Bayesian regression model with application to a study of food insecurity in household level: a cross sectional study

Abstract

Background: Food insecurity is a situation in which access to sufficient food is limited at times during the year by a lack of money and other resources. Even though several efforts were made to recover food security, still it is a critical social problem that needs immediate attention from policy and other decision makers especially in Ethiopia. The objective of the paper was to identify the significant predictors of food insecurity at household level in the given District. Method: A cross-sectional survey study was employed among 305 households selected using systematic random sampling technique. The data was collected using structured interviewer administrative questionnaire. Descriptive statistics was used to assess the prevalence of food insecurity status, and Bayesian estimation on binary logistic regression was used to identify the significant predictors of household food insecurity. Gibbs sampler algorithm was employed on Win BUGS software. Convergence of algorithm was assessed by using time series plot, density plot and auto correlation plot. Result: The prevalence of household food insecurity was 59% in the study District. From Bayesian estimation, the significant predictors of food insecurity were sex of household head, agro-ecological zone, loan status, access to agricultural training, age of household head, marital status of household head, family size, agricultural land size, tropical livestock unit, and soil fertility of agricultural land. Conclusion: The result shows that the households headed by male; who had own land, who land fertile soil, and those who took agricultural training were less likely to be food insecure. On the other hand, households with large family size, small farm land size and less tropical livestock unit were more likely to be food insecure. Hence, to increase food production and productivity of the farmers, proper attention should be given to improve soil fertility of agricultural land. Creating access to credit to households and providing them with agricultural training and family planning should be also emphasized.