Opioid Prescribing in the 2016 Medicare Fee-for-Service Population.

BACKGROUND AND OBJECTIVES: Opioid use and misuse are prevalent and remain a national crisis. This study identified beneficiary characteristics associated with filling opioid prescriptions, variation in opioid dosing, and opioid use with average daily doses (ADDs) equal to 120 morphine milligram equivalents (MMEs) or more in the 100% Medicare fee-for-service (FFS) population. DESIGN, SETTING, PARTICIPANTS, AND MEASUREMENTS: In a cohort of FFS beneficiaries with 12 months of Medicare Part D coverage in 2016, we examined patient factors associated with filling an opioid prescription (n = 20,880,490) and variation in ADDs (n = 7,325,031) in a two-part model. Among those filling opioids, we also examined the probability of ADD equal to 120 MMEs or more via logistic regression. RESULTS: About 35% of FFS beneficiaries had one or more opioid prescription fills in 2016 and 1.5% had ADDs equal to 120 MMEs or more. Disability-eligible beneficiaries and beneficiaries with multiple chronic conditions were more likely to fill opioids, to have higher ADDs or were more likely to have ADD equal to 120 MMEs or more. Beneficiaries with chronic obstructive pulmonary disease (COPD) were more likely to fill opioids (odds ratio (OR) = 1.47, 95% confidence interval (CI) = 1.46-1.47), have higher ADDs (rate ratio = 1.06, 95% CI = 1.06-1.06) when filled and were more likely to have ADD equal to 120 MMEs or more (OR = 1.23, 95% CI = 1.21-1.24). Finally, black and Hispanic beneficiaries were less likely to fill opioids, had lower overall doses and were less likely to have ADDs equal to 120 MMEs or more compared to white beneficiaries. CONCLUSION: Several beneficiary subgroups have underappreciated risk of adverse events associated with ADD equal to 120 MMEs or more that may benefit from opioid optimization interventions that balance pain management and adverse event risk, especially beneficiaries with COPD who are at risk for respiratory depression.

Gender Differences in Patient Experience Across Medicare Advantage Plans.

BACKGROUND: Medicare beneficiaries annually select fee-for-service Medicare or a private Medicare insurance (managed care) plan; information about plan performance on quality measures can inform their decisions. Although there is drill-down information available regarding quality variation by race and ethnicity, there remains a dearth of evidence regarding the extent to which care varies by other key beneficiary characteristics, such as gender. We measured gender differences for six patient experience measures and how gender gaps differ across Medicare plans. METHODS: We used data from 300,979 respondents to the 2015-2016 Medicare Advantage Consumer Assessment of Healthcare Providers and Systems surveys. We fit case mix-adjusted linear mixed-effects models to estimate gender differences and evaluate heterogeneity in differences across health plans. RESULTS: Nationally, women's experiences were better than men's (p < .05) by 1 percentage point on measures involving interactions with administrative staff (+1.6 percentage point for customer service) and timely access to care (+1.1 percentage point for getting care quickly), but worse on a measure that may involve negotiation with physicians (getting needed care). Gender gaps varied across plans, particularly for getting care quickly and getting needed care, where plan-level differences of up to 5 to 6 percentage points were observed. CONCLUSIONS: Although the average national differences in patient experience by gender were generally small, gender gaps were larger in some health plans and for specific measures. This finding indicates opportunities for health plans with larger gender gaps to implement quality improvement efforts.

Inpatient care experiences differ by preferred language within racial/ethnic groups.

