Epidemiology最新文献_第5页

Regression-based Proximal Causal Inference for Right-censored Time-to-event Data. 基于回归的右截尾时间到事件数据的近端因果推理。

IF 4.4 2区医学

Epidemiology Pub Date : 2025-09-01 Epub Date: 2025-06-13 DOI: 10.1097/EDE.0000000000001884

Kendrick Qijun Li, George C Linderman, Xu Shi, Eric J Tchetgen Tchetgen

{"title":"Regression-based Proximal Causal Inference for Right-censored Time-to-event Data.","authors":"Kendrick Qijun Li, George C Linderman, Xu Shi, Eric J Tchetgen Tchetgen","doi":"10.1097/EDE.0000000000001884","DOIUrl":"10.1097/EDE.0000000000001884","url":null,"abstract":"Unmeasured confounding is a major concern in obtaining credible inferences about causal effects from observational data. Proximal causal inference is an emerging methodological framework to detect and potentially account for confounding bias by carefully leveraging a pair of negative control exposure and outcome variables, also known as treatment and outcome confounding proxies. Although regression-based proximal causal inference is well-developed for binary and continuous outcomes, analogous proximal causal inference regression methods for right-censored time-to-event outcomes are currently lacking. In this paper, we propose a novel two-stage regression proximal causal inference approach for right-censored survival data under an additive hazard structural model. We provide theoretical justification for the proposed approach tailored to different types of negative control outcomes, including continuous, count, and right-censored time-to-event variables. We illustrate the approach with an evaluation of the effectiveness of right heart catheterization among critically ill patients using data from the SUPPORT study. Our method is implemented in the open-access R package \"pci2s.\"","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"694-704"},"PeriodicalIF":4.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144289400","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}

引用次数: 0

Beta Approach for Risk Summarization: An Empirical Bayes Method for Summarizing Pregnancy History to Predict Later Health Outcomes. BARS：一种总结妊娠史以预测后期健康结果的经验贝叶斯方法。

IF 4.4 2区医学

Epidemiology Pub Date : 2025-09-01 Epub Date: 2025-05-28 DOI: 10.1097/EDE.0000000000001880

Mary V Díaz-Santana, Molly Rogers, Clarice R Weinberg

{"title":"Beta Approach for Risk Summarization: An Empirical Bayes Method for Summarizing Pregnancy History to Predict Later Health Outcomes.","authors":"Mary V Díaz-Santana, Molly Rogers, Clarice R Weinberg","doi":"10.1097/EDE.0000000000001880","DOIUrl":"10.1097/EDE.0000000000001880","url":null,"abstract":"Reproductive complications tend to recur. The risk of gestational diabetes is much higher in the second pregnancy if it occurred in the first. Such recurrence risks are regarded as reflecting heterogeneity among couples in their inherent risk. Pregnancy complications not only predict their own recurrence but have been shown to be associated with different later health problems like hypertension and heart disease. Epidemiologically considering reproductive history as a risk factor has been challenging, however, because women vary in their number of pregnancies and there's no obvious way to account for both prior occurrences and prior nonoccurrences. We propose a simple empirical Bayes approach, the Beta Approach for Risk Summarization (BARS). We apply BARS to retrospective data reported at enrollment in a large cohort, the Sister Study, to estimate propensity to gestational diabetes, and use that to predict subsequent occurrences of gestational diabetes based on successively updated pregnancy histories. We assess the calibration of our predictive model for gestational diabetes and demonstrate that it works well. We then apply the method to prospective data from the Sister Study, revisiting an earlier paper that linked gestational diabetes to the risk of breast cancer, but now using BARS and additional person time.","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"591-598"},"PeriodicalIF":4.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12279077/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144156695","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}

引用次数: 0

Early Detection of Dengue Outbreaks: Transmission Model Analysis of a Dengue Outbreak in a Remote Setting in Ecuador. 登革热疫情的早期发现：厄瓜多尔偏远地区登革热疫情的传播模式分析

IF 4.4 2区医学

Epidemiology Pub Date : 2025-09-01 Epub Date: 2025-06-03 DOI: 10.1097/EDE.0000000000001874

