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Published in: BMC Public Health 1/2022

Open Access 01-12-2022 | Obesity | Research

Geographical disparities in obesity prevalence: small-area analysis of the Chilean National Health Surveys

Authors: Alejandro Sepúlveda-Peñaloza, Francisco Cumsille, Marcela Garrido, Patricia Matus, Germán Vera-Concha, Cinthya Urquidi

Published in: BMC Public Health | Issue 1/2022

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Abstract

Background

Previous representative health surveys conducted in Chile evidenced a high obesity prevalence rate among adults, especially in female and urban areas. Nevertheless, these have limited utility for targeted interventions and local source allocation for prevention. This study analyzes the increments in obesity prevalence rates in populations ≥15 years of age and the geographic variation at the regional level. We also assessed whether the obesity rates have different patterns on a smaller geographic level than national and regional ones.

Methods

This ecological study analyzed data from two representative national samples of adolescents and adults ≥15 years old, who participated in the last Chilean health surveys, 2009 (n = 5412) and 2016 (n = 6233). Obesity (body mass index≥30 kg/m2) rates were calculated on the national, regional, and Health service (HS) levels, being HS the smallest unit of analysis available. Obesity rates and relative increase to early identify target populations and geographic areas, with 95% confidence intervals (95% CI), were calculated using the sampling design of the national surveys, at the national and regional level, and by gender, age groups, and socioeconomic status. The Fay-Herriot (FH) models, using auxiliary data, were fitted for obesity rate estimates at the HS level.

Results

The relative increase in obesity rate was 37.1% (95%CI 23.3–52.9) at the national level, with a heterogeneous geographic distribution at the regional one. Southern regions had the highest obesity rates in both surveys (Aysén: 35.2, 95%CI 26.9–43.5 in 2009, 44.3 95%CI 37–51.7 in 2016), but higher increases were predominantly in the northern and central areas of the country (relative increase 91.1 95%CI 39.6–110.1 in Valparaiso and 81.6 95%CI 14.4–196.2 in Tarapacá). Obesity rates were higher in females, older age, and lower socioeconomic groups; nevertheless, relative increases were higher in the opposite ones. The FH estimates showed an obesity rates variation at the HS level, where higher rates tend to converge to specific HS areas of each region.

