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Objectives: This study delineates activities of daily living (ADL) and instrumental activities of daily living (IADL) black-white disparity trends by age, period, and cohort (APC) and explores sociodemographic contributors of cohort-based disparity trends., Method: We utilized multiple cross-sectional waves of National Health Interview Survey data (1982-2009) to describe APC trends of ADL and IADL disparities using a cross-classified random effect model. Further, we decomposed the cohort-based disparity trends using Fairlie's decomposition method for nonlinear outcomes., Results: The crossover ADL and IADL disparities (whites > blacks) occurring at age 75 increased with age and reached a plateau at age of 80, whereas period-based ADL and IADL disparities remained constant for the past 3 decades. The cohort disparity trends for both disabilities showed a decline with each successive cohort except for ADL disparity among women., Discussion: We examined the role of aging on racial disparity in disability and found support for the racial crossover effect. Further, the racial disparity in disability will disappear should the observed pattern of declining cohort-based ADL and IADL disparities persist. Although education, income, and marital status are important sociodemographic contributors to cohort disparity trends, future studies should investigate individual behavioral health determinants and cohort-specific characteristics that explain the cohort-based racial difference in ADL and IADL disabilities., (© The Author 2014. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

This paper uses data from the U.S. National Health Interview Surveys (N = 1,513,097) to describe and explain temporal patterns in black-white health disparities with models that simultaneously consider the unique effects of age, period, and cohort. First, we employ cross-classified random effects age-period-cohort (APC) models to document black-white disparities in self-rated health across temporal dimensions. Second, we use decomposition techniques to shed light on the extent to which socio-economic shifts in cohort composition explain the age and period adjusted racial health disparities across successive birth cohorts. Third, we examine the extent to which exogenous conditions at the time of birth help explain the racial disparities across successive cohorts. Results show that black-white disparities are wider among the pre-1935 cohorts for women, falling thereafter; disparities for men exhibit a similar pattern but exhibit narrowing among cohorts born earlier in the century. Differences in socioeconomic composition consistently contribute to racial health disparities across cohorts; notably, marital status differences by race emerge as an increasingly important explanatory factor in more recent cohorts for women whereas employment differences by race emerge as increasingly salient in more recent cohorts for men. Finally, our cohort characteristics models suggest that cohort economic conditions at the time of birth (percent large family, farm or Southern birth) help explain racial disparities in health for both men and women., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

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 1997-2004 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.

The persistence of the black health disadvantage has been a puzzling component of health in the United States in spite of general declines in rates of morbidity and mortality over the past century. Studies that have focused on well-established individual-level determinants of health such as socio-economic status and health behaviors have been unable to fully explain these disparities. Recent research has begun to focus on other factors such as racism, discrimination, and segregation. Variation in neighborhood context-socio-demographic composition, social aspects, and built environment-has been postulated as an additional explanation for racial disparities, but few attempts have been made to quantify its overall contribution to the black/white health gap. This analysis is an attempt to generate an estimate of place effects on explaining health disparities by utilizing data from the U.S. National Health Interview Survey (NHIS) (1989-1994), combined with a methodology for identifying residents of the same blocks both within and across NHIS survey cross-sections. Our results indicate that controlling for a single point-in-time measure of residential context results in a roughly 15-76% reduction of the black/white disparities in self-rated health that were previously unaccounted for by individual-level controls. The contribution of residential context toward explaining the black/white self-rated health gap varies by both age and gender such that contextual explanations of disparities decline with age and appear to be smaller among females.

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.