Epidemiology最新文献

{"title":"Combining Observational Studies to Reduce Multiple Biases.","authors":"Stephen R Cole, Paul N Zivich, Bonnie E Shook-Sa, David B Richardson, Michael G Hudgens, Jessie K Edwards","doi":"10.1097/EDE.0000000000001999","DOIUrl":"10.1097/EDE.0000000000001999","url":null,"abstract":"<p><p>Epidemiology stands to benefit greatly from combining data sources with complementary strengths. We propose a study design and estimators to combine information from multiple observational studies to simultaneously address confounding and outcome measurement error. Using inverse probability weighted, g-computation, and augmented inverse probability weighted estimators, we show how to combine information from two studies wherein the first study is subject to outcome misclassification (but has adequate confounder control) and the second study has gold-standard outcomes (but inadequate confounder control). Monte Carlo experiments demonstrate that the proposed estimators remove both confounding and measurement biases and provide appropriate 95% confidence interval coverage, while standard analyses fall short. Fusion designs offer a principled approach to combine data from multiple sources to address multiple biases in epidemiologic research.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"565-574"},"PeriodicalIF":4.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13182983/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147948176","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":"Industrial Air Toxicant Exposure and Individual Mortality: Evidence from the Americans' Changing Lives Cohort, 1986-2019.","authors":"Reed T DeAngelis, Lindsey Burnside, David Rigby, Paul Mohai, Devon Payne-Sturges, Margaret T Hicken","doi":"10.1097/EDE.0000000000002005","DOIUrl":"10.1097/EDE.0000000000002005","url":null,"abstract":"<p><strong>Background: </strong>Exposure to industrial air toxicants remains a leading environmental health risk in the United States.</p><p><strong>Methods: </strong>Our prospective cohort study examines the extent to which industrial air toxicant exposure accounts for individual mortality risk throughout the adult life course. Individual data come from Americans' Changing Lives, a nationally representative US cohort who were followed for six waves between 1986 and 2019 (N = 3329). Industrial air toxicant data come from the Environmental Protection Agency's Risk-Screening Environmental Indicators Geographic Microdata; toxicants are recorded at each wave within five circular buffers surrounding respondents' homes at increasing radii (1-25 km). We employ accelerated failure time-shared frailty survival models that account for unmeasured between-person differences in mortality risk, and other individual- and area-level characteristics measured over time.</p><p><strong>Results: </strong>We report three key findings. First, associations between air toxicants and mortality are nonlinear, such that respondents who reside in areas with relatively low toxicant levels still live for 1-2 fewer years, on average, compared with peers residing in the least polluted areas. Second, the association between air toxicant exposure and mortality increases across larger buffer areas. For instance, compared with their counterparts in the least polluted buffers, respondents who reside within the most polluted 1-km buffers during the study period live for 2 fewer years, on average, while those in the most polluted 25-km buffers live for 4 fewer years. Third, several of these associations are increased for respondents who identify as non-Hispanic Black (vs. White), report lower incomes, or reside in high-poverty census tracts.</p><p><strong>Conclusion: </strong>Our study corroborates that industrial air toxicants remain significant environmental health risks in the United States, especially for socioeconomically disadvantaged populations.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"709-718"},"PeriodicalIF":4.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13367425/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147962656","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":"Differential Misclassification by Time: A Proposed Validation Substudy Design to Account for Time Trends in Bias Parameters.","authors":"Katherine A Lawson-Michod, Julie Barberio, Richard F MacLehose, Thomas P Ahern, Timothy L Lash, Lindsay J Collin","doi":"10.1097/EDE.0000000000002008","DOIUrl":"10.1097/EDE.0000000000002008","url":null,"abstract":"<p><strong>Background: </strong>Data from validation substudies help quantify bias due to misclassification of an analytic variable by estimating classification parameters. Studies with long enrollment or follow-up may be susceptible to changes in classification parameters over time, which would have important implications for validation substudy design and quantitative bias analysis. Herein, we provide guidance for sampling validation data to account for time trends in classification parameters.</p><p><strong>Methods: </strong>Using a simulated cohort of 10,000 observations, we induced exposure misclassification under three scenarios: absence of a time trend, but expected change in exposure prevalence; linear time trends in exposure misclassification; and logarithmic time trends in exposure misclassification. Validation sampling was conducted at the beginning, middle, and end of follow-up. These validation data were then used to impute positive and negative predictive values for the entire cohort over the study period as a function of time. We compared these imputed values with the true values.</p><p><strong>Results: </strong>We demonstrated that, in the presence of a time trend, purposeful sampling allows for estimation of the changing positive and negative predictive values over the course of the study. Estimation of predictive values was accurate under both the linear and logarithmic scenarios, and was closest to the truth when a large proportion of exposure/outcome strata was sampled for the validation substudy.</p><p><strong>Conclusions: </strong>When a time trend in classification parameters exists, designs that allow estimation of time-varying predictive values should be used instead of conventional validation study designs that estimate a single summary classification parameter over the study period.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"582-590"},"PeriodicalIF":4.