Waiting Times for Cataract Surgery in Scotland since 2002 and the Effect of Austerity: An Interrupted Time Series Analysis
Bibliographic record
Abstract
PURPOSE: In Scotland, in 2002, the National Waiting Times Unit was launched to reduce NHS waiting times. This was accompanied by a series of waiting time targets across the NHS in Scotland. The purpose of this study is to analyse changes in equality of access to treatment by socioeconomic deprivation associated with this initiative. METHODS: Trends in annual cataract rates were calculated using secondary care admissions' Scottish Morbidity Record (SMR01) data on NHS funded elective cataract procedures for patients treated in Scotland from 01 April 1997 to 31 March 2019. An interrupted time series model was used to analyse socioeconomic differences in waiting times by deprivation quintile over three time periods; pre and post waiting time initiative, and post austerity. RESULTS: Cataract Surgical Rates more than doubled from 3,723 per million population in 1997/1998 to 7,896 per million population in 2018/2019. Mean waiting time fell from 129.5 days in 1997/1998 to 87.7 days in 2018/2019. Inequality in mean waiting time between most and least deprived cataract patients increased by 1.34 days per quarter between 01 April 1997 and 30 June 2002 and following the waiting time initiative fell by 0.41 days per quarter through to 31 March 2010; and then decreased by 0.002 days per quarter between 01 April 2010 and 31 March 2019. CONCLUSION: The waiting time initiative had a major impact on reducing inequality in waiting times between socioeconomic groups. The onset of austerity in 2010 was associated with a very small and insignificant increase in inequality.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".