Spatial Analysis Risk Factors of Pneumonia Incidence in Toddlers Gowa Regency
Bibliographic record
Abstract
Background: Pneumonia is one of the highest causes of death in children under five years old in the world. Globally, the number of under-five deaths due to pneumonia is estimated to reach up to 700,000 cases per year. Objectives: This study aimed to spatially analyze the risk factors for pneumonia incidence among under-fives in Gowa Regency in 2021-2023. Methods: This study used an analytic observational with an ecological study design. The population in this study was all cases of pneumonia among under-fives in Gowa Regency in 2021-2023, totaling 1,634 cases. The sample size in this study was 18 subdistricts with the sample selection technique using the exhaustive sampling method. Results: There was a relationship between population density (r=0.470 p=0.000), poor population (r=0.422 p=0.001) and incomplete immunization status (r=0.457 p=0.000) with the incidence of pneumonia among under-fives in Gowa Regency in 2021-2023. Meanwhile, there was no association between undernutrition status (r=0.250 p=0.068) with the incidence of pneumonia among under-fives in Gowa Regency in 2021-2023. Conclusion: Although undernutrition status did not show a statistically significant association in this study, it remains an important risk factor in the susceptibility of under-fives to pneumonia and other infections. Children with undernutrition status have a weak immune system, making them susceptible to disease complications. Therefore, nutritional interventions such as the provision of supplementary food, increasing exclusive breastfeeding coverage, and nutrition education to parents still need to be developed in a sustainable manner.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".