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

The periodic evaluation of health care services is a primary concern for many institutions. We consider services provided by nursing homes with the aim of ranking a set of these structures with respect to their effect on resident health status. Since the overall health status is not directly observa...

Full description

Saved in:
Bibliographic Details
Published in:Biometrical journal 2018-09, Vol.60 (5), p.962-978
Main Authors: Montanari, Giorgio E., Doretti, Marco, Bartolucci, Francesco
Format: Article
Language:English
Subjects:
clustered data
Drug Combinations
Evaluation
Health care
health status assessment
Humans
Likelihood Functions
Markov Chains
Models, Statistical
Multilevel
Multivariate Analysis
nonignorable dropout
Nurses
Nursing homes
Nursing Homes - statistics & numerical data
random‐effects approach
Ranking
Statistical analysis
Sulfanilamides
Time Factors
Transition probabilities
Trimethoprim
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The periodic evaluation of health care services is a primary concern for many institutions. We consider services provided by nursing homes with the aim of ranking a set of these structures with respect to their effect on resident health status. Since the overall health status is not directly observable, and given the longitudinal and multilevel structure of the available data, we rely on latent variable models and, in particular, on a multilevel latent Markov model where residents and nursing homes are the first and the second level units, respectively. The model includes individual covariates to account for resident characteristics. The impact of nursing home membership is modelled through a pair of 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 accounts for nonignorable dropout due to resident death, which typically occurs in these contexts. The motivating dataset is gathered from the Long Term Care Facilities programme, a health care protocol implemented in Umbria (Italy). Our results show that differences in performance between nursing homes are statistically significant.
ISSN:0323-3847
1521-4036
DOI:10.1002/bimj.201700223