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Abstract

An estimated 31.5 million Americans have a mobility limitation. Health care administrative data could be a valuable resource for research on this population but methods for cohort identification are lacking. We developed and tested an algorithm to reliably identify adults with mobility limitation in U.S. Department of Veterans Affairs health care data. We linked diagnosis, encounter, durable medical equipment, and demographic data for 964 veterans to their self-reported mobility limitation from the Medicare Current Beneficiary Survey. We evaluated performance of logistic regression models in classifying mobility limitation. The binary approach (yes/no limitation) had good sensitivity (70%) and specificity (79%), whereas the multilevel approach did not perform well. The algorithms for predicting a binary mobility limitation outcome performed well at discriminating between veterans who did and did not have mobility limitation. Future work should focus on multilevel approaches to predicting mobility limitation and samples with greater proportions of women and younger adults.

Keywords

mobility limitation administrative data algorithm veterans

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Development of a Predictive Algorithm to Identify Adults With Mobility Limitations Using VA Health Care Administrative Data

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