Identifying risk factors of anemia among women of reproductive age in Rwanda – a cross-sectional study using secondary data from the Rwanda demographic and health survey 2014/2015
Bibliographic record
Abstract
BACKGROUND: Anemia among Women of Reproductive Age (WRA) continues to be among the major public health problems in many developing countries, including Rwanda, where it increased in prevalence between the 2015 and 2010 Rwanda Demographic and Health Survey (RDHS) reports. A thorough understanding of its risk factors is necessary to design better interventions. However, to the best of our knowledge, no study has been conducted in Rwanda on a nationally representative sample to assess factors associated with anemia among WRA. Accordingly, this study was conducted to address such gap. METHODS: A quantitative, cross-sectional study was conducted using data from the RDHS 2014-2015. The study population consisted of 6680 WRA who were tested for anemia during the survey. Anemia was defined as having a hemoglobin level equal to or below 10.9 g/dl for a pregnant woman, and hemoglobin level equal to or below 11.9 g/dl for a non-pregnant woman. Pearson's chi-squared test and multiple logistic regression were conducted for bivariate and multivariable analysis, respectively. RESULTS: The prevalence of anemia among WRA was 19.2% (95% CI: 18.0-20.5). Four factors were found to be associated with lower odds of anemia, including being obese (OR: 0.61, 95% CI: 0.40-0.91), being in the rich category (OR: 0.74, 95% CI: 0.63-0.87), sleeping under a mosquito net (OR: 0.85, 95% CI: 0.74-0.98), and using hormonal contraceptives (OR: 0.61, 95% CI: 0.50-0.73). Five factors were associated with higher odds of anemia, including being underweight (OR: 1.39, 95% CI: 1.09-1.78), using an intrauterine device (OR: 1.98, 95% CI: 1.05-3.75), being separated or widowed (OR: 1.35, 95% CI: 1.09-1.67), and living in the Southern province (OR: 1.45, 95% CI: 1.11-1.89) or in the Eastern province (OR: 1.41, 95% CI: 1.06-1.88). CONCLUSION: Anemia continues to pose public health challenges; novel public health interventions should consider geographic variations in anemia risk, seek to improve women's economic statuses, and strengthen iron supplementation especially for Intrauterine device users. Additionally, given the association between anemia and malaria, interventions to prevent malaria should be enhanced.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".