Prevalence of anemia and its associated factors among married women in 19 sub-Saharan African countries
Bibliographic record
Abstract
BACKGROUND: Sub-Saharan Africa (SSA) remains the region with the highest burden of anemia globally. Since anemia has both health and non-health-related consequences, its reduction is one of the Sustainable Development Goals. Therefore, this study aimed to examine the prevalence of anemia and its associated factors among married women in SSA. METHODS: Using Stata version-14 software, the analysis was done on 89,029 married women from the Demographic and Health Surveys of 19 countries in SSA. Pearson Chi-Square test and Binary logistic regression analyses were used to examine the factors associated with anemia. The results were presented using adjusted Odds Ratio (aOR) at a 95% Confidence Interval (CI). A p-value less than or equal to 0.05 (p ≤ 0.05) was considered statistically significant. RESULTS: The pooled analysis showed that 49.7% of married women were anemic. Of these, 1.04% and 15.05% were severely and moderately anemic respectively, and the rest 33.61% were mildly anemic. Husband education (primary school-aOR = 0.84, 95% CI; 0.71-0.99), wealth index (middle-aOR = 0.81, 95% CI; 0.68-0.96, richer-aOR = 0.69, 95% CI; 0.57-0.84, richest-aOR = 0.68, 95% CI; 0.51-0.91), modern contraceptive use (yes-aOR = 0.68, 95% CI; 0.56-0.81) and religion (Muslim-aOR = 1.27, 95% CI; 1.11-1.46, others-aOR = 0.73, 95% CI; 0.59-0.90) were factors associated with anemia among married women. CONCLUSION: The findings show that nearly half of the married women are affected by anemia. Enhancing partners' educational levels, and economic empowerment of women, strengthening family planning services, and working with religious leaders to reduce the perception and religious beliefs related to food restrictions can be the main focus to reduce the burden of anemia among married women in SSA.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".