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.S. Census Bureau at the block level for the 2020 Census are not biased in any consistent direction. However, subpopulation totals defined by age, race, and ethnicity are highly variable. Additionally, we find that inconsistencies across the three sources are amplified in large blocks defined in terms of land area or by total housing units, blocks in suburban areas, and blocks that lack broadband access.

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. We assessed feasibility and acceptability of START and the RCT, and we conducted an intent-to-treat analysis on data from the electronic medical record and patient interviews at baseline and 1-month postdischarge. The study compared RCT outcomes (medication for alcohol or opioid use disorder, linkage to follow-up care after discharge, substance use, hospital readmission) between arms by fitting logistic and linear regression models. FINDINGS: Of 38 START patients, 97 % met with the addiction medicine specialist and care manager; 89 % received ≥8 of 10 intervention components. All patients receiving START found it to be somewhat or very acceptable. START patients had higher odds of initiating medication during the inpatient stay (OR 6.26, 95 % CI = 2.38-16.48, p < .001) and being linked to follow-up care (OR 5.76, 95 % CI = 1.86-17.86, p < .01) compared to usual care patients (N = 50). The study found no significant differences between groups in drinking or opioid use; patients in both groups reported using fewer substances at the 1-month follow-up. CONCLUSIONS: Pilot data suggest START and RCT implementation are feasible and acceptable and that START may facilitate medication initiation and linkage to follow-up for inpatients with an alcohol or opioid use disorder. A larger trial should assess effectiveness, covariates, and moderators of intervention effects.

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. economy and health care system since 2006, there are concerns that continued reliance on PPI Survey data might result in PE payments that do not accurately capture the resources that are typically required to provide services. In this final phase of a study on PE methodology, the authors address how the Centers for Medicare & Medicaid Services (CMS) might improve the methodology used in PE rate-setting, update data that inform PE rates, or both. The authors conclude that this information is best provided by a survey; therefore, they focus on the advantages and disadvantages of survey-based approaches. They also describe the use of a lean model survey instrument, as well as partnering with another agency to collect data. Finally, the authors describe a virtual town hall meeting held in June 2021 to give stakeholders an opportunity to provide feedback on PE data collection and rate-setting. The system of data and methods that CMS uses to support PE rate-setting is complex; thus, CMS must take into account a number of competing priorities when considering changes to the system. With this in mind, the authors offer a number of near- and longer-term recommendations.

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. Our findings showcase that these two models should, by default, include higher-order moments and focusing only on first moments can result in substantial bias in estimated treatment effect estimates from both models that could be avoided using higher moments.

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

Background: 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. Mixture models are appropriate for bimodal data because they assume that the sampled population is composed of several underlying subpopulations.Objectives: To introduce a novel mixture modeling approach to analyze bimodal substance use frequency data.Methods: We reviewed existing models used to analyze substance use frequency outcomes and developed multiple alternative variants of a finite mixture model. We applied all methods to data from a randomized controlled study in which 30-day alcohol abstinence was the primary outcome. Study data included 73 individuals (38 men and 35 women). Models were implemented in the software packages SAS, Stata, and Stan.Results: Shortcomings of existing approaches include: 1) inability to model outcomes with multiple modes, 2) invalid statistical inferences, including anti-conservative p-values, 3) sensitivity of results to the arbitrary choice to model days of substance use versus days of substance abstention, and 4) generation of predictions outside the range of common substance use frequency outcomes. Our mixture model variants avoided all of these shortcomings.Conclusions: Standard models of substance use frequency outcomes can be problematic, sometimes overstating treatment effectiveness. The mixture models developed improve the analysis of bimodal substance use frequency.

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. METHODS: We employ a spatial propensity score matching method to account for "geographic confounding", which occurs when the confounding factors, whether observed or unobserved, vary by geographic region. We augment the propensity score and outcome models with spatial random effects, which are assigned scaled Besag-York-Mollié priors to address spatial clustering and improve inferences by borrowing information across neighboring geographic regions. We apply this approach to a study exploring racial disparities in diabetes specialty care between non-Hispanic black and non-Hispanic white veterans. We construct multiple global estimates of the risk difference in diabetes care: a crude unadjusted estimate, an estimate based solely on patient-level matching, and an estimate that incorporates both patient and spatial information. RESULTS: In simulation we show that in the presence of an unmeasured geographic confounder, ignoring spatial heterogeneity results in increased relative bias and mean squared error, whereas incorporating spatial random effects improves inferences. In our study of racial disparities in diabetes specialty care, the crude unadjusted estimate suggests that specialty care is more prevalent among non-Hispanic blacks, while patient-level matching indicates that it is less prevalent. Hierarchical spatial matching supports the latter conclusion, with a further increase in the magnitude of the disparity. CONCLUSIONS: These results highlight the importance of accounting for spatial heterogeneity in propensity score analysis, and suggest the need for clinical care and management strategies that are culturally sensitive and racially inclusive.

