Background: Misclassification of Medicare beneficiaries’ race/ethnicity in administrative data sources is frequently overlooked and a limitation in health disparities research. Objective: To compare the validity of 2 race/ethnicity variables found in Medicare administrative data [enrollment database (EDB) and Research Triangle Institute (RTI) race] against a gold-standard source also available in the Medicare data warehouse: the self-reported race/ethnicity variable on the home health Outcome and Assessment Information Set (OASIS). Subjects: Medicare beneficiaries over the age of 18 who received home health care in 2015 (N=4,243,090). Measures: Percent agreement, sensitivity, specificity, positive predictive value, and Cohen κ coefficient. Results: The EDB and RTI race variable have high validity for black race and low validity for American Indian/Alaskan Native race. Although the RTI race variable has better validity than the EDB race variable for other races, κ values suggest room for future improvements in classification of whites (0.90), Hispanics (0.87), Asian/Pacific Islanders (0.77), and American Indian/Alaskan Natives (0.44). Discussion: The status quo of using “good-enough for government” race/ethnicity variables contained in Medicare administrative data for minority health disparities research can be improved through the use of self-reported race/ethnicity data, available in the Medicare data warehouse. Health services and policy researchers should critically examine the source of race/ethnicity variables used in minority health and health disparities research. Future work to improve the accuracy of Medicare beneficiaries’ race/ethnicity data should incorporate and augment the self-reported race/ethnicity data contained in assessment and survey data, available within the Medicare data warehouse.

Background: Rigorous measurement of organizational performance requires large, unbiased samples to allow inferences to the population. Studies of organizations, including hospitals, often rely on voluntary surveys subject to nonresponse bias. For example, hospital administrators with concerns about performance are more likely to opt-out of surveys about organizational quality and safety, which is problematic for generating inferences.Objective: The objective of this study was to describe a novel approach to obtaining a representative sample of organizations using individuals nested within organizations, and demonstrate how resurveying nonrespondents can allay concerns about bias from low response rates at the individual-level.Methods: We review and analyze common ways of surveying hospitals. We describe the approach and results of a double-sampling technique of surveying nurses as informants about hospital quality and performance. Finally, we provide recommendations for sampling and survey methods to increase response rates and evaluate whether and to what extent bias exists.Results: The survey of nurses yielded data on over 95% of hospitals in the sampling frame. Although the nurse response rate was 26%, comparisons of nurses’ responses in the main survey and those of resurveyed nonrespondents, which yielded nearly a 90% response rate, revealed no statistically significant differences at the nurse-level, suggesting no evidence of nonresponse bias.Conclusions: Surveying organizations via random sampling of front-line providers can avoid the self-selection issues caused by directly sampling organizations. Response rates are commonly misinterpreted as a measure of representativeness; however, findings from the double-sampling approach show how low response rates merely increase thepotential fornonresponse bias but do not confirm it.

The Institute of Medicine concluded in To Err Is Human in 1999 that transformation of nurse work environments was needed to reduce patient harm. We studied 535 hospitals in four large states at two...