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Disaggregating Health Differences and Disparities With Machine Learning and Observed-to-expected Ratios: Application

Paula D Strassle^1,2, Samantha D Minc³, Corey A Kalbaugh⁴

¹From the Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD.

Epidemiology (Cambridge, Mass.)

|July 9, 2025

Summary

Major lower limb amputations due to peripheral artery disease show persistent racial disparities. Even after accounting for clinical, hospital, and social factors, implicit bias likely contributes to these preventable amputation rates.

Keywords:

Healthcare disparities Machine learning Major lower limb amputation Peripheral artery disease Rural health Social determinants of health Surgical

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Area of Science:

Vascular Surgery
Health Disparities Research
Public Health Policy

Background:

Major lower limb amputation is a severe complication of peripheral artery disease (PAD).
Racial, ethnic, and rural disparities in PAD amputation rates are significant but poorly understood.
Identifying the underlying causes of these disparities is crucial for developing targeted interventions.

Purpose of the Study:

To investigate the contribution of clinical, hospital, and social determinants of health (SDOH) to racial and rural disparities in major lower limb amputations among PAD patients.
To quantify the extent to which these factors explain observed amputation rate differences across demographic groups.

Main Methods:

Analysis of 2017-2019 Florida, Georgia, Maryland, Mississippi, and New York inpatient data for patients aged 40+ with PAD.
Utilized three models (unadjusted, adjusted for clinical factors, adjusted for clinical, hospital, and SDOH factors via LASSO regression) to estimate expected amputation numbers.
Calculated observed-to-expected amputation ratios to identify and quantify disparities.

Main Results:

The study included 1,577,061 hospitalizations and 21,233 major lower limb amputations (1.4%).
After adjusting for clinical factors, disparities were noted among rural Black, Hispanic, Native American, and White patients, and non-rural Black and Native American patients.
Further adjustment for hospital factors and SDOH eliminated disparities for rural White patients but disparities persisted for rural Black, Hispanic, Native American, and non-rural Black and Native American patients.

Conclusions:

Clinical factors alone do not fully explain the observed amputation rate disparities.
Hospital factors and social determinants of health also do not fully account for these disparities.
The persistence of disparities after accounting for known factors suggests a role for implicit bias in major lower limb amputations among peripheral artery disease patients.