Wang J, Buto P, Ferguson EL
… +9 more, Chen R, Pederson A, Choi M, Murchland AR, Ackley SF, Blacker D, Hayes-Larson E, Mayeda ER, Glymour MM
Epidemiology
· 2026 Jun · PMID 42348262
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BACKGROUND: Participants in the All of Us research study differ from the U.S. population in myriad characteristics, limiting the generalizability of findings. A statistical reweighting tool to improve generalizability wo...BACKGROUND: Participants in the All of Us research study differ from the U.S. population in myriad characteristics, limiting the generalizability of findings. A statistical reweighting tool to improve generalizability would enhance the scientific value of the data. METHODS: To account for differences between All of Us and the nationally representative 1999-2018 National Health and Nutrition Examination Survey (NHANES), we generated selection weights using four models incorporating sociodemographic, self-reported health, and clinical characteristics. We assessed covariate balance and compared predictors of all-cause mortality in weighted All of Us to NHANES using the ratio of hazard ratios (RHRs), where an RHR of one indicates unbiased estimates in (weighted) All of Us relative to NHANES. RESULTS: Weighting improved balance on measured variables between All of Us and NHANES. Among the four weighting models, the most complex model which included sociodemographic, health, and clinical variables and their interactions achieved HRs in All of Us most similar to those in NHANES. For example, the RHR for hypertension for unweighted All of Us vs. NHANES (RHR=1.5; 95% CI=1.4 to 1.7) was reduced to 1.2 (95% CI=0.9 to 1.5) after applying weights from the clinical-interaction model. Even in this model, 17 of 35 HRs evaluated diverged by >20% (RHR <0.8 or >1.2) between weighted All of Us and NHANES. CONCLUSIONS: Predictors of mortality in All of Us differ from those in the U.S. population both in their distribution and in their associations with mortality. Reweighting can mitigate selection bias, but no model we tested comprehensively achieved generalizability.
Eisenberg-Guyot J, Prins SJ, Minh A
… +2 more, Hajat A, Renson A
Epidemiology
· 2026 Jun · PMID 42223287
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BACKGROUND: Given established relationships between social class and mortality, the growing concentration of income, wealth, and power among business owners in the U.S. may have increased mortality inequities across clas...BACKGROUND: Given established relationships between social class and mortality, the growing concentration of income, wealth, and power among business owners in the U.S. may have increased mortality inequities across classes. To investigate this hypothesis, we analyzed temporal changes in mortality inequities between owners and non-owners. METHODS: Our sample included respondents ages 25-64 in the 1984, 1989, 1994, and 1999-2013 Panel Study of Income Dynamics with mortality follow-up through 2022 (respondents: 22,103; observations: 103,965). Business owners were individuals with personal or family ownership of, or direct financial interest in, a business in the prior year. Using g-computation, we estimated how inequities between owners and non-owners in 10-year age-adjusted mortality risks changed from 1984-2013. Next, we analyzed whether any changes were attributable to shifting social stratification. Finally, we analyzed whether growing income and wealth disparities between owners and non-owners exacerbated inequities. RESULTS: In 1984, non-owners had 1.4 times (95% CI: 1.1, 1.8) greater 10-year age-adjusted mortality risks than owners. In 2013, the figure was 2.3 (95% CI: 1.8, 3.0), yielding a ratio of risk ratios (RRR) of 1.7 (95% CI: 1.1, 2.5). After social-stratification-adjustment, within-year inequities lessened; however, increases across years attenuated only somewhat (2013 vs 1984 RRR: 1.5 [95% CI: 0.99, 2.2]). Finally, we did not find that increases in inequities across years would have lessened if income and wealth distributions had remained at 1984 levels. CONCLUSIONS: Mortality inequities between owners and non-owners have increased and cannot be fully explained by social stratification and individual-level income and wealth distributions.
Evidence-informed infectious disease policy requires estimates of the health effects of infections. However, pathogenic variation, whereby the risk of adverse outcomes depends on the strain-specific characteristics of th...Evidence-informed infectious disease policy requires estimates of the health effects of infections. However, pathogenic variation, whereby the risk of adverse outcomes depends on the strain-specific characteristics of the pathogen, might complicate the study of many infectious agents. Here, we consider the interpretation of epidemiologic studies of infectious diseases when there is such heterogeneity in strain-specific effects and when information on strain composition is unavailable. We use potential outcomes and causal inference theory for analyses in the presence of multiple versions of treatment to argue that oft-reported quantities in these studies have a causal interpretation that depends on population frequencies of infecting strains. Moreover, as in other contexts where the treatment-variation-irrelevance assumption might be violated, transportability requires additional considerations, beyond those needed for noncompound exposures. This discussion, considering potential heterogeneity in strain-specific effects, will facilitate the interpretation of epidemiologic studies and also highlight the value of pathogen subtype data.
