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A Nonlinear Dynamic Factor Model of Health and Medical Treatment

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Abstract

Quantitative assessments of the relationship between health and medical treatment are of great importance to policy makers. However, simply looking at the raw correlation between health and medical care is unlikely to give the right answer because of endogeneity problems. We overcome these problems by formulating and estimating a tractable dynamic factor model of health and medical treatment where individual observed health outcomes are driven by the individual's latent health stock. The dynamics of latent health reflects both exogenous health depreciation and endogenous health investments. Our model allows us to investigate the effect of medical treatment on current health, as well as on future medical treatment and health outcomes. We estimate the model by maximum simulated likelihood and minimum distance methods using a rich longitudinal data set from Italy obtained by merging a number of administrative archives. These data contain detailed information on medical drug use, hospitalization, and mortality for a representative sample of elderly hypertensive patients. Our findings show that medical care consumption is highly correlated over time, and this relationship depends on both permanent and time-varying observed and unobserved heterogeneity. They also show that medical drug use significantly maintains future health levels and prevents transitions to worse health. These results suggest that policies aimed at increasing the awareness and the compliance of hypertensive patients help reduces cardiovascular risks and consequent hospitalization and mortality.

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  • Franco Peracchi & Claudio Rossetti, 2019. "A Nonlinear Dynamic Factor Model of Health and Medical Treatment," CSEF Working Papers 524, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    • Handle: RePEc:sef:csefwp:524
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    Cited by:

    1. Matteo Barigozzi & Marc Hallin, 2023. "Dynamic Factor Models: a Genealogy," Papers 2310.17278, arXiv.org, revised Jan 2024.

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    More about this item

    Keywords

    Dynamic panel data models; latent variable models; factor models; maximum simulated likelihood; minimum distance method; health dynamics; medical treatment; drug consumption; mortality;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • J14 - Labor and Demographic Economics - - Demographic Economics - - - Economics of the Elderly; Economics of the Handicapped; Non-Labor Market Discrimination

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