GIS and Health: Enhancing Disease Surveillance and Intervention through Spatial Epidemiology
Abstract: The success of an evidence-based intervention depends on precise and accurate evaluation of available data and information. Here, the use of robust methods for evidence evaluation is important. Epidemiology, in its conventional form, relies on statistics and mathematics to draw inferences on disease dynamics in affected populations. Interestingly, most of the data used tend to have spatial aspects to them. However, most of these statistical and mathematical methods tend to either neglect these spatial aspects or consider them as artefacts, thereby biasing the resultant estimates. Thankfully, spatial methods allow for evidence evaluation and prediction in epidemiologic data while considering their inherent spatial characteristics. This, thus, promises more precise and accurate estimates.This thesis documents and illustrates the contribution spatial methods and spatial thinking makes to epidemiology through studies carried out in two countries with different heath-data quality realities, Uganda and Sweden. To be able to use spatial methods for epidemiology studies, proper spatial data need to be available, which is not the case in Uganda. Consequently, this study had two main aims: (1) It proposed and implemented a novel way of spatially-enabling patient registry systems in settings where the existing infrastructures do not allow for the collection of patient-level spatial details, prerequisites for fine-scale spatial analyses; (2) Where spatial data were available, spatial methods were used to study associative relationships between health outcomes and exposure factors. Spatial econometrics approaches, especially spatially autoregressive regression models were adopted. Also, consistent with location-specific epidemiologic intervention, the advantages of using spatial scan statistics, Geographically Weighted (Poisson) Regression and local entropy maps to distil model parameter estimates into their inherent spatial heterogeneities were illustrated. Our results illustrated that through the use of mobile and web technologies and leveraging on existing spatial data pools, systems that enable recording and storage of geospatially referenced patient records can be created. Also, spatial methods outperformed conventional statistical approaches, giving refined and more accurate parameter estimates. Finally, our study illustrates that the use of local spatial methods can inform policy and intervention better through the identification of areas with elevated disease burden or those areas worth additional scrutiny as illustrated by our study of HIV-TB coinfection areas in Uganda, the areas with high CVD-air pollution associations in Sweden, and areas with consistently high joint mortality burden for CVD and cancer among the Swedish elderly.Overall, the incorporation of spatial approaches and spatial thinking in epidemiology cannot be overemphasized. First, by enabling the capture of fine-scale personal-level spatial data, our study promises more robust analyses and seamless data integration. Secondly, associative analyses using spatial methods showed improved results. Thirdly, identification of the areas with elevated disease burden makes identifying the primary drivers of the observed local patterns more informed and focused. Ultimately, our results inform healthcare policy and strategic intervention as the most affected areas can easily be zoned out. Therefore, by illustrating these benefits, this study contributes to epidemiology, through spatial methods, especially in the aspects of disease surveillance, informing policy, and driving possible effective intervention.
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