Differences in Health Care Utilization of High-Need and High-Cost Patients of Federally Funded Health Centers Versus Other Primary Care Providers.

BACKGROUND: Primary care providers (PCP) differ in their ability to address the needs and reduce use of costly services among complex Medicaid beneficiaries. Among PCPs, Health Resources and Services Administration (HRSA)-funded health centers (HCs) are shown to provide high-value care. OBJECTIVE: We compared health care utilization of complex Medicaid managed care beneficiaries whose PCPs were HCs versus 3 other groups. RESEARCH DESIGN: Cross-sectional study using propensity score matching comparing health care use by provider type, controlling for demographics, health status, and other covariates. SUBJECTS: California Medicaid administrative data for complex adult managed care beneficiaries with at least 1 primary care visit in 2018. MEASURES: Primary and specialty care evaluation & management visits and services; emergency department (ED) visits; and hospitalizations. PCPs included HCs, clinics not funded by HRSA, solo, and group practice providers. RESULTS: HRSA-funded HCs had lower predicted rates of specialty evaluation & management and other services than all others; lower predicted probability of any ED visits than clinics not funded by HRSA [54% (95% CI: 53%-55%) vs. 56% (95% CI: 55%-57%)] and group practice providers [51% (95% CI: 51%-52%) vs. 52% (95% CI: 52%-53%)]; and lower PP of any hospitalizations than solo [20% (95% CI: 19%-20%) vs. 23% (95% CI: 22%-24%)] and group practice providers [21% (95% CI: 20%-21%) vs. 24% (95% CI: 23%-24%)]. CONCLUSIONS: Differences in HC care delivery and practices were associated with lower use of specialty, ED, and hospitalization visits compared with other PCPs for complex Medicaid managed care beneficiaries. Understanding the underlying reasons for these utilization differences may promote better outcomes among these patients.

Weight management practices of health center providers in the United States.

BACKGROUND: We examined weight management counseling practices of Health Resources and Services Administration-funded health center (HC) providers for patients with overweight (POW) and obesity (POB) status, focusing on weight-related conditions, risk factors, and health care utilization. METHOD: We used a nationally representative cross-sectional survey of HC patients and multilevel generalized structural equation logistic regression models to assess the association of provider counseling practices for POW and POB and by three obesity classes. Dependent variables included being told by the HC provider that weight was a problem, receiving a diet or exercise recommendation, referral to a nutritionist, or receiving weight loss prescriptions. Independent variables included weight-related conditions such as diabetes and hypertension, risk factors such as smoking, and health service utilization such as five or more primary care visits. RESULTS: All POB classes had higher odds of receiving all five counseling interventions than POW. Patients with diabetes and high cholesterol had higher odds of diet recommendations (OR = 1.8) and nutritionist referrals (OR = 2.3), while patients with cardiovascular disease had higher odds of nutritionist referral (OR = 2.0) and receiving weight loss prescriptions (OR = 2.6). Respondents with POB class III and diabetes had higher odds of receiving exercise recommendations (OR = 3.4), while POB class 1 and had hypertension had lower odds of nutritionist referral (OR = 0.3). CONCLUSIONS: Variations in HC primary care providers' weight management counseling practices between POW and POB present missed opportunities for consistent practice and early intervention. Assessing providers' counseling practices for patients with comorbid conditions is essential to the successful management of the obesity crisis.

Beyond "Chilling Effects": Latinx and Asian Immigrants' Experiences With Enforcement and Barriers to Health Care.

OBJECTIVES: Immigration enforcement policies are associated with immigrants' barriers to health care. Current evidence suggests that enforcement creates a "chilling effect" in which immigrants avoid care due to fear of encountering enforcement. Yet, there has been little examination of the impact of immigrants' direct encounters with enforcement on health care access. We examined some of the first population-level data on Asian and Latinx immigrants' encounters with law and immigration enforcement and assessed associations with health care access. METHODS: We analyzed the 2018 and 2019 Research on Immigrant Health and State Policy survey in which Asian and Latinx immigrants in California (n=1681) reported on 7 enforcement experiences (eg, racial profiling and deportation). We examined the associations between measures of individual and cumulative enforcement experiences and the usual sources of care and delay in care. RESULTS: Latinx, compared with Asian respondents, reported the highest levels of enforcement experiences. Almost all individual enforcement experiences were associated with delaying care for both groups. Each additional cumulative experience was associated with a delay in care for both groups (OR=1.30, 95% CI 1.10-1.50). There were no associations with the usual source of care. CONCLUSION: Findings confirm that Latinx immigrants experience high levels of encounters with the enforcement system and highlight new data on Asian immigrants' enforcement encounters. Direct experiences with enforcement have a negative relationship with health care access. Findings have implications for health systems to address the needs of immigrants affected by enforcement and for changes to health and immigration policy to ensure immigrants' access to care.

