S Harrison Farber1,2, Joao Ricardo Nickenig Vissoci2,3,4, Tu M Tran2,3, Anthony T Fuller1,2,3, Elissa K Butler5, Luciano Andrade2, Catherine Staton2,3,6, Fredrick Makumbi7, Samuel Luboga8, Christine Muhumuza7, Didacus B Namanya9, Jeffrey G Chipman10, Moses Galukande11, Michael M Haglund12,13. 1. Duke University School of Medicine, Durham, NC, USA. 2. Division of Duke Global Neurosurgery and Neuroscience, Department of Neurosurgery, Duke University Medical Center, 4508 Hospital South, Durham, NC, 27710, USA. 3. Duke University Global Health Institute, Durham, NC, USA. 4. Department of Medicine, Faculdade Inga, Maringa, PR, Brazil. 5. University of Minnesota Medical School, Minneapolis, MN, USA. 6. Division of Emergency Medicine, Department of Surgery, Duke University Medical Center, Durham, NC, USA. 7. Makerere University School of Public Health, Kampala, Uganda. 8. Department of Anatomy, Makerere University School of Medicine, Kampala, Uganda. 9. Ministry of Health, Government of Uganda, Kampala, Uganda. 10. Department of Surgery, University of Minnesota, Minneapolis, MN, USA. 11. Department of Surgery, Makerere University College of Health Sciences, Kampala, Uganda. 12. Division of Duke Global Neurosurgery and Neuroscience, Department of Neurosurgery, Duke University Medical Center, 4508 Hospital South, Durham, NC, 27710, USA. michael.haglund@duke.edu. 13. Duke University Global Health Institute, Durham, NC, USA. michael.haglund@duke.edu.
Abstract
BACKGROUND: Globally, a staggering five billion people lack access to adequate surgical care. Sub-Saharan Africa represents one of the regions of greatest need. We sought to understand how geographic factors related to unmet surgical need (USN) in Uganda. METHODS: We performed a geographic information system analysis of a nationwide survey on surgical conditions performed in 105 enumeration areas (EAs) representing the national population. At the district level, we determined the spatial autocorrelation of the following study variables: prevalence of USN, hub distance (distance from EA to the nearest surgical center), area of coverage (geographic catchment area of each center), tertiary facility transport time (average respondent-reported travel time), and care availability (rate of hospital beds by population and by district). We then used local indicators of spatial association (LISA) and spatial regression to identify any significant clustering of these study variables among the districts. RESULTS: The survey enumerated 4248 individuals. The prevalence of USN varied from 2.0-45 %. The USN prevalence was highest in the Northern and Western Regions. Moran's I bivariate analysis indicated a positive correlation between USN and hub distance (p = 0.03), area of coverage (p = 0.02), and facility transport time (p = 0.03). These associations were consistent nationally. The LISA analysis showed a high degree of clustering among sets of districts in the Northern Sub-Region. CONCLUSIONS: This study demonstrates a statistically significant association between USN and the geographic variables examined. We have identified the Northern Sub-Region as the highest priority areas for financial investment to reduce this unmet surgical disease burden.
BACKGROUND: Globally, a staggering five billion people lack access to adequate surgical care. Sub-Saharan Africa represents one of the regions of greatest need. We sought to understand how geographic factors related to unmet surgical need (USN) in Uganda. METHODS: We performed a geographic information system analysis of a nationwide survey on surgical conditions performed in 105 enumeration areas (EAs) representing the national population. At the district level, we determined the spatial autocorrelation of the following study variables: prevalence of USN, hub distance (distance from EA to the nearest surgical center), area of coverage (geographic catchment area of each center), tertiary facility transport time (average respondent-reported travel time), and care availability (rate of hospital beds by population and by district). We then used local indicators of spatial association (LISA) and spatial regression to identify any significant clustering of these study variables among the districts. RESULTS: The survey enumerated 4248 individuals. The prevalence of USN varied from 2.0-45 %. The USN prevalence was highest in the Northern and Western Regions. Moran's I bivariate analysis indicated a positive correlation between USN and hub distance (p = 0.03), area of coverage (p = 0.02), and facility transport time (p = 0.03). These associations were consistent nationally. The LISA analysis showed a high degree of clustering among sets of districts in the Northern Sub-Region. CONCLUSIONS: This study demonstrates a statistically significant association between USN and the geographic variables examined. We have identified the Northern Sub-Region as the highest priority areas for financial investment to reduce this unmet surgical disease burden.
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