Economic Analysis of Rotavirus‐Associated Diarrhea in the Metropolitan Toronto and Peel Regions of Ontario
Bibliographic record
Abstract
OBJECTIVE: To measure the economic cost of rotavirus-associated diarrhea for a selected group of families, in a nonexperimental setting, and to estimate the factors that influence these costs. DESIGN: Use and other socioeconomic data from a family survey (the Pediatric Rotavirus Epidemiology Study for Immunization) of children who tested positive for rotavirus were collected for the metropolitan Toronto and Peel regions of Ontario during the rotavirus season of 1997-1998. Service costs were estimated from provider data. A statistical regression analysis was used to explain the variances of provincial health care costs, prescription drug costs and indirect (work-loss) costs. SETTING: Data were collected in hospitals, emergency rooms, paediatric practices, primary care clinics and licensed daycare centres. Hospital coverage was wide, but community coverage was not. PATIENTS AND OTHER PARTICIPANTS: Children with diarrhea were tested for rotavirus. Those who tested positive and whose parents consented for their children to participate were included in the study. INTERVENTIONS: None MAIN OUTCOME MEASURES: The main outcomes were provincial health care costs, drug costs, nonmedical costs and the number of days of work missed by parents per child, as well as factors that determine these costs. RESULTS: Children in the most severe category incurred costs of $2,663/person, and those in the least severe categories incurred costs of approximately $350/person. The most important determinant to explain provincial health care costs was the number of health problems that the child had before having rotavirus. Costs due to work loss of parents were considerable for children in all severity groups and were influenced by family working conditions. CONCLUSIONS: When considering the economic implications of rotavirus, prior health status should be considered and indirect costs should be recognized for their importance.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".