Ensuring Equitable Care in Diabetes Management Among Patients of Health Resources & Services Administration-Funded Health Centers in the United States.

Aim: To explore whether there are racial/ethnic differences in diabetes management and outcomes among adult health center (HC) patients with type 2 diabetes. Methods: We analyzed data from the 2014 Health Center Patient Survey, a national sample of HC patients. We examined indicators of diabetes monitoring (A1C testing, annual foot/eye doctor visits, and cholesterol checks) and care management (specialist referrals, individual treatment plan, and receipt of calls/appointments/home visits). We also examined diabetes-specific outcomes (blood glucose levels, diabetes-related emergency department [ED] visits/hospitalizations, and diabetes self-management confidence) and general outcomes (number of doctor visits, ED visits, and hospitalizations). We used multilevel logistic regression models to examine racial/ethnic disparities by the above indicators. Results: We found racial/ethnic parity in A1C testing, eye doctor visits, and diabetes-specific outcomes. However, Hispanics/Latinos (odds ratio [OR] 0.26), non-Hispanic African Americans (OR 0.25), and Asians (OR 0.11) were less likely to receive a cholesterol check than Whites. Non-Hispanic African Americans (OR 0.43) were less likely to have frequent doctor visits, while Hispanic/Latino patients (OR 0.45) were less likely to receive an individual treatment plan. Conclusion: HCs largely provide equitable diabetes care but have room for improvement in some indicators. Tailored efforts such as culturally competent care and health education for some racial/ethnic groups may be needed to improve diabetes management and outcomes.

Factors associated with frequent emergency department visits among health centre patients receiving primary care.

RATIONALE, AIMS AND OBJECTIVES: We sought to examine specific care-seeking behaviours and experiences, access indicators, and patient care management approaches associated with frequency of emergency department (ED) visits among patients of Health Resources and Services Administration-funded health centres that provide comprehensive primary care to low-income and uninsured patients. METHOD: We used cross-sectional data of a most recent nationally representative sample of health centre adult patients aged 18-64 (n = 4577) conducted between October 2014 and April 2015. These data were merged with the 2014 Uniform Data System to incorporate health centre characteristics. We measured care-seeking behaviours by whether the patient called the health centre afterhours, for an urgent appointment, or talked to a provider about a concern. Access to care indicators included health centre continuity of care and receipt of transportation or translation services. We included receipt of care coordination and specialist referral as care management indicators. We used a multilevel multinomial logistic regression model to identify the association of independent variables with number of ED visits (4 or more visits, 2-3 visits, 1 visit, vs. 0 visits), controlling for predisposing, enabling, and need characteristics. RESULTS: Calling the health centre after-hours (OR = 2.41) or for urgent care (OR = 2.53), and being referred to specialists (OR = 2.36) were associated with higher odds of four or more ED visits versus none. Three or more years of continuity with the health centre (OR = 0.32) was also associated with lower odds of four or more ED visits versus none. CONCLUSIONS: Findings underscore opportunities to reduce higher frequency of ED visits in health centres, which are primary care providers to many low-income populations. Our findings highlight the potential importance of improving patient retention, better access to providers afterhours or for urgent visits, and access to specialist as areas of care in need of improvement.

Easy to use and validated predictive models to identify beneficiaries experiencing homelessness in Medicaid administrative data.

