Abstract
Annual preventive care is essential for diabetes patients to reduce the risk of complications including hypoglycemic events and blindness. Our aim was to examine the relative efficiency of Diabetes Belt (DB) and non-Diabetes Belt (NDB) counties in providing recommended preventive care for Medicare beneficiaries with diabetes using available health professional resources and to understand county-level socioeconomic factors associated with inefficient provision of preventive care. A data envelopment analysis (DEA) model was developed to assess relative efficiency of counties in providing diabetes preventive care. Logistic regression was performed to identify socioeconomic characteristics associated with inefficiencies. We used Medicare claims data to extract individual-level information of diabetes preventive service use and obtained county-level estimates of health resources information from the Area Health Resources File. More than 80% of counties had more than 10% inefficiencies on average. Compared to counties in the NDB, the odds of being inefficient were 2.44 times more likely in the DB (OR 2.44, CI 1.67–3.58). Counties with lower median income, with a smaller proportion of non-Hispanic Black population, and in a rural area had higher odds of being inefficient in providing preventive care. Our DEA results showed that counties in the DB and NDB were mostly inefficient. The availability of care providers may be less of a problem than how efficiently the resources are used in providing preventive care. Identifying sources of inefficiency within each community with low resource utilization and developing targeted strategies is needed to improve uptake of preventive care cost-effectively.
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Funding
The study was supported in part by the National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases (1R01DK113295). This funding supported HK, MWS, SK, RB, RA, AM, TM, and JML. The study sponsor had no role in study design, collection, analysis, interpretation of data, writing the report, or the decision to submit the report for publication.
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HK, MS, and JML developed the project. HK, MS, SK, and SZ performed data analysis. HK, MS, SK, and JML drafted the main manuscript text. All authors performed interpretation of findings and significant edits and review of the manuscript.
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10742_2023_310_MOESM3_ESM.png
Supplementary file3: Figure A1. Light colored counties represent the counties that are inefficient (efficiency score < 0.90) in all years 2009–2015. Dark colored counties represent the counties that had an efficiency score ≥ 0.90 in at least one year over the period. The Diabetes Belt (DB) counties are shown in orange and the non-Diabetes Belt (NDB) counties are shown in blue. Dots represent cities with a population of 40,000 or more according to the 2010 census and major highways are displayed. (PNG 255 KB)
10742_2023_310_MOESM4_ESM.gif
Supplementary file4: Supplementary File A2. GIF presenting county level efficiency scores for 2009–2015. The Diabetes Belt (DB) counties are shown in orange and the non-Diabetes Belt (NDB) counties are shown in blue. Dots represent cities with a population of 40,000 or more according to the 2010 census and major highways are displayed. (GIF 266 KB)
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Kang, H., Sohn, MW., Kim, S. et al. Diabetes Belt has lower efficiency in providing diabetes preventive care than surrounding counties. Health Serv Outcomes Res Method 24, 200–210 (2024). https://doi.org/10.1007/s10742-023-00310-5
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DOI: https://doi.org/10.1007/s10742-023-00310-5