Evaluating the medical direct costs associated with prematurity during the initial hospitalization in Rwanda: a prevalence based cost of illness study
Bibliographic record
Abstract
Abstract Background Prematurity is still the leading cause of global neonatal mortality, Rwanda included, even though advanced medical technology has improved survival. Initial hospitalization of premature babies (PBs) is associated with high costs which have an impact on Rwanda’s health budget. In Rwanda, these costs are not known, while knowing them would allow better planning, hence the purpose and motivation for this research. Methods This was a prospective cost of illness study using a prevalence approach conducted in 5 hospitals (University Teaching Hospital of Butare, Gisenyi, Masaka, Muhima, and Ruhengeri). It included PBs admitted from June to July 2021 followed up prospectively to determine the medical direct costs (MDC) by enumerating the cost of all inputs. Descriptive analyses and ordinary least squares regression were used to illustrate factors associated with and predictive of mean cost. The significance level was set at p < 0.05. Results A total of 123 PBs were included. Very preterm and moderate PBs were 36.6% and 23.6% respectively and the average birth weight (BW) was 1724 g (SD: 408.1 g). The overall mean MDC was $237.7 per PB (SD: $294.9) representing 28% of Gross Domestic Product (GDP) per capita per year. Costs per PB varied with weight category, prematurity degree, hospital level, and length of stay (LoS) among other variables. MDC was dominated by drugs and supplies (65%) with oxygen being an influential driver of MDC accounting for 38.4% of total MDC. Birth weight, oxygen therapy, and hospital level were significant MDC predictive factors. Conclusion This study provides an in-depth understanding of MDC of initial hospitalization of PBs in Rwanda. It also indicates predictive factors, including birth weight, which can be managed through measures to prevent or delay preterm birth. Implication for prematurity prevention and management The results suggest a need to revise the benefits and entitlements of insured people to include drugs and interventions not covered that are essential and where there are no alternatives. Having oxygen plants in hospitals may reduce oxygen-related costs. Furthermore, interventions to reduce prematurity should be evaluated using cost-effectiveness analysis since its overall burden is high.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".