American journal of epidemiology最新文献

Interventional pharmacoepidemiology applies quantitative analysis of patterns of medication use and outcomes to help design, guide and then evaluate programs to improve prescription drug use and outcomes. Surveillance of prescribing and drug-taking in large populations is increasingly practical because of the proliferation of detailed data on medication use decisions, often based on paid claims billing data. At the same time, increasingly granular clinical information is available on patient characteristics and outcomes. This can offer important opportunities to identify problematic use, focus interventions to address them, and measure their impact. Alexander et al (Am J Epidemiol. 0000;000(00):0000-0000) review the need for such research and provide methodological guidance for its performance. While randomized controlled trials of such interventions are ideal, real-world considerations often require other evaluation strategies, including stepped-wedge designs and interrupted time-series analysis. As drug therapy becomes more powerful and more costly, and the risks of poor medication choices as well as under-use of effective treatments become even better understood, the health care system will increasingly rely on such approaches to assess current patterns of prescribing and patient adherence, target programs to address problem areas, and measure the effectiveness of such interventions.

The quality of the inferences we make from pathogen sequence data is determined by the number and composition of pathogen sequences that make up the sample used to drive that inference. However, there remains limited guidance on how to best structure and power studies when the end goal is phylogenetic inference. One question that we can attempt to answer with molecular data is whether some people are more likely to transmit a pathogen than others. Here we present an estimator to quantify differential transmission, as measured by the ratio of reproductive numbers between people with different characteristics, using transmission pairs linked by molecular data, along with a sample size calculation for this estimator. We also provide extensions to our method to correct for imperfect identification of transmission linked pairs, overdispersion in the transmission process, and group imbalance. We validate this method via simulation and provide tools to implement it in an R package, phylosamp.

This study aims to understand availability of school-based infectious disease surveillance data (e.g., COVID-19 cases, student absences) based on experiences during the COVID-19 pandemic using a national sample of public K-12 schools (n = 1,602). Based on surveys administered to school administrators throughout the 2021-2022 school year, we found high levels of missingness data for school-level COVID-19 cases, quarantines, and student absenteeism, increasing missingness over time, and variations in missingness by school characteristics (e.g., school size) and protocols (e.g., having a school-based system to report at-home COVID-19 tests). For the same sample of schools, using data requests to health departments, we found similarly high levels of missingness of school-level COVID-19 case data and varying approaches in data collection. Developing nationally standardized case definitions-and systems to surveil or collect and monitor school-based infectious disease outcomes early in a public health emergency-may be helpful in producing actionable data.

We examined the association between rapid opioid reduction or discontinuation and self-harm, suicide attempt, and suicide death among high-dose long-term opioid therapy (HD-LTOT) patient and examined effect measure modification by individual and neighborhood-level characteristics. Using private insurance data from North Carolina, this retrospective cohort study covered January 2006 to September 2018, with up to four years of follow-up. Participants included patients aged 18-64 years who were prescribed HD-LTOT. Time-varying exposure was ever exposed to rapid opioid reduction or discontinuation vs never exposed. The outcomes were self-harm or suicide attempt, suicide death, and the combined outcome. We estimated cumulative incidence and used Fine-Gray models to estimate sub-distribution hazard ratios (HRs). There were 21,450 HD-LTOT patients. In year 1, rapid opioid reduction or discontinuation was not associated with the combined outcome, HR: 1.09 (95% CI: 0.61-1.96). However, in years 2-4, rapid opioid reduction or discontinuation was associated with higher hazard of the combined outcome, HR: 2.77 (95% CI: 1.45-5.27). This association was stronger among patients with mental health conditions and those residing in underserved neighborhoods. These findings underscore the importance of provider training in adhering to guideline-concordant gradual tapering, offering mental health support, and ensuring patient safety throughout the tapering process.

Individuals with low socioeconomic position (SEP) experience greater rates of alcohol-attributable mortality, contributing to health inequities in mortality and life expectancy. We examined the association between SEP and alcohol-attributable mortality by sex/gender and age in Canada. Census records from the 2006 Canadian Census Health and Environment Cohort (ages 12+; n=5,038,790) were linked to mortality data from 2006-2019. SEP was measured by educational attainment and household income. Poisson and Fine and Gray sub-distribution hazard models estimated rate differences (RD) per 100,000 person-years and hazard ratios (HR). Both educational attainment and household income were inversely associated with alcohol-attributable mortality. Absolute SEP inequities were greater among men than women, with a RD of 30.81 (95% CI: 28.04, 33.57) for men and 9.86 (95% CI: 8.49, 11.22) for women when comparing the lowest to the highest income quintile. Age-stratified analyses showed absolute SEP inequities were most pronounced in middle and older adulthood, above age 30 for women and age 50 for men, with smaller RDs in ages 12-29. Relative SEP inequities were similar in women and men, with greater HRs at younger ages. Public health policies addressing social determinants and population-level alcohol policies should consider patterning of SEP inequities by sex/gender and age group.

