Doubly robust evaluation of high-dimensional surrogate markers.

When evaluating the effectiveness of a treatment, policy, or intervention, the desired measure of efficacy may be expensive to collect, not routinely available, or may take a long time to occur. In these cases, it is sometimes possible to identify a surrogate outcome that can more easily, quickly, or cheaply capture the effect of interest. Theory and methods for evaluating the strength of surrogate markers have been well studied in the context of a single surrogate marker measured in the course of a randomized clinical study. However, methods are lacking for quantifying the utility of surrogate markers when the dimension of the surrogate grows. We propose a robust and efficient method for evaluating a set of surrogate markers that may be high-dimensional. Our method does not require treatment to be randomized and may be used in observational studies. Our approach draws on a connection between quantifying the utility of a surrogate marker and the most fundamental tools of causal inference-namely, methods for robust estimation of the average treatment effect. This connection facilitates the use of modern methods for estimating treatment effects, using machine learning to estimate nuisance functions and relaxing the dependence on model specification. We demonstrate that our proposed approach performs well, demonstrate connections between our approach and certain mediation effects, and illustrate it by evaluating whether gene expression can be used as a surrogate for immune activation in an Ebola study.

Do Hospital Characteristics Predict Racial-and-Ethnic Disparities in Patient Experience? National Results From the HCAHPS Survey.

OBJECTIVE: Assess whether hospital characteristics associated with better patient experiences overall are also associated with smaller racial-and-ethnic disparities in inpatient experience. BACKGROUND: Hospitals that are smaller, non-profit, and serve high proportions of White patients tend to be high-performing overall, but it is not known whether these hospitals also have smaller racial-and-ethnic disparities in care. RESEARCH DESIGN: We used linear mixed-effect regression models to predict a summary measure that averaged eight Hospital CAHPS (HCAHPS) measures (Nurse Communication, Doctor Communication, Staff Responsiveness, Communication about Medicines, Discharge Information, Care Coordination, Hospital Cleanliness, and Quietness) from patient race-and-ethnicity, hospital characteristics (size, ownership, racial-and-ethnic patient-mix), and interactions of race-and-ethnicity with hospital characteristics. SUBJECTS: Inpatients discharged from 4,365 hospitals in 2021 who completed an HCAHPS survey ( N =2,288,862). RESULTS: While hospitals serving larger proportions of Black and Hispanic patients scored lower on all measures, racial-and-ethnic disparities were generally smaller for Black and Hispanic patients who received care from hospitals serving higher proportions of patients in their racial-and-ethnic group. Experiences overall were better in smaller and non-profit hospitals, but racial-and-ethnic differences were slightly larger. CONCLUSIONS: Large, for-profit hospitals and hospitals serving higher proportions of Black and Hispanic patients tend to be lower performing overall but have smaller disparities in patient experience. High-performing hospitals might look at low-performing hospitals for how to provide less disparate care whereas low-performing hospitals may look to high-performing hospitals for how to improve patient experience overall.

Measuring Equity in the Hospital Setting: An HCAHPS Application of the Health Equity Summary Score.

BACKGROUND: Health care quality varies by patient factors, including race-and-ethnicity and preferred language. Addressing inequities requires identifying them and incentivizing equity. OBJECTIVES: We apply an approach first implemented in the Medicare Advantage setting to measure equity in patient experiences by race-and-ethnicity [Asian American and Native Hawaiian or Pacific Islander (AA and NHPI), Black, Hispanic, vs. White] and language preference (English-preferring vs. another-language-preferring). We identify characteristics of hospitals providing high-quality equitable care. RESEARCH DESIGN: We estimated, standardized, and combined performance measures into a Health Equity Summary Score (HESS) using 2016-2019 Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey data. The HCAHPS HESS considered current cross-sectional performance, within-hospital improvement, and overall improvement by race-and-ethnicity and language preference. SUBJECTS: A total of 3333 US hospitals with 2019 HCAHPS Star Ratings. RESULTS: The HCAHPS HESS was calculable for 44% of hospitals. High-scoring (4-5 diamonds on a 1-diamond to 5-diamond scale) hospitals tended to be smaller than intermediate-scoring [3 diamonds (14% of high-scoring hospitals had <100 beds vs. 7% of intermediate-scoring hospitals, P <0.001) and were less often for-profit (20% vs. 31%, P <0.001)]. While a significant percentage (29%) of patients served by high-scoring hospitals were AA and NHPI, Black, or Hispanic, and 9% were another-language-preferring, there were smaller proportions of Black and Hispanic patients in high-scoring versus other hospitals. HESS performance was negatively associated with the percentage of patients preferring another language to English. HESS scores were moderately correlated with overall Star Ratings ( r =0.70). CONCLUSIONS: The HCAHPS HESS and practices of high-scoring hospitals could promote more equitable patient experiences.