Luo Y, Rivas I, Foraster M
… +24 more, Galmes T, Arevalo G, Barril L, Basagaña X, Bustamante M, Cirach M, Domínguez A, Jerrett M, Gascon M, Lao J, Mazarico E, Morillas À, Moreno T, Nieuwenhuijsen M, Persavento C, Raimbault B, Querol X, Sentís A, Schwartz J, Tonne C, Gómez-Roig MD, Llurba E, Sunyer J, Dadvand P
Epidemiology
· 2026 May · PMID 42179135
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BACKGROUND: Maternal air pollution exposure has been associated with impaired fetal growth, yet most studies have overlooked microenvironmental and personal exposures. OBJECTIVES: To examine associations between maternal...BACKGROUND: Maternal air pollution exposure has been associated with impaired fetal growth, yet most studies have overlooked microenvironmental and personal exposures. OBJECTIVES: To examine associations between maternal air pollution exposure in key microenvironments (home, workplace, and commuting route) and fetal growth (birth weight and small for gestational age) using three modelling approaches and personal, home-indoor, and home-outdoor monitoring. METHODS: We used data from 1024 pregnant women in the Barcelona Life Study Cohort (2018-2021). Exposure to nitrogen dioxide, black carbon, and fine particulate matter (PM2.5) and its metallic constituents (copper, iron, and zinc) in each microenvironment were estimated using land use regression models, dispersion models, and hybrid land use regression-dispersion models, and combined with time-activity data to estimate total microenvironment exposures. Personal, home-indoor, and home-outdoor nitrogen dioxide concentrations were measured using passive samplers. Associations with birth weight and small for gestational age were evaluated using linear and logistic mixed-effects models. RESULTS: Higher nitrogen dioxide and black carbon exposure at home and in total microenvironments, estimated by land use regression, dispersion, and hybrid models, were associated with lower birth weight. Increased black carbon exposure in the workplace (hybrid model) and PM2.5 exposure both at home (land use regression model) and in total microenvironments (land use regression and dispersion models) were also associated with reduced birth weight, as were higher home, workplace, and total microenvironmental exposure to metallic components of PM2.5 in land use regression models, although higher workplace PM2.5-zinc was associated with higher birth weight in hybrid models. Higher personal and home-outdoor nitrogen dioxide exposure were further associated with reduced birth weight. Similar patterns were observed for small for gestational age. CONCLUSION: Maternal air pollution exposure was associated with impaired fetal growth. Home-based exposure estimates and short-term nitrogen dioxide measurements may serve as practical exposure proxies during pregnancy.
Epidemiology
· 2026 May · PMID 42171367
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BACKGROUND: Internalizing and externalizing symptoms are key mental health indicators that frequently co-occur in young populations. This study examined transition probabilities between none/low-symptom, exclusive intern...BACKGROUND: Internalizing and externalizing symptoms are key mental health indicators that frequently co-occur in young populations. This study examined transition probabilities between none/low-symptom, exclusive internalizing (e.g., depression, anxiety), exclusive externalizing (e.g., aggression, impulsivity), and co-occurring states in young adulthood and assessed associations with substance use. METHODS: Using four waves (Wave 4-7) of the Population Assessment of Tobacco and Health Study (2016-2023), this prospective cohort study applied Markov multistate transition modeling to estimate transition probabilities across 4 mental health states based on self-reported past 12-month symptoms. We then examined how current (daily, non-daily) nicotine use, past 30-day binge drinking, cannabis use, prescription drug misuse, and sociodemographic characteristics were associated with transitions between mental health states among baseline young adults (18-24 years). RESULTS: In the sample (N=5,575), the baseline prevalences of none/low symptom, exclusive internalizing, exclusive externalizing, and co-occurring states were 62.5%, 12.1%, 10.4%, and 13.9%, respectively. Sizable transition probabilities from internalizing or externalizing symptoms to co-occurring symptoms were observed in both short-term (21.6% and 19.7% at Wave 2) and long-term (14.7% and 14.4% at Wave 4) analyses. Co-occurring states demonstrated greater stability (43.9% short-term; 16.4% long-term) than internalizing or externalizing symptom states. Nicotine use was associated with transitions from none/low to internalizing symptoms; binge drinking and prescription drug misuse with transitions to externalizing symptoms; and cannabis use with transitions to both internalizing and externalizing symptoms. CONCLUSIONS: Transitions to co-occurring symptoms are frequent in young adulthood, and may be associated with patterns of substance use.
Epidemiology
· 2026 May · PMID 42165509
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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...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.
