The Contribution of First-name Information to the Accuracy of Racial-and-Ethnic Imputations Varies by Sex and Race-and-Ethnicity Among Medicare Beneficiaries.

BACKGROUND: Data on race-and-ethnicity that are needed to measure health equity are often limited or missing. The importance of first name and sex in predicting race-and-ethnicity is not well understood. OBJECTIVE: The objective of this study was to compare the contribution of first-name information to the accuracy of basic and more complex racial-and-ethnic imputations that incorporate surname information. RESEARCH DESIGN: We imputed race-and-ethnicity in a sample of Medicare beneficiaries under 2 scenarios: (1) with only sparse predictors (name, address, sex) and (2) with a rich set (adding limited administrative race-and-ethnicity, demographics, and insurance). SUBJECTS: A total of 284,627 Medicare beneficiaries who completed the 2014 Medicare Consumer Assessment of Healthcare Providers and Systems survey and reported race-and-ethnicity were included. RESULTS: Hispanic, non-Hispanic Asian/Pacific Islander, and non-Hispanic White racial-and-ethnic imputations are more accurate for males than females under both sparse-predictor and rich-predictor scenarios; adding first-name information increases accuracy more for females than males. In contrast, imputations of non-Hispanic Black race-and-ethnicity are similarly accurate for females and males, and first names increase accuracy equally for each sex in both sparse-predictor and rich-predictor scenarios. For all 4 racial-and-ethnic groups, incorporating first-name information improves prediction accuracy more under the sparse-predictor scenario than under the rich-predictor scenario. CONCLUSION: First-name information contributes more to the accuracy of racial-and-ethnic imputations in a sparse-predictor scenario than in a rich-predictor scenario and generally narrows sex gaps in accuracy of imputations.

How Do Gender Differences in Quality of Care Vary Across Medicare Advantage Plans?

BACKGROUND: Healthcare Effectiveness Data and Information Set (HEDIS) quality measures have long been used to compare care across health plans and to study racial/ethnic and socioeconomic disparities among Medicare Advantage (MA) beneficiaries. However, possible gender differences in seniors' quality of care have received less attention. OBJECTIVE: To test for the presence and nature of any gender differences in quality of care across MA Plans, overall and by domain; to identify those most at risk of poor care. DESIGN: Cross-sectional analysis of individual-level HEDIS measure scores from 23.8 million records using binomial mixed-effect models to estimate the effect of gender on performance. For each measure, we assess variation in gender gaps and their correlation with plan performance. PARTICIPANTS: Beneficiaries from 456 MA plans in 2011-2012 HEDIS data. MAIN MEASURES: Performance on 32 of 34 HEDIS measures which were available in both measurement years. The two excluded measures had mean performance scores below 10%. KEY RESULTS: Women experienced better quality of care than men for 22/32 measures, with most pertaining to screening or treatment. Men experienced better quality on nine measures, including four related to cardiovascular disease and three to potentially harmful drug-disease interactions. Plans varied substantially in the magnitude of gender gaps for 21/32 measures; in general, the gender gap in quality of care was least favorable to men in low-performing plans. CONCLUSIONS: Women generally experienced better quality of care than men. However, women experienced poorer care for cardiovascular disease-related intermediate outcomes and potentially harmful drug-disease interactions. Quality improvement may be especially important for men in low-performing plans and for cardiovascular-related care and drug-disease interactions for women. Gender-stratified reporting could reveal gender gaps, identify plans for which care varies by gender, and motivate efforts to address faults and close the gaps in the delivery system.

Developing predictive models of health literacy.

