Local Population Characteristics and Hemoglobin A1c Testing Rates among Diabetic Medicare Beneficiaries
Skinner, Jonathan S.
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CitationYasaitis, Laura C., Thomas Bubolz, Jonathan S. Skinner, and Amitabh Chandra. 2014. “Local Population Characteristics and Hemoglobin A1c Testing Rates among Diabetic Medicare Beneficiaries.” PLoS ONE 9 (10): e111119. doi:10.1371/journal.pone.0111119. http://dx.doi.org/10.1371/journal.pone.0111119.
AbstractBackground: Proposed payment reforms in the US healthcare system would hold providers accountable for the care delivered to an assigned patient population. Annual hemoglobin A1c (HbA1c) tests are recommended for all diabetics, but some patient populations may face barriers to high quality healthcare that are beyond providers' control. The magnitude of fine-grained variations in care for diabetic Medicare beneficiaries, and their associations with local population characteristics, are unknown. Methods: HbA1c tests were recorded for 480,745 diabetic Medicare beneficiaries. Spatial analysis was used to create ZIP code-level estimated testing rates. Associations of testing rates with local population characteristics that are outside the control of providers – population density, the percent African American, with less than a high school education, or living in poverty – were assessed. Results: In 2009, 83.3% of diabetic Medicare beneficiaries received HbA1c tests. Estimated ZIP code-level rates ranged from 71.0% in the lowest decile to 93.1% in the highest. With each 10% increase in the percent of the population that was African American, associated HbA1c testing rates were 0.24% lower (95% CI −0.32–−0.17); for identical increases in the percent with less than a high school education or the percent living in poverty, testing rates were 0.70% lower (−0.95–−0.46) and 1.6% lower (−1.8–−1.4), respectively. Testing rates were lowest in the least and most densely populated ZIP codes. Population characteristics explained 5% of testing rate variations. Conclusions: HbA1c testing rates are associated with population characteristics, but these characteristics fail to explain the vast majority of variations. Consequently, even complete risk-adjustment may have little impact on some process of care quality measures; much of the ZIP code-related variations in testing rates likely result from provider-based differences and idiosyncratic local factors not related to poverty, education, or race.
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