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Predicting Length of Hospice Stay: An Application of Quantile Regression
Brystana G Kaufman1,2, David Klemish3, Cordt T Kassner4
11 Department of Health Policy and Management, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina.
Background:
Use of the Medicare hospice benefit has been associated with high-quality care at the end of life, and hospice length of use in particular has been used as a proxy for appropriate timing of hospice enrollment. Quantile regression has been underutilized as an alternative tool to model distributional changes in hospice length of use and hospice payments outside of the mean.
Objective:
To test for heterogeneity in the relationship between patient characteristics and hospice outcomes across the distribution of hospice days.
Setting:
Medicare Beneficiary Summary File and survey data (2014) for hospice beneficiaries in North and South Carolina with common terminal diagnoses.
Measurements:
Distributional shifts associated with patient characteristics were evaluated at the 25th and 75th percentiles of hospice days and hospice payments using quantile regressions and compared to the mean shift estimated by ordinary least squares (OLS) regression.
Principal Findings:
Significant (p < 0.001) heterogeneity in the marginal effects on hospice days and costs was observed, with patient characteristics associated with generally larger shifts in the 75th percentile than the 25th percentile. Mean effects estimated by OLS regression overestimate the magnitude of the median marginal effects for all patient characteristics except for race. Results for hospice payments in 2014 were similar.
Conclusions:
Methodological decisions can have a meaningful impact in the evaluation of factors influencing hospice length of use or cost.
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