed no increased risks from use of the broad category of atypical antipsychotics, although a significantly increased risk was found with olanzapine. 46 In our study, we found increased risks of acute kidney injury with both typical and atypical antipsychotics, with risks being higher for haloperidol than for risperidone and quetiapine.

In a meta-analysis of observational studies, antipsychotic use was associated with increased risks of hip fracture among people with dementia. 41 A self-controlled case series study of older adult patients (≥65 years) also reported increased risks of falls and fracture after initiation of antipsychotics, but incidence was found to be even higher in the 14 days before treatment started. 43 Similar findings were also reported in another study, suggesting that the risks observed during the treatment periods might not be attributable to the antipsychotics alone. 42 Although we cannot eliminate confounding in our study, we minimised this risk by adjusting for a large number of both clinical and non-clinical characteristics that might have influenced treatment assignment. We also found no increased risks associated with current or recent antipsychotic use for the negative control outcome (appendicitis and cholecystitis).

Our study found that the risks of stroke and heart failure were higher for typical antipsychotics than for atypical antipsychotics, but risks of venous thromboembolism and myocardial infarction were similar between the two drug classes. We also found no significant differences between haloperidol and risperidone in risks of these four outcomes, but significantly increased risks for stroke, venous thromboembolism, and heart failure for haloperidol versus quetiapine. Previous studies of elderly patients have reported similar risks for cardiovascular or cerebrovascular events associated with use of typical and atypical antipsychotics, 17 74 75 76 but risks of these outcomes and of all cause mortality were increased with haloperidol versus risperidone. 21 76 For fracture and pneumonia, we found that risks were higher in association with typical antipsychotics than atypical antipsychotics and for haloperidol versus risperidone or quetiapine. The findings from previous studies comparing these risks by antipsychotic types have been inconsistent. 30 31 32 74 75

Strengths and limitations of this study

A key strength of this study was the investigation of a wide range of adverse events in a large population based cohort, and the reporting of both relative and absolute risk differences over multiple periods. Previous studies commonly focused on a single outcome or type of outcome, such as cerebrovascular events, and on the reporting of relative risks. By examining the same cohort at risk, we were able to directly compare the hazards of multiple outcomes without differential biases between the cohorts. In addition, we only included patients with a clinician recorded diagnosis of dementia, and we adjusted for many variables that might have influenced the probability of antipsychotic initiation, seeking to minimise confounding by indication. CPRD is one of the largest primary care databases in the world, and it is broadly representative of the UK population. 47 48 49 The database includes all prescriptions issued in participating primary care practices in the UK, and it is recognised as a high quality resource to support international pharmacovigilance. 77 The longitudinal nature of CPRD, with linked data from secondary care and mortality records, enabled us to capture the study outcomes from multiple sources, as well as information on prescribing and comorbidities. 78 79 Our findings were also robust to different classifications of usage periods and we found no associations between current and recent antipsychotic use with the development of the negative control outcome (appendicitis and cholecystitis). However, a significant association with past use was observed that we are unable to explain.

As with all observational studies, residual confounding cannot be excluded. For example, polypharmacy is common among elderly people, which could lead to drug-drug interactions and potentially confound our findings. 80 81 We also did not have information on indications for antipsychotics treatment. We minimised the risk of confounding using propensity score methods to control for imbalances in measurable patient characteristics between antipsychotic users and their matched comparators. However, unlike randomised control trials, which, if properly conducted, could account for both observed and unobserved differences between treated and untreated groups, the propensity score method can only adjust for the observed differences between two groups. Additionally, our choice of covariates was based on the literature and discussions with clinical experts and was not formally structured using, for example, a directed acyclic graph. Although the strong associations with pneumonia in the first seven days of antipsychotic initiation may partially be attributed to reverse causality, however, it is less likely to explain associations over longer periods. We also found no increased risk for appendicitis and cholecystitis during current and recent use—our negative control outcome that was included to detect potential unmeasured confounding. 53 Another limitation of our study is that although prescriptions issued in primary care are reliable in CPRD, information on dosage is not well recorded and information on drug adherence or prescriptions issued while patients are in hospital is not available. 48 Misclassification of drug use is therefore a potential problem. As with other electronic health data that are routinely collected for administrative rather than research purposes, potential issues exist with coding errors, missing or incomplete information, and variations in data quality between practices and healthcare settings. Although the data undergo quality checks before being released and our use of the linked data would have helped to deal with such problems, we were restricted to data coded in patients’ electronic health records. In addition, despite the representativeness of the CPRD data, care should be taken in making inferences beyond the population studied. Our sex specific investigations were also conducted as post hoc analyses. By using existing matched sets but restricting the comparators to those of the same sex as the antipsychotic user to whom they were matched, the number of comparators was greatly reduced. Although we found some evidence of differences in hazards for stroke, pneumonia, and acute kidney injury between male and female antipsychotic users, further research is needed to validate these findings.

Policy implications

The mechanisms underlying the links between antipsychotics and the outcomes in our study are not fully understood. In the UK, US, and Europe, current regulatory warnings for using antipsychotics to treat behavioural and psychological symptoms of dementia were mostly based on evidence of increased risks for stroke and mortality. 8 11 22 23 24 25 26 We found a considerably wider range of harms associated with antipsychotic use in people with dementia, and the risks of harm were highest soon after initiation. Our findings must be seen in the context of trial evidence of at best modest benefit on behavioural and psychological symptoms of dementia. The efficacy of antipsychotics in the management of behavioural and psychological symptoms of dementia remains inconclusive. 82 83 84 85 Atypical antipsychotics, including risperidone, which is one of two antipsychotics licensed in the UK for the treatment of behavioural and psychological symptoms of dementia, have the strongest evidence base, but the benefits are only modest. 82 85

Any potential benefits of antipsychotic treatment therefore need to be weighed against the risk of serious harm across multiple outcomes. Although there may be times when an antipsychotic prescription is the least bad option, clinicians should actively consider the risks, considering patients’ pre-existing comorbidities and living support. The NNH reported in this study can help to inform clinical judgements on the appropriateness of treatments, taking account of the modest potential benefits reported in clinical trials. When prescriptions of such drugs are needed, treatment plans should be reviewed regularly with patients and their carers to reassess the need for continuing treatment. 9 In addition, given the higher risks of adverse events in the early days after drug initiation, clinical examinations should be taken before, and clinical reviews conducted shortly after, the start of treatment. Our study reaffirms that these drugs should only be prescribed for the shortest period possible. 9 Although regulators have made efforts to limit the use of these drugs to people with the most severe behavioural and psychological symptoms of dementia, 8 82 86 antipsychotic prescribing in dementia remains common and has even increased in recent years. 4 5 87 88 If such trends continue, further communication on the associated risks could be considered by guideline developers or regulators after a review of the totality of evidence. Greater accountability and monitoring in the use of these drugs may be called for, and additional legal reforms may be required to regulate adherence. 89 In recent years, other psychotropic drugs such as antidepressants, benzodiazepines, mood stabilisers, and anticonvulsants have been prescribed instead of antipsychotics for the treatment of behavioural and psychological symptoms of dementia. 28 90 91 These drugs, however, also pose their own risks. Further research is needed into safer drug treatment of behavioural and psychological symptoms of dementia and more efficacious, easy to deliver, initial non-drug treatments.

Conclusions

Antipsychotic use is associated with a wide range of serious adverse outcomes in people with dementia, with relatively large absolute risks of harm for some outcomes. These risks should be considered in future regulatory decisions, alongside cerebrovascular events and mortality. Any potential benefits of antipsychotic treatment need to be weighed against risk of serious harm, and treatment plans should be reviewed regularly. The effect of antipsychotics on behavioural and psychological symptoms of dementia is modest at best, but the proportion of people with dementia prescribed antipsychotics has increased in recent years. Our finding that antipsychotics are associated with a wider range of risks than previously known is therefore of direct relevance to guideline developers, regulators, and clinicians considering the appropriateness of antipsychotic prescribing for behavioural and psychological symptoms of dementia.

What is already known on this topic

Despite safety concerns, antipsychotics continue to be frequently prescribed for the management of behavioural and psychological symptoms of dementia

Current regulatory warnings for the treatment of behavioural and psychological symptoms of dementia using antipsychotics are based on evidence of increased risks of stroke and death

Evidence for other adverse outcomes is less conclusive or is more limited among people with dementia, and comparisons of risks for multiple adverse events are also difficult owing to different study designs and populations

What this study adds

Antipsychotic use in people with dementia was associated with increased risks of stroke, venous thromboembolism, myocardial infarction, heart failure, fracture, pneumonia, and acute kidney injury, compared with non-use, but not ventricular arrhythmia

Relative hazards were highest for pneumonia, acute kidney injury, stroke, and venous thromboembolism, and absolute risk and risk difference between antipsychotic users and their matched comparators was largest for pneumonia

Risks of these wide ranging adverse outcomes need to be considered before prescribing antipsychotic drugs to people with dementia

Ethics statements

Ethical approval.

This study was approved by the Clinical Practice Research Datalink’s (CPRD) independent scientific advisory committee (protocol 18_168). CPRD also has ethical approval from the Health Research Authority to support research using anonymised patient data (research ethics committee reference 21/EM/0265). 92 Individual patient consent was not required as all data were deidentified.

Data availability statement

Electronic health records are, by definition, considered sensitive data in the UK by the Data Protection Act and cannot be shared via public deposition because of information governance restriction in place to protect patient confidentiality. Access to Clinical Practice Research Datalink (CPRD) data is subject to protocol approval via CPRD’s research data governance process. For more information see https://cprd.com/data-access . Linked secondary care data from Hospital Episodes Statistics, mortality data from the Office for National Statistics, and index of multiple deprivation data can also be requested from CPRD.

Acknowledgments

We thank Hayley Gorton and Thomas Allen for their contribution to the protocol development, Evan Kontopantelis for his statistical advice, and members of our patient and public involvement team, Antony Chuter and Jillian Beggs, for their contributions to this project. This study is based on data from the Clinical Practice Research Datalink obtained under licence from the UK Medicines and Healthcare Products Regulatory Agency (MHRA). The data are provided by patients and collected by the NHS as part of its care and support. Hospital Episode Statistics and Office for National Statistics mortality data are subject to Crown copyright (2022) protection, reused with the permission of The Health and Social Care Information Centre, all rights reserved. The interpretation and conclusions contained in this study are those of the authors alone, and not necessarily those of the MHRA, National Institute of Health and Care Research, NHS, or Department of Health and Social Care. The study protocol was approved by Clinical Practice Research Datalink’s independent scientific advisory committee (reference: 18_168). We would like to acknowledge all the data providers and general practices who make anonymised data available for research.

Contributors: All authors conceived and designed the study and acquired, analysed, or interpreted the data. BG, DRM, TvS, AJA, and DMA reviewed the clinical codes. PLHM conducted the statistical analyses and wrote the first draft of the manuscript. All authors critically revised the manuscript for important intellectual content and approved the final version. AJA, DMA, RAE, BG, DRM, AS, and TvS obtained the funding. PLHM is the guarantor. The corresponding author (PLHM) attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: This study was funded by the National Institute for Health and Care Research (NIHR, RP-PG-1214-20012). MJC, AJA, and DMA were supported by the NIHR Greater Manchester Patient Safety Translational Research Centre (PSTRC-2016-003) at the time of this study and are now supported by the NIHR Greater Manchester Patient Safety Research Collaboration (NIHR204295). The funders had no role in the study design; in the collection, analysis, or interpretation of data; in the writing of the report; or in the decision to submit the article for publication. PLHM has full access to all data and all authors have full access to the statistical reports and tables in the study. PLHM takes responsibility for the integrity of the data and the accuracy of the data analysis.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: BG reports research grants from the National Institute for Health and Care Research (NIHR). DRM was awarded a Wellcome Trust Clinical Research Development Fellowship (214588/Z/18/Z). AS reports a research grant from the NIHR. RAE reports research grants from the NIHR and NHS England, and travel costs to attend a roundtable dinner discussion on medication errors, House of Commons, Westminster, on 29 March 2022. TvS reports research grants from the NIHR. AJA is national clinical director for prescribing for NHS England and reports research grants from the NIHR. DMA reports research grants from the NIHR, AbbVie, Almirall, Celgene, Eli Lilly, Janssen, Novartis, UCB, and the Leo Foundation. All other authors declare no support from any organisation for the submitted work (except those listed in the funding section); no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Transparency: The lead author (PLHM: the manuscript’s guarantor) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

Dissemination to participants and related patient and public communities: This study used anonymised electronic health records from the CPRD and it is therefore not possible to disseminate the findings directly to individuals whose data we used. This study is part of a National Institute for Health and Care Research (NIHR) funded programme (RP-PG-1214-20012): Avoiding patient harm through the application of prescribing safety indicators in English general practices (PRoTeCT). We have experienced patient and public involvement members aligned to the programme who we will consult in the results dissemination. In addition, senior author DMA is director of NIHR Greater Manchester Patient Safety Research Collaboration (GMPSRC), and co-authors MJC and AJA are affiliated with it. The Patient Safety Research Collaboration has a community of public contributors including patients, carers, and people accessing health and social care services. The authors will work with this network to disseminate findings.

Provenance and peer review: Not commissioned; externally peer reviewed.

This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/ .

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• Environmental economics
• Environmental health
• Interdisciplinary studies
• Projection and prediction

Global projections of macroeconomic climate-change damages typically consider impacts from average annual and national temperatures over long time horizons 1 , 2 , 3 , 4 , 5 , 6 . Here we use recent empirical findings from more than 1,600 regions worldwide over the past 40 years to project sub-national damages from temperature and precipitation, including daily variability and extremes 7 , 8 . Using an empirical approach that provides a robust lower bound on the persistence of impacts on economic growth, we find that the world economy is committed to an income reduction of 19% within the next 26 years independent of future emission choices (relative to a baseline without climate impacts, likely range of 11–29% accounting for physical climate and empirical uncertainty). These damages already outweigh the mitigation costs required to limit global warming to 2 °C by sixfold over this near-term time frame and thereafter diverge strongly dependent on emission choices. Committed damages arise predominantly through changes in average temperature, but accounting for further climatic components raises estimates by approximately 50% and leads to stronger regional heterogeneity. Committed losses are projected for all regions except those at very high latitudes, at which reductions in temperature variability bring benefits. The largest losses are committed at lower latitudes in regions with lower cumulative historical emissions and lower present-day income.

