Social and Environmental Determinants of Childhood Stunting in Indonesia: National Cross-Sectional Study
Bibliographic record
Abstract
Background: The cause-effect of stunting is known as a complex factor, including family, environmental, social, and cultural factors, in stunting among children. Yet, the latest updated associated factors emphasizing on social and environmental factors are still limited. Objective: This study aimed to analyze the latest evidence on the factors associated with stunting, with a particular focus on various factors. Methods: A secondary data analysis using the 2023 Indonesia Health Survey (Survei Kesehatan Indonesia [SKI] 2023) was conducted. This study analyzed a total of 78,049 (or 81,068 if weighted) children aged 5 years and younger who had a complete response to all interest variables. Bivariate analysis using the Pearson χ2 test with a P value of <.05 for determining a significant association and a multivariate analysis for further analysis of the association between the outcome and each predictor were implemented. Results: The prevalence of stunting in this study was 15,958/78,049 children (19.69%). In the adjusted analysis, immunization status (adjusted odds ratio [aOR] 1.34, 95% CI 1.22-1.48; P<.001) and KPS (Kartu Perlindungan Sosial; Social Protection Card) ownership (aOR 1.13, 95% CI 1.05-1.21; P<.001) were significantly associated with higher odds of stunting. Conversely, children in the wealthiest quintile were significantly less likely to experience stunting compared to those from the poorest families (aOR 0.47, 95% CI 0.42-0.52; P<.001). Other variables, such as household water sources (aOR 1.18, 95% CI 1.00-1.37; P=.04), and geographical location, particularly in Sulawesi (aOR 1.23, 95% CI 1.14-1.33; P<.001) and Papua and Maluku (aOR 1.20, 95% CI 1.08-1.33; P<.001), were also significantly associated with increased odds of stunting. Conclusions: Not receiving immunization, consuming water from unimproved sources, ownership of a Social Protection Card, and living in regions such as eastern Indonesia were significantly associated with childhood stunting. These findings emphasize that social and environmental factors remain critical determinants of stunting. Improving multifaceted and holistic interventions, with a focus on immunization coverage, good water access, social protection, and reducing regional disparities, is essential to accelerate progress toward stunting reduction targets.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".