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A multilevel latent Markov model for the evaluation of nursing homes' performance

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  • Montanari, Giorgio E.
  • Doretti, Marco
  • Bartolucci, Francesco

Abstract

The periodic evaluation of health care services is a primary concern for many institutions. In this work, we focus on nursing home services with the aim to produce a ranking of a set of nursing homes based on their capability to improve - or at least to keep unchanged - the health status of the patients they host. As the overall health status is not directly observable, latent variable models represent a suitable approach. Moreover, given the longitudinal and multilevel structure of the available data, we rely on a multilevel latent Markov model where patients and nursing homes are the first and the second level units, respectively. The model includes individual covariates to account for the patient case-mix and the impact of nursing home membership is modeled through a pair of correlated random effects affecting the initial distribution and the transition probabilities between different levels of health status. Through the prediction of these random effects we obtain a ranking of the nursing homes. Furthermore, the proposed model is designed to address non-ignorable dropout, which typically occurs in these contexts because some elderly patients die before completing the survey. We apply our model to the Long Term Care Facilities dataset, a longitudinal dataset gathered from Regione Umbria (Italy). Our results are robust to the sensitivity parameter involved (the number of latent states) and show that differences in nursing homes' performances are statistically significant. The authors certify that they have the right to deposit this contribution in its published format with MPRA.

Suggested Citation

  • Montanari, Giorgio E. & Doretti, Marco & Bartolucci, Francesco, 2017. "A multilevel latent Markov model for the evaluation of nursing homes' performance," MPRA Paper 80691, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:80691
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    1. Harvey Goldstein & Michael J. R. Healy, 1995. "The Graphical Presentation of a Collection of Means," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 158(1), pages 175-177, January.
    2. Francesco Bartolucci & Monia Lupparelli, 2016. "Pairwise Likelihood Inference for Nested Hidden Markov Chain Models for Multilevel Longitudinal Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 216-228, March.
    3. Francesco Bartolucci & Fulvia Pennoni & Giorgio Vittadini, 2011. "Assessment of School Performance Through a Multilevel Latent Markov Rasch Model," Journal of Educational and Behavioral Statistics, , vol. 36(4), pages 491-522, August.
    4. Matthew Stephens, 2000. "Dealing with label switching in mixture models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 795-809.
    5. Altman, Rachel MacKay, 2007. "Mixed Hidden Markov Models: An Extension of the Hidden Markov Model to the Longitudinal Data Setting," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 201-210, March.
    6. Antonello Maruotti, 2011. "Mixed Hidden Markov Models for Longitudinal Data: An Overview," International Statistical Review, International Statistical Institute, vol. 79(3), pages 427-454, December.
    7. Evelyn Kitagawa, 1964. "Standardized comparisons in population research," Demography, Springer;Population Association of America (PAA), vol. 1(1), pages 296-315, March.
    8. S. Bacci & S. Pandolfi & F. Pennoni, 2014. "A comparison of some criteria for states selection in the latent Markov model for longitudinal data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(2), pages 125-145, June.
    9. Jesse D. Raffa & Joel A. Dubin, 2015. "Multivariate longitudinal data analysis with mixed effects hidden Markov models," Biometrics, The International Biometric Society, vol. 71(3), pages 821-831, September.
    10. Jeroen K. Vermunt & Rolf Langeheine & Ulf Bockenholt, 1999. "Discrete-Time Discrete-State Latent Markov Models with Time-Constant and Time-Varying Covariates," Journal of Educational and Behavioral Statistics, , vol. 24(2), pages 179-207, June.
    11. Bartolucci, Francesco & Montanari, Giorgio E. & Pandolfi, Silvia, 2015. "Three-step estimation of latent Markov models with covariates," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 287-301.
    12. Makai, Peter & Brouwer, Werner B.F. & Koopmanschap, Marc A. & Stolk, Elly A. & Nieboer, Anna P., 2014. "Quality of life instruments for economic evaluations in health and social care for older people: A systematic review," Social Science & Medicine, Elsevier, vol. 102(C), pages 83-93.
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    More about this item

    Keywords

    clustered data; health status evaluation; non-ingorable dropout; random effects;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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