Variations in the persistence of health expenditures and the implications for the design of capitation payments in Taiwan. (July 2015)

• Record Type: Journal Article
• Authors: Ku, Li-Jung Elizabeth; Chiou, Meng-Jiun; Liu, Li-Fan
• Is Part Of: Journal of health services research & policy. Volume 20:Number 3(2015:Jul.)
• Page Start: 146
• Page End: 153
• Publication Date: 2015-07
• Subjects: capitation -- health care expenditures -- national health insurance; Medical care -- Periodicals; Medical policy -- Periodicals; Public health -- Periodicals; 362.105
• DOI: 10.1177/1355819615577711
• Languages: English
• ISSNs: 1355-8196

Abstract

Objectives: The National Health Insurance (NHI) system in Taiwan launched a trial capitation provider payment programme in 2011, with the capitation formula based on patients' average NHI expenditure in the previous year. This study seeks to examine the concentration and persistence of health care expenditure among the elderly, and to assess the performance of the current capitation formula in predicting future high-cost users. Methods: This study analysed NHI expenditures for a nationally representative sample of people aged 65 years and over who took part in Taiwan's National Health Interview Survey, 2005. Expenditure concentration was assessed by the proportion of NHI expenditures attributable to four groups by expenditure percentile. Four transition probability matrixes examined changes in a person's position in the expenditure percentiles and generalized estimation equation models were estimated to identify significant predictors of a patient being in the top 10% of users. Results: Between 2005 and 2009, the top 10% of users on average accounted for 55% of total NHI expenditures. Of the top 10% in 2005, 39% retained this position in 2006. However, expenditure persistence was the highest (77%) among the bottom 50% of users. NHI expenditure percentiles in both the baseline year and the prior year, and chronic conditions all significantly predicted future high expenditures. The model including chronic conditions performed better in predicting the top 10% of users (c-statistics increased from 0.772 to 0.904) than the model without. Conclusions: Given the increase in predictive ability, adding chronic conditions and baseline health care use data to Taiwan's capitation payment formula would correctly identify more high users.