Härkänen T, Tapanainen H, Sares-Jäske L
… +3 more, Männistö S, Kaartinen NE, Paalanen L
Epidemiology
· 2026 Mar · PMID 41505395
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BACKGROUND: Projections for major health outcomes are crucial for decision-making to enable early interventions. Pooling results from meta-analyses with estimates based on individual participant data can reduce uncertain...BACKGROUND: Projections for major health outcomes are crucial for decision-making to enable early interventions. Pooling results from meta-analyses with estimates based on individual participant data can reduce uncertainty and improve generalizability. However, current methods for meta-meta-analyses of nonlinear functions have remained underdeveloped. METHODS: We proposed a novel meta-analysis method to pool pointwise nonlinear function literature estimates with parameter estimates, applied here to the association of dietary change scenarios with mortality and ischemic heart disease (IHD) based on Finnish individual participant data. We linked individual-level demographic and risk factor data from the Health 2000, FINRISK 2007, FINRISK 2012, and FinHealth 2017 surveys (n = 20,784) with follow-up data on outcomes obtained from national health registers. We applied a Poisson multistate model and microsimulation to project state probabilities. RESULTS: Pooling reduced uncertainty in the hazard ratio estimates and in the health projections. We estimated that a two-thirds reduction in red and processed meat consumption would decrease the prevalence of IHD by 2 (95% prediction intervals [PI] 1, 4) percentage points (%pt) in the 2017 cohort and deaths by 2%pt (95% PI 1, 4) by 2050. We estimated that a 100% increase in whole grain consumption would reduce IHD by 2%pt (95% PI 0, 3) and deaths by 2%pt (95% PI 0, 3). CONCLUSIONS: Our flexible meta-analysis method allowed the pooling of nonlinear estimates reported in the literature without detailed technical information. Our estimates support the hypothesis that more plant-based diets reduce mortality and IHD prevalence. The most beneficial scenarios included reductions in red and processed meat and increments in whole grain consumption.
Zivich PN, Klose M, DeMonte JB
… +3 more, Shook-Sa BE, Cole SR, Edwards JK
Epidemiology
· 2026 May · PMID 41490323
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BACKGROUND: 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 p...BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: The proposed implementation can greatly simplify estimation of pooled logistic regression models, which is especially important when relying on the bootstrap for inference.
Epidemiology
· 2026 Mar · PMID 41490058
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Causal inference is the goal of randomized trials and many observational studies. The first step in a formal causal inference framework is to define the causal estimand, and in both types of study this can be done mathem...Causal inference is the goal of randomized trials and many observational studies. The first step in a formal causal inference framework is to define the causal estimand, and in both types of study this can be done mathematically or, equivalently, by specifying an ideal trial: a hypothetical perfect randomized experiment (with representative sample, perfect adherence, etc). The target trial framework is increasingly used for causal inference in observational studies, but clarity is lacking in how a target trial should be specified and how it relates to an ideal trial. Here, we review the mathematical and ideal trial approaches to defining a causal estimand, highlighting their equivalence and the need to balance practical relevance and feasibility of estimation regardless of approach. We then consider the question of how a target trial should be specified, outlining the challenges of a recommended approach, commonly seen in applications, that puts the focus heavily on the feasibility of estimation: to specify the target trial such that it is closely aligned with the observational data (e.g., uses the same eligibility criteria). We argue that with this "aligned" approach, biases may remain relative to the estimand of ultimate practical interest, defined by the ideal trial, that mirror the often-overlooked biases of actual trials. We conclude that consideration of the ideal trial and of how the target trial and its emulation or the actual trial differ from it is necessary to identify and manage all bias sources in both settings. An example from respiratory epidemiology is used for illustration.
Shapiro AN, Mohammed S, Horsburgh CR
… +2 more, Jenkins HE, White LF
Epidemiology
· 2026 Mar · PMID 41481913
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BACKGROUND: Despite being the leading cause of death, the global tuberculosis (TB) burden is ill-defined. Existing methods to estimate incidence are time and/or resource-intensive and often inaccurate. Back-calculation w...BACKGROUND: Despite being the leading cause of death, the global tuberculosis (TB) burden is ill-defined. Existing methods to estimate incidence are time and/or resource-intensive and often inaccurate. Back-calculation was developed to estimate HIV incidence by considering reported cases to be a convolution of the disease duration and the incidence of new cases. New estimates of TB natural history parameters allow us to develop Bayesian back-calculation methods for TB to assign case notification data to the time point of onset of disease. METHODS: Recorded counts of TB cases are underestimates of the true burden of disease, so we include a multiplier derived from prevalence to notification ratios to account for underreporting. We assume a Poisson distribution for notifications and incidence and use a penalized-likelihood before smooth estimates. We estimate sex-stratified TB incidence for Vietnam, Cambodia, and the Philippines via Markov chain Monte Carlo. RESULTS: Annual estimated TB incidence was, on average 19% greater than recorded notifications. TB incidence among males was on average 3.8% higher than females in Vietnam, 1.3% in Cambodia, and 2.5% higher in the Philippines. CONCLUSIONS: These estimates account for the delay between bacteriologically positive subclinical disease and notification and, as such, may be more temporally accurate than existing methods.