OBJECTIVE: To develop easy to use and validated predictive models to identify beneficiaries experiencing homelessness from administrative data. DATA SOURCES: We pooled enrollment and claims data from enrollees of the California Whole Person Care (WPC) Medicaid demonstration program that coordinated the care of a subset of Medicaid beneficiaries identified as high utilizers in 26 California counties (25 WPC Pilots). We also used public directories of social service and health care facilities. STUDY DESIGN: Using WPC Pilot-reported homelessness status, we trained seven supervised learning algorithms with different specifications to identify beneficiaries experiencing homelessness. The list of predictors included address- and claims-based indicators, demographics, health status, health care utilization, and county-level homelessness rate. We then assessed model performance using measures of balanced accuracy (BA), sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (area under the curve [AUC]). DATA COLLECTION/EXTRACTION METHODS: We included 93,656 WPC enrollees from 2017 to 2018, 37,441 of whom had a WPC Pilot-reported homelessness indicator. PRINCIPAL FINDINGS: The random forest algorithm with all available indicators had the best performance (87% BA and 0.95 AUC), but a simpler Generalized Linear Model (GLM) also performed well (74% BA and 0.83 AUC). Reducing predictors to the top 20 and top five most important indicators in a GLM model yields only slightly lower performance (86% BA and 0.94 AUC for the top 20 and 86% BA and 0.91 AUC for the top five). CONCLUSIONS: Large samples can be used to accurately predict homelessness in Medicaid administrative data if a validated homelessness indicator for a small subset can be obtained. In the absence of a validated indicator, the likelihood of homelessness can be calculated using county rate of homelessness, address- and claim-based indicators, and beneficiary age using a prediction model presented here. These approaches are needed given the rising prevalence of homelessness and the focus of Medicaid and other payers on addressing homelessness and its outcomes.

Staffing transformation following Patient-Centered Medical Home recognition among Health Resources & Services Administration-funded health centers.

INTRODUCTION: Patient-Centered Medical Home (PCMH) recognition is designed to promote whole-person team-based and integrated care. PURPOSE: Our goal was to assess changes in staffing infrastructure that promoted team-based and integrated care delivery before and after PCMH recognition in Health Resources & Services Administration (HRSA)-funded health centers (HCs). METHODOLOGY/APPROACH: We identified changes in staffing 2 years before and 3 years after PCMH recognition using 2010-2019 Uniform Data System data among three cohorts of HCs that received PCMH recognition in 2013 ( n = 346), 2014 ( n = 207), and 2015 ( n = 115). Our outcomes were team-based ratio (full-time equivalent medical and nonmedical providers and staff to one primary care physician) and a multidisciplinary staff ratio (allied medical and nonmedical staff to 1,000 patients). We used mixed-effects Poisson regression models. RESULTS: The earlier cohorts served fewer complex patients and were larger before PCMH recognition. Three years following recognition, the 2013 and 2014 cohorts had significantly larger team-based ratios, and all three cohorts had significantly larger multidisciplinary staff ratios. Cohorts varied, however, in the type of staff that drove this change. Both ratios increased in the longer term. CONCLUSION: Our study suggests that growth in team-based and multidisciplinary staff ratios in each cohort may have been due to a combination of HCs' perceptions of need for specific services, HRSA funding, and technical assistance opportunities. POLICY IMPLICATIONS: Further research is needed to understand barriers such as costs of employing a multidisciplinary staff, particularly those that cannot directly bill for services as well as whether such changes lead to practice transformation and improved quality of care.

European immigrant health policies, immigrants' health, and immigrants' access to healthcare.

OBJECTIVE: Evidence indicates presence of immigrant health disparities in the European Union (EU) and the United States (US). We examined the association between immigrant health policies and the gap in health status, unmet needs and service use between immigrants and citizens, in the EU and US. METHODS: We used the Migrant Integration Policy Index (MIPEX), European Health Interview Survey, and National Health Interview Survey for 2014. Our independent variables of interest were MIPEX Health strand score and citizenship. Our dependent variables were four measures of health status (self-reported poor health; severely limited in general activities; two or more comorbidities; one or more ambulatory care sensitive conditions) and four measures of health access and utilization (unmet need due to non-financial reasons; could not afford needed health care; one or more primary care visits last year; any hospitalization last year). We conducted linear probability models and presented the marginal effects of each outcome in percentage points. FINDINGS: We found that immigrant-friendly health policies were significantly associated with better health and less unmet need due to non-financial reasons. CONCLUSION: Our findings supported the promotion of immigrant-friendly and a 'Health-in-All Policies' (HiAP) approach to preserve the health of immigrants.

The Impact of a Primary Care Telepsychiatry Program on Outcomes of Managed Care Older Adults.