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Projections of the macroeconomic damage caused by future climate change are crucial to informing public and policy debates about adaptation, mitigation and climate justice. On the one hand, adaptation against climate impacts must be justified and planned on the basis of an understanding of their future magnitude and spatial distribution 9 . This is also of importance in the context of climate justice 10 , as well as to key societal actors, including governments, central banks and private businesses, which increasingly require the inclusion of climate risks in their macroeconomic forecasts to aid adaptive decision-making 11 , 12 . On the other hand, climate mitigation policy such as the Paris Climate Agreement is often evaluated by balancing the costs of its implementation against the benefits of avoiding projected physical damages. This evaluation occurs both formally through cost–benefit analyses 1 , 4 , 5 , 6 , as well as informally through public perception of mitigation and damage costs 13 .

Projections of future damages meet challenges when informing these debates, in particular the human biases relating to uncertainty and remoteness that are raised by long-term perspectives 14 . Here we aim to overcome such challenges by assessing the extent of economic damages from climate change to which the world is already committed by historical emissions and socio-economic inertia (the range of future emission scenarios that are considered socio-economically plausible 15 ). Such a focus on the near term limits the large uncertainties about diverging future emission trajectories, the resulting long-term climate response and the validity of applying historically observed climate–economic relations over long timescales during which socio-technical conditions may change considerably. As such, this focus aims to simplify the communication and maximize the credibility of projected economic damages from future climate change.

In projecting the future economic damages from climate change, we make use of recent advances in climate econometrics that provide evidence for impacts on sub-national economic growth from numerous components of the distribution of daily temperature and precipitation 3 , 7 , 8 . Using fixed-effects panel regression models to control for potential confounders, these studies exploit within-region variation in local temperature and precipitation in a panel of more than 1,600 regions worldwide, comprising climate and income data over the past 40 years, to identify the plausibly causal effects of changes in several climate variables on economic productivity 16 , 17 . Specifically, macroeconomic impacts have been identified from changing daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall that occur in addition to those already identified from changing average temperature 2 , 3 , 18 . Moreover, regional heterogeneity in these effects based on the prevailing local climatic conditions has been found using interactions terms. The selection of these climate variables follows micro-level evidence for mechanisms related to the impacts of average temperatures on labour and agricultural productivity 2 , of temperature variability on agricultural productivity and health 7 , as well as of precipitation on agricultural productivity, labour outcomes and flood damages 8 (see Extended Data Table 1 for an overview, including more detailed references). References  7 , 8 contain a more detailed motivation for the use of these particular climate variables and provide extensive empirical tests about the robustness and nature of their effects on economic output, which are summarized in Methods . By accounting for these extra climatic variables at the sub-national level, we aim for a more comprehensive description of climate impacts with greater detail across both time and space.

Constraining the persistence of impacts

A key determinant and source of discrepancy in estimates of the magnitude of future climate damages is the extent to which the impact of a climate variable on economic growth rates persists. The two extreme cases in which these impacts persist indefinitely or only instantaneously are commonly referred to as growth or level effects 19 , 20 (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for mathematical definitions). Recent work shows that future damages from climate change depend strongly on whether growth or level effects are assumed 20 . Following refs.  2 , 18 , we provide constraints on this persistence by using distributed lag models to test the significance of delayed effects separately for each climate variable. Notably, and in contrast to refs.  2 , 18 , we use climate variables in their first-differenced form following ref.  3 , implying a dependence of the growth rate on a change in climate variables. This choice means that a baseline specification without any lags constitutes a model prior of purely level effects, in which a permanent change in the climate has only an instantaneous effect on the growth rate 3 , 19 , 21 . By including lags, one can then test whether any effects may persist further. This is in contrast to the specification used by refs.  2 , 18 , in which climate variables are used without taking the first difference, implying a dependence of the growth rate on the level of climate variables. In this alternative case, the baseline specification without any lags constitutes a model prior of pure growth effects, in which a change in climate has an infinitely persistent effect on the growth rate. Consequently, including further lags in this alternative case tests whether the initial growth impact is recovered 18 , 19 , 21 . Both of these specifications suffer from the limiting possibility that, if too few lags are included, one might falsely accept the model prior. The limitations of including a very large number of lags, including loss of data and increasing statistical uncertainty with an increasing number of parameters, mean that such a possibility is likely. By choosing a specification in which the model prior is one of level effects, our approach is therefore conservative by design, avoiding assumptions of infinite persistence of climate impacts on growth and instead providing a lower bound on this persistence based on what is observable empirically (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for further exposition of this framework). The conservative nature of such a choice is probably the reason that ref.  19 finds much greater consistency between the impacts projected by models that use the first difference of climate variables, as opposed to their levels.

We begin our empirical analysis of the persistence of climate impacts on growth using ten lags of the first-differenced climate variables in fixed-effects distributed lag models. We detect substantial effects on economic growth at time lags of up to approximately 8–10 years for the temperature terms and up to approximately 4 years for the precipitation terms (Extended Data Fig. 1 and Extended Data Table 2 ). Furthermore, evaluation by means of information criteria indicates that the inclusion of all five climate variables and the use of these numbers of lags provide a preferable trade-off between best-fitting the data and including further terms that could cause overfitting, in comparison with model specifications excluding climate variables or including more or fewer lags (Extended Data Fig. 3 , Supplementary Methods Section  1 and Supplementary Table 1 ). We therefore remove statistically insignificant terms at later lags (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). Further tests using Monte Carlo simulations demonstrate that the empirical models are robust to autocorrelation in the lagged climate variables (Supplementary Methods Section  2 and Supplementary Figs. 4 and 5 ), that information criteria provide an effective indicator for lag selection (Supplementary Methods Section  2 and Supplementary Fig. 6 ), that the results are robust to concerns of imperfect multicollinearity between climate variables and that including several climate variables is actually necessary to isolate their separate effects (Supplementary Methods Section  3 and Supplementary Fig. 7 ). We provide a further robustness check using a restricted distributed lag model to limit oscillations in the lagged parameter estimates that may result from autocorrelation, finding that it provides similar estimates of cumulative marginal effects to the unrestricted model (Supplementary Methods Section 4 and Supplementary Figs. 8 and 9 ). Finally, to explicitly account for any outstanding uncertainty arising from the precise choice of the number of lags, we include empirical models with marginally different numbers of lags in the error-sampling procedure of our projection of future damages. On the basis of the lag-selection procedure (the significance of lagged terms in Extended Data Fig. 1 and Extended Data Table 2 , as well as information criteria in Extended Data Fig. 3 ), we sample from models with eight to ten lags for temperature and four for precipitation (models shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). In summary, this empirical approach to constrain the persistence of climate impacts on economic growth rates is conservative by design in avoiding assumptions of infinite persistence, but nevertheless provides a lower bound on the extent of impact persistence that is robust to the numerous tests outlined above.

Committed damages until mid-century

We combine these empirical economic response functions (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) with an ensemble of 21 climate models (see Supplementary Table 5 ) from the Coupled Model Intercomparison Project Phase 6 (CMIP-6) 22 to project the macroeconomic damages from these components of physical climate change (see Methods for further details). Bias-adjusted climate models that provide a highly accurate reproduction of observed climatological patterns with limited uncertainty (Supplementary Table 6 ) are used to avoid introducing biases in the projections. Following a well-developed literature 2 , 3 , 19 , these projections do not aim to provide a prediction of future economic growth. Instead, they are a projection of the exogenous impact of future climate conditions on the economy relative to the baselines specified by socio-economic projections, based on the plausibly causal relationships inferred by the empirical models and assuming ceteris paribus. Other exogenous factors relevant for the prediction of economic output are purposefully assumed constant.

A Monte Carlo procedure that samples from climate model projections, empirical models with different numbers of lags and model parameter estimates (obtained by 1,000 block-bootstrap resamples of each of the regressions in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) is used to estimate the combined uncertainty from these sources. Given these uncertainty distributions, we find that projected global damages are statistically indistinguishable across the two most extreme emission scenarios until 2049 (at the 5% significance level; Fig. 1 ). As such, the climate damages occurring before this time constitute those to which the world is already committed owing to the combination of past emissions and the range of future emission scenarios that are considered socio-economically plausible 15 . These committed damages comprise a permanent income reduction of 19% on average globally (population-weighted average) in comparison with a baseline without climate-change impacts (with a likely range of 11–29%, following the likelihood classification adopted by the Intergovernmental Panel on Climate Change (IPCC); see caption of Fig. 1 ). Even though levels of income per capita generally still increase relative to those of today, this constitutes a permanent income reduction for most regions, including North America and Europe (each with median income reductions of approximately 11%) and with South Asia and Africa being the most strongly affected (each with median income reductions of approximately 22%; Fig. 1 ). Under a middle-of-the road scenario of future income development (SSP2, in which SSP stands for Shared Socio-economic Pathway), this corresponds to global annual damages in 2049 of 38 trillion in 2005 international dollars (likely range of 19–59 trillion 2005 international dollars). Compared with empirical specifications that assume pure growth or pure level effects, our preferred specification that provides a robust lower bound on the extent of climate impact persistence produces damages between these two extreme assumptions (Extended Data Fig. 3 ).

Estimates of the projected reduction in income per capita from changes in all climate variables based on empirical models of climate impacts on economic output with a robust lower bound on their persistence (Extended Data Fig. 1 ) under a low-emission scenario compatible with the 2 °C warming target and a high-emission scenario (SSP2-RCP2.6 and SSP5-RCP8.5, respectively) are shown in purple and orange, respectively. Shading represents the 34% and 10% confidence intervals reflecting the likely and very likely ranges, respectively (following the likelihood classification adopted by the IPCC), having estimated uncertainty from a Monte Carlo procedure, which samples the uncertainty from the choice of physical climate models, empirical models with different numbers of lags and bootstrapped estimates of the regression parameters shown in Supplementary Figs. 1 – 3 . Vertical dashed lines show the time at which the climate damages of the two emission scenarios diverge at the 5% and 1% significance levels based on the distribution of differences between emission scenarios arising from the uncertainty sampling discussed above. Note that uncertainty in the difference of the two scenarios is smaller than the combined uncertainty of the two respective scenarios because samples of the uncertainty (climate model and empirical model choice, as well as model parameter bootstrap) are consistent across the two emission scenarios, hence the divergence of damages occurs while the uncertainty bounds of the two separate damage scenarios still overlap. Estimates of global mitigation costs from the three IAMs that provide results for the SSP2 baseline and SSP2-RCP2.6 scenario are shown in light green in the top panel, with the median of these estimates shown in bold.

Damages already outweigh mitigation costs

We compare the damages to which the world is committed over the next 25 years to estimates of the mitigation costs required to achieve the Paris Climate Agreement. Taking estimates of mitigation costs from the three integrated assessment models (IAMs) in the IPCC AR6 database 23 that provide results under comparable scenarios (SSP2 baseline and SSP2-RCP2.6, in which RCP stands for Representative Concentration Pathway), we find that the median committed climate damages are larger than the median mitigation costs in 2050 (six trillion in 2005 international dollars) by a factor of approximately six (note that estimates of mitigation costs are only provided every 10 years by the IAMs and so a comparison in 2049 is not possible). This comparison simply aims to compare the magnitude of future damages against mitigation costs, rather than to conduct a formal cost–benefit analysis of transitioning from one emission path to another. Formal cost–benefit analyses typically find that the net benefits of mitigation only emerge after 2050 (ref.  5 ), which may lead some to conclude that physical damages from climate change are simply not large enough to outweigh mitigation costs until the second half of the century. Our simple comparison of their magnitudes makes clear that damages are actually already considerably larger than mitigation costs and the delayed emergence of net mitigation benefits results primarily from the fact that damages across different emission paths are indistinguishable until mid-century (Fig. 1 ).

Although these near-term damages constitute those to which the world is already committed, we note that damage estimates diverge strongly across emission scenarios after 2049, conveying the clear benefits of mitigation from a purely economic point of view that have been emphasized in previous studies 4 , 24 . As well as the uncertainties assessed in Fig. 1 , these conclusions are robust to structural choices, such as the timescale with which changes in the moderating variables of the empirical models are estimated (Supplementary Figs. 10 and 11 ), as well as the order in which one accounts for the intertemporal and international components of currency comparison (Supplementary Fig. 12 ; see Methods for further details).

Damages from variability and extremes

Committed damages primarily arise through changes in average temperature (Fig. 2 ). This reflects the fact that projected changes in average temperature are larger than those in other climate variables when expressed as a function of their historical interannual variability (Extended Data Fig. 4 ). Because the historical variability is that on which the empirical models are estimated, larger projected changes in comparison with this variability probably lead to larger future impacts in a purely statistical sense. From a mechanistic perspective, one may plausibly interpret this result as implying that future changes in average temperature are the most unprecedented from the perspective of the historical fluctuations to which the economy is accustomed and therefore will cause the most damage. This insight may prove useful in terms of guiding adaptation measures to the sources of greatest damage.

Estimates of the median projected reduction in sub-national income per capita across emission scenarios (SSP2-RCP2.6 and SSP2-RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ). a , Impacts arising from all climate variables. b – f , Impacts arising separately from changes in annual mean temperature ( b ), daily temperature variability ( c ), total annual precipitation ( d ), the annual number of wet days (>1 mm) ( e ) and extreme daily rainfall ( f ) (see Methods for further definitions). Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Nevertheless, future damages based on empirical models that consider changes in annual average temperature only and exclude the other climate variables constitute income reductions of only 13% in 2049 (Extended Data Fig. 5a , likely range 5–21%). This suggests that accounting for the other components of the distribution of temperature and precipitation raises net damages by nearly 50%. This increase arises through the further damages that these climatic components cause, but also because their inclusion reveals a stronger negative economic response to average temperatures (Extended Data Fig. 5b ). The latter finding is consistent with our Monte Carlo simulations, which suggest that the magnitude of the effect of average temperature on economic growth is underestimated unless accounting for the impacts of other correlated climate variables (Supplementary Fig. 7 ).