Antipsychotics and the risk of diabetes and death among adults with serious mental illnesses.

BACKGROUND: Individuals with schizophrenia exposed to second-generation antipsychotics (SGA) have an increased risk for diabetes, with aripiprazole purportedly a safer drug. Less is known about the drugs' mortality risk or whether serious mental illness (SMI) diagnosis or race/ethnicity modify these effects. METHODS: Authors created a retrospective cohort of non-elderly adults with SMI initiating monotherapy with an SGA (olanzapine, quetiapine, risperidone, and ziprasidone, aripiprazole) or haloperidol during 2008-2013. Three-year diabetes incidence or all-cause death risk differences were estimated between each drug and aripiprazole, the comparator, as well as effects within SMI diagnosis and race/ethnicity. Sensitivity analyses evaluated potential confounding by indication. RESULTS: 38 762 adults, 65% White and 55% with schizophrenia, initiated monotherapy, with haloperidol least (6%) and quetiapine most (26·5%) frequent. Three-year mortality was 5% and diabetes incidence 9.3%. Compared with aripiprazole, haloperidol and olanzapine reduced diabetes risk by 1.9 (95% CI 1.2-2.6) percentage points, or a 18.6 percentage point reduction relative to aripiprazole users' unadjusted risk (10.2%), with risperidone having a smaller advantage. Relative to aripiprazole users' unadjusted risk (3.4%), all antipsychotics increased mortality risk by 1.1-2.2 percentage points, representing 32.4-64.7 percentage point increases. Findings within diagnosis and race/ethnicity were generally consistent with overall findings. Only quetiapine's higher mortality risk held in sensitivity analyses. CONCLUSIONS: Haloperidol's, olanzapine's, and risperidone's lower diabetes risks relative to aripiprazole were not robust in sensitivity analyses but quetiapine's higher mortality risk proved robust. Findings expand the evidence on antipsychotics' risks, suggesting a need for caution in the use of quetiapine among individuals with SMI.

Effects of the COVID-19 early pandemic on delivery outcomes among women with and without COVID-19 at birth.

BACKGROUND: The COVID-19 pandemic may influence delivery outcomes through direct effects of infection or indirect effects of disruptions in prenatal care. We examined early pandemic-related changes in birth outcomes for pregnant women with and without a COVID-19 diagnosis at delivery. METHODS: We compared four delivery outcomes-preterm delivery (PTD), severe maternal morbidity (SMM), stillbirth, and cesarean birth-between 2017 and 2019 (prepandemic) and between April and December 2020 (early-pandemic) using interrupted time series models on 11.8 million deliveries, stratified by COVID-19 infection status at birth with entropy weighting for historical controls, from the Healthcare Cost and Utilization Project across 43 states and the District of Columbia. RESULTS: Relative to 2017-2019, women without COVID-19 at delivery in 2020 had lower odds of PTD (OR = 0.93; 95% CI = 0.92-0.94) and SMM (OR = 0.88; 95% CI = 0.85-0.91) but increased odds of stillbirth (OR = 1.04; 95% CI = 1.01-1.08). Absolute effects were small across race/ethnicity groups. Deliveries with COVID-19 had an excess of each outcome, by factors of 1.07-1.46 for outcomes except SMM at 4.21. The effect for SMM was more pronounced for Asian/Pacific Islander non-Hispanic (API; OR = 10.51; 95% CI = 5.49-20.14) and Hispanic (OR = 5.09; 95% CI = 4.29-6.03) pregnant women than for White non-Hispanic (OR = 3.28; 95% CI = 2.65-4.06) women. DISCUSSION: Decreasing rates of PTD and SMM and increasing rates of stillbirth among deliveries without COVID-19 were small but suggest indirect effects of the pandemic on maternal outcomes. Among pregnant women with COVID-19 at delivery, adverse effects, particularly SMM for API and Hispanic women, underscore the importance of addressing health disparities.

