Spatial pattern analysis of the impact of community food environments on foetal macrosomia, preterm births and low birth weight

Submitted: 25 October 2023
Accepted: 13 March 2024
Published: 7 May 2024
Abstract Views: 286
PDF: 142
Supplementary Materials: 5
HTML: 1
Publisher's note
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

Community food environments (CFEs) have a strong impact on child health and nutrition and this impact is currently negative in many areas. In the Republic of Argentina, there is a lack of research evaluating CFEs regionally and comprehensively by tools based on geographic information systems (GIS). This study aimed to characterize the spatial patterns of CFEs, through variables associated with its three dimensions (political, individual and environmental), and their association with the spatial distribution in urban localities in Argentina. CFEs were assessed in 657 localities with ≥5,000 inhabitants. Data on births and CFEs were obtained from nationally available open-source data and through remote sensing. The spatial distribution and presence of clusters were assessed using hotspot analysis, purely spatial analysis (SaTScan), Moran’s Index, semivariograms and spatially restrained multivariate clustering. Clusters of low risk for LBW, macrosomia, and preterm births were observed in the central-east part of the country, while high-risk clusters identified in the North, Centre and South. In the central-eastern region, low-risk clusters were found coinciding with hotspots of public policy coverage, high night-time light, social security coverage and complete secondary education of the household head in areas with low risk for negative outcomes of the birth variables studied, with the opposite with regard to households with unsatisfied basic needs and predominant land use classes in peri-urban areas of crops and herbaceous cover. These results show that the exploration of spatial patterns of CFEs is a necessary preliminary step before developing explanatory models and generating novel findings valuable for decision-making.

Dimensions

Altmetric

PlumX Metrics

Downloads

Download data is not yet available.