OBJECTIVE: To describe differences in patient experiences of hospital care by preferred language within racial/ethnic groups. DATA SOURCE: 2014-2015 HCAHPS survey data. STUDY DESIGN: We compared six composite measures for seven languages (English, Spanish, Russian, Portuguese, Chinese, Vietnamese, and Other) within applicable subsets of five racial/ethnic groups (Hispanics, Asian/Pacific Islanders, American Indian/Alaska Natives, Blacks, and Whites). We measured patient-mix adjusted overall, between- and within-hospital differences in patient experience by language, using linear regression. DATA COLLECTION METHODS: Surveys from 5 480 308 patients discharged from 4517 hospitals 2014-2015. PRINCIPAL FINDINGS: Within each racial/ethnic group, mean reported experiences for non-English-preferring patients were almost always worse than their English-preferring counterparts. Language differences were largest and most consistent for Care Coordination. Within-hospital differences by language were often larger than between-hospital differences and were largest for Care Coordination. Where between-hospital differences existed, non-English-preferring patients usually attended hospitals whose average patient experience scores for all patients were lower than the average scores for the hospitals of their English-preferring counterparts. CONCLUSIONS: Efforts should be made to increase access to better hospitals for language minorities and improve care coordination and other facets of patient experience in hospitals with high proportions of non-English-preferring patients, focusing on cultural competence and language-appropriate services.

Geographic context of black-white disparities in Medicare CAHPS patient experience measures.

OBJECTIVE: To examine whether black-white patient experience disparities vary by geography and within-county contextual factors. DATA SOURCES: 321 300 Medicare beneficiaries responding to the 2015-2016 Medicare Consumer Assessment of Health care Providers and Systems (MCAHPS) Surveys; 2010 Census data for several within-county contextual factors. STUDY DESIGN: Mixed-effects regression models predicted three MCAHPS patient experience measures for black and white beneficiaries from geographic random effects, contextual fixed effects, and beneficiary-level case-mix adjustors. PRINCIPAL FINDINGS: Black-white disparities in patient experiences were smaller in counties with higher average patient experiences. Black-white disparities in patient experiences were not associated with county-level poverty or racial segregation. However, county racial segregation and some measures of poverty were significantly associated with all beneficiaries' level of health care access. Getting Needed Care scores were higher with greater racial segregation, while Getting Care Quickly scores were lower with higher poverty and racial segregation. CONCLUSIONS: Efforts to reduce black-white disparities in patient experiences should focus on areas with low average patient experiences. Attempts to reduce disparities in timely access to health care should target primarily black, low-income, and racially and economically segregated areas. Positive associations of racial segregation with accessing needed care were unexpected.

Indirect Estimation of Race/Ethnicity for Survey Respondents Who Do Not Report Race/Ethnicity.

BACKGROUND: Researchers are increasingly interested in measuring race/ethnicity, but some survey respondents skip race/ethnicity items. OBJECTIVES: The main objectives of this study were to investigate the extent to which racial/ethnic groups differ in skipping race/ethnicity survey items, the degree to which this reflects reluctance to disclose race/ethnicity, and the utility of imputing missing race/ethnicity. RESEARCH DESIGN: We applied a previously developed method for imputing race/ethnicity from administrative data (Medicare Bayesian Improved Surname and Geocoding 2.0) to data from a national survey where race/ethnicity was usually self-reported, but was sometimes missing. A linear mixed-effects regression model predicted the probability of self-reporting race/ethnicity from imputed racial/ethnic probabilities. SUBJECTS: In total, 508,497 Medicare beneficiaries responding to the 2013-2014 Medicare Consumer Assessment of Healthcare Providers and Systems surveys were included in this study. MEASURES: Self-reported race/ethnicity and estimated racial/ethnic probabilities. RESULTS: Black beneficiaries were most likely to not self-report their race/ethnicity (6.6%), followed by Hispanic (4.7%) and Asian/Pacific Islander (4.7%) beneficiaries. Non-Hispanic whites were the least likely to skip these items (3.2%). The 3.7% overall rate of missingness is similar to adjacent demographic items. General patterns of item missingness rather than a specific reluctance to disclose race/ethnicity appears to explain the elevated rate of missing race/ethnicity among Asian/Pacific Islander and Hispanic beneficiaries and most but not all among Black beneficiaries. Adding imputed cases to the data set did not substantially alter the estimated overall racial/ethnic distribution, but it did modestly increase sample size and statistical power. CONCLUSIONS: It may be worthwhile to impute race/ethnicity when this information is unavailable in survey data sets due to item nonresponse, especially when missingness is high.