In terms of the relative contributions of the different climatic components to overall damages, we find that accounting for daily temperature variability causes the largest increase in overall damages relative to empirical frameworks that only consider changes in annual average temperature (4.9 percentage points, likely range 2.4–8.7 percentage points, equivalent to approximately 10 trillion international dollars). Accounting for precipitation causes smaller increases in overall damages, which are—nevertheless—equivalent to approximately 1.2 trillion international dollars: 0.01 percentage points (−0.37–0.33 percentage points), 0.34 percentage points (0.07–0.90 percentage points) and 0.36 percentage points (0.13–0.65 percentage points) from total annual precipitation, the number of wet days and extreme daily precipitation, respectively. Moreover, climate models seem to underestimate future changes in temperature variability 25 and extreme precipitation 26 , 27 in response to anthropogenic forcing as compared with that observed historically, suggesting that the true impacts from these variables may be larger.

The distribution of committed damages

The spatial distribution of committed damages (Fig. 2a ) reflects a complex interplay between the patterns of future change in several climatic components and those of historical economic vulnerability to changes in those variables. Damages resulting from increasing annual mean temperature (Fig. 2b ) are negative almost everywhere globally, and larger at lower latitudes in regions in which temperatures are already higher and economic vulnerability to temperature increases is greatest (see the response heterogeneity to mean temperature embodied in Extended Data Fig. 1a ). This occurs despite the amplified warming projected at higher latitudes 28 , suggesting that regional heterogeneity in economic vulnerability to temperature changes outweighs heterogeneity in the magnitude of future warming (Supplementary Fig. 13a ). Economic damages owing to daily temperature variability (Fig. 2c ) exhibit a strong latitudinal polarisation, primarily reflecting the physical response of daily variability to greenhouse forcing in which increases in variability across lower latitudes (and Europe) contrast decreases at high latitudes 25 (Supplementary Fig. 13b ). These two temperature terms are the dominant determinants of the pattern of overall damages (Fig. 2a ), which exhibits a strong polarity with damages across most of the globe except at the highest northern latitudes. Future changes in total annual precipitation mainly bring economic benefits except in regions of drying, such as the Mediterranean and central South America (Fig. 2d and Supplementary Fig. 13c ), but these benefits are opposed by changes in the number of wet days, which produce damages with a similar pattern of opposite sign (Fig. 2e and Supplementary Fig. 13d ). By contrast, changes in extreme daily rainfall produce damages in all regions, reflecting the intensification of daily rainfall extremes over global land areas 29 , 30 (Fig. 2f and Supplementary Fig. 13e ).

The spatial distribution of committed damages implies considerable injustice along two dimensions: culpability for the historical emissions that have caused climate change and pre-existing levels of socio-economic welfare. Spearman’s rank correlations indicate that committed damages are significantly larger in countries with smaller historical cumulative emissions, as well as in regions with lower current income per capita (Fig. 3 ). This implies that those countries that will suffer the most from the damages already committed are those that are least responsible for climate change and which also have the least resources to adapt to it.

Estimates of the median projected change in national income per capita across emission scenarios (RCP2.6 and RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ) are plotted against cumulative national emissions per capita in 2020 (from the Global Carbon Project) and coloured by national income per capita in 2020 (from the World Bank) in a and vice versa in b . In each panel, the size of each scatter point is weighted by the national population in 2020 (from the World Bank). Inset numbers indicate the Spearman’s rank correlation ρ and P -values for a hypothesis test whose null hypothesis is of no correlation, as well as the Spearman’s rank correlation weighted by national population.

To further quantify this heterogeneity, we assess the difference in committed damages between the upper and lower quartiles of regions when ranked by present income levels and historical cumulative emissions (using a population weighting to both define the quartiles and estimate the group averages). On average, the quartile of countries with lower income are committed to an income loss that is 8.9 percentage points (or 61%) greater than the upper quartile (Extended Data Fig. 6 ), with a likely range of 3.8–14.7 percentage points across the uncertainty sampling of our damage projections (following the likelihood classification adopted by the IPCC). Similarly, the quartile of countries with lower historical cumulative emissions are committed to an income loss that is 6.9 percentage points (or 40%) greater than the upper quartile, with a likely range of 0.27–12 percentage points. These patterns reemphasize the prevalence of injustice in climate impacts 31 , 32 , 33 in the context of the damages to which the world is already committed by historical emissions and socio-economic inertia.

Contextualizing the magnitude of damages

The magnitude of projected economic damages exceeds previous literature estimates 2 , 3 , arising from several developments made on previous approaches. Our estimates are larger than those of ref.  2 (see first row of Extended Data Table 3 ), primarily because of the facts that sub-national estimates typically show a steeper temperature response (see also refs.  3 , 34 ) and that accounting for other climatic components raises damage estimates (Extended Data Fig. 5 ). However, we note that our empirical approach using first-differenced climate variables is conservative compared with that of ref.  2 in regard to the persistence of climate impacts on growth (see introduction and Methods section ‘Empirical model specification: fixed-effects distributed lag models’), an important determinant of the magnitude of long-term damages 19 , 21 . Using a similar empirical specification to ref.  2 , which assumes infinite persistence while maintaining the rest of our approach (sub-national data and further climate variables), produces considerably larger damages (purple curve of Extended Data Fig. 3 ). Compared with studies that do take the first difference of climate variables 3 , 35 , our estimates are also larger (see second and third rows of Extended Data Table 3 ). The inclusion of further climate variables (Extended Data Fig. 5 ) and a sufficient number of lags to more adequately capture the extent of impact persistence (Extended Data Figs. 1 and 2 ) are the main sources of this difference, as is the use of specifications that capture nonlinearities in the temperature response when compared with ref.  35 . In summary, our estimates develop on previous studies by incorporating the latest data and empirical insights 7 , 8 , as well as in providing a robust empirical lower bound on the persistence of impacts on economic growth, which constitutes a middle ground between the extremes of the growth-versus-levels debate 19 , 21 (Extended Data Fig. 3 ).

Compared with the fraction of variance explained by the empirical models historically (<5%), the projection of reductions in income of 19% may seem large. This arises owing to the fact that projected changes in climatic conditions are much larger than those that were experienced historically, particularly for changes in average temperature (Extended Data Fig. 4 ). As such, any assessment of future climate-change impacts necessarily requires an extrapolation outside the range of the historical data on which the empirical impact models were evaluated. Nevertheless, these models constitute the most state-of-the-art methods for inference of plausibly causal climate impacts based on observed data. Moreover, we take explicit steps to limit out-of-sample extrapolation by capping the moderating variables of the interaction terms at the 95th percentile of the historical distribution (see Methods ). This avoids extrapolating the marginal effects outside what was observed historically. Given the nonlinear response of economic output to annual mean temperature (Extended Data Fig. 1 and Extended Data Table 2 ), this is a conservative choice that limits the magnitude of damages that we project. Furthermore, back-of-the-envelope calculations indicate that the projected damages are consistent with the magnitude and patterns of historical economic development (see Supplementary Discussion Section  5 ).

Missing impacts and spatial spillovers

Despite assessing several climatic components from which economic impacts have recently been identified 3 , 7 , 8 , this assessment of aggregate climate damages should not be considered comprehensive. Important channels such as impacts from heatwaves 31 , sea-level rise 36 , tropical cyclones 37 and tipping points 38 , 39 , as well as non-market damages such as those to ecosystems 40 and human health 41 , are not considered in these estimates. Sea-level rise is unlikely to be feasibly incorporated into empirical assessments such as this because historical sea-level variability is mostly small. Non-market damages are inherently intractable within our estimates of impacts on aggregate monetary output and estimates of these impacts could arguably be considered as extra to those identified here. Recent empirical work suggests that accounting for these channels would probably raise estimates of these committed damages, with larger damages continuing to arise in the global south 31 , 36 , 37 , 38 , 39 , 40 , 41 , 42 .

Moreover, our main empirical analysis does not explicitly evaluate the potential for impacts in local regions to produce effects that ‘spill over’ into other regions. Such effects may further mitigate or amplify the impacts we estimate, for example, if companies relocate production from one affected region to another or if impacts propagate along supply chains. The current literature indicates that trade plays a substantial role in propagating spillover effects 43 , 44 , making their assessment at the sub-national level challenging without available data on sub-national trade dependencies. Studies accounting for only spatially adjacent neighbours indicate that negative impacts in one region induce further negative impacts in neighbouring regions 45 , 46 , 47 , 48 , suggesting that our projected damages are probably conservative by excluding these effects. In Supplementary Fig. 14 , we assess spillovers from neighbouring regions using a spatial-lag model. For simplicity, this analysis excludes temporal lags, focusing only on contemporaneous effects. The results show that accounting for spatial spillovers can amplify the overall magnitude, and also the heterogeneity, of impacts. Consistent with previous literature, this indicates that the overall magnitude (Fig. 1 ) and heterogeneity (Fig. 3 ) of damages that we project in our main specification may be conservative without explicitly accounting for spillovers. We note that further analysis that addresses both spatially and trade-connected spillovers, while also accounting for delayed impacts using temporal lags, would be necessary to adequately address this question fully. These approaches offer fruitful avenues for further research but are beyond the scope of this manuscript, which primarily aims to explore the impacts of different climate conditions and their persistence.

Policy implications

We find that the economic damages resulting from climate change until 2049 are those to which the world economy is already committed and that these greatly outweigh the costs required to mitigate emissions in line with the 2 °C target of the Paris Climate Agreement (Fig. 1 ). This assessment is complementary to formal analyses of the net costs and benefits associated with moving from one emission path to another, which typically find that net benefits of mitigation only emerge in the second half of the century 5 . Our simple comparison of the magnitude of damages and mitigation costs makes clear that this is primarily because damages are indistinguishable across emissions scenarios—that is, committed—until mid-century (Fig. 1 ) and that they are actually already much larger than mitigation costs. For simplicity, and owing to the availability of data, we compare damages to mitigation costs at the global level. Regional estimates of mitigation costs may shed further light on the national incentives for mitigation to which our results already hint, of relevance for international climate policy. Although these damages are committed from a mitigation perspective, adaptation may provide an opportunity to reduce them. Moreover, the strong divergence of damages after mid-century reemphasizes the clear benefits of mitigation from a purely economic perspective, as highlighted in previous studies 1 , 4 , 6 , 24 .

Historical climate data

Historical daily 2-m temperature and precipitation totals (in mm) are obtained for the period 1979–2019 from the W5E5 database. The W5E5 dataset comes from ERA-5, a state-of-the-art reanalysis of historical observations, but has been bias-adjusted by applying version 2.0 of the WATCH Forcing Data to ERA-5 reanalysis data and precipitation data from version 2.3 of the Global Precipitation Climatology Project to better reflect ground-based measurements 49 , 50 , 51 . We obtain these data on a 0.5° × 0.5° grid from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) database. Notably, these historical data have been used to bias-adjust future climate projections from CMIP-6 (see the following section), ensuring consistency between the distribution of historical daily weather on which our empirical models were estimated and the climate projections used to estimate future damages. These data are publicly available from the ISIMIP database. See refs.  7 , 8 for robustness tests of the empirical models to the choice of climate data reanalysis products.

Future climate data

Daily 2-m temperature and precipitation totals (in mm) are taken from 21 climate models participating in CMIP-6 under a high (RCP8.5) and a low (RCP2.6) greenhouse gas emission scenario from 2015 to 2100. The data have been bias-adjusted and statistically downscaled to a common half-degree grid to reflect the historical distribution of daily temperature and precipitation of the W5E5 dataset using the trend-preserving method developed by the ISIMIP 50 , 52 . As such, the climate model data reproduce observed climatological patterns exceptionally well (Supplementary Table 5 ). Gridded data are publicly available from the ISIMIP database.

Historical economic data

Historical economic data come from the DOSE database of sub-national economic output 53 . We use a recent revision to the DOSE dataset that provides data across 83 countries, 1,660 sub-national regions with varying temporal coverage from 1960 to 2019. Sub-national units constitute the first administrative division below national, for example, states for the USA and provinces for China. Data come from measures of gross regional product per capita (GRPpc) or income per capita in local currencies, reflecting the values reported in national statistical agencies, yearbooks and, in some cases, academic literature. We follow previous literature 3 , 7 , 8 , 54 and assess real sub-national output per capita by first converting values from local currencies to US dollars to account for diverging national inflationary tendencies and then account for US inflation using a US deflator. Alternatively, one might first account for national inflation and then convert between currencies. Supplementary Fig. 12 demonstrates that our conclusions are consistent when accounting for price changes in the reversed order, although the magnitude of estimated damages varies. See the documentation of the DOSE dataset for further discussion of these choices. Conversions between currencies are conducted using exchange rates from the FRED database of the Federal Reserve Bank of St. Louis 55 and the national deflators from the World Bank 56 .

Future socio-economic data

Baseline gridded gross domestic product (GDP) and population data for the period 2015–2100 are taken from the middle-of-the-road scenario SSP2 (ref.  15 ). Population data have been downscaled to a half-degree grid by the ISIMIP following the methodologies of refs.  57 , 58 , which we then aggregate to the sub-national level of our economic data using the spatial aggregation procedure described below. Because current methodologies for downscaling the GDP of the SSPs use downscaled population to do so, per-capita estimates of GDP with a realistic distribution at the sub-national level are not readily available for the SSPs. We therefore use national-level GDP per capita (GDPpc) projections for all sub-national regions of a given country, assuming homogeneity within countries in terms of baseline GDPpc. Here we use projections that have been updated to account for the impact of the COVID-19 pandemic on the trajectory of future income, while remaining consistent with the long-term development of the SSPs 59 . The choice of baseline SSP alters the magnitude of projected climate damages in monetary terms, but when assessed in terms of percentage change from the baseline, the choice of socio-economic scenario is inconsequential. Gridded SSP population data and national-level GDPpc data are publicly available from the ISIMIP database. Sub-national estimates as used in this study are available in the code and data replication files.

