Epidemiology
· 2025 Jul · PMID 40257114
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BACKGROUND: Exposure to fine particulate matter (PM 2.5 ) during pregnancy has been associated with adverse birth outcomes. However, limited evidence exists on the effects of specific PM 2.5 components. We investigated t...BACKGROUND: Exposure to fine particulate matter (PM 2.5 ) during pregnancy has been associated with adverse birth outcomes. However, limited evidence exists on the effects of specific PM 2.5 components. We investigated the association of prenatal exposure to PM 2.5 and its components with birth outcomes and mortality at age <5 years in four metropolitan cities in South Korea. METHODS: We obtained data from Statistic Korea linking birth records for 2013-2015 to death records under age 5 years. Data for PM 2.5 and 10 of its components were collected from four monitoring stations. We calculated exposures during pregnancy and each trimester for a total of 324,566 births. We used logistic regression to estimate the associations between exposure and risk of preterm birth (PTB) (<37 weeks), low birth weight (<2.5 kg), small for gestational age (birth weight <10 th percentile for the same gestational age), and under-5 mortality. RESULTS: An interquartile range (8.7 µg/m 3 ) increase in exposure to PM 2.5 during the entire pregnancy was associated with increased odds of PTB (odds ratio [OR] = 1.17; 95% confidence interval [CI] = 1.11, 1.23). We observed no association with low birth weight, small for gestational age, or under-5 mortality for the entire pregnancy exposure. Elemental carbon and secondary inorganic aerosols showed higher effect estimates for PTB than did other components. CONCLUSIONS: In urban populations of South Korea, exposure to PM 2.5 during pregnancy was associated with an increased risk of PTB. Different components showed varying associations with adverse birth outcomes.
Ahrenfeldt LJ, Søndergaard J, Laro M
… +2 more, Möller S, Stripp TA
Epidemiology
· 2025 May · PMID 40214143
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BACKGROUND: Previous evidence shows that religious service attendance is associated with lower mortality among women and fewer hospitalizations among men. However, it is unclear if similar associations exist for other ac...BACKGROUND: Previous evidence shows that religious service attendance is associated with lower mortality among women and fewer hospitalizations among men. However, it is unclear if similar associations exist for other activities. METHODS: This cohort study examines the associations between various activities and their engagement levels on mortality and hospitalizations among 2987 Danes aged 40+ years, interviewed in SHARE from 2004 to 2007. The study followed individuals in the Danish registers until 2018. We estimated relative and absolute risks of mortality at age 90 and used negative binomial regressions for hospitalizations, including adjustment for several potential confounders. RESULTS: Overall, activity participation was associated with lower mortality. Specifically, individuals participating in voluntary or charity work (relative risks [RR] = 0.85; 95% CI = 0.76, 0.95; risk differences [RD] = -0.11; 95% CI = -0.18, -0.04), helping others (RR = 0.88; 95% CI = 0.82, 0.95; RD = -0.09; 95% CI = -0.14, -0.04), or participating in sports or social clubs (RR = 0.89; 95% CI = 0.81, 0.98; RD = -0.09; 95% CI = -0.15, -0.03) exhibited lower mortality. We found lower mortality among women who took part in a religious organization. Our results showed fewer hospitalizations for men who engaged in 3+ activities, and for men who took part in a religious organization. For some activities, the strength of associations varied with activity frequency; for example, caring for the sick was associated with fewer hospitalizations but only when done less than once a week. CONCLUSIONS: Several activities were associated with lower mortality, particularly among women. However, the reduced hospitalizations observed among men attending religious services did not extend to other activities.