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. Drawing on birth certificate data from 2007-2014, during which 21 states enacted E-Verify mandates, we find that these mandates are associated with a decline in birthweight and gestational age for infants born to immigrant mothers with demographic profiles matching the undocumented population in their state as well as for infants of native-born mothers. In observing negative trends for both immigrants and natives, our findings do not support the hypothesis that E-Verify has a distinct impact on immigrant health; however, the broader economic, political, and demographic contexts that coincide with these policies, which likely impact the broader community of both immigrants and natives, may pose risks to infant health.

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. We demonstrate that in the presence of unmeasured geographic confounding, spatial propensity score matching in addition to the spatial negative binomial hurdle outcome model yields improved performance compared to the outcome model alone. In the motivating application, we construct three estimates of racial differences in hospitalizations: the risk difference in admission, the mean difference in number of inpatient days among those hospitalized, and the mean difference in number of inpatient days across all patients (hospitalized and non-hospitalized). Results indicate that non-Hispanic black veterans with type 2 diabetes have a lower risk of hospital admission and a greater number of inpatient days on average. The latter result is especially important considering that we observed much smaller effect sizes in analyses that did not incorporate spatial matching. These results emphasize the need to address geographic confounding in health disparity studies.

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. Through a series of simulations, we show that ignoring spatial variation results in increased absolute bias and mean squared error, while the spatial doubly robust estimator performs well under various levels of spatial heterogeneity and moderate sample sizes. In the motivating application, we construct three global estimates of the risk difference between race groups: an unadjusted estimate, a doubly robust estimate that adjusts only for patient-level information, and a hierarchical spatial doubly robust estimate. Results indicate a gradual reduction in the risk difference at each stage, with the inclusion of spatial random effects providing a 20% reduction compared to an estimate that ignores spatial heterogeneity. Smoothed maps indicate poor glycemic control across Alabama and southern Georgia, areas comprising the so-called "stroke belt." These results suggest the need for community-specific interventions to target diabetes in geographic areas of greatest need.

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). PRINCIPAL FINDINGS: A mixture of multivariate normal distributions can classify physician organization cost curves into clusters defined by total cost level, shape, and within-cluster variation. K-means clustering does not accommodate differing levels of within-cluster variation and resulted in more clusters being allocated to unstable cost curves. A mixture of regressions approach focuses overly on anomalous trajectories and is sensitive to model coding. CONCLUSIONS: Statistical clustering can be used to form sampling strata when longitudinal measures are of primary interest. Many clustering algorithms are available; the choice of the clustering algorithm can strongly impact the resulting strata because various algorithms focus on different aspects of the observed data.

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.S. military and their families. These counseling services are collectively called non-medical counseling within the DoD and are offered through the Military and Family Life Counseling (MFLC) and Military OneSource programs. RAND's National Defense Research Institute was asked to evaluate these programs to determine whether they are effective in improving outcomes and whether effectiveness varies by problem type and/or population. Two online surveys were provided to program participants-the first two to three weeks after their initial session and the second three months later. Surveys were designed to gain information on 1) problem severity and overall problem resolution, 2) resolution of stress and anxiety, 3) problem interference with work and daily life, 4) connection to other services and referrals, 5) experiences with MFLC and Military OneSource programs, and 6) perceptions of non-medical counselors. The majority of participants experienced a decrease in problem severity and a reduction in reported frequency of feeling stressed or anxious as a result of their problem following counseling. These improvements were sustained or continued to improve in the three months after initiation of counseling. Non-medical counseling was not universally successful, however, and a small minority expressed dissatisfaction with the program or their counselor. Collectively these findings suggest a number of policy implications and programmatic improvements of interest to program leadership in the Office of the Secretary of Defense.

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. We build on this earlier work by extending the models to semicontinuous outcomes, or outcomes that are a mixture of continuous and discrete distributions. This results in multivariate session effects, for which we allow temporal dependencies of various orders. We illustrate our methods using data from a group-based intervention to treat substance abuse and depression, focusing on the outcome of average number of drinks per day. Alcohol and other drug abuse are frequently treated in a group therapy setting. If 2 clients attend the some of the same sessions, we might expect that-on average-their posttreatment outcomes would be more similar than if they had not attended any sessions together. Hence, if participants are allowed to enroll in therapy on a rolling basis, irregular patterns of session attendance can induce complex relationships between participant outcomes. Statistical methods have been developed previously to account for rolling admission group therapy when the outcomes are normally distributed. In the case of alcohol and other drug use interventions, however, a substantial fraction of participants often report zero use after treatment. We extend previous work to build models that accommodate semicontinuous outcomes, which are a mixture of continuous and discrete distributions, for such situations. We find that modern Bayesian statistical methods and software allow users to efficiently estimate nonstandard models such as these. We illustrate our methods using data from a group-based intervention to treat substance abuse and depression, focusing on the outcome of average number of drinks per day. We find that the intervention is associated with a drop in the probability of any drinking, but find no evidence of a change in the amount of drinking, conditional on some drinking. (PsycINFO Database Record