OBJECTIVE: The goal of this study was to assess the outcomes of a primary-based telepsychiatry intervention program for older managed care enrollees with depression/anxiety and with limited access to in-person psychiatric care. DESIGN: A pre-post design was used to examine service use (n = 218) and severity of depression (n = 204). Enrollment, claims, and depression and anxiety score data were obtained from the medical group. The implementation process and self-reported outcomes were examined. SETTING AND PARTICIPANTS: The program was funded by the Senior Care Action Network (SCAN) group and implemented by a large medical group serving older adults who were identified as needing outpatient psychiatric care, including those with psychiatric hospitalizations, depression/anxiety disorders, comorbid substance use disorders, or other multiple comorbidities. METHODS: Poisson regressions were used to examine changes in predicted rates of outpatient services, emergency department visits, and hospitalizations up to 24 months prior and 24 months following the first telepsychiatry visit. Changes in predicted severity of depression up to 2 quarters prior and 3 quarters following the first telepsychiatry visit were examined. RESULTS: The number of outpatient services declined significantly by 0.24 per patient per 6-month time frame following the first telepsychiatry visit. The number of emergency department visits and hospitalizations also declined after the first visit (0.07 and 0.03 per patient per 6-month time frame, respectively). Depression severity scores also declined in the quarters following the first visit (1.52). The medical group reported improvements in both wait time for appointments and no-show rates with the integration of telepsychiatry in primary care. CONCLUSIONS AND IMPLICATIONS: The telepsychiatry program lowered service use, depression severity, and increased better access to psychiatry care. The findings highlight the potential benefits of sustaining and expanding the telepsychiatry program by SCAN and other plans facing a limited supply of psychiatrists.

A Systematic Literature Review of Health Center Efforts to Address Social Determinants of Health.

Health centers (HCs) play a crucial and integral role in addressing social determinants of health (SDOH) among vulnerable and underserved populations, yet data on SDOH assessment and subsequent actions is limited. We conducted a systematic review to understand the existing evidence of integration of SDOH into HC primary-care practices. Database searches yielded 3,516 studies, of which 41 articles met the inclusion criteria. A majority of studies showed that HCs primarily captured patient-level rather than community-level SDOH data. Studies also showed that HCs utilized SDOH in electronic health records but capabilities varied widely. A few studies indicated that HCs measured health-related outcomes of integrating SDOH data. The review highlighted that many knowledge gaps exist in the collection, use, and assessment of impact of these data on outcomes, and future research is needed to address this knowledge gap.

Measuring Behavioral Health Integration in Primary Care.

Behavioral health integration (BHI) within primary care settings is shown to improve outcomes. However, achieving BHI requires identifying best practices and a reliable tool that can be used to measure existing levels and progress toward BHI. The objective of this study was to develop and apply a conceptual framework to measure BHI, test the approach, and examine challenges to achieving BHI. Surveys and interviews were conducted with key informants within 17 designated public hospitals in California at the midpoint of participating in a 5-year project to establish BHI. A framework and coding methodology were developed to assess BHI best practices at each hospital. BHI status was assessed in the domains of infrastructure and process. Each domain included 5 themes such as electronic health record integration and functionality (infrastructure) and interprovider communication (process). Themes were assessed using a 6-point scale for various activities under a theme and associated weights. Theme-specific values were standardized from 0% to 100% to compare BHI scores between hospitals. Overall progress toward BHI ranged from scores of 52% to 83% (mean 63%) and indicated greater contribution of infrastructure versus process implementation. Within the infrastructure domain, scores were higher for having institutional and provider support, but lagged in establishing provider proximity. Within the process domain, scores were highest for implementation of behavioral health screening, but were frequently lower for other themes such as use of care coordination and referral processes. Further research is needed to test the robustness of this approach in other settings.

Differential associations of homelessness with emergency department visits and hospitalizations by race, ethnicity, and gender.

