Elective surgery waiting time prioritisation to improve population health gains and reduce health inequalities

Introduction

Waiting times for elective procedures in the National Health Service (NHS) in England have been increasing over time. If decision makers wish to prioritise efforts to reduce waiting lists in order to maximise health then it is important to understand how the health impact varies between different procedures. If decision makers are also interested in the distributional impact of varying waiting time, then additional information on the impact by Index of Multiple Deprivation (IMD) quintile group is informative.

The aim of this scoping study is to explore the feasibility of estimating the health impact of changes in waiting times across elective procedures in the NHS in England. We have previously developed a practical framework and this report presents the application of it via the development of a model applied to eight procedures.

Methods

We developed a Markov model capturing health pