Lack of household clustering of malaria in a complex humanitarian emergency: implications for active case detection
Bibliographic record
Abstract
BACKGROUND: Malaria contributes to elevated morbidity and mortality in populations displaced by conflict in tropical zones. In an attempt to reduce malaria transmission in an internally displaced persons (IDP) camp in eastern Democratic Republic of Congo (DRC), we tested a strategy of active case detection of household contacts of malaria cases. METHODS: Prospective community-based survey. RESULTS: From a convenience sample of 100 febrile patients under 5 years of age from the IDP camp presenting to a nearby clinic for management of a fever episode, 19 cases of uncomplicated malaria and 81 controls with non-malarial febrile illness (NFMI) were diagnosed. We engaged community health workers in the IDP camp to screen their household contacts for malaria using rapid diagnostic tests. We detected 29 cases of malaria through this active case-finding procedure. Household contacts of children with uncomplicated malaria were no more likely to have positive Plasmodium falciparum antigenemia than controls with NFMI (OR 0.89, 95% CI 0.33 to 2.4, p = 1.0), suggesting that malaria cases did not cluster at the household level. However, household contacts reporting mild symptoms at the time of community survey (headache, myalgia) had a higher odds of malaria than asymptomatic individuals (OR 14 (95% CI 4.2-48), p ≤ 0.001 and 18 (95% CI 5.9-54), p ≤ 0.001, respectively). CONCLUSION: Screening household contacts of malaria cases was not an efficient case-finding strategy in a Congolese IDP camp. Symptom-based screening may be a simpler and cost-effective method to identify individuals at increased risk of malaria for targeted screening and treatment in an IDP camp.
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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 | 0.050 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".