David Bann

Should original research articles routinely contain prominent policy claims or broad calls to action? Growing emphasis on research impact might be welcome yet have unintended consequences (eg, incentivizing overextrapolation and undermining the perceived objectivity of scientists). We examined 45 807 abstracts from 10 leading Epidemiology and Public Health journals (1990-2024). Using a large language model with human validation, we classified policy claims and mapped trends. Claims markedly increased from 17.6% to 35.8%, with wide variation across countries and journals (>60% vs <4%). Keywords linked to higher claim rates differed by topic and time: some corresponded to topics with clear causal evidence of harm as well as topics with notable advocacy. Claims were most common in qualitative or cross-sectional studies, and less common in cohort, quasi-experimental, or experimental studies. We argue that these patterns reflect a research culture increasingly oriented toward claiming policy relevance—and incentives that encourage attaching claims to single studies. Our findings raise questions about how scientists and journals balance evidence, advocacy, and scientific credibility. Ensuring that policy claims and calls to action remain commensurate with evidence will be central to building trust as policy impact continues to be incentivized.

Background: Population–based cohort studies are increasingly expected to demonstrate benefits for public health and wider society. However, there is limited systematic evidence on what such impact entails or how it is generated and sustained. To address this gap, we examined researcher perspectives on the impact of cohort studies. Methods: We conducted, to our knowledge, the first quantitative study of researcher views on cohort impact, recruiting active cohort researchers through national and international networks between August and December 2025. The anonymous cross–sectional survey captured researcher characteristics, perceived contributions, impact processes, challenges, and open–ended reflections. Results: A total of 163 cohort researchers participated, primarily from Australia (42%) and the UK (23%). Participants perceived their work as informing a wide range of societal issues and reported investing an average of 24% of their work time in impact–related activities. While most respondents (73%) believed their research leads to tangible policy or practice change, two–thirds indicated that impact is rarely or never demonstrable shortly after study completion (67%) and seldom attributable to a single study (67%). Key concerns included pressure to overstate contributions (80%), perceived disadvantages for cohort studies in impact assessments (78%), and inadequate skills or resources to achieve impact (65%). Conclusions: Cohort researchers perceive their work as generating broad societal contributions and invest substantial effort in supporting impact. However, they face systemic challenges in both achieving and demonstrating impact. These findings highlight the need for impact frameworks that better capture complexity, long–term influence, and cumulative contributions, while mitigating unintended consequences. Key Messages: Researchers perceive population–based cohort studies as generating broad societal value and contributing evidence relevant to complex societal challenges. Cohort impact is described as unfolding over long timescales and rarely attributable to single studies; features that sit uneasily within short–term, attribution–focused impact models. Current impact frameworks appear poorly aligned with long–term research investments and may create unintended pressures to overstate research contributions.

Social scientists have long sought to investigate whether the predictors of educational attainment (EA) have changed across time. Here, we provide insights by incorporating genetic predictors of education in three nationally representative British birth cohorts born in 1946, 1958, and 1970. We investigated whether individual characteristics as proxied by polygenic indexes (PGIs) for EA and cognition have become more relevant to educational success over time and whether returns to genetic predisposition were moderated by early life socioeconomic background. We present three findings. First, associations between the EA PGI and attainment increased over time, with increasing incremental variance explained by the EA PGI. Second, associations between the cognition PGI and attainment were broadly consistent across cohorts, and there was no clear change in explained variance. Since the EA PGI captures multiple traits related to educational success, factors other than those related to cognition may have become more relevant over time. Third, we observed strong evidence of interaction: Associations between the EA PGI and EA were disproportionately larger among those from more advantaged socioeconomic backgrounds. The strength and pattern of associations varied when using EA PGIs that were less conservatively filtered for SNPs. Our findings suggest EA is influenced by social and genetic factors both independently and jointly. Genetic liability and social background could be considered as two forms of inherited advantage which synergistically influence education attainment.