Imputation of race/ethnicity to enable measurement of HEDIS performance by race/ethnicity.

OBJECTIVE: To improve an existing method, Medicare Bayesian Improved Surname Geocoding (MBISG) 1.0 that augments the Centers for Medicare & Medicaid Services' (CMS) administrative measure of race/ethnicity with surname and geographic data to estimate race/ethnicity. DATA SOURCES/STUDY SETTING: Data from 284 627 respondents to the 2014 Medicare CAHPS survey. STUDY DESIGN: We compared performance (cross-validated Pearson correlation of estimates and self-reported race/ethnicity) for several alternative models predicting self-reported race/ethnicity in cross-sectional observational data to assess accuracy of estimates, resulting in MBISG 2.0. MBISG 2.0 adds to MBISG 1.0 first name, demographic, and coverage predictors of race/ethnicity and uses a more flexible data aggregation framework. DATA COLLECTION/EXTRACTION METHODS: We linked survey-reported race/ethnicity to CMS administrative and US census data. PRINCIPAL FINDINGS: MBISG 2.0 removed 25-39 percent of the remaining MBISG 1.0 error for Hispanics, Whites, and Asian/Pacific Islanders (API), and 9 percent for Blacks, resulting in correlations of 0.88 to 0.95 with self-reported race/ethnicity for these groups. CONCLUSIONS: MBISG 2.0 represents a substantial improvement over MBISG 1.0 and the use of CMS administrative data on race/ethnicity alone. MBISG 2.0 is used in CMS' public reporting of Medicare Advantage contract HEDIS measures stratified by race/ethnicity for Hispanics, Whites, API, and Blacks.

The Limited English Proficient Population: Describing Medicare, Medicaid, and Dual Beneficiaries.

Purpose: The Limited English Proficient (LEP) population experiences well-documented suboptimal health outcomes and substandard provider experiences. The lack of national estimates on the size of the LEP population relative to the healthcare setting makes examining health outcomes for this population very difficult. This analysis addresses this limitation by publishing population estimates for LEP persons enrolled in Medicare, Medicaid, and Duals (enrolled in Medicare and Medicaid). Focusing on the Medicare and Medicaid programs provides an important foundation as these programs are not only the largest insurers in the United States but are also governed by legislation that requires LEP persons to receive equitable access to care. Methods: Data from the 2014 American Community Survey Public Use Microdata Sample (ACS PUMS) were used to produce national estimates and measures of statistical accuracy for the LEP population enrolled in Medicare and/or Medicaid (LEPMM). Results: In 2014, there were approximately 8.7 million LEP persons enrolled in Medicare, Medicaid, or both programs (Duals). The LEPMM was concentrated along the western and eastern coastlines and the southwestern region, with California and New York each containing more than 1 million LEPMMs. The LEPMM was also highly diverse with varying disability status, and most were racial or ethnic minorities and elderly. Conclusion: These findings provide a foundation for measuring an understudied and at-risk population that will enable population health professionals to develop effective culturally and linguistically, and appropriate services and policies that address health disparities in the LEPMM.

Racial/Ethnic Health Disparities Among Rural Adults-United States, 2012-2015: MMWR Surveillance Summaries / November 17, 2017 / 66(23);1-9.

