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

  title={A hierarchical latent class model for predicting disability small area counts from survey data},
  author={Enrico Fabrizi and Giorgio Eduardo Montanari and M. Giovanna Ranalli},
  journal={Journal of the Royal Statistical Society: Series A (Statistics in Society)},
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… 
A novel approach to latent class modelling: identifying the various types of body mass index individuals
An extension to latent class modelling is proposed, which serves to unveil a more detailed picture of the determinants of BMI, and a simple and generic way of parameterizing both the class probabilities and the statistical representation of behaviours within each class is proposed.
Multivariate Small Area Estimation for Health Indicators
In order to improve the overall health condition of a population, accurate estimates of health indicators are required at a fine spatial scale, such as the administrative units of a country or
Statistical models for small area public health intelligence on chronic morbidity
Local indicators of chronic morbidity are needed to conduct needs assessments, plan health care services, allocate funds and monitor health inequalities. Model-based estimation is increasingly
A Bayesian spatial categorical model for prediction to overlapping geographical areas in sample surveys
A Bayesian model using latent processes, underpinned by a non‐stationary spatial basis that combines Moran's I and multiresolution basis functions with a small but representative set of knots is developed that can be highly effective and gives more accurate estimates for areas defined by the target geography than several existing models.
Improving small area estimates of disability: combining the American Community Survey with the Survey of Income and Program Participation
The Survey of Income and Program Participation (SIPP) is designed to make national level estimates of changes in income, eligibility for and participation in transfer programmes, household and family
Hierarchical Bayes small‐area estimation with an unknown link function
This paper relax the assumption of a known link function by not specifying its form and estimating it from the data, and a hierarchical Bayes method of estimating area means is developed using a Markov chain Monte Carlo method for posterior computations.
Multivariate small area estimation for multidimensional well-being indicators
Small area estimation (SAE) plays a crucial role in the social sciences due to the growing need for reliable and accurate estimates for small domains. In the study of wellbeing, for example,
Bivariate Hierarchical Bayesian Model for Combining Summary Measures and their Uncertainties from Multiple Sources
It is often of interest to combine available estimates of a similar quantity from multiple data sources. When the corresponding variances of each estimate are also available, a model should take into
Social Integration of Second Generation Students in the Italian School System
This work proposes a method for the construction of a quantitative indicator capturing social integration of second generation students in the Italian school system according to areas defined by nationality of the students and administrative region in which they attend school.
The Determinants of Women’s Empowerment in Bangladesh: The Role of Partner’s Attitudes and Participation in Microcredit Programmes
ABSTRACT This paper employs data from the Bangladesh Demographic and Health Survey (2004) to explore how women’s empowerment is related to partner’s attitudes, participation in microcredit programmes


Latent variable modeling of disability in people aged 65 or more
This paper aims at classifying, on the basis of their disability profile, the population of elderly and quantifying the number of those with a very low level of functioning in a central region of
Aggregated measures of functional disability in a nationally representative sample of disabled people: analysis of dimensionality according to gender and severity of disability
IADL/ADL items can be combined in a single scale to measure severity of functional disability in females, but not in males, and separate aggregated scores must be considered for each subdomain, basic mobility and self-care, in order to measure the severity of ADL disability.
Bayesian Inferences of Latent Class Models with an Unknown Number of Classes
A Bayesian framework is proposed to perform the joint estimation of the number of latent classes and model parameters and applies the reversible jump Markov chain Monte Carlo to analyze finite mixtures of multivariate multinomial distributions.
Estimating Latent Structure Models with Categorical Variables: One-Step Versus Three-Step Estimators
We study the properties of a three-step approach to estimating the parameters of a latent structure model for categorical data and propose a simple correction for a common source of bias. Such models
Grade of membership and latent structure models with application to disability survey data
This work examines a relatively new latent structure model, the Grade of Membership (GoM) model, integrating the GoM language and ideas with more standard statistical literature on latent variable models, and presents a general class of mixed membership models that unifies the latent structure of theGoM model and two other mixed Membership models that recently appeared in the genetics and the machine learning literatures.
Bayesian Variable Selection for Latent Class Models
A latent class model with class probabilities that depend on subject‐specific covariates is developed and a Bayesian variable selection approach is proposed and implemented to obtain model‐averaged estimates of quantities of interest such as marginal inclusion probabilities of predictors.
Latent class logistic regression: application to marijuana use and attitudes among high school seniors
Summary.  Analysing the use of marijuana is challenging in part because there is no widely accepted single measure of individual use. Similarly, there is no single response variable that effectively
Bayesian model checking for multivariate outcome data
This paper considers small-area estimation with lung cancer mortality data, and discusses the choice of upper-level model for the variation over areas. Inference about the random effects for the