Milkovska E, Wouterse B, Issa J
… +1 more, van Baal P
Epidemiology
· 2025 Jul · PMID 40202801
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BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic caused substantial health losses but not much is known about how these are distributed across the population. We aimed to estimate the distribution of years of...BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic caused substantial health losses but not much is known about how these are distributed across the population. We aimed to estimate the distribution of years of life lost (YLL) due to COVID-19 and investigate its variation across the Dutch population, taking into account preexisting differences in health. METHODS: We used linked administrative data covering the entire 50+ Dutch population over 2012-2018 (n = 6,102,334) to estimate counterfactual individual-level life expectancy for those who died from COVID-19 in 2020 and 2021. We estimated survival models and used Cox-LASSO and Cox-Elastic Net to perform variable selection among the large set of potential predictors in our data. Using individual-level life expectancy predictions, we generated the distribution of YLL due to COVID-19 for the entire 50+ population by age and income. RESULTS: On average, we estimate that individuals who died of COVID-19 had a counterfactual life expectancy about 28% lower than that of the rest of the population. Within this average, there was substantial heterogeneity, with 20% of all individuals who died of COVID-19 having an estimated life expectancy exceeding that of the age-specific population average. Both the richest and poorest COVID-19 decedents lost the same average number of YLL, which were similarly dispersed. CONCLUSION: Accounting for preexisting health problems is crucial when estimating YLL due to COVID-19. While average life expectancy among COVID-19 decedents was substantially lower than for the rest of the population, the popular notion that only the frail died from COVID-19 is not true.
Epidemiology
· 2025 Jul · PMID 40197649
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BACKGROUND: Payday loans refer to high-interest, short-term loans. These loans can provide immediate financial relief for individuals with limited access to traditional credit. However, the predatory nature of payday loa...BACKGROUND: Payday loans refer to high-interest, short-term loans. These loans can provide immediate financial relief for individuals with limited access to traditional credit. However, the predatory nature of payday loans may portend increased financial strain and adverse public health consequences. METHODS: We examine whether state-level temporal variation in payday loan restrictions over a 20-year period (2000-2019) corresponds with a reduction in preterm births: a leading cause of infant mortality in the United States (US). Between 2000 and 2019, 10 US states and the District of Columbia imposed restrictions on payday lending at varied time points. We use data on preterm births provided by the Centers of Disease Control's WONDER database (2000-2019) and apply staggered difference-in-difference approaches to examine whether preterm births (per 100 live births) declined among states that imposed payday lending restrictions, relative to states that never imposed restrictions. We also control for state-specific time propensity of preterm births, derived through time-series analysis. RESULTS: Results indicate a decline in the preterm births by approximately 0.22 per 100 live births (95% confidence interval: -0.31, -0.13) within the first 3 years of payday loan restrictions, which corresponds to 4512 fewer than expected preterm births. CONCLUSION: Our findings are consistent with the hypothesis that state-level payday lending restrictionsare associated with a reduction in preterm births.
Luukkonen J, Einiö E, Tarkiainen L
… +2 more, Martikainen P, Remes H
Epidemiology
· 2025 Jul · PMID 40185681
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BACKGROUND: Little is known about how alcohol policies experienced in adolescence are associated with later health. We assess whether the age of exposure to stricter alcohol policies is associated with later alcohol-attr...BACKGROUND: Little is known about how alcohol policies experienced in adolescence are associated with later health. We assess whether the age of exposure to stricter alcohol policies is associated with later alcohol-attributable hospitalizations and mortality. We take advantage of an alcohol advertising ban and alcohol tax increases introduced in 1975-1977 with relatively stable alcohol policies before and after. METHODS: We used Finnish register data on birth cohorts 1950-1964 (1,175,878 individuals) to assess cohort-wise hazard ratios for the first incidence of alcohol-attributable hospitalization and mortality, and mortality due to external and other causes at ages 21-54 years. RESULTS: Men who were aged 19-25 at the time of the restrictive reform had similar risks for alcohol-attributable hospitalization and mortality to the reference group of those aged 18-legal drinking age-at the time of reform. For those underage at the time, hospitalization and mortality rates were incrementally smaller cohort by cohort. For example, men who were 17 at the time of the reform had lower hazard ratios of alcohol-attributable hospitalization: 0.91 (95% confidence interval: 0.87, 0.95) as did those who were 13 (0.85; 95% confidence interval: 0.81, 0.89). The findings were similar for external-cause mortality, and similar yet more uncertain for women. In contrast, mortality from other causes declined continuously from cohort to cohort. CONCLUSIONS: Our findings are consistent with the hypothesis that stricter alcohol policies in adolescence reduce harmful alcohol consumption patterns extending into adulthood and manifesting as lower alcohol-related harm to health.
