The impact of regional deprivation and individual socio‐economic status on the prevalence of Type 2 diabetes in Germany. A pooled analysis of five population‐based studies
Bibliographic record
Abstract
AIM: Our objective was to test the hypothesis that the prevalence of Type 2 diabetes increases with increasing regional deprivation even after controlling for individual socio-economic status. METHODS: We pooled cross-sectional data from five German population-based studies. The data set contained information on n = 11,688 study participants (men 50.1%) aged 45-74 years, of whom 1008 people had prevalent Type 2 diabetes (men 56.2%). Logistic multilevel regression was performed to estimate odds ratios (OR) and 95% confidence intervals (CI) for diabetes prevalence. We controlled for sex, age and lifestyle risk factors, individual socio-economic status and regional deprivation, based on a new small-area deprivation measure, the German Index of Multiple Deprivation. RESULTS: Adjusted for sex, age, body mass index (BMI), physical activity, smoking status and alcohol consumption, the prevalence of Type 2 diabetes showed a stepwise increase in risk with increasing area deprivation [OR 1.88 (95% CI 1.16-3.04) in quintile 4 and OR 2.14 (95% CI 1.29-3.55) in quintile 5 compared with the least deprived quintile 1], even after controlling for individual socio-economic status. Focusing on individual socio-economic status alone, the risk of having diabetes was significantly higher for low compared with medium or high educational level [OR 1.46 (95% CI 1.24-1.71)] and for the lowest compared with the highest income group [OR 1.53 (95% CI 1.18-1.99)]. CONCLUSION: Regional deprivation plays a significant part in the explanation of diabetes prevalence in Germany independently of individual socio-economic status. The results of the present study could help to target public health measures in deprived regions.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.012 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".