Geospatial Clustering of Mobile Phone Use and Tuberculosis Health Outcomes Among African Health Systems

BACKGROUND: While multiple studies have documented the impacts of mobile phone use on TB health outcomes for varied settings, it is not immediately clear what the spatial patterns of TB treatment completion rates among African countries are. This paper used Exploratory Spatial Data Analysis (ESDA) techniques to explore the clustering spatial patterns of TB treatment completion rates in 53 African countries and also their relationships with mobile phone use. Using an ESDA approach to identify countries with low TB treatment completion rates and reduced mobile phone use is the first step toward addressing issues related to poor TB outcomes. METHODS: TB notifications and treatment data from 2000 through 2015 that were obtained from the World Bank database were used to illustrate a descriptive epidemiology of TB treatment completion rates among African health systems. Spatial clustering patterns of TB treatment completion rates were assessed using differential local Moran's I techniques, and local spatial analytics was performed using local Moran's I tests. Relationships between TB treatment completion rates and mobile phone use were evaluated using ESDA approach. RESULT: -values. Algeria and Senegal had significant clusters across the study periods, while Democratic Republic of Congo, Niger, South Africa, and Cameroon had significant clusters in at least two time-periods. ESDA identified statistically significant associations between TB treatment completion rates and mobile phone use. Countries with higher rates of mobile phone use showed higher TB treatment completion rates overall, indicating enhanced program uptake (p < 0.05). CONCLUSION: Study findings provide systematic evidence to inform policy regarding investments in the use of mHealth to optimize TB health outcomes. African governments should identify turnaround strategies to strengthen mHealth technologies and improve outcomes

Cluster analysis · eHealth · Geospatial analysis · Healthcare system · mHealth · Mobile phone · Phone · Tuberculosis · COVID-19 epidemiological studies · Data-Driven Disease Surveillance · Tuberculosis Research and Epidemiology · Health Informatics

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