Health‐related quality of life in subjects with and without Type 2 diabetes: pooled analysis of five population‐based surveys in Germany
Bibliographic record
Abstract
AIMS: To estimate population values of health-related quality of life (HRQL) in subjects with and without Type 2 diabetes mellitus across several large population-based survey studies in Germany. Systematic differences in relation to age and sex were of particular interest. METHODS: Individual data from four population-based studies from different regions throughout Germany and the nationwide German National Health Interview and Examination Survey (GNHIES98) were included in a pooled analysis of primary data (N = 9579). HRQL was assessed using the generic index instrument SF-36 (36-item Short Form Health Survey) or its shorter version, the SF-12 (12 items). Regression analysis was carried out to examine the association between Type 2 diabetes and the two component scores derived from the SF-36/SF-12, the physical component summary score (PCS-12) and the mental component summary score (MCS-12), as well as interaction effects with age and sex. RESULTS: The PCS-12 differed significantly by -4.1 points in subjects with Type 2 diabetes in comparison with subjects without Type 2 diabetes. Type 2 diabetes was associated with significantly lower MCS-12 in women only. Higher age was associated with lower PCS-12, but with an increase in MCS-12, for subjects with and without Type 2 diabetes. CONCLUSIONS: Pooled analysis of population-based primary data offers HRQL values for subjects with Type 2 diabetes in Germany, stratified by age and sex. Type 2 diabetes has negative consequences for HRQL, particularly for women. This underlines the burden of disease and the importance of diabetes prevention. Factors that disadvantage women with Type 2 diabetes need to be researched more thoroughly.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".