Geospatial Analysis of Unmet Surgical Need in Uganda: An Analysis of SOSAS Survey Data



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.


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.


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.


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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We thank the Uganda Bureau of Statistics for methodological advice and for providing randomized EAs and the Uganda Ministry of Health and Makerere College of Health Sciences for institutional support. We thank the following enumerators and field supervisors for their dedication to data quality and the field supervisors for their leadership of implementation: Samuel Kagongwe, Mark Kashaija, Sheila Kisakye, Mable Luzze, and Hassard Sempeera. We benefitted from the generous collaboration of the Gates Institute for Population and Reproductive Health at Johns Hopkins Bloomberg School of Public Health, specifically Professors Scott Radloff and Amy Tsui. We are grateful to the Surgeons OverSeas organization, and in particular Dr. Reinou Groen, Dr. Shailvi Gupta, and Dr. Adam Kushner, for guidance from initial design to analysis of SOSAS and this specific report. We thank Ashley Morgan for editing this manuscript. We dedicate this report in memory of Mr. Charles Bambaiha, Mr. Allan Ssekindi, and Ms. Irene Tusiime.

Author contributions

SHF, TMT, ATF, and EKB conducted the literature search. TMT, LA, and JRV made the tables and figures. ATF, EKB, CS, FM, SL, CM, DBN, JGC, MG, and MMH designed the study. ATF, TMT, and CM supervised data collection. SHF, JRV, TMT, ATF, EKB, LA, CS, FM, SL, JGC, MG, and MMH analyzed and interpreted the data. DBN helped interpret the data. SHF, JRV, and TMT wrote the report. All authors commented on and critically revised the manuscript.


Funding was provided by the Duke Global Health Institute, Duke University Department of Neurosurgery, University of Minnesota Department of Surgery, Makerere University College of Health Sciences, and Johnson and Johnson Family of Companies. Dr. Staton would like to acknowledge salary support funding from the Fogarty International Center (Staton, K01 TW010000-01A1). Funding sources played no role in study design, data collection, data analysis, or writing of the manuscript. All authors had full access to the data and had final responsibility for the decision to submit for publication.

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Correspondence to Michael M. Haglund.

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Farber, S.H., Vissoci, J.R.N., Tran, T.M. et al. Geospatial Analysis of Unmet Surgical Need in Uganda: An Analysis of SOSAS Survey Data. World J Surg 41, 353–363 (2017).

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  • Spatial Autocorrelation
  • Travel Time
  • Geographical Access
  • Care Availability
  • Geographic Information System Analysis