Glaucoma treatment and deprivation: time-series analysis of general practice prescribing in England
Bibliographic record
Abstract
BACKGROUND: Despite advances in glaucoma management, topical eyedrop treatment has been paramount, with prostaglandin analogues (PGAs) being first-line agents. While late presentation is linked with deprivation, there is no recent research examining associations between deprivation and prescribing within primary care. AIM: To explore PGA prescribing in general practice over a 6-year timeline, assessing associations with deprivation. DESIGN & SETTING: Analysis of NHS Business Services Authority (NHSBSA) data for general practice prescribing in England from April 2016-March 2022. METHOD: Glaucoma treatments by GP prescribers were extracted, identifying ~9.11-9.58 million prescriptions/annum. Data were linked to Index of Multiple Deprivation (IMD) quintiles of GP practices. Crude rates per 1000 population were calculated using population data from NHS Digital. Time-series analyses facilitated comparison in prescribing nationally and in deprived areas. Autoregressive Integrated Moving Average (ARIMA) modelling facilitated measurement of synchrony between time series using cross correlation. RESULTS: PGAs and fixed combination eyedrops accounted for approximately two-thirds of glaucoma-related prescribing. Prescriptions per month increased slightly over a 6-year timeline, but rates per 1000 population reduced in 2020-2021 during the COVID-19 pandemic. The number of PGA prescriptions dispensed in deprived areas was lower than all other quintiles. Cross-correlation analysis indicates a lag of ~12 months between average PGA prescribing nationally versus more deprived areas. CONCLUSION: The rate of PGA prescribing in primary care was substantially lower in deprived versus affluent areas, with delayed uptake of PGAs in more deprived areas of ~12 months. Further research is needed to explore reasons for this discrepancy, permitting strategies to be developed to reduce unwarranted variation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".