Yu M, Liu TH, Li KQ
… +5 more, Jewell N, Tchetgen Tchetgen E, Small D, Shi X, Wang B
Epidemiology
· 2026 Mar · PMID 41474934
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Test-negative designs (TNDs) are widely used for postmarket evaluation of vaccine effectiveness (VE), particularly in cases when randomized trials are not feasible. Unlike classical TNDs, which only include healthcare se...Test-negative designs (TNDs) are widely used for postmarket evaluation of vaccine effectiveness (VE), particularly in cases when randomized trials are not feasible. Unlike classical TNDs, which only include healthcare seekers with symptoms, recent TNDs have involved individuals with various reasons for testing, especially in an outbreak setting. While including these data can increase sample size and hence improve precision, concerns have been raised about whether they introduce bias into the current framework of TNDs, thereby demanding a formal statistical examination of this modified design. In this article, using statistical derivations, causal graphs, and numerical demonstrations, we show that the standard odds ratio estimator may be biased if various reasons for testing are not taken into account. To eliminate this bias, we identify three categories of reasons for testing, namely symptoms, mandatory screening, and case contact tracing, and characterize associated statistical properties and estimands. Based on our characterization, we show how to consistently estimate each estimand via stratification. Furthermore, we describe when these estimands correspond to the same VE parameter and, when appropriate, propose a stratified estimator that can incorporate multiple reasons for testing and improve precision. We demonstrate the performance of our proposed method through simulation studies and a real-data analysis.
Galmiche S, Comin E, Dell'Aniello S
… +2 more, Balayla J, Suissa S
Epidemiology
· 2026 May · PMID 41468587
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BACKGROUND: 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...BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Willis MD, Sheng C, Lovett SM
… +8 more, Feldscher T, Sims KD, Francis B, Hicks JM, Holder EX, Wise LA, Cozier YC, Wesselink AK
Epidemiology
· 2026 Mar · PMID 41397260
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BACKGROUND: Structural racism can manifest in contemporary neighborhoods via historical policies or programs. For example, the Home Owners' Loan Corporation, a government-backed program from the 1930s, systematically div...BACKGROUND: Structural racism can manifest in contemporary neighborhoods via historical policies or programs. For example, the Home Owners' Loan Corporation, a government-backed program from the 1930s, systematically diverted wealth away from Black neighborhoods. The reproductive health consequences of this racist program remain understudied. We evaluated associations between residence in a historically redlined neighborhood and fecundability, the per-cycle probability of conception. METHODS: We used data from the Black Women's Health Study, a US cohort of Black women who were aged 21-69 years in 1995 and were followed up with biannual questionnaires. Experiences of infertility (i.e., tried for ≥12 months to conceive without success) were captured on several questionnaires. A 2011 supplemental module collected pregnancy histories between 1995 and 2011, including planning status and time to conception. We linked geocoded addresses to historical Home Owners' Loan Corporation grades (A ["best"] to D ["hazardous," i.e., redlined]). Using proportional probabilities regression models with generalized estimating equations, we estimated fecundability ratios and 95% confidence intervals (CIs). RESULTS: Our analysis included 818 planned pregnancy attempts from 674 participants (mean age = 33.9 years). Relative to participants residing in neighborhoods with the highest grades (A or B), adjusted models showed reduced fecundability among participants who resided in lower graded neighborhoods (C: 0.91, 95% CI: 0.77, 1.09; D: 0.82, 95% CI: 0.68, 0.99). CONCLUSIONS: In this cohort of US Black women, contemporary residence in a historically redlined neighborhood was associated with reduced fecundability. Our findings highlight the importance of exploring how historical neighborhood disinvestment affects reproductive health.
