Epidemiology最新文献

{"title":"Extending inferences from a randomized trial to trial-eligible and treatment-candidate target populations: examples of generalizability and transportability.","authors":"Jessica C Young, Issa J Dahabreh, Stefan James, David Erlinge, Ole Fröbert, Anita Berglund, Rebecca Rylance, Miguel A Hernán, Anthony A Matthews","doi":"10.1097/EDE.0000000000001994","DOIUrl":"https://doi.org/10.1097/EDE.0000000000001994","url":null,"abstract":"<p><strong>Background: </strong>When decision makers use evidence from a randomized trial to inform population-level decisions, the target population they envision rarely aligns with the population of individuals who enrolled in the trial. Here, we extend inferences from the VALIDATE-SWEDEHEART randomized trial (hereafter, the index trial), which compared the effects of bivalirudin and heparin during percutaneous coronary intervention on the risk of death, reinfarction, and bleeding, to two clinically relevant target populations: first, the trial-eligible population of individuals eligible for the index trial regardless of enrollment, and second, the treatment-candidate population of individuals who are considered candidates for bivalirudin and heparin under routine care, regardless of eligibility for the index trial.</p><p><strong>Methods: </strong>Using data from the index trial, we fit logistic regression models for the outcome at 180 days in each group based on assigned treatment. We then standardized risk estimates to the baseline covariate distribution of the trial-eligible and treatment-candidate target populations, which were characterized using data from Swedish healthcare registries.</p><p><strong>Results: </strong>The estimated risk difference comparing bivalirudin vs. heparin was -1.1% (-3.1%, 0.9%) in the trial-eligible population and -1.0% (-3.0%, 1.0%) in the treatment-candidate population. The corresponding risk ratios were 0.92 (0.80, 1.07) and 0.93 (0.80, 1.07), respectively, aligning closely with estimates from the index trial. Absolute risks in each treatment group were, however, between 0.8 and 1.2 percentage points higher in comparison with the index trial.</p><p><strong>Conclusions: </strong>Estimated risk ratios for the broader trial-eligible and treatment-candidate populations generally align with the findings from the index trial. While trials provide essential evidence for healthcare, questions often arise about wider, clinically relevant populations beyond the population of trial participants. By leveraging data from trials and observational data sources, we can attempt to address questions in these wider target populations.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147835294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sibling Comparison Designs to Assess Social Exposures and Empirical Tools to Guide Interpretation: An Illustrative Study of Childhood Income and Subsequent Mental Disorders.","authors":"Linda Ejlskov, Buket Öztürk Esen, Tomáš Formánek, Christian Hakulinen, Nanna Weye, John J McGrath, Carsten Bøcker Pedersen, Oleguer Plana-Ripoll","doi":"10.1097/EDE.0000000000001937","DOIUrl":"10.1097/EDE.0000000000001937","url":null,"abstract":"<p><strong>Background: </strong>Sibling comparison designs are increasingly used to strengthen causal claims about social exposures and health outcomes, yet methodologic challenges in interpreting their results remain insufficiently addressed. This study develops empirical approaches to help assess whether sibling comparison estimates provide reliable evidence for causal relationships.</p><p><strong>Methods: </strong>We used childhood family income and severe mental disorders in a Danish nationwide cohort (n = 643,623; 403,963 siblings born 1986-1996) as an example. We applied three complementary approaches: negative control analyses using pseudo-siblings (unrelated individuals with similar income differences as real siblings) to isolate exposure variability effects from shared familial confounding effects; assessment of sibling age structure, exposure correlation, and variation patterns to establish whether meaningful contrasts exist between siblings; and critical period assumption evaluation through age-specific income measurement.</p><p><strong>Results: </strong>Family income at age 14 was associated with decreased mental disorder risk in the population-wide analysis [adjusted hazard ratio (aHR) = 0.78; 95% confidence interval (CI): 0.76, 0.81] but showed no association using a sibling comparison design (aHR = 1.02; 95% CI: 0.94, 1.11). The pseudo-sibling cohort matched on income also showed substantial attenuation (aHR = 0.93; 95% CI: 0.85, 1.01), while pseudo-siblings not matched on income showed no attenuation. Income associations were similar across childhood measurement ages 0-14 (aHR range = 0.67-0.82).</p><p><strong>Conclusions: </strong>In this example, estimates from the sibling comparison design may reflect limited exposure variability within families and unmet life course model assumptions, rather than or in addition to the removal of shared familial confounding. The empirical approaches we developed help researchers distinguish methodologic factors from genuine null findings, and are available with R code for implementation.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"298-306"},"PeriodicalIF":4.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145762295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Separate and Joint Associations of Own and Spousal Depression with Mortality in Couples.","authors":"Toshiaki Komura, Säde Stenlund, Laura D Kubzansky","doi":"10.1097/EDE.0000000000001993","DOIUrl":"https://doi.org/10.1097/EDE.0000000000001993","url":null,"abstract":"<p><strong>Background: </strong>Depression causes a substantial burden on a person's health and well-being. However, evidence is limited regarding whether depression of one person in a marital relationship may affect the other partner's health. We assessed whether depression in one partner within a married couple might contribute to the other partner's mortality hazard, also considering whether only one or both partners have depression.