Critical care capacity in Africa: Post-pandemic ICU capacity, service readiness, and patient profiles across public and private hospitals in Ethiopia
Bibliographic record
Abstract
Background The COVID-19 pandemic highlighted critical care disparities in low-resource settings. Ethiopia implemented interventions to strengthen ICU capacity post-pandemic, yet gaps persist. This study assesses national ICU capacity following the COVID-19 pandemic, including private facilities, and identifies challenges to equitable critical care delivery. Method A cross-sectional nationwide assessment was conducted in public and private hospitals across Ethiopia. Data were collected through site visits and standardized questionnaires, assessing ICU capacity, staffing, equipment, protocols, and emergency preparedness. Patient-level data were also collected to assess common admission characteristics. Findings were compared with pre-COVID-19 baseline data to evaluate progress and identify gaps following the implementation of the interventions. Result A total of 159 facilities were included; 117 out of 159 (73.5%) were governmental, and 42 out of 159 (26·4%) were private facilities, assessing a total of 1028 beds. Post-COVID-19, public ICU facilities increased from 51 to 117, with beds increasing from 324 to 762. Private facilities contributed 266 beds (25.9% of national capacity). Improvements included 24/7 ICU-trained physician availability (52.1% vs. 29.0% pre-COVID) and disaster preparedness plans (21.4% vs. 6.0%). Persistent gaps included advanced hemodynamic monitoring (5/117 public, 3/42 private facilities) and organ support (9/117 public ICUs). Among 279 admissions (mean age, 39.1 years; 55.2% male), neurological (32.1%) and respiratory (25.8%) conditions predominated, with sepsis accounting for 29.4% of cases. Hypertension (25.1%) and diabetes (17.2%) were common comorbidities. Interpretation This study reveals significant growth in Ethiopia’s ICU infrastructure and workforce following the COVID-19 pandemic, with a threefold increase in ICU beds and improved distribution. However, gaps in advanced monitoring, organ support, and referral coordination reveal systemic issues in the readiness of critical care. The high incidence of sepsis among mostly young patients indicates the need for targeted investment in essential emergencies and critical care at all facility levels. Strengthening public-private integration, standardizing referral protocols, and scaling low-cost, high-impact interventions are key for enhancing equity and outcomes in resource-limited settings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".