Antibiotic Shortages among Public Sector Hospitals Across Sub-Saharan Africa: A Protocol for an Electronic Survey to Gain Understanding
Bibliographic record
Abstract
Background: Antibiotic shortages in public hospitals across sub-Saharan Africa represent a growing public health crisis, increasing antimicrobial resistance (AMR). While European countries have conducted several surveys on medicine shortages, similar data are scarce in sub-Saharan Africa. Ensuring the availability of critical antibiotics among hospitals across Africa is essential for effective treatment of infectious diseases and for implementing targeted Antimicrobial Stewardship Programmes (ASPs) to reduce AMR. Objective: To evaluate the scope, causes and potential solutions regarding antibiotic shortages in public sector hospitals across sub-Saharan Africa. Subsequently, use the findings to make recommendations for future strategies, including ASPs. Methods: A cross-sectional descriptive survey will be undertaken among hospital pharmacists, nurses, physicians and other healthcare professionals across sub-Saharan Africa. An electronic questionnaire, based on the European Association of Hospital Pharmacists (EAHP) model and available in English, French and Portuguese, will gather data on the frequency, types, causes and proposed solutions to antibiotic shortages in hospitals. The survey will run for two months, leveraging existing professional networks to enhance participation. Open-ended responses will be summarised in in Excel. Descriptive statistics will include frequencies, percentages, means and standard deviations, and will be calculated using STATA ® to summarise both categorical and continuous variables. Discussion and Conclusion: This study will provide comprehensive data on the prevalence and drivers of antibiotic shortages in public hospitals in this important region. The findings will inform national and regional health policies, strengthen supply chain resilience and support ASP implementation. This will be the first time that such a comprehensive survey will be conducted across sub-Saharan Africa as part of the drive to reduce AMR.
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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 | 0.051 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.047 | 0.015 |
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".