Risk Factors Associated With Dengue in District Khyber, Khyber Pakhtunkhwa, Pakistan, From September 2 to November 24, 2019: Case-Control Study
Bibliographic record
Abstract
Background Dengue is a vector-borne disease endemic to Pakistan as well as to Khyber Pakhtunkhwa. A total of 300 confirmed cases of dengue were reported in District Khyber in 2019, where a prompt response was initiated by the Disease Surveillance and Response Unit. Subsequently, a study was planned to identify the risk factors associated with dengue and to propose recommendations for containment of the disease. Objective This study aimed to assess the risk factors associated with dengue in District Khyber and to provide recommendations for improving the existing system and preventing dengue. Methods A case-control study was conducted in District Khyber from September 2 to November 24, 2019. Cases were enrolled from health care facilities based on predefined criteria, which included the presence of clinical signs and symptoms as well as laboratory confirmation of dengue NS1 antigen. Controls were enrolled from the community at a case-control ratio of 1:2. Data were collected using a pretested questionnaire in face-to-face interviews. Results A total of 300 cases were enrolled; 87% (263) of cases were men. This study found that 45% (269/601; odds ratio [OR] 15, 95% CI 9.9-24.07; P<.001) of enrolled participants did not use bed nets and 39% (236/601; OR 1.7, 95% CI 1.1-2.4; P<.001) did not use mosquito repellents. A total of 39% (233/601; OR 16.6, 95% CI 11.11-24.93; P<.001) of enrolled participants who tested positive for dengue were neighbors, 35% (213/601; OR 1.47, 95% CI 1.04-2.0; P<.001) lived in a joint family, and 40% (241/601; OR 3.32, 95% CI 2.3-4.7; P<.001) slept outdoors; these factors were significantly associated with dengue. Conclusions Dengue is a preventable disease and can be controlled by the proper use of bed nets and mosquito repellents, modification of sleeping habits, and improvement in family structure. Dengue management training for health care personnel and community awareness are recommended.
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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) | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".