Distribution and Determinants of Low Birth Weight in Developing Countries
Bibliographic record
Abstract
OBJECTIVES: Low birth weight (LBW) is a major public health concern, especially in developing countries, and is frequently related to child morbidity and mortality. This study aimed to identify key determinants that influence the prevalence of LBW in selected developing countries. METHODS: Secondary data analysis was conducted using 10 recent Demography and Health Surveys from developing countries based on the availability of the required information for the years 2010 to 2013. Associations of demographic, socioeconomic, community-based, and individual factors of the mother with LBW in infants were evaluated using multivariate logistic regression analysis. RESULTS: The overall prevalence of LBW in the study countries was 15.9% (range, 9.0 to 35.1%). The following factors were shown to have a significant association with the risk of having an LBW infant in developing countries: maternal age of 35 to 49 years (adjusted odds ratio [aOR], 1.7; 95% confidence interval [CI], 1.2 to 3.1; p<0.01), inadequate antenatal care (ANC) (aOR, 1.7; 95% CI, 1.1 to 2.8; p<0.01), illiteracy (aOR, 1.5; 95% CI, 1.1 to 2.7; p<0.001), delayed conception (aOR, 1.8; 95% CI, 1.4 to 2.5; p<0.001), low body mass index (aOR, 1.6; 95% CI, 1.2 to 2.1; p<0.001) and being in the poorest socioeconomic stratum (aOR, 1.4; 95% CI, 1.1 to 1.8; p<0.001). CONCLUSIONS: This study demonstrated that delayed conception, advanced maternal age, and inadequate ANC visits had independent effects on the prevalence of LBW. Strategies should be implemented based on these findings with the goal of developing policy options for improving the overall maternal health status in developing 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 | 0.002 |
| 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 | 0.001 | 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".