Epidemiology
· 2025 Jul · PMID 40185679
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BACKGROUND: Health selection into neighborhoods describes unhealthy people moving disproportionately to lower-income neighborhoods, producing observable socioeconomic gradients sometimes falsely attributed to neighborhoo...BACKGROUND: Health selection into neighborhoods describes unhealthy people moving disproportionately to lower-income neighborhoods, producing observable socioeconomic gradients sometimes falsely attributed to neighborhood effects on health. We investigated residential mobility outcomes and their relationship to baseline health using population-level data linkages in Ontario, Canada. METHODS: We included Canadian Community Health Survey respondents ages 25 to 64 between 2005 and 2014 (n = 93,235). We assessed baseline health using self-reported health and multimorbidity. We captured moves using health administrative data and the Canadian census. We fit multinomial logistic regression models with a six-category residential mobility outcome: (1) nonmovers from low-income neighborhoods; (2) nonmovers from high-income neighborhoods; (3) movers from low-income to low-income; (4) movers from low-income to high-income; (5) movers from high-income to low-income; and (6) movers from high-income to high-income. We adjusted models for the Canadian Community Health Survey cycle, age, sex, household income, immigrant status, and residential instability. RESULTS: Compared with those with very good or excellent health, respondents reporting fair or poor health at baseline had higher odds of moving from low- to low-income neighborhoods (Adjusted odds ratios [aOR] = 1.73; 95% confidence interval [CI] = 1.46, 2.05), moving from high- to low-income (aOR = 1.64; 95% CI = 1.35, 1.98), moving from low- to high-income (aOR = 1.26; 95% CI = 1.04, 1.54), and not moving within low-income (aOR = 1.36; 1.23, 1.51) relative to not moving within high-income. Results were consistent for objective health measures, comparing respondents with at least four chronic conditions to those with one or none. CONCLUSIONS: In a large, population-based study, both subjective and objective measures of health had a strong relationship with residential mobility outcomes.
Li G, Gerlovin H, Figueroa Muñiz MJ
… +10 more, Wise JK, Madenci AL, Robins JM, Aslan M, Cho K, Gaziano JM, Lipsitch M, Casas JP, Hernán MA, Dickerman BA
Epidemiology
· 2025 May · PMID 40173425
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The interventional effects approach to causal mediation analysis is increasingly common in epidemiologic research given its potential to address policy-relevant questions about hypothetical mediator interventions. Multip...The interventional effects approach to causal mediation analysis is increasingly common in epidemiologic research given its potential to address policy-relevant questions about hypothetical mediator interventions. Multiple imputation is widely used for handling multivariable missing data in epidemiologic studies. However, guidance is lacking on best practices for using multiple imputation when estimating interventional mediation effects, specifically regarding the role of missingness mechanism in the performance of the method, how to appropriately specify the multiple imputation model when g-computation is used for effect estimation, and appropriate variance estimation. To address this gap, we conducted simulations based on the Victorian Adolescent Health Cohort Study. We considered seven missingness mechanisms, involving varying assumptions regarding the influence of an intermediate confounder, a mediator, and/or the outcome on missingness in key variables. We compared the performance of complete case analysis, six multiple imputation approaches by fully conditional specification, differing in how the imputation model was tailored, and a "substantive model compatible" multiple imputation-fully conditional specification approach. We evaluated MIBoot (multiple imputation, then bootstrap) and BootMI (bootstrap, then multiple imputation) approaches for variance estimation. All multiple imputation approaches, apart from those clearly diverging from best practice, yielded approximately unbiased estimates when none of the intermediate confounder, mediator, and outcome variables influenced missingness in any of these variables and nonnegligible bias otherwise. We observed the largest bias for interventional effects when each of the intermediate confounders, mediators, and outcomes influenced their own missingness. BootMI returned variance estimates with a smaller bias than MIBoot.
