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

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

Abstract

type="main" xml:id="rssa12112-abs-0001"> 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 (‘Nomenclature des unités territoriales 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.

Suggested Citation

  • Enrico Fabrizi & Giorgio E. Montanari & M. Giovanna Ranalli, 2016. "A hierarchical latent class model for predicting disability small area counts from survey data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(1), pages 103-131, January.
    • Handle: RePEc:bla:jorssa:v:179:y:2016:i:1:p:103-131
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    File URL: http://hdl.handle.net/10.1111/rssa.2016.179.issue-1
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    Cited by:

    1. K. Shuvo Bakar & Nicholas Biddle & Philip Kokic & Huidong Jin, 2020. "A Bayesian spatial categorical model for prediction to overlapping geographical areas in sample surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 535-563, February.
    2. Sarah Brown & William Greene & Mark Harris, 2020. "A novel approach to latent class modelling: identifying the various types of body mass index individuals," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 983-1004, June.
    3. Francesco Giovinazzi & Daniela Cocchi, 2022. "Social Integration of Second Generation Students in the Italian School System," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 160(1), pages 287-307, February.
    4. Jerry J. Maples, 2017. "Improving small area estimates of disability: combining the American Community Survey with the Survey of Income and Program Participation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 1211-1227, October.

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