Disparities in Potentially Preventable Hospitalizations Between American Indian and Alaska Native and Non-Hispanic White Medicare Enrollees.

Med Care

*Colorado School of Public Health, Centers for American Indian and Alaska Native Health, University of Colorado, Aurora †Department of Health & Behavioral Sciences, University of Colorado Denver, Denver, CO ‡California Rural Indian Health Board, Sacramento, CA.

Published: June 2017

Objective: A number of health care initiatives seek to improve health outcomes by increasing access to outpatient services while reducing preventable acute events. We evaluated disparities between American Indian and Alaska Native (AI/AN) and non-Hispanic white (white) Medicare enrollees in access to outpatient preventive, primary, and specialty services by comparing their potentially preventable hospitalizations (PPHs).

Research Design: The study population included 121,311 adult AI/AN Medicare enrollees registered to use services funded by the Indian Health Service and 5,915,011 adult white enrollees living in the same counties. Medicare 2010 data and a nationally recognized algorithm were used to identify PPHs.

Results: Among AI/AN Medicare enrollees, 58.6% had either diabetes, cardiovascular disease, or both conditions; the AI/AN age-adjusted prevalence of either or both conditions was 1.2 times that of the white enrollees (P<0.001). The age-adjusted PPH rate for all AI/ANs was 74 admissions per 1000 adults, 1.5 times that of white enrollees (P<0.001). Nearly 90% of AI/AN PPHs were among AI/ANs with diabetes, cardiovascular disease, or both conditions; their PPH rate was 114 admissions per 1000 adults, 1.2 times that of white enrollees (P<0.001) with those conditions.

Conclusions: Differences in disease burden and access to outpatient services may partly explain the higher PPH rates for AI/AN Medicare enrollees. The health care quality measure used in this study (PPH) was developed for the US general population. It is important to consider AI/AN socioeconomic and other characteristics when interpreting findings for such measures and enhancing programs and policies to improve AI/AN health outcomes.

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http://dx.doi.org/10.1097/MLR.0000000000000698DOI Listing

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