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. Specifically, we propose and evaluate three approaches for selecting the optimal number of trees for the GBM in the twang package in R. Normally, the twang package in R iteratively selects the optimal number of trees as that which maximizes balance between the treatment groups being considered. Because the selected number of trees may lead to highly variable propensity score weights, we examine alternative ways to tune the number of trees used in the estimation of propensity score weights such that we sacrifice some balance on the pre-treatment covariates in exchange for less variable weights. We use simulation studies to illustrate these methods and to describe the potential advantages and disadvantages of each method. We apply these methods to two case studies: one examining the effect of dog ownership on the owner's general health using data from a large, population-based survey in California, and a second investigating the relationship between abstinence and a long-term economic outcome among a sample of high-risk youth.

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. In this article, we compare two promising propensity score estimation methods (for time-invariant binary exposures) when assessing the average treatment effect on the treated: the generalized boosted models and covariate-balancing propensity scores, with the main objective to provide analysts with some rules-of-thumb when choosing between these two methods. We compare the methods across different dimensions including the presence of extraneous variables, the complexity of the relationship between exposure or outcome and covariates, and the residual variance in outcome and exposure. We found that when noncomplex relationships exist between outcome or exposure and covariates, the covariate-balancing method outperformed the boosted method, but under complex relationships, the boosted method performed better. We lay out criteria for when one method should be expected to outperform the other with no blanket statement on whether one method is always better than the other.

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. DATA SOURCES: We estimated surgical times using Medicare 2011 anesthesia claims and New York Statewide Planning and Research Cooperative System 2011 OR times. Estimated times were validated using data from the National Surgical Quality Improvement Program. We compared our time estimates to those used by Medicare in the fee schedule. STUDY DESIGN: We estimate surgical times via piecewise linear median regression models. PRINCIPAL FINDINGS: Using 3.0 million observations of anesthesia and OR times, we estimated surgical time for 921 procedures. Correlation between these time estimates and directly measured surgical time from the validation database was 0.98. Our estimates of surgical time were shorter than the Medicare fee schedule estimates for 78 percent of procedures. CONCLUSIONS: Anesthesia and OR times can be used to measure surgical time and thereby improve the payment for surgical procedures in the Medicare fee schedule.

Who Are the Men Caring for Maltreated Youth? Male Primary Caregivers in the Child Welfare System.

The goal of this study is to better understand the characteristics of men who act as primary caregivers of maltreated children. We examined differences between male primary caregivers (fathers) for youth involved in the child welfare system and female primary caregivers (mothers). We conducted secondary data analyses of the National Survey of Child and Adolescent Well-Being-II baseline data. Overall, primary caregiving fathers and mothers were more similar than different, though a few differences were revealed. Compared to mothers, fathers tended to be older and were more likely to be employed, with a higher household income and older children. Fathers and mothers did not differ in terms of depression or parenting behavior, but there was evidence that mothers have more problems with drug use compared to fathers. Compared to fathers, mothers reported higher levels of internalizing and externalizing problems in their children. Children with male primary caregivers were more likely to have experienced physical abuse but less likely to have experienced emotional abuse or witnessed domestic violence than children with female primary caregivers. These findings may help to inform researchers, practitioners, and policy makers on how to address the needs of male caregivers and their children.

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. Qualitative data analysis software was used to code thematic content and identify representative segments of text. Participating radiologists also completed an accompanying survey designed to supplement focus group discussions. RESULTS: Twenty-six radiologists participated in four focus group discussions. The following major themes related to the radiologists' perceptions after decision support implementation were identified: no substantial change in radiologists' interactions with referring clinicians; no substantial change in radiologist work flow, including protocol-writing time; and no perceived increase in imaging appropriateness. Radiologists provided suggestions for improvements in the decision support system, including increasing the usability of clinical data captured, and expressed a desire to have greater involvement in future development and implementation efforts. CONCLUSION: Overall, radiologists from health care systems involved in the Medicare Imaging Demonstration did not perceive that decision support had a substantial effect, either positive or negative, on their professional roles and responsibilities. Radiologists expressed a desire to improve efficiencies and quality of care by having greater involvement in future efforts.

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. We model the random effects via intrinsic conditionally autoregressive priors, improving small-area estimation through maximum spatiotemporal smoothing. We adopt a Bayesian modeling approach based on an asymmetric Laplace distribution and develop an efficient posterior sampling scheme that relies solely on conjugate full conditionals. We apply our model to data from the Duke support repository, a large georeferenced database containing health and financial data for Duke Health System patients residing in Durham County, North Carolina.

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. Small changes in ordering patterns were noted, but decision support systems were unable to assign appropriateness ratings to many orders, thus limiting the potential effectiveness of decision support. Many opportunities to refine decision support systems have been identified.