Epidemiology最新文献

{"title":"An Improved Pooled Logistic Regression Implementation.","authors":"Paul N Zivich, Mark Klose, Justin B DeMonte, Bonnie E Shook-Sa, Stephen R Cole, Jessie K Edwards","doi":"10.1097/EDE.0000000000001945","DOIUrl":"10.1097/EDE.0000000000001945","url":null,"abstract":"<p><strong>Background: </strong>Pooled logistic regression is a popular tool for survival analyses in epidemiology, but can face computational challenges. Commonly, these challenges are addressed through widening time intervals or using a parametric functional form for time. We propose a third option to reduce the computational burden without constraining the functional form for time.</p><p><strong>Methods: </strong>The proposed algorithm operates by restricting the long data set to rows that correspond to unique event times. However, our approach is only compatible when modeling time most flexibly with disjoint indicators. We compared the standard implementation to the proposed implementation in SAS, R, and Python using a publicly available data set.</p><p><strong>Results: </strong>For the example considered, both implementations provided the same point estimates, but the proposed implementation was between 6 and 68 times faster depending on the software.</p><p><strong>Conclusions: </strong>The proposed implementation can greatly simplify estimation of pooled logistic regression models, which is especially important when relying on the bootstrap for inference.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"311-314"},"PeriodicalIF":4.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12778973/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145905884","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":"Antibiotics and Preterm Delivery: The Prevalent New-user Cohort Design to Resolve Immortal Time Bias.","authors":"Simon Galmiche, Eros Comin, Sophie Dell'Aniello, Jacques Balayla, Samy Suissa","doi":"10.1097/EDE.0000000000001947","DOIUrl":"10.1097/EDE.0000000000001947","url":null,"abstract":"<p><strong>Background: </strong>Observational studies of the association between antibiotics and preterm delivery report conflicting findings, with some potentially affected by immortal time bias. We assessed the effects of third-trimester antibiotic use on preterm delivery and low birthweight, using a study design that accounts for immortal time bias.</p><p><strong>Methods: </strong>We used the UK's Clinical Practice Research Datalink to identify pregnant females aged 12-50, over the period 2002 to 2016, reaching 27 weeks of gestation without antibiotic use until that point. We applied the prevalent new-user design, matching each third-trimester antibiotic initiator with a reference nonuser at the same gestational day, using time-conditional propensity scores. The 2 matched groups were compared on the incidence of preterm delivery and low birthweight. The full cohort was also analyzed with antibiotic use considered as time-fixed and time-varying exposures.</p><p><strong>Results: </strong>The cohort included 207,027 pregnancies, with 16,865 initiating antibiotics matched to 16,865 nonusers. The hazard ratio (HR) of preterm delivery with third-trimester antibiotic use was 1.14 [95% confidence interval (CI): 1.04, 1.24], compared with nonuse. With time-fixed exposure, subject to immortal time bias, the HR was 0.78 (95% CI: 0.73, 0.83), while with time-varying exposure, the HR was 1.23 (95% CI: 1.16, 1.32). The HR of low birthweight with antibiotic initiation was 1.07 (95% CI: 0.93, 1.25) compared with 0.91 (95% CI: 0.83, 1.00) under the time-fixed approach.</p><p><strong>Conclusions: </strong>Using the prevalent new-user design, which emulates a randomized trial, antibiotic use late in pregnancy was associated with a modestly increased incidence of preterm delivery. Previous inconclusive studies may have resulted from observational methods that introduced, or insufficiently addressed, immortal time bias.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"355-362"},"PeriodicalIF":4.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145862546","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":"What Makes Something Worth Knowing? Epistemology and Public Health Impact.","authors":"Katherine M Keyes","doi":"10.1097/EDE.0000000000001957","DOIUrl":"10.1097/EDE.0000000000001957","url":null,"abstract":"","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"371-373"},"PeriodicalIF":4.