Citations

Almli LM, Ely DM, Ailes EC, Abouk R, Grosse SD et al., 2020. Infant mortality attributable to birth defects—United States, 2003–2017. Morb Mortal Wkly Rep 69:25. DOI: https://doi.org/10.15585/mmwr.mm6902a1
Alvarez Di Fino EM, 2020. Aplicación de tecnologías geoespaciales para el análisis de la seguridad alimentaria y nutricional en la ciudad de Córdoba, Argentina. PhD Thesis; University of ….
Amarasinghe GS, Agampodi TC, Mendis V, Agampodi SB, 2022. The geo-spatial perspective of biological, social and environmental determinants of early pregnancy anaemia in rural Sri Lanka: Need for context-specific approaches on prevention. Geospat Health 17:1110. DOI: https://doi.org/10.4081/gh.2022.1110
Andrada MJ, 2014. Niveles de mortalidad y vulnerabilidad social en el noroeste argentino. 2001-2010.
Caspi CE, Sorensen G, Subramanian SV, Kawachi I, 2012. The local food environment and diet: a systematic review. Health Place 18:1172-87. DOI: https://doi.org/10.1016/j.healthplace.2012.05.006
Campero MN, Marinelli MV, Scavuzzo CM, Roman MD, 2022. Community Food Environments, exploring a geomatic vision of regional scale. In 2022 IEEE Biennial Congress of Argentina (ARGENCON) (pp. 1-8). DOI: https://doi.org/10.1109/ARGENCON55245.2022.9940098
Castro MJ, 2020. Programación fetal. Rev Digit Postgrado 9:e214. DOI: https://doi.org/10.37910/RDP.2020.9.2.e214
Celemín JP, Mikkelsen C, Velázquez G, 2015. La calidad de vida desde una perspectiva geográfica: Integración de indicadores objetivos y subjetivos. Rev Univ Geogr 24:63-84.
Celemín JP, Velázquez GA, 2017. Spatial Analysis of the Relationship Between a Life Quality Index, HDI and Poverty in the Province of Buenos Aires and the Autonomous City of Buenos Aires, Argentina. Soc Indic Res 134:1-21. DOI: https://doi.org/10.1007/s11205-017-1777-z
Elorriaga N, Moyano DL, López MV, Cavallo AS, Gutierrez L, et al., 2021. Urban Retail Food Environments: Relative Availability and Prominence of Exhibition of Healthy vs. Unhealthy Foods at Supermarkets in Buenos Aires, Argentina. Int J Environ Res Public Health 18:944. DOI: https://doi.org/10.3390/ijerph18030944
De Grande P, Rodriguez G, 2023. Censo Nacional de Población, Hogares y Viviendas 2022 - Resultados provisionales. Retrieved 11 August, 2023. Available from: https://mapa.poblaciones.org/map/151701
Esri, 2022. Clustering multivariante restringido espacialmente (Estadística espacial). Available from: https://pro.arcgis.com/en/pro-app/3.0/tool-reference/spatial-statistics/spatially-constrained-multivariate-clustering.htm
García-De la Torre JI, Rodríguez-Valdez A, Delgado-Rosas A, 2016. Factores de riesgo de macrosomía fetal en pacientes sin diabetes mellitus gestacional. Ginecol Obstet Mex 84:164-171.
Glanz K, Sallis JF, Saelens BE, Frank LD, 2005. Healthy nutrition environments: concepts and measures. Am J Health Promot 19:330-3. DOI: https://doi.org/10.4278/0890-1171-19.5.330
Gutierrez LE, Elorriaga N, Gibbons L, Melendi S, Chaparro M, Calandrelli M, Lanas F, Mores N, Ponzo J, Poggio R, Berrueta M, Irazola V, 2021. Attributes of the food and physical activity built environments from the Southern Cone of Latin America. Sci Data 8:291. DOI: https://doi.org/10.1038/s41597-021-01073-9
Instituto Nacional de Estadísticas y Censos (INDEC), 2010. Censo 2010. Disponible en: https://www.indec.gob.ar/indec/web/Nivel4-Tema-2-41-135.
Júnior EA, Peixoto AB, Zamarian ACP, Júnior JE, Tonni G, 2017. Macrosomia. Best Pract Res Clin Obstet Gynaecol 38:83-96. DOI: https://doi.org/10.1016/j.bpobgyn.2016.08.003
Kramer MS, Barros FC, Demissie K, Liu S, Kiely J, Joseph KS, 2005. Does reducing infant mortality depend on preventing low birthweight? An analysis of temporal trends in the Americas. Paediatr Perinat Epidemiol 19:445-51. DOI: https://doi.org/10.1111/j.1365-3016.2005.00681.x
Kulldorff M, 2022. SaTScan™ User Guide for version 10.1. Available from: https://www.satscan.org/techdoc.html
Lu C, Zhang W, Zheng X, Sun J, Chen L, et al., 2020. Combined effects of ambient air pollution and home environmental factors on low birth weight. Chemosphere 240:124836. DOI: https://doi.org/10.1016/j.chemosphere.2019.124836
Lytle LA, Sokol RL, 2017. Measures of the food environment: A systematic review of the field, 2007–2015. Health Place 44:18-34. DOI: https://doi.org/10.1016/j.healthplace.2016.12.007
Mena C, Sepúlveda C, Fuentes E, Ormazábal Y, Palomo I, 2018. Spatial analysis for the epidemiological study of cardiovascular diseases: A systematic literature search. Geospat Health 13:587. DOI: https://doi.org/10.4081/gh.2018.587
