Spatial Analysis of Prediabetes and Associated Risk Factor Prevalence Among Late Adolescents in San Luis Potosí, México
Bibliographic record
Abstract
Introduction The prevalence of prediabetes is increasing worldwide. However, the determinants that contribute to its onset in young individuals remain poorly understood. An essential aspect of directing control and preventive initiatives is comprehending the geographical distribution of these disorders and pinpointing regions with a high prevalence. Objective The objective of this study was to determine the spatial distribution of prediabetes and associated risk factor prevalence among late adolescents in the metropolitan area of San Luis Potosí (MASLP), México, during the years 2008, 2009, and 2010. Methods This was a cross-sectional study that included 15,672 participants between the ages of 18 and 21 years. We made a cartographic overlay of the body mass index (BMI), systolic blood pressure (BP), diastolic BP, and impaired fasting glucose (IFG) using a 2010 marginalization index. Results The prevalence of prediabetes was 5.5%, whereas the prevalence of overweight and obesity remained stable for three years. However, in 2010, the prevalence of both diastolic BP and prediabetes increased. Spatial analysis revealed that the urban basic geostatistical area (Area Geo-Estadística Básica (AGEB)), with 11-20 participants with prediabetes, was mainly concentrated in the areas of medium and low marginalization throughout all years. Conclusion We found a yearly increase in prediabetes prevalence and increased diastolic BP. Prediabetes varies across the region. MASLP has statistically detected significant high hot spots in prediabetes. The findings of this study are valuable for directing resource distribution and highlighting intervention programs that target modifiable prediabetes predictors. To improve health coverage, it is necessary to consider different local realities.
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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 | 0.001 |
| 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 | 0.000 |
| 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".