Application of Causal Forest Model to Examine Treatment Effect Heterogeneity in Substance Use Disorder Psychosocial Treatments.

Objectives: Heterogeneity of treatment effect (HTE) is a concern in substance use disorder (SUD) treatments but has not been rigorously examined. This exploratory study applied a causal forest approach to examine HTE in psychosocial SUD treatments, considering multiple covariates simultaneously.

Methods: Data from 12 randomized controlled trials of nine psychosocial treatments were obtained from the National Institute on Drug Abuse Clinical Trials Network.

The San Francisco Health Systems Collaborative: Public Health and Health Care Delivery Systems' Response to the Covid-19 Pandemic.

The Covid-19 pandemic challenged health care delivery systems worldwide. Many acute care hospitals in communities that experienced surges in cases and hospitalizations had to make decisions such as rationing scarce resources. Hospitals serving low-income communities, communities of color, and those in other historically marginalized or vulnerable groups reported the greatest operational impacts of surges.

Trajectories of craving in the course of pharmacotherapy trials for methamphetamine use disorder.

Aims: The aim of this study was to measure trajectories of craving for methamphetamine during the course of pharmacotherapy trials for methamphetamine use disorder.

Design, Setting And Participants: Craving trajectories were identified using Group-Based Trajectory Modeling. The association of craving trajectories with drug use trajectories was examined using a dual trajectory model.

Sensitivity analysis for principal ignorability violation in estimating complier and noncomplier average causal effects.

An important strategy for identifying principal causal effects (popular estimands in settings with noncompliance) is to invoke the principal ignorability (PI) assumption. As PI is untestable, it is important to gauge how sensitive effect estimates are to its violation. We focus on this task for the common one-sided noncompliance setting where there are two principal strata, compliers and noncompliers.

Multiple imputation for propensity score analysis with covariates missing at random: some clarity on "within" and "across" methods.

In epidemiology and the social sciences, propensity score methods are popular for estimating treatment effects using observational data, and multiple imputation is popular for handling covariate missingness. However, how to appropriately use multiple imputation for propensity score analysis is not completely clear. This paper aims to bring clarity on the consistency (or lack thereof) of methods that have been proposed, focusing on the "within" approach (where the effect is estimated separately in each imputed dataset and then the multiple estimates are combined) and the "across" approach (where typically propensity scores are averaged across imputed datasets before being used for effect estimation).

Methods for Integrating Trials and Non-experimental Data to Examine Treatment Effect Heterogeneity.

Estimating treatment effects conditional on observed covariates can improve the ability to tailor treatments to particular individuals. Doing so effectively requires dealing with potential confounding, and also enough data to adequately estimate effect moderation. A recent influx of work has looked into estimating treatment effect heterogeneity using data from multiple randomized controlled trials and/or observational datasets.

Identification of complier and noncomplier average causal effects in the presence of latent missing-at-random (LMAR) outcomes: a unifying view and choices of assumptions.

The study of treatment effects is often complicated by noncompliance and missing data. In the one-sided noncompliance setting where of interest are the complier and noncomplier average causal effects, we address outcome missingness of the latent missing at random type (LMAR, also known as latent ignorability). That is, conditional on covariates and treatment assigned, the missingness may depend on compliance type.

Comparison of methods that combine multiple randomized trials to estimate heterogeneous treatment effects.

Individualized treatment decisions can improve health outcomes, but using data to make these decisions in a reliable, precise, and generalizable way is challenging with a single dataset. Leveraging multiple randomized controlled trials allows for the combination of datasets with unconfounded treatment assignment to better estimate heterogeneous treatment effects. This article discusses several nonparametric approaches for estimating heterogeneous treatment effects using data from multiple trials.

Estimation of place-based vulnerability scores for HIV viral non-suppression: an application leveraging data from a cohort of people with histories of using drugs.

The relationships between place (e.g., neighborhood) and HIV are commonly investigated.

The Importance of Equity Value Judgments and Estimator-Estimand Alignment in Measuring Disparity and Identifying Targets to Reduce Disparity.

The choice of which covariates to adjust for (so-called allowability designation (AD)) in health disparity measurements reflects value judgments about inequitable versus equitable sources of health differences, which is paramount for making inferences about disparity. Yet, many off-the-shelf estimators used in health disparity research are not designed with equity considerations in mind, and they imply different ADs. We demonstrated the practical importance of incorporating equity concerns in disparity measurements through simulations, motivated by the example of reducing racial disparities in hypertension control via interventions on disparities in treatment intensification.

