Whites have better overall access to care than other beneficiaries with Medicare in the absence of major health conditions. Disparities in getting care quickly and immunizations are smaller among beneficiaries with greater disease burden, perhaps as a function of integration into the health care system gained through management of health issues. These results underscore the importance of outreach to minorities with low utilization and few or no major health conditions.

Objective. Single-year estimates of health disparities in small racial/ethnic groups are often insufficiently precise to guide policy, whereas estimates that are pooled over multiple years may not accurately describe current conditions. While collecting additional data is costly, innovative analytic approaches may improve the accuracy and utility of existing data. We developed an application of the Kalman filter in order to make more efficient use of extant data. Data Source. We used 1997We used -2004 National Health Interview Survey data on the prevalence of health outcomes for two racial/ethnic subgroups: American Indians/ Alaska Natives and Chinese Americans. Study Design. We modified the Kalman filter to generate more accurate current-year prevalence estimates for small racial/ethnic groups by efficiently aggregating past years of cross-sectional survey data within racial/ethnic groups. We compared these new estimates and their accuracy to simple current-year prevalence estimates. Principal Findings. For 18 of 19 outcomes, the modified Kalman filter approach reduced the error of current-year estimates for each of the two groups by 20-35 percent--equivalent to increasing current-year sample sizes for these groups by 56-135 percent.Conclusions. This approach could increase the accuracy of health measures for small groups using extant data, with virtually no additional cost other than those related to analytical processes.Key Words. Health disparities, Chinese, American Indians, borrowing strength, cross-sectional data Eliminating racial/ethnic health disparities is a major federal policy goal; however, most national health surveys have limited ability to assess the health of racial/ethnic population subgroups (e.g., national origin subgroups of r Health Research and Educational Trust

Since 2006, Medicare beneficiaries have been able to obtain prescription drug coverage through standalone prescription drug plans or their Medicare Advantage (MA) health plan, options exercised in 2015 by 72 percent of beneficiaries. Using data from community-dwelling Medicare beneficiaries older than age sixty-four in 700 plans surveyed from 2007 to 2014, we compared beneficiaries' assessments of Medicare prescription drug coverage when provided by standalone plans or integrated into an MA plan. Beneficiaries in standalone plans consistently reported less positive experiences with prescription drug plans (ease of getting medications, getting coverage information, and getting cost information) than their MA counterparts. Because MA plans are responsible for overall health care costs, they might have more integrated systems and greater incentives than standalone prescription drug plans to provide enrollees medications and information effectively, including, since 2010, quality bonus payments to these MA plans under provisions of the Affordable Care Act.

Most national health surveys do not permit precise measurement of the health of racial/ethnic subgroups that comprise <1 per cent of the U.S. population. We identify three potentially promising sample design strategies for increasing the accuracy of national health estimates for a small target subgroup when used to supplement a small probability sample of that group and apply these strategies to American Indians/Alaska Natives (AI/AN) and Chinese using National Health Interview Survey data. These sample design strategies include (1) complete sampling of targets within households, (2) oversampling selected macrogeographic units, and (3) oversampling from an incomplete list frame. Stage (1) is promising for Chinese and AI/AN; (2) works for both groups, but it would be more cost-effective for AI/AN because of their greater residential concentration; (3) is somewhat effective for groups like Chinese with viable surname lists, but not for AI/AN. Both (2) and (3) efficiently improve measurement precision when the supplement is the same size as the existing core sample, with diminishing additional returns as the supplement grows relative to the core sample, especially for (3). To avoid large design effects, the oversampled geographic areas or lists must have good coverage of the target population. To reduce costs, oversampled geographic tracts and lists must consist primarily of targets. These techniques can be used simultaneously to substantially increase effective sample sizes (ESSs). For example, (1) and (2) in combination can be used to multiply the nominal sample size of AI/AN or Chinese by 8 and the ESS by 4.

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.