A hierarchical latent class model for predicting disability small area counts from survey data

Enrico Fabrizi, Giorgio E. Montanari, M. Giovanna Ranalli

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

We consider the estimation of the number of severely disabled people by using data from the Italian survey on ‘Health conditions and appeal to Medicare’. In this survey, disability is indirectly measured by using a set of categorical items, which consider a set of functions concerning the ability of a person to accomplish everyday tasks. Latent class models can be employed to classify the population according to different levels of a latent variable connected with disability.The survey is designed to provide reliable estimates at the level of administrative regions (‘Nomenclaturedesunit ´esterritoriales statistiques ’, level 2), whereas local authorities are interested in quantifying the number of people who belong to each latent class at a subregional level. Therefore, small area estimation techniques should be used. The challenge is that the variable of interest is not observed. Adopting a full Bayesian approach, we base small area estimation on a latent class model in which the probability of belonging to each latent class changes with covariates and the influence of age is learnt from the data by using penalized splines. Demmler–Reinsch bases are shown to improve speed and mixing of Markov chain Monte Carlo chains used to simulate posteriors.
Original languageEnglish
Pages (from-to)103-131
Number of pages29
JournalJOURNAL OF THE ROYAL STATISTICAL SOCIETY. SERIES A. STATISTICS IN SOCIETY
Volume179
DOIs
Publication statusPublished - 2016

Keywords

  • Demmler–Reinsch bases
  • Health interview survey
  • Non-parametric regression
  • Penalized splines
  • Small area estimation
  • Unit level model

Fingerprint

Dive into the research topics of 'A hierarchical latent class model for predicting disability small area counts from survey data'. Together they form a unique fingerprint.

Cite this