Geospatial Modeling of 1-Hour and 2-Hour Access to Surgical Care in Uganda: A National Scale-Up Analysis. Article Swipe
Brian Kasagga
,
Asha Nagawa
,
J. C. Harrison
,
Emmanuel Elobu
,
Andrew Curtis
,
Rik Lubbers
,
Caroline Peace Nsodi
,
Moses Galukande
,
Doruk Ozgediz
,
Rachel Koch
,
Nicolas Ray
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-7937670/v1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-7937670/v1
Background Equitable access to surgery remains a challenge in low-resource settings. In Uganda, National and Regional Referral Hospitals (NRHs, RRHs) and District Hospitals (DHs) are the primary providers of bellwether surgical procedures, while some Health Centre IV (HCIV, mini-hospitals) currently mostly offer cesarean sections. Expanding HCIV capacity to perform all three bellwether procedures (emergency cesarean section, laparotomy, and open fracture fixation) could significantly improve timely surgical access. The “golden hour” for trauma care and the two-hour standard for bellwether procedures are key benchmarks for surgical access. Objective To model population coverage under 1h and 2h access to existing facilities providing Bellwether surgical procedures in Uganda and to evaluate the impact on coverage of equipping HCIVs to perform all Bellwether procedures. Methods Using AccessMod 5, we modeled travel times to surgical facilities under two scenarios: (1) Main hospitals comprising National and Regional Referral Hospitals (NRHS, RRHs) and District Hospitals (DHs) only, and (2) Expanded scenario - Main hospitals, and upgraded HCIVs as fully bellwether-capable. Inputs included gridded population count, road networks, land cover, hydrography, and elevation. Expert-based travel speeds were stratified by land cover and road class. We compared unimodal (walking only) and bimodal (walking plus motorized) travel scenarios, estimating population coverage within 1-hour and 2-hour intervals stratified by region. Results In the unimodal (walking-only) model, 9.7% of Uganda’s population could reach a main hospital within 1 hour, and 20.4% within 2 hours. When HCIVs were included in the expanded scenario, coverage increased to 18.4% within 1 hour and 37.9% within 2 hours. In the bimodal model, 1-hour access improved from 74.9% with main hospitals alone to 91.6% with HCIVs, a gain of 16.7%. The Northern and Western regions experienced the largest improvements in 1-hour access, with increases of 20.6% and 26.9%, respectively. In the bimodal model, 2-hour access rose from 96.7% with main hospitals only to 98.7% after adding HCIVs. Conclusion Geospatial modeling shows that motorized transport substantially improves timely access to surgical care, and equipping HCIVs to perform all bellwether procedures markedly increases 1-hour access, particularly in underserved northern and western districts. Strategic investment in emergency prehospital systems and upgrading HCIVs to bellwether-capable facilities can enhance equity, close regional gaps, and align Uganda with global surgical benchmarks, addressing critical needs in trauma and emergency surgery. Trial registration Not applicable
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- article
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- https://doi.org/10.21203/rs.3.rs-7937670/v1
- https://www.researchsquare.com/article/rs-7937670/latest.pdf
- OA Status
- gold
- OpenAlex ID
- https://openalex.org/W4415650578
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4415650578Canonical identifier for this work in OpenAlex
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https://doi.org/10.21203/rs.3.rs-7937670/v1Digital Object Identifier
- Title
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Geospatial Modeling of 1-Hour and 2-Hour Access to Surgical Care in Uganda: A National Scale-Up Analysis.Work title
- Type
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articleOpenAlex work type
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2025Year of publication
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2025-10-29Full publication date if available
- Authors
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Brian Kasagga, Asha Nagawa, J. C. Harrison, Emmanuel Elobu, Andrew Curtis, Rik Lubbers, Caroline Peace Nsodi, Moses Galukande, Doruk Ozgediz, Rachel Koch, Nicolas RayList of authors in order
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https://doi.org/10.21203/rs.3.rs-7937670/v1Publisher landing page
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https://www.researchsquare.com/article/rs-7937670/latest.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.researchsquare.com/article/rs-7937670/latest.pdfDirect OA link when available
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0Total citation count in OpenAlex
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| abstract_inverted_index.regions | 278 |
| abstract_inverted_index.remains | 6 |