INTRODUCTION: Low health literacy (LHL) remains a formidable barrier to improving health care quality and outcomes. Given the lack of precision of single demographic characteristics to predict health literacy, and the administrative burden and inability of existing health literacy measures to estimate health literacy at a population level, LHL is largely unaddressed in public health and clinical practice. To help overcome these limitations, we developed two models to estimate health literacy. METHODS: We analyzed data from the 2003 National Assessment of Adult Literacy (NAAL), using linear regression to predict mean health literacy scores and probit regression to predict the probability of an individual having 'above basic' proficiency. Predictors included gender, age, race/ethnicity, educational attainment, poverty status, marital status, language spoken in the home, metropolitan statistical area (MSA) and length of time in U.S. RESULTS: All variables except MSA were statistically significant, with lower educational attainment being the strongest predictor. Our linear regression model and the probit model accounted for about 30% and 21% of the variance in health literacy scores, respectively, nearly twice as much as the variance accounted for by either education or poverty alone. CONCLUSIONS: Multivariable models permit a more accurate estimation of health literacy than single predictors. Further, such models can be applied to readily available administrative or census data to produce estimates of average health literacy and identify communities that would benefit most from appropriate, targeted interventions in the clinical setting to address poor quality care and outcomes related to LHL.

The National Health Plan Collaborative to Reduce Disparities and Improve Quality.

BACKGROUND: Despite numerous reports and initiatives, progress in reducing racial/ethnic disparities in health care has been slow. The National Health Plan Collaborative (NHPC), a novel public-private partnership between nine health plans covering approximately 95 million lives, leading learning and research organizations, the Agency for Healthcare Research and Quality, and the Robert Wood Johnson Foundation, was established in December 2004 to address these disparities. PROGRESS TO DATE: The health plans were able to overcame initial challenges in obtaining information on race/ethnicity of their enrollees and examined their diabetes performance measure to assess disparities in care. By February 2006, the initial nine plans that had joined the NHPC progressed from focusing solely on data collection and management issues and were engaged in outreach activities to members, providers, or community or had completed capacity development for disparities work. Five plans had implemented one or more pilot interventions. Plans also addressed unanticipated challenges, such as sorting through large amounts of data to target disparities. CHALLENGES AND LESSONS LEARNED: Because many of the plans are complex national entities with varying regional and departmental structures, simply achieving coordination of disparities activities across the organization has been a major challenge and, in many cases, a major breakthrough. CONCLUSIONS: The NHPC represents a model of shared learning and innovation through which health plans are tackling racial/ethnic disparities. Now that most of the plans have some data on their enrollees with diabetes and have begun targeting disparities, they want to capitalize on their collective industry strength to influence policy on issues related to disparities.

Mapping the Gaps: Gender Differences in Preventive Cardiovascular Care among Managed Care Members in Four Metropolitan Areas.

BACKGROUND: Prior research documents gender gaps in cardiovascular risk management, with women receiving poorer quality routine care on average, even in managed care systems. Although population health management tools and quality improvement efforts have led to better overall care quality and narrowing of racial/ethnic gaps for a variety of measures, we sought to quantify persistent gender gaps in cardiovascular risk management and to assess the performance of routinely used commercial population health management tools in helping systems narrow gender gaps. METHODS: Using 2013 through 2014 claims and enrollment data from more than 1 million members of a large national health insurance plan, we assessed performance on seven evidence-based quality measures for the management of coronary artery disease and diabetes mellitus, a cardiac risk factor, across and within four metropolitan areas. We used logistic regression to adjust for region, demographics, and risk factors commonly tracked in population health management tools. FINDINGS: Low-density lipoprotein (LDL) cholesterol control (LDL < 100 mg/dL) rates were 5 and 15 percentage points lower for women than men with diabetes mellitus (p < .0001), and coronary artery disease (p < .0001), respectively. Adjusted analyses showed women were more likely to have gaps in LDL control, with an odds ratio of 1.31 (95% confidence interval, 1.27-1.38) in diabetes mellitus and 1.88 (95% confidence interval, 1.65-2.10) in coronary artery disease. CONCLUSIONS: Given our findings that gender gaps persist across both clinical and geographic variation, we identified additional steps health plans can take to reduce disparities. For measures where gaps have been consistently identified, we recommend that gender-stratified quality reporting and analysis be used to complement widely used algorithms to identify individuals with unmet needs for referral to population health and wellness behavior support programs.

One In Five Fewer Heart Attacks: Impact, Savings, And Sustainability In San Diego County Collaborative.