PROBLEM/CONDITION: Rural communities often have worse health outcomes, have less access to care, and are less diverse than urban communities. Much of the research on rural health disparities examines disparities between rural and urban communities, with fewer studies on disparities within rural communities. This report provides an overview of racial/ethnic health disparities for selected indicators in rural areas of the United States. REPORTING PERIOD: 2012-2015. DESCRIPTION OF SYSTEM: Self-reported data from the 2012-2015 Behavioral Risk Factor Surveillance System were pooled to evaluate racial/ethnic disparities in health, access to care, and health-related behaviors among rural residents in all 50 states and the District of Columbia. Using the National Center for Health Statistics 2013 Urban-Rural Classification Scheme for Counties to assess rurality, this analysis focused on adults living in noncore (rural) counties. RESULTS: Racial/ethnic minorities who lived in rural areas were younger (more often in the youngest age group) than non-Hispanic whites. Except for Asians and Native Hawaiians and other Pacific Islanders (combined in the analysis), more racial/ethnic minorities (compared with non-Hispanic whites) reported their health as fair or poor, that they had obesity, and that they were unable to see a physician in the past 12 months because of cost. All racial/ethnic minority populations were less likely than non-Hispanic whites to report having a personal health care provider. Non-Hispanic whites had the highest estimated prevalence of binge drinking in the past 30 days. INTERPRETATION: Although persons in rural communities often have worse health outcomes and less access to health care than those in urban communities, rural racial/ethnic minority populations have substantial health, access to care, and lifestyle challenges that can be overlooked when considering aggregated population data. This study revealed difficulties among non-Hispanic whites as well, primarily related to health-related risk behaviors. Across each population, the challenges vary. PUBLIC HEALTH ACTION: Stratifying data by different demographics, using community health needs assessments, and adopting and implementing the National Culturally and Linguistically Appropriate Services Standards can help rural communities identify disparities and develop effective initiatives to eliminate them, which aligns with a Healthy People 2020 overarching goal: achieving health equity.

Racial/Ethnic Health Disparities Among Rural Adults - United States, 2012-2015.

PROBLEM/CONDITION: Rural communities often have worse health outcomes, have less access to care, and are less diverse than urban communities. Much of the research on rural health disparities examines disparities between rural and urban communities, with fewer studies on disparities within rural communities. This report provides an overview of racial/ethnic health disparities for selected indicators in rural areas of the United States. REPORTING PERIOD: 2012-2015. DESCRIPTION OF SYSTEM: Self-reported data from the 2012-2015 Behavioral Risk Factor Surveillance System were pooled to evaluate racial/ethnic disparities in health, access to care, and health-related behaviors among rural residents in all 50 states and the District of Columbia. Using the National Center for Health Statistics 2013 Urban-Rural Classification Scheme for Counties to assess rurality, this analysis focused on adults living in noncore (rural) counties. RESULTS: Racial/ethnic minorities who lived in rural areas were younger (more often in the youngest age group) than non-Hispanic whites. Except for Asians and Native Hawaiians and other Pacific Islanders (combined in the analysis), more racial/ethnic minorities (compared with non-Hispanic whites) reported their health as fair or poor, that they had obesity, and that they were unable to see a physician in the past 12 months because of cost. All racial/ethnic minority populations were less likely than non-Hispanic whites to report having a personal health care provider. Non-Hispanic whites had the highest estimated prevalence of binge drinking in the past 30 days. INTERPRETATION: Although persons in rural communities often have worse health outcomes and less access to health care than those in urban communities, rural racial/ethnic minority populations have substantial health, access to care, and lifestyle challenges that can be overlooked when considering aggregated population data. This study revealed difficulties among non-Hispanic whites as well, primarily related to health-related risk behaviors. Across each population, the challenges vary. PUBLIC HEALTH ACTION: Stratifying data by different demographics, using community health needs assessments, and adopting and implementing the National Culturally and Linguistically Appropriate Services Standards can help rural communities identify disparities and develop effective initiatives to eliminate them, which aligns with a Healthy People 2020 overarching goal: achieving health equity.

Examination of race disparities in physical inactivity among adults of similar social context.

OBJECTIVE: The objective of the study was to determine whether race disparities in physical inactivity are present among urban low-income Blacks and Whites living in similar social context. DESIGN: This analysis included Black and White respondents ( > or = 18 years) from the Exploring Health Disparities in Integrated Communities-Southwest Baltimore (EHDIC-SWB; N=1350) Study and the National Health Interview Survey (NHIS; N = 67790). Respondents who reported no levels of moderate or vigorous physical activity, during leisure time, over a usual week were considered physically inactive. RESULTS: After controlling for confounders, Blacks had higher adjusted odds of physical inactivity compared to Whites in the national sample (odds ratio [OR] = 1.40; 95% confidence interval [CI] =1.30-1.51). In EHDIC-SWB, Blacks and Whites had a similar odds of physical inactivity (OR = 1.09; 95% CI .86-1.40). CONCLUSION: Social context contributes to our understanding of racial disparities in physical inactivity.