</p><p><strong>Methods: </strong>We examined a nationally representative sample comprising 8,442 older US adults within 4,225 couples from the Health and Retirement Study. Using the Center for Epidemiologic Studies Depression Scale, we characterized depression status as follows: i) no depression in either partner, ii) depression in respondent only, iii) depression in spouse only, and iv) depression in respondent and their spouse. Associations between couples' depression status on individuals' mortality hazards over 11 years were estimated using Cox proportional hazards models adjusted for 27 characteristics of the individual, their spouse, and their household.</p><p><strong>Results: </strong>We observed a higher mortality hazard when only the respondent had depression (hazard ratio (HR): 1.44 [95% confidence interval (CI): 1.26, 1.66]), as well as a modestly elevated hazard when only their spouse exhibited depression (HR: 1.18 [95% CI: 1.01, 1.39]). When both partners had depression, we observed a jointly elevated mortality hazard (HR: 1.53 [95% CI: 1.26, 1.86]).</p><p><strong>Conclusions: </strong>Results suggest the harmful effects of depression could extend beyond the individual to spouses' physical health. Future studies on health effects of depression should incorporate familial contexts.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147812539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel Psychometric Indicator Assessments: The Relative Excess Correlation and Associated Matrices.","authors":"Tyler J VanderWeele, R Noah Padgett","doi":"10.1097/EDE.0000000000001963","DOIUrl":"10.1097/EDE.0000000000001963","url":null,"abstract":"<p><p>For a series of indicators used to assess psychosocial constructs, we propose reporting new types of correlation matrices to gain greater insight into the relation of the indicators with one another. What we define as the observed residual correlation (ORC) matrix can give insight as to whether, when a given indicator is above the indicator-average scores across all indicators for that individual, what other indicators might be anticipated to be above that individual's average score as well. What we define as the relative excess correlation (REC) matrix can give insight, for each pair of indicators, whether the strength of that particular correlation is above or below what might have been anticipated based on the correlation of each of those two indicators with all of the others. The ORC and REC matrices will, generally, have numerous negative entries even if all of the raw correlations between each pair of indicators are positive. We discuss the properties of, and the relations between, these correlation matrices, and their analogues for covariances. The positive deviations of the REC matrix entries from zero also can help identify clusters of indicators that are more strongly related to one another, providing insights somewhat analogous to factor analysis, but without the need for decisions concerning rotations or the number of factors. However, the ORC and REC matrices can also be used purely descriptively to provide insights into understanding the relation of indicators with one another.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":"37 3","pages":"315-324"},"PeriodicalIF":4.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13034737/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147590965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding Algorithmic Fairness for Clinical Prediction in Terms of Subgroup Net Benefit and Health Equity.","authors":"Jose Benitez-Aurioles, Alice Joules, Irene Brusini, Niels Peek, Matthew Sperrin","doi":"10.1097/EDE.0000000000001949","DOIUrl":"10.1097/EDE.0000000000001949","url":null,"abstract":"<p><p>There are concerns about the fairness of clinical prediction models. \"Fair\" models are defined as those for which their performance or predictions are not inappropriately influenced by protected attributes such as ethnicity, gender, or socioeconomic status. Researchers have raised concerns that current algorithmic fairness paradigms enforce strict egalitarianism in healthcare, leveling down the performance of models in higher-performing subgroups instead of improving it in lower-performing ones. We propose assessing the fairness of a prediction model by expanding the concept of net benefit, using it to quantify and compare the clinical impact of a model in different subgroups. We use this to explore how a model distributes benefits across a population, its impact on health inequalities, and its role in the achievement of health equity. We show how resource constraints might introduce necessary trade-offs between health equity and other objectives of healthcare systems. We showcase our proposed approach with the development of two clinical prediction models: (1) a prognostic type 2 diabetes model used by clinicians to enroll patients into a preventive care lifestyle intervention programme and (2) a lung cancer screening algorithm used to allocate diagnostic scans across the population. This approach helps modelers better understand if a model upholds health equity by considering its performance in a clinical and social context.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"386-396"},"PeriodicalIF":4.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145942847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Re: Confounders, Mediators, or Colliders: What Types of Shared Covariates Does a Sibling Comparison Design Control For?","authors":"Alyssa Bilinski","doi":"10.1097/EDE.0000000000001959","DOIUrl":"10.1097/EDE.0000000000001959","url":null,"abstract":"","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":"37 3","pages":"e6-e7"},"PeriodicalIF":4.