The healthcare and economic burden associated with inadequate risk factor control for type 2 diabetes in Hong Kong: A population‐based modelling study
Bibliographic record
Abstract
AIM: To estimate the healthcare and economic burden associated with improved risk factor control for people with type 2 diabetes in Hong Kong over 10 years. MATERIALS AND METHODS: We obtained population-based data from electronic healthcare records of the Hong Kong Hospital Authority. Risk factor targets were defined by American Diabetes Association guidelines. We applied a validated patient-level diabetes outcomes model (Chinese Hong Kong Integrated Modelling and Evaluation) to estimate the health and economic outcomes for all individuals with type 2 diabetes (n = 526 672) in Hong Kong in 2021. Immediate risk factor control was compared to baseline over 10 years. Costs were estimated from a healthcare provider perspective. RESULTS: Most people (84.9%) failed to achieve optimal combined risk factors control (glycated haemoglobin, blood pressure and low-density lipoprotein-cholesterol) at baseline. Combined control was associated with population-level increases in quality-adjusted life-years (QALYs) of 17 605 and healthcare cost savings of US$ 106.7 million over 10 years. Glycaemic control solely yielded the greatest QALY increases and had the highest cost savings (US$ 29.0 million) over 10 years. CONCLUSIONS: The substantial population health and economic burden of inadequate risk factor control for individuals with diabetes in Hong Kong can potentially be mitigated through enhanced adherence, highlighting the need for effective and intensive interventions.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".