Is Bigger Better?: A Closer Look at Small Health Systems in the United States.

BACKGROUND: Research on US health systems has focused on large systems with at least 50 physicians. Little is known about small systems. OBJECTIVES: Compare the characteristics, quality, and costs of care between small and large health systems. RESEARCH DESIGN: Retrospective, repeated cross-sectional analysis. SUBJECTS: Between 468 and 479 large health systems, and between 608 and 641 small systems serving fee-for-service Medicare beneficiaries, yearly between 2013 and 2017. MEASURES: We compared organizational, provider and beneficiary characteristics of large and small systems, and their geographic distribution, using multiple Medicare and Internal Revenue Service administrative data sources. We used mixed-effects regression models to estimate differences between small and large systems in claims-based Healthcare Effectiveness Data and Information Set (HEDIS) quality measures and HealthPartners' Total Cost of Care measure using a 100% sample of Medicare fee-for-service claims. We fit linear spline models to examine the relationship between the number of a system's affiliated physicians and its quality and costs. RESULTS: The number of both small and large systems increased from 2013 to 2017. Small systems had a larger share of practice sites (43.1% vs. 11.7% for large systems in 2017) and beneficiaries (51.4% vs. 15.5% for large systems in 2017) in rural areas or small towns. Quality performance was lower among small systems than large systems (-0.52 SDs of a composite quality measure) and increased with system size up to ∼75 physicians. There was no difference in total costs of care. CONCLUSIONS: Small systems are a growing source of care for rural Medicare populations, but their quality performance lags behind large systems. Future studies should examine the mechanisms responsible for quality differences.

Measuring Inconsistency in Quality Across Patient Groups to Target Quality Improvement.

BACKGROUND: Quality improvement (QI) may be aimed at improving care for all patients, or it may be targeted at only certain patient groups. Health care providers have little guidance when determining when targeted QI may be preferred. OBJECTIVES: The aim was to develop a method for quantifying performance inconsistency and guidelines for when inconsistency indicates targeted QI, which we apply to the performance of health plans for different patient groups. RESEARCH DESIGN AND MEASURES: Retrospective analysis of 7 Health Care Effectiveness Data and Information Set (HEDIS) measures of clinical care quality. SUBJECTS: All Medicare Advantage (MA) beneficiaries eligible for any of 7 HEDIS measures 2015-2018. RESULTS: MA plans with higher overall performance tended to be less inconsistent in their performance (r=-0.2) across groups defined by race-and-ethnicity and low-income status (ie, dual eligibility for Medicaid or receipt of Low-Income Subsidy). Plan characteristics were usually associated with only small differences in inconsistency. The characteristics associated with differences in consistency [eg, size, Health Maintenance Organization (HMO) status] were also associated with differences in overall performance. We identified 9 (of 363) plans that had large inconsistency in performance across groups (>0.8 SD) and investigated the reasons for inconsistency for 2 example plans. CONCLUSIONS: This newly developed inconsistency metric may help those designing and evaluating QI efforts to appropriately determine when targeted QI is preferred. It can be used in settings where performance varies across groups, which can be defined by patient characteristics, geographic areas, hospital wards, etc. Effectively targeting QI efforts is essential in today's resource-constrained health care environment.

Trends in US Ambulatory Care Patterns During the COVID-19 Pandemic, 2019-2021.

