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Disaggregating Health Differences and Disparities With Machine Learning and Observed-to-expected Ratios: Application to Major Lower Limb Amputation.

Publication ,  Journal Article
Strassle, PD; Minc, SD; Kalbaugh, CA; Donneyong, MM; Ko, JS; McGinigle, KL
Published in: Epidemiology
November 1, 2025

BACKGROUND: Major lower limb amputation is a devastating but preventable complication of peripheral artery disease. It is unclear whether racial and ethnic and rural differences in amputation rates are due to clinical, hospital, or structural factors. METHODS: We included all peripheral artery disease hospitalizations of patients ≥40 years old between 2017 and 2019 in Florida, Georgia, Maryland, Mississippi, or New York (HCUP State Inpatient Databases). We estimated the expected number of amputations using three models: (1) unadjusted, (2) adjusted for clinical factors, and (3) adjusted for clinical factors, hospital factors, and social determinants of health using least absolute shrinkage and selection operator (LASSO). We calculated and compared observed-to-expected ratios and quantified the role of these factors in amputation rates. RESULTS: Overall, 1,577,061 hospitalizations (990,152 unique patients) and 21,233 major lower limb amputations (1.4%) were included. After accounting for clinical differences, we observed amputation disparities among rural Black, Hispanic, Native American, and White patients and nonrural Black and Native American patients. After accounting for hospital factors and social determinants of health, disparities were no longer present among rural White adults (0.93, 95% confidence interval [CI]: 0.77, 1.09); however, disparities persisted among rural Black (1.26, 95% CI: 1.01, 1.51), Hispanic (1.50, 95% CI: 0.89, 2.12), and Native American patients (1.13, 95% CI: 0.68, 1.58) and nonrural Black (1.12, 95% CI: 1.09, 1.15) and Native American (1.15, 95% CI: 0.86, 1.44) patients. CONCLUSION: Clinical factors did not fully explain differences in amputation rates, and hospital factors and social determinants of health did not fully explain disparities. These findings provide additional evidence that implicit bias is associated with amputation disparities.

Duke Scholars

Published In

Epidemiology

DOI

EISSN

1531-5487

Publication Date

November 1, 2025

Volume

36

Issue

6

Start / End Page

841 / 848

Location

United States

Related Subject Headings

  • United States
  • Social Determinants of Health
  • Rural Population
  • Peripheral Arterial Disease
  • Middle Aged
  • Male
  • Machine Learning
  • Lower Extremity
  • Humans
  • Hospitalization
 

Citation

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Chicago
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Strassle, P. D., Minc, S. D., Kalbaugh, C. A., Donneyong, M. M., Ko, J. S., & McGinigle, K. L. (2025). Disaggregating Health Differences and Disparities With Machine Learning and Observed-to-expected Ratios: Application to Major Lower Limb Amputation. Epidemiology, 36(6), 841–848. https://doi.org/10.1097/EDE.0000000000001892
Strassle, Paula D., Samantha D. Minc, Corey A. Kalbaugh, Macarius M. Donneyong, Jamie S. Ko, and Katharine L. McGinigle. “Disaggregating Health Differences and Disparities With Machine Learning and Observed-to-expected Ratios: Application to Major Lower Limb Amputation.Epidemiology 36, no. 6 (November 1, 2025): 841–48. https://doi.org/10.1097/EDE.0000000000001892.
Strassle PD, Minc SD, Kalbaugh CA, Donneyong MM, Ko JS, McGinigle KL. Disaggregating Health Differences and Disparities With Machine Learning and Observed-to-expected Ratios: Application to Major Lower Limb Amputation. Epidemiology. 2025 Nov 1;36(6):841–8.
Strassle, Paula D., et al. “Disaggregating Health Differences and Disparities With Machine Learning and Observed-to-expected Ratios: Application to Major Lower Limb Amputation.Epidemiology, vol. 36, no. 6, Nov. 2025, pp. 841–48. Pubmed, doi:10.1097/EDE.0000000000001892.
Strassle PD, Minc SD, Kalbaugh CA, Donneyong MM, Ko JS, McGinigle KL. Disaggregating Health Differences and Disparities With Machine Learning and Observed-to-expected Ratios: Application to Major Lower Limb Amputation. Epidemiology. 2025 Nov 1;36(6):841–848.

Published In

Epidemiology

DOI

EISSN

1531-5487

Publication Date

November 1, 2025

Volume

36

Issue

6

Start / End Page

841 / 848

Location

United States

Related Subject Headings

  • United States
  • Social Determinants of Health
  • Rural Population
  • Peripheral Arterial Disease
  • Middle Aged
  • Male
  • Machine Learning
  • Lower Extremity
  • Humans
  • Hospitalization