Climate variables

Following recent literature 3 , 7 , 8 , we calculate an array of climate variables for which substantial impacts on macroeconomic output have been identified empirically, supported by further evidence at the micro level for plausible underlying mechanisms. See refs.  7 , 8 for an extensive motivation for the use of these particular climate variables and for detailed empirical tests on the nature and robustness of their effects on economic output. To summarize, these studies have found evidence for independent impacts on economic growth rates from annual average temperature, daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall. Assessments of daily temperature variability were motivated by evidence of impacts on agricultural output and human health, as well as macroeconomic literature on the impacts of volatility on growth when manifest in different dimensions, such as government spending, exchange rates and even output itself 7 . Assessments of precipitation impacts were motivated by evidence of impacts on agricultural productivity, metropolitan labour outcomes and conflict, as well as damages caused by flash flooding 8 . See Extended Data Table 1 for detailed references to empirical studies of these physical mechanisms. Marked impacts of daily temperature variability, total annual precipitation, the number of wet days and extreme daily rainfall on macroeconomic output were identified robustly across different climate datasets, spatial aggregation schemes, specifications of regional time trends and error-clustering approaches. They were also found to be robust to the consideration of temperature extremes 7 , 8 . Furthermore, these climate variables were identified as having independent effects on economic output 7 , 8 , which we further explain here using Monte Carlo simulations to demonstrate the robustness of the results to concerns of imperfect multicollinearity between climate variables (Supplementary Methods Section  2 ), as well as by using information criteria (Supplementary Table 1 ) to demonstrate that including several lagged climate variables provides a preferable trade-off between optimally describing the data and limiting the possibility of overfitting.

We calculate these variables from the distribution of daily, d , temperature, T x , d , and precipitation, P x , d , at the grid-cell, x , level for both the historical and future climate data. As well as annual mean temperature, \({\bar{T}}_{x,y}\) , and annual total precipitation, P x , y , we calculate annual, y , measures of daily temperature variability, \({\widetilde{T}}_{x,y}\) :

the number of wet days, Pwd x , y :

and extreme daily rainfall:

in which T x , d , m , y is the grid-cell-specific daily temperature in month m and year y , \({\bar{T}}_{x,m,{y}}\) is the year and grid-cell-specific monthly, m , mean temperature, D m and D y the number of days in a given month m or year y , respectively, H the Heaviside step function, 1 mm the threshold used to define wet days and P 99.9 x is the 99.9th percentile of historical (1979–2019) daily precipitation at the grid-cell level. Units of the climate measures are degrees Celsius for annual mean temperature and daily temperature variability, millimetres for total annual precipitation and extreme daily precipitation, and simply the number of days for the annual number of wet days.

We also calculated weighted standard deviations of monthly rainfall totals as also used in ref.  8 but do not include them in our projections as we find that, when accounting for delayed effects, their effect becomes statistically indistinct and is better captured by changes in total annual rainfall.

Spatial aggregation

We aggregate grid-cell-level historical and future climate measures, as well as grid-cell-level future GDPpc and population, to the level of the first administrative unit below national level of the GADM database, using an area-weighting algorithm that estimates the portion of each grid cell falling within an administrative boundary. We use this as our baseline specification following previous findings that the effect of area or population weighting at the sub-national level is negligible 7 , 8 .

Empirical model specification: fixed-effects distributed lag models

Following a wide range of climate econometric literature 16 , 60 , we use panel regression models with a selection of fixed effects and time trends to isolate plausibly exogenous variation with which to maximize confidence in a causal interpretation of the effects of climate on economic growth rates. The use of region fixed effects, μ r , accounts for unobserved time-invariant differences between regions, such as prevailing climatic norms and growth rates owing to historical and geopolitical factors. The use of yearly fixed effects, η y , accounts for regionally invariant annual shocks to the global climate or economy such as the El Niño–Southern Oscillation or global recessions. In our baseline specification, we also include region-specific linear time trends, k r y , to exclude the possibility of spurious correlations resulting from common slow-moving trends in climate and growth.

The persistence of climate impacts on economic growth rates is a key determinant of the long-term magnitude of damages. Methods for inferring the extent of persistence in impacts on growth rates have typically used lagged climate variables to evaluate the presence of delayed effects or catch-up dynamics 2 , 18 . For example, consider starting from a model in which a climate condition, C r , y , (for example, annual mean temperature) affects the growth rate, Δlgrp r , y (the first difference of the logarithm of gross regional product) of region r in year y :

which we refer to as a ‘pure growth effects’ model in the main text. Typically, further lags are included,

and the cumulative effect of all lagged terms is evaluated to assess the extent to which climate impacts on growth rates persist. Following ref.  18 , in the case that,

the implication is that impacts on the growth rate persist up to NL years after the initial shock (possibly to a weaker or a stronger extent), whereas if

then the initial impact on the growth rate is recovered after NL years and the effect is only one on the level of output. However, we note that such approaches are limited by the fact that, when including an insufficient number of lags to detect a recovery of the growth rates, one may find equation ( 6 ) to be satisfied and incorrectly assume that a change in climatic conditions affects the growth rate indefinitely. In practice, given a limited record of historical data, including too few lags to confidently conclude in an infinitely persistent impact on the growth rate is likely, particularly over the long timescales over which future climate damages are often projected 2 , 24 . To avoid this issue, we instead begin our analysis with a model for which the level of output, lgrp r , y , depends on the level of a climate variable, C r , y :

Given the non-stationarity of the level of output, we follow the literature 19 and estimate such an equation in first-differenced form as,

which we refer to as a model of ‘pure level effects’ in the main text. This model constitutes a baseline specification in which a permanent change in the climate variable produces an instantaneous impact on the growth rate and a permanent effect only on the level of output. By including lagged variables in this specification,

we are able to test whether the impacts on the growth rate persist any further than instantaneously by evaluating whether α L  > 0 are statistically significantly different from zero. Even though this framework is also limited by the possibility of including too few lags, the choice of a baseline model specification in which impacts on the growth rate do not persist means that, in the case of including too few lags, the framework reverts to the baseline specification of level effects. As such, this framework is conservative with respect to the persistence of impacts and the magnitude of future damages. It naturally avoids assumptions of infinite persistence and we are able to interpret any persistence that we identify with equation ( 9 ) as a lower bound on the extent of climate impact persistence on growth rates. See the main text for further discussion of this specification choice, in particular about its conservative nature compared with previous literature estimates, such as refs.  2 , 18 .

We allow the response to climatic changes to vary across regions, using interactions of the climate variables with historical average (1979–2019) climatic conditions reflecting heterogenous effects identified in previous work 7 , 8 . Following this previous work, the moderating variables of these interaction terms constitute the historical average of either the variable itself or of the seasonal temperature difference, \({\hat{T}}_{r}\) , or annual mean temperature, \({\bar{T}}_{r}\) , in the case of daily temperature variability 7 and extreme daily rainfall, respectively 8 .

The resulting regression equation with N and M lagged variables, respectively, reads:

in which Δlgrp r , y is the annual, regional GRPpc growth rate, measured as the first difference of the logarithm of real GRPpc, following previous work 2 , 3 , 7 , 8 , 18 , 19 . Fixed-effects regressions were run using the fixest package in R (ref.  61 ).

Estimates of the coefficients of interest α i , L are shown in Extended Data Fig. 1 for N  =  M  = 10 lags and for our preferred choice of the number of lags in Supplementary Figs. 1 – 3 . In Extended Data Fig. 1 , errors are shown clustered at the regional level, but for the construction of damage projections, we block-bootstrap the regressions by region 1,000 times to provide a range of parameter estimates with which to sample the projection uncertainty (following refs.  2 , 31 ).

Spatial-lag model

In Supplementary Fig. 14 , we present the results from a spatial-lag model that explores the potential for climate impacts to ‘spill over’ into spatially neighbouring regions. We measure the distance between centroids of each pair of sub-national regions and construct spatial lags that take the average of the first-differenced climate variables and their interaction terms over neighbouring regions that are at distances of 0–500, 500–1,000, 1,000–1,500 and 1,500–2000 km (spatial lags, ‘SL’, 1 to 4). For simplicity, we then assess a spatial-lag model without temporal lags to assess spatial spillovers of contemporaneous climate impacts. This model takes the form:

in which SL indicates the spatial lag of each climate variable and interaction term. In Supplementary Fig. 14 , we plot the cumulative marginal effect of each climate variable at different baseline climate conditions by summing the coefficients for each climate variable and interaction term, for example, for average temperature impacts as:

These cumulative marginal effects can be regarded as the overall spatially dependent impact to an individual region given a one-unit shock to a climate variable in that region and all neighbouring regions at a given value of the moderating variable of the interaction term.

Constructing projections of economic damage from future climate change

We construct projections of future climate damages by applying the coefficients estimated in equation ( 10 ) and shown in Supplementary Tables 2 – 4 (when including only lags with statistically significant effects in specifications that limit overfitting; see Supplementary Methods Section  1 ) to projections of future climate change from the CMIP-6 models. Year-on-year changes in each primary climate variable of interest are calculated to reflect the year-to-year variations used in the empirical models. 30-year moving averages of the moderating variables of the interaction terms are calculated to reflect the long-term average of climatic conditions that were used for the moderating variables in the empirical models. By using moving averages in the projections, we account for the changing vulnerability to climate shocks based on the evolving long-term conditions (Supplementary Figs. 10 and 11 show that the results are robust to the precise choice of the window of this moving average). Although these climate variables are not differenced, the fact that the bias-adjusted climate models reproduce observed climatological patterns across regions for these moderating variables very accurately (Supplementary Table 6 ) with limited spread across models (<3%) precludes the possibility that any considerable bias or uncertainty is introduced by this methodological choice. However, we impose caps on these moderating variables at the 95th percentile at which they were observed in the historical data to prevent extrapolation of the marginal effects outside the range in which the regressions were estimated. This is a conservative choice that limits the magnitude of our damage projections.

Time series of primary climate variables and moderating climate variables are then combined with estimates of the empirical model parameters to evaluate the regression coefficients in equation ( 10 ), producing a time series of annual GRPpc growth-rate reductions for a given emission scenario, climate model and set of empirical model parameters. The resulting time series of growth-rate impacts reflects those occurring owing to future climate change. By contrast, a future scenario with no climate change would be one in which climate variables do not change (other than with random year-to-year fluctuations) and hence the time-averaged evaluation of equation ( 10 ) would be zero. Our approach therefore implicitly compares the future climate-change scenario to this no-climate-change baseline scenario.

The time series of growth-rate impacts owing to future climate change in region r and year y , δ r , y , are then added to the future baseline growth rates, π r , y (in log-diff form), obtained from the SSP2 scenario to yield trajectories of damaged GRPpc growth rates, ρ r , y . These trajectories are aggregated over time to estimate the future trajectory of GRPpc with future climate impacts:

in which GRPpc r , y =2020 is the initial log level of GRPpc. We begin damage estimates in 2020 to reflect the damages occurring since the end of the period for which we estimate the empirical models (1979–2019) and to match the timing of mitigation-cost estimates from most IAMs (see below).

For each emission scenario, this procedure is repeated 1,000 times while randomly sampling from the selection of climate models, the selection of empirical models with different numbers of lags (shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) and bootstrapped estimates of the regression parameters. The result is an ensemble of future GRPpc trajectories that reflect uncertainty from both physical climate change and the structural and sampling uncertainty of the empirical models.

Estimates of mitigation costs

We obtain IPCC estimates of the aggregate costs of emission mitigation from the AR6 Scenario Explorer and Database hosted by IIASA 23 . Specifically, we search the AR6 Scenarios Database World v1.1 for IAMs that provided estimates of global GDP and population under both a SSP2 baseline and a SSP2-RCP2.6 scenario to maintain consistency with the socio-economic and emission scenarios of the climate damage projections. We find five IAMs that provide data for these scenarios, namely, MESSAGE-GLOBIOM 1.0, REMIND-MAgPIE 1.5, AIM/GCE 2.0, GCAM 4.2 and WITCH-GLOBIOM 3.1. Of these five IAMs, we use the results only from the first three that passed the IPCC vetting procedure for reproducing historical emission and climate trajectories. We then estimate global mitigation costs as the percentage difference in global per capita GDP between the SSP2 baseline and the SSP2-RCP2.6 emission scenario. In the case of one of these IAMs, estimates of mitigation costs begin in 2020, whereas in the case of two others, mitigation costs begin in 2010. The mitigation cost estimates before 2020 in these two IAMs are mostly negligible, and our choice to begin comparison with damage estimates in 2020 is conservative with respect to the relative weight of climate damages compared with mitigation costs for these two IAMs.

Data availability

Data on economic production and ERA-5 climate data are publicly available at https://doi.org/10.5281/zenodo.4681306 (ref. 62 ) and https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 , respectively. Data on mitigation costs are publicly available at https://data.ene.iiasa.ac.at/ar6/#/downloads . Processed climate and economic data, as well as all other necessary data for reproduction of the results, are available at the public repository https://doi.org/10.5281/zenodo.10562951  (ref. 63 ).

Code availability

All code necessary for reproduction of the results is available at the public repository https://doi.org/10.5281/zenodo.10562951  (ref. 63 ).

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Acknowledgements

We gratefully acknowledge financing from the Volkswagen Foundation and the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH on behalf of the Government of the Federal Republic of Germany and Federal Ministry for Economic Cooperation and Development (BMZ).

Open access funding provided by Potsdam-Institut für Klimafolgenforschung (PIK) e.V.

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All authors contributed to the design of the analysis. M.K. conducted the analysis and produced the figures. All authors contributed to the interpretation and presentation of the results. M.K. and L.W. wrote the manuscript.

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Extended data figures and tables

Extended data fig. 1 constraining the persistence of historical climate impacts on economic growth rates..

The results of a panel-based fixed-effects distributed lag model for the effects of annual mean temperature ( a ), daily temperature variability ( b ), total annual precipitation ( c ), the number of wet days ( d ) and extreme daily precipitation ( e ) on sub-national economic growth rates. Point estimates show the effects of a 1 °C or one standard deviation increase (for temperature and precipitation variables, respectively) at the lower quartile, median and upper quartile of the relevant moderating variable (green, orange and purple, respectively) at different lagged periods after the initial shock (note that these are not cumulative effects). Climate variables are used in their first-differenced form (see main text for discussion) and the moderating climate variables are the annual mean temperature, seasonal temperature difference, total annual precipitation, number of wet days and annual mean temperature, respectively, in panels a – e (see Methods for further discussion). Error bars show the 95% confidence intervals having clustered standard errors by region. The within-region R 2 , Bayesian and Akaike information criteria for the model are shown at the top of the figure. This figure shows results with ten lags for each variable to demonstrate the observed levels of persistence, but our preferred specifications remove later lags based on the statistical significance of terms shown above and the information criteria shown in Extended Data Fig. 2 . The resulting models without later lags are shown in Supplementary Figs. 1 – 3 .

Extended Data Fig. 2 Incremental lag-selection procedure using information criteria and within-region R 2 .

