Bibliographic record
Abstract
OBJECTIVES: As economic evaluation methods evolve, their applicability to special populations, such as children, has received increased scrutiny. The objective was to review paediatric health economic evaluations published over the last quarter century, comment on trends, discuss gaps between developed and developing nations, and point to future directions for research. METHODS: Data compiled for the Paediatric Economic Database Evaluation (PEDE) project to 2003 were used to describe temporal and geographic trends and evaluate the frequency of intervention categories and conditions studied. RESULTS: The volume of paediatric health economic evaluations rose rapidly since 1980. Studies of infective/parasitic diseases, congenital anomalies and complications of pregnancy accounted for the majority. Prevention rather than treatment was emphasized. Most evaluations performed since 1998 (78%) were cost-effectiveness analyses. Cost-utility analyses were rare. The US produced half of all publications, with the U.K. contributing 12%. Economic evaluations from developing countries were uncommon, despite an urgent need for evidence-based decision-making in these regions. The interventions studied reflected local health priorities; HIV and malaria prevention were more commonly studied in developing nations, whereas treatments for asthma and birth malformations were more often evaluated in developed nations. CONCLUSIONS: Despite global initiatives to combat disease, developing nations rely on foreign research to inform implementation of local health programs. There is a need for better methods for data transfer and extrapolation. Future research must focus on paediatric models of costs and consequences and the development of tools to measure long-term effects.
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 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 | 0.023 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.005 |
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; both teacher heads agree on what is shown here.
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".