Using national health weeks to deliver deworming to children: lessons from Mexico
Bibliographic record
Abstract
Mexico established national health weeks (NHWs) in the early 1980s to promote childhood vaccinations. Because of the cumulative worldwide peer-reviewed scientific evidence, the recommendations of the World Health Organization and other international organisations, the political will of the Mexican government and the infrastructure provided by the NHWs, deworming was added to the NHWs in 1993. In addition to the Ministry of Health, several other government organisations participated in administering the deworming component. Tens of millions of school-age and preschool children between the ages of 2 years and 14 years now receive deworming (a single 400 mg dose of albendazole) approximately every 8 months. Between 1993 and 1998 evaluations were carried out in over 90,000 children to determine the effect of NHWs on the prevalence of geohelminth infections. In 1993, the overall prevalence of Ascaris was 20% and that of Trichuris was 15%. Prevalences decreased significantly over time (p <0.001). Treatment efficacy for Ascaris ranged from 91.6% to 85.3%, and for Trichuris, from 97.9% to 42.6%. In 1998, after conducting 12 NHWs with deworming, the respective prevalences were Ascaris 8% and Trichuris 11%. The experience of Mexico in integrating albendazole into its NHWs shows how deworming can be delivered to large numbers of at-risk children using an existing infrastructure. The NHW approach may be generalisable in other countries with successful national vaccination campaigns. The challenge remaining is to sustain the deworming programme until other longer-term behavioural, environmental and socioeconomic changes can be implemented.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".