Predicting Disability among Community‐Dwelling Medicare Beneficiaries Using Claims‐Based Indicators

Abstract

Objectives. To assess the feasibility of using existing claims-based algorithms to identify community-dwelling Medicare beneficiaries with disability based solely on the conditions for which they are being treated, and improving on these algorithms by combining them in predictive models. Data Source. Data on 12,415 community-dwelling fee-for-service Medicare beneficiaries who first responded to the Medicare Current Beneficiary Survey (MCBS) in 2003-2006. Study Design. Logistic regression models in which six claims-based disability indicators are used to predict self-reported disability. Receiver operating characteristic (ROC) curves were used to assess the performance of the predictive models. Principal Findings. The predictive performance of the regression-based models is better than that of the individual claims-based indicators. At a predicted probability threshold chosen to maximize the sum of sensitivity and specificity, sensitivity is 0.72 for beneficiaries age 65 or older and specificity is 0.65. For those under 65, sensitivity is 0.54 and specificity is 0.67. The findings also suggest ways to improve predictive performance for specific disability populations of interest to researchers. Conclusions. Predictive models that incorporate multiple claims-based indicators provide an improved tool for researchers seeking to identify people with disabilities in claims data. Key Words. Self-reported disability, Medicare, claims-based algorithms, receiver operating characteristic (ROC) curves Claims data are a potentially powerful tool for research on the long-term service and support (LTSS) needs of people with disabilities. Identification of people with disabilities in claims data-particularly those who live in the community and might benefit from home-and community-based service (HCBS) interventions-is helpful in assessments of interventions designed to improve

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“…Due to the absence of similar studies on mobility limitation in the extant literature, we compare our results with those that focused on identification of any limitations in ADLs. Previous studies predicting any limitation in ADLs had similar performance as ours (Ben-Shalom & Stapleton,2016; AUC = 0.75; Faurot et al, 2015; AUC = 0.85). Our focus on mobility limitation specifically can support the identification and study of this population and answering policy relevant questions targeted to people with mobility limitation.…”

“…Previous research has used administrative data to identify similar constructs, but they have been confounded with more general functional limitations, limited to specific health conditions, or only, in part, related to mobility limitation. These included identifying people with a disability related to any limitations in activities of daily living (ADLs) and instrumental ADL (IADLs; Ben-Shalom & Stapleton, 2016; Davidoff et al, 2013) or focusing on aspects of frailty (Faurot et al, 2015). However, grouping mobility limitation with all other functional limitations or frailty may lead to aggregation bias and misunderstanding of the nature of relationships that exist for people with mobility limitation specifically.…”

Patient Perception of Physicians and Medication Adherence Among Older Adults With Hypertension

“…The Patient Perception of Physicians Scale was constructed using items from the 2007 MCBS. The MCBS data have been collected since 1991, and there have been many studies utilizing these data to assess long-term care (Lee et al, 2016; Moyo, Huang, Simoni-Wastila, & Harrington, 2018; Shen, Zuckerman, Palmer, & Stuart, 2015), health care cost and utilization (Shen et al, 2015), disability (Ben-Shalom & Stapleton, 2016; Hennessy et al, 2015), Medicare plans (Henning-Smith, Casey, & Moscovice, 2017; Jacobs & Buntin, 2015), and patient satisfaction (Bogner et al, 2015). MCBS survey items include a variety of questions asking about sociodemographic characteristics, clinical characteristics, health status and functioning, access to care, health care resource use, and health care costs.…”

Inpatient Medicaid Usage and Expenditure Patterns After Changes in Supplemental Nutrition Assistance Program Benefit Levels

“…To increase the specificity of this algorithm, as done previously (22), we identified the individuals with the highest likelihood of having a disability and compared them to the other groups. After each discharged person was assigned a likelihood for having a disability, data for each group was collapsed by month.…”