Importance: Following reductions in US ambulatory care early in the pandemic, it remains unclear whether care consistently returned to expected rates across insurance types and services. Objective: To assess whether patients with Medicaid or Medicare-Medicaid dual eligibility had significantly lower than expected return to use of ambulatory care rates than patients with commercial, Medicare Advantage, or Medicare fee-for-service insurance. Design, Setting, and Participants: In this retrospective cohort study examining ambulatory care service patterns from January 1, 2019, through February 28, 2021, claims data from multiple US payers were combined using the Milliman MedInsight research database. Using a difference-in-differences design, the extent to which utilization during the pandemic differed from expected rates had the pandemic not occurred was estimated. Changes in utilization rates between January and February 2020 and each subsequent 2-month time frame during the pandemic were compared with the changes in the corresponding months from the year prior. Age- and sex-adjusted Poisson regression models of monthly utilization counts were used, offsetting for total patient-months and stratifying by service and insurance type. Exposures: Patients with Medicaid or Medicare-Medicaid dual eligibility compared with patients with commercial, Medicare Advantage, or Medicare fee-for-service insurance, respectively. Main Outcomes and Measures: Utilization rates per 100 people for 6 services: emergency department, office and urgent care, behavioral health, screening colonoscopies, screening mammograms, and contraception counseling or HIV screening. Results: More than 14.5 million US adults were included (mean age, 52.7 years; 54.9% women). In the March-April 2020 time frame, the combined use of 6 ambulatory services declined to 67.0% (95% CI, 66.9%-67.1%) of expected rates, but returned to 96.7% (95% CI, 96.6%-96.8%) of expected rates by the November-December 2020 time frame. During the second COVID-19 wave in the January-February 2021 time frame, overall utilization again declined to 86.2% (95% CI, 86.1%-86.3%) of expected rates, with colonoscopy remaining at 65.0% (95% CI, 64.1%-65.9%) and mammography at 79.2% (95% CI, 78.5%-79.8%) of expected rates. By the January-February 2021 time frame, overall utilization returned to expected rates as follows: patients with Medicaid at 78.4% (95% CI, 78.2%-78.7%), Medicare-Medicaid dual eligibility at 73.3% (95% CI, 72.8%-73.8%), commercial at 90.7% (95% CI, 90.5%-90.9%), Medicare Advantage at 83.2% (95% CI, 81.7%-82.2%), and Medicare fee-for-service at 82.0% (95% CI, 81.7%-82.2%; P < .001; comparing return to expected utilization rates among patients with Medicaid and Medicare-Medicaid dual eligibility, respectively, with each of the other insurance types). Conclusions and Relevance: Between March 2020 and February 2021, aggregate use of 6 ambulatory care services increased after the preceding decrease in utilization that followed the onset of the COVID-19 pandemic. However, the rate of increase in use of these ambulatory care services was significantly lower for participants with Medicaid or Medicare-Medicaid dual eligibility than for those insured by commercial, Medicare Advantage, or Medicare fee-for-service.

Rates of Disenrollment From Medicare Advantage Plans Are Higher for Racial/Ethnic Minority Beneficiaries.

BACKGROUND: Each year, about 10% of Medicare Advantage (MA) enrollees voluntarily switch to another MA contract, while another 2% voluntarily switch from MA to fee-for-service Medicare. Voluntary disenrollment from MA plans is related to beneficiaries' negative experiences with their plan, disrupts the continuity of care, and conflicts with goals to reduce Medicare costs. Little is known about racial/ethnic disparities in voluntary disenrollment from MA plans. OBJECTIVE: The objective of this study was to investigate differences in rates of voluntary disenrollment from MA plans by race/ethnicity. SUBJECTS: A total of 116,770,319 beneficiaries enrolled in 736 MA plans in 2015. METHODS: Differences in rates of disenrollment across racial/ethnic groups [Asian or Pacific Islander (API), Black, Hispanic, and White] were summarized using 4 types of logistic regression models: adjusted and unadjusted models estimating overall differences and adjusted and unadjusted models estimating within-plan differences. Unadjusted overall models included only racial/ethnic group probabilities as predictors. Adjusted overall models added age, sex, dual eligibility, disability, and state of residence as control variables. Between-plan differences were estimated by subtracting within-plan differences from overall differences. RESULTS: Adjusted rates of disenrollment were significantly (P<0.001) higher for Hispanic (+1.2 percentage points), Black (+1.2 percentage points), and API beneficiaries (+2.4 percentage points) than for Whites. Within states, all 3 racial/ethnic minority groups tended to be concentrated in higher disenrollment plans. Within plans, API beneficiaries voluntarily disenrolled considerably more often than otherwise similar White beneficiaries. CONCLUSION: These findings suggest the need to address cost, information, and other factors that may create barriers to racial/ethnic minority beneficiaries' enrollment in plans with lower overall disenrollment rates.

