Evaluating the Accuracy of 2020 Census Block-Level Estimates in California.

In this study, we provide an assessment of data accuracy from the 2020 Census. We compare block-level population totals from a sample of 173 census blocks in California across three sources: (1) the 2020 Census, which has been infused with error to protect respondent confidentiality; (2) the California Neighborhoods Count, the first independent enumeration survey of census blocks; and (3) projections based on the 2010 Census and subsequent American Community Surveys. We find that, on average, total population counts provided by the U.

Pilot randomized controlled trial of a hospital-based substance use treatment and recovery team (START) to improve initiation of medication for alcohol or opioid use disorder and linkage to follow-up care.

Objectives: We conducted a pilot randomized controlled trial (RCT) to explore whether a hospital inpatient addiction consult team (Substance Use Treatment and Recovery Team [START]) based on collaborative care was feasible, acceptable to patients, and whether it could improve uptake of medication in the hospital and linkage to care after discharge, as well as reduce substance use and hospital readmission. The START consisted of an addiction medicine specialist and care manager who implemented a motivational and discharge planning intervention.

Methods: We randomized inpatients age ≥ 18 with a probable alcohol or opioid use disorder to receive START or usual care.

Practice Expense Data Collection and Methodology: Phase II Final Report.

Each year, Medicare allocates tens of billions of dollars for indirect practice expense (PE) across services on the basis of data from the Physician Practice Information (PPI) Survey, which reflects 2006 expenses. Because these data are not regularly updated, and because there have been significant changes in the U.S.

Higher Moments for Optimal Balance Weighting in Causal Estimation.

We expand upon a simulation study that compared three promising methods for estimating weights for assessing the average treatment effect on the treated for binary treatments: generalized boosted models, covariate-balancing propensity scores, and entropy balance. The original study showed that generalized boosted models can outperform covariate-balancing propensity scores, and entropy balance when there are likely to be nonlinear associations in both the treatment assignment and outcome models and when the other two models are fine-tuned to obtain balance only on first-order moments. We explore the potential benefit of using higher-order moments in the balancing conditions for covariate-balancing propensity scores and entropy balance.

Appropriate analyses of bimodal substance use frequency outcomes: a mixture model approach.

In addiction research, outcome measures are often characterized by bimodal distributions. One mode can be for individuals with low substance use and the other mode for individuals with high substance use. Applying standard statistical procedures to bimodal data may result in invalid inference.

Propensity score matching for multilevel spatial data: accounting for geographic confounding in health disparity studies.

Background: Diabetes is a public health burden that disproportionately affects military veterans and racial minorities. Studies of racial disparities are inherently observational, and thus may require the use of methods such as Propensity Score Analysis (PSA). While traditional PSA accounts for patient-level factors, this may not be sufficient when patients are clustered at the geographic level and thus important confounders, whether observed or unobserved, vary by geographic location.

Employer Verification Mandates and Infant Health.

In recent decades, several states have enacted their own immigration enforcement policies. This reflects substantial variation in the social environments faced by immigrants and native-born citizens, and has raised concerns about unintended consequences. E-Verify mandates, which require employers to use an electronic system to ascertain legal status as a pre-requisite for employment, are a common example of this trend.

Analysis of racial differences in hospital stays in the presence of geographic confounding.

Using recent methods for spatial propensity score modeling, we examine differences in hospital stays between non-Hispanic black and non-Hispanic white veterans with type 2 diabetes. We augment a traditional patient-level propensity score model with a spatial random effect to create a matched sample based on the estimated propensity score. We then use a spatial negative binomial hurdle model to estimate differences in both hospital admissions and inpatient days.

Sample selection in the face of design constraints: Use of clustering to define sample strata for qualitative research.

Objective: To sample 40 physician organizations stratified on the basis of longitudinal cost of care measures for qualitative interviews in order to describe the range of care delivery structures and processes that are being deployed to influence the total costs of caring for patients.

Data Sources: Three years of physician organization-level total cost of care data (n = 156 in California) from the Integrated Healthcare Association's value-based pay-for-performance program.

Study Design: We fit total cost of care data using mixture and K-means clustering algorithms to segment the population of physician organizations into sampling strata based on 3-year cost trajectories (ie, cost curves).

An Evaluation of U.S. Military Non-Medical Counseling Programs.

This study evaluates two programs offered by the U.S. Department of Defense (DoD) that provide short-term, solution-focused counseling for common personal and family issues to members of the U.

Bayesian models for semicontinuous outcomes in rolling admission therapy groups.

Alcohol and other drug abuse are frequently treated in a group therapy setting. If participants are allowed to enroll in therapy on a rolling basis, irregular patterns of participant overlap can induce complex correlations of participant outcomes. Previous work has accounted for common session attendance by modeling random effects for each therapy session, which map to participant outcomes via a multiple membership construction when modeling normally distributed outcome measures.

Addressing geographic confounding through spatial propensity scores: a study of racial disparities in diabetes.

Motivated by a study exploring differences in glycemic control between non-Hispanic black and non-Hispanic white veterans with type 2 diabetes, we aim to address a type of confounding that arises in spatially referenced observational studies. Specifically, we develop a spatial doubly robust propensity score estimator to reduce bias associated with geographic confounding, which occurs when measured or unmeasured confounding factors vary by geographic location, leading to imbalanced group comparisons. We augment the doubly robust estimator with spatial random effects, which are assigned conditionally autoregressive priors to improve inferences by borrowing information across neighboring geographic regions.