Human-induced climate change has led to more frequent and severe flooding around the globe. We examined the association between flood risk and the prevalence of coronary heart disease, high blood pressure, asthma, and poor mental health in the United States, while taking into account different levels of social vulnerability. We aggregated flood risk variables from First Street Foundation data by census tract and used principal component analysis to derive a set of 5 interpretable flood risk factors. The dependent variables were census-tract level disease prevalences generated by the Centers for Disease Control and Prevention. Bayesian spatial conditional autoregressive models were fit on these data to quantify the relationship between flood risk and health outcomes under different stratifications of social vulnerability. We show that 3 flood risk principal components had small but significant associations with each of the health outcomes across the different stratifications of social vulnerability. Our analysis gives, to our knowledge, the first United States-wide estimates of the associated effects of flood risk on specific health outcomes. We also show that social vulnerability is an important moderator of the relationship between flood risk and health outcomes. Our approach can be extended to other ecological studies that examine the health impacts of climate hazards. This article is part of a Special Collection on Environmental Epidemiology.

US Asian adults and people with limited English proficiency (LEP) confront mental health treatment receipt disparities. At the intersection of racial and language injustice, Asian adults with LEP may face even greater disparity, but studies have not assessed this through explicitly intersectional approaches. Using 2019 and 2020 National Survey of Drug Use and Health data, we computed disparities in mental health treatment among those with mental illness comparing: non-Hispanic (NH) Asian adults with LEP to NH White adults without LEP (joint disparity), NH Asian adults without LEP to NH White adults without LEP (referent race disparity), NH Asian adults with LEP to those without LEP (referent LEP disparity), and the joint disparity versus the sum of referent disparities (excess intersectional disparity). In age- and gender-adjusted analyses, excess intersectional disparity was 26.8% (95% CI, -29.8 to 83.4) of the joint disparity in 2019 and 63.0% (95% CI, 29.1-96.8) in 2020. The 2019 joint disparity was 1.37 (95% CI, 0.31-2.42) times that if the race-related disparity did not vary by LEP, and if LEP-related disparity did not vary by race; this figure was 2.70 (95% CI, 0.23-5.17) in 2020. These findings highlight the necessity of considering the intersection of race and LEP in addressing mental health treatment disparities. This article is part of a Special Collection on Mental Health.

In 1952, James Watt, a young US Public Health Service (PHS) infectious disease epidemiologist, was appointed-amid wide surprise-director of the US National Heart Institute (NHI) where he served until 1961. He skillfully advanced epidemiologic research methods and study conduct nationally while also establishing epidemiology in the administrative hierarchy of the institute. Watt soon turned to development of an effective program in international cardiovascular disease (CVD) epidemiology under auspices of the World Health Organization (WHO) at the United Nations in Geneva. That effort resulted in the 1959 appointment of Zdenek Fejfar, a young Czech clinical investigator, as director of the WHO CVD Unit. The coming together of Watt and Fejfar, with a joint focus on improved methods and population comparisons, helped establish a vigorous international community of CVD epidemiology. Their collaboration and friendship remained active and close throughout their career assignments and thereafter, as documented in this story.

Most previous studies found an elevated risk of endometrial cancer among women with polycystic ovary syndrome (PCOS). However, these had highly varying methods for ascertainment of PCOS diagnoses and limitations such as few exposed women and short follow-up. In this cohort study, we investigated the association between PCOS and endometrial cancer among women born in Denmark between January 1, 1940, and December 31, 1993 (n = 1 719 121). Data in this study, including PCOS and endometrial cancer diagnoses and covariates, were derived from nationwide registers. We used Cox proportional hazards regression models to calculate hazard ratios (HRs) and 95% CIs. A total of 7862 endometrial cancer cases were identified during 23.7 years of follow-up (IQR, 37.7-61.9). We found an increased risk of endometrial cancer among women with PCOS compared with women without PCOS (HR = 3.02; 95% CI, 2.03-4.49). The risk was increased for premenopausal women (HR = 5.82; 95% CI, 3.64-9.30), whereas no marked association was seen for postmenopausal women. However, for postmenopausal women, results were limited by few cases and young age at the end of follow-up. Mounting evidence of an increased risk for endometrial cancer among women with PCOS reinforces the need for prevention and early detection. This article is part of a Special Collection on Gynecological Cancers.