| abstract_inverted_index.surgery | 5 |
| abstract_inverted_index.systems | 349 |
| abstract_inverted_index.western | 342 |
| abstract_inverted_index.(walking | 190, 194 |
| abstract_inverted_index.District | 22, 147 |
| abstract_inverted_index.Expanded | 153 |
| abstract_inverted_index.National | 14, 139 |
| abstract_inverted_index.Northern | 275 |
| abstract_inverted_index.Referral | 17, 142 |
| abstract_inverted_index.Regional | 16, 141 |
| abstract_inverted_index.capacity | 47 |
| abstract_inverted_index.cesarean | 43, 55 |
| abstract_inverted_index.compared | 188 |
| abstract_inverted_index.coverage | 91, 112, 201, 241 |
| abstract_inverted_index.critical | 370 |
| abstract_inverted_index.evaluate | 108 |
| abstract_inverted_index.existing | 98 |
| abstract_inverted_index.expanded | 239 |
| abstract_inverted_index.fracture | 60 |
| abstract_inverted_index.hospital | 224 |
| abstract_inverted_index.improved | 259 |
| abstract_inverted_index.improves | 319 |
| abstract_inverted_index.included | 165, 236 |
| abstract_inverted_index.markedly | 333 |
| abstract_inverted_index.modeling | 313 |
| abstract_inverted_index.northern | 340 |
| abstract_inverted_index.regional | 360 |
| abstract_inverted_index.scenario | 154 |
| abstract_inverted_index.section, | 56 |
| abstract_inverted_index.standard | 77 |
| abstract_inverted_index.surgery. | 376 |
| abstract_inverted_index.surgical | 31, 66, 85, 102, 130, 323, 367 |
| abstract_inverted_index.two-hour | 76 |
| abstract_inverted_index.unimodal | 189, 213 |
| abstract_inverted_index.upgraded | 159 |
| abstract_inverted_index.AccessMod | 123 |
| abstract_inverted_index.Equitable | 2 |
| abstract_inverted_index.Expanding | 45 |
| abstract_inverted_index.Hospitals | 18, 23, 143, 148 |
| abstract_inverted_index.Objective | 87 |
| abstract_inverted_index.Strategic | 344 |
| abstract_inverted_index.challenge | 8 |
| abstract_inverted_index.currently | 40 |
| abstract_inverted_index.emergency | 347, 375 |
| abstract_inverted_index.equipping | 114, 326 |
| abstract_inverted_index.fixation) | 61 |
| abstract_inverted_index.hospitals | 137, 264, 304 |
| abstract_inverted_index.increased | 242 |
| abstract_inverted_index.increases | 287, 334 |
| abstract_inverted_index.intervals | 206 |
| abstract_inverted_index.motorized | 316 |
| abstract_inverted_index.networks, | 170 |
| abstract_inverted_index.providers | 28 |
| abstract_inverted_index.providing | 100 |
| abstract_inverted_index.scenario, | 240 |
| abstract_inverted_index.sections. | 44 |
| abstract_inverted_index.settings. | 11 |
| abstract_inverted_index.transport | 317 |
| abstract_inverted_index.upgrading | 351 |
| abstract_inverted_index.“golden | 69 |
| abstract_inverted_index.(emergency | 54 |
| abstract_inverted_index.Background | 1 |
| abstract_inverted_index.Bellwether | 101, 119 |
| abstract_inverted_index.Conclusion | 311 |
| abstract_inverted_index.Geospatial | 312 |
| abstract_inverted_index.Uganda’s | 218 |
| abstract_inverted_index.addressing | 369 |
| abstract_inverted_index.applicable | 380 |
| abstract_inverted_index.bellwether | 30, 52, 79, 331 |
| abstract_inverted_index.benchmarks | 83 |
| abstract_inverted_index.comprising | 138 |
| abstract_inverted_index.districts. | 343 |
| abstract_inverted_index.elevation. | 175 |
| abstract_inverted_index.estimating | 199 |
| abstract_inverted_index.facilities | 99, 131, 355 |
| abstract_inverted_index.hospitals, | 157 |
| abstract_inverted_index.investment | 345 |
| abstract_inverted_index.motorized) | 196 |
| abstract_inverted_index.population | 90, 167, 200, 219 |
| abstract_inverted_index.procedures | 53, 80, 103, 332 |
| abstract_inverted_index.scenarios, | 198 |
| abstract_inverted_index.scenarios: | 134 |
| abstract_inverted_index.stratified | 180, 207 |
| abstract_inverted_index.benchmarks, | 368 |
| abstract_inverted_index.experienced | 279 |
| abstract_inverted_index.laparotomy, | 57 |
| abstract_inverted_index.prehospital | 348 |
| abstract_inverted_index.procedures, | 32 |
| abstract_inverted_index.procedures. | 120 |
| abstract_inverted_index.underserved | 339 |
| abstract_inverted_index.Expert-based | 176 |
| abstract_inverted_index.hydrography, | 173 |
| abstract_inverted_index.improvements | 282 |
| abstract_inverted_index.low-resource | 10 |
| abstract_inverted_index.particularly | 337 |
| abstract_inverted_index.registration | 378 |
| abstract_inverted_index.respectively. | 292 |
| abstract_inverted_index.significantly | 63 |
| abstract_inverted_index.substantially | 318 |
| abstract_inverted_index.(walking-only) | 214 |
| abstract_inverted_index.mini-hospitals) | 39 |
| abstract_inverted_index.bellwether-capable | 354 |
| abstract_inverted_index.bellwether-capable. | 163 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| cited_by_percentile_year | |
| countries_distinct_count | 4 |
| institutions_distinct_count | 11 |
| citation_normalized_percentile |