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Local population characteristics and hemoglobin A1c testing rates among diabetic medicare beneficiaries.

Yasaitis LC, Bubolz T, Skinner JS, Chandra A - PLoS ONE (2014)

Bottom Line: Testing rates were lowest in the least and most densely populated ZIP codes.Population characteristics explained 5% of testing rate 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.

View Article: PubMed Central - PubMed

Affiliation: Harvard Center for Population and Development Studies, Harvard University, Cambridge, Massachusetts, United States of America.

ABSTRACT

Background: 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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Related in: MedlinePlus

Multiple Regression Results: Hemglobin A1c Testing Rate Differences Associated With Population Characteristics.ZIP code-level HbA1c testing rates regressed on local population characteristics. Bars represent the 95% CI around the estimated association between each covariate and local HbA1c testing rates.
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pone-0111119-g004: Multiple Regression Results: Hemglobin A1c Testing Rate Differences Associated With Population Characteristics.ZIP code-level HbA1c testing rates regressed on local population characteristics. Bars represent the 95% CI around the estimated association between each covariate and local HbA1c testing rates.

Mentions: To make this comparison of sociodemographic factors and quality measures more formally, we performed a multiple linear regression at the ZIP code-level. Figure 4 presents the results. We regressed the raw rate from each ZIP code on the percent of the local population that was African American, the percent with less than a high school education, and the percent living below 100% FPL, as well as indicators for the decile of population density (with the least populated decile as reference). Each 10% increase in the percent of a ZIP's population that was African American was associated with a 0.24% decrease (95% CI −0.32–−0.17) in HbA1c testing rates. The corresponding values for 10% increases in the percent of the population that had less than a high school education or were living below 100% FPL were a 0.7% (95% CI −0.95–−0.46) decrease and a 1.6% decrease (95% CI −1.84–−1.42), respectively. The least densely populated ZIP codes had the lowest testing rates. Rates increased noticeably in the 2nd and 3rd most populated deciles, by 1.63% (95% CI 1.14–2.12) and 2.58% (95% CI 2.09–3.08), each relative to the first decile, and then decreased again with increasing population density. Testing rates in the 10th (most densely populated) decile of ZIP codes were not significantly different from those in the 1st. The total r-squared from the regression was 0.048. In sensitivity analyses, our main findings were consistent when we used spatially smoothed HbA1c rates rather than raw rates, or when we restricted the sample to ZIP codes whose raw rates were based on larger populations (the r-squared did increase with these alternative approaches, particularly using the smoothed outcome variable, but was never higher than 0.09).


Local population characteristics and hemoglobin A1c testing rates among diabetic medicare beneficiaries.

Yasaitis LC, Bubolz T, Skinner JS, Chandra A - PLoS ONE (2014)

Multiple Regression Results: Hemglobin A1c Testing Rate Differences Associated With Population Characteristics.ZIP code-level HbA1c testing rates regressed on local population characteristics. Bars represent the 95% CI around the estimated association between each covariate and local HbA1c testing rates.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4215926&req=5

pone-0111119-g004: Multiple Regression Results: Hemglobin A1c Testing Rate Differences Associated With Population Characteristics.ZIP code-level HbA1c testing rates regressed on local population characteristics. Bars represent the 95% CI around the estimated association between each covariate and local HbA1c testing rates.
Mentions: To make this comparison of sociodemographic factors and quality measures more formally, we performed a multiple linear regression at the ZIP code-level. Figure 4 presents the results. We regressed the raw rate from each ZIP code on the percent of the local population that was African American, the percent with less than a high school education, and the percent living below 100% FPL, as well as indicators for the decile of population density (with the least populated decile as reference). Each 10% increase in the percent of a ZIP's population that was African American was associated with a 0.24% decrease (95% CI −0.32–−0.17) in HbA1c testing rates. The corresponding values for 10% increases in the percent of the population that had less than a high school education or were living below 100% FPL were a 0.7% (95% CI −0.95–−0.46) decrease and a 1.6% decrease (95% CI −1.84–−1.42), respectively. The least densely populated ZIP codes had the lowest testing rates. Rates increased noticeably in the 2nd and 3rd most populated deciles, by 1.63% (95% CI 1.14–2.12) and 2.58% (95% CI 2.09–3.08), each relative to the first decile, and then decreased again with increasing population density. Testing rates in the 10th (most densely populated) decile of ZIP codes were not significantly different from those in the 1st. The total r-squared from the regression was 0.048. In sensitivity analyses, our main findings were consistent when we used spatially smoothed HbA1c rates rather than raw rates, or when we restricted the sample to ZIP codes whose raw rates were based on larger populations (the r-squared did increase with these alternative approaches, particularly using the smoothed outcome variable, but was never higher than 0.09).

Bottom Line: Testing rates were lowest in the least and most densely populated ZIP codes.Population characteristics explained 5% of testing rate 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.

View Article: PubMed Central - PubMed

Affiliation: Harvard Center for Population and Development Studies, Harvard University, Cambridge, Massachusetts, United States of America.

ABSTRACT

Background: 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.

Show MeSH
Related in: MedlinePlus