Epidemiology
· 2025 Jul · PMID 40167173
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BACKGROUND: The population attributable fraction corresponds to the reduction of the outcome had individuals (counter-to-fact) not experienced the exposure scaled by the observed incidence. Estimators proposed by Levin a...BACKGROUND: The population attributable fraction corresponds to the reduction of the outcome had individuals (counter-to-fact) not experienced the exposure scaled by the observed incidence. Estimators proposed by Levin and Miettinen implicitly assume the study population is a random sample of the target population, which is not always the case. METHODS: In our example, we estimate the reduction in AIDS or death among women diagnosed with HIV in the United States in 2008, had they not had a history of injection drug use. To transport risk estimates from 1164 women in the Women's Interagency HIV Study to the 11,282 women diagnosed with HIV in the United States in 2008, we use the inverse probability of treatment and the inverse odds of sampling weighting. We estimate the variance of the population attributable fraction with a nonparametric bootstrap and M-estimation using the sandwich variance estimator. RESULTS: The population attributable fraction estimated in the observed sample was 0.21 (95% confidence interval: 0.13, 0.29). After transporting the population attributable fraction to the target population, it was 0.13 (95% confidence interval: 0.065, 0.19). CONCLUSIONS: Defining the target population and identification conditions allows for a clearer interpretation of the population attributable fraction.
Epidemiology
· 2025 Jul · PMID 40167168
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BACKGROUND: Recent work conceptually unifying selection and collider-restriction bias as threats to internal validity implies that their impact on observed associations should similarly align. We reviewed epidemiologic l...BACKGROUND: Recent work conceptually unifying selection and collider-restriction bias as threats to internal validity implies that their impact on observed associations should similarly align. We reviewed epidemiologic literature to summarize existing knowledge about the impact of selection and collider bias. METHODS: We systematically searched for peer-reviewed, methodologic articles and general epidemiology textbooks published in English from 1 January 2000 to 12 July 2024. We included sources that focused on internal validity and discussed the magnitude or direction of selection or collider bias. We abstracted conclusions about the likely magnitude and direction of bias, which stratum or strata are affected when restricting analyses to a subset, and the conditions under which the consequences of bias were evaluated. RESULTS: We retained 33 of 5508 identified articles and 12 of 205 textbooks for data abstraction. Overall, we found that collider bias articles conveyed its impact as minimal while selection bias sources described variable effects. We also observed that most collider bias sources evaluated bias under the sharp null (assuming no relationship between the exposure and outcome) and found differences between how selection and collider bias sources discussed the role of interaction and the strata affected. CONCLUSIONS: Although collider-restriction and selection bias affecting internal validity are considered theoretically equivalent, conclusions differ about their consequences for study results. Investigating collider bias not under the sharp null and considering the role of both multiplicative and additive interaction between the causes of a collider may improve our ability to predict and quantify its impact on internal validity.
Han DH, Leventhal AM, Stokes AC
… +4 more, Audrain-McGovern JE, Eckel SP, Liu J, Harlow AF
Epidemiology
· 2025 Jul · PMID 40164563
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BACKGROUND: Prior studies examining the association of cannabis use with nicotine abstinence did not distinguish between individuals co-using nicotine and cannabis versus those who switched from nicotine to exclusive can...BACKGROUND: Prior studies examining the association of cannabis use with nicotine abstinence did not distinguish between individuals co-using nicotine and cannabis versus those who switched from nicotine to exclusive cannabis use; these may have different effects on nicotine abstinence. We examined associations of cannabis use uptake with subsequent nicotine abstinence approximately 1 year later among adults using cigarettes and/or e-cigarettes. METHODS: Using six waves of the Population Assessment of Tobacco and Health Study (2013-2021), we assessed transitions from exclusive nicotine use prebaseline (time t ) to (1) exclusive cannabis use, (2) nicotine-cannabis co-use, (3) nonuse of both nicotine and cannabis, and (4) continued exclusive nicotine use at baseline ( t + 1) as exposure variables. Analyses examined associations with nicotine abstinence (from both cigarettes and e-cigarettes) at 1-year follow-up ( t + 2). RESULTS: Among 8382 adults (19,618 observations) reporting exclusive nicotine use prebaseline, 1% transitioned to exclusive cannabis use, 9% to nicotine-cannabis co-use, and 9% to nonuse of both drugs; 81% were still using nicotine exclusively at baseline. Transition to nicotine-cannabis co-use (6%) versus exclusive nicotine use (10%) was inversely associated with nicotine abstinence at follow-up (adjusted relative risk [aRR] = 0.68; 95% confidence interval [CI] = 0.55, 0.83). Transition to exclusive cannabis use (72%) was positively associated with nicotine abstinence compared with continued exclusive nicotine use (10%; aRR = 4.66; 95% CI = 3.83, 5.67) and with similar nicotine abstinence at follow-up (72%) compared with nonuse of both drugs (65%; aRR=0.98; 95% CI = 0.81, 1.18). CONCLUSION: Co-use of nicotine and cannabis was associated with lower nicotine abstinence. Switching to exclusive cannabis use was associated with similar or greater nicotine abstinence.