Starting from a panel-based fixed-effects distributed lag model estimating the effects of climate on economic growth using the real historical data (as in equation ( 4 )) with ten lags for all climate variables (as shown in Extended Data Fig. 1 ), lags are incrementally removed for one climate variable at a time. The resulting Bayesian and Akaike information criteria are shown in a – e and f – j , respectively, and the within-region R 2 and number of observations in k – o and p – t , respectively. Different rows show the results when removing lags from different climate variables, ordered from top to bottom as annual mean temperature, daily temperature variability, total annual precipitation, the number of wet days and extreme annual precipitation. Information criteria show minima at approximately four lags for precipitation variables and ten to eight for temperature variables, indicating that including these numbers of lags does not lead to overfitting. See Supplementary Table 1 for an assessment using information criteria to determine whether including further climate variables causes overfitting.

Extended Data Fig. 3 Damages in our preferred specification that provides a robust lower bound on the persistence of climate impacts on economic growth versus damages in specifications of pure growth or pure level effects.

Estimates of future damages as shown in Fig. 1 but under the emission scenario RCP8.5 for three separate empirical specifications: in orange our preferred specification, which provides an empirical lower bound on the persistence of climate impacts on economic growth rates while avoiding assumptions of infinite persistence (see main text for further discussion); in purple a specification of ‘pure growth effects’ in which the first difference of climate variables is not taken and no lagged climate variables are included (the baseline specification of ref.  2 ); and in pink a specification of ‘pure level effects’ in which the first difference of climate variables is taken but no lagged terms are included.

Extended Data Fig. 4 Climate changes in different variables as a function of historical interannual variability.

Changes in each climate variable of interest from 1979–2019 to 2035–2065 under the high-emission scenario SSP5-RCP8.5, expressed as a percentage of the historical variability of each measure. Historical variability is estimated as the standard deviation of each detrended climate variable over the period 1979–2019 during which the empirical models were identified (detrending is appropriate because of the inclusion of region-specific linear time trends in the empirical models). See Supplementary Fig. 13 for changes expressed in standard units. Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Extended Data Fig. 5 Contribution of different climate variables to overall committed damages.

a , Climate damages in 2049 when using empirical models that account for all climate variables, changes in annual mean temperature only or changes in both annual mean temperature and one other climate variable (daily temperature variability, total annual precipitation, the number of wet days and extreme daily precipitation, respectively). b , The cumulative marginal effects of an increase in annual mean temperature of 1 °C, at different baseline temperatures, estimated from empirical models including all climate variables or annual mean temperature only. Estimates and uncertainty bars represent the median and 95% confidence intervals obtained from 1,000 block-bootstrap resamples from each of three different empirical models using eight, nine or ten lags of temperature terms.

Extended Data Fig. 6 The difference in committed damages between the upper and lower quartiles of countries when ranked by GDP and cumulative historical emissions.

Quartiles are defined using a population weighting, as are the average committed damages across each quartile group. The violin plots indicate the distribution of differences between quartiles across the two extreme emission scenarios (RCP2.6 and RCP8.5) and the uncertainty sampling procedure outlined in Methods , which accounts for uncertainty arising from the choice of lags in the empirical models, uncertainty in the empirical model parameter estimates, as well as the climate model projections. Bars indicate the median, as well as the 10th and 90th percentiles and upper and lower sixths of the distribution reflecting the very likely and likely ranges following the likelihood classification adopted by the IPCC.

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Kotz, M., Levermann, A. & Wenz, L. The economic commitment of climate change. Nature 628 , 551–557 (2024). https://doi.org/10.1038/s41586-024-07219-0

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Climate-smart agriculture: adoption, impacts, and implications for sustainable development

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• Published: 29 April 2024
• Volume 29 , article number  44 , ( 2024 )

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• Wanglin Ma   ORCID: orcid.org/0000-0001-7847-8459 1 &
• Dil Bahadur Rahut   ORCID: orcid.org/0000-0002-7505-5271 2  

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The 19 papers included in this special issue examined the factors influencing the adoption of climate-smart agriculture (CSA) practices among smallholder farmers and estimated the impacts of CSA adoption on farm production, income, and well-being. Key findings from this special issue include: (1) the variables, including age, gender, education, risk perception and preferences, access to credit, farm size, production conditions, off-farm income, and labour allocation, have a mixed (either positive or negative) influence on the adoption of CSA practices; (2) the variables, including labour endowment, land tenure security, access to extension services, agricultural training, membership in farmers’ organizations, support from non-governmental organizations, climate conditions, and access to information consistently have a positive impact on CSA adoption; (3) diverse forms of capital (physical, social, human, financial, natural, and institutional), social responsibility awareness, and digital advisory services can effectively promote CSA adoption; (4) the establishment of climate-smart villages and civil-society organizations enhances CSA adoption by improving their access to credit; (5) CSA adoption contributes to improved farm resilience to climate change and mitigation of greenhouse gas emissions; (6) CSA adoption leads to higher crop yields, increased farm income, and greater economic diversification; (7) integrating CSA technologies into traditional agricultural practices not only boosts economic viability but also contributes to environmental sustainability and health benefits; and (8) there is a critical need for international collaboration in transferring technology for CSA. Overall, the findings of this special issue highlight that through targeted interventions and collaborative efforts, CSA can play a pivotal role in achieving food security, poverty alleviation, and climate resilience in farming communities worldwide and contribute to the achievements of the United Nations Sustainable Development Goals.

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1 Introduction

Climate change reduces agricultural productivity and leads to greater instability in crop production, disrupting the global food supply and resulting in food and nutritional insecurity. In particular, climate change adversely affects food production through water shortages, pest outbreaks, and soil degradation, leading to significant crop yield losses and posing significant challenges to global food security (Kang et al. 2009 ; Läderach et al. 2017 ; Arora 2019 ; Zizinga et al. 2022 ; Mirón et al. 2023 ). United Nations reported that the human population will reach 9.7 billion by 2050. In response, food-calorie production will have to expand by 70% to meet the food demand of the growing population (United Nations 2021 ). Hence, it is imperative to advocate for robust mitigation strategies that counteract the negative impacts of climate change and enhance the flexibility and speed of response in smallholder farming systems.

A transformation of the agricultural sector towards climate-resilient practices can help tackle food security and climate change challenges successfully. Climate-smart agriculture (CSA) is an approach that guides farmers’ actions to transform agrifood systems towards building the agricultural sector’s resilience to climate change based on three pillars: increasing farm productivity and incomes, enhancing the resilience of livelihoods and ecosystems, and reducing and removing greenhouse gas emissions from the atmosphere (FAO 2013 ). Promoting the adoption of CSA practices is crucial to improve smallholder farmers’ capacity to adapt to climate change, mitigate its impact, and help achieve the United Nations Sustainable Development Goals.

Realizing the benefits of adopting CSA, governments in different countries and international organizations such as the Consultative Group on International Agricultural Research (CGIAR), the Food and Agriculture Organisation (FAO) of the United Nations, and non-governmental organizations (NGOs) have made great efforts to scale up and out the CSA. For example, climate-smart villages in India (Alam and Sikka 2019 ; Hariharan et al. 2020 ) and civil society organizations in Africa, Asia, and Latin America (Waters-Bayer et al. 2015 ; Brown 2016 ) have been developed to reduce information costs and barriers and bridge the gap in finance access to promote farmers’ adoption of sustainable agricultural practices, including CSA. Besides, agricultural training programs have been used to enhance farmers’ knowledge of CSA and their adoption of the technology in Ghana (Zakaria et al. 2020 ; Martey et al. 2021 ).

As a result, smallholder farmers worldwide have adopted various CSA practices and technologies (e.g., integrated crop systems, drop diversification, inter-cropping, improved pest, water, and nutrient management, improved grassland management, reduced tillage and use of diverse varieties and breeds, restoring degraded lands, and improved the efficiency of input use) to reach the objectives of CSA (Kpadonou et al. 2017 ; Zakaria et al. 2020 ; Khatri-Chhetri et al. 2020 ; Aryal et al. 2020a ; Waaswa et al. 2022 ; Vatsa et al. 2023 ). In the Indian context, technologies such as laser land levelling and the happy seeder have been promoted widely for their potential in climate change adaptation and mitigation, offering benefits in terms of farm profitability, emission reduction, and water and land productivity (Aryal et al. 2020b ; Keil et al. 2021 ). In some African countries such as Tanzania and Kenya, climate-smart feeding practices in the livestock sector have been suggested to tackle challenges in feed quality and availability exacerbated by climate change, aiming to improve livestock productivity and resilience (García de Jalón et al. 2017 ; Shikuku et al. 2017 ; Radeny et al. 2022 ).

Several studies have investigated the factors influencing farmers’ decisions to adopt CSA practices. They have focused on, for example, farmers’ characteristics (e.g., age, gender, and education), farm-level characteristics (e.g., farm size, land fertility, and land tenure security), socioeconomic factors (e.g., economic conditions), institutional factors (e.g., development programs, membership in farmers’ organizations, and access to agricultural training), climate conditions, and access to information (Aryal et al. 2018 ; Tran et al. 2020 ; Zakaria et al. 2020 ; Kangogo et al. 2021 ; Diro et al. 2022 ; Kifle et al. 2022 ; Belay et al. 2023 ; Zhou et al. 2023 ). For example, Aryal et al. ( 2018 ) found that household characteristics (e.g., general caste, education, and migration status), plot characteristics (e.g., tenure of plot, plot size, and soil fertility), distance to market, and major climate risks are major factors determining farmers’ adoption of multiple CSA practices in India. Tran et al. ( 2020 ) reported that age, gender, number of family workers, climate-related factors, farm characteristics, distance to markets, access to climate information, confidence in the know-how of extension workers, membership in social/agricultural groups, and attitude toward risk are the major factors affecting rice farmers’ decisions to adopt CSA technologies in Vietnam. Diro et al.’s ( 2022 ) analysis revealed that coffee growers’ decisions to adopt CSA practices are determined by their education, extension (access to extension services and participation on field days), and ownership of communication devices, specifically radio in Ethiopia. Zhou et al. 2023 ) found that cooperative membership significantly increases the adoption of climate-smart agricultural practices among banana-producing farmers in China. These studies provide significant insights regarding the factors influencing farmers’ decisions regarding CSA adoption.

A growing body of studies have also estimated the effects of CSA adoption. They have found that CSA practices enhance food security and dietary diversity by increasing crop yields and rural incomes (Amadu et al. 2020 ; Akter et al. 2022 ; Santalucia 2023 ; Tabe-Ojong et al. 2023 ; Vatsa et al. 2023 ; Omotoso and Omotayo 2024 ). For example, Akter et al. ( 2022 ) found that adoption of CSA practices was positively associated with rice, wheat, and maize yields and household income, contributing to household food security in Bangladesh. By estimating data from rice farmers in China, Vatsa et al. ( 2023 ) reported that intensifying the adoption of climate-smart agricultural practices improved rice yield by 94 kg/mu and contributed to food security. Santalucia ( 2023 ) and Omotoso and Omotayo ( 2024 ) found that adoption of CSA practices (improved maize varieties and maize-legume intercropping) increases household dietary diversity and food security among smallholders in Tanzania and Nigeria, respectively.

Agriculture is crucial in climate change, accounting for roughly 20% of worldwide greenhouse gas (GHG) emissions. Additionally, it is responsible for approximately 45% of the global emissions of methane, a potent gas that significantly contributes to heat absorption in the atmosphere. CSA adoption improves farm resilience to climate variability (e.g., Makate et al. 2019 ; Jamil et al. 2021 ) and mitigates greenhouse gas emissions (Israel et al. 2020 ; McNunn et al. 2020 ). For example, Makate et al. ( 2019 ) for southern Africa and Jamil et al. ( 2021 ) for Pakistan found that promoting CSA innovations is crucial for boosting farmers’ resilience to climate change. McNunn et al. ( 2020 ) reported that CSA adoption significantly reduces greenhouse gas emissions from agriculture by increasing soil organic carbon stocks and decreasing nitrous oxide emissions.

Although a growing number of studies have enriched our understanding of the determinants and impacts of ICT adoption, it should be emphasized that no one-size-fits-all approach exists for CSA technology adoption due to geographical and environmental variability. The definitions of CSA should also be advanced to better adapt to changing climate and regional production conditions. Clearly, despite the extensive research on CSA, several gaps remain. First, there is a lack of comprehensive studies that consolidate findings across different geographical regions to inform policymaking effectively. The calls for studies on literature review and meta-analysis to synthesize the findings of the existing studies to make our understanding generalized. Second, although the literature on determinants of CSA adoption is becoming rich, there is a lack of understanding of how CSA adoption is influenced by different forms of capital, social responsibility awareness of farmers’ cultivating family farms, and digital advisory services. Third, there is a lack of understanding of how climate-smart villages and civil society organizations address farmers’ financial constraints and encourage them to adopt modern sustainable agricultural practices, including CSA practices. Fourth, very few studies have explored how CSA adoption influences the benefit–cost ratio of farm production, factor demand, and input substitution. Fifth, no previous studies have reported the progress of research on CSA. Addressing these gaps is crucial for designing and implementing effective policies and programs that support the widespread adoption of CSA practices, thereby contributing to sustainable agricultural development and climate resilience.

We address the research gaps mentioned above and extend the findings in previous studies by organizing a Special Issue on “Climate-Smart Agriculture: Adoption, Impacts, and Implications for Sustainable Development” in the Mitigation and Adaptation Strategies for Global Change (MASGC) journal. We aim to collect high-quality theoretical and applied research papers discussing CSA and seek to comprehensively understand the associations between CSA and sustainable rural and agricultural development. To achieve this goal, we aim to find answers to these questions: What are the CSA practices and technologies (either single or multiple) that are currently adopted in smallholder farming systems? What are the key barriers, challenges, and drivers of promoting CSA practices? What are the impacts of adopting these practices? Answers to these questions will help devise appropriate solutions for promoting sustainable agricultural production and rural development. They will also provide insights for policymakers to design appropriate policy instruments to develop agricultural practices and technologies and promote them to sustainably enhance the farm sector’s resilience to climate change and increase productivity.