Hannah Van Wyk, Andrew F Brouwer, Gwenyth O Lee, Sully Márquez, Paulina Andrade, Edward L Ionides, Josefina Coloma, Joseph N S Eisenberg

{"title":"Early Detection of Dengue Outbreaks: Transmission Model Analysis of a Dengue Outbreak in a Remote Setting in Ecuador.","authors":"Hannah Van Wyk, Andrew F Brouwer, Gwenyth O Lee, Sully Márquez, Paulina Andrade, Edward L Ionides, Josefina Coloma, Joseph N S Eisenberg","doi":"10.1097/EDE.0000000000001874","DOIUrl":"10.1097/EDE.0000000000001874","url":null,"abstract":"Background: Pathogen transmission of an outbreak generally begins well before it is identified by a surveillance system, particularly for infectious diseases in which a high proportion of cases are subclinical, as is the case for arboviruses. We aimed to ascertain the most likely date of the primary case (the first infection, whether detected or not) in an outbreak.Methods: Using data from a 2019 dengue outbreak in a rural, riverine town in Northwestern Ecuador, we investigated potential undetected dengue virus transmission before the outbreak detected in mid-May. The outbreak was preceded by four reported cases on 9 February, 13 February, 28 March, and 2 May. Using a hidden Markov model, we estimate the most likely date of the primary case for different assumed case reporting fractions.Results: For all reporting fractions, the most likely primary case occurred near the 2 February candidate index cases, ranging from 7 February to 12 February, over 2 months before the main outbreak. Individual simulations showed that earlier and later primary cases were also possible. Our results suggest that the dengue virus was circulating in the community for around 3 months before the outbreak.Conclusions: Surveillance systems that can detect low-level transmission in the early stages of an outbreak can provide time to intervene before the exponential phase of the outbreak, with the potential to substantially reduce transmission and disease burden.","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"636-645"},"PeriodicalIF":4.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12404040/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144208031","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}

引用次数: 0

The Authors Respond. 作者回应。

IF 4.4 2区医学

Epidemiology Pub Date : 2025-08-19 DOI: 10.1097/EDE.0000000000001910

Frances Em Albers, Margarita Moreno-Betancur, Roger L Milne, Dallas R English, Brigid M Lynch, S Ghazaleh Dashti

引用次数: 0

Re. Pre-diagnostic exposures and cancer survival: Can a meaningful causal estimand be specified? 关于诊断前暴露与癌症生存：是否可以确定有意义的因果估计？

IF 4.4 2区医学

Epidemiology Pub Date : 2025-08-19 DOI: 10.1097/EDE.0000000000001909

Matthew M Coates, Charles J Wolock, Onyebuchi A Arah

引用次数: 0

Erratum: Effect Modification in Settings with "Truncation by Death". 勘误表。

IF 4.4 2区医学

Epidemiology Pub Date : 2025-07-14 DOI: 10.1097/EDE.0000000000001897

引用次数: 0

Liacine Bouaoun, Winner of the 2025 Rothman Prize. Liacine Bouaoun， 2025年罗斯曼奖得主。

IF 4.7 2区医学

Epidemiology Pub Date : 2025-07-01 Epub Date: 2025-05-29 DOI: 10.1097/EDE.0000000000001870

引用次数: 0

Generalizing and Transporting Causal Inferences from Randomized Trials in the Presence of Trial Engagement Effects. 在试验参与效应的存在下，从随机试验中归纳和传递因果推论。

IF 4.4 2区医学

Epidemiology Pub Date : 2025-07-01 Epub Date: 2025-04-23 DOI: 10.1097/EDE.0000000000001863

