Factors associated with higher healthcare costs in individuals living with arthritis : evidence from the quantile regression approach

Objective: To examine the factors associated with higher healthcare cost in women with arthritis, using generalized linear models (GLMs) and quantile regression (QR). Methods: This is a cross-sectional healthcare cost study of individuals with arthritis that focused on older Australian women. Cost data were drawn from the Medicare Australia datasets. Results: GLM results show that healthcare cost was significantly associated with various socio-demographic and health factors. Although QR analysis results show the same direction of association between these factors and healthcare cost as in the GLMs, they indicate progressively increased effect sizes at the 50th, 75th, 90th and 95th percentiles. Conclusion: Findings suggest traditional regression models such as GLMs that assume a single rate of change to accurately describe the relationships between explanatory variables and healthcare costs across the entire distribution of cost can produce biased results. QR should be considered in future healthcare cost research.