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147590976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Where Do Target Trials Come From? Specifying the Protocol of a Target Trial When Repurposing Data for Causal Inference.","authors":"Miguel A Hernán, Barbra A Dickerman, Sonja A Swanson, Issa J Dahabreh","doi":"10.1097/EDE.0000000000001951","DOIUrl":"10.1097/EDE.0000000000001951","url":null,"abstract":"<p><p>Descriptions of the target trial framework often appear to assume that investigators knew the protocol of the target trial from the start of the investigation. In practical applications, however, the protocol of the target trial is constrained by the observational data that investigators have access to. When using preexisting observational databases, the target trial protocol typically needs to be iteratively developed as investigators learn about the data. Here we examine the process by which investigators adapt their original causal question, operationalized into the protocol of a hypothetical trial (the index trial), to the available data. This adaptive process results in a final causal question, operationalized into the protocol of another hypothetical trial (the target trial), that depends both on the original causal question and on the available data. As a result, prespecification of an emulatable target trial protocol is not generally possible because adaptations are expected after inspecting the data. The adaptive nature of the specification of the target trial protocol raises important questions about the types of data examinations that are permissible to guide the adaptations, the procedures for transparent reporting of each adaptation and its rationale, and the possibility of prespecifying the rules that will govern the investigators' decisions to adapt the protocol to the data.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":"37 3","pages":"282-286"},"PeriodicalIF":4.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147590985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Limitations (With Apologies to Sir Philip Sidney).","authors":"Stephen J Mooney","doi":"10.1097/EDE.0000000000001950","DOIUrl":"10.1097/EDE.0000000000001950","url":null,"abstract":"","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"e6"},"PeriodicalIF":4.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145892479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"State Minimum Wages and Food Insecurity Among Households Receiving Government Food Assistance.","authors":"Krista Neumann, Barbara A Laraia, Corinne A Riddell","doi":"10.1097/EDE.0000000000001955","DOIUrl":"10.1097/EDE.0000000000001955","url":null,"abstract":"<p><strong>Background: </strong>While the Supplemental Nutrition Assistance Program (SNAP) aims to reduce food insecurity among low-income households, nearly half of recipients remain food insecure. Increasing state minimum wages could help improve food security, but because SNAP benefits are income-dependent, net effects are unclear.</p><p><strong>Methods: </strong>Using the US Current Population Survey Food Security Supplement (2002-2019), we linked households interviewed in two consecutive Decembers to create a 2-year panel. The primary sample included SNAP recipient households with at least one adult working in year 1. The exposure was the average effective state minimum wage (2019 $) for each state and year. We estimated prevalence differences in food insecurity per $1 increase in minimum wage using a within-household linear fixed-effects model adjusting for time-varying economic confounders and concurrent safety-net policies. We investigated variation in the effect by household and family structure, race and ethnicity, and educational attainment using stratified models.</p><p><strong>Results: </strong>Overall estimates were most compatible with protective effects (prevalence difference per 10,000 households: -298, 95% confidence interval [CI]: -673, 77). The strongest protective estimates were for senior-headed (-1,472, 95% CI: -2,869, -76), Hispanic (-865, 95% CI: -1,638, -92), and some college households (-988, 95% CI: -1,664, -312). Estimates for Indigenous households were imprecise and possibly harmful (900, 95% CI: -736, 2,537). Most other subgroup estimates were near zero.</p><p><strong>Conclusions: </strong>Increased minimum wages may modestly support food security for working SNAP households. As SNAP benefit rules evolve, these findings suggest that minimum-wage policies can complement and reinforce the program's goals to protect low-income households from food hardship.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"397-406"},"PeriodicalIF":4.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146156576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Framework for Thinking About the Potential Public Health Impact of Epidemiologic Research.","authors":"Catherine R Lesko, Lauren C Zalla, Rachael K Ross, Jacqueline E Rudolph, Emily R Smith, Jessie K Edwards","doi":"10.1097/EDE.0000000000001939","DOIUrl":"10.1097/EDE.0000000000001939","url":null,"abstract":"<p><p>An impactful epidemiologic question is one that, if answered, could inform meaningful action to reduce the burden of disease in the population it concerns. We propose a set of factors that could be used for discussing, evaluating, and communicating the public health impact of epidemiologic studies. These factors pertain to the burden and distribution of disease, the potential for an intervention to alter the disease burden, and the context in which the study is conducted. The disease burden is characterized by the number of cases, severity or cost of disease, and distribution of disease across the population. The potential for intervention is characterized by the mutability of the exposure itself, the prevalence and distribution of other causes of the disease in the population, the prevalence of the exposure and risk of the outcome under the natural course (before any intervention), and the feasibility of intervening. An epidemiologic question need not be impactful along all these factors to make answering it worthwhile. However, answering epidemiologic questions with more of these factors present will likely have a greater public health impact than answering questions for which these factors are absent. We hope that collecting these factors into a single framework may aid students and senior epidemiologists alike when organizing arguments for the value of their own work or attempting to evaluate the impact of others' work.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"363-370"},"PeriodicalIF":4.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12707578/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145761580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}