Evaluation of long-term health care services through a latent Markov model with covariates

Giorgio E. Montanari () and Silvia Pandolfi
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Giorgio E. Montanari: University of Perugia

Statistical Methods & Applications, 2018, vol. 27, issue 1, No 8, 173 pages

Abstract: Abstract We focus on the evaluation of the long-term health care services provided to elderly patients by nursing homes of four different health districts in the Umbria region (Italy). To this end, we analyze data coming from a longitudinal survey aimed at assessing several aspects of patient health conditions and develop an extended version of the latent Markov model with covariates, which allows us to deal with dropout and intermittent missing data patterns that are common in longitudinal studies. Maximum likelihood estimates are obtained by a two-step approach that allows for fast estimation of model parameters and prevents some drawbacks of the standard maximum likelihood method encountered in the presence of many response variables and covariates. In the application to the observed data, we show how to obtain indicators of the effectiveness of the health care services delivered by each health district, by means of a resampling procedure.

Keywords: Informative dropout; Latent class model; Missing responses; Nursing homes; Two-step estimation (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10260-017-0390-2

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