Multilevel modelling of sociodemographic predictors of various levels of anaemia among women in Mali
Bibliographic record
Abstract
OBJECTIVE: Anaemia currently affects 40-80% of women in Africa. While risk factors for anaemia have been intensively studied, research has rarely compared risk factors between mild anaemia and moderate/severe anaemia. Also, the contribution of neighbourhood to the prevalence of anaemia has been rarely studied. The aim of the present study was to identify and compare individual and contextual factors associated with various levels of anaemia among women. DESIGN: A multilevel analysis of data from the 2001 Mali Demographic and Health Survey (n 3763) was carried out. Outcomes variables were mild anaemia, moderate-to-severe anaemia and any anaemia. Multilevel regression analyses were performed for each outcome. SETTING: Mali, West Africa. SUBJECTS: Women (n 3763) aged 15 to 49 years, including 512 pregnant women. RESULTS: Among the eleven potential risk factors included in the models, two factors were associated with mild anaemia (BMI and education), three with any anaemia (pregnancy, BMI and education) and six with moderate-to-severe anaemia (pregnancy, BMI, education, wealth, childhood residency and region of residence). Clustering of anaemia within communities was 20% for moderate-to-severe anaemia and 13% for mild anaemia. Despite significant differences in the prevalence of anaemia across regions in Mali, no difference between regions in the risk of mild anaemia was found and only the region of Gao showed a significantly higher risk of moderate-to-severe anaemia. CONCLUSIONS: The findings indicate that sociodemographic risk factors as well as clustering of anaemia varies with the severity of anaemia. Specific studies are needed to identify risk factors of mild anaemia as well as its consequences, as mild anaemia accounts for 20-40% of total prevalence of anaemia in Africa.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".