Hailu EM, Riddell CA, Tamene M
… +2 more, Carmichael SL, Mujahid MS
Epidemiology
· 2026 Mar · PMID 41397239
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BACKGROUND: Drivers of persistent racial-ethnic inequities in severe maternal morbidity are poorly understood. This study examined how neighborhood-level structural racism measures shape risk of severe maternal morbidity...BACKGROUND: Drivers of persistent racial-ethnic inequities in severe maternal morbidity are poorly understood. This study examined how neighborhood-level structural racism measures shape risk of severe maternal morbidity among Black mothers. METHODS: Data are from live hospital births in California between 1997 and 2019 at ≥20 weeks' gestation (N = 555,511). We leveraged information from the U.S. Census Bureau and the American Community Survey to determine neighborhood (census tract) measures of structural racism across six domains (homeownership, unemployment, poverty, educational attainment, racialized economic deprivation, and racial residential segregation). We used (1) an additive composite index (quartile 1 [low]-quartile 4 [high]) and (2) latent class analysis to characterize four structural racism typologies. We examined associations across both measurement approaches using mixed-effects logistic regression models with neighborhood random intercepts, adjusting for maternal age, education, and hospital payer/insurance information. RESULTS: Black mothers living in neighborhoods scoring high (quartile 4) on the additive composite index had 13% higher risk of severe maternal morbidity than those in neighborhoods scoring low (quartile 1) (95% confidence interval = 1.04, 1.24). Models evaluating latent class typologies also revealed that Black mothers living in neighborhoods characterized by consistently high racial inequity in unemployment, racialized economic deprivation, and racial residential segregation across the study period had a 12% higher risk of severe maternal morbidity compared with those in neighborhoods consistently scoring low in the domains examined (95% confidence interval = 1.03, 1.23). CONCLUSIONS: Our findings support the hypothesis that neighborhood-level measures of structural racism influence the risk of severe maternal morbidity among Black mothers.
Lesko CR, Zalla LC, Ross RK
… +3 more, Rudolph JE, Smith ER, Edwards JK
Epidemiology
· 2026 May · PMID 41397236
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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, e...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.
Gittins M, Palmer B, Xiang H
… +3 more, Chowdary P, Collins P, Bird SM
Epidemiology
· 2026 Mar · PMID 41397235
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BACKGROUND: Between 1970 and 1991, when viral inhibition reduced the risk, people with a bleeding disorder in the United Kingdom had their missing clotting factors replaced with plasma products derived from donated plasm...BACKGROUND: Between 1970 and 1991, when viral inhibition reduced the risk, people with a bleeding disorder in the United Kingdom had their missing clotting factors replaced with plasma products derived from donated plasma at risk of infection. We analyzed longer-term survival of people with bleeding disorders exposed to plasma products. METHODS: The National Haemophilia Database documents people with bleeding disorders registered, treated before, and alive on 1 January 1992. We estimated all-cause mortality proportional hazard ratios for exposure groups (Human immunodeficiency virus (HIV)/Hepatitis C virus (HCV) coinfected, HCV-diagnosed, and HCV-status unknown) versus HCV antibody negative, within distinct epochs: 1992-1999; 2000-2009; 2010-2019. We estimated years of life lost by epoch and exposure group versus UK general population lifetables or via parametric survival models compared with people with bleeding disorders negative or unknown for HCV antibodies. Models were adjusted for sex, age band at 1 January 1992, bleeding disorder, and severity. RESULTS: Of 6282 people with bleeding disorders who met inclusion criteria, 15% were HIV/HCV coinfected, 32% HCV antibody positive, and 28% HCV antibody negative. Compared with HCV-negative, those HIV/HCV coinfected had an all-cause mortality hazard ratio of 4.2 (95% confidence interval: 2.9, 6.0) and HCV+ of 2.2 (1.7, 2.8) in 2010 to 2019. Years of life lost for 2014 to 2019 were 740 (95% confidence interval: 440, 1030) for HCV+ persons and 270 (130, 400) for HIV/HCV coinfected persons, compared with HCV unknown or negative persons. CONCLUSIONS: People with bleeding disorders in the United Kingdom infected before, but alive at, 1 January 1992, were still at increased risk of death 3 decades postimplementation of HCV screening of blood supplies.
Ejlskov L, Esen BÖ, Formánek T
… +5 more, Hakulinen C, Weye N, McGrath JJ, Pedersen CB, Plana-Ripoll O
Epidemiology
· 2026 May · PMID 41397234
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BACKGROUND: 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 address...BACKGROUND: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.
