Determinants of Adapted Improved Sanitation in Rural and Urban of Bangladesh and Pakistan

Abstract

This study focuses on the socioeconomic and demographic determinants of improved sanitation adaptation in South Asia, mainly in Bangladesh and Pakistan. For the purpose of mass population study, cross-sectional datasets were obtained for both Bangladesh and Pakistan. The survey is based on a two-stage stratified sample of households. This study hypothesized the status of sanitation into three categorically distributed dependent variables which are “improved sanitation facility”, “unimproved sanitation facility” and “no sanitation facility”. According to the World Health Organization, these variables are supported to categorize the situation of sanitation by the Joint Monitoring Program categorization of toilet facilities. The “improved sanitation facilities” includes Flush or pour flushed toilets connected to sewers, Flush or pour flushed toilets or latrines connected to pits or septic tanks, and ventilated improved pit (VIP) latrines, Pit latrines with slab, Composting toilets including twin-pit latrines and container-based systems. The “unimproved sanitation facilities” includes Pit latrines without slab, Bucket latrines and Hanging latrines. And the “no facility” category includes open defecation (bush/field/desert/road/beach etc.). Wealth Index of the household, Gender of head of the household, marital status of the respondent, Education of household head, Locality of the household, and Province/ division are the categorical explanatory variables used in this study; family size of the household and age of the head of the household are two numerical explanatory variables that are taken as important demographic factors. Two candidate models were constructed for predicting no facility, unimproved sanitation, and improved sanitation respectively. In the sample, only 1.26% of households of Bangladesh do not have any toilet facility while Pakistan has 10.1% households without any toilet facility.  The indicators, all three component models, and two data sets demonstrate agreement in the analysis. There is no discernible upward trend in model performance year after year. All of the comparison results show that the ordinal logistic model outperforms the multinomial regression model. According to the AIC, BIC, and log probability statistics, the highest education of family members is a more relevant element in a household's avoiding status in Pakistan. The indicators, all three component models, and two data sets demonstrate agreement in the analysis. There is no discernible upward trend in model performance year after year. All of the comparison results show that the ordinal logistic model outperforms the multinomial regression model.