Assessing the Multiple Dimensions of Poverty. Data Mining Approaches to the 2004–14 Health and Demographic Surveillance System in Cuatro Santos, Nicaragua

Bibliographic Data

ID22072258
AuthorsC Källestål (0000-0002-3206-6528, Uppsala University, corresponding author), Elmer Zelaya Blandón (Universidad Nacional Autónoma de Nicaragua-León), Rodolfo Pena (0009-0008-8496-8452, Uppsala University), Wilton Pérez (0000-0002-9109-2224, Uppsala University), Mariela Contreras (0000-0002-6376-2219, Uppsala University), Lars Ake Persson (0000-0003-0710-7954, Uppsala University), Lars-Åke Persson, Oleg Sysoev (0000-0002-3092-4162, Linköping University), Katarina Ekholm Selling (0000-0001-8036-168X, Uppsala University)
Year2020
Volume7
Pages409-409
Publication date2020-01-29
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2019.00409
PMID32064243
OpenAlexW3003642024
LanguageEN
Citations received6
References cited19

We identified clusters of multiple dimensions of poverty according to the capability approach theory by applying data mining approaches to the Cuatro Santos Health and Demographic Surveillance database, Nicaragua. Four municipalities in northern Nicaragua constitute the Cuatro Santos area, with 25,893 inhabitants in 5,966 households (2014). A local process analyzing poverty-related problems, prioritizing suggested actions, was initiated in 1997 and generated a community action plan 2002-2015. Interventions were school breakfasts, environmental protection, water and sanitation, preventive healthcare, home gardening, microcredit, technical training, university education stipends, and use of the Internet. In 2004, a survey of basic health and demographic information was performed in the whole population, followed by surveillance updates in 2007, 2009, and 2014 linking households and individuals. Information included the house material (floor, walls) and services (water, sanitation, electricity) as well as demographic data (birth, deaths, migration). Data on participation in interventions, food security, household assets, and women's self-rated health were collected in 2014. A K-means algorithm was used to cluster the household data (56 variables) in six clusters. The poverty ranking of household clusters using the unsatisfied basic needs index variables changed when including variables describing basic capabilities. The households in the fairly rich cluster with assets such as motorbikes and computers were described as modern. Those in the fairly poor cluster, having different degrees of food insecurity, were labeled vulnerable. Poor and poorest clusters of households were traditional, e.g., in using horses for transport. Results displayed a society transforming from traditional to modern, where the forerunners were not the richest but educated, had more working members in household, had fewer children, and were food secure. Those lagging were the poor, traditional, and food insecure. The approach may be useful for an improved understanding of poverty and to direct local policy and interventions

Data mining · Data science · Environmental health · Geography · Health care · Health data · Political science · Poverty · Child Nutrition and Water Access · Computer Science · Food Security and Health in Diverse Populations · Income, Poverty, and Inequality · Medicine

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