Finally, 19 papers were selected after a rigorous peer-review process and published in this special issue. We collected 10 papers investigating the determinants of CSA adoption. Among them, four papers investigated the determinants of CSA adoption among smallholders by reviewing and summarizing the findings in the literature and conducting a meta-analysis. Three papers explored the role of social-economic factors on ICT adoption, including capital, social responsibility awareness, and digital advisory services. Besides, three papers examined the associations between external development interventions, including climate-smart villages and civil-society initiatives, and CSA adoption. We collected eight papers exploring the impacts of CSA adoption. Among them, one paper conducted a comprehensive literature review to summarize the impacts of CSA adoption on crop yields, farm income, and environmental sustainability. Six papers estimated the impacts of CSA adoption on crop yields and farm income, and one paper focused on the impact of CSA adoption on factor demand and input substitution. The last paper included in this special issue delved into the advancements in technological innovation for agricultural adaptation within the context of climate-smart agriculture.

The structure of this paper is as follows: Section  2 summarizes the papers received in this special issue. Section  3 introduces the international conference that was purposely organized for the special issue. Section  4 summarizes the key findings of the 19 papers published in the special issue, followed by a summary of their policy implications, presented in Section  5 . The final section provides a brief conclusion.

2 Summary of received manuscripts

The special issue received 77 submissions, with the contributing authors hailing from 22 countries, as illustrated in Fig.  1 . This diversity highlights the global interest and wide-ranging contributions to the issue. Notably, over half of these submissions (53.2%) originated from corresponding authors in India and China, with 29 and 12 manuscripts, respectively. New Zealand authors contributed six manuscripts, while their Australian counterparts submitted four. Following closely, authors from the United Kingdom and Kenya each submitted three manuscripts. Authors from Thailand, Pakistan, Japan, and Germany submitted two manuscripts each. The remaining 12 manuscripts came from authors in Vietnam, Uzbekistan, the Philippines, Nigeria, the Netherlands, Malaysia, Italy, Indonesia, Ghana, Ethiopia, Brazil, and Bangladesh.

Distributions of 77 received manuscripts by corresponding authors' countries

Among the 77 received manuscripts, 30 were desk-rejected by the guest editors because they did not meet the aims and scope of the special issue, and the remaining 47, considered candidate papers for the special issue, were sent for external review. The decision on each manuscript was made based on review reports of 2–4 experts in this field. The guest editors also read and commented on each manuscript before they made decisions.

3 ADBI virtual international conference

3.1 selected presentations.

The guest editors from Lincoln University (New Zealand) and the Asian Development Bank Institute (ADBI) (Tokyo, Japan) organized a virtual international conference on the special issue theme “ Climate-Smart Agriculture: Adoption, Impacts, and Implications for Sustainable Development ”. The conference was organized on 10–11 October 2023 and was supported by the ADBI. Footnote 1 As previously noted, the guest editors curated a selection of 47 manuscripts from the pool of 77 submissions, identifying them as potential candidates for inclusion in the special issue, and sent them out for external review. Given the logistical constraints of orchestrating a two-day conference, the guest editors ultimately extended invitations to 20 corresponding authors. These authors were invited to present their work at the virtual international conference.

Figure  2 illustrates the native countries of the presenters, showing that the presenters were from 10 different countries. Most of the presenters were from India, accounting for 40% of the presenters. This is followed by China, where the four presenters were originally from. The conference presentations and discussions proved immensely beneficial, fostering knowledge exchange among presenters, discussants, and participants. It significantly allowed presenters to refine their manuscripts, leveraging the constructive feedback from discussants and fellow attendees.

Distributions of selected presentations by corresponding authors' countries

3.2 Keynote speeches

The guest editors invited two keynote speakers to present at the two-day conference. They were Prof. Edward B. Barbier from the Colorado State University in the United States Footnote 2 and Prof. Tatsuyoshi Saijo from Kyoto University of Advanced Science in Japan. Footnote 3

Prof. Edward Barbier gave a speech, “ A Policy Strategy for Climate-Smart Agriculture for Sustainable Rural Development ”, on 10th October 2023. He outlined a strategic approach for integrating CSA into sustainable rural development, particularly within emerging markets and developing economies. He emphasized the necessity of CSA and nature-based solutions (NbS) to tackle food security, climate change, and rural poverty simultaneously. Highlighting the substantial investment needs and the significant role of international and domestic financing, Prof. Barbier advocated reducing harmful subsidies in agriculture, forestry, fishing, and fossil fuel consumption to redirect funds toward CSA and NbS investments. He also proposed the implementation of a tropical carbon tax as an innovative financing mechanism. By focusing on recycling environmentally harmful subsidies and leveraging additional funding through public and private investments, Prof. Barbier’s strategy aims to foster a “win–win” scenario for climate action and sustainable development, underscoring the urgency of adopting comprehensive policies to mobilize the necessary resources for these critical investments.

Prof. Tatsuyoshi Saijo, gave his speech, “ Future Design ”, on 11th October 2023. He explored the significant impact of the Haber–Bosch process on human civilization and the environment. Prof. Saijo identifies this process, which synthetically fixed nitrogen from the atmosphere to create ammonia for fertilizers and other products, as the greatest invention from the twentieth century to the present, fundamentally transforming the world’s food production and enabling the global population and industrial activities to expand dramatically. He also discussed the environmental costs of this technological advancement, including increased greenhouse gas emissions, pollution, and contribution to climate change. Prof. Saijo then introduced the concept of “Future Design” as a method to envision and implement sustainable social systems that consider the well-being of future generations. He presented various experiments and case studies from Japan and beyond, showing how incorporating perspectives of imaginary future generations into decision-making processes can lead to more sustainable choices. By doing so, Prof. Saijo suggested that humanity can address the “Intergenerational Sustainability Dilemma” and potentially avoid the ecological overshoot and collapse faced by past civilizations like Easter Island. He called for a redesign of social systems to activate “futurability”, where individuals derive happiness from decisions that benefit future generations, ultimately aiming to ensure the long-term survival of humankind amidst environmental challenges.

4 Summary of published articles

As a result of a rigorous double-anonymized reviewing process, the special issue accepted 19 articles for publication. These studies have investigated the determinants and impacts of CSA adoption. Table 1 in the Appendix summarises the CSA technologies and practices considered in each paper. Below, we summarize the key findings of the contributions based on their research themes.

4.1 Determinants of CSA adoption among smallholders

4.1.1 influencing factors of csa adoption from literature review.

Investigating the factors influencing farmers’ adoption of CSA practices through a literature review helps offer a comprehensive understanding of the multifaceted determinants of CSA adoption. Investigating the factors influencing farmers’ adoption of CSA practices through a literature review helps provide a comprehensive understanding of the determinants of CSA adoption. Such analyses help identify consistent trends and divergences in how different variables influence farmers’ CSA adoption decisions. In this special issue, we collected four papers that reviewed the literature and synthesized the factors influencing farmers’ decisions to adopt CSA.

Li, Ma and Zhu’s paper, “ A systematic literature review of factors influencing the adoption of climate-smart agricultural practices ”, conducted a systematic review of the literature on the adoption of CSA, summarizing the definitions of CSA practices and the factors that influence farmers’ decisions to adopt these practices. The authors reviewed 190 studies published between 2013 and 2023. They broadly defined CSA practices as “agricultural production-related and unrelated practices that can help adapt to climate change and increase agricultural outputs”. Narrowly, they defined CSA practices as “agricultural production-related practices that can effectively adapt agriculture to climate change and reinforce agricultural production capacity”. The review identified that many factors, including age, gender, education, risk perception, preferences, access to credit, farm size, production conditions, off-farm income, and labour allocation, have a mixed (positive or negative) influence on the adoption of CSA practices. Variables such as labour endowment, land tenure security, access to extension services, agricultural training, membership in farmers’ organizations, support from non-governmental organizations (NGOs), climate conditions, and access to information were consistently found to positively influence CSA practice adoption.

Thottadi and Singh’s paper, “ Climate-smart agriculture (CSA) adaptation, adaptation determinants and extension services synergies: A systematic review ””, reviewed 45 articles published between 2011 and 2022 to explore different CAS practices adopted by farmers and the factors determining their adoption. They found that CSA practices adopted by farmers can be categorized into five groups. These included resilient technologies (e.g., early maturing varieties, drought-resistant varieties, and winter ploughing), management strategies (e.g., nutrient management, water management, and pest management), conservation technologies (e.g., vermicomposting and residue management, drip and sprinkler irrigation, and soil conservation), diversification of income security (e.g., mixed farming, livestock, and crop diversification), and risk mitigation strategies (e.g., contingent planning, adjusting plant dates, and crop insurance). They also found that farmers’ decisions to adopt CSA practices are mainly determined by individual characteristics (age, gender, and education), socioeconomic factors (income and wealth), institutional factors (social group, access to credit, crop insurance, distance, land tenure, and rights), behavioural factors (climate perception, farmers’ perception on CSA, Bookkeeping), and factor endowments (family labour, machinery, and land size). The authors emphasized that extension services improved CSA adaptation by reducing information asymmetry.

Naveen, Datta, Behera and Rahut’s paper, “ Climate-Smart Agriculture in South Asia: Exploring Practices, Determinants, and Contribution to Sustainable Development Goals ”, offered a comprehensive systematic review of 78 research papers on CSA practice adoption in South Asia. Their objective was to assess the current implementation of CSA practices and to identify the factors that influence farmers’ decisions to adopt these practices. They identified various CSA practices widely adopted in South Asia, including climate-resilient seeds, zero tillage, water conservation, rescheduling of planting, crop diversification, soil conservation and water harvesting, and agroforestry. They also identified several key factors that collectively drive farmers’ adoption of CSA practices. These included socioeconomic factors (age, education, livestock ownership, size of land holdings, and market access), institutional factors (access to information and communication technology, availability of credit, input subsidies, agricultural training and demonstrations, direct cash transfers, and crop insurance), and climatic factors (notably rising temperatures, floods, droughts, reduced rainfall, and delayed rainfall).

Wang, Wang and Fu’s paper, “ Can social networks facilitate smallholders’ decisions to adopt Climate-smart Agriculture technologies? A three-level meta-analysis ”, explored the influence of social networks on the adoption of CSA technologies by smallholder farmers through a detailed three-level meta-analysis. This analysis encompassed 26 empirical studies, incorporating 150 effect sizes. The authors reported a modest overall effect size of 0.065 between social networks and the decision-making process for CSA technology adoption, with an 85.21% variance observed among the sample effect sizes. They found that over half (55.17%) of this variance was attributed to the differences in outcomes within each study, highlighting the impact of diverse social network types explored across the studies as significant contributors. They did not identify publication bias in this field. Among the three types of social networks (official-advising network, peer-advising network, and kinship and friendship network), kinship and friendship networks are the most effective in facilitating smallholders’ decisions to adopt climate-smart agriculture technologies.

4.1.2 Socioeconomic factors influencing CSA adoption

We collected three papers highlighting the diverse forms of capital, social responsibility awareness, and effectiveness of digital advisory services in promoting CSA in India, China and Ghana. These studies showcase how digital tools can significantly increase the adoption of CSA technologies, how social responsibility can motivate CSA practices and the importance of various forms of capital in CSA strategy adoption.

Sandilya and Goswami’s paper, “ Effect of different forms of capital on the adoption of multiple climate-smart agriculture strategies by smallholder farmers in Assam, India ”, delved into the determinants behind the adoption of CSA strategies by smallholder farmers in Nagaon district, India, a region notably prone to climate adversities. The authors focused on six types of capital: physical, social, human, financial, natural, and institutional. They considered four CSA practices: alternate land use systems, integrated nutrient management, site-specific nutrient management, and crop diversification. Their analyses encompassed a dual approach, combining a quantitative analysis via a multivariate probit model with qualitative insights from focus group discussions. They found that agricultural cooperatives and mobile applications, both forms of social capital, play a significant role in facilitating the adoption of CSA. In contrast, the authors also identified certain barriers to CSA adoption, such as the remoteness of farm plots from all-weather roads (a component of physical capital) and a lack of comprehensive climate change advisories (a component of institutional capital). Furthermore, the authors highlighted the beneficial impact of irrigation availability (a component of physical capital) on embracing alternate land use and crop diversification strategies. Additionally, the application of indigenous technical knowledge (a component of human capital) and the provision of government-supplied seeds (a component of institutional capital) were found to influence the adoption of CSA practices distinctly.

Ye, Zhang, Song and Li’s paper, “ Social Responsibility Awareness and Adoption of Climate-smart Agricultural Practices: Evidence from Food-based Family Farms in China ”, examined whether social responsibility awareness (SRA) can be a driver for the adoption of CSA on family farms in China. Using multiple linear regression and hierarchical regression analyses, the authors analyzed data from 637 family farms in five provinces (Zhejiang, Shandong, Henan, Heilongjiang, and Hebei) in China. They found that SRA positively impacted the adoption of CSA practice. Pro-social motivation and impression management motivation partially and completely mediated the relationship between SRA and the adoption of CSA practices.

Asante, Ma, Prah and Temoso’s paper, “ Promoting the adoption of climate-smart agricultural technologies among maize farmers in Ghana: Using digital advisory services ”, investigated the impacts of digital advisory services (DAS) use on CSA technology adoption and estimated data collected from 3,197 maize farmers in China. The authors used a recursive bivariate probit model to address the self-selection bias issues when farmers use DAS. They found that DAS notably increases the propensity to adopt drought-tolerant seeds, zero tillage, and row planting by 4.6%, 4.2%, and 12.4%, respectively. The average treatment effect on the treated indicated that maize farmers who use DAS are significantly more likely to adopt row planting, zero tillage, and drought-tolerant seeds—by 38.8%, 24.9%, and 47.2%, respectively. Gender differences in DAS impact were observed; male farmers showed a higher likelihood of adopting zero tillage and drought-tolerant seeds by 2.5% and 3.6%, respectively, whereas female farmers exhibited a greater influence on the adoption of row planting, with a 2.4% probability compared to 1.5% for males. Additionally, factors such as age, education, household size, membership in farmer-based organizations, farm size, perceived drought stress, perceived pest and disease incidence, and geographic location were significant determinants in the adoption of CSA technologies.

4.1.3 Climate-smart villages and CSA adoption

Climate-Smart Villages (CSVs) play a pivotal role in promoting CSA by significantly improving farmers’ access to savings and credit, and the adoption of improved agricultural practices among smallholder farmers. CSV interventions demonstrate the power of community-based financial initiatives in enabling investments in CSA technologies. In this special issue, we collected two insightful papers investigating the relationship between CSVs and the adoption of CSA practices, focusing on India and Kenya.

