Malaria in the Limbé River valley of northern Haiti: a hospital-based retrospective study, 1975-1997
Bibliographic record
Abstract
In the Limbé River valley of northern Haiti a retrospective study at the Bon Samaritain Hospital (BSH) determined the total number of cases and the cyclical nature of malaria from 1975 through 1997, examined the relationship between rainfall and malaria from 1975 through 1985, and compared the incidence of malaria at that hospital with general trends for Haiti for 1975 through 1996 as reported by the World Health Organization (WHO). During 1975-1997, 27,078 positive cases of malaria were diagnosed at BSH; 50% of these cases occurred during 16 weeks out of the year, during a summer peak in June and July and a winter peak in December and January. For 1975-1985, there was no significant correlation between the incidence of malaria and annual rainfall. The strongest correlation was observed between weekly rainfall and weekly incidence of malaria when the data was staggered to allow a lag of 9-11 weeks between rainfall and new malaria cases. The lag period is explained by the time required for the creation of breeding sites after rain, the life cycles of the Anopheles albimanus mosquito and the Plasmodium falciparum parasite, and the incubation period for falciparum malaria. The incidence of malaria in the Limbé River valley loosely followed the trends in all of Haiti and also supported WHO reports indicating that malaria in Haiti has been in a general decline since the mid-1980s. By showing the seasonal trends for malaria in the Limbé valley and the relationship between rainfall and malaria over an extended time period, this study provides a means to measure the effectiveness of malaria control efforts in the region.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".