OBJECTIVE: To investigate the differential associations of homelessness with emergency department (ED) visits and hospitalizations by race, ethnicity, and gender. DATA SOURCES: California Medicaid enrollment and claims. STUDY DESIGN: We identified beneficiaries experiencing homelessness (BEH) and those who did not (NBEH) using diagnosis and place of service codes and residential addresses. Outcomes include four ED visit measures and four hospitalization measures. We compared the use of these services by BEH to NBEH overall and by race, ethnicity, and gender groups in regression models controlling for covariates. DATA COLLECTION: We used a sample of Medicaid beneficiaries who met eligibility criteria for a California Medicaid demonstration program in 2017 and 2018 but were not enrolled in the program. We identified 473,069 BEH, and the rest (1,948,422) were considered NBEH. We used the 2018 data for utilization analyses and most covariates. We constructed lagged measures of health conditions using 2017 data. PRINCIPAL FINDINGS: We found that homelessness was significantly associated with 0.34 more ED visits (p < 0.01) and a higher likelihood of frequent ED visits (2.77 percentage points [pp], p < 0.01), any ED visits due to mental health conditions (0.79 pp, p < 0.01), and any ED visits due to substance use disorders (1.47 pp, p < 0.01). Experiencing homelessness was also significantly associated with 0.03 more hospitalizations (p < 0.01), a higher likelihood of frequent hospitalizations (0.68 pp, p < 0.01) and high frequent hospitalizations (0.28 pp, p < 0.01), and a longer length of stay (0.53 days, p < 0.01). We found a larger association for American Indian and Alaska Native, Black, Native Hawaii or Pacific Islander, and White populations than that for Asian and Hispanic populations. The associations are larger for males than females. CONCLUSIONS: Our findings identified distinct utilization patterns by race, ethnicity, and gender. They indicated the need for developing race, ethnicity, and gender-specific strategies to reduce ED visits and hospitalizations of BEH.

The Relative Contribution of Social Determinants of Health Among Health Resources and Services Administration-Funded Health Centers.

Frameworks for identifying and assessing social determinants of health (SDOH) are effective for developing long-term societal policies to promote health and well-being, but may be less applicable in clinical settings. The authors compared the relative contribution of a specific set of SDOH indicators with several measures of health status among patients served by health centers (HCs). The 2014 Health Center Patient Survey was used to identify a sample of HC patient adults 18 years and older that reported the HC as their usual source of care (n = 5024). The authors examined the relationship between SDOH indicators organized in categories (health behaviors, access and utilization, social factors, economic factors, quality of care, physical environment) with health status measures (fair or poor health, diabetes, hypertension, cardiovascular disease, depression, or anxiety) using logistic regressions and predicted probabilities. Findings indicated that access to care and utilization indicators had the greatest relative contribution to all health status measures, but the relative contribution of other SDOH indicators varied. For example, access indicators had the highest predicted probability in the model with fair or poor health as the dependent variable (72.4%) and the model with hypertension as the dependent variable (47.4%). However, the second highest predicted probability was for social indicators (54.1%) in the former model and physical environment (44.7%) indicators in the latter model. These findings have implications for HCs that serve as the primary point of access to medical care in underserved communities and to mitigate SDOH particularly for patients with diabetes, depression, or anxiety.

Intersection of complexity and high utilization among health center patients aged 18 to 64 years.

OBJECTIVES: Existing literature indicates that multimorbidity, mental health (MH) conditions, substance use disorders (SUDs), and social determinants of health are hallmarks of high-need, high-cost patients. Health Resources and Services Administration-funded health centers (HCs) provide care to nearly 30 million patients, but data on their patients' complexity and utilization patterns are limited. We identified subgroups of HC patients based on latent concepts of complexity and utilization. STUDY DESIGN: We used cross-sectional national data from the 2014 Health Center Patient Survey and latent class analyses to identify distinct and homogenous groups of complex high-utilizing patients aged 18 to 64 years. METHODS: We included indicators of chronic conditions (CCs), MH, SUD risk, and health behavior to measure complexity. We used number of outpatient and emergency department visits in the past year to measure utilization. RESULTS: HC patients were separated in 9 distinct groups based on 3 complexity latent classes (MH, multiple CCs, and low risk) and 3 utilization classes (low, high, and superutilizers). Conditions associated with each subgroup differed. The highest prevalence of bipolar disorder (45%) and high SUD risk (6%) was observed among MH superutilizers, whereas the highest prevalence of cardiovascular disease (48%) and obesity (96%) was seen among CC superutilizers. Most MH superutilizer patients concurrently had MH conditions and obesity and were smokers, but most CC superutilizer patients concurrently had hypertension, obesity, and cardiovascular disease. CONCLUSIONS: Our examination of complexity and utilization indicated distinct HC patient populations. Managing the care of each group may require different targeted intervention approaches such as multidisciplinary care teams that include MH providers or specialists.

