Predicting poverty. Data mining approaches to the health and demographic surveillance system in Cuatro Santos, Nicaragua

• Main Authors: C. Källestål (Author), E. Blandón Zelaya (Author), R. Peña (Author), W. Peréz (Author), M. Contreras (Author), L. Å. Persson (Author), O. Sysoev (Author), K. Ekholm Selling (Author)
• Format: Book
• Published: BMC, 2019-10-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.1186/s12939-019-1054-7; 1475-9276
• Keywords: Poverty; Prediction; Conditional inference trees; Conditional random forest analyses; Datamining; Education; Public aspects of medicine; RA1-1270
• Source: International Journal for Equity in Health, Vol 18, Iss 1, Pp 1-12 (2019)

Abstract Background In order to further identify the needed interventions for continued poverty reduction in our study area Cuatro Santos, northern Nicaragua, we aimed to elucidate what predicts poverty, measured by the Unsatisfied Basic Need index. This analysis was done by using decision tree methodology applied to the Cuatro Santos health and demographic surveillance databases. Methods Using variables derived from the health and demographic surveillance update 2014, transferring individual data to the household level we used the decision tree framework Conditional Inference trees to predict the outcome "poverty" defined as two to four unsatisfied basic needs using the Unsatisfied Basic Need Index. We further validated the trees by applying Conditional random forest analyses in order to assess and rank the importance of predictors about their ability to explain the variation of the outcome "poverty." The majority of the Cuatro Santos households provided information and the included variables measured housing conditions, assets, and demographic experiences since the last update (5 yrs), earlier participation in interventions and food security during the last 4 weeks. Results Poverty was rare in households that have some assets and someone in the household that has a higher education than primary school. For these households participating in the intervention that installed piped water with water meter was most important, but also when excluding this variable, the resulting tree showed the same results. When assets were not taken into consideration, the importance of education was pronounced as a predictor for welfare. The results were further strengthened by the validation using Conditional random forest modeling showing the same variables being important as predicting the outcome in the CI tree analysis. As assets can be a result, rather than a predictor of more affluence our results in summary point specifically to the importance of education and participation in the water installation intervention as predictors for more affluence. Conclusion Predictors of poverty are useful for directing interventions and in the Cuatro Santos area education seems most important to prioritize. Hopefully, the lessons learned can continue to develop the Cuatro Santos communities as well as development in similar poor rural settings around the world.