Epidemiology最新文献

{"title":"A Novel Transmission Model-based Method to Evaluate Population Attribution Methods for Norovirus and Rotavirus Diarrhea.","authors":"Dehao Chen, Kayoko Shioda, Andrew F Brouwer, Alicia N M Kraay, Andreas Handel, Benjamin A Lopman, Elizabeth T Rogawski McQuade, Kristin N Nelson","doi":"10.1097/EDE.0000000000002004","DOIUrl":"10.1097/EDE.0000000000002004","url":null,"abstract":"<p><strong>Background: </strong>The estimate of diarrhea burden attributed to a specific enteric pathogen-the population attributable fraction (PAF)-depends on the specific calculation method. Two conventional methods are commonly used to estimate the PAF for enteric infections: the \"detection-as-etiology\" method, which defines the PAF as the pathogen prevalence in diarrheal cases; and the \"odds-ratio\" method, which expresses the PAF as a function of the odds ratio between pathogen detection and diarrhea. A third, less frequently used method uses the risk ratio to quantify the strength of infection.</p><p><strong>Methods: </strong>We compared each conventional PAF (detection-as-etiology, odds-ratio, or risk-ratio PAF) to a model-based PAF derived from a transmission model of enteric infection. We quantified deviation as the crude difference from this model-based PAF. We fitted the transmission model to site-specific qPCR data for norovirus and rotavirus detection from an eight-country birth cohort studying enteric infections (MAL-ED) and used the equilibrium states to calculate the model-based PAF.</p><p><strong>Results: </strong>For both pathogens, the odds-ratio and risk-ratio deviations were small at all sites (ranging from -5% to +3%), whereas the detection-as-etiology method consistently overestimated the PAF, and its deviation was the largest of the conventional methods.</p><p><strong>Conclusions: </strong>Our mechanistic model provides an independent alternative to conventional methods, quantifying pathogen-specific enteric burden and the deviations in those methods. Our model suggests the detection-as-etiology PAF estimates consistently deviated from our model-based reference, and validates the odds-ratio and risk-ratio methods as feasible, low-deviation measures for quantifying enteric burden.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"620-628"},"PeriodicalIF":4.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147962715","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":"Generalizing Findings in Nested Cohorts.","authors":"Ashley I Naimi, Ya-Hui Yu, Sara M Parisi, Lisa M Bodnar","doi":"10.1097/EDE.0000000000002012","DOIUrl":"10.1097/EDE.0000000000002012","url":null,"abstract":"<p><p>Many research findings are based on a nested subset of an original cohort followed over time, but results are rarely generalized back to the original population. Here, we conduct a \"proof of concept\" analysis demonstrating the application of simple methods to generalize the association between prepregnancy obesity and preeclampsia in women from the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers to Be (nuMoM2b, target sample), using a subset of women whose follow-up was extended to 3 years postpartum in the Heart Health Study (nuMoM2b-HHS, source sample). We constructed inverse probability of selection weights and estimated three risk ratios for the association between obesity and preeclampsia: in the target nuMoM2b sample (N = 9,920), in the source nuMoM2b-HHS sample (N = 4,486), and in the weighted source nuMoM2b-HHS sample generalized back to the target sample using inverse probability of selection weights (pseudo N = 4,468). In the target, source, and weighted samples, the estimated risk ratios (95% confidence intervals) were 2.0 (1.7, 2.3), 2.2 (1.8, 2.6), and 2.0 (1.6, 2.4), respectively. We discuss the assumptions involved in generalizing study findings and methods available for doing so. When relevant, researchers should deploy methods for generalizing study nested cohort findings back to a target sample.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":"37 5","pages":"578-581"},"PeriodicalIF":4.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148618968","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":"Are We the Same, or Are We Different?: Untangling Education Policy Effects from State and Birth-year Differences.","authors":"Dana E Goin, Corinne A Riddell","doi":"10.1097/EDE.0000000000002003","DOIUrl":"10.1097/EDE.0000000000002003","url":null,"abstract":"","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"662-666"},"PeriodicalIF":4.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147971427","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":"Causal Estimands for Analyses of Averted and Avertible Outcomes due to Infectious Disease Interventions: Erratum.","authors":"Katherine M Jia, Christopher B Boyer, Jacco Wallinga, Marc Lipsitch","doi":"10.1097/EDE.0000000000001976","DOIUrl":"10.1097/EDE.0000000000001976","url":null,"abstract":"","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":"37 5","pages":"e19"},"PeriodicalIF":4.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148619002","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":"Reflections on frameworks for defining causal estimands in both observational studies and trials: Response to Labrecque et al.","authors":"Margarita Moreno-Betancur, Rushani Wijesuriya, John B Carlin","doi":"10.1097/EDE.0000000000002040","DOIUrl":"https://doi.org/10.1097/EDE.0000000000002040","url":null,"abstract":"","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148864118","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":"Timothy J. Wade, 1967-2026.","authors":"Andrey I Egorov, John M Colford","doi":"10.1097/EDE.0000000000002026","DOIUrl":"https://doi.org/10.1097/EDE.0000000000002026","url":null,"abstract":"","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148577483","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":"Spatial Heterogeneity in Synergistic Effects of Extreme Heat and NO 2 Exposures on Cardiorespiratory Hospitalizations in California.","authors":"Yiqun Ma, Chen Chen, Rosana Aguilera, Alexander Gershunov, Michael Jerrett, Rachel Connolly, Miriam E Marlier, Tarik Benmarhnia","doi":"10.1097/EDE.0000000000001970","DOIUrl":"10.1097/EDE.0000000000001970","url":null,"abstract":"<p><strong>Background: </strong>Extreme heat and high air pollution levels may co-occur and act synergistically. The synergistic health effects of extreme heat and nitrogen dioxide (NO 2 ) , and the fine-scale spatial heterogeneity in such joint effects, remain unclear. We aimed to investigate the synergistic effect of extreme heat and NO 2 exposures on cardiorespiratory hospitalizations in California and explore its spatial heterogeneity.