Epidemiology最新文献

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.

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.

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.

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.