Calibration of a stochastic health evolution model using NHIS data

Aparna Gupta and Zhisheng Li ()

Physica A: Statistical Mechanics and its Applications, 2011, vol. 390, issue 20, 3524-3540

Abstract: This paper presents and calibrates an individual’s stochastic health evolution model. In this health evolution model, the uncertainty of health incidents is described by a stochastic process with a finite number of possible outcomes. We construct a comprehensive health status index (HSI) to describe an individual’s health status, as well as a health risk factor system (RFS) to classify individuals into different risk groups. Based on the maximum likelihood estimation (MLE) method and the method of nonlinear least squares fitting, model calibration is formulated in terms of two mixed-integer nonlinear optimization problems. Using the National Health Interview Survey (NHIS) data, the model is calibrated for specific risk groups. Longitudinal data from the Health and Retirement Study (HRS) is used to validate the calibrated model, which displays good validation properties. The end goal of this paper is to provide a model and methodology, whose output can serve as a crucial component of decision support for strategic planning of health related financing and risk management.

Keywords: Health evolution model; Health status measurements; Health risk factors; Sampling; Mixed-integer program (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:390:y:2011:i:20:p:3524-3540

DOI: 10.1016/j.physa.2011.04.003

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