摘要：In Ethiopia, food insecurity remains a major public health challenge. Agroecosystem has a potential to determine food insecurity. Therefore, this study aimed to determine the spatial pattern of household food insecurity across different agroecosystems in East Gojjam Zone. An agroecosystem-linked cross-sectional survey was done among 3108 households after selecting using multistage cluster sampling. The study area is divided into hilly and mountainous highlands, midland plains with black soil, midland plains with brown soil, midland plains with red soil and lowlands of Abay valley. Data were collected on sociodemographic variables, household food access and geographical location after five days training and pretesting of the tool to maintain data quality. Data were entered using Epi Info version 3.5 and exported to SaTScan and SPSS 20 for further analysis. To identify the most likely clusters using SaTScan software, the log likelihood ratio (LLR) and P less than 0.05 were considered as the level of significance. The overall prevalence of household food insecurity was found to be 65.3% (95% CI 63.5, 67.00). The lowlands of the Abay valley (70.6%, 95% CI 66.9, 74.2) and hilly and mountainous highlands (69.8%, 95% CI 65.9, 73.3) showed a significantly higher prevalence compared to midland plains with black soil (61.7%, 95% CI 58.1, 65.6), midland plains with red soil (63.5%, 95% CI 59.9, 65.0) and midland plains with brown soil (61.5%, 95% CI 57.4, 65.3). SaTScan spatial analysis identified hilly and mountainous highlands (LLR: 11.64; P 0.0088) and lowlands of the Abay valley (LLR: 8.23; P 0.025) as the most likely primary and secondary clusters, respectively. The lowlands of Abay valley and hilly and mountainous highlands were the most vulnerable areas of food insecurity. Concerned bodies that are working to mitigate food insecurity shall consider microlevel food insecurity variations during planning interventions. Further research is needed to determine the temporal variation of household food insecurity. Also, it is very important to validate the spatial analysis results applicability to design geographically targeted interventions.