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147282922","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":"Development and Validation of Gestational Age Estimation Algorithms for Nonlive Births in Administrative Healthcare Databases.","authors":"Yongtai Cho, Eun-Young Choi, Hyesung Lee, Yunha Noh, Jung Yeol Han, Seung-Ah Choe, Hoon Kim, Ju-Young Shin","doi":"10.1097/EDE.0000000000001956","DOIUrl":"10.1097/EDE.0000000000001956","url":null,"abstract":"<p><strong>Background: </strong>Algorithms to estimate gestational age (GA) for nonlive births have been developed in other administrative healthcare databases, but their applicability in Korea remains unknown. We adapted algorithms developed in the United States and evaluated their validity in Korean healthcare claims data.</p><p><strong>Methods: </strong>Using the National Health Information Database of South Korea, we linked GA information on influenza vaccination from the national vaccination registry to establish a reference standard. Nonlive births were stratified into spontaneous/induced abortions and stillbirths. Four algorithms were tested: (1) assigning outcome-specific GA, (2) adjusting GA based on ultrasound scan records, (3) a regression model using gestational markers as predictors, and (4) a random forest model. Algorithms were evaluated by the proportion of estimates within 1-4 weeks of the reference standard and the mean squared error (MSE). External validation was conducted using an independent dataset.</p><p><strong>Results: </strong>Random forests performed best for both spontaneous/induced abortions (MSE 1.68 weeks 2 ) and stillbirths (MSE 0.97 weeks 2 ), with 92.6% (95% confidence interval 91.6, 93.4) and 97.4% (96.2, 98.3) of predictions falling within 2 weeks of the reference standard, respectively. However, in the external validation set, the ultrasound record-based adjustment approach performed similarly to the random forest approach for both spontaneous/induced abortions (MSE 8.37 vs. 8.15 weeks 2 ) and stillbirths (MSE 12.42 vs. 12.52 weeks 2 ).</p><p><strong>Conclusions: </strong>Deterministic approaches may be preferable for estimating GA of nonlive births in the National Health Information Database, as they are simpler to implement and perform comparably to model-based algorithms. These algorithms can support pregnancy research in the Korean population.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"336-344"},"PeriodicalIF":4.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146156611","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":"Differential Reporting of Severe Maternal Morbidity on US Birth Certificate and Claims Data by Race and Ethnicity.","authors":"Beth L Pineles, Anthony D Harris, Lisa Pineles, Esa M Davis, K S Joseph, Enrique Schisterman, Laurence S Magder, Katherine E Goodman","doi":"10.1097/EDE.0000000000001954","DOIUrl":"10.1097/EDE.0000000000001954","url":null,"abstract":"<p><strong>Background: </strong>US birth certificates are the only complete, national source of severe maternal morbidity (SMM) data, but state and local studies have identified data quality concerns. The objective of this study was to compare reporting of SMM in birth certificate versus hospital claims data and evaluate differences by patient race/ethnicity.</p><p><strong>Methods: </strong>We compared incidence rates of blood transfusion, hysterectomy, intensive care unit admission, uterine rupture, and third/fourth degree perineal laceration between 2019 deliveries in the United States birth certificate and the Premier Healthcare Database, overall and stratifying by maternal race/ethnicity. We then computed incidence rate ratios (IRRs) computed between datasets, and fit logistic regression models of race/ethnicity on SMM.</p><p><strong>Results: </strong>Comparing 3,467,934 birth certificate deliveries with 3,450,569 Premier deliveries (n = 905,766 preweighting for national representativeness), incidence rates of SMMs were lower in birth certificate compared with Premier data, and these rate differentials varied by maternal race/ethnicity. For example, among non-Hispanic white patients, the incidence rate of blood transfusions in birth certificate data was 50% that of the incidence rate in the Premier claims dataset (IRR: 0.50, 95% confidence interval [CI] = 0.47, 0.52). Among all other races/ethnicities, the incidence rate of blood transfusions was even lower relative to the claims data (IRR range: 0.29-0.39). Adjusted odds ratios for SMM in non-Hispanic Black and Hispanic patients versus non-Hispanic white patients were closer to the null in birth certificate than Premier data (e.g., compared with non-Hispanic white patients, non-Hispanic Black patients had a 16% higher adjusted odds in the birth certificate [95% CI = 1.10, 1.21] data versus an 84% higher adjusted odds of blood transfusion in Premier data [95% CI = 1.79, 1.89]).</p><p><strong>Conclusions: </strong>Birth certificates report substantially less SMM than claims data, with a greater differential in reporting for non-Hispanic Black and Hispanic patients that may bias birth certificate-based research findings.