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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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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. |
format | Online Article Text |
id | pubmed-7084380 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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