Optimizing Variance-Bias Trade-off in the TWANG Package for Estimation of Propensity Scores.

While propensity score weighting has been shown to reduce bias in treatment effect estimation when selection bias is present, it has also been shown that such weighting can perform poorly if the estimated propensity score weights are highly variable. Various approaches have been proposed which can reduce the variability of the weights and the risk of poor performance, particularly those based on machine learning methods. In this study, we closely examine approaches to fine-tune one machine learning technique (generalized boosted models [GBM]) to select propensity scores that seek to optimize the variance-bias trade-off that is inherent in most propensity score analyses.

The Right Tool for the Job: Choosing Between Covariate-balancing and Generalized Boosted Model Propensity Scores.

Estimating the causal effect of an exposure (vs. some control) on an outcome using observational data often requires addressing the fact that exposed and control groups differ on pre-exposure characteristics that may be related to the outcome (confounders). Propensity score methods have long been used as a tool for adjusting for observed confounders in order to produce more valid causal effect estimates under the strong ignorability assumption.

Estimating Surgical Procedure Times Using Anesthesia Billing Data and Operating Room Records.

Objective: The median time required to perform a surgical procedure is important in determining payment under Medicare's physician fee schedule. Prior studies have demonstrated that the current methodology of using physician surveys to determine surgical times results in overstated times. To measure surgical times more accurately, we developed and validated a methodology using available data from anesthesia billing data and operating room (OR) records.

JOURNAL CLUB: Radiologists' Perceptions of Computerized Decision Support: A Focus Group Study From the Medicare Imaging Demonstration Project.

Objective: The purpose of this study was to discern radiologists' perceptions regarding the implementation of a decision support system intervention as part of the Medicare Imaging Demonstration project and the effect of decision support on radiologists' interactions with ordering clinicians, their radiology work flow, and appropriateness of advanced imaging.

Subjects And Methods: A focus group study was conducted with a diverse sample of radiologists involved in interpreting advanced imaging studies at Medicare Imaging Demonstration project sites. A semistructured moderator guide was used, and all focus group discussions were recorded and transcribed verbatim.

Development of a Model for the Validation of Work Relative Value Units for the Medicare Physician Fee Schedule.

The Centers for Medicare & Medicaid Services (CMS) uses the resource-based relative value scale to pay physicians and other practitioners for professional services. The work values measure the relative levels of professional time and intensity (physical effort, skills, and stress) associated with providing services. CMS asked RAND to develop a model to validate the work values using external data sources.

Medicare Imaging Demonstration Final Evaluation: Report to Congress.

Increasing use of advanced medical imaging is often cited as a key driver of cost growth in medical spending. In 2011, the Medicare Imaging Demonstration from the Centers for Medicare & Medicaid Services began testing whether exposing ordering clinicians to appropriateness guidelines for advanced imaging would reduce ordering inappropriate images. The evaluation examined trends in advanced diagnostic imaging utilization starting January 1, 2009-more than two years before the beginning of the demonstration-to November 30, 2013-two months after the close of the demonstration.

A spatiotemporal quantile regression model for emergency department expenditures.

Motivated by a recent study of geographic and temporal trends in emergency department care, we develop a spatiotemporal quantile regression model for the analysis of emergency department-related medical expenditures. The model yields distinct spatial patterns across time for each quantile of the response distribution, which is important in the spatial analysis of expenditures, as there is often little spatiotemporal variation in mean expenditures but more pronounced variation in the extremes. The model has a hierarchical structure incorporating patient-level and region-level predictors as well as spatiotemporal random effects.

Alcohol Pharmacology Education Partnership: Using Chemistry and Biology Concepts To Educate High School Students about Alcohol.

We developed the Alcohol Pharmacology Education Partnership (APEP), a set of modules designed to integrate a topic of interest (alcohol) with concepts in chemistry and biology for high school students. Chemistry and biology teachers ( = 156) were recruited nationally to field-test APEP in a controlled study. Teachers obtained professional development either at a conference-based workshop (NSTA or NCSTA) or via distance learning to learn how to incorporate the APEP modules into their teaching.

Estimating the causal effects of cumulative treatment episodes for adolescents using marginal structural models and inverse probability of treatment weighting.

Background: Substance use treatment is rarely a one-time event for individuals with substance use disorders. Sustained reductions in substance use and its related symptoms may result from multiple treatment episodes.

Methods: We use a marginal structural model with inverse-probability-of-treatment weighting to estimate the causal effects of cumulative treatment experiences over a period of 9 months on drug use at the end of 1-year among 2870 adolescents receiving care in community-based treatment settings.

Multiple-Shrinkage Multinomial Probit Models with Applications to Simulating Geographies in Public Use Data.

Multinomial outcomes with many levels can be challenging to model. Information typically accrues slowly with increasing sample size, yet the parameter space expands rapidly with additional covariates. Shrinking all regression parameters towards zero, as often done in models of continuous or binary response variables, is unsatisfactory, since setting parameters equal to zero in multinomial models does not necessarily imply "no effect.

The effect of North Carolina hospital payor mix on dental-related pediatric emergency room utilization.

Objectives: We examined the effect of hospital payor mix on the proportion of pediatric emergency department (ED) visits that were dental related.

Methods: We used the North Carolina (NC) Emergency Room Discharge Database from 2007 to 2009 to estimate the relationship between the percent of pediatric ED patients that were covered by Medicaid and the percent of pediatric ED visits that were dental related. Hospital-level fixed effects controlled for unobserved hospital-level characteristics.