Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Jun 22;15(6):e0235009.
doi: 10.1371/journal.pone.0235009. eCollection 2020.

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

Gloria A Aguayo  1 , Anna Schritz  2 , Maria Ruiz-Castell  1 , Luis Villarroel  3 , Gonzalo Valdivia  3 , Guy Fagherazzi  1 , Daniel R Witte  4   5 , Andrew Lawson  6
Affiliations

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

Gloria A Aguayo et al. PLoS One. .

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.

PubMed Disclaimer

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Diabetes posterior mean prevalence and hotspots.
(A1) Posterior mean prevalence, all areas. (A2) Posterior mean prevalence, Central area. Colours are in increasing gradient for prevalence (light yellow 5–7% to dark red 15–17% of diabetes prevalence). (B1) Hotspots, All areas. (B2) Hotspots, Central areas. The hotspot is shown in dark grey (exceedance probability significant for ≥ 11%). North: 1 = Parinacota; 2 = Arica; 3 = Iquique; 4 = Tamarugal; 5 = Tocopilla; 6 = El Loa; 7 = Antofagasta; 8 = Chañaral; 9 = Copiapó; 10 = Huasco; 11 = Elqui; 12 = Limarí; 13 = Choapa; Centre: 14 = San Antonio; 15 = Petorca; 16 = Valparaíso; 17 = Quillota; 18 = Los Andes; 19 = San Felipe; 20 = Chacabuco; 21 = Santiago; 22 = Melipilla; 23 = Talagante; 24 = Maipo; 25 = Cordillera; 26 = Cardenal Caro; 27 = Cachapoal; 28 = Colchagua; 29 = Cauquenes; 30 = Curicó; 31 = Linares; 32 = Talca; 33 = Arauco; 34 = Concepción; 35 = Ñuble; 36 = Biobio; South: 37 = Malleco; 38 = Cautín; 39 = Ranco; 40 = Valdivia; 41 = Chiloé; 42 = Llanquihue; 43 = Palena; 44 = Osorno; Far South: 45 = Coyhaique; 46 = General Carrera; 47 = Aisén; 48 = Capitan Prat; 49 = Antártica Chilena; 50 = Última Esperanza; 51 = Magallanes; 52 = Tierra del Fuego.Republished from http://labgeo.ufro.cl/ under a CC BY license, with permission from C. Albers, original copyright 2020.
Fig 2
Fig 2. Obesity posterior mean prevalence and hotspots.
(A1) Posterior mean prevalence, all areas. (A2) Posterior mean prevalence, Central area. Colours are in increasing gradient for prevalence (light yellow 19–22% to dark red 37–41% of obesity prevalence). (B1) Hotspots, All areas. (B2) Hotspots, Central areas. Hotspots are shown in dark grey (exceedance probability significant for ≥ 30%). North: 1 = Parinacota; 2 = Arica; 3 = Iquique; 4 = Tamarugal; 5 = Tocopilla; 6 = El Loa; 7 = Antofagasta; 8 = Chañaral; 9 = Copiapó; 10 = Huasco; 11 = Elqui; 12 = Limarí; 13 = Choapa; Centre: 14 = San Antonio; 15 = Petorca; 16 = Valparaíso; 17 = Quillota; 18 = Los Andes; 19 = San Felipe; 20 = Chacabuco; 21 = Santiago; 22 = Melipilla; 23 = Talagante; 24 = Maipo; 25 = Cordillera; 26 = Cardenal Caro; 27 = Cachapoal; 28 = Colchagua; 29 = Cauquenes; 30 = Curicó; 31 = Linares; 32 = Talca; 33 = Arauco; 34 = Concepción; 35 = Ñuble; 36 = Biobio; South: 37 = Malleco; 38 = Cautín; 39 = Ranco; 40 = Valdivia; 41 = Chiloé; 42 = Llanquihue; 43 = Palena; 44 = Osorno; Far South: 45 = Coyhaique; 46 = General Carrera; 47 = Aisén; 48 = Capitan Prat; 49 = Antártica Chilena; 50 = Última Esperanza; 51 = Magallanes; 52 = Tierra del Fuego.Republished from http://labgeo.ufro.cl/ under a CC BY license, with permission from C. Albers, original copyright 2020.
Fig 3
Fig 3. Hypertension posterior mean prevalence and hotspots.
