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Healthcare Expenditure And Utilization Assessment: Bayesian Sensitivity Analyses For Hidden Subpopulations In Weighted Sampling | 17898
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I
n countries such as the United States, the exploding medical cost is the key cause of the current healthcare crisis. Hence,
accurate assessments of the healthcare expenditure, utilization and their associated risk factors are essential in understanding
the key issues for the purpose of future improvements. In this paper, we propose several Bayesian model-based approaches for
sensitivity analyses on population healthcare expenditure and utilization assessments under complex models. In particular,
the proposed methods adjust for a potential impact from a hidden sub-population that cannot be reached by weighted
sampling, which may be the case for public healthcare surveys such as the Medical Expenditure Panel Survey of the United
States. Bayesian models are presented for estimating population medical expenditure and health care utilization, as well as
measurements of associations with a binary risk factor. In order to understand the performance of the models, large-sample
limits of the posteriors were obtained for all the models. Using Medical Expenditure Panel Survey data, in which individuals
with higher expenditures and more frequent health care visits are more likely to be included, we illustrate how the assumption
on the hidden proportion of never-respondents may impact the final estimates of expenditure, utilization, and measurements
of associations with a binary risk factor.
Biography
Michelle Xia is an Assistant Professor of statistics who works in Bayesian methods for model identification issues, with applications in medical, health and
insurance areas. She completed her PhD degree in 2013 from the Department of Statistics at the University of British Columbia, under the supervision of Professor
Paul Gustafson. In addition to her academic qualifications, she has over seven years of professional experiences in actuarial science, predictive modeling and
biostatistics.
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