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Identifying hotspots of cardiometabolic outcomes based on a Bayesian approach: The example of Chile

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Identifying hotspots of cardiometabolic outcomes based on a Bayesian approach : The example of Chile. / Aguayo, Gloria A; Schritz, Anna; Ruiz-Castell, Maria; Villarroel, Luis; Valdivia, Gonzalo; Fagherazzi, Guy; Witte, Daniel R; Lawson, Andrew.

I: PLOS ONE, Bind 15, Nr. 6, e0235009, 06.2020.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

Harvard

Aguayo, GA, Schritz, A, Ruiz-Castell, M, Villarroel, L, Valdivia, G, Fagherazzi, G, Witte, DR & Lawson, A 2020, 'Identifying hotspots of cardiometabolic outcomes based on a Bayesian approach: The example of Chile', PLOS ONE, bind 15, nr. 6, e0235009. https://doi.org/10.1371/journal.pone.0235009

APA

Aguayo, G. A., Schritz, A., Ruiz-Castell, M., Villarroel, L., Valdivia, G., Fagherazzi, G., Witte, D. R., & Lawson, A. (2020). Identifying hotspots of cardiometabolic outcomes based on a Bayesian approach: The example of Chile. PLOS ONE, 15(6), [e0235009]. https://doi.org/10.1371/journal.pone.0235009

CBE

Aguayo GA, Schritz A, Ruiz-Castell M, Villarroel L, Valdivia G, Fagherazzi G, Witte DR, Lawson A. 2020. Identifying hotspots of cardiometabolic outcomes based on a Bayesian approach: The example of Chile. PLOS ONE. 15(6):Article e0235009. https://doi.org/10.1371/journal.pone.0235009

MLA

Vancouver

Aguayo GA, Schritz A, Ruiz-Castell M, Villarroel L, Valdivia G, Fagherazzi G o.a. Identifying hotspots of cardiometabolic outcomes based on a Bayesian approach: The example of Chile. PLOS ONE. 2020 jun;15(6). e0235009. https://doi.org/10.1371/journal.pone.0235009

Author

Aguayo, Gloria A ; Schritz, Anna ; Ruiz-Castell, Maria ; Villarroel, Luis ; Valdivia, Gonzalo ; Fagherazzi, Guy ; Witte, Daniel R ; Lawson, Andrew. / Identifying hotspots of cardiometabolic outcomes based on a Bayesian approach : The example of Chile. I: PLOS ONE. 2020 ; Bind 15, Nr. 6.

Bibtex

@article{534a59515f384d158fb8cb80e299d83b,
title = "Identifying hotspots of cardiometabolic outcomes based on a Bayesian approach: The example of Chile",
abstract = "BACKGROUND: There 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.METHODS: Using 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.RESULTS: We 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.CONCLUSIONS: The 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.",
author = "Aguayo, {Gloria A} and Anna Schritz and Maria Ruiz-Castell and Luis Villarroel and Gonzalo Valdivia and Guy Fagherazzi and Witte, {Daniel R} and Andrew Lawson",
year = "2020",
month = jun,
doi = "10.1371/journal.pone.0235009",
language = "English",
volume = "15",
journal = "P L o S One",
issn = "1932-6203",
publisher = "public library of science",
number = "6",

}

RIS

TY - JOUR

T1 - Identifying hotspots of cardiometabolic outcomes based on a Bayesian approach

T2 - The example of Chile

AU - Aguayo, Gloria A

AU - Schritz, Anna

AU - Ruiz-Castell, Maria

AU - Villarroel, Luis

AU - Valdivia, Gonzalo

AU - Fagherazzi, Guy

AU - Witte, Daniel R

AU - Lawson, Andrew

PY - 2020/6

Y1 - 2020/6

N2 - BACKGROUND: There 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.METHODS: Using 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.RESULTS: We 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.CONCLUSIONS: The 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.

AB - BACKGROUND: There 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.METHODS: Using 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.RESULTS: We 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.CONCLUSIONS: The 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.

U2 - 10.1371/journal.pone.0235009

DO - 10.1371/journal.pone.0235009

M3 - Journal article

C2 - 32569307

VL - 15

JO - P L o S One

JF - P L o S One

SN - 1932-6203

IS - 6

M1 - e0235009

ER -