ArticlePloS one2020
Identifying hotspots of cardiometabolic outcomes based on a Bayesian approach: The example of Chile.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Familial Hypercholesterolemia in Latin America: Unmet Needs, Barriers, and Challenges.Journal of lipid and atherosclerosis · 2026Review
- MODELING RACIAL/ETHNIC DIFFERENCES IN COVID-19 INCIDENCE WITH COVARIATES SUBJECT TO NON-RANDOM MISSINGNESS.The annals of applied statistics · 2023Article
- Spatiotemporal high-resolution prediction and mapping: methodology and application to dengue disease.Journal of geographical systems · 2022Article
- Bayesian spatiotemporal forecasting and mapping of COVID-19 risk with application to West Java Province, Indonesia.Journal of regional science · 2021Article
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8 authors.
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Abstract
backgroundThere is a need to identify priority zones for cardiometabolic prevention. Disease mapping in countries with high heterogeneity in the geographic distribution of the population is challenging. Our goal was to map the cardiometabolic health and identify hotspots of disease using data from a national health survey.
methodsUsing Chile as a case study, we applied a Bayesian hierarchical modelling. We performed a cross-sectional analysis of the 2009-2010 Chilean Health Survey. Outcomes were diabetes (all types), obesity, hypertension, and high LDL cholesterol. To estimate prevalence, we used individual and aggregated data by province. We identified hotspots defined as prevalence in provinces significantly greater than the national prevalence. Models were adjusted for age, sex, their interaction, and sampling weight. We imputed missing data. We applied a joint outcome modelling approach to capture the association between the four outcomes.
resultsWe analysed data from 4,780 participants (mean age (SD) 46 (19) years; 60% women). The national prevalence (percentage (95% credible intervals) for diabetes, obesity, hypertension and high LDL cholesterol were 10.9 (4.5, 19.2), 30.0 (17.7, 45.3), 36.4 (16.4, 57.6), and 13.7 (3.4, 32.2) respectively. Prevalence of diabetes was lower in the far south. Prevalence of obesity and hypertension increased from north to far south. Prevalence of high LDL cholesterol was higher in the north and south. A hotspot for diabetes was located in the centre. Hotspots for obesity were mainly situated in the south and far south, for hypertension in the centre, south and far south and for high LDL cholesterol in the far south.
conclusionsThe distribution of cardiometabolic risk factors in Chile has a characteristic pattern with a general trend to a north-south gradient. Our approach is reproducible and demonstrates that the Bayesian approach enables the accurate identification of hotspots and mapping of disease, allowing the identification of areas for cardiometabolic prevention.
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