Prescription distribution and inequities in diabetes care: A comparative analysis of continuous glucose monitoring access by diabetes status, ethnicity and socio‐economic factors in England
Bibliographic record
Abstract
BACKGROUND: Diabetes affects over 3.3 million people in England, creating a significant health and economic burden. Continuous glucose monitoring (CGM) improves diabetes management but remains unevenly accessible, especially among Black and minority groups who face onset at younger ages, higher diabetes rates and complications. Updated NICE guidelines promote CGM access for all people with T1D and certain people with T2D, yet data on prescribing patterns in England are limited. This study investigates CGM prescribing across integrated care boards (ICBs) and primary care networks (PCNs), focusing on ethnicity and deprivation, to identify and address access disparities. METHODS: Cross-sectional analysis of publicly available data examined CGM prescribing patterns across England's PCNs, focusing on ethnicity and socio-economic factors. Data from OpenPrescribing, the National Diabetes Audit and Public Health England were analysed through descriptive and inferential statistics, including regression and Intraclass Correlation Coefficient (ICC) calculations, to assess disparities in prescribing ratio per 1000 people. RESULTS: Significant disparities in CGM prescribing across PCNs and ICBs are identified, shaped by ethnicity, age and socio-economic factors. The mean items prescription ratio is 4.87 per 1000 people, ranging from 0.26 to 11.59. People with T1D are generally younger, with only 15.5% over 65, compared to 52.0% in T2D. White individuals represent 83.6% of T1D cases, while South Asians and Afro-Caribbeans are more prevalent in T2D (14.5% and 5.3%, respectively). ICBs with below-average CGM prescribing have a higher percentage of Afro-Caribbean and South Asian populations compared to ICBs with above-average prescribing. For T1D, Afro-Caribbean representation is 6.7 (SD:7.0) in lower-prescribing ICBs versus 2.1 (SD:2.8) in higher-prescribing ICBs, and for T2D, it is 8.4 (10.4) versus 1.8 (SD:3.4) South Asian representation in low-prescribing ICBs is 10.6 (SD:13.7) for T1D and 21.9 (SD:20.5) for T2D, compared to 3.2 (SD:4.9) for T1D and 6.5 (SD:9.7) for T2D in higher-prescribing ICBs. CGM prescribing variance attributed to ethnicity and deprivation is 46.6% in T1D and 77.3% in T2D, indicating considerable socio-demographic impact. CONCLUSION: This study reveals significant ethnic disparities in CGM access, with Afro-Caribbean and South Asian groups facing a reduced prescribing ratio per 1000 people. Consistent NICE guideline adoption and targeted outreach are needed to improve equity in CGM access.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".