T K T Lo1, Lynne Parkinson2, Michelle Cunich3, Julie Byles1. 1. a 1 Research Centre for Gender, Health & Ageing, The University of Newcastle, HMRI Building, C/- University Drive, Callaghan, NSW 2308, Australia. 2. b 2 Human Health and Social Sciences/Higher Education Division, Central Queensland University, Bruce Highway, Rockhampton Qld 4702, Australia. 3. c 3 The University of Sydney, Charles Perkins Centre, Research and Education Hub, The University of Sydney, NSW 2006, Australia.
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.
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.
Authors: Margaret A Olsen; Fang Tian; Anna E Wallace; Katelin B Nickel; David K Warren; Victoria J Fraser; Nandini Selvam; Barton H Hamilton Journal: Ann Surg Date: 2017-02 Impact factor: 12.969