Epidemiology最新文献

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.

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.

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.

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.

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.