Spatial analysis of factors linked to Community Food Environments in argentine urban localities, during 2018-2019

In Argentina, food environments and health aspects related to them, such as low birth weight (lbw) and macrosomia, have not yet been studied using geoscience tools at a regional scale. This work aims to spatially describe the distribution of birth weight and associated food environment variables in Argentina during the 2018-2019 period. Methods: using open data, the prevalence of lbw and macrosomia was characterized for the period 2018-2019, in 657 urban localities in Argentina (>5000 inhabitants). Based on the community food environments model, socioeconomic, demographic, and environmental variables (from remote sensing) were characterized, which were grouped into three environment dimensions: policy, environmental, and individual. A description of the spatial distribution of each variable was conducted. An optimized hot spot analysis (arcgis pro 3.1) and a purely spatial analysis (satscan) were carried out. Cartography was processed with qgis 3.22. Results: low-risk clusters for lbw and macrosomia were observed in places with lower food insecurity, altitude, proportion of households with unsatisfied basic needs (ubn), and higher percentage of social security coverage. High-risk clusters for lbw coincide with areas of lower food insecurity, infant mortality rate, percentage of households with female headship, ubn, and higher altitude and social security coverage. Furthermore, a high-risk cluster for macrosomia was observed in areas with higher food insecurity, vegetation index, precipitation, percentage of households with ubn, and lower social security coverage. Discussion: it is important to study the spatial behavior of environment variables before conducting explanatory or predictive modeling, as these often render the spatial relationships between them invisible.