Incentivizing Excellent Care to At-Risk Groups with a Health Equity Summary Score.

BACKGROUND: Social risk factors (SRFs) such as minority race-and-ethnicity or low income are associated with quality-of-care, health, and healthcare outcomes. Organizations might prioritize improving care for easier-to-treat groups over those with SRFs, but measuring, reporting, and further incentivizing quality-of-care for SRF groups may improve their care. OBJECTIVE: To develop, as a proof-of-concept, a Health Equity Summary Score (HESS): a succinct, easy-to-understand score that could be used to promote high-quality care to those with SRFs in Medicare Advantage (MA) health plans, which provide care for almost twenty million older and disabled Americans and collect extensive quality measure and SRF data. DESIGN: We estimated, standardized, and combined performance scores for two sets of quality measures for enrollees in 2013-2016 MA health plans, considering both current levels of care, within-plan improvement, and nationally benchmarked improvement for those with SRFs (specifically, racial-and-ethnic minority status and dual-eligibility for Medicare and Medicaid). PARTICIPANTS: All MA plans with publicly reported quality scores and 500 or more 2016 enrollees. MAIN MEASURES: Publicly reported clinical quality and patient experience measures. KEY RESULTS: Almost 90% of plans measured for MA Star Ratings received a HESS; plans serving few patients with SRFs were excluded. The summary score was moderately positively correlated with publicly reported overall Star Ratings (r = 0.66-0.67). High-scoring plans typically had sizable enrollment of both racial-and-ethnic minorities (38-42%) and dually eligible beneficiaries (29-38%). CONCLUSIONS: We demonstrated the feasibility of developing and estimating a HESS that is intended to promote and incentivize excellent care for racial-and-ethnic minorities and dually eligible MA enrollees. The HESS measures SRF-specific performance and does not simply duplicate overall plan Star Ratings. It also identifies plans that provide excellent care to large numbers of those with SRFs. Our methodology could be extended to other SRFs, quality measures, and settings.

Evaluation of longitudinal surrogate markers.

The use of surrogate markers to examine the effectiveness of a treatment has the potential to decrease study length and identify effective treatments more quickly. Most available methods to investigate the usefulness of a surrogate marker involve restrictive parametric assumptions and tend to focus on settings where the surrogate is measured at a single point in time. However, in many clinical settings, the potential surrogate marker is often measured repeatedly over time, and thus, the surrogate marker information is a trajectory of measurements. In addition, it is often difficult in practice to correctly specify the relationship between a treatment, primary outcome, and surrogate marker trajectory. In this paper, we propose a model-free definition for the proportion of the treatment effect on the primary outcome that is explained by the treatment effect on the longitudinal surrogate markers. We propose three novel flexible methods to estimate this proportion, develop the asymptotic properties of our estimators, and investigate the robustness of the estimators under multiple settings via a simulation study. We apply our proposed procedures to an AIDS clinical trial dataset to examine a trajectory of CD4 counts as a potential surrogate.

Pilot Randomized Controlled Trial of an Intervention to Improve Coping with Intersectional Stigma and Medication Adherence Among HIV-Positive Latinx Sexual Minority Men.

