Dengue occurrence relations and serology: cross-sectional analysis of results from the Guerrero State, Mexico, baseline for a cluster-randomised controlled trial of community mobilisation for dengue prevention
Bibliographic record
Abstract
BACKGROUND: The Mexican arm of the Camino Verde trial of community mobilisation for dengue prevention covered three coastal regions of Guerrero state: Acapulco, Costa Grande and Costa Chica. A baseline cross-sectional survey provided data for community mobilisation and for adapting the intervention design to concrete conditions in the intervention areas. METHODS: Trained field teams constructed community profiles in randomly selected clusters, based on observation and key informant interviews. In each household they carried out an entomological inspection of water containers, collected information on socio-demographic variables and cases of dengue illness among household members in the last year, and gathered paired saliva samples from children aged 3-9 years, which were subjected to ELISA testing to detect recent dengue infection. We examined associations with dengue illness and recent dengue infection in bivariate and then multivariate analysis. RESULTS: In 70/90 clusters, key informants were unable to identify any organized community groups. Some 1.9% (1029/55,723) of the household population reported dengue illness in the past year, with a higher rate in Acapulco region. Among children 3-9 years old, 6.1% (392/6382) had serological evidence of recent dengue infection. In all three regions, household use of anti-mosquito products, household heads working, and households having less than 5 members were associated with self-reported dengue illness. In Acapulco region, people aged less than 25 years, those with a more educated household head and those from urban sites were also more likely to report dengue illness, while in Costa Chica and Costa Grande, females were more likely to report dengue illness. Among children aged 3-9 years, those aged 3-4 years and those living in Acapulco were more likely to have evidence of recent dengue infection. CONCLUSIONS: The evidence from the baseline survey provided important support for the design and implementation of the trial intervention. The weakness of community leadership and the relatively low rates of self-reported dengue illness were challenges that the Mexican intervention team had to overcome. The higher dengue illness occurrence among women in Costa Grande and Costa Chica may help explain why women participated more than men in activities during the Camino Verde trial.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".