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Title: Essays in Modeling Health Care Expenditures With A Focus On Singapore
Keywords: Elderly, Health care expenditures, Catastrophic health care expenditure risk, Modeling, Health care financing, Optimal deductible
Issue Date: 30-Dec-2010
Citation: HIMANI AGGARWAL (2010-12-30). Essays in Modeling Health Care Expenditures With A Focus On Singapore. ScholarBank@NUS Repository.
Abstract: Modeling and predicting both average and extreme hospitalization expenditures and financing health care expenditures within the Singapore context are the key issues addressed in this thesis. As a precursor to modeling, the first essay (second chapter) examines how the elderly Singaporeans offset their hospital bill. The government subsidy that a patient receives, on average, covers 60% of the total charges of a hospitalization episode. After accounting for the government subsidy, medical savings accounts of the patient and family member, together, offset about 68% of the subsidized hospital bill. The contribution of insurance (both the government and private) is small, covering only 15% of the subsidized bill. Direct out-of-pocket payment comprises 6.3% of the subsidized bill. The second essay (third chapter) explores the best model to predict the mean inpatient expenditure incurred by the elderly Singaporeans and estimates the impact of various covariates such as demographic characteristics, clinical factors, outcome of hospitalization, length of stay, insurance status on mean expenditure via marginal and incremental effects. The findings show that, compared to ward C, the average bill of a hospitalization episode in ward A is higher by S$8,241 and the bill in ward B1 is higher by S$5,686. The difference between ward B2 and ward C bills narrows down to S$657. In case of a surgical operation, the average bill per episode is approximately S$1,043 more than the episodes without any operation and the difference in case of an implant is S$2,411. The average bill is S$876 more in the event of death of a patient in the hospital. For patients who had payouts from the government insurance, other things being equal, the mean expense per admission is S$288 higher than for patients without payout. In case of private health insurance, the difference is S$395. If slope parameters remain the same, the model can be used for out-of-sample predictions through intercept adjustments as the expenditure profile shifts over time. In the third essay (fourth chapter), the probabilities of incurring catastrophic health care expenditures by the elderly Singaporeans are predicted and factors that increase the likelihood of facing such expenditures are determined. The results show that the probability of incurring hospitalization expenditure more than S$10,000 by the elderly with the government insurance varies from 1.7 to 8.8% while the corresponding probability range for the elderly without the government insurance is 0.9 to 4.9%. This difference is more pronounced for private insurance (2.9-10.8% versus 0.9-5.3%). Among different diseases afflicting the elderly, the probability of catastrophic expenditure is highest for musculoskeletal diseases. The oldest old face the lowest probability of catastrophic expenditure. As in the second essay, the model can be adapted for out-of-sample predictions through intercept adjustments. A significant proportion of the elderly are not covered by any health insurance in Singapore. One of the reasons cited for low uptake of the catastrophic illness insurance offered by the government is that it is characterized by high deductibles. In the fourth essay (fifth chapter), a simulation model is developed to estimate the size of optimal deductible for the government insurance in Singapore. The result shows that the optimal deductible is S$1000 (in 2007 dollars) for the base case scenario which matches the deductible component of the government health insurance.
Appears in Collections:Ph.D Theses (Open)

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