Should original research routinely contain prominent policy claims, such as recommendations for policymakers or broad calls to action? Growing emphasis on “research impact” might be welcome but also have unintended consequences that include risks of overextrapolation, the blurring of roles between scientists and advocates, and potential erosion of scientific credibility. To inform this debate, we examined 45,807 abstracts from ten leading Epidemiology and Public Health journals (1990–2024). Using a large language model with human validation, we classified policy claims and mapped their prevalence by time, country, journal, field of study, and study design. Policy claims markedly increased in frequency from 17.6% in 1990-1999 to 35.8% in 2020-2024, with wide variation across countries (>40% in Italy and Australia vs 60% in some vs <6% in others). Keywords linked to higher claim rates differed by topic and time: some corresponded to topics with clear causal evidence (“tobacco”), others to topics with more complex causal evidence and notable researcher advocacy (“health inequalities” and “COVID-19”). Claims were most common in qualitative or cross-sectional studies, and less common in cohort, quasi-experimental, or experimental studies. We argue that these patterns reflect a research culture increasingly oriented toward claiming policy relevance—and incentives that encourage attaching claims to single studies. Our findings raise questions about how scientists and journals balance evidence, advocacy, and credibility. Ensuring that policy claims remain commensurate with evidence will be central to build trust as policy impact continues to be incentivised. We discuss alternative ways for researchers and publishers to engage meaningfully with policy beyond attaching claims to individual studies, and share our data and scripts to catalyse further work in this area.

Polygenic indexes (PGIs) — DNA-based predictors of individual phenotypes — have become essential tools across biomedical and social sciences. We introduce Version 2 of the Polygenic Index Repository, which expands phenotype coverage from 47 to 61, increases the number of participating datasets from 11 to 20, and adopts a more consistent and improved methodology for PGI construction. For 16 phenotypes, we leverage summary statistics from an updated GWAS meta-analysis with greater statistical power compared to the original release, thereby improving the PGI’s predictive power. To improve power for family-based analyses, we provide imputed parental PGIs in all datasets with first-degree relatives and offer a framework for interpreting results from analyses that control for parental PGIs. We illustrate the utility of parental PGIs using two applications: (1) comparing PGI associations with and without parental PGI controls for all phenotypes in two Repository datasets with family data, and (2) for BMI and diastolic blood pressure, exploring the contribution of causal versus non-causal components of PGI associations to the imperfect portability of PGIs across subgroups within a genetic ancestry. Collectively, the updates enhance predictive performance, broaden the Repository’s scope, and introduce novel resources that reduce confounding bias and improve interpretability.

Recent advances in artificial intelligence (AI) - particularly generative AI - present new opportunities to accelerate, or even automate, epidemiological research. Unlike disciplines based on physical experimentation, a sizable fraction of Epidemiology relies on secondary data analysis and thus is well-suited for such augmentation. Yet, it remains unclear which specific tasks can benefit from AI interventions or where roadblocks exist. Awareness of current AI capabilities is also mixed. Here, we map the landscape of epidemiological tasks using existing datasets - from literature review to data access, analysis, writing up, and dissemination - and identify where existing AI tools offer efficiency gains. While AI can increase productivity in some areas such as coding and administrative tasks, its utility is constrained by limitations of existing AI models (e.g. hallucinations in literature reviews) and human systems (e.g. barriers to accessing datasets). Through examples of AI-generated epidemiological outputs, including fully AI-generated papers, we demonstrate that recently developed agentic systems can now design and execute epidemiological analysis, albeit to varied quality (see https://github.com/edlowther/automated-epidemiology). Epidemiologists have new opportunities to empirically test and benchmark AI systems; realising the potential of AI will require two-way engagement between epidemiologists and engineers.

Polygenic indexes (PGIs) — DNA-based predictors of individual phenotypes — have become essential tools across biomedical and social sciences. We introduce Version 2 of the Polygenic Index Repository, which expands phenotype coverage from 47 to 61, increases the number of participating datasets from 11 to 20, and adopts a more consistent and improved methodology for PGI construction. For 16 phenotypes, we leverage summary statistics from an updated GWAS meta-analysis with greater statistical power compared to the original release, thereby improving the PGI’s predictive power. To improve power for family-based analyses, we provide imputed parental PGIs in all datasets with first-degree relatives and offer a framework for interpreting results from analyses that control for parental PGIs. We illustrate the utility of parental PGIs using two applications: (1) comparing PGI associations with and without parental PGI controls for all phenotypes in two Repository datasets with family data, and (2) for BMI and diastolic blood pressure, exploring the contribution of causal versus non-causal components of PGI associations to the imperfect portability of PGIs across subgroups within a genetic ancestry. Collectively, the updates enhance predictive performance, broaden the Repository’s scope, and introduce novel resources that reduce confounding bias and improve interpretability.