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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it