Access to urban acute care services in high- vs. middle-income countries: an analysis of seven cities
Bibliographic record
Abstract
PURPOSE: Cities are expanding rapidly in middle-income countries, but their supply of acute care services is unknown. We measured acute care services supply in seven cities of diverse economic background. METHODS: In a cross-sectional study, we compared cities from two high-income (Boston, USA and Paris, France), three upper-middle-income (Bogota, Colombia; Recife, Brazil; and Liaocheng, China), and two lower-middle-income (Chennai, India and Kumasi, Ghana) countries. We collected standardized data on hospital beds, intensive care unit beds, and ambulances. Where possible, information was collected from local authorities. We expressed results per population (from United Nations) and per acute illness deaths (from Global Burden of Disease project). RESULTS: Supply of hospital beds where intravenous fluids could be delivered varied fourfold from 72.4/100,000 population in Kumasi to 241.5/100,000 in Boston. Intensive care unit (ICU) bed supply varied more than 45-fold from 0.4/100,000 population in Kumasi to 18.8/100,000 in Boston. Ambulance supply varied more than 70-fold. The variation widened when supply was estimated relative to disease burden (e.g., ICU beds varied more than 65-fold from 0.06/100 deaths due to acute illnesses in Kumasi to 4.11/100 in Bogota; ambulance services varied more than 100-fold). Hospital bed per disease burden was associated with gross domestic product (GDP) (R (2) = 0.88, p = 0.01), but ICU supply was not (R (2) = 0.33, p = 0.18). No city provided all requested data, and only two had ICU data. CONCLUSIONS: Urban acute care services vary substantially across economic regions, only partially due to differences in GDP. Cities were poor sources of information, which may hinder their future planning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".