https://doi.org/10.4081/gh.2023.1212
A geospatial analysis of cardiometabolic diseases and their risk factors considering environmental features in a midsized city in Argentina
Submitted: 11 May 2023
Accepted: 19 September 2023
Published: 23 October 2023
Accepted: 19 September 2023
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
New approaches to the study of cardiometabolic disease (CMD) distribution include analysis of built environment (BE), with spatial tools as suitable instruments. We aimed to characterize the spatial dissemination of CMD and the associated risk factors considering the BE for people attending the Non-Invasive Cardiology Service of Hospital Nacional de Clinicas in Córdoba City, Argentina during the period 2015-2020. We carried out an observational, descriptive, cross-sectional study performing non-probabilistic convenience sampling. The final sample included 345 people of both sexes older than 35 years. The CMD data were collected from medical records and validated techniques and BE information was extracted from Landsat-8 satellite products. A geographic information system (GIS) was constructed to assess the distribution of CMD and its risk factors in the area. Out of the people sampled, 41% showed the full metabolic syndrome and 22.6% only type-2 diabetes mellitus (DM2), a cluster of which was evidenced in north-western Córdoba. The risk of DM2 showed an association with high values of the normalized difference vegetation index (NDVI) (OR= 0.81; 95% CI: - 0.30 to 1.66; p=0.05) and low normalized difference built index (NDBI) values that reduced the probability of occurrence of DM2 (OR= -1.39; 95% CI: -2.62 to -0.17; p=0.03). Considering that the results were found to be linked to the environmental indexes, the study of BE should include investigation of physical space as a fundamental part of the context in which people develop medically within society. The novel collection of satellite-generated information on BE proved efficient.
Alvarez Di Fino EM, 2020. Aplicación de tecnologías geoespaciales para el análisis de la seguridad alimentaria y nutricional en la ciudad de Córdoba, Argentina (Master thesis). Instituto de Altos Estudios Espaciales Mario Gulich, Argentina.
Alvarez Di Fino EM, Defagó MD, Scavuzzo CM, 2019. Spatial analysis applied to nutritional epidemiology. (Conference presentation abstract at the XVIII Workshop on Information Processing and Control (RPIC) IEEE held in 2019. Doi.org/10.1109/RPIC.2019.8882136
DOI: https://doi.org/10.1109/RPIC.2019.8882136
Arrieta EM, Fischer CG, Aguiar S, Geri M, Fernández RJ, Coquet JB, Scavuzzo CM, Rieznik A, León A, González AD, Jobbágy EG. (2022). The health, environmental, and economic dimensions of future dietary transitions in Argentina. Sustain Sci 1-17.
DOI: https://doi.org/10.1007/s11625-021-01087-7
Barrera R, 2017. Cuestionario Internacional de actividad física (IPAQ). Rev Enferm Trabajo 7:49-54.
Benziger CP, Zavala Loayza JA, Bernabe Ortiz A, Gilman RH, Checkley W, Smeeth L, Malaga G, Miranda JJ, CRONICAS Cohort Study group, 2018. Low prevalence of ideal cardiovascular health in Peru. Heart (British Cardiac Society) 104:1251–1256.
DOI: https://doi.org/10.1136/heartjnl-2017-312255
Booth KM, Pinkston MM, Poston WSC, 2005. Obesity and the built environment. J Am Diet Assoc 105:10-117.
DOI: https://doi.org/10.1016/j.jada.2005.02.045
Chandrabose M, Rachele JN, Gunn L, Kavanagh A, Owen N, Turrell G, Giles-Corti B, Sugiyama T, 2019. Built environment and cardio‐metabolic health: systematic review and meta‐analysis of longitudinal studies. Obesity Rev 20;41-54.
DOI: https://doi.org/10.1111/obr.12759
Chuvieco E, Cocero D, Riano D, Martin P, Martınez-Vega J, De La Riva J, Pérez F, 2004. Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating. Remote Sensing Environ 92:322-331.