Systematic Review of Care Coordination Interventions Linking Health and Social Services for High-Utilizing Patient Populations.

Recognizing that social factors influence patient health outcomes and utilization, health systems have developed interventions to address patients' social needs. Care coordination across the health care and social service sectors is a distinct and important strategy to address social determinants of health, but limited information exists about how care coordination operates in this context. To address this gap, the authors conducted a systematic review of peer-reviewed publications that document the coordination of health care and social services in the United States. After a structured elimination process, 25 publications of 19 programs were synthesized to identify patterns in care coordination implementation. Results indicate that patient needs assessment, in-person patient contact, and standardized care coordination protocols are common across programs that bridge health care and social services. Publications discussing these programs often provide limited detail on other key elements of care coordination, especially the nature of referrals and care coordinator caseload. Additional research is needed to document critical elements of program implementation and to evaluate program impacts.

Trends in access to care among rural patients served at HRSA-funded health centers.

PURPOSE: Nearly one-fifth of Americans live in rural areas and experience multiple socioeconomic and health disparities. Health Resources and Services Administration (HRSA)-funded health centers (HCs) provide comprehensive primary care in rural communities. However, no prior research has examined trends in access to care in rural HC patients. We examined the change in access to care among patients served at rural HRSA-funded HCs in the United States between 2009 and 2014. METHODS: We compared patients by year to examine measures of access using multilevel generalized structural equation logistic regression models with random effects. We used the 2009 and 2014 cross-sectional Health Center Patient Surveys and identified 2,625 adult rural HC patients. Dependent variables were subjective (unmet need/delay in medical care, mental health, dental care, and prescription medications) and objective measures (preventive care and other health care utilization) in access to care. Our independent variable of interest was time, comparing access in 2009 and 2014. RESULTS: Rural HC patients reported higher predicted probability of influenza vaccine receipt (37% vs 51%), and lower unmet (25% vs 14%) and delayed medical care (36% vs 18%) between 2009 and 2014. Any emergency department visits in the last year increased (32% vs 46%) and mammogram (70% vs 55%) and Pap test (83% vs 72%) screening rates decreased. CONCLUSIONS: Observed increases in access to care among rural HC patients are positive developments but the challenges to access care still persist. Remote services, such as telehealth, could be cost-effective means of improving access to care among rural patients with limited provider supply.

Overview of Ten Child Mental Health Clinical Outcome Measures: Testing of Psychometric Properties with Diverse Client Populations in the U.S.

While many standardized assessment measures exist to track child mental health treatment outcomes, the degree to which such tools have been adequately tested for reliability and validity across race, ethnicity, and class is uneven. This paper examines the corpus of published tests of psychometric properties for the ten standardized measures used in U.S. child outpatient care, with focus on breadth of testing across these domains. Our goal is to assist care providers, researchers, and legislators in understanding how cultural mismatch impacts measurement accuracy and how to select tools appropriate to the characteristics of their client populations. We also highlight avenues of needed research for measures that are in common use. The list of measures was compiled from (1) U.S. state Department of Mental Health websites; (2) a survey of California county behavioral health agency directors; and (3) exploratory literature scans of published research. Ten measures met inclusion criteria; for each one a systematic review of psychometrics literature was conducted. Diversity of participant research samples was examined as well as differences in reliability and validity by gender, race or ethnicity, and socio-economic class. All measures showed adequate reliability and validity, however half lacked diverse testing across all three domains and all lacked testing with Asian American/Pacific Islander and Native American children. ASEBA, PSC, and SDQ had the broadest testing.

Mental Health Staffing at HRSA-Funded Health Centers May Improve Access to Care.

OBJECTIVE: The study objective was to examine the association between mental health staffing at health centers funded by the Health Resources and Services Administration (HRSA) and patients' receipt of mental health treatment. METHODS: Data were from the 2014 HRSA-funded Health Center Patient Survey and the 2013 Uniform Data System. Colocation of any mental health staff, including psychiatrists, psychologists, and other licensed staff, was examined. The outcomes of interest were whether a patient received any mental treatment and received any such treatment on site (at the health center). Analyses were conducted with multilevel generalized structural equation logistic regression models for 4,575 patients ages 18-64. RESULTS: Patients attending health centers with at least one mental health full-time equivalent (FTE) per 2,000 patients had a higher predicted probability of receiving mental health treatment (32%) compared with those attending centers with fewer than one such FTE (24%) or no such staffing (22%). Among patients who received this treatment, those at health centers with no staffing had a significantly lower predicted probability of receiving such treatment on site (28%), compared with patients at health centers with fewer than one such FTE (49%) and with at least one such FTE (65%). The predicted probability of receiving such treatment on site was significantly higher if there was a colocated psychiatrist versus no psychiatrist (58% versus 40%). CONCLUSIONS: Colocating mental health staff at health centers increases the probability of patients' access to such treatment on site as well as from off-site providers.