Diversity Differences Among Cardiovascular Fellowships Across Five Geographic Regions in the United States.

Introduction Diversity and inclusion in cardiovascular fellowships are necessary for addressing the healthcare needs of diverse patient populations. However, regional disparities in the diversity of these programs persist, diminishing efforts to create a representative workforce. We observe the regional differences in the diversity of cardiovascular fellowship programs, focusing on gender, doctorate designation, and graduation within the United States (US) or other.

San Francisco's Citywide COVID-19 Response: Strategies to Reduce COVID-19 Severity and Health Disparities, March 2020 Through May 2022.

San Francisco implemented one of the most intensive, comprehensive, multipronged COVID-19 pandemic responses in the United States using 4 core strategies: (1) aggressive mitigation measures to protect populations at risk for severe disease, (2) prioritization of resources in neighborhoods highly affected by COVID-19, (3) timely and adaptive data-driven policy making, and (4) leveraging of partnerships and public trust. We collected data to describe programmatic and population-level outcomes. The excess all-cause mortality rate in 2020 in San Francisco was half that seen in 2019 in California as a whole (8% vs 16%).

Brief Report: Interruptions in HIV Prevention and Treatment During the COVID-19 Pandemic: A Cross-Sectional Study of Transgender and Gender Nonbinary Adults in the United States.

Background: Transgender and gender nonbinary (TNB) people have been disproportionately affected by HIV and the COVID-19 pandemic. This study explored the prevalence of HIV prevention and treatment (HPT) interruptions during the pandemic and identified factors associated with these interruptions.

Setting: Data were drawn from LITE Connect, a US-based, nationwide, online, self-administered survey designed to examine the experiences of TNB adults during the COVID-19 pandemic.

Causal mediation analysis: From simple to more robust strategies for estimation of marginal natural (in)direct effects.

This paper aims to provide practitioners of causal mediation analysis with a better understanding of estimation options. We take as inputs two familiar strategies (weighting and model-based prediction) and a simple way of combining them (weighted models), and show how a range of estimators can be generated, with different modeling requirements and robustness properties. The primary goal is to help build intuitive appreciation for robust estimation that is conducive to sound practice.

COVID-19, HIV, and Syphilis Contact Tracing: What Have We Learned and Where Are We Headed?

Contact tracing is a core public health intervention for a range of communicable diseases, in which the primary goal is to interrupt disease transmission and decrease morbidity. In this article, we present lessons learned from COVID-19, HIV, and syphilis in San Francisco to illustrate factors that shape the effectiveness of contact tracing programs and to highlight the value of investing in a robust disease intervention workforce with capacity to pivot rapidly in response to a range of emerging disease trends and outbreak response needs.

Comparing the performance of statistical methods that generalize effect estimates from randomized controlled trials to much larger target populations.

Policymakers use results from randomized controlled trials to inform decisions about whether to implement treatments in target populations. Various methods - including inverse probability weighting, outcome modeling, and Targeted Maximum Likelihood Estimation - that use baseline data available in both the trial and target population have been proposed to generalize the trial treatment effect estimate to the target population. Often the target population is significantly larger than the trial sample, which can cause estimation challenges.

Clarifying causal mediation analysis: Effect identification via three assumptions and five potential outcomes.

Causal mediation analysis is complicated with multiple effect definitions that require different sets of assumptions for identification. This article provides a systematic explanation of such assumptions. We define five potential outcome types whose means are involved in various effect definitions.

UVB-Induced Microvesicle Particle Release and Its Effects on the Cutaneous Microenvironment.

Ultraviolet B radiation (UVB) has profound effects on human skin that results in a broad spectrum of immunological local and systemic responses and is the major cause of skin carcinogenesis. One important area of study in photobiology is how UVB is translated into effector signals. As the skin is exposed to UVB light, subcellular microvesicle particles (MVP), a subtype of bioactive extracellular vesicles, are released causing a variety of local and systemic immunological effects.

Contact-Tracing Outcomes Among Household Contacts of Fully Vaccinated Coronavirus Disease 2019 (COVID-19) Patients: San Francisco, California, 29 January-2 July 2021.

Background: The extent to which vaccinated persons diagnosed with coronavirus disease 2019 (COVID-19) can transmit to other vaccinated and unvaccinated persons is unclear.

Methods: Using data from the San Francisco Department of Public Health, this report describes outcomes of household contact tracing during 29 January-2 July 2021, where fully vaccinated patients with COVID-19 were the index case in the household.

Results: Among 248 fully vaccinated patients with breakthrough infections, 203 (82%) were symptomatic and 105 were identified as the index patient within their household.