DOI: https://doi.org/10.1016/j.rse.2004.01.019
Curtis AJ, Lee WA, 2010. Spatial patterns of diabetes related health problems for vulnerable populations in Los Angeles. Int J Health Geograph 9:43.
DOI: https://doi.org/10.1186/1476-072X-9-43
Cravey AJ, Washburn SA, Gesler WM, Arcury TA, Skelly AH, 2001. Developing socio-spatial knowledge networks: A qualitative methodology for chronic disease prevention. Soc Sci Med 52:763-1775.
DOI: https://doi.org/10.1016/S0277-9536(00)00295-1
Dadvand P, Bartoll X, Basagaña X, Dalmau-Bueno A, Martinez D, Ambros A, Cirach M, Triguero-Mas M, Gascon M, Borrell C, Nieuwenhuijsen MJ, 2016. Green spaces and General Health: Roles of mental health status, social support, and physical activity. Environ Int 91:161–167.
DOI: https://doi.org/10.1016/j.envint.2016.02.029
Defagó MD, Perovic NR, Aguinaldo CA, Actis AB, 2009. Desarrollo de un programa informático para estudios nutricionales. Revista Panamericana de Salud Pública, 25:362-366.
DOI: https://doi.org/10.1590/S1020-49892009000400011
Den Braver NR, Lakerveld J, Rutters F, Schoonmade LJ, Brug J, Beulens JWJ, 2018. Built environmental characteristics and diabetes: a systematic review and meta-analysis. BMC Med 16:1-26.
DOI: https://doi.org/10.1186/s12916-017-0997-z
Donovan GH, Butry DT, Michael YL, Prestemon JP, Liebhold AM, Gatziolis D, Mao MY, 2013. The relationship between trees and human health: evidence from the spread of the emerald ash borer. Am J Prev Med 44:139–145.
DOI: https://doi.org/10.1016/j.amepre.2012.09.066
Drewnowski A, Buszkiewicz J, Aggarwal A, Rose C, Gupta S, Bradshaw A, 2019. Obesity and the Built Environment: A Reappraisal. Obesity 28:22-30.
DOI: https://doi.org/10.1002/oby.22672
Emadi M, Delavari S, Bayati M, 2021. Global socioeconomic inequality in the burden of communicable and non-communicable diseases and injuries: an analysis on global burden of disease study 2019. BMC Public Health 21:1-13.
DOI: https://doi.org/10.1186/s12889-021-11793-7
Earth Observing System, 2022. Índice de Agua De Diferencia Normalizada. /https://eos.com/es/make-an-analysis/ndwi/
Ferreira SRG, Chiavegatto Filho ADP, Lebrão ML, Duarte YADO, Laurenti R, 2018. Cardiometabolic diseases. Revista Brasileira de Epidemiologia 21:e180008.
DOI: https://doi.org/10.1590/1980-549720180008.supl.2
Flack JM, Adekola B, 2020. Blood pressure and the new ACC/AHA hypertension guidelines. Trends Cardiovasc Med 30:160-164.
DOI: https://doi.org/10.1016/j.tcm.2019.05.003
Gassasse Z, Smith D, Finer S, Gallo V, 2017. Association between urbanisation and type 2 diabetes: an ecological study. BMJ Global Health 2:e000473.
DOI: https://doi.org/10.1136/bmjgh-2017-000473
Gesler WM, Hayes M, Arcury TA, Skelly AH, Nash S, Soward AC, 2004. Use of mapping technology in health intervention research. Nursing Outlook 52:142-146.
DOI: https://doi.org/10.1016/j.outlook.2004.01.009
Gómez Vicario I, 2014. Análisis de sellado de suelos en varios municipios de la Comunidad de Madrid: comparación de la pérdida de suelo entre 1987 y 2011. (Tesis de Maestría). Universidad Complutense de Madrid, España.
Goossens GH, 2017. The metabolic phenotype in obesity: fat mass, body fat distribution, and adipose tissue function. Obesity Facts 10:207-215.
