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

International Journal of Environmental Research and Public Health
Andrew B LawsonGloria A Aguayo

Abstract

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...Continue Reading

References

Jun 17, 2004·Environmental Health Perspectives·Sylvia RichardsonPaul Elliott
Oct 12, 2005·Statistics in Medicine·Mary M Louie, Eric D Kolaczyk
Jul 25, 2009·Statistics in Medicine·David LunnNicky Best
Dec 3, 2014·Spatial and Spatio-temporal Epidemiology·Cici ChenThomas Lumely
Oct 1, 2015·Statistical Methods in Medical Research·Mehreteab AregayRussell S Kirby
Jul 4, 2017·Statistics in Medicine·K WatjouY Vandendijck
Aug 2, 2017·Spatial and Spatio-temporal Epidemiology·Mehreteab AregayKevin Watjou
Oct 11, 2017·Spatial Statistics·Y VandendijckN Hens

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