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Article

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

1
Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29466, USA
2
Luxembourg Institute of Health, 1A-B, rue Thomas Edison, Strassen, L-1445 Luxembourg City, Luxembourg
3
Public Health Department, School of Medicine, Pontificia Universidad Católica de Chile, Diagonal Paraguay 362, Santiago 8330077, Chile
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(5), 1682; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17051682
Received: 17 January 2020 / Revised: 25 February 2020 / Accepted: 3 March 2020 / Published: 5 March 2020
(This article belongs to the Special Issue GIS and Spatial Modelling for Environmental Epidemiology)
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. View Full-Text
Keywords: Bayesian modeling; multivariate; multi-scale; spatial correlation; sample weights Bayesian modeling; multivariate; multi-scale; spatial correlation; sample weights
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MDPI and ACS Style

Lawson, A.; Schritz, A.; Villarroel, L.; Aguayo, G.A. Multi-Scale Multivariate Models for Small Area Health Survey Data: A Chilean Example. Int. J. Environ. Res. Public Health 2020, 17, 1682. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17051682

AMA Style

Lawson A, Schritz A, Villarroel L, Aguayo GA. Multi-Scale Multivariate Models for Small Area Health Survey Data: A Chilean Example. International Journal of Environmental Research and Public Health. 2020; 17(5):1682. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17051682

Chicago/Turabian Style

Lawson, Andrew, Anna Schritz, Luis Villarroel, and Gloria A. Aguayo 2020. "Multi-Scale Multivariate Models for Small Area Health Survey Data: A Chilean Example" International Journal of Environmental Research and Public Health 17, no. 5: 1682. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17051682

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