Lawson Ung, Tyler J VanderWeele, Issa J Dahabreh

{"title":"Generalizing and Transporting Causal Inferences from Randomized Trials in the Presence of Trial Engagement Effects.","authors":"Lawson Ung, Tyler J VanderWeele, Issa J Dahabreh","doi":"10.1097/EDE.0000000000001863","DOIUrl":"10.1097/EDE.0000000000001863","url":null,"abstract":"Trial engagement effects are effects of trial participation on the outcome that are not mediated by treatment assignment. Most work on extending (generalizing or transporting) causal inferences from a randomized trial to a target population has, explicitly or implicitly, assumed that trial engagement effects are absent, allowing evidence about the effects of the treatments examined in trials to be applied to nonexperimental settings. Here, we define novel causal estimands and present identification results for generalizability and transportability analyses in the presence of trial engagement effects. Our approach allows for trial engagement effects under assumptions of no causal interaction between trial participation and treatment assignment on the absolute or relative scales. We show that under these assumptions, even in the presence of trial engagement effects, the trial data can be combined with covariate data from the target population to identify average treatment effects in the context of usual care as implemented in the target population (i.e., outside the experimental setting). The identifying observed data functionals under these no-interaction assumptions are the same as those obtained under the stronger identifiability conditions that have been invoked in prior work. Therefore, our results suggest a new interpretation for previously proposed generalizability and transportability estimators. This interpretation may be useful in analyses under causal structures where background knowledge suggests that trial engagement effects are present but interactions between trial participation and treatment are negligible.","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"500-510"},"PeriodicalIF":4.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143958959","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}

引用次数: 0

Abortion Ratios After First-trimester Exposure to Teratogenic Medication in People with Disabilities. 残疾人妊娠早期接触致畸药物后的流产率。

IF 4.4 2区医学

Epidemiology Pub Date : 2025-07-01 Epub Date: 2025-03-24 DOI: 10.1097/EDE.0000000000001851

Andi Camden, Isobel Sharpe, Hong Lu, Hilary K Brown

引用次数: 0

Modeling Time-varying Dispersion to Improve Estimation of the Short-term Health Effect of Environmental Exposure in a Time-series Design. 在时间序列设计中建立时变离散度模型以改进对环境暴露的短期健康影响的估计。

IF 4.4 2区医学

Epidemiology Pub Date : 2025-07-01 Epub Date: 2025-03-31 DOI: 10.1097/EDE.0000000000001856

Danlu Zhang, Stefanie T Ebelt, Noah C Scovronick, Howard H Chang

{"title":"Modeling Time-varying Dispersion to Improve Estimation of the Short-term Health Effect of Environmental Exposure in a Time-series Design.","authors":"Danlu Zhang, Stefanie T Ebelt, Noah C Scovronick, Howard H Chang","doi":"10.1097/EDE.0000000000001856","DOIUrl":"10.1097/EDE.0000000000001856","url":null,"abstract":"Background: Time-series models for count outcomes are routinely used to estimate short-term health effects of environmental exposures. The dispersion parameter is universally assumed to be constant over the study period.Objective: The aim is to examine whether dispersion depends on time-varying covariates in a case study of emergency department visits in Atlanta during 1999-2009 and to evaluate approaches for addressing time-varying dispersion.Methods: Using the double generalized linear model framework, we jointly modeled the Poisson log-linear mean and dispersion to estimate associations between emergency department visits for respiratory diseases and daily ozone concentrations. We conducted a simulation study to evaluate the impact of time-varying overdispersion on health effect estimation when constant overdispersion is assumed and developed an analytic code for implementing double generalized linear model using R.Results: We found dispersion to depend on calendar date and meteorology. Assuming constant dispersion, the relative risk (RR) per interquartile range increase in 3-day moving ozone exposure was 1.037 (95% confidence interval: 1.024, 1.050). In the multivariable dispersion model, the RR was reduced to 1.029 (95% confidence interval: 1.020, 1.039), but with a large (26%) reduction in log RR standard error. The positive associations for ozone were robust against different dispersion model specifications. Simulation study results also demonstrated that when time-varying dispersion is present, it can lead to a larger standard error assuming constant dispersion.Conclusion: When the outcome exhibits large dispersion in a time-series analysis, allowing for covariate-dependent time-varying dispersion can improve inference, particularly by increasing estimation precision.","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"450-457"},"PeriodicalIF":4.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12122218/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143751752","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}

引用次数: 0