Cordes J, Glynn RJ, Walker AM
… +1 more, Schneeweiss SS
Epidemiology
· 2026 Mar · PMID 41397233
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Publisher ↗
BACKGROUND: Randomized trials of major adverse cardiovascular events found no effect of dipeptidyl-peptidase-4 inhibitors (DPP-4i) medications compared with second-generation sulfonylureas, while nonrandomized studies es...BACKGROUND: Randomized trials of major adverse cardiovascular events found no effect of dipeptidyl-peptidase-4 inhibitors (DPP-4i) medications compared with second-generation sulfonylureas, while nonrandomized studies estimated a benefit of DPP-4i. Socioeconomic residual confounding was thought to be implicated. We compared area-level prescribing density and physician prescribing preference as candidate instrumental variables for the effect of DPP-4i medications on major adverse cardiovascular events. METHODS: Using Medicare claims data, we built two cohorts emulating randomized trials of sitagliptin or saxagliptin starters, each compared with sulfonylurea starters. The proportion of DPP-4i prescribing in a ZIP Code tabulation area defined the area-level prescribing density instrumental variable at various cutoffs (0% vs. 100% to <50% vs. ≥50%). Patients' physician prescribing history using the same proportion cutoffs was the physician prescribing preference candidate instrumental variable. An instantaneous physician preference instrumental variable used a physician's most recent prescription. We adjusted two-stage instrumental variable regression models for propensity score quintiles. RESULTS: Unadjusted analyses for sitagliptin and saxagliptin, each compared with sulfonylurea, estimated a reduced risk of major adverse cardiovascular events [sitagliptin hazard ratio (HR) = 0.86; 95% confidence interval = 0.83, 0.88]; saxagliptin (HR = 0.68; 0.64, 0.73). All instrumental variables were strong and reduced covariate imbalance. Analyses of area-level prescribing density found no meaningful difference for sitagliptin (0% vs. 100% HR = 1.1; 0.79, 1.6). Analyses of physician prescribing preference estimated reduced risk for sitagliptin (<50% vs. ≥50% HR = 0.69; 0.48, 0.98). Instantaneous physician prescribing preference analyses showed little to no difference for sitagliptin (HR = 0.86; 0.60, 1.1) and saxagliptin (HR = 0.98; 0.56, 1.7). CONCLUSIONS: Candidate instrumental variables focusing on short-term prescribing preference hold promise over area-based variables but remain inefficient.
Ozluk P, Romine J, Sylwestrzak G
… +1 more, Hamad R
Epidemiology
· 2026 Mar · PMID 41364631
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BACKGROUND: School closures during the coronavirus disease 2019 pandemic disrupted children's education, socialization, and access to mental health resources, raising concerns about long-term effects on childrens' mental...BACKGROUND: School closures during the coronavirus disease 2019 pandemic disrupted children's education, socialization, and access to mental health resources, raising concerns about long-term effects on childrens' mental health. The objective was to evaluate the impacts of pandemic-era school reopenings on children's mental health and healthcare expenditures. Variation in the timing of school reopenings created a unique quasi-experiment. METHODS: We used difference-in-differences analysis to examine how staggered implementation of school reopenings affected diagnoses with depression, anxiety, and attention-deficit/hyperactivity disorder, and related healthcare spending among school-aged children during March 2020-June 2021 across 24 California counties. Data were drawn from medical claims from the second largest private health insurer in the state (N = 185,735). RESULTS: School reopening was associated with a 1.2%-point drop in monthly prevalence of mental health diagnoses [95% confidence interval (CI) = -1.59, -0.74], and a 10.6% (95% CI = -13.4%, -7.8%) drop in related healthcare spending. The mental health conditions that saw the largest differential between in-person and remote school districts were anxiety and depression. Effects were strongest among girls. CONCLUSIONS: In-person learning is an important component of children's mental health. These results are informative for future policymaking during public health crises, to balance infection risk with the need for socialization and other critical resources that schools provide to children.