We developed and pilot-tested an eight-session community-based cognitive behavior therapy group intervention to improve coping with intersectional stigma, address medical mistrust, and improve antiretroviral treatment adherence. Seventy-six HIV-positive Latinx sexual minority men (SMM; 38 intervention, 38 wait-list control) completed surveys at baseline, and 4- and 7-months post-baseline. Adherence was electronically monitored. Intention-to-treat, repeated-measures regressions showed improved adherence in the intervention vs. control group from baseline to follow-up [electronically monitored: b (95% CI) 9.24 (- 0.55, 19.03), p = 0.06; self-reported: b (95% CI) 4.50 (0.70, 8.30), p = .02]. Intervention participants showed marginally decreased negative religious coping beliefs in response to stigma [b (95% CI) = - 0.18 (- 0.37, 0.01), p = .06], and significantly lower medical mistrust [b (95% CI) = - 0.47 (- 0.84, - 0.09), p = .02]. Our intervention holds promise for improving HIV outcomes by empowering Latinx SMM to leverage innate resilience resources when faced with stigma.ClinicalTrials.gov ID (TRN): NCT03432819, 01/31/2018.

RESUMEN: Hemos desarrollado un estudio piloto para poner a prueba un programa de ocho-sesiones de terapia cognitivo-conductual basado en un grupo de comunidad para abordar el estigma interseccional, la desconfianza médica y mejorar la adherencia al tratamiento antirretroviral. Setenta y seis hombres Latinos de minorías sexuales VIH positivos (38 en el grupo de intervención, 38 en el grupo de control de lista de espera) completaron encuestas al inicio, y cuatro y siete meses después de la línea de base. La adherencia fue medida electrónicamente. Los resultados del análisis mostraron una mejor adherencia en el grupo de intervención en comparación al grupo de control desde el inicio hasta el seguimiento [monitoreado electrónicamente: b (95% IC) 9.24 (− 0.55, 19.03), p = .06; y autoreporte: b (95% IC) 4.50 (0.70, 8.30), p = .02]. Los participantes del grupo de intervención mostraron una disminución marginal en las creencias negativas de afrontamiento religioso al estigma [b (95% IC) − 0.18 (− 0.37, 0.01), p = .06], y significativamente menor desconfianza médica [b (95% IC) − 0.47 (− 0.84, − 0.09), p = .02]. Nuestra intervención es prometedora para mejorar los resultados del VIH al empoderar a hombres Latinos de minorías sexuales para tomar ventaja de los recursos de resiliencia innatos cuando se enfrentan al estigma.

Unveiling an 'invisible population': health, substance use, sexual behavior, culture, and discrimination among urban American Indian/Alaska Native adolescents in California.

Objectives: There are limited public health data on urban American Indian/Alaska Native (AI/AN) populations, particularly adolescents. The current study attempted to address gaps by providing descriptive information on experiences of urban AI/AN adolescents across northern, central, and southern California.Design: We describe demographics and several behavioral health and cultural domains, including: alcohol and other drug (AOD) use, risky sexual behavior, mental and physical health, discrimination experiences, involvement in traditional practices, and cultural pride and belonging. We recruited 185 urban AI/AN adolescents across northern, central, and southern California from 2014 to 2017 who completed a baseline survey as part of a randomized controlled intervention trial.Results: Average age was 15.6 years; 51% female; 59% of adolescents that indicated AI/AN descent also endorsed another race or ethnicity. Rates of AOD use in this urban AI/AN sample were similar to rates for Monitoring the Future. About one-third of adolescents reported ever having sexual intercourse, with 15% reporting using alcohol or drugs before sex. Most reported good mental and physical health. Most urban AI/AN adolescents participated in traditional practices, such as attending Pow Wows and learning their tribal history. Adolescents also reported discrimination experiences, including being a victim of racial slurs and discrimination by law enforcement.Conclusions: This study describes a select sample of California urban AI/AN adolescents across several behavioral health and cultural domains. Although these adolescents reported numerous discrimination experiences and other stressors, findings suggest that this sample of urban AI/AN teens may be particularly resilient with regard to behavioral health.

Distinguishing High-Performing Health Systems Using a Composite of Publicly Reported Measures of Ambulatory Care.