Mikkelsen C, Ares S, Gordziejczuk M, 2016. Dinámica socioterritorial del espacio rural en Argentina. En: Velázquez G. (dir.) Geografía y calidad de vida en Argentina. Análisis regional y departamental (2010). Tandil: IGEHCS/CIG.
Mogollón-Pastrán SC, García-Ubaque JC, Martínez-Martínez, S, 2020. Determinantes sociales de la mortalidad infantil en municipios de frontera en Colombia, 2005-2011. Rev Fac Med, 68(2), 269-278. DOI: https://doi.org/10.15446/revfacmed.v68n2.77750
Momeni M, Danaei M, Kermani AJ, Bakhshandeh M, Foroodnia S, Mahmoudabadi Z, Amirzadeh R, Safizadeh H, 2017. Prevalence and Risk Factors of Low Birth Weight in the Southeast of Iran. Int J Prev Med 8:12. DOI: https://doi.org/10.4103/ijpvm.IJPVM_112_16
Ncube CN, Enquobahrie DA, Albert SM, Herrick AL, Burke JG, 2016. Association of neighborhood context with offspring risk of preterm birth and low birthweight: A systematic review and meta-analysis of population-based studies. Soc Sci Med 153:156-64. DOI: https://doi.org/10.1016/j.socscimed.2016.02.014
Niembro A, Dondo M, Civitaresi HM, 2016. La manifestación territorial de las desigualdades socioeconómicas en Argentina: del diagnóstico a las políticas públicas. Población y sociedad 23:79-123.
Pan American Health Organization (PAHO), 2021. Sistemas Alimentarios para la salud: Resumen informativo. Disponible en: https://iris.paho.org/bitstream/handle/10665.2/56525/9789275325520_spa.pdf?sequence=1&isAllowed=y.
Pérez-Ferrer C, Auchincloss AH, de Menezes MC, Kroker-Lobos MF, Cardoso LO, Barrientos-Gutierrez T, 2019. The food environment in Latin America: a systematic review with a focus on environments relevant to obesity and related chronic diseases. Public Health Nutr 22:3447-64. DOI: https://doi.org/10.1017/S1368980019002891
Piña Borrego CE, 2020. Cambio climático, inseguridad alimentaria y obesidad infantil. Rev Cubana Salud Pública 45:e1964.
Ramírez-López MT, Vázquez Berrios M, Arco González R, Blanco Velilla RN, Decara del Olmo J, et al., 2015. El papel de la dieta materna en la programación metabólica y conductual: Revisión de los mecanismos biológicos implicados. Nutr Hosp 32:2433-45.
Safi-Stibler S, Gabory A, 2020. Epigenetics and the Developmental Origins of Health and Disease: Parental environment signalling to the epigenome, critical time windows and sculpting the adult phenotype. Semin Cell Dev Biol 97:172-180. DOI: https://doi.org/10.1016/j.semcdb.2019.09.008
Santamaría MS, Malla MS, 2006. Notas sobre el manejo del software geoestadístico Variowin. Parte I: Cálculo del variograma. Rev Inst Investig Fac Minas Metal Cienc Geogr 9(18):82-90.
Secretaría de Gobierno de Salud [Government Secretariat of Health], 2019. 2da Encuesta Nacional de Nutrición y Salud (ENNyS 2). Resumen Ejecutivo. [2nd National Nutrition and Health Survey (ENNyS 2). Executive Summary.] Available from: https://cesni-biblioteca.org/wp-content/uploads/2019/10/0000001565cnt-ennys2_resumen-ejecutivo-20191.pdf.
Seijo M, Spira C, Chaparro M, Elorriaga N, Rubinstein A, García-Elorrio E, Irazola V, 2021. Development of physical activity and food built environment quality indicators for chronic diseases in Argentina. Health Promot Int 36:1554-1565. DOI: https://doi.org/10.1093/heapro/daaa138
Suerungruang S, Sornlorm K, Laohasiriwong W, Mahato RK, 2023. Spatial association and modelling of under-5 mortality in Thailand, 2020. Geospat Health 18:1220. DOI: https://doi.org/10.4081/gh.2023.1220
Teixidó Trujillo D, 2015. Meteorología y salud infantil: una aplicaciónal caso de Nigeria. Degree Thesis. University of La Laguna, España.
Turner C, Aggarwal A, Walls H, Herforth A, Drewnowski A, et al., 2018. Concepts and critical perspectives for food environment research: a global framework with implications for action in low-and middle-income countries. Global Food Sec 18:93-101. DOI: https://doi.org/10.1016/j.gfs.2018.08.003
United Nations, 2021. Global Nutrition Report. The state of global nutrition. Disponible en: https://www.un.org/nutrition/sites/www.un.org.nutrition/files/global_nutrition_report_2021.pdf
Uwiringiyimana V, Veldkamp A, Amer S, 2019. Stunting spatial pattern in Rwanda: An examination of the demographic, socio-economic and environmental determinants. Geospat Health 14:820. DOI: https://doi.org/10.4081/gh.2019.820
World Health Organization (WHO), 2008. Climate change and health. Available from: https://iris.who.int/handle/10665/126809

How to Cite

Campero, M. N., Scavuzzo, C. M., Scavuzzo, C. M., & Román, M. D. (2024). Spatial pattern analysis of the impact of community food environments on foetal macrosomia, preterm births and low birth weight. Geospatial Health, 19(1). https://doi.org/10.4081/gh.2024.1249