Gabriel M Leung1, Keith Y K Tin, Wai-Sum Chan. 1. Department of Community Medicine and School of Public Health, Faculty of Medicine Building, University of Hong Kong, 21 Sassoon Road, Pokfulam, Hong Kong, China. gmleung@hku.hk
Abstract
OBJECTIVE: To derive actuarial projection estimates of Hong Kong's total domestic health expenditure to the year 2033. METHODS: Disaggregating health expenditure by age, sex, unit cost and utilisation level, we estimated future health spending by projecting utilisation (by public/private, inpatient/outpatient care) to reflect demographic changes and associated increase in demand (from higher expectations and greater intensity of care), and then multiplying such by the projected unit costs (incorporating the impact of key cost drivers such as public expectations, technological changes and potential productivity gains) to obtain total expenditure estimates. RESULTS: The model was most sensitive to the excess health care price inflation rate, i.e. the annual price/cost growth of medical goods and services over and above per capita GDP growth. Population ageing and growth per se, without taking into account related technologic innovation for chronic conditions that particularly afflict older adults, contribute relatively little to overall spending growth. Given the model assumptions, it is possible to limit total health spending to below 10% of GDP by 2033, where the public share would gradually decline from the current 57% to between 46% and 49%. CONCLUSIONS: Expenditure control through global budgeting, technology assessment and demand-side constraints should be considered although their effectiveness remains inconclusive.
OBJECTIVE: To derive actuarial projection estimates of Hong Kong's total domestic health expenditure to the year 2033. METHODS: Disaggregating health expenditure by age, sex, unit cost and utilisation level, we estimated future health spending by projecting utilisation (by public/private, inpatient/outpatient care) to reflect demographic changes and associated increase in demand (from higher expectations and greater intensity of care), and then multiplying such by the projected unit costs (incorporating the impact of key cost drivers such as public expectations, technological changes and potential productivity gains) to obtain total expenditure estimates. RESULTS: The model was most sensitive to the excess health care price inflation rate, i.e. the annual price/cost growth of medical goods and services over and above per capita GDP growth. Population ageing and growth per se, without taking into account related technologic innovation for chronic conditions that particularly afflict older adults, contribute relatively little to overall spending growth. Given the model assumptions, it is possible to limit total health spending to below 10% of GDP by 2033, where the public share would gradually decline from the current 57% to between 46% and 49%. CONCLUSIONS: Expenditure control through global budgeting, technology assessment and demand-side constraints should be considered although their effectiveness remains inconclusive.
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