Prevalence, Trends and Predictors of Small Size Babies in Nigeria: Analysis of Data from Two Recent Nigeria Demographic and Health Surveys
Bibliographic record
Abstract
Background: Despite low birth weight (LBW) role on child growth, development, and survival in developing countries, it has not been given the desired priority in terms of research, at the national level in Nigeria. Our study aims to estimate the trend in the prevalence of small size babies and to identify its predictors using nationally representative data. Methods: We used the 2013 and 2018 data from the Nigeria Demographic and Health Survey using the statistical methods of descriptive analysis and logistic regression modelling. Results: The proportion of babies reported to have small size at birth in Nigeria declined from 14.9% in 2013 to 13.7% in 2018. Various factors from demographic, socio-economic, and health-seeking behaviour were identified as significant predictors. Women who received iron pills and tetanus toxoids during pregnancy had at most 79% and 80% less risk of having small size babies, respectively, than those who received none of these two. Female children had at least 21% more chance of being small in size than male children. Other key predictors were geopolitical region, maternal age at child birth, maternal literacy level, wealth status, religion, source of water supply, number of ANC visits during pregnancy, and desirousness of pregnancy. Conclusion: In light of the adverse effects of low birth weight on child well-being, we recommend the implementation and prioritization of active, resourceful public health interventions that account for the findings of this study, if Nigeria is to sustain the progress achieved so far in reducing its current high rate.
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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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".