Socio‐economic differences in cardiovascular disease risk factor prevalence in people with type 2 diabetes in Scotland: a cross‐sectional study
Bibliographic record
Abstract
Abstract Aim To describe the association between socio‐economic status and prevalence of key cardiovascular risk factors in people with type 2 diabetes in Scotland. Methods A cross‐sectional study of 264 011 people with type 2 diabetes in Scotland in 2016 identified from the population‐based diabetes register. Socio‐economic status was defined using quintiles of the area‐based Scottish Index of Multiple Deprivation (SIMD) with quintile (Q)1 and Q5 used to identify the most‐ and least‐deprived fifths of the population, respectively. Logistic regression models adjusted for age, sex, health board, history of cardiovascular disease and duration of diabetes were used to estimate odds ratios (ORs) for Q1 compared with Q5 for each risk factor. Results The mean (sd) age of the study population was 66.7 (12.8) years, 56% were men, 24% were in Q1 and 15% were in Q5. Crude prevalence in Q1/Q5 was 24%/8.8% for smoking, 62%/49% for BMI ≥ 30 kg/m2, 44%/40% for HbA1c ≥ 58 mmol/mol (7.5%), 31%/31% for systolic blood pressure (SBP) ≥ 140 mmHg, and 24%/25% for total cholesterol ≥ 5 mmol/l, respectively. ORs [95% confidence intervals (CI)] were 3.08 (2.95–3.21) for current smoking, 1.48 (1.44–1.52) for BMI ≥ 30 kg/m2, 1.11 (1.08–1.15) for HbA1c ≥ 58 mmol/mol (7.5%), 1.03 (1.00–1.06) for SBP ≥ 140 mmHg and 0.87 (0.84–0.90) for total cholesterol ≥ 5 mmol/l. Conclusions Socio‐economic deprivation is associated with higher prevalence of smoking, BMI ≥ 30 kg/m2 and HbA1c ≥ 58 mmol/mol (7.5%), and lower prevalence of total cholesterol ≥ 5 mmol/l among people with type 2 diabetes in Scotland. Effective approaches to reducing inequalities are required as well as reducing risk factor prevalence across the whole population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".