Conclusion

Obesity rates and relative increase are diverse across subnational levels and substantially differ from the national estimates, highlighting a pattern that converges to areas with low-middle income households. Our results emphasize geographical disparities in obesity prevalence among adults and adolescents.
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Literature
1.
go back to reference Worldwide trends in body-mass index. Underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. Lancet Lond Engl. 2016;390(10113):2627–42. Worldwide trends in body-mass index. Underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. Lancet Lond Engl. 2016;390(10113):2627–42.
2.
go back to reference Popkin BM, Corvalan C, Grummer-Strawn LM. Dynamics of the double burden of malnutrition and the changing nutrition reality. Lancet Lond Engl. 2020;395(10217):65–74.CrossRef Popkin BM, Corvalan C, Grummer-Strawn LM. Dynamics of the double burden of malnutrition and the changing nutrition reality. Lancet Lond Engl. 2020;395(10217):65–74.CrossRef
3.
go back to reference Musinguzi G, Ndejjo R, Ssinabulya I, Bastiaens H, van Marwijk H, Wanyenze RK. Cardiovascular risk factor mapping and distribution among adults in Mukono and Buikwe districts in Uganda: small area analysis. BMC Cardiovasc Disord. 2020;20(1):284.CrossRef Musinguzi G, Ndejjo R, Ssinabulya I, Bastiaens H, van Marwijk H, Wanyenze RK. Cardiovascular risk factor mapping and distribution among adults in Mukono and Buikwe districts in Uganda: small area analysis. BMC Cardiovasc Disord. 2020;20(1):284.CrossRef
4.
go back to reference Li Z, Hsiao Y, Godwin J, Martin BD, Wakefield J, Clark SJ, et al. Changes in the spatial distribution of the under-five mortality rate: small-area analysis of 122 DHS surveys in 262 subregions of 35 countries in Africa. PLoS One. 2019;14(1):e0210645.CrossRef Li Z, Hsiao Y, Godwin J, Martin BD, Wakefield J, Clark SJ, et al. Changes in the spatial distribution of the under-five mortality rate: small-area analysis of 122 DHS surveys in 262 subregions of 35 countries in Africa. PLoS One. 2019;14(1):e0210645.CrossRef
5.
go back to reference Cully G, Corcoran P, Leahy D, Griffin E, Dillon C, Cassidy E, et al. Method of self-harm and risk of self-harm repetition: findings from a national self-harm registry. J Affect Disord. 2019;246:843–50.CrossRef Cully G, Corcoran P, Leahy D, Griffin E, Dillon C, Cassidy E, et al. Method of self-harm and risk of self-harm repetition: findings from a national self-harm registry. J Affect Disord. 2019;246:843–50.CrossRef
6.
go back to reference Nguipdop-Djomo P, Rodrigues LC, Abubakar I, Mangtani P. Small-area level socio-economic deprivation and tuberculosis rates in England: an ecological analysis of tuberculosis notifications between 2008 and 2012. PLoS One. 2020;15(10):e0240879.CrossRef Nguipdop-Djomo P, Rodrigues LC, Abubakar I, Mangtani P. Small-area level socio-economic deprivation and tuberculosis rates in England: an ecological analysis of tuberculosis notifications between 2008 and 2012. PLoS One. 2020;15(10):e0240879.CrossRef
7.
go back to reference Rohleder S, Stock C, Bozorgmehr K. Socioeconomic deprivation is inversely associated with measles incidence: a longitudinal small-area analysis, Germany, 2001 to 2017. Eurosurveillance. 2021;26(17):1900755.CrossRef Rohleder S, Stock C, Bozorgmehr K. Socioeconomic deprivation is inversely associated with measles incidence: a longitudinal small-area analysis, Germany, 2001 to 2017. Eurosurveillance. 2021;26(17):1900755.CrossRef
8.
go back to reference Tewara MA, Mbah-Fongkimeh PN, Dayimu A, Kang F, Xue F. Small-area spatial statistical analysis of malaria clusters and hotspots in Cameroon;2000–2015. BMC Infect Dis. 2018;18(1):636.CrossRef Tewara MA, Mbah-Fongkimeh PN, Dayimu A, Kang F, Xue F. Small-area spatial statistical analysis of malaria clusters and hotspots in Cameroon;2000–2015. BMC Infect Dis. 2018;18(1):636.CrossRef
9.
go back to reference Panczak R, Luta X, Maessen M, Stuck AE, Berlin C, Schmidlin K, et al. Regional variation of cost of Care in the Last 12 months of life in Switzerland. Med Care. 2017;55(2):155–63.CrossRef Panczak R, Luta X, Maessen M, Stuck AE, Berlin C, Schmidlin K, et al. Regional variation of cost of Care in the Last 12 months of life in Switzerland. Med Care. 2017;55(2):155–63.CrossRef
