Harmonising Incompatible Datasets to Enable GIS Use to Study Non-communicable Diseases in Tonga

We investigated the use of geographic information systems (GIS) to study the prevalence of non-communicable diseases (NCDs) in association with area deprivation within Tongatapu, the largest island in Tonga. This is a case study to determine what is possible to achieve since various data issues influenced by institutional and organizational barriers exist, affecting data integration and GIS use. A data conversion framework was developed using geographic conversion tables (GCTs) to process existing data into a compatible format and create new geographies to analyse the prevalence of NCDs and area deprivation across different levels of geography. Area deprivation was higher in the rural district and was associated with the prevalence of NCDs. However, at lower levels of geography, the distribution and patterns of NCDs, and area deprivation were unclear. This was influenced by the methods of data collection, recording and dissemination. There is a need for a national action plan outlining the standard operating procedures for all stakeholders to adhere to, and thereby produce and disseminate comprehensive, reliable, and high-quality data. Otherwise, data will be collected for basic reporting but impractical for sophisticated analysis and research. We suggest an investigation into dasymetric mapping to disaggregate population data and develop automating processes for large national datasets.

Automated summary

This study investigated the use of GIS to analyze the prevalence of non-communicable diseases in relation to area deprivation in Tongatapu. Data integration and GIS use were affected by institutional and organizational barriers. A data conversion framework was developed to analyze the prevalence of diseases and area deprivation at different levels of geography. The study found a higher level of area deprivation in rural areas, which was associated with the prevalence of non-communicable diseases.