Changes in hyperglycaemia-related testing for prediabetes and type 2 diabetes mellitus management: a prospective, cross-sectional survey of 16 years of general practice data from Australia
Bibliographic record
Abstract
Abstract Background The rising prevalence of prediabetes increases the population risk of type 2 diabetes mellitus (T2DM), metabolic syndrome and cardiovascular disease. Early identification by General Practitioners (GPs) provides opportunities for lifestyle modifications that can lower these risks. Methods This study examined 16 years of hyperglycaemia-related testing for patients in Australia aged 13 years or older with, or at risk of a diagnosis of T2DM. The Bettering the Evaluation and Care of Health (BEACH) study is a national cross-sectional survey, with a single-stage, cluster sampling design. Approximately 1,000 GPs were randomly selected annually (2000/01–20,015/16) from across Australia, who each recorded details of 100 consecutive clinical encounters with consenting patients. Point estimates were adjusted for intracluster correlation and GP characteristics. Results Fifteen thousand six hundred seventy nine GPs recorded details of 1,387,190 clinical encounters with patients aged 13 + years. Prediabetes and T2DM were managed at 0.25% (95% CI: 0.24–0.27%) and 3.68% (95% CI: 3.62–3.73%) of encounters respectively. By the end of the study, the proportion of encounters where prediabetes was managed was 2.3 times higher and for T2DM, 1.5 times higher. The proportion of prediabetes (55.9%, 95% CI: 53.9–57.8%) and T2DM (27.3%, 95% CI: 26.7–27.9%) management occasions where one or more hyperglycaemia-related tests were requested were relatively stable. However, differences in the types of tests were observed. For prediabetes, glucose tolerance tests were most common but from 2014/15, requests for HbA1c tests began to increase. For T2DM, HbA1c tests were most common, and requests for one or more glucose tests gradually declined. Conclusion The observed 16-year annual trends align with the rising incidence of prediabetes and T2DM. GPs appeared to be strongly influenced by changes to the national insurance scheme and clinical guidelines for hyperglycaemia-related pathology testing. However, some GPs may have been pre-empting policy changes as there was also evidence of ‘unendorsed’ testing, notably for prediabetes, that warrants further investigation. The increasing proportion of encounters for prediabetes, coupled with a high proportion of management occasions where pathology was requested have substantial resource implications. Calls to lower the risk threshold for prediabetes screening therefore warrant an economic analysis. Ongoing, reliable, up-to-date data is needed to inform clinical practice guidelines and policy in Australia.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".