Geospatial Analysis On Stunting Prevalence And Strategies

Abstract

Stunting is a condition in which children under the age of five fail to thrive due to chronic malnutrition, resulting in the child being too short for his age. The issue of short children reflects the community's socioeconomic conditions because the nutritional problems displayed by short children are chronic. Stunting is influenced by environmental factors and geographical conditions (population density, climatic conditions, and inadequate sanitation) in addition to maternal characteristics and child-rearing patterns, so spatial analysis is critical in overcoming this problem at the rural level. Various stunting prevention programs have been implemented, but they have not been effective, and there has not been an adequate reduction. This research aims to determine the relationship between geographical conditions and the distribution of stunting sufferers, as well as stunting prevention strategies, in Bulukumba Regency. Secondary data were obtained from reports of stunting cases at the Bulukumba District Health Office, and interviews were used in this study. The chi-square test was used in data analysis to determine the relationship between variables, specifically the incidence of stunting and geographical conditions. Meanwhile, the empirical bayesian smoothing rates developed by Clayton and Kaldor (1987) are used in the Geoda software program version 1.6.7 to identify the distribution of cases because stunting cases are not fully representative if they occur in a larger population but are not densely populated due to a larger area. This study was carried out in Bulukumba Regency with a population of 24-59-month-old children. The findings indicate that there is a link between population density and stunting distribution. This research serves as the foundation for decision-makers to develop relevant strategic policies to accelerate stunting prevention in Bulukumba Regency.