Cowan KN, Boudreault J, LeMasters K
… +1 more, Brinkley-Rubinstein L
Epidemiology
· 2026 May · PMID 42159416
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BACKGROUND: Temperature extremes are becoming more common due to climate change. Due to infrastructural issues and the demographic characteristics of the incarcerated population, incarcerated people are at high risk of p...BACKGROUND: Temperature extremes are becoming more common due to climate change. Due to infrastructural issues and the demographic characteristics of the incarcerated population, incarcerated people are at high risk of poor health outcomes during extreme temperature events. However, limited empirical evidence exists, with no prior studies on US federal prisons. Our goal was to estimate associations between ambient temperature and all-cause mortality in federal prisons. METHODS: Data on mortality in federal prisons from 2009 to 2021 were linked with daily mean temperature data. We employed a case crossover design and distributed lag non-linear models to estimate the temperature-mortality relationship for deaths occurring during the hot (May-September) and cold (November-March) months throughout the US. Odds ratios were estimated comparing the odds of mortality at extreme temperature percentiles (5th and 95 th percentiles) to the median temperature at each prison during the study period from 2009-2021. RESULTS: Of 5,182 deaths from 2009 to 2021, most were recorded during hot (42%) and cold (41%) months. The cumulative effect of extreme hot (95 th percentile) and cold (5 th percentile) temperatures was protective, yet imprecise. At a the 2-day lag for extreme hot temperatures and a same-day lag for extreme cold temperatures, we observed increased odds of mortality in federal prison. CONCLUSIONS: Our analysis of mortality in federal prisons yielded different results from previous analyses of mortality in state prisons. Future studies are needed to improve exposure estimation of indoor temperatures and quantify air conditioning access in US prisons.
Epidemiology
· 2026 May · PMID 42153772
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BACKGROUND: Low educational attainment was strongly associated with higher COVID-19 mortality. Whether this association is causal remains controversial. We evaluated the impact of historical state-level compulsory school...BACKGROUND: Low educational attainment was strongly associated with higher COVID-19 mortality. Whether this association is causal remains controversial. We evaluated the impact of historical state-level compulsory schooling laws (CSLs) on older individuals' pandemic-era mortality via natural experiment. METHODS: We defined the US population of older adults using the 2019 American Community Survey (ACS) and included all US death certificates March 2019-December 2021. We linked each individual to state-level CSLs at school-age. We estimated effects of state-level CSLs on individual-level COVID-19 mortality and all-cause mortality pre-pandemic (March 2019-February 2020), pandemic year 1 (March 2020-February 2021) and pandemic year 2 (March 2021-December 2021), controlling for birth year, sex, race and ethnicity, state-level demographics, and state-of-birth fixed effects. We also examined effect heterogeneity by sex and racial identity. RESULTS: Each additional year of policy-mandated compulsory schooling was associated with lower odds of COVID-19 mortality (pandemic year 1 Odds Ratio [OR] for 8 vs 9 years: 1.16 (95% Confidence Interval [CI]: 1.11 to 1.20); OR for 10 vs 9 years 0.96 (95% CI: 0.93 to 0.98); similar estimates in year 2). The relationship between CSLs and COVID-19 mortality roughly mirrored the relationship with all-cause mortality immediately before and during the pandemic. The gradient was steepest among Black women. CONCLUSIONS: Older adults born into states requiring longer schooling experienced markedly lower mortality during the pandemic, suggesting historic investments in children's education reduced pandemic-era mortality decades later. Our findings support a causal relationship between population-level education policy and long-term population health.
Epidemiology
· 2026 May · PMID 42149656
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Exposure to industrial air toxicants remains a leading environmental health risk in the United States (US). Our prospective cohort study examines the extent to which industrial air toxicant exposure accounts for individu...Exposure to industrial air toxicants remains a leading environmental health risk in the United States (US). 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 = 3,329). 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 radiuses (1-km to 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. We report three key findings. First, associations between air toxicants and mortality are non-linear, such that respondents who reside in areas with relatively low toxicant levels still live for 1-2 fewer years, on average, compared to peers residing in the least polluted areas. Second, the association between air toxicant exposure and mortality increases across larger buffer areas. For instance, compared to 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. Our study corroborates that industrial air toxicants remain significant environmental health risks in the US, especially for socioeconomically disadvantaged populations.
Chen D, Shioda K, Brouwer AF
… +5 more, Kraay ANM, Handel A, Lopman BA, McQuade ETR, Nelson K
Epidemiology
· 2026 May · PMID 42149607
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BACKGROUND: 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 e...BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: Our mechanistic model provides an independent alternative to conventional methods, quantifying pathogens-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.