Villalba, Joshi, Daum and Venus’s paper, “ Financing Climate-Smart Agriculture: A Case Study from the Indo-Gangetic Plains ”, investigated the adoption and financing of CSA technologies in India, focusing on two capital-intensive technologies: laser land levelers and happy seeders. Conducted in Karnal, Haryana, within the framework of Climate-Smart-Villages, the authors combined data from a household survey of 120 farmers, interviews, and focus group discussions with stakeholders like banks and cooperatives. The authors found that adoption rates are high, with 77% for laser land levelers and 52% for happy seeders, but ownership is low, indicating a preference for renting from Custom-Hiring Centers. Farmers tended to avoid formal banking channels for financing, opting instead for informal sources like family, savings, and money lenders, due to the immediate access to credit and avoidance of bureaucratic hurdles. The authors suggested that institutional innovations and governmental support could streamline credit access for renting CSA technologies, emphasizing the importance of knowledge transfer, capacity building, and the development of digital tools to inform farmers about financing options. This research highlights the critical role of financing mechanisms in promoting CSA technology adoption among smallholder farmers in climate-vulnerable regions.

Asseldonk, Oostendorp, Recha, Gathiaka, Mulwa, Radeny Wattel and Wesenbeeck’s paper, “ Distributional impact of climate‑smart villages on access to savings and credit and adoption of improved climate‑smart agricultural practices in the Nyando Basin, Kenya ”, investigated the impact of CSV interventions in Kenya on smallholder farmers’ access to savings, credit, and adoption of improved livestock breeds as part of CSA practices. The authors employed a linear probability model to estimate a balanced panel of 118 farm households interviewed across 2017, 2019, and 2020. They found that CSV interventions significantly increased the adoption of improved livestock breeds and membership in savings and credit groups, which further facilitated the adoption of these improved breeds. The findings highlighted that community-based savings and loan initiatives effectively enable farmers to invest in CSA practices. Although there was a sustained positive trend in savings and loans group membership, the adoption of improved livestock did not show a similar sustained increase. Moreover, the introduction of improved breeds initially benefited larger livestock owners more. However, credit availability was found to reduce this inequity in ownership among participants, making the distribution of improved livestock more equitable within CSVs compared to non-CSV areas, thus highlighting the potential of CSV interventions to reduce disparities in access to improved CSA practices.

4.1.4 Civil-society initiatives and CSA adoption

Civil society initiatives are critical in promoting CSA by embedding its principles across diverse agricultural development projects. These initiatives enhance mitigation, adaptation, and food security efforts for smallholder farmers, demonstrating the importance of varied implementation strategies to address the challenges of CSA. We collected one paper investigating how civil society-based development projects in Asia and Africa incorporated CSA principles to benefit smallholder farmers and local communities.

Davila, Jacobs, Nadeem, Kelly and Kurimoto’s paper, “ Finding climate smart agriculture in civil-society initiatives ”, scrutinized the role of international civil society and non-government organizations (NGOs) in embedding CSA principles within agricultural development projects aimed at enhancing mitigation, adaptation, and food security. Through a thematic analysis of documentation from six projects selected on the basis that they represented a range of geographical regions (East Africa, South, and Southeast Asia) and initiated since 2009, the authors assessed how development programs incorporate CSA principles to support smallholder farmers under CSA’s major pillars. They found heterogeneous application of CSA principles across the projects, underscoring a diversity in implementation strategies despite vague definitions and focuses of CSA. The projects variedly contributed to greening and forests, knowledge exchange, market development, policy and institutional engagement, nutrition, carbon and climate action, and gender considerations.

4.2 Impacts of CSA adoption

4.2.1 impacts of csa adoption from literature review.

A comprehensive literature review on the impacts of CSA adoption plays an indispensable role in bridging the gap between theoretical knowledge and practical implementation in the agricultural sector. In this special issue, we collected one paper that comprehensively reviewed the literature on the impacts of CSA adoption from the perspective of the triple win of CSA.

Zheng, Ma and He’s paper, “ Climate-smart agricultural practices for enhanced farm productivity, income, resilience, and Greenhouse gas mitigation: A comprehensive review ”, reviewed 107 articles published between 2013–2023 to distill a broad understanding of the impacts of CSA practices. The review categorized the literature into three critical areas of CSA benefits: (a) the sustainable increase of agricultural productivity and incomes; (b) the adaptation and enhancement of resilience among individuals and agrifood systems to climate change; and (c) the reduction or avoidance of greenhouse gas (GHG) emissions where feasible. The authors found that CSA practices significantly improved farm productivity and incomes and boosted technical and resource use efficiency. Moreover, CSA practices strengthened individual resilience through improved food consumption, dietary diversity, and food security while enhancing agrifood systems’ resilience by mitigating production risks and reducing vulnerability. Additionally, CSA adoption was crucial in lowering Greenhouse gas emissions and fostering carbon sequestration in soils and biomass, contributing to improved soil quality.

4.2.2 Impacts on crop yields and farm income

Understanding the impact of CSA adoption on crop yields and income is crucial for improving agricultural resilience and sustainability. In this special issue, we collected three papers highlighting the transformative potential of CSA practices in boosting crop yields, commercialization, and farm income. One paper focuses on India and the other concentrates on Ghana and Kenya.

Tanti, Jena, Timilsina and Rahut’s paper, “ Enhancing crop yields and farm income through climate-smart agricultural practices in Eastern India ”, examined the impact of CSA practices (crop rotation and integrated soil management practices) on crop yields and incomes. The authors used propensity score matching and the two-stage least square model to control self-selection bias and endogeneity and analyzed data collected from 494 farm households in India. They found that adopting CSA practices increases agricultural income and paddy yield. The crucial factor determining the adoption of CSA practices was the income-enhancing potential to transform subsistence farming into a profoundly ingrained farming culture.

Asante, Ma, Prah and Temoso’s paper, “ Farmers’ adoption of multiple climate-smart agricultural technologies in Ghana: Determinants and impacts on maize yields and net farm income ”, investigated the factors influencing maize growers’ decisions to adopt CSA technologies and estimated the impact of adopting CSA technologies on maize yields and net farm income. They considered three CSA technology types: drought-resistant seeds, row planting, and zero tillage. The authors used the multinomial endogenous switching regression model to estimate the treatment effect of CSA technology adoption and analyze data collected from 3,197 smallholder farmers in Ghana. They found that farmer-based organization membership, education, resource constraints such as lack of land, access to markets, and production shocks such as perceived pest and disease stress and drought are the main factors that drive farmers’ decisions to adopt CSA technologies. They also found that integrating any CSA technology or adopting all three CSA technologies greatly enhances maize yields and net farm income. Adopting all three CSA technologies had the largest impact on maize yields, while adopting row planting and zero tillage had the greatest impact on net farm income.

Mburu, Mburu, Nyikal, Mugera and Ndambi’s paper, “ Assessment of Socioeconomic Determinants and Impacts of Climate-Smart Feeding Practices in the Kenyan Dairy Sector ”, assessed the determinants and impacts of adopting climate-smart feeding practices (fodder and feed concentrates) on yield, milk commercialization, and household income. The authors used multinomial endogenous switching regression to account for self-selection bias arising from observable and unobservable factors and estimated data collected from 665 dairy farmers in Kenya. They found that human and social capital, resource endowment, dairy feeding systems, the source of information about feeding practices, and perceived characteristics were the main factors influencing farmers’ adoption of climate-smart feeding practices. They also found that combining climate-smart feed concentrates and fodder significantly increased milk productivity, output, and dairy income. Climate-smart feed concentrates yielded more benefits regarding dairy milk commercialization and household income than climate-smart fodder.

4.2.3 Impacts on crop yields

Estimating the impacts of CSA adoption on crop yields is crucial for enhancing food security, improving farmers’ resilience to climate change, and guiding policy and investment towards sustainable agricultural development. In this special issue, we collected one paper that provided insights into this field.

Singh, Bisaria, Sinha, Patasaraiya and Sreerag’s paper, “ Developing A Composite Weighted Indicator-based Index for Monitoring and Evaluating Climate-Smart Agriculture in India ”, developed a composite index based on a weighted index to calculate the Climate Smart Score (CSS) at the farm level in India and tested the relationship between computed CSS and farm-level productivity. Through an intensive literature review, the authors selected 34 indicators, which were then grouped into five dimensions for calculating CSS. These dimensions encompassed governance (e.g., land ownership, subsidized fertilizer, and subsidized seeds), farm management practices (mulching, zero tillage farming, and inter-cropping and crop diversification), environment management practices (e.g., not converting forested land into agricultural land and Agroforestry/plantation), energy management (e.g., solar water pump and Biogas digester), and awareness and training (e.g., knowledge of climate-related risk and timely access to weather and agro-advisory). They tested the relationship between CSS and farm productivity using data collected from 315 farmers. They found that improved seeds, direct seeding of rice, crop diversification, zero tillage, agroforestry, crop residue management, integrated nutrient management, and training on these practices were the most popular CSA practices the sampled farmers adopted. In addition, there was a positive association between CSS and paddy, wheat, and maize yields. This finding underscores the beneficial impact of CSA practices on enhancing farm productivity.

4.2.4 Impacts on incomes and benefit–cost ratio

Understanding the income effects of CSA adoption is crucial for assessing its impact on household livelihoods, farm profitability, and income diversity. Quantifying income enhancements would contribute to informed decision-making and investment strategies to improve farming communities’ economic well-being. In this special issue, we collected two papers looking into the effects of CSA adoption on income.

Sang, Chen, Hu and Rahut’s paper, “ Economic benefits of climate-smart agricultural practices: Empirical investigations and policy implications ”, investigated the impact of CSA adoption intensity on household income, net farm income, and income diversity. They used the two-stage residual inclusion model to mitigate the endogeneity of CSA adoption intensity and analyzed the 2020 China Rural Revitalization Survey data. They also used the instrumental-variable-based quantile regression model to investigate the heterogeneous impacts of CSA adoption intensity. The authors found that the education level of the household head and geographical location determine farmers’ adoption intensity of CSAs.CSA practices. The higher levels of CSA adoption were positively and significantly associated with higher household income, net farm income, and income diversity. They also found that while the impact of CSA adoption intensity on household income escalates across selected quantiles, its effect on net farm income diminishes over these quantiles. Additionally, the study reveals that CSA adoption intensity notably enhances income diversity at the 20th quantile only.

Kandulu, Zuo, Wheeler, Dusingizimana and Chagund’s paper, “ Influence of climate-smart technologies on the success of livestock donation programs for smallholder farmers in Rwanda ”, investigated the economic, environmental, and health benefits of integrating CSA technologies —specifically barns and biogas plants—into livestock donation programs in Rwanda. Employing a stochastic benefit–cost analysis from the perspective of the beneficiaries, the authors assessed the net advantages for households that receive heifers under an enhanced program compared to those under the existing scheme. They found that incorporating CSA technologies not only boosts the economic viability of these programs but also significantly increases the resilience and sustainability of smallholder farming systems. More precisely, households equipped with cows and CSA technologies can attain net benefits up to 3.5 times greater than those provided by the current program, with the benefit–cost ratios reaching up to 5. Furthermore, biogas technology reduces deforestation, mitigating greenhouse gas emissions, and lowering the risk of respiratory illnesses, underscoring the multifaceted advantages of integrating such innovations into livestock donation initiatives.

4.2.5 Impacts on factor demand and input substitution

Estimating the impacts of CSA adoption on factor demand and input substitution is key to optimizing resource use, reducing environmental footprints, and ensuring agricultural sustainability by enabling informed decisions on efficient input use and technology adoption. In this field, we collected one paper that enriched our understanding in this field. Understanding the impacts of CSA adoption on factor demand, input substitution, and financing options is crucial for promoting sustainable farming in diverse contexts. In this special issue, we collected one paper comprehensively discussing how CSA adoption impacted factor demand and input substitution.

Kehinde, Shittu, Awe and Ajayi’s paper, “ Effects of Using Climate-Smart Agricultural Practices on Factor Demand and Input Substitution among Smallholder Rice Farmers in Nigeria ”, examined the impacts of agricultural practices with CSA potential (AP-CSAPs) on the demand of labour and other production factors (seed, pesticides, fertilizers, and mechanization) and input substitution. The AP-CSAPs considered in this research included zero/minimum tillage, rotational cropping, green manuring, organic manuring, residue retention, and agroforestry. The authors employed the seemingly unrelated regression method to estimate data collected from 1,500 smallholder rice farmers in Nigeria. The authors found that labour and fertilizer were not easily substitutable in the Nigerian context; increases in the unit price of labour (wage rate) and fertilizer lead to a greater budget allocation towards these inputs. Conversely, a rise in the cost of mechanization services per hectare significantly reduced labour costs while increasing expenditure on pesticides and mechanization services. They also found that most AP-CSAPs were labour-intensive, except for agroforestry, which is labor-neutral. Organic manure and residue retention notably conserved pesticides, whereas zero/minimum tillage practices increased the use of pesticides and fertilizers. Furthermore, the demand for most production factors, except pesticides, was found to be price inelastic, indicating that price changes do not significantly alter the quantity demanded.

4.3 Progress of research on CSA

Understanding the progress of research on CSA is essential for identifying and leveraging technological innovations—like greenhouse advancements, organic fertilizer products, and biotechnological crop improvements—that support sustainable agricultural adaptation. This knowledge enables the integration of nature-based strategies, informs policy, and underscores the importance of international cooperation in overcoming patent and CSA adoption challenges to ensure global food security amidst climate change. We collected one paper in this field.

Tey, Brindal, Darham and Zainalabidin’s paper, “ Adaptation technologies for climate-smart agriculture: A patent network analysis ”, delved into the advancements in technological innovation for agricultural adaptation within the context of CSA by analyzing global patent databases. The authors found that greenhouse technologies have seen a surge in research and development (R&D) efforts, whereas composting technologies have evolved into innovations in organic fertilizer products. Additionally, biotechnology has been a significant focus, aiming to develop crop traits better suited to changing climate conditions. A notable emergence is seen in resource restoration innovations addressing climate challenges. These technologies offer a range of policy options for climate-smart agriculture, from broad strategies to specific operational techniques, and pave the way for integration with nature-based adaptation strategies. However, the widespread adoption and potential impact of these technologies may be hindered by issues related to patent ownership and the path dependency this creates. Despite commercial interests driving the diffusion of innovation, international cooperation is clearly needed to enhance technology transfer.

