Measuring Multidimensional Poverty in a Complex Environment; Identifying the Sensitive Links

The central hypothesis of this study is that a holistic, systems-based approach employing multiple analytical tools is useful for identifying the most sensitive links within complex communities to down-scale global development priorities such as the United Nations Sustainable Development Goals. Results of latent factor regression, canonical correlation analysis, and structural equation modeling were compared for multiple, publically-available data sets for two rural regions in Brazil and Guatemala. The results of this study confirm previously reported findings, and collectively support the central hypothesis demonstrating a pathway for linking global priorities with the complex realities of \u27on-the-ground\u27 development conditions in specific communities

Utilizing Structural Equation Modeling in the Development of a Standardized Intervention Assessment Tool

There are numerous approaches to measuring multidimensional poverty; these include the Human Development Index and the Multidimensional Poverty Index among others [1]. However, a gap in the literature is found when intervention assessment tools are investigated. The idea of creating a standardized assessment tool would allow for a deeper understanding of poverty on a per community basis. Structural Equation Modeling (SEM) offers a robust platform in which to establish such a tool. An overview of SEM and several other general approaches to data aggregation are addressed. The notion of a standardized intervention assessment tool is discussed; this is focused on utilizing the SEM platform for this tool. Further, previous works by Divelbiss [2] and Voth-Gaeddert [3], [4] are discussed. To date SEM has shown to handle adaptability of differing environments positively. Divelbiss reported on the SEM multivariable poverty model within villages of Guatemala and Voth-Gaeddert reports on the applicability of this model used in a dissimilar environment in Brazil. These findings suggest feasibility in the utilization of a SEM platform for a standardized intervention assessment tool

Utilizing Structural Equation Modeling As an Evaluation Tool for Critical Parameters of the Biosand Filter in a Pilot Study in Para, Brazil

Biosand filters (BSFs) have been used to provide potable water to many communities throughout the world. A significant number of laboratory and field studies have demonstrated the effectiveness of the BSF if utilized properly. However, our prior work suggests that multiple factors contribute to the effectiveness of the BSF in a community setting. These factors include: household education levels (HELs), socio-economic status (SES), additional interventions for sanitation and hygiene, water sources, and the relationship between treatment and storage. A structural equation model (SEM) was constructed to evaluate the contribution of these factors to diarrheal occurrence in impoverished households in two Brazilian communities along the Amazon River. The results of this study showed that HEL was the most important factor in reducing diarrhea, and the presence of a BSF was near ineffective. Furthermore, interventions for sanitation and hygiene as well as SES all contributed to a reduction in diarrheal occurrence. This study demonstrates that SEM can provide a platform to evaluate the complex interaction among factors contributing to a reduction in the occurrence of diarrhea. Furthermore, the results of this study highlight the importance of a holistic approach to the deployment of technology-driven solutions such as the BSF

Utilizing Structural Equation Modeling to Correlate Biosand Filter Performance and Occurrence of Diarrhea in the Village of Enseado Do Aritapera in Para, Brazil

Previously, our earlier work demonstrated the use of structural equation modeling to evaluate the effectiveness of point-of-use biosand filters (BSF) to reduce the occurrence of diarrhea in rural Guatemala. While prior research in laboratory and field locations has documented the effectiveness of BSF to remove agents of diarrhea, experience in field sites suggests that multiple local factors greatly influence the occurrence of diarrhea. In addition to the BSF, this study evaluated household education level, socioeconomic status, water source and handling, and sanitation as factors impacting the occurrence of diarrhea for households in the village of Enseado do Aritapera in Para, Brazil. Of the 18 correlations examined, 16 were negatively correlated, and the strongest correlation was between the utilization of an \u27improved\u27 water source and the reduction of the occurrence of diarrhea within the household. While proper operation and maintenance of the BSF was found to have a negative correlation with the occurrence of diarrhea, it was not the most influential factor. This result supports the prior findings from our earlier work suggesting that more research is needed to evaluate which intervention should be prioritized for maximum return on investment with aid distribution