Hayes-Larson E, Andrews RM, Kezios KL
… +33 more, Bercu A, Rouanet A, Helmer C, Crane PK, Gibbons LE, Klinedinst BS, McEvoy LK, Nichols E, Weuve J, Rajan KB, Hwang PH, Mez J, Farina M, Shaw C, Sims KD, Therneau T, Petersen RC, Bouteloup V, Gross AL, Albert M, Morris JC, Masters CL, Resnick SM, Maruff P, Manly JJ, Turney IC, Vonk JMJ, Avila-Rieger J, Weigand A, Chen R, Wang J, Proust-Lima C, Mayeda ER
Epidemiology
· 2025 Jul · PMID 40164562
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BACKGROUND: Cognitive change is an important factor in understanding dementia. Estimating effects of exposures on cognitive change requires choosing an analytical timescale, typically time on study or current age. There...BACKGROUND: Cognitive change is an important factor in understanding dementia. Estimating effects of exposures on cognitive change requires choosing an analytical timescale, typically time on study or current age. There is limited consensus regarding timescale choice in epidemiologic cognitive aging research. METHODS: Using a coordinated analytic approach in 10 cohorts of older adults, we evaluated whether estimated effects of two exposures on memory change differed depending on timescale (time on study or current age). We modeled effects of apolipoprotein-E ( APOE ) ε4 genotype (a time-invariant exposure) and diabetes (a potentially time-varying/acquired exposure evaluated at baseline) using mixed-effects models with linear and nonlinear time specifications for both timescales. RESULTS: Among APOE ε4 carriers, model-estimated memory scores at baseline (time on study timescale) or at each cohort's median baseline age (current age timescale) were lower, and the average rate of decline was faster than among APOE ε4 noncarriers. Model-estimated memory scores at baseline or at median baseline age were generally similar across baseline diabetes status, with variability across cohorts in the diabetes-memory change association. In some cohorts, trends in diabetes-memory change associations differed in direction across timescales. CONCLUSIONS: Timescale was largely inconsequential for estimated effects of APOE genotype, but yielded differences in estimated diabetes effects, raising questions about the appropriate scale. The current age scale may be problematic because diabetes was measured heterogeneously in age across individuals, a common issue in aging cohorts. Our work demonstrates approaches to evaluate alternative timescales, including in multicohort analyses, and highlights potential implications for estimated exposure effects on cognitive change.
Cole SR, Breskin A, Shook-Sa BE
… +3 more, Zivich PN, Hudgens MG, Edwards JK
Epidemiology
· 2025 Jul · PMID 40164560
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Epidemiologists rely on statistical approaches to estimate parameters of interest, like the prevalence of exposure or the incidence of disease. Influence functions are a powerful statistical concept that may be used for...Epidemiologists rely on statistical approaches to estimate parameters of interest, like the prevalence of exposure or the incidence of disease. Influence functions are a powerful statistical concept that may be used for the development and application of quantitative methods in epidemiology and more broadly. The influence function quantifies the impact that individual observations have on the estimator and is broadly useful for obtaining consistent variance estimators. Influence functions are relevant for many quantitative methods used by epidemiologists, providing a unifying framework to obtain variance estimates for ordinary least squares, generalized estimating equations, and inverse probability weighting. Further, they are pivotal for deriving methods such as augmented inverse probability weighted and targeted maximum likelihood estimators. Here we describe some basic facts about influence functions and detail two simple examples.
