Utilization of deworming medication and its associated factors among pregnant married women in 26 sub-Saharan African countries: a multi-country analysis
Bibliographic record
Abstract
BACKGROUND: Deworming is one of the strategies to reduce the burden of anemia among pregnant women. Globally, pregnant women in sub-Saharan Africa are more affected by anemia. Therefore, this study examined both the coverage and demographic, socioeconomic, and women empowerment-related factors associated with the utilization of deworming medication among pregnant married women in sub-Saharan Africa. METHODS: We used data from the most recent Demographic and Health Surveys of 26 countries in sub-Saharan Africa conducted between 2010 and 2019. Using Stata version-14 software, analysis was done on 168,910 pregnant married women. Bivariate and multivariable logistic regression analyses were conducted to examine the factors associated with the utilization of deworming medication. The results were presented using adjusted odds ratios (aORs) at 95% confidence intervals (CIs). RESULTS: The pooled results showed that about 50.7% (95% CI 48.2-53.3%) of pregnant married women in the studied countries took deworming medications, and this varied from as high as 84.1% in Sierra Leone to as low as 2% in Angola. Regarding sub-regional coverage, the highest and lowest coverages were seen in East Africa (67.6%, 95% CI 66.0-69.1%) and West Africa (24.3%, 95% CI 22.4-26.4%) respectively. We found higher odds of utilization of deworming medication among older pregnant married women (aOR=1.93, 95% CI 1.32-2.84), women with educated husbands (aOR=1.40, 95% CI 1.11-1.77), wealthier women (aOR=3.12, 95% CI 1.95-4.99), women exposed to media (aOR=1.46, 95% CI 1.18-1.80), and those who had four or more antenatal care visits (aOR=1.51, 95% CI 1.24-1.83). CONCLUSIONS: Enhancing women's education, disseminating information about maternal healthcare services through mass media, and ensuring that women from economically disadvantaged households benefit from national economic growth can be considered as deworming medication improvement strategies in sub-Saharan Africa. Moreover, providing more attention to adolescents or young pregnant women and increasing the number of antenatal care visits could be considered to increase deworming uptake among pregnant married women.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".