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Abstract
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.
Financial & competing interests disclosure
The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.
No writing assistance was utilized in the production of this manuscript.
In assessing the factors associated with healthcare costs, generalized linear models with logarithmic link function and gamma distribution for cost have been commonly used.
Traditional regression methods such as generalized linear models assume a single rate of change that can accurately describe the relationship between an explanatory variable and the healthcare cost across the entire cost distribution.
Individuals with different levels of healthcare utilization and cost may be heterogeneous groups; there may be different factors affecting the healthcare cost at different degrees in different groups.
Our results from assessing the factors associated with higher healthcare cost in older Australian women with arthritis using quantile regression show that various significant factors (e.g., socio-demographic and health variables) have different effects on cost at different percentiles.
These findings indicate that traditional regression methods may not accurately describe the relationships between explanatory variables and cost for the entire population.
As quantile regression can be used to estimate the effect of explanatory variables at specific conditional quantiles of cost, its use should be considered in future health economic research.