Before 2011 rates of hospitalization for heart attacks were about the same in San Diego County as they were in the rest of California. In 2011 a multistakeholder population health collaborative consisting of partners at the federal, state, and local levels launched Be There San Diego. The collaborative's goal was to reduce cardiovascular events through the spread of best practices aimed at improving control of hypertension, lipid levels, and blood sugar and through patient and medical community activation. Using hospital discharge data for the period 2007-16, we compared acute myocardial infarction (AMI) hospitalization rates in San Diego County and the rest of the state before and after the demonstration project started. AMI hospitalization rates decreased by 22 percent in San Diego County versus 8 percent in the rest of the state, with an estimated 3,826 AMI hospitalizations avoided and $86 million in savings in San Diego. Results show that a science-based health collaborative can improve outcomes while lowering costs, and efforts are under way to ensure the collaborative's sustainability.

Does quality of care for cardiovascular disease and diabetes differ by gender for enrollees in managed care plans?

PURPOSE: To assess gender differences in the quality of care for cardiovascular disease and diabetes for enrollees in managed care plans. METHODS: We obtained data from 10 commercial and 9 Medicare plans and calculated performance on 6 Health Employer Data and Information Set (HEDIS) measures of quality of care (beta-blocker use after myocardial infarction [MI], low-density lipoprotein cholesterol [LDL-C] check after a cardiac event, and in diabetics, whether glycosylated hemoglobin [HgbA1c], LDL cholesterol, nephropathy, and eyes were checked) and a 7th HEDIS-like measure (angiotensin-converting enzyme [ACE] inhibitor use for congestive heart failure). A smaller number of plans provided HEDIS scores on 4 additional measures that require medical chart abstraction (control of LDL-C after cardiac event, blood pressure control in hypertensive patients, and HgbA1c and LDL-C control in diabetics). We used logistic regression models to adjust for age, race/ethnicity, socioeconomic status, and plan. MAIN FINDINGS: Adjusting for covariates, we found significant gender differences on 5 of 11 measures among Medicare enrollees, with 4 favoring men. Similarly, among commercial enrollees, we found significant gender differences for 8 of 11 measures, with 6 favoring men. The largest disparity was for control of LDL-C among diabetics, where women were 19% less likely to achieve control among Medicare enrollees (relative risk [RR] = 0.81; 95% confidence interval [CI] = 0.64-0.99) and 16% less likely among commercial enrollees (RR = 0.84; 95%CI = 0.73-0.95). CONCLUSION: Gender differences in the quality of cardiovascular and diabetic care were common and sometimes substantial among enrollees in Medicare and commercial health plans. Routine monitoring of such differences is both warranted and feasible.

Differences in HIV care between patients with and without severe mental illness.

OBJECTIVE: This study explored how HIV care differs for infected persons with and without severe mental illness. METHODS: Data were obtained through interviews with and chart review of 295 patients with severe mental illness and HIV from public mental health agencies in Los Angeles County and New York City. Data were compared with data from 1,294 HIV patients without severe mental illness from a separate national probability sample. Measures were difficulty obtaining care, whether patients recommend their HIV care provider, hospital problem score, functional health status, and disability days. RESULTS: In Los Angeles, HIV patients with severe mental illness were more likely than those without severe mental illness to have difficulty obtaining care (p<.001); to not recommend their provider (10% versus 5%, p=.007); and to have problematic hospital care (p=.001), poor health status (p=.001), and more disability days (p<.001). In New York City, HIV patients with severe mental illness were more likely than patients without severe mental illness to have difficulty obtaining care (p=.002) and not recommend their provider (p=.02). The relationship between severe mental illness and health status in Los Angeles and access in New York City became insignificant after adjustment for sociodemographic factors, drug use, and CD4 cell count. Further adjustment for higher case management rates among HIV patients with severe mental illness reduced disparities only in the West. CONCLUSIONS: Patients with severe mental illness experienced more problems with HIV care than patients without severe mental illness, although high case management rates for patients with severe mental illness may have offset some problems.

Differential diffusion of HIV technologies by gender: the case of highly active antiretroviral therapy.

