Latent variable modeling of disability in people aged 65 or more

Montanari, Giorgio Eduardo; Ranalli, Maria Giovanna

doi:10.1007/s10260-010-0148-6

Cited by 9 publications

(17 citation statements)

References 21 publications

(17 reference statements)

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“…Item fit analysis has been conducted on the data set at hand here and results are consistent with those of Montanari et al . (). Details are not reported for brevity but are available from the authors.…”

Section: The Datamentioning

confidence: 97%

“…Montanari et al . () considered the same set of items to provide a classification of the elderly. In addition, they used tools that were developed for the validation of Rasch models and performed item fit analysis to assess which items provide a unidimensional latent construct.…”

Section: The Datamentioning

confidence: 99%

“…using model (2) for which

log \{\frac{P false(Q_{i j} = c false)}{P false(Q_{i j} = 1 false)}\} = {italicα}_{0 c} c = 2, \dots, C .

This reproduces what would be done within a three‐step approach and what has been done by Montanari et al . () to obtain population estimates.…”

Section: The Latent Class Small Area Modelmentioning

confidence: 99%

“…A person is considered disabled if, excluding temporary limitations, he or she expresses some kind of confinement (first item) or he or she expresses the maximum degree of difficulty in at least one of the others items. Montanari et al (2011) considered the same set of items to provide a classification of the elderly. In addition, they used tools that were developed for the validation of Rasch models and performed item fit analysis to assess which items provide a unidimensional latent construct.…”

Section: The Datamentioning

confidence: 99%

“…Montanari et al . () used this approach on data from the HCAMS to classify people and could identify one class as composed of those in a condition of severe disability. In this paper we follow this approach, but we extend it to tackle the following issues.…”

Section: Introductionmentioning

confidence: 99%

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A Hierarchical Latent Class Model for Predicting Disability Small Area Counts from Survey Data

Fabrizi

Montanari

Ranalli

2015

Journal of the Royal Statistical Society Series A: Statistics in Society

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This article considers the estimation of the number of severely disabled people using data from the Italian survey on Health Conditions and Appeal to Medicare. Disability is indirectly measured using a set of categorical items, which survey 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, however, is designed to provide reliable estimates at the level of Administrative Regions (NUTS2 level), while local authorities are interested in quantifying the amount of population that belongs to each latent class at a sub-regional level. Therefore, small area estimation techniques should be used. The challenge of the present application 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 using penalized splines. DeimmlerReisch bases are shown to improve speed and mixing of MCMC chains used to simulate posteriors.

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Section: The Datamentioning

confidence: 97%

Section: The Datamentioning

confidence: 99%

“…using model (2) for which

log \{\frac{P false(Q_{i j} = c false)}{P false(Q_{i j} = 1 false)}\} = {italicα}_{0 c} c = 2, \dots, C .

This reproduces what would be done within a three‐step approach and what has been done by Montanari et al . () to obtain population estimates.…”

Section: The Latent Class Small Area Modelmentioning

confidence: 99%

Section: The Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Hierarchical Latent Class Model for Predicting Disability Small Area Counts from Survey Data

Fabrizi

Montanari

Ranalli

2015

Journal of the Royal Statistical Society Series A: Statistics in Society

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Cross-national health comparisons using the Rasch model: findings from the 2012 US Health and Retirement Study and the 2012 Mexican Health and Aging Study

et al. 2018

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Measurement of the severity of disability in community-dwelling adults and older adults: interval-level measures for accurate comparisons in large survey data sets

Buz

Rodríguez

2016

BMJ Open

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ObjectivesTo (1) create a single metric of disability using Rasch modelling to be used for comparing disability severity levels across groups and countries, (2) test whether the interval-level measures were invariant across countries, sociodemographic and health variables and (3) examine the gains in precision using interval-level measures relative to ordinal scores when discriminating between groups known to differ in disability.DesignCross-sectional, population-based study.Setting/participantsData were drawn from the Survey of Health, Ageing and Retirement in Europe (SHARE), including comparable data across 16 countries and involving 58 489 community-dwelling adults aged 50+.Main outcome measuresA single metric of disability composed of self-care and instrumental activities of daily living (IADLs) and functional limitations. We examined the construct validity through the fit to the Rasch model and the know-groups method. Reliability was examined using person separation reliability.ResultsThe single metric fulfilled the requirements of a strong hierarchical scale; was able to separate persons with different levels of disability; demonstrated invariance of the item hierarchy across countries; and was unbiased by age, gender and different health conditions. However, we found a blurred hierarchy of ADL and IADL tasks. Rasch-based measures yielded gains in relative precision (11–116%) in discriminating between groups with different medical conditions.ConclusionsEqual-interval measures, with person-invariance and item-invariance properties, provide epidemiologists and researchers with the opportunity to gain better insight into the hierarchical structure of functional disability, and yield more reliable and accurate estimates of disability across groups and countries. Interval-level measures of disability allow parametric statistical analysis to confidently examine the relationship between disability and continuous measures so frequent in health sciences (eg, cholesterol, blood pressure, C reactive protein).

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Latent variable modeling of disability in people aged 65 or more

Abstract: Categorical variables, Latent variables model, Rasch model, Partial credit model, Activities of daily living,

Cited by 9 publications

References 21 publications

A Hierarchical Latent Class Model for Predicting Disability Small Area Counts from Survey Data

A Hierarchical Latent Class Model for Predicting Disability Small Area Counts from Survey Data

Cross-national health comparisons using the Rasch model: findings from the 2012 US Health and Retirement Study and the 2012 Mexican Health and Aging Study

Measurement of the severity of disability in community-dwelling adults and older adults: interval-level measures for accurate comparisons in large survey data sets

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