Differential effects of socio-demographic factors on maternal haemoglobin concentration in three sub-Saharan African Countries
Bibliographic record
Abstract
Abstract Objective To investigate the effects of socio-demographic factors on maternal haemoglobin (Hb) at different points of the conditional distribution of Hb concentration. Methods We analysed the Demographic and Health Surveys data from Ghana, Democratic Republic of the Congo (DRC) and Mozambique, using Hb concentration of mothers aged 15-49 years as an outcome of interest. We utilise quantile regression to estimate the effects of the socio-demographic factors across specific points of the maternal Hb concentration. Results The results showed crucial differences in the effects of socio-demographic factors along the conditional distribution of Hb concentration. In Ghana, maternal education had a positive effect on Hb concentration in the 5 th and 10 th quantiles. The positive effect of education on maternal Hb concentration occurred across all quantiles in Mozambique, with the largest effect at the lowest quantile (5 th ) and the smallest effect at the highest quantile (90 th ). In contrast, maternal education had a negative effect on the Hb concentration of mothers in the 50 th , 75 th and 90 th quantiles in DRC. Maternal body mass index (BMI) had a positive effect on Hb concentration of mothers in the 5 th , 10 th , 50 th and 90 th , and 5 th to 50 th quantiles in Ghana and Mozambique, respectively. Breastfeeding had a significant positive effect on Hb concentration across all countries, with the largest effect on Hb concentration of mothers in the lower quantiles. All the household wealth indices had positive effects on maternal Hb concentration across quantiles in Mozambique, with the largest effect among mothers in the upper quantiles. However, in Ghana, living in a poor wealth index was inversely related with Hb concentration of mothers in the 5 th and 10 th quantiles. Conclusions Our results showed that the effects of socio-demographic factors on maternal Hb concentration vary along its distribution. Interventions to address maternal anaemia should take these variations into account to identify the most vulnerable groups. What this study adds Quantile regression can be used effectively to analyse anaemia data Socio-demographic factors have differential effects on Hb at different points of its distribution Interpreting results based on the mean effect (as in OLS) only provides a partial picture Breastfeeding has positive effect on maternal Hb concentration The use of multicountry data revealed differences and commonalities between countries
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".