5 Summary of key policy implications

The collection of 19 papers in this special issue sheds light on the critical aspects of promoting farmers’ adoption of CSA practices, which eventually help enhance agricultural productivity and resilience, reduce greenhouse gas emissions, improve food security and soil health, offer economic benefits to farmers, and contribute to sustainable development and climate change adaptation. We summarize and discuss the policy implications derived from this special issue from the following four aspects:

5.1 Improving CSA adoption through extension services

Extension services help reduce information asymmetry associated with CSA adoption and increase farmers’ awareness of CSA practices’ benefits, costs, and risks while addressing their specific challenges. Therefore, the government should improve farmers’ access to extension services. These services need to be inclusive and customized to meet the gender-specific needs and the diverse requirements of various farming stakeholders. Additionally, fostering partnerships between small and medium enterprises and agricultural extension agents is crucial for enhancing the local availability of CSA technologies. Government-sponsored extension services should prioritize equipping farmers with essential CSA skills, ensuring they are well-prepared to implement these practices. This structured approach will streamline the adoption process and significantly improve the effectiveness of CSA initiatives.

5.2 Facilitating CSA adoption through farmers’ organizations

Farmers’ organizations, such as village cooperatives, farmer groups, and self-help groups, play a pivotal role in facilitating farmers’ CSA adoption and empowering rural women’s adoption through effective information dissemination and the use of agricultural apps. Therefore, the government should facilitate the establishment and development of farmers’ organizations and encourage farmers to join those organizations as members. In particular, the proven positive impacts of farmer-based organizations (FBOs) highlight the importance of fostering collaborations between governments and FBOs. Supporting farmer cooperatives with government financial and technical aid is essential for catalyzing community-driven climate adaptation efforts. Furthermore, the successful use of DAS in promoting CSA adoption underscores the need for government collaboration with farmer groups to expand DAS utilization. This includes overcoming usage barriers and emphasizing DAS’s reliability as a source of climate-smart information. By establishing and expanding digital hubs and demonstration centres in rural areas, farmers can access and experience DAS technologies firsthand, leading to broader adoption and integration into their CSA practices.

5.3 Enhancing CSA adoption through agricultural training and education

Agricultural training and education are essential in enhancing farmers’ adoption of CSA. To effectively extend the reach of CSA practices, the government should prioritize expanding rural ICT infrastructure investments and establish CSA training centres equipped with ICT tools that target key demographics such as women and older people, aiming to bridge the digital adoption gaps. Further efforts should prioritize awareness and training programs to ensure farmers can access weather and agro-advisory services. These programs should promote the use of ICT-based tools through collaborations with technology providers and include regular CSA training and the establishment of demonstration fields that showcase the tangible benefits of CSA practices.

Education plays a vital role in adopting CAPs, suggesting targeted interventions such as comprehensive technical training to assist farmers with limited educational backgrounds in understanding the value of CAPs, ultimately improving their adoption rates. Establishing robust monitoring mechanisms is crucial to maintaining farmer engagement and success in CSA practices. These mechanisms will facilitate the ongoing adoption and evaluation of CSA practices and help educate farmers on the long-term benefits. Centralizing and disseminating information about financial products and subsidies through various channels, including digital platforms tailored to local languages and contexts, is essential. This approach helps educate farmers on financing options and requirements, supporting the adoption of CSA technologies among smallholder farmers. Lastly, integrating traditional and local knowledge with scientific research and development can effectively tailor CSA initiatives. This integration requires the involvement of a range of stakeholders, including NGOs, to navigate the complexities of CSA and ensure that interventions are effective but also equitable and sustainable. The enhanced capacity of institutions and their extension teams will further support these CSA initiatives.

5.4 Promoting CSA adoption through establishing social networks and innovating strategies

The finding that social networks play a crucial role in promoting the adoption of CSA suggests that implementing reward systems to incentivize current CSA adopters to advocate for climate-smart practices within their social circles could be an effective strategy to promote CSA among farmers. The evidence of a significant link between family farms’ awareness of social responsibility and their adoption of CSA highlights that governments should undertake initiatives, such as employing lectures and pamphlets, to enhance family farm operating farmers’ understanding of social responsibility. The government should consider introducing incentives that foster positive behavioural changes among family farms to cultivate a more profound commitment to social responsibility. The government can also consider integrating social responsibility criteria into the family farm awards and recognition evaluation process. These measures would encourage family farms to align their operations with broader social and environmental goals, promoting CSA practices.

Combining traditional incentives, such as higher wages and access to improved agricultural inputs, with innovative strategies like community-driven development for equipment sharing and integrating moral suasion with Payment for Ecosystem Services would foster farmers’ commitment to CSA practices. The finding that technological evolution plays a vital role in shaping adaptation strategies for CSA highlights the necessity for policy instruments that not only leverage modern technologies but also integrate them with traditional, nature-based adaptation strategies, enhancing their capacity to address specific CSA challenges. Policymakers should consider the region’s unique socioeconomic, environmental, and geographical characteristics when promoting CSA, moving away from a one-size-fits-all approach to ensure the adaptability and relevance of CSA practices across different agricultural landscapes. They should foster an environment that encourages the reporting of all research outcomes to develop evidence-based policies that are informed by a balanced view of CSA’s potential benefits and limitations.

Finally, governance is critical in creating an enabling environment for CSA adoption. Policies should support CSA practices and integrate environmental sustainability to enhance productivity and ecosystem health. Development programs must offer financial incentives, establish well-supported voluntary schemes, provide robust training programs, and ensure the wide dissemination of informational tools. These measures are designed to help farmers integrate CAPs into their operations, improving economic and operational sustainability.

6 Concluding remarks

This special issue has provided a wealth of insights into the adoption and impact of CSA practices across various contexts, underscoring the complexity and multifaceted nature of CSA implementation. The 19 papers in this special issue collectively emphasize the importance of understanding local conditions, farmer characteristics, and broader socioeconomic and institutional factors that influence CSA adoption. They highlight the crucial role of extension services, digital advisory services, social responsibility awareness, and diverse forms of capital in facilitating the adoption of CSA practices. Moreover, the findings stress the positive impact of CSA on farm productivity, income diversification, and resilience to climate change while also pointing out the potential for CSA practices to address broader sustainability goals.

Significantly, the discussions underline the need for policy frameworks that are supportive and adaptive, tailored to specific regional and local contexts to promote CSA adoption effectively. Leveraging social networks, enhancing access to financial products and mechanisms, and integrating technological innovations with traditional agricultural practices are vital strategies for scaling CSA adoption. Furthermore, the discussions advocate for a balanced approach that combines economic incentives with moral persuasion and community engagement to foster sustainable agricultural practices.

These comprehensive insights call for concerted efforts from policymakers, researchers, extension agents, and the agricultural community to foster an enabling environment for CSA. Such an environment would support knowledge exchange, financial accessibility, and the adoption of CSA practices that contribute to the resilience and sustainability of agricultural systems in the face of climate change. As CSA continues to evolve, future research should focus on addressing the gaps identified, exploring innovative financing and technology dissemination models, and assessing the long-term impacts of CSA practices on agricultural sustainability and food security. This special issue lays the groundwork for further exploration and implementation of CSA practices, aiming to achieve resilient, productive, and sustainable agricultural systems worldwide and contribute to the achievements of the United Nations Sustainable Development Goals.

Data availability

No new data were created or analyzed during this study. Data sharing is not applicable to this article.

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Acknowledgements

We want to thank all the authors who have submitted papers for the special issue and the reviewers who reviewed manuscripts on time. We acknowledge the Asian Development Bank Institute (ADBI) for supporting the virtual international conference on “ Climate-smart Agriculture: Adoption, Impacts, and Implications for Sustainable Development ” held on 10-11 October 2023. Special thanks to the invited keynote speakers, Prof. Edward Barbier and Prof. Tatsuyoshi Saijo. Finally, we would like to express our thanks, gratitude, and appreciation to the session chairs (Prof. Anita Wreford, Prof. Jianjun Tang, Prof. Alan Renwick, and Assoc. Prof. Sukanya Das), ADBI supporting team (Panharoth Chhay, Mami Nomoto, Mami Yoshida, and Raja Rajendra Timilsina), and discussants who made substantial contributions to the conference.

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Ma, W., Rahut, D.B. Climate-smart agriculture: adoption, impacts, and implications for sustainable development. Mitig Adapt Strateg Glob Change 29 , 44 (2024). https://doi.org/10.1007/s11027-024-10139-z

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Use of publicly available large language models (LLMs) resulted in changes in breast imaging reports classification that could have a negative effect on patient management, according to a new international study published in the journal Radiology . The study findings underscore the need to regulate these LLMs in scenarios that require high-level medical reasoning, researchers said.

LLMs are a type of artificial intelligence (AI) widely used today for a variety of purposes. In radiology, LLMs have already been tested in a wide variety of clinical tasks, from processing radiology request forms to providing imaging recommendations and diagnosis support.

Publicly available generic LLMs like ChatGPT (GPT-3.5 and GPT-4) and Google Gemini (formerly Bard) have shown promising results in some tasks. Importantly, however, they are less successful at more complex tasks requiring a higher level of reasoning and deeper clinical knowledge, such as providing imaging recommendations. Users seeking medical advice may not always understand the limitations of these untrained programs.

"Evaluating the abilities of generic LLMs remains important as these tools are the most readily available and may unjustifiably be used by both patients and non-radiologist physicians seeking a second opinion ," said study co-lead author Andrea Cozzi, M.D., Ph.D., radiology resident and post-doctoral research fellow at the Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, in Lugano, Switzerland.

Dr. Cozzi and colleagues set out to test the generic LLMs on a task that pertains to daily clinical routine but where the depth of medical reasoning is high and where the use of languages other than English would further stress LLMs' capabilities. They focused on the agreement between human readers and LLMs for the assignment of Breast Imaging Reporting and Data System (BI-RADS) categories, a widely used system to describe and classify breast lesions.

The Swiss researchers partnered with an American team from Memorial Sloan Kettering Cancer Center in New York City and a Dutch team at the Netherlands Cancer Institute in Amsterdam.

The study included BI-RADS classifications of 2,400 breast imaging reports written in English, Italian and Dutch. Three LLMs—GPT-3.5, GPT-4 and Google Bard (now renamed Google Gemini)—assigned BI-RADS categories using only the findings described by the original radiologists. The researchers then compared the performance of the LLMs with that of board-certified breast radiologists.

The agreement for BI-RADS category assignments between human readers was almost perfect. However, the agreement between humans and the LLMs was only moderate. Most importantly, the researchers also observed a high percentage of discordant category assignments that would result in negative changes in patient management. This raises several concerns about the potential consequences of placing too much reliance on these widely available LLMs.

According to Dr. Cozzi, the results highlight the need for regulation of LLMs when there is a highly likely possibility that users may ask them health-care-related questions of varying depth and complexity.

"The results of this study add to the growing body of evidence that reminds us of the need to carefully understand and highlight the pros and cons of LLM use in health care," he said. "These programs can be a wonderful tool for many tasks but should be used wisely. Patients need to be aware of the intrinsic shortcomings of these tools, and that they may receive incomplete or even utterly wrong replies to complex questions."

The Swiss researchers were supervised by the co-senior author Simone Schiaffino, M.D. The American team was led by the co-first author Katja Pinker, M.D., Ph.D., and the Dutch team was led by the co-senior author Ritse M. Mann, M.D., Ph.D.

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Over 40% of Americans see China as an enemy, a Pew report shows. That's a five-year high

W ASHINGTON (AP) — More than 40% of Americans now label China as an enemy, up from a quarter two years ago and reaching the highest level in five years, according to an annual Pew Research Center survey released Wednesday.

Half of Americans think of China as a competitor, and only 6% consider the country a partner, according to the report. The findings come as the Biden administration is seeking to stabilize U.S.-China relations to avoid miscalculations that could result in clashes, while still trying to counter the world's second-largest economy on issues from Russia's war in Ukraine to Taiwan and human rights.

Secretary of State Antony Blinken and Treasury Secretary Janet Yellen have both recently visited China in the administration's latest effort to “responsibly” manage the competition with Beijing. Despite those overtures, President Joe Biden has been competing with former President Donald Trump, the presumptive Republican nominee in November's election, on being tough on China .

The Pew report, which is drawn from an April 1-7 survey of a sample of 3,600 U.S. adults, found that roughly half of Americans think limiting China’s power and influence should be a top U.S. foreign policy priority. Only 8% don’t think it should be a priority at all.

For the fifth year in a row, about eight in 10 Americans report an unfavorable view of China, the Pew report said.

“Today, 81% of U.S. adults see the country unfavorably, including 43% who hold a very unfavorable opinion. Chinese President Xi Jinping receives similarly negative ratings,” the report said.

About eight in 10 Americans say they have little or no confidence in Xi to do the right thing regarding world affairs. About 10% said they have never heard of him.

American attitudes toward China have turned largely critical after the U.S. launched a trade war against China in 2018 and since the emergence of COVID-19, which was first reported in China. Beijing’s human rights record, its closeness to Russia and its policies toward Taiwan and Hong Kong also have left Americans with negative views of the country, according to Pew’s previous analyses.

At the same time, the U.S. government has been overt about competing with China on economic and diplomatic issues.

Following that, 42% of Americans say China is an enemy of the U.S., the highest level since 2021, when Pew began asking the question.

The share is much larger among Republicans and Republican-leaning independents, Pew said, with 59% of them describing China as an enemy, compared with 28% of Democrats and those leaning Democratic.

Older Americans, conservative Republicans and those with a sour view of the U.S. economy are more critical of China and more likely to consider the country an enemy, the report said.

“Americans also see China more negatively when they think China’s influence in the world has gotten stronger in recent years or when they think China has a substantial amount of influence on the U.S. economy,” said Christine Huang, a Pew research associate.

“Even pessimism about the U.S. economy is related to how Americans evaluate China: Those who think the economic situation in the U.S. is bad are more likely to see China unfavorably and to see it as an enemy,” she added.

Pew said a nationally representative sample of 3,600 respondents filled out online surveys and that the margin of error was plus or minus 2.1 percentage points.