Factors influencing the use of malaria prevention strategies by women in Senegal: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: The World Health Organization (WHO) recommends the use of insecticide-treated nets (ITNs) and intermittent preventive treatment in pregnancy (IPTp) as a cost-effective intervention for the prevention of malaria during pregnancy in endemic areas. This study was conducted to investigate: (1) the extent of use of both IPTp and ITNs, and (2) conduct multinomial regression to identify factors affecting the optimal usage of IPTp and ITNs among women with a recent pregnancy in Senegal. METHODS: Data was drawn from the 2013-2014 Demographic and Health Survey. A total of 4616 women aged 15-49 years old, who had a recent pregnancy were analyzed. Multinomial logistic regression model was used to assess factors associated with optimal uptake of malaria preventive strategies (both IPTp and ITN use). RESULTS: Amongst women who had a recent pregnancy, less than half of them used ITNs (46.84%) however, 80.35% reported taking IPTp during their last pregnancy. Overall, 37.51% reported using the optimal malaria preventive strategies. Women aged 35-49 years and living in the richer or middle wealth quintile were more likely to use optimal prevention methods. Pregnant women living in Diourbel, Saint-Louis, Thies, Louga, Fatick and Matam were more likely to use both IPTp-SP and ITNs compared to those living in Dakar. Additionally, women who initiated antenatal care in at least at 6 weeks of pregnancy or who attended four antenatal visits or more were more likely to use optimal malaria preventive methods during pregnancy. CONCLUSIONS: This study has shown important factors that influence the uptake of malaria prevention methods during pregnancy in Senegal. These findings highlight the need for targeted preventive strategies when designing and implementing policies aimed at improving the uptake of these measures during pregnancy in Senegal.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".