We sought to examine whether diffusion of new HIV technologies differed by gender in the United States, the source of any such disparities, and whether disparities narrow over time as technologies become more established. In particular, we assess how rates of use of highly active antiretroviral therapy (HAART) varied between males and females during the late 1990s, when HAART was rapidly diffusing. We examined data from a prospective cohort study of a national probability sample of 1421 HIV-infected adults in medical care who were enrolled in the HIV Cost and Services Utilization Study (HCSUS) from January 1996 to December 1998. We found that HAART use substantially increased between 1996 and early 1998 for all groups. Women were less likely to receive HAART at all time points, although the diffusion of HAART between 1996 and 1998 reduced gender disparities. Gender disparities in 1998 were only partially explained by women's lower income and educational levels. We conclude that HAART therapy diffused more slowly to HIV-positive females than other groups. Policies that reduce the impact of income and education inequalities on health care may help to narrow gender disparities for new HIV technologies, but other factors may also disadvantage women.

Improving Population Health Through an Innovative Collaborative: The Be There San Diego Data for Quality Group.

In 2012, leaders from disparate health care organizations established a data group aligned around a regional goal of preventing heart attacks and strokes in San Diego. The group---now named the Be There San Diego Data for Quality (DFQ) Group---is a safe venue for medical directors and other quality-improvement leaders to share performance data on quality-of-care measures for diabetes, hypertension, and cardiovascular disease, as well as insights, lessons learned, and challenges faced by each organization in treating these conditions. The DFQ Group has focused its efforts on improving the quality of services provided by each participating health care organization, and has placed a strong emphasis on analyzing trends in combined quality data to better understand the health of the entire San Diego population. By fostering collaboration among organizations that collectively serve a large portion of the local population and other key community stakeholders, the DFQ Group has helped form the foundation of a unique, multifaceted, multi-stakeholder, regional effort that is gaining national attention and funding for its community-driven approach.

Racial and ethnic disparities in care: the perspectives of cardiologists.

BACKGROUND: Despite extensive documentation of racial and ethnic disparities in care, provider awareness of disparities has been thought to be low. To be effective, educational efforts for physicians must consider providers' knowledge and beliefs about what causes disparities and what can be done about them. METHODS AND RESULTS: We conducted a Web-based survey of 344 cardiologists to determine their level of awareness of disparities and views of underlying causes. Responses were assessed by means of 5-point Likert scales. Thirty-four percent of cardiologists agreed that disparities existed in care overall in the US healthcare system, and 33% agreed that disparities existed in cardiovascular care. Only 12% felt disparities existed in their own hospital setting, and even fewer, 5%, thought disparities existed in the care of their own patients. Despite this, most respondents rated the strength of the evidence about disparities as "very strong" or "strong." Respondents identified many potential causes for disparities in care but were more likely to endorse patient and system level factors (eg, insurance status or adherence) rather than provider level factors. CONCLUSIONS: Cardiologists' awareness of disparities in care remains low, and awareness is inversely proportional to proximity to their own practice setting.

Looking forward: cross-cutting issues in the collection and use of racial/ethnic data.

Availability of reliable and valid race/ethnicity data is essential for monitoring and improving quality of care for minority groups. We explore the limitations and challenges posed by existing means of data collection and discuss issues that need to be considered as the data are analyzed and used.

Use of geocoding and surname analysis to estimate race and ethnicity.

