Human Resource and Funding Constraints for Essential Surgery in District Hospitals in Africa: A Retrospective Cross-Sectional Survey
Bibliographic record
Abstract
BACKGROUND: There is a growing recognition that the provision of surgical services in low-income countries is inadequate to the need. While constrained health budgets and health worker shortages have been blamed for the low rates of surgery, there has been little empirical data on the providers of surgery and cost of surgical services in Africa. This study described the range of providers of surgical care and anesthesia and estimated the resources dedicated to surgery at district hospitals in three African countries. METHODS AND FINDINGS: We conducted a retrospective cross-sectional survey of data from eight district hospitals in Mozambique, Tanzania, and Uganda. There were no specialist surgeons or anesthetists in any of the hospitals. Most of the health workers were nurses (77.5%), followed by mid-level providers (MLPs) not trained to provide surgical care (7.8%), and MLPs trained to perform surgical procedures (3.8%). There were one to six medical doctors per hospital (4.2% of clinical staff). Most major surgical procedures were performed by doctors (54.6%), however over one-third (35.9%) were done by MLPs. Anesthesia was mainly provided by nurses (39.4%). Most of the hospital expenditure was related to staffing. Of the total operating costs, only 7% to 14% was allocated to surgical care, the majority of which was for obstetric surgery. These costs represent a per capita expenditure on surgery ranging from US$0.05 to US$0.14 between the eight hospitals. CONCLUSION: African countries have adopted different policies to ensure the provision of surgical care in their respective district hospitals. Overall, the surgical output per capita was very low, reflecting low staffing ratios and limited expenditures for surgery. We found that most surgical and anesthesia services in the three countries in the study were provided by generalist doctors, MLPs, and nurses. Although more information is needed to estimate unmet need for surgery, increasing the funds allocated to surgery, and, in the absence of trained doctors and surgeons, formalizing the training of MLPs appears to be a pragmatic and cost-effective way to make basic surgical services available in underserved areas. Please see later in the article for the Editors' Summary.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".