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"345-354"},"PeriodicalIF":4.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146028683","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":"Applying the Target Study Conceptual Model to Measure Racial and Ethnic Disparities in Hypertension Treatment Intensification.","authors":"Aster Meche, Romsai T Boonyasai, Yea-Jen Hsu, Raquel C Greer, Hemalkumar B Mehta, G Caleb Alexander, Jodi B Segal, Lisa A Cooper, John W Jackson","doi":"10.1097/EDE.0000000000001964","DOIUrl":"10.1097/EDE.0000000000001964","url":null,"abstract":"<p><strong>Background: </strong>This tutorial paper demonstrates the application of a conceptual model to measure racial and ethnic disparities in treatment intensification among adults with hypertension consistent with the Institute of Medicine (IOM) definition. The IOM defines disparity as differences in health care quality that are not due to access-related factors, clinical needs, preferences, and appropriateness of intervention (referred to as allowable covariates).</p><p><strong>Methods: </strong>We used a conceptual model called the Target Study to estimate annual disparities in the probability of treatment intensification between Black and White patients seen at primary care clinics within a large healthcare system in the Mid-Atlantic region from 2018 to 2022, using electronic medical record data. We emulated the specified target study through an appropriate study design and the use of inverse probability weighting to balance allowable covariates while measuring disparities.</p><p><strong>Results: </strong>Unadjusted analyses showed a higher percentage of treatment intensification for Black patients compared with White patients, with annual differences ranging from 2 to 4 percentage points. For example, in 2020, the unadjusted difference was three percentage points (95% confidence interval: 1%, 5%). After adjusting for allowable covariates via the emulated target study, Black patients had consistently lower percentages of treatment intensification by 3-4 percentage points each year. For example, in 2020, the adjusted disparity was -3 percentage points (95% confidence interval: -4%, -1%).</p><p><strong>Conclusions: </strong>The Target Study can implement the IOM definition for measuring disparities in health care. Future research should explore its application across different populations to better understand and address health disparities.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":"37 3","pages":"376-385"},"PeriodicalIF":4.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13045737/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147590903","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":"Jørn Olsen, 20 May 1946-5 January 2026.","authors":"","doi":"10.1097/EDE.0000000000001961","DOIUrl":"https://doi.org/10.1097/EDE.0000000000001961","url":null,"abstract":"","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":"37 3","pages":"281"},"PeriodicalIF":4.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147697754","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":"Mind the Gap: Addressing Missing Person Time When Estimating Outcome Incidence in Longitudinal Data.","authors":"Jacqueline E Rudolph, Rachael K Ross, Lauren C Zalla, Shruti H Mehta, Gregory D Kirk, Becky L Genberg, Bryan Lau, Catherine R Lesko","doi":"10.1097/EDE.0000000000001958","DOIUrl":"10.1097/EDE.0000000000001958","url":null,"abstract":"<p><strong>Background: </strong>Longitudinal data often include gaps in observation when outcomes (and other variables) are unmeasured due to missed study visits or dropout. We explore the fundamentals of data gaps and use simulation to compare approaches for handling data gaps when estimating outcome incidence.</p><p><strong>Methods: </strong>We generated a simulation of 1000 individuals across 10 study visits. We used 4 data-generating mechanisms: (1) missingness was independent of the outcome; (2) there was a baseline common cause of missingness and the outcome; (3) there was a time-varying common cause; and (4) the outcome directly affected future missingness. We estimated the risk and rate of the first outcome occurrence (generated as a transient, repeated, and permanent outcome), using crude and adjusted approaches, across 1000 iterations, and compared bias and empirical standard error.</p><p><strong>Results: </strong>Under Scenario 1, in crude analyses, results were unbiased when censoring before a data gap but not when allowing participants to return. Under scenarios 2-4, all crude approaches were biased. Inverse probability of censoring weights and multiple imputation were relatively unbiased across scenarios and outcome types; multiple imputation was more precise. Inverse probability of observation weights was biased when the outcome was permanent and was less precise than either of the other two approaches.