BACKGROUND: Payers and policymakers are rewarding high-performing health care providers on the basis of summaries of overall quality performance, and the methods they use for measuring performance are increasingly important. OBJECTIVE: To develop and evaluate a measure that ranks health care systems by ambulatory care quality. DESIGN: Systems were ranked using a composite model that summarizes individual measures of quality, accounts for their correlation, and does not require health care systems to report every measure. The composite measure's suitability was evaluated by examining whether it captured the quality indicated by component measures (validity), whether differences in rank between health care systems were larger than statistical noise (reliability), and whether year-to-year changes in rank were small (stability). SETTING: California and Minnesota, 2014 to 2016. PARTICIPANTS: 55 health care systems. MEASUREMENTS: Publicly reported measures of ambulatory care quality. RESULTS: The composite measure was valid in that it was broadly representative of the component measures and was not dominated by any single measure. The measure was reliable because the ranks for 93% of California systems and 80% of Minnesota systems were unlikely to be more than 2 places lower or higher. The measure was stable because fewer than half of systems changed ranks by more than 2 ranks from year to year. LIMITATION: The analysis is limited to available measures of ambulatory care quality and includes only 2 states. CONCLUSION: This composite measure uses publicly reported data to produce valid, reliable, and stable ranks of ambulatory care quality for health care systems in Minnesota and California, and this approach could be used in other applications. PRIMARY FUNDING SOURCE: Agency for Healthcare Research and Quality.

Identifying as American Indian/Alaska Native in Urban Areas: Implications for Adolescent Behavioral Health and Well-Being.

American Indian and Alaska Native (AI/AN) youth exhibit multiple health disparities, including high rates of alcohol and other drug (AOD) use, violence and delinquency, and mental health problems. Approximately 70% of AI/AN youth reside in urban areas, where negative outcomes on behavioral health and well-being are often high. Identity development may be particularly complex in urban settings, where youth may face more fragmented and lower density AI/AN communities, as well as mixed racial-ethnic ancestry and decreased familiarity with AI/AN lifeways. This study examines racial-ethnic and cultural identity among AI/AN adolescents and associations with behavioral health and well-being by analyzing quantitative data collected from a baseline assessment of 185 AI/AN urban adolescents from California who were part of a substance use intervention study. Adolescents who identified as AI/AN on their survey reported better mental health, less alcohol and marijuana use, lower rates of delinquency, and increased happiness and spiritual health.

Provision of Social Services and Health Care Quality in US Community Health Centers, 2017.

Objectives. To describe the types of social services provided at community health centers (CHCs), characteristics of CHCs providing these services, and the association between on-site provision and health care quality.Methods. We surveyed CHCs in 12 US states and the District of Columbia during summer 2017 (n = 208) to identify referral to and provision of services to address 8 social needs. Regression models estimated factors associated with the provision of social services by CHCs and the association between providing services and health care quality (an 8-item composite).Results. CHCs most often offered on-site assistance for needs related to food or nutrition (43%), interpersonal violence (32%), and housing (30%). Participation in projects with community-based organizations was associated with providing services on-site (odds ratio = 2.48; P = .018). On-site provision was associated with better performance on measures of health care quality (e.g., each additional social service was associated with a 4.3 percentage point increase in colorectal cancer screenings).Conclusions. Some CHCs provide social services on-site, and this was associated with better performance on measures of health care quality.Public Health Implications. Health care providers are increasingly seeking to identify and address patients' unmet social needs, and on-site provision of services is 1 strategy to consider.

Using Ancillary Sociodemographic Data to Identify Sexual Minority Adults Among Those Responding "Something Else" or "Don't Know" to Sexual Orientation Questions.