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147962737","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":"Riesz Representers for the Rest of Us.","authors":"Nicholas T Williams, Oliver J Hines, Kara E Rudolph","doi":"10.1097/EDE.0000000000001996","DOIUrl":"10.1097/EDE.0000000000001996","url":null,"abstract":"<p><p>The application of semiparametric efficient estimators, particularly those that leverage machine learning, is rapidly expanding within epidemiology and causal inference. This literature is increasingly invoking the Riesz representation theorem and Riesz regression. This paper aims to introduce the Riesz representation theorem to an epidemiologic audience, explaining what it is and why it is useful, using step-by-step worked examples.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"596-602"},"PeriodicalIF":4.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147980894","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":"Against Resignation on Prespecification: Reproducibility and Target Trial Emulation.","authors":"Sarah F Ackley","doi":"10.1097/EDE.0000000000001997","DOIUrl":"10.1097/EDE.0000000000001997","url":null,"abstract":"","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"e14"},"PeriodicalIF":4.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147971432","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":"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":"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 versus 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":"603-611"},"PeriodicalIF":4.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13390982/pdf/","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gut Health, Systemic Inflammation, and Linear Growth Among Indonesian Infants: Findings From the Action Against Stunting Hub Observation Cohort: Erratum.","authors":"","doi":"10.1097/EDE.0000000000002015","DOIUrl":"10.1097/EDE.0000000000002015","url":null,"abstract":"","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"e21"},"PeriodicalIF":4.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148263671","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":"Transporting Results from a Trial to an External Target Population When Trial Participation Impacts Adherence: Erratum.","authors":"Rachael K Ross, Iván Díaz, Amy J Pitts, Elizabeth A Stuart, Kara E Rudolph","doi":"10.1097/EDE.0000000000001971","DOIUrl":"10.1097/EDE.0000000000001971","url":null,"abstract":"","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":"37 5","pages":"e20"},"PeriodicalIF":4.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148618986","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":"Target Trial Emulation Without Matching: A More Efficient Approach for Evaluating Vaccine Effectiveness Using Observational Data.","authors":"Emily Wu, Elizabeth Rogawski McQuade, Mats J Stensrud, Razieh Nabi, David Benkeser","doi":"10.1097/EDE.0000000000001982","DOIUrl":"10.1097/EDE.0000000000001982","url":null,"abstract":"<p><p>Real-world vaccine effectiveness (VE) has increasingly been studied using matching-based approaches, particularly in observational cohort studies that follow the target trial emulation framework. Although matching is appealing in its simplicity, it has important limitations in terms of clarity of the target estimand and the precision with which it is estimated. Moreover, defining causal estimands of VE requires care, because vaccine uptake often occurs over calendar time when infection dynamics may also be rapidly changing. We propose a causal estimand of VE that summarizes VE over calendar time, similar to how vaccine efficacy is summarized in randomized controlled trials. We describe the identification of our estimand and propose simple-to-implement estimators based on two hazard regression models. We apply our proposed estimator in simulations and in a study assessing the effectiveness of the Pfizer-BioNTech COVID-19 vaccine to prevent SARS-CoV-2 infections in children 5-11 years old. In both settings, we find that our proposed estimator yields similar scientific inferences while providing significant efficiency gains over commonly used matching-based estimators.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"612-619"},"PeriodicalIF":4.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147580628","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":"Changes in Community-level Pedestrian Stops Following Overdose Prevention Center Implementation in New York City: An Augmented Synthetic Control Approach.","authors":"Ignacio Bórquez, Bennett Allen, Cale Basaraba, Audrey Renson, Brandi Moore, Brandon D L Marshall, Magdalena Cerdá","doi":"10.1097/EDE.0000000000002000","DOIUrl":"10.1097/EDE.0000000000002000","url":null,"abstract":"<p><strong>Background: </strong>Overdose prevention centers (OPCs) may reduce public drug use and, with it, policing of people who use drugs in the communities surrounding these sites. We applied an augmented synthetic control method to assess changes in pedestrian stops before and after the November 2021 opening of two OPCs (Washington Heights and East Harlem) in New York City.</p><p><strong>Methods: </strong>We retrieved pedestrian stop information from the New York Police Department's Stop, Question, and Frisk program from January 2017 to December 2024, and created bimonthly averages using 5- and 10-minute walking buffers surrounding the OPCs and 57 donor sites (syringe service and opioid treatment programs) as outcomes. Covariates were derived from the American Community Survey, New York Police Department Calls for Services, and SafeGraph pedestrian mobility estimates.</p><p><strong>Results: </strong>The opening of the Washington Heights OPC was associated with a reduction of 2.8 bimonthly average pedestrian stops in the post-intervention period when using 5-minute walking buffers, although results were compatible with increases and reductions (95% confidence interval [CI] = -9.4, 4.0). For 10-minute walking buffers, results were compatible with a wide range of reductions (average treatment effect on the treated [ATT] = -9.2 [95% CI = -18.3, -1.3]). East Harlem OPC showed larger point estimates when examining both distances (ATT = -8.4 [95% CI = -12.2, -4.5] and ATT = -13.7 [95% CI = -22.1, -4.2] with 5- and 10-minute walking buffers, respectively). For both sites, permutation tests suggested that these reductions fell within the range of possible donor-unit placebo effects.</p><p><strong>Conclusion: </strong>Our study shows limited evidence of an effect of New York City's first two OPCs on pedestrian stops in their immediate vicinity, with a potential decrease concentrated in the first 2 years at the East Harlem location.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"667-678"},"PeriodicalIF":4.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147948094","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}