Epidemiology
· 2025 Jul · PMID 40164543
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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....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.
Edwards JK, Breger TL, Cole SR
… +21 more, Zivich PN, Shook-Sa BE, Sadinski LM, Westreich D, Edmonds A, Ramirez C, Ofotokun I, Kassaye SG, Brown TT, Konkle-Parker D, Stosor V, Bolan R, Krier S, Jones DL, D'Souza G, Cohen M, Tien PC, Taylor T, Anastos K, Drummond MB, Floris-Moore M
Epidemiology
· 2025 Jul · PMID 40125846
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BACKGROUND: Epidemiologists frequently employ right censoring to handle missing outcome, covariate, or exposure data incurred when participants have large gaps between study visits or stop attending study visits entirely...BACKGROUND: Epidemiologists frequently employ right censoring to handle missing outcome, covariate, or exposure data incurred when participants have large gaps between study visits or stop attending study visits entirely. But, if participants who are censored are more or less likely to experience outcomes of interest than those not censored, such censoring could introduce bias in estimated measures. METHODS: We examined how censoring after two consecutive missed visits may affect mortality results from the Multicenter AIDS Cohort Study (MACS) and Women's Interagency HIV Study (WIHS). MACS and WIHS provide linkages to vital statistics registries, such that mortality data were available for all participants, regardless of whether they attended study visits. RESULTS: In a gold standard analysis that did not censor after two consecutive missed visits, 10-year mortality was 23% (95% CI: 22, 24) in MACS and 21% (95% CI: 20, 23) in WIHS. Estimated mortality was modestly reduced by 0%-5% across subgroups when censoring at missed visits. Applying inverse probability of censoring weights partially removed this attenuation. CONCLUSIONS: While mortality was slightly elevated after two consecutive missed visits in MACS and WIHS, censoring at two consecutive missed visits did not substantially alter estimated mortality, particularly after applying inverse probability of censoring weights.
Do V, McBrien HK, Edmondson D
… +2 more, Kioumourtzoglou MA, Casey JA
Epidemiology
· 2025 Jul · PMID 40125837
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BACKGROUND: Power outages are common. They can result in exposure to extreme temperatures by shutting off temperature-controlling devices, and thereby also cause stress. Consequently, outages may precipitate cardiovascul...BACKGROUND: Power outages are common. They can result in exposure to extreme temperatures by shutting off temperature-controlling devices, and thereby also cause stress. Consequently, outages may precipitate cardiovascular disease (CVD)-related hospitalizations. We assessed this relationship among older adults. METHODS: We leveraged 2017-2018 data from 245,452 New York State Medicare Fee-for-Service beneficiaries (65+ years) with 390,530 CVD hospitalizations. Using NY Department of Public Services data, we calculated total hours without power 1 day, 1-2 days, and 1-3 days before case and control periods, with an outage ZIP Code Tabulation Area (ZCTA)-hour defined based on ≥10% of customers in a ZCTA-hour without power in primary analyses. We used a case-crossover study design and ran conditional logistic regression to assess associations separately within each urbanicity level: New York City (NYC), non-NYC urban, and rural areas. We additionally stratified models by warm versus cool season, individual-level age and sex, and ZCTA-level socioeconomic factors. Secondarily, we considered emergency (n = 298,910) and nonemergency hospitalizations separately. RESULTS: We generally observed null associations between power outages and all CVD hospitalizations across New York State and within subgroups. For example, in NYC, we observed a rate ratio of 1.05 (95% confidence interval: 0.85, 1.30) for each additional power outage hour 1 day prior. CONCLUSIONS: The case-crossover design we used eliminated time-fixed confounding, but there were a limited number of exposed cases, limiting statistical power. Future studies should investigate co-occurring severe weather, span additional years, and evaluate other and broader geographic areas.