Hotspots and Regional Variation in Smoking Prevalence Among 514 Districts in Indonesia: Analysis of Basic Health Research 2018
Bibliographic record
Abstract
BACKGROUND: The prevalence among adult men in Indonesia is among the highest in the world. Objective: Our study examines the hotspots and regional variation in smoking prevalence among 514 districts in Indonesia. METHODS: Taking advantage of the latest national health survey (Basic Health Research, Riskesdas 2018), which included smoking prevalence representative at the district level. We assessed the smoking prevalence among male and female adults (15+ years) and youth (13-14 years). We conducted geospatial analyses, using ArcMap 10.6, including quintile analysis (mapping the smoking prevalence by quintile for each district) and hotspot analysis (using Getis-Ord Gi* statistics to produce the hotspots, areas with a significantly higher density of advertisements). We also conducted quantitative analyses, using Stata 15.1, on geographic disparity, including region and urbanicity. RESULTS: We found huge disparity in smoking prevalence between districts, ranging from 9 to 81% for men, 0 to 50% for, 0 to 41% for women, and 0 to 50% for girls. We found up to 62 and 47 smoking hotspots among males and females, respectively. The poorest districts had significantly higher smoking prevalence among men but lower smoking prevalence among boys, and less educated districts had higher smoking prevalence among women. CONCLUSION: There were significant hotspots and regional variations among 514 districts in Indonesia.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 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".