Consumer Assessment of Healthcare Providers and Systems (CAHPS®) survey of experiences with ambulatory healthcare for Asians and non-Hispanic Whites in the United States.

BACKGROUND: Differences in experiences of care reported by Asian Americans (Asians) compared to non-Hispanic Whites (Whites) may be due to lack of measurement invariance. METHODS: We evaluated the three-factor structure and the equivalence of responses to the Consumer Assessment of Healthcare Providers and Systems (CAHPS®) Clinical and Group (CG-CAHPS) Adult Visit Survey 1.0 and compared care experiences of Asians and Whites. Thirteen questions were used to elicit reports about specific aspects of care and two questions assessed overall care perceptions. This analysis of the CAHPS database included 769 providers and 266,327 respondents. Most surveys (98%) were administered by mail and the rest (2%) by phone. Only 0.5% of the surveys were administered in Spanish. The sample was 64% female, 89% White and 2% Asian, 39% 65 years or older, and 32% were high school graduates or less. RESULTS: A three-factor model was supported by categorical confirmatory factor analysis using weighted least squares with mean and variance adjustment: confirmatory fit index (CFI) = 0.99 and root mean squared error of approximation (RMSEA) = 0.03). A multi-group configural invariance model also fit the data well: (CFI = 0.993, RMSEA = 0.031). Regression models indicated that Asians reported worse access, lower scores on office staff courtesy and helpfulness and rating their doctors and were less likely to recommend their doctors to family/friends than did Whites. CONCLUSIONS: Use of the CG-CAHPS Adult Visit Survey 1.0 to assess perceptions of care by Asians and Whites is supported. Quality improvement efforts are needed to address worse experiences of care among Asians in the United States.

Evaluating inpatient adverse outcomes under California's Delivery System Reform Incentive Payment Program.

OBJECTIVE: The California Delivery System Reform Incentive Payment Program (DSRIP) provided incentive payments to Designated Public Hospitals (DPHs) to improve quality of care. We assessed the program's impact on reductions in sepsis mortality, central line-associated bloodstream infections (CLABSIs), venous thromboembolisms (VTEs), and hospital-acquired pressure ulcers (HAPUs). DATA SOURCES: We used 2009-2014 discharge data from California hospitals. STUDY DESIGN: We used a pre-post study design with a comparison group. We constructed propensity scores and used them to assign inverse probability weights according to their similarity to DPH discharges. Interaction term coefficients of time trends and treatment group provided significance testing. DATA EXTRACTION: We used Patient Safety Indicators for CLABSI, HAPU, and VTE, and constructed a sepsis mortality measure. PRINCIPAL FINDINGS: Discharges from DPHs and non-DPHs both saw decreases in the four outcomes over the DSRIP period (2010-2014). The difference-in-difference estimator (DD) for sepsis was only significant during two time periods, comparing 2010 with 2012 (DD: -2.90 percent, 95% CI: -5.08, -0.72 percent) and 2010 with 2014 (DD: -5.74, 95% CI: -8.76 percent, -2.72 percent); the DD estimator was not significant comparing 2010 with 2012 (DD: -1.30, 95% CI: -3.18 percent, 0.58 percent) or comparing 2010 with 2013 (DD: -3.05 percent, 95% CI: -6.50 percent, 0.40 percent). For CLABSI, we did not find any meaningful differences between DPHs and non-DPHs across the four time periods. For HAPU and VTE, the only significant DD estimator compared 2014 with 2010. CONCLUSIONS: We did not find that DPHs participating in DSRIP outperformed non-DPHs during the DSRIP program. Our results were robust to multiple sensitivity analyses. Given multiple concurrent inpatient safety initiatives, it was challenging to assign improvements over time periods to DSRIP.