BACKGROUND: General population surveys are increasingly offering broader response options for questions on sexual orientation-for example, not only gay or lesbian, but also "something else" (SE) and "don't know" (DK). However, these additional response options are potentially confusing for those who may not know what the terms mean. Researchers studying sexual orientation-based disparities face difficult methodological trade-offs regarding how best to classify respondents identifying with the SE and DK categories. OBJECTIVES: Develop respondent-level probabilities of sexual minority orientation without excluding or misclassifying the potentially ambiguous SE and DK responses. Compare 3 increasingly inclusive analytic approaches for estimating health disparities using a single item: (a) omitting SE and DK respondents; (b) classifying SE as sexual minority and omitting DK; and (c) a new approach classifying only SE and DK respondents with >50% predicted probabilities of being sexual minorities as sexual minority. MATERIALS AND METHODS: We used the sociodemographic information and follow-up questions for SE and DK respondents in the 2013-2014 National Health Interview Survey to generate predicted probabilities of identifying as a sexual minority adult. RESULTS: About 94% of the 144 SE respondents and 20% of the 310 DK respondents were predicted to identify as a sexual minority adult, with higher probabilities for younger, wealthier, non-Hispanic white, and urban-dwelling respondents. Using a more specific definition of sexual minority orientation improved the precision of health and health care disparity estimates. CONCLUSIONS: Predicted probabilities of sexual minority orientation may be used in this and other surveys to improve representation and categorization of those who identify as a sexual minority adult.

Variance component score test for time-course gene set analysis of longitudinal RNA-seq data.

As gene expression measurement technology is shifting from microarrays to sequencing, the statistical tools available for their analysis must be adapted since RNA-seq data are measured as counts. It has been proposed to model RNA-seq counts as continuous variables using nonparametric regression to account for their inherent heteroscedasticity. In this vein, we propose tcgsaseq, a principled, model-free, and efficient method for detecting longitudinal changes in RNA-seq gene sets defined a priori. The method identifies those gene sets whose expression varies over time, based on an original variance component score test accounting for both covariates and heteroscedasticity without assuming any specific parametric distribution for the (transformed) counts. We demonstrate that despite the presence of a nonparametric component, our test statistic has a simple form and limiting distribution, and both may be computed quickly. A permutation version of the test is additionally proposed for very small sample sizes. Applied to both simulated data and two real datasets, tcgsaseq is shown to exhibit very good statistical properties, with an increase in stability and power when compared to state-of-the-art methods ROAST (rotation gene set testing), edgeR, and DESeq2, which can fail to control the type I error under certain realistic settings. We have made the method available for the community in the R package tcgsaseq.

Doubly robust matching estimators for high dimensional confounding adjustment.

Valid estimation of treatment effects from observational data requires proper control of confounding. If the number of covariates is large relative to the number of observations, then controlling for all available covariates is infeasible. In cases where a sparsity condition holds, variable selection or penalization can reduce the dimension of the covariate space in a manner that allows for valid estimation of treatment effects. In this article, we propose matching on both the estimated propensity score and the estimated prognostic scores when the number of covariates is large relative to the number of observations. We derive asymptotic results for the matching estimator and show that it is doubly robust in the sense that only one of the two score models need be correct to obtain a consistent estimator. We show via simulation its effectiveness in controlling for confounding and highlight its potential to address nonlinear confounding. Finally, we apply the proposed procedure to analyze the effect of gender on prescription opioid use using insurance claims data.

Analysis of multiple diverse phenotypes via semiparametric canonical correlation analysis.

Studying multiple outcomes simultaneously allows researchers to begin to identify underlying factors that affect all of a set of diseases (i.e., shared etiology) and what may give rise to differences in disorders between patients (i.e., disease subtypes). In this work, our goal is to build risk scores that are predictive of multiple phenotypes simultaneously and identify subpopulations at high risk of multiple phenotypes. Such analyses could yield insight into etiology or point to treatment and prevention strategies. The standard canonical correlation analysis (CCA) can be used to relate multiple continuous outcomes to multiple predictors. However, in order to capture the full complexity of a disorder, phenotypes may include a diverse range of data types, including binary, continuous, ordinal, and censored variables. When phenotypes are diverse in this way, standard CCA is not possible and no methods currently exist to model them jointly. In the presence of such complications, we propose a semi-parametric CCA method to develop risk scores that are predictive of multiple phenotypes. To guard against potential model mis-specification, we also propose a nonparametric calibration method to identify subgroups that are at high risk of multiple disorders. A resampling procedure is also developed to account for the variability in these estimates. Our method opens the door to synthesizing a wide array of data sources for the purposes of joint prediction.