Spatial Autocorrelation of Diarrhea Cases in West Java Province in 2023
Abstract
Introduction: Diarrhea have been being a significant public health threat for the community due to its impact on morbidity and even mortality especially among infants and toddlers. Understanding the pattern of diarrhea, how the key factors influence the prevalence of the disease and which areas are at the highest risk could help in controlling diarhhea.
Objective: Through spatial autocorrelation analysis of diarrhea prevalence with several risk factors, such as open defecation behavior, population density, access to proper sanitation, availability of drinking water facilities, and the number of health centers in West Java Province in 2023, this study aims to identify which districts/cities in the province are at high risk of diarrhea.
Method: This ecological study analyzed 27 districts/cities in West Java Province in 2023 using secondary data from Open Data Jabar. The dependent variable was the number of diarrhea cases, while independent variables included key factors influencing the prevalence of diarrhea. The Moran index was used for autocorrelation tests, The study used Geoda software version 1.22.
Result: The study found positive autocorrelation pattern between diarrhea prevalence and population density as well as access to proper sanitation. Negative autocorrelatios found for the other factors i.e. the number of drinking water facilities, the number of villages that stopped open defecation and the number of health centers. The risk analysis reveals four districts categorized as high risk of diarrhea: Depok City, Bekasi City, Bogor City, and Cianjur City. Depok City has the highest score of 12, due to high population density and lack of access to proper sanitation. Bekasi, Bogor City, and Cianjur have the next highest scores, with proper sanitation being the largest contributing factor.
Conclusion: Autocorrelation analysis can help understand diarrhea patterns and factors influencing its prevalence, provide guidance for program implementation and prioritization to address the most high risk areas.
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