10.
go back to reference Wairoto KG, Joseph NK, Macharia PM, Okiro EA. Determinants of subnational disparities in antenatal care utilisation: a spatial analysis of demographic and health survey data in Kenya. BMC Health Serv Res. 2020;20(1):665.CrossRef Wairoto KG, Joseph NK, Macharia PM, Okiro EA. Determinants of subnational disparities in antenatal care utilisation: a spatial analysis of demographic and health survey data in Kenya. BMC Health Serv Res. 2020;20(1):665.CrossRef
11.
go back to reference Le A, Judd SE, Allison DB, Oza-Frank R, Affuso O, Safford MM, et al. The geographic distribution of obesity in the US and the potential regional differences in misreporting of obesity. Obesity. 2014;22(1):300–6.CrossRef Le A, Judd SE, Allison DB, Oza-Frank R, Affuso O, Safford MM, et al. The geographic distribution of obesity in the US and the potential regional differences in misreporting of obesity. Obesity. 2014;22(1):300–6.CrossRef
12.
go back to reference Lahti-Koski M, Taskinen O, Similä M, Männistö S, Laatikainen T, Knekt P, et al. Mapping geographical variation in obesity in Finland. Eur J Pub Health. 2008;18(6):637–43.CrossRef Lahti-Koski M, Taskinen O, Similä M, Männistö S, Laatikainen T, Knekt P, et al. Mapping geographical variation in obesity in Finland. Eur J Pub Health. 2008;18(6):637–43.CrossRef
13.
go back to reference Zhang X, Zhang M, Zhao Z, Huang Z, Deng Q, Li Y, et al. Geographic variation in prevalence of adult obesity in China: results from the 2013-2014 National Chronic Disease and risk factor surveillance. Ann Intern Med. 2020;172(4):291–3.CrossRef Zhang X, Zhang M, Zhao Z, Huang Z, Deng Q, Li Y, et al. Geographic variation in prevalence of adult obesity in China: results from the 2013-2014 National Chronic Disease and risk factor surveillance. Ann Intern Med. 2020;172(4):291–3.CrossRef
14.
go back to reference Qiu G, Liu X, Amiranti AY, Yasini M, Wu T, Amer S, et al. Geographic clustering and region-specific determinants of obesity in the Netherlands. Geospatial. Health. 2020;15(1). Qiu G, Liu X, Amiranti AY, Yasini M, Wu T, Amer S, et al. Geographic clustering and region-specific determinants of obesity in the Netherlands. Geospatial. Health. 2020;15(1).
15.
go back to reference Keramat SA, Alam K, Al-Hanawi MK, Gow J, Biddle SJH, Hashmi R. Trends in the prevalence of adult overweight and obesity in Australia, and its association with geographic remoteness. Sci Rep. 2021;11. Keramat SA, Alam K, Al-Hanawi MK, Gow J, Biddle SJH, Hashmi R. Trends in the prevalence of adult overweight and obesity in Australia, and its association with geographic remoteness. Sci Rep. 2021;11.
16.
go back to reference Mills CW, Johnson G, Huang TTK, Balk D, Wyka K. Use of small-area estimates to Describe County-level geographic variation in prevalence of extreme obesity among US adults. JAMA Netw Open. 2020;3(5):e204289–9.CrossRef Mills CW, Johnson G, Huang TTK, Balk D, Wyka K. Use of small-area estimates to Describe County-level geographic variation in prevalence of extreme obesity among US adults. JAMA Netw Open. 2020;3(5):e204289–9.CrossRef
17.
go back to reference Erdei G, Bakacs M, Illés É, Nagy B, Kaposvári C, Mák E, et al. Substantial variation across geographic regions in the obesity prevalence among 6-8 years old Hungarian children (COSI Hungary 2016). BMC Public Health. 2018;18(1):611.CrossRef Erdei G, Bakacs M, Illés É, Nagy B, Kaposvári C, Mák E, et al. Substantial variation across geographic regions in the obesity prevalence among 6-8 years old Hungarian children (COSI Hungary 2016). BMC Public Health. 2018;18(1):611.CrossRef
18.
go back to reference Zgodic A, Eberth JM, Breneman C, Wende ME, Kaczynski AT, Liese AD, et al. Estimates of childhood overweight and obesity at the region, state, and county levels: a multilevel small area estimation approach. Am J Epidemiol. 2021; kwab176. Zgodic A, Eberth JM, Breneman C, Wende ME, Kaczynski AT, Liese AD, et al. Estimates of childhood overweight and obesity at the region, state, and county levels: a multilevel small area estimation approach. Am J Epidemiol. 2021; kwab176.
