American Journal of Epidemiology

Lasso regression is widely used for large-scale propensity score (PS) estimation in healthcare database studies. In these settings, previous work has shown that undersmoothing (overfitting) Lasso PS models can improve confounding control, but it can also cause problems of non-overlap in covariate distributions. It remains unclear how to select the degree of undersmoothing when fitting large-scale Lasso PS models to improve confounding control while avoiding issues that can result from reduced covariate overlap...

Depressive symptoms have rapidly accelerated among recent US adolescent birth cohorts, yet there remains little understanding of trends among racialized and minoritized groups. These groups may experience depressive symptoms due to the deleterious effects of structural racism. Using 2005-2020 Monitoring the Future survey data, we examine all racialized groups using within-group analyses to observe trends in high depressive symptoms across cohorts. Generally, across racialized groups and ages, the odds of high depressive symptoms increased in recent birth cohorts...

No abstract text is available yet for this article.

No abstract text is available yet for this article.

No abstract text is available yet for this article.

Epidemiologists are attempting to address research questions of increasing complexity by developing novel methods for combining information from diverse sources. Cole et al. (Am J Epidemiol. 2022;XXX(XX):XXXX-XXXX) provide two examples of combining information to draw inferences about a population proportion. In this commentary, we consider combining information to learn about a target population as an epidemiological activity and distinguish it from more conventional meta-analyses. We examine possible rationales for combining information and discuss broad methodological considerations, with an emphasis on aspects of study design, including the selection among candidate data sources and the sampling of observations from these sources...

Difference-in-differences and synthetic control methods have become common study designs for evaluating the effects of policy changes, including health policies. They also have potential for providing real-world effectiveness and safety evidence in pharmacoepidemiology. To effectively add to the toolkit of the field, however, designs-including both their benefits and drawbacks-must be well understood. Quasi-experimental designs provide an opportunity to estimate the average treatment effect on the treated without requiring the measurement of all possible confounding factors, and to assess population-level effects...

An important challenge to addressing the opioid overdose crisis is the lack of information on the size of the population of people who misuse opioids (PWMO) in local areas. This estimate is needed for better resource allocation, estimation of treatment and overdose outcome rates using appropriate denominators (i.e., the population at risk), and proper evaluation of intervention effects. In this study, we used a Bayesian hierarchical spatio-temporal integrated abundance model that integrates multiple types of county-level surveillance outcome data, state-level information on opioid misuse, and covariates to estimate the latent (hidden) counts and prevalence of PWMO across New York State counties (2007-2018)...

No abstract text is available yet for this article.

No abstract text is available yet for this article.

Routinely collected testing data has been a vital resource for public health response during the COVID-19 pandemic and has revealed the extent to which Black and Hispanic persons have borne a disproportionate burden of SARS-CoV-2 infections and hospitalizations in the United States. However, missing race and ethnicity data and missed infections due to testing disparities limit the interpretation of testing data and obscure the true toll of the pandemic. We investigated potential bias arising from these two types of missing data through a case study in Holyoke, Massachusetts during the pre-vaccination phase of the pandemic...

Distributed networks and other multi-site studies assess drug safety and effectiveness in diverse populations by pooling information. Targeting groups of clinical or policy interest (including specific sites or site combinations) and applying weights based on effect measure modifiers (EMMs) prior to pooling estimates within multi-site studies may increase interpretability and improve precision. We simulated a four-site study, standardized each site using inverse odds weights (IOW) to resemble the three smallest sites or the smallest site, estimated IOW-weighted RDs, and combined estimates with inverse variance weights (IVW)...

Many ecological studies examine health outcomes and disparities using administrative boundaries such as census tracts, counties, or states. These boundaries help us to understand the patterning of health by place along with impacts of policies implemented at these levels. However, additional geo-political units, or units with both geographic and political meaning, such as congressional districts, present further opportunities to connect research with public policy. We provide a step-by-step guide in how to conduct disparities-focused analysis at the congressional district level, and as an applied case study we use geocoded vital statistics data from 2010-2015 to examine levels and disparities of infant mortality (IM) and deaths of despair (DoD) in the 19 U...