Health behaviors and all-cause mortality in African American men.

Because of the excess burden of preventable chronic diseases and premature death among African American men, identifying health behaviors to enhance longevity is needed. We used data from the Third National Health and Nutrition Examination Survey 1988-1994 (NHANES III) and the NHANES III Linked Mortality Public-use File to determine the association between health behaviors and all-cause mortality and if these behaviors varied by age in 2029 African American men. Health behaviors included smoking, drinking, physical inactivity, obesity, and a healthy eating index score. Age was categorized as 25-44 years (n = 1,045), 45-64 years (n = 544), and 65 years and older (n = 440). Cox regression analyses were used to estimate the relationship between health behaviors and mortality within each age-group. All models were adjusted for marital status, education, poverty-to-income ratio, insurance status, and number of health conditions. Being a current smoker was associated with an increased risk of mortality in the 25- to 44-year age-group, whereas being physically inactive was associated with an increased risk of mortality in the 45- to 64-year age-group. For the 65 years and older age-group, being overweight or obese was associated with decreased mortality risk. Efforts to improve longevity should focus on developing age-tailored health promoting strategies and interventions aimed at smoking cessation and increasing physical activity in young and middle-aged African American men.

Association between race, place, and preventive health screenings among men: findings from the exploring health disparities in integrated communities study.

African American men consistently report poorer health and have lower participation rates in preventive screening tests than White men. This finding is generally attributed to race differences in access to care, which may be a consequence of the different health care markets in which African American and White men typically live. This proposition is tested by assessing race differences in use of preventive screenings among African American and White men residing within the same health care marketplace. Logistic regression was used to examine the association between race and physical, dental, eye and foot examinations, blood pressure and cholesterol checks, and colon and prostate cancer screenings in men in the Exploring Health Disparities in Integrated Communities in Southwest Baltimore Study. After adjusting for covariates, African American men had greater odds of having had a physical, dental, and eye examination; having had their blood pressure and cholesterol checked; and having been screened for colon and prostate cancer than White men. No race differences in having a foot examination were observed. Contrary to most findings, African American men had a higher participation rate in preventive screenings than White men. This underscores the importance of accounting for social context in public health campaigns targeting preventive screenings in men.

Association of secondhand smoke exposure with nicotine dependence among Black smokers.

INTRODUCTION: Exposure to secondhand smoke (SHS) is influenced by norms and regulations, socioeconomic status and immediate personal interactions. SHS exposure may occur in various settings, including the living space, workplace, and other social environments. This study examines the association between exposure to SHS and nicotine dependence among current smokers. METHODS: A cross-sectional sample of 246 Black (60% male and 40% female) current smokers age 40 and older, from Baltimore, Maryland and Washington, D.C, responded to an interviewer-administered questionnaire. We examined nicotine dependence using clinical guidelines based on the Diagnostic and Statistical Manual of Mental Disorders, Text Revision (2000). We performed multivariate logistic regression to assess the association between SHS and nicotine dependence. RESULTS: SHS exposure in the current home environment and exposure in settings outside the home, as well as difficulty to quit smoking and heaviness of smoking, were associated with nicotine dependence. After adjustment for age, gender, education, income, employment status, current alcohol consumption, history of marijuana use, and number of cigarettes smoked per day; exposure to SHS at home only, and in both current home environment and other settings, continued to be associated with clinically-defined nicotine dependence (OR=2.25; 95% CI 1.05, 4.86 vs. OR=2.31; 95% CI 1.03, 5.18), respectively. DISCUSSION: These findings highlight the relative importance of examining SHS exposure in personal (residential and automobile) and public (workplace and outdoor) settings by current smokers. Promotion of smoke-free environments may reduce the prevalence of nicotine dependence among current smokers.