DOI: https://doi.org/10.1159/000471488
Hipp JA, Chalise N, 2015. Peer reviewed: spatial analysis and correlates of county-level diabetes prevalence, 2009–2010. Preventing Chronic Dis 12:E08.
DOI: https://doi.org/10.5888/pcd12.140404
Hirschler V, Oestreicher K, Maccallini G, Aranda C, 2010. Relationship between obesity and metabolic syndrome among Argentinean elementary school children. Clin Biochem 43:435-441.
DOI: https://doi.org/10.1016/j.clinbiochem.2009.11.003
Instituto Nacional de Estadísticas y Censos de la República Argentina (INDEC), 2010. Censo Nacional de Población, Hogares y Viviendas 2010. https://www.indec.gob.ar/ftp/cuadros/poblacion/censo2010_tomo1.pdf
Kovalskys I, Rigotti A, Koletzko B, Fisberg M, Gómez G, Herrera-Cuenca M, Cortés Sanabria LY, Yépez García MC, Pareja RG, Zimberg IZ, Del Arco A, Zonis L, Previdelli AN, Guajardo V, Moreno LA, Fisberg R; ELANS Study Group, 2019. Latin American consumption of major food groups: Results from the ELANS study. 2019. PLoS ONE 14:e0225101.
DOI: https://doi.org/10.1371/journal.pone.0225101
Kuldorff M, Heffernan R, Hartman J, Assuncao RM, Mostashari F, 2005. A space-time permutation scan statistic for the early detection of disease outbreaks. PloS Med 2:e59.
DOI: https://doi.org/10.1371/journal.pmed.0020059
Lago-Peñas S, Rivera B, Cantarero D, Casal B, Pascual M, Blázquez-Fernández C, Reyes F, 2020. The impact of socioeconomic position on non-communicable diseases: what do we know about it? Perspect Public Health 141:158–176.
DOI: https://doi.org/10.1177/1757913920914952
Liu W, Lin R, Liu A, Du L, Chen Q, 2010. Prevalence and association between obesity and metabolic syndrome among Chinese elementary school children: a school-based survey. BMC Public Health 10:1-7.
DOI: https://doi.org/10.1186/1471-2458-10-780
Malambo P, Kengne AP, De Villiers A, Lambert EV, Puoane T, 2016. Built environment, selected risk factors and major cardiovascular disease outcomes: a systematic review. PloS One 11:e0166846.
DOI: https://doi.org/10.1371/journal.pone.0166846
Matozinhos FP, Felisbino-Mendes MS, Gomes CS, Jansen AK, Machado IE, Lana FCF, Carvalho-Malta D, Velaquez-Melendez G, 2017. Cardiovascular health in Brazilian state capitals. Revista Latino-Americana de Enfermagem 25:e2843.
DOI: https://doi.org/10.1590/1518-8345.1327.2843
Mena C, Sepúlveda C, Fuentes E, Ormazábal Y, Palomo I, 2018. Spatial analysis for the epidemiological study of cardiovascular diseases: A systematic literature search. Geospatial health, 13:587.
DOI: https://doi.org/10.4081/gh.2018.587
MohammadEbrahimi S, Kiani B, Rahmatinejad Z, Baral S, Hashtarkhani S, Dehghan-Tezerjani M, Zare E, Arian M, Kiani F, Gouya MM, Dadras MN, Karamouzian M, 2022. Geospatial epidemiology of hospitalized patients with a positive influenza assay: A nationwide study in Iran, 2016-2018. PLoS One 17:e0278900.
DOI: https://doi.org/10.1371/journal.pone.0278900
Müller-Riemenschneider F, Petrunoff N, Yao J, Ng A, Sia A, Ramiah A, Wong M, Han J, Tai BC, Uijtdewilligen L, 2020. Effectiveness of prescribing physical activity in parks to improve health and wellbeing - the park prescription randomized controlled trial. Int J Behav Nutr Phys Act 2020;17:42.