OBJECTIVE: To review two indirect methods, geocoding and surname analysis, for estimating race/ethnicity as a means for health plans to assess disparities in care. STUDY DESIGN: Review of published articles and unpublished data on the use of geocoding and surname analyses. PRINCIPAL FINDINGS: Few published studies have evaluated use of geocoding to estimate racial and ethnic characteristics of a patient population or to assess disparities in health care. Three of four studies showed similar estimates of the proportion of blacks and one showed nearly identical estimates of racial disparities, regardless of whether indirect or more direct measures (e.g., death certificate or CMS data) were used. However, accuracy depended on racial segregation levels in the population and region assessed and geocoding was unreliable for identifying Hispanics and Asians/Pacific Islanders. Similarly, several studies suggest surname analyses produces reasonable estimates of whether an enrollee is Hispanic or Asian/Pacific Islander and can identify disparities in care. However, accuracy depends on the concentrations of Asians or Hispanics in areas assessed. It is less accurate for women and more acculturated and higher SES persons due intermarriage, name changes, and adoption. Surname analysis is not accurate for identifying African Americans. Recent unpublished analyses suggest plans can successfully use a combined geocoding/surname analyses approach to identify disparities in care in most regions. Refinements based on Bayesian methods may make geocoding/surname analyses appropriate for use in areas where the accuracy is currently poor, but validation of these preliminary results is needed. CONCLUSIONS: Geocoding and surname analysis show promise for estimating racial/ethnic health plan composition of enrollees when direct data on major racial and ethnic groups are lacking. These data can be used to assess disparities in care, pending availability of self-reported race/ethnicity data.

Patterns of HIV care for patients with serious mental illness.

Individuals with serious mental illness are at higher risk for HIV than are members of the general population. Although studies have shown that individuals with serious mental illness experience less adequate care and worse physical health outcomes than comparable patients without serious mental illness, little is known about HIV care among individuals with serious mental illness who become infected with HIV. In the present study, we describe patterns of highly active antiretroviral treatment (HAART) use and physician monitoring received by 154 patients with serious mental illness infected with HIV. Participants were recruited from mental health agencies in Los Angeles, California. Data from 762 HIV-only patients from a separate Western U.S. probability sample were used for comparison. High proportions of serious mental illness patients with HIV in our sample appeared to be receiving adequate HIV care. Fifty-one percent of all serious mental illness patients with serious mental illness with HIV were taking HAART, and the majority received close monitoring of their CD4 counts (84%) and viral loads (82%) throughout a 1-year period. HAART use and patterns of CD4 count and viral load monitoring did not differ significantly between patients with both serious mental illness and HIV, and patients with HIV only (all p > 0.05). Specialized programs providing assistance to serious mental illness populations with HIV may be helping to narrow health care disparities as a result of having serious mental illness.

An HIV collaborative in the VHA: do advanced HIT and one-day sessions change the collaborative experience?

BACKGROUND: Many organizations participate in quality collaboratives, yet the return on investment of the associated time and costs is unclear. METHOD: Semistructured interviews, surveys, and direct observation were used to assess experiences, improvement activities, and costs associated with participation in a year-long modified Institute for Healthcare Improvement-style collaborative designed to improve HIV care within the Veterans Health Administration. All nine sites had access to automated patient registries and semi-automated clinical measure reports; five sites also received computerized clinical reminders. Three one-day learning sessions were conducted. RESULTS: Participants reported that burden was small and value high, although many suggested that more time for peer-to peer learning would have been helpful. Teams averaged five quality improvement activities per site and most reported improvements in HIV care processes. The average annual cost per site was dollars 28,000 but costs varied considerably by site. DISCUSSION: Shortened learning sessions and the incorporation of health information technology can reduce some of the costs and burdens associated with collaboratives, yet peer-to-peer interaction and local organizational factors remain important to ensuring perceived effectiveness of collaboratives.

When Race/Ethnicity Data Are Lacking: Using Advanced Indirect Estimation Methods to Measure Disparities.

A key aim of U.S. health care reforms is to ensure equitable care while improving quality for all Americans. Limited race/ethnicity data in health care records hamper efforts to meet this goal. Despite improvements in access and quality, gaps persist, particularly among persons belonging to racial/ethnic minority and low-income groups. This study describes the use of indirect estimation methods to produce probabilistic estimates of racial/ethnic populations to monitor health care utilization and improvement. One method described, called Bayesian Indirect Surname Geocoding, uses a person's Census surname and the racial/ethnic composition of their neighborhood to produce a set of probabilities that a given person belongs to one of a set of mutually exclusive racial/ethnic groups. Advances in methods for estimating race/ethnicity are enabling health plans and other health care organizations to overcome a long-standing barrier to routine monitoring and actions to reduce disparities in care. Though these new estimation methods are promising, practical knowledge and guidance on how to most effectively apply newly available race/ethnicity data to address disparities can be greatly extended.

