Pediatric cryptosporidiosis: An evaluation of health care and societal costs in Peru, Bangladesh and Kenya
Bibliographic record
Abstract
Cryptosporidium is a leading cause of pediatric diarrhea in resource-limited settings; yet, few studies report the health care costs or societal impacts of this protozoan parasite. Our study examined direct and indirect costs associated with symptomatic cryptosporidiosis in infants younger than 12 months in Kenya, Peru and Bangladesh. Inputs to the economic burden model, such as disease incidence, population size, health care seeking behaviour, hospital costs, travel costs, were extracted from peer-reviewed literature, government documents, and internationally validated statistical tools for each country. Indirect losses (i.e. caregiver income loss, mortality, and growth faltering) were also estimated. Our findings suggest that direct treatment costs per symptomatic cryptosporidiosis episode were highest in Kenya ($59.01), followed by Peru ($23.32), and Bangladesh ($7.62). The total annual economic impacts for the 0-11 month cohorts were highest in Peru ($41.5M; range $0.88-$599.3M), followed by Kenya ($37.4M; range $1.6-$804.5M) and Bangladesh ($9.6M, range $0.28-$91.5M). For all scenarios, indirect societal costs far outweighed direct treatment costs. These results highlight the critical need for innovative improvements to current prevention, diagnostic and treatment strategies available in resource poor settings, as well as the need for solutions that span multiple disciplines including food and water safety, sanitation and livestock production.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".