DOI: https://doi.org/10.1186/s12966-020-00941-8
Oberto MG, Perovic NR, Celi A, Marchiori GN, Flores D, Carrizo LN, Defagó MD, 2020. Intervención extensionista en salud cardiovascular en un hospital escuela. Available from: https://ri.conicet.gov.ar/handle/11336/144682
Organización Panamericana de la Salud, 2021. Enfermedades no transmisibles. https://www.paho.org/es/temas/enfermedades-no-transmisibles
Paquet C, Coffee NT, Haren MT, Howard NJ, Adams RJ, Taylor AW, Daniel M, 2014. Food environment, walkability, and public open spaces are associated with incident development of cardio-metabolic risk factors in a biomedical cohort. Health Place 28:173-176.
DOI: https://doi.org/10.1016/j.healthplace.2014.05.001
Pereira G, Foster S, Martin K, Christian H, Boruff BJ, Knuiman M, Giles-Corti B, 2012. The association between neighborhood greenness and cardiovascular disease: an observational study. BMC Public Health 12:466.
DOI: https://doi.org/10.1186/1471-2458-12-466
Perovic NR, Defago MD, Aguinaldo A, Joekes S, Actis AB, 2015. Validity and reproducibility of a food frequency questionnaire to assess lipid and phytochemical intake. Revista de Facultad de Ciencias Medicas Universidad Nacional de Córdoba 72:69-77.
Picone N, 2017. Comparación de imágenes satelitales Sentinel 2 y Landsat 8 en el estudio de áreas urbanas. In Congreso Nacional de Geografía de Universidades Nacionales, At Resistencia, Chaco, Argentina.
Popkin BM, Reardon T, 2018. Obesity and the food system transformation in Latin America. Obesity Reviews 19:1028-1064.
DOI: https://doi.org/10.1111/obr.12694
Popkin BM, Adair LS, Ng SW, 2012. Global nutrition transition and the pandemic of obesity in developing countries. Nutrition Rev 70:3-21.
DOI: https://doi.org/10.1111/j.1753-4887.2011.00456.x
Pou SA, Tumas N, Aballay LR, 2020. Nutrition Transition and Obesity Trends in Argentina Within the Latin American Context. Obesity Diabetes 9-19.
DOI: https://doi.org/10.1007/978-3-030-53370-0_2
Quantum, 2015. Qgis development team–qgis geographic information system. Open source geospatial foundation project. Available from: https://qgis.org/es/site/
Ramírez R, Agredo RA, 2012. El sedentarismo es un factor predictor de hipertrigliceridemia, obesidad central y sobrepeso. Revista Colombiana de Cardiología 19:75-79.
DOI: https://doi.org/10.1016/S0120-5633(12)70109-2
Ross R, Neeland IJ, Yamashita S, Shai I, Seidell J, Magni P, Santos RD, Arsenault B, Cuevas A, Hu FB, Griffin BA, Zambon A, Barter P, Fruchart JC, Eckel RH, Matsuzawa Y, Després, JP, 2020. La circunferencia de la cintura como un signo vital en la práctica clínica: una declaración de consenso del Grupo de Trabajo sobre Obesidad Visceral de la IAS y el ICCR. Reseñas de la naturaleza. Endocrinología 16:177–189.
DOI: https://doi.org/10.1038/s41574-019-0310-7
Scarlatta VR, Defagó MD, 2020. Urbanización y entornos alimentarios relacionados a la salud cardiovascular en la ciudad de La Calera, Córdoba, Argentina, 2016-2017. Revista de Salud Pública 24:18-30.
DOI: https://doi.org/10.31052/1853.1180.v24.n2.28515
Schwingshackl L, Hoffmann G, 2014. Comparison of the long-term effects of high-fat v. low-fat diet consumption on cardiometabolic risk factors in subjects with abnormal glucose metabolism: a systematic review and meta-analysis. Br J Nutrition 111:2047-2058.