19.
go back to reference Zhao Y-Q, Norton D, Hanrahan L. Small area estimation and childhood obesity surveillance using electronic health records. PLoS One. 2021;16(2):e0247476.CrossRef Zhao Y-Q, Norton D, Hanrahan L. Small area estimation and childhood obesity surveillance using electronic health records. PLoS One. 2021;16(2):e0247476.CrossRef
20.
go back to reference Osayomi T, Orhiere MA. Small-area variations in overweight and obesity in an urban area of Nigeria: the role of fast food outlets. Bull Geogr Socio-Econ Ser. 2017;38(38):93–108. Osayomi T, Orhiere MA. Small-area variations in overweight and obesity in an urban area of Nigeria: the role of fast food outlets. Bull Geogr Socio-Econ Ser. 2017;38(38):93–108.
21.
go back to reference Shrestha N, Mishra SR, Ghimire S, Gyawali B, Pradhan PMS, Schwarz D. Application of single-level and multi-level modeling approach to examine geographic and socioeconomic variation in underweight, overweight and obesity in Nepal: findings from NDHS 2016. Sci Rep. 2020;10(1):2406.CrossRef Shrestha N, Mishra SR, Ghimire S, Gyawali B, Pradhan PMS, Schwarz D. Application of single-level and multi-level modeling approach to examine geographic and socioeconomic variation in underweight, overweight and obesity in Nepal: findings from NDHS 2016. Sci Rep. 2020;10(1):2406.CrossRef
22.
go back to reference Albala C, Vio F, Kain J, Uauy R. Nutrition transition in Latin America: the case of Chile. Nutr Rev. 2001;59(6):170–6.CrossRef Albala C, Vio F, Kain J, Uauy R. Nutrition transition in Latin America: the case of Chile. Nutr Rev. 2001;59(6):170–6.CrossRef
23.
go back to reference Hernández Bonivento J, Ramírez Figueroa H, Parrao Cartagena A, Salazar Gómez L, González Castro J. Godoy Berthet C. Índice de Desarrollo Comunal. Chile 2020. Universidad Autónoma de Chile. 2020; Available from: https://repositorio.uautonoma.cl. (Cited 2021 Dec 28). Hernández Bonivento J, Ramírez Figueroa H, Parrao Cartagena A, Salazar Gómez L, González Castro J. Godoy Berthet C. Índice de Desarrollo Comunal. Chile 2020. Universidad Autónoma de Chile. 2020; Available from: https://​repositorio.​uautonoma.​cl. (Cited 2021 Dec 28).
24.
go back to reference Fay RE, Herriot RA. Estimates of income for small places: an application of James-stein procedures to census data. J Am Stat Assoc. 1979;74(366a):269–77.CrossRef Fay RE, Herriot RA. Estimates of income for small places: an application of James-stein procedures to census data. J Am Stat Assoc. 1979;74(366a):269–77.CrossRef
26.
27.
go back to reference Asosega KA, Adebanji AO, Abdul IW. Spatial analysis of the prevalence of obesity and overweight among women in Ghana. BMJ Open. 2021;11(1):e041659.CrossRef Asosega KA, Adebanji AO, Abdul IW. Spatial analysis of the prevalence of obesity and overweight among women in Ghana. BMJ Open. 2021;11(1):e041659.CrossRef
28.
go back to reference Pan X-F, Wang L, Pan A. Epidemiology and determinants of obesity in China. Lancet Diabetes Endocrinol. 2021;9(6):373–92.CrossRef Pan X-F, Wang L, Pan A. Epidemiology and determinants of obesity in China. Lancet Diabetes Endocrinol. 2021;9(6):373–92.CrossRef
29.
go back to reference Kamel Boulos MN, Koh K. Smart city lifestyle sensing, big data, geo-analytics and intelligence for smarter public health decision-making in overweight, obesity and type 2 diabetes prevention: the research we should be doing. Int J Health Geogr. 2021;20(1):12.CrossRef Kamel Boulos MN, Koh K. Smart city lifestyle sensing, big data, geo-analytics and intelligence for smarter public health decision-making in overweight, obesity and type 2 diabetes prevention: the research we should be doing. Int J Health Geogr. 2021;20(1):12.CrossRef
Metadata
Title
Geographical disparities in obesity prevalence: small-area analysis of the Chilean National Health Surveys
Authors
Alejandro Sepúlveda-Peñaloza
Francisco Cumsille
Marcela Garrido
Patricia Matus
Germán Vera-Concha
Cinthya Urquidi
Publication date
01-12-2022
Publisher
BioMed Central
Keywords
Obesity
Obesity
Published in
BMC Public Health / Issue 1/2022
Electronic ISSN: 1471-2458
DOI
https://doi.org/10.1186/s12889-022-13841-2

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