Few methods have been used to characterize repeatedly measured biomarkers of chemical mixtures. We applied latent profile analysis (LPA) to serum concentrations of four perfluoroalkyl and polyfluoroalkyl substances (PFAS) at four timepoints from gestation to age 12 years. We evaluated the relations between profiles and z-scores of height, body mass index, fat mass index, and lean body mass index at age 12 years (n = 218). We compared LPA findings with an alternative approach for cumulative PFAS mixtures using g-computation to estimate the effect of simultaneously increasing the area under the curve (AUC) for all PFAS...

We synthesized the epidemiologic evidence on the associations between per- and polyfluoroalkyl substances (PFAS) exposure and breast cancer risk. Our systematic review and meta-analysis included 18 and 11 articles, respectively, covering studies up to February 2023. The summary relative risks (RR) estimated by random-effects meta-analyses did not support an association between PFAS and overall breast cancer risk (e.g., a natural log (ln)-unit increase in serum/plasma concentrations [ng/mL] for perfluorooctanoate [PFOA] RR=0...

Across high-income countries, adolescent emotional concerns have been increasing in prevalence over the past two decades and it is unclear why this is occurring, including if and how substance use relates to these changing trends. On the other hand, substance use has been generally declining, and little is known about the role of emotional concerns in these trends. Several studies have explored the changes in co-occurring substance use and emotional concerns among adolescents over time, with mixed results and inconsistent messaging about the implications of the findings...

Targeted Maximum Likelihood Estimation (TMLE) is increasingly used for doubly robust causal inference, but how missing data should be handled when using TMLE with data-adaptive approaches is unclear. Based on the Victorian Adolescent Health Cohort Study, we conducted a simulation study to evaluate eight missing data methods in this context: complete-case analysis, extended TMLE incorporating outcome-missingness model, missing covariate missing indicator method, five multiple imputation (MI) approaches using parametric or machine-learning models...

In 2008, Oregon expanded its Medicaid program using a lottery, creating a rare opportunity to study the effects of Medicaid coverage using a randomized controlled design (Oregon Health Insurance Experiment). Analysis showed that Medicaid coverage lowered the risk of depression. However, this effect may vary between individuals, and the identification of individuals likely to benefit the most has the potential to improve the effectiveness and efficiency of the Medicaid program. By applying the machine learning causal forest to data from this experiment, we found substantial heterogeneity in the effect of Medicaid coverage on depression; individuals with high predicted benefit were older and had more physical or mental health conditions at baseline...

The statistical analysis of omics data poses a great computational challenge given its ultra-high dimensional nature and frequent between-features correlation. In this work, we extended the Iterative Sure Independence Screening (ISIS) algorithm by pairing ISIS with elastic-net (Enet) and two versions of adaptive Enet (AEnet and MSAEnet) to efficiently improve feature selection and effect estimation in omics research. We subsequently used genome-wide human blood DNA methylation data from American Indians of the Strong Heart Study (N=2,235 participants), measured in 1989-1991, to compare the performance (predictive accuracy, coefficient estimation and computational efficiency) of SIS-paired regularization methods to Bayesian shrinkage and traditional linear regression to identify epigenomic multi-marker of body mass index...

This article introduces Bayesian spatial smoothing models for disease mapping, a specific application of small area estimation where the full universe of data is known, to a wider audience of public health professionals using firearm suicide as a motivating example. Besag, York and Mollié (BYM) Poisson spatial and space-time smoothing models were fit to firearm suicide counts for the years 2014-2018. County raw death rates in 2018 ranged from 0-24.81 deaths per 10,000 people. However, the highest mortality rate was highly unstable based on only 2 deaths in a population of approximately 800, and 82...