DOI: https://doi.org/10.1017/S0007114514000464
Seron P, Irazola V, Rubinstein A, Calandrelli M, Ponzo J, Olivera H, Gutierrez L, Elorriaga N, Poggio R, Lanas F, 2018. Ideal Cardiovascular Health in the southern cone of Latin America. Public Health 156:132–139.
DOI: https://doi.org/10.1016/j.puhe.2017.12.017
Schlundt DG, Hargreaves MK, McClellan L, 2006. Geographic clustering of obesity, diabetes, and hypertension in Nashville, Tennessee. J Ambulatory Care Manag 29:125-132.
DOI: https://doi.org/10.1097/00004479-200604000-00005
Tang TS, Ayala GX, Cherrington A, Rana G, 2011. A review of volunteer-based peer support interventions in diabetes. Diabetes Spectrum 24:85-98.
DOI: https://doi.org/10.2337/diaspect.24.2.85
Tasic I, Lovic D, 2018. Hypertension and cardiometabolic disease. Front Biosci 10:166-174.
DOI: https://doi.org/10.2741/s506
Tobias DK, Chen M, Manson JE, Ludwig DS, Willett W, Hu FB, 2015. Effect of low-fat diet interventions versus other diet interventions on long-term weight change in adults: a systematic review and meta-analysis. Lancet Diabetes Endocrinol 3:968-979.
DOI: https://doi.org/10.1016/S2213-8587(15)00367-8
Voss S, Schneider A, Huth C, Wolf K, Markevych I, Schwettmann L, Rathmann W, Peters A, Breitner S, 2021. Long-term exposure to air pollution, road traffic noise, residential greenness, and prevalent and incident metabolic syndrome: Results from the population-based KORA F4/FF4 cohort in Augsburg, Germany. Environ Int 147:106364.
DOI: https://doi.org/10.1016/j.envint.2020.106364
Wang X, Bao W, Liu J, Ouyang YY, Wang D, Rong S, Xiao X, Shan ZL, Zhang Y, Yao P, Liu LG, 2013. Inflammatory markers and risk of type 2 diabetes: a systematic review and meta-analysis. Diabetes Care 36:166-75.
DOI: https://doi.org/10.2337/dc12-0702
WHO, 2022. Non-communicable diseases. Accessed on 18 Sept. 2023. Available from https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases
WHO, 2011. Waist Circumference and Waist–Hip Ratio: Report of a WHO Expert Consultation 8–11 December 2008. Geneva, Switzerland: 2011
Xu Y, Wang L, 2015. GIS-based analysis of obesity and the built environment in the US. Cartography Geogr Inform Sci 42:9-21.
DOI: https://doi.org/10.1080/15230406.2014.965748
Yang BY, Liu KK, Markevych I, Knibbs LD, Bloom MS, Dharmage SC, Lin S, Morawska L, Heinrich J, Jalaludin B, Gao M, Guo Y, Zhou Y, Huang WZ, Yu HY, Zeng XW, Hu LW, Hu Q, Dong GH, 2020. Association between residential greenness and metabolic syndrome in Chinese adults. Environ Int 135:105388.
DOI: https://doi.org/10.1016/j.envint.2019.105388
Zapata Bedoya S, Walteros Acero DM, Mercado M, 2023. Modelos geoespaciales para control de brotes de SARS-CoV-2 en Cartagena y Barranquilla, Colombia, 2020. Revista Panamericana de Salud Pública, 46, e26.
DOI: https://doi.org/10.26633/RPSP.2022.26
How to Cite
Campero, M. N., Scavuzzo, C. M., Andreo, V., Mileo, M. S., Franzois, M. B., Oberto, M. G., … Defagó, M. D. (2023). A geospatial analysis of cardiometabolic diseases and their risk factors considering environmental features in a midsized city in Argentina. Geospatial Health, 18(2). https://doi.org/10.4081/gh.2023.1212
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