(A1) Posterior mean prevalence, all areas. (A2) Posterior mean prevalence, Central area. Colours are in increasing gradient for prevalence (light yellow 17–27% to dark red 47–58% of hypertension prevalence). (B1) Hotspots, All areas. (B2) Hotspots, Central areas. Hotspots are shown in dark grey (exceedance probability significant for ≥ 36%). North: 1 = Parinacota; 2 = Arica; 3 = Iquique; 4 = Tamarugal; 5 = Tocopilla; 6 = El Loa; 7 = Antofagasta; 8 = Chañaral; 9 = Copiapó; 10 = Huasco; 11 = Elqui; 12 = Limarí; 13 = Choapa; Centre: 14 = San Antonio; 15 = Petorca; 16 = Valparaíso; 17 = Quillota; 18 = Los Andes; 19 = San Felipe; 20 = Chacabuco; 21 = Santiago; 22 = Melipilla; 23 = Talagante; 24 = Maipo; 25 = Cordillera; 26 = Cardenal Caro; 27 = Cachapoal; 28 = Colchagua; 29 = Cauquenes; 30 = Curicó; 31 = Linares; 32 = Talca; 33 = Arauco; 34 = Concepción; 35 = Ñuble; 36 = Biobio; South: 37 = Malleco; 38 = Cautín; 39 = Ranco; 40 = Valdivia; 41 = Chiloé; 42 = Llanquihue; 43 = Palena; 44 = Osorno; Far South: 45 = Coyhaique; 46 = General Carrera; 47 = Aisén; 48 = Capitan Prat; 49 = Antártica Chilena; 50 = Última Esperanza; 51 = Magallanes; 52 = Tierra del Fuego. Republished from http://labgeo.ufro.cl/ under a CC BY license, with permission from C. Albers, original copyright 2020.
Fig 4
Fig 4. High LDL cholesterol posterior mean prevalence and hotspots.
(A1) Posterior mean prevalence, all areas. (A2) Posterior mean prevalence, Central area. Colours are in increasing gradient for prevalence (light yellow 5–10% to dark red 20–25% of high LDL cholesterol prevalence). (B1) Hotspots, All areas. (B2) Hotspots, Central areas. Hotspots are shown in dark grey (exceedance probability significant for ≥ 14%). North: 1 = Parinacota; 2 = Arica; 3 = Iquique; 4 = Tamarugal; 5 = Tocopilla; 6 = El Loa; 7 = Antofagasta; 8 = Chañaral; 9 = Copiapó; 10 = Huasco; 11 = Elqui; 12 = Limarí; 13 = Choapa; Centre: 14 = San Antonio; 15 = Petorca; 16 = Valparaíso; 17 = Quillota; 18 = Los Andes; 19 = San Felipe; 20 = Chacabuco; 21 = Santiago; 22 = Melipilla; 23 = Talagante; 24 = Maipo; 25 = Cordillera; 26 = Cardenal Caro; 27 = Cachapoal; 28 = Colchagua; 29 = Cauquenes; 30 = Curicó; 31 = Linares; 32 = Talca; 33 = Arauco; 34 = Concepción; 35 = Ñuble; 36 = Biobio; South: 37 = Malleco; 38 = Cautín; 39 = Ranco; 40 = Valdivia; 41 = Chiloé; 42 = Llanquihue; 43 = Palena; 44 = Osorno; Far South: 45 = Coyhaique; 46 = General Carrera; 47 = Aisén; 48 = Capitan Prat; 49 = Antártica Chilena; 50 = Última Esperanza; 51 = Magallanes; 52 = Tierra del Fuego. Republished from http://labgeo.ufro.cl/ under a CC BY license, with permission from C. Albers, original copyright 2020.
See this image and copyright information in PMC

References

    1. Roth GA, Johnson C, Abajobir A, Abd-Allah F, Abera SF, Abyu G, et al. Global, regional, and national burden of cardiovascular diseases for 10 causes, 1990 to 2015. Journal of the American College of Cardiology. 2017;70(1):1–25. 10.1016/j.jacc.2017.04.052 - DOI - PMC - PubMed
    1. Yeates K, Lohfeld L, Sleeth J, Morales F, Rajkotia Y, Ogedegbe O. A global perspective on cardiovascular disease in vulnerable populations. Canadian Journal of Cardiology. 2015;31(9):1081–93. 10.1016/j.cjca.2015.06.035 - DOI - PMC - PubMed
    1. Popkin BM. Nutrition Transition and the Global Diabetes Epidemic. Current diabetes reports. 2015;15(9):64 10.1007/s11892-015-0631-4 - DOI - PMC - PubMed
    1. Albala C, Vio F, Kain J, Uauy R. Nutrition transition in Chile: determinants and consequences. Public health nutrition. 2002;5(1a):123–8. 10.1079/PHN2001283 - DOI - PubMed
    1. Ministerio de Salud DdPS, Departamento de Epidemiología. Encuesta de Salud Chile 2003 [Chile Health Survey 2003]. 2003.

Substances