</p><p><strong>Conclusion: </strong>Crude approaches allowing participants to return following a data gap are not recommended because they can be biased even when missingness and the outcome are independent. Instead, one should either censor or handle the data gap using multiple imputation.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"287-297"},"PeriodicalIF":4.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12893332/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146156618","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":"Considerations for the Analysis of Urinary Environmental Chemical Concentrations During Pregnancy.","authors":"Danielle R Stevens, Kaleigh Hinton, Katie M O'Brien, Jessie P Buckley, Barrett M Welch, Jason A Watts, Antonia M Calafat, Julianne C Botelho, Elena Sinkovskaya, Ann Przybylska, George Saade, Alfred Abuhamad, Kelly K Ferguson","doi":"10.1097/EDE.0000000000001960","DOIUrl":"10.1097/EDE.0000000000001960","url":null,"abstract":"<p><strong>Background: </strong>Pregnancy physiology may impact indicators of hydration, affecting exposure assessment in gestational studies with urinary chemical biomarkers.</p><p><strong>Methods: </strong>We aimed to characterize hydration and demonstrate the impact of different methods for standardizing urinary chemical biomarker concentrations on exposure descriptives in the Human Placenta and Phthalates Study (n = 303, 2017-2020), a prospective pregnancy cohort with eight study visits between 12 and 38 weeks of gestation. We assessed trajectories and predictors of hydration using urine flow rate (UFR) and specific gravity (SG). Likewise, we examined trajectories and predictors of mono-n-butyl phthalate (MBP), for which concentrations were unstandardized or standardized via UFR (analyte excretion rates), SG alone (Boeniger method), or covariate-adjusted SG (O'Brien method). We used generalized additive mixed effects models to examine trajectories. We used linear mixed effects models to investigate participant demographic and pregnancy characteristics influencing UFR and SG, and agreement between unstandardized and standardized MBP concentrations.</p><p><strong>Results: </strong>As pregnancy progressed, SG declined linearly, whereas UFR varied in a nonlinear manner. Several demographic and pregnancy characteristics, notably race and ethnicity, were associated with UFR and SG. Unstandardized and standardized MBP concentrations showed good agreement, with lower agreement observed between concentrations standardized using UFR (analyte excretion rates) relative to SG (Boeniger and O'Brien methods). Nevertheless, trajectories and predictors of MBP concentrations were similar across all standardization approaches.</p><p><strong>Conclusions: </strong>Despite systematic variations in hydration across gestation and by demographic and pregnancy characteristics, methods commonly used for standardizing urinary concentrations of chemical biomarkers were robust to differences in the hydration indicator as well as the standardization method employed.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"325-335"},"PeriodicalIF":4.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13034755/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146206648","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":"Discretizing Continuous Event Time Data.","authors":"Rachael K Ross, Jacqueline E Rudolph, Lauren C Zalla, Catherine R Lesko","doi":"10.1097/EDE.0000000000001946","DOIUrl":"10.1097/EDE.0000000000001946","url":null,"abstract":"<p><p>Although data may capture continuous event times or event times with high resolution (e.g., day), some statistical analyses require the discretization of time into intervals and assigning each event (i.e., outcome or loss to follow-up [LTFU]) to the start or end of an interval. First, using a simulated example, we showed that outcomes should be assigned to the end of the interval. Next, we considered four approaches for assigning LTFU events in a simulated example and in 20 real datasets. Comparing the resulting cumulative risk curves with the curve using continuous time, one approach always had the least error: assigning LTFU to the start or end of the interval, depending on which was closest to the continuous event time. This approach was superior to always censoring at the beginning or end of the interval.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":"37 3","pages":"307-310"},"PeriodicalIF":4.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147590962","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}