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Multi-Scale Multivariate Models for Small Area Health Survey Data: A Chilean Example

Background: We propose a general approach to the analysis of multivariate health outcome data where geo-coding at different spatial scales is available. We propose multiscale joint models which address the links between individual outcomes and also allow for correlation between areas. The models are...

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Autores principales: Lawson, Andrew, Schritz, Anna, Villarroel, Luis, Aguayo, Gloria A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7084380/
https://www.ncbi.nlm.nih.gov/pubmed/32150815
http://dx.doi.org/10.3390/ijerph17051682
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author Lawson, Andrew
Schritz, Anna
Villarroel, Luis
Aguayo, Gloria A.
author_facet Lawson, Andrew
Schritz, Anna
Villarroel, Luis
Aguayo, Gloria A.
author_sort Lawson, Andrew
collection PubMed
description Background: We propose a general approach to the analysis of multivariate health outcome data where geo-coding at different spatial scales is available. We propose multiscale joint models which address the links between individual outcomes and also allow for correlation between areas. The models are highly novel in that they exploit survey data to provide multiscale estimates of the prevalences in small areas for a range of disease outcomes. Results The models incorporate both disease specific, and common disease spatially structured components. The multiple scales envisaged is where individual survey data is used to model regional prevalences or risks at an aggregate scale. This approach involves the use of survey weights as predictors within our Bayesian multivariate models. Missingness has to be addressed within these models and we use predictive inference which exploits the correlation between diseases to provide estimates of missing prevalances. The Case study we examine is from the National Health Survey of Chile where geocoding to Province level is available. In that survey, diabetes, Hypertension, obesity and elevated low-density cholesterol (LDL) are available but differential missingness requires that aggregation of estimates and also the assumption of smoothed sampling weights at the aggregate level. Conclusions: The methodology proposed is highly novel and flexibly handles multiple disease outcomes at individual and aggregated levels (i.e., multiscale joint models). The missingness mechanism adopted provides realistic estimates for inclusion in the aggregate model at Provincia level. The spatial structure of four diseases within Provincias has marked spatial differentiation, with diabetes and hypertension strongly clustered in central Provincias and obesity and LDL more clustered in the southern areas.
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spelling pubmed-70843802020-03-24 Multi-Scale Multivariate Models for Small Area Health Survey Data: A Chilean Example Lawson, Andrew Schritz, Anna Villarroel, Luis Aguayo, Gloria A. Int J Environ Res Public Health Article Background: We propose a general approach to the analysis of multivariate health outcome data where geo-coding at different spatial scales is available. We propose multiscale joint models which address the links between individual outcomes and also allow for correlation between areas. The models are highly novel in that they exploit survey data to provide multiscale estimates of the prevalences in small areas for a range of disease outcomes. Results The models incorporate both disease specific, and common disease spatially structured components. The multiple scales envisaged is where individual survey data is used to model regional prevalences or risks at an aggregate scale. This approach involves the use of survey weights as predictors within our Bayesian multivariate models. Missingness has to be addressed within these models and we use predictive inference which exploits the correlation between diseases to provide estimates of missing prevalances. The Case study we examine is from the National Health Survey of Chile where geocoding to Province level is available. In that survey, diabetes, Hypertension, obesity and elevated low-density cholesterol (LDL) are available but differential missingness requires that aggregation of estimates and also the assumption of smoothed sampling weights at the aggregate level. Conclusions: The methodology proposed is highly novel and flexibly handles multiple disease outcomes at individual and aggregated levels (i.e., multiscale joint models). The missingness mechanism adopted provides realistic estimates for inclusion in the aggregate model at Provincia level. The spatial structure of four diseases within Provincias has marked spatial differentiation, with diabetes and hypertension strongly clustered in central Provincias and obesity and LDL more clustered in the southern areas. MDPI 2020-03-05 2020-03 /pmc/articles/PMC7084380/ /pubmed/32150815 http://dx.doi.org/10.3390/ijerph17051682 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lawson, Andrew
Schritz, Anna
Villarroel, Luis
Aguayo, Gloria A.
Multi-Scale Multivariate Models for Small Area Health Survey Data: A Chilean Example
title Multi-Scale Multivariate Models for Small Area Health Survey Data: A Chilean Example
title_full Multi-Scale Multivariate Models for Small Area Health Survey Data: A Chilean Example
title_fullStr Multi-Scale Multivariate Models for Small Area Health Survey Data: A Chilean Example
title_full_unstemmed Multi-Scale Multivariate Models for Small Area Health Survey Data: A Chilean Example
title_short Multi-Scale Multivariate Models for Small Area Health Survey Data: A Chilean Example
title_sort multi-scale multivariate models for small area health survey data: a chilean example
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7084380/
https://www.ncbi.nlm.nih.gov/pubmed/32150815
http://dx.doi.org/10.3390/ijerph17051682
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