</p><p><strong>Methods: </strong>Daily ZIP Code Tabulation Area (ZCTA)-level hospitalization data, 2000-2019, were obtained from the California Department of Health Care Access and Information. Extreme heat and NO 2 exposures were defined as days when the heat index and NO 2 concentration exceeded a ZCTA-specific threshold, respectively. We first investigated the state-level synergistic effects with a case-crossover analysis and then estimated the ZCTA-specific synergistic effects using a within-community matched design combined with a spatial Bayesian hierarchical model. Finally, we explored how community characteristics modify these effects using meta-regressions.</p><p><strong>Results: </strong>We found mild synergistic effects of extreme heat and NO 2 exposures on cardiorespiratory hospitalizations at the state level, with a relative excess risk due to interaction (RERI) of 0.005 (95% confidence interval = -0.002, 0.011). Yet, great spatial heterogeneity was observed, with ZCTA-specific RERIs ranging from -1.08 to 2.22. Higher RERIs were observed in communities with lower socioeconomic status, higher population density, reduced green space, greater proportions of racial and ethnic minority residents, and higher historical temperatures.</p><p><strong>Conclusions: </strong>Our findings underscore the need for adaptation policies that integrate compound exposures to heat and air pollution and inform the development of targeted intervention strategies to protect vulnerable communities.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"437-446"},"PeriodicalIF":4.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13218587/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147369157","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":"The Illusion of the \"Self-correcting\" Nature of Science.","authors":"George Davey Smith","doi":"10.1097/EDE.0000000000001987","DOIUrl":"https://doi.org/10.1097/EDE.0000000000001987","url":null,"abstract":"","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":"37 4","pages":"e8"},"PeriodicalIF":4.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148053496","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":"Informed Presence in Electronic Health Record Data: Illustrating Bias and Bias Reduction Approaches in Longitudinal Analyses.","authors":"Daniel T Vader, Di Shu, Rebecca A Hubbard, Craig L K Boge, Anna Sharova, Kevin Downes, Yun Li","doi":"10.1097/EDE.0000000000001974","DOIUrl":"10.1097/EDE.0000000000001974","url":null,"abstract":"<p><p>Electronic health record (EHR) systems capture patient information inconsistently, with patients generally contributing more data when they are sick than when they are healthy. This creates \"informed presence,\" systematic differences between captured and noncaptured data, potentially biasing association estimates. There is growing interest in methods that account for informed presence, but practical approaches for conceptualizing, identifying, and addressing this bias in applied EHR research have received limited attention. Focusing on longitudinal settings, we present a conceptual framework for informed presence bias, which arises when data capture depends on exposure and outcome, and thus the visit process acts as a collider. We then illustrate methods that aim to reduce bias by reweighting or resampling observed data to approximate conditional independence between the visit process and the outcome. We illustrate these methods using longitudinal EHR data from pediatric solid organ transplant recipients (N = 271) to examine the association between steroids and cytomegalovirus viremia, where the frequency of cytomegalovirus testing varies across patients and over time. Incidence rate ratios decreased from 1.83 (95% CI = 1.02, 3.28) in a naïve analysis to 1.37 (0.73, 2.57) when accounting for informed presence using inverse intensity weighting. Incidence rate ratio estimates from bootstrapped inverse intensity weighting were 1.37 (0.71, 2.27) and 1.40 (0.73, 2.68) from multiple outputation. These results show the anticipated attenuation of effect estimates after accounting for informed presence bias. When analyzing irregularly measured EHR data, we recommend (1) identifying the expected observation process using conceptual diagrams, (2) assessing dependence in the observation process, and (3) accounting for outcome dependence in statistical analysis.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"523-531"},"PeriodicalIF":4.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147480119","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":"Improving Assessment of Vaccine Effectiveness by Coupling Test-negative Design Studies with Survival Models.","authors":"Shangchen Song, Matt D T Hitchings, Yang Yang, Ira M Longini","doi":"10.1097/EDE.0000000000001972","DOIUrl":"10.1097/EDE.0000000000001972","url":null,"abstract":"<p><p>The test-negative design (TND) has become a widely used observational study design for evaluating vaccine effectiveness, especially during the COVID-19 pandemic. Traditionally, TND has been viewed as a variant of the case-control study and largely limited to use with logistic regression models. In this paper, we first establish that TND can be framed as a special case of a cohort study, thereby opening the door to a wider range of analytical approaches. We then introduce the Prentice, Williams, and Peterson gap-time (PWP-GT) frailty model as a novel method for analyzing TND data, accounting for recurrent infections and time-dependent vaccination status. Through extensive simulation studies, we demonstrate that the proposed model outperforms conventional models commonly applied in TND-based vaccine effectiveness studies. Finally, we apply our method to data from the National COVID Cohort Collaborative, estimating the effectiveness of full and booster doses of Pfizer's COVID-19 vaccines against both initial infection and reinfection during the Omicron variant circulation period in a real-world setting.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"417-426"},"PeriodicalIF":4.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13275164/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147765980","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}