Epidemiology
· 2026 Mar · PMID 41355391
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BACKGROUND: Data on lactation status for research are often collected through surveys. Information on human milk feeding collected at routine healthcare visits and stored in electronic health records (EHR) is an emerging...BACKGROUND: Data on lactation status for research are often collected through surveys. Information on human milk feeding collected at routine healthcare visits and stored in electronic health records (EHR) is an emerging source of data for lactation research. We compared information on milk feeding obtained from structured EHR fields with survey data. METHODS: We included participants from five US healthcare systems in the Managing Our Mood survey. Individuals had a live birth (March 2022-October 2023), depression diagnosis during pregnancy, and ≥1 record of human milk feeding information in their or their infant's EHR. We compared information from EHR data up to ten months after delivery with survey data collected 3-4 months after delivery as the reference. We assessed agreement on lactation status (human milk feeding ever and at survey) using percent agreement, Cohen's kappa, sensitivity, specificity, positive predictive value, and negative predictive value overall and by characteristics. RESULTS: According to survey data, the prevalence of human milk feeding ever was 93.2% and was 73.0% at the time of survey among 281 eligible individuals. Agreement between data sources for ever and for human milk feeding at the survey was ≥92% with kappas ≥0.77. EHR and survey data agreed on human milk feeding ever for 97.3% (95% confidence interval: 94.6%, 98.7%) and on human milk feeding at the time of the survey for 98.0% (95% confidence interval: 95.1%, 99.2%) of those who reported yes to these practices on the survey. These measurements were lower among individuals with fewer records. CONCLUSIONS: There was substantial agreement on lactation status between EHR and survey data. These findings suggest that lactation information from structured EHR may be used for epidemiologic research.
Poveda NE, Elliott MR, Mehta NK
… +1 more, Cunningham SA
Epidemiology
· 2026 Jan · PMID 41342794
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BACKGROUND: Obesity dynamics early in life are likely important for long-term health, but have only been described piecemeal, because nationally representative longitudinal datasets are few and have limited follow-up dur...BACKGROUND: Obesity dynamics early in life are likely important for long-term health, but have only been described piecemeal, because nationally representative longitudinal datasets are few and have limited follow-up duration. METHODS: We created a synthetic cohort by combining two US nationally representative datasets, the Early Childhood Longitudinal Study, Kindergarten Class of 1998-1999 (ECLS98; N = 21,120; ages 4-16 years; birth cohort 1991-1994), and the National Longitudinal Survey of Youth 1997 (NLSY97; N = 8,984; ages 12-41 years; birth cohort 1980-1984). We used the older-age cohort to impute future weight trajectories of children in the younger-age cohort by matching based on subject-level body mass index trajectories estimated via linear mixed models. We projected trajectories to age 41 years in 2035 for children observed up to a mean age of 13.5 years in 2007. RESULTS: The synthetic cohort (N = 10,102) showed that obesity prevalence increases from 10.0% at age 4 years to 56.3% at age 41 years. Obesity incidence peaks at ages 8 years (4.00/100 person-years [PY] [3.29-4.73]), 26 years (4.48/100 PY [3.04-5.92]), and 38 years (3.60/100 PY [0.00-8.91]). CONCLUSIONS: This synthetic cohort approach can be used to characterize dynamics of obesity and other conditions by maximizing data from shorter "life segments." Findings suggest that today's young adults will continue to become heavier as they age. In addition to prevention before kindergarten entry, other periods for obesity prevention could be middle childhood, mid-twenties, and late thirties.
Boyer CB, Li KQ, Shi X
… +1 more, Tchetgen Tchetgen EJ
Epidemiology
· 2026 Jan · PMID 41342793
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Publisher ↗
The test-negative design (TND) is widely used to evaluate vaccine effectiveness in real-world settings. In a TND study, individuals with similar symptoms who seek care are tested, and effectiveness is estimated by compar...The test-negative design (TND) is widely used to evaluate vaccine effectiveness in real-world settings. In a TND study, individuals with similar symptoms who seek care are tested, and effectiveness is estimated by comparing vaccination histories of test-positive cases and test-negative controls. The TND is often justified on the grounds that it reduces confounding due to unmeasured health-seeking behavior, although this has not been formally described using potential outcomes. At the same time, concerns persist that conditioning on test receipt can introduce selection bias. We provide a formal justification of the TND under an assumption of odds ratio equi-confounding, where unmeasured confounders affect test-positive and test-negative individuals equivalently on the odds ratio scale. Health-seeking behavior is one plausible example. We also show that these results hold under the outcome-dependent sampling used in TNDs. We discuss the design implications of the equi-confounding assumption and provide alternative estimators for the marginal risk ratio among the vaccinated under equi-confounding, including outcome modeling and inverse probability weighting estimators as well as a semiparametric estimator that is doubly robust. When equi-confounding does not hold, we suggest a straightforward sensitivity analysis that parameterizes the magnitude of the deviation on the odds ratio scale. A simulation study evaluates the empirical performance of our proposed estimators under a wide range of scenarios. Finally, we discuss broader uses of test-negative outcomes to de-bias cohort studies in which testing is triggered by symptoms.