Use of geocoding in managed care settings to identify quality disparities.

Tracking quality-of-care measures is essential for improving care, particularly for vulnerable populations. Although managed care plans routinely track quality measures, few examine whether their performance differs by enrollee race/ethnicity or socioeconomic status (SES), in part because plans do not collect that information. We show that plans can begin examining and targeting potential disparities using indirect measures of enrollee race/ethnicity and SES based on geocoding. Using such measures, we demonstrate disparities within both Medicare+Choice and commercial plans on Health Plan Employer Data and Information Set (HEDIS) measures of diabetes and cardiovascular care, including instances in which race/ethnicity and SES have distinct effects.

Imputing Missing Race/Ethnicity in Pediatric Electronic Health Records: Reducing Bias with Use of U.S. Census Location and Surname Data.

OBJECTIVE: To assess the utility of imputing race/ethnicity using U.S. Census race/ethnicity, residential address, and surname information compared to standard missing data methods in a pediatric cohort. DATA SOURCES/STUDY SETTING: Electronic health record data from 30 pediatric practices with known race/ethnicity. STUDY DESIGN: In a simulation experiment, we constructed dichotomous and continuous outcomes with pre-specified associations with known race/ethnicity. Bias was introduced by nonrandomly setting race/ethnicity to missing. We compared typical methods for handling missing race/ethnicity (multiple imputation alone with clinical factors, complete case analysis, indicator variables) to multiple imputation incorporating surname and address information. PRINCIPAL FINDINGS: Imputation using U.S. Census information reduced bias for both continuous and dichotomous outcomes. CONCLUSIONS: The new method reduces bias when race/ethnicity is partially, nonrandomly missing.

Patterns of care for open-angle glaucoma in managed care.

OBJECTIVES: To describe patterns of care for primary open-angle glaucoma (POAG) and assess conformance with the American Academy of Ophthalmology's Preferred Practice Pattern (PPP). METHODS: We obtained administrative, survey, and eye care records data on 395 working-age patients with POAG enrolled in 6 managed care plans between 1997 and 1999. We assessed processes of care at the initial and follow-up visits, control of intraocular pressure (IOP), intervals between visits and visual field tests, and adjustments in therapy. RESULTS: We found high rates of performance on most recommended processes during initial evaluations, although only 53% of patients received an optic nerve head photograph or drawing and only 1% had a target IOP level documented. Recommended processes were performed at 80% to 97% of follow-up visits. Using loose criteria for control, IOP was controlled in 66% of follow-up visits for patients with mild glaucoma and 52% of visits for patients with moderate to severe glaucoma. Intervals between visits and visual field tests were generally consistent with PPP recommendations. Adjustments in therapy were more likely with worse control of IOP, although adjustments occurred in only half of visits where the IOP was 30 mm Hg or higher. CONCLUSIONS: Our study suggests that, in many respects, patients with POAG are receiving care that is consistent with the PPP. However, care is falling short on several key aspects, and POAG may be undertreated relative to standards for IOP control established in recent clinical trials.

Improving women's quality of care for cardiovascular disease and diabetes: the feasibility and desirability of stratified reporting of objective performance measures.

Despite growing recognition of significant morbidity and mortality among women from cardiovascular disease, management of primary and secondary cardiac risk factors continues to be suboptimal for many women. Although there is a good deal of room to improve the care for cardiovascular disease and diabetes in men, existing gender differences in performance suggest much can be gained by specifically assessing and monitoring quality of care for these conditions in women. In this paper, we describe recent work showing gender differences in quality of ambulatory care in managed care plans with some plans having substantial gender differences on widely used measures of the quality of primary and secondary prevention of cardiac disease. We then discuss potential benefits of and barriers to routine reporting of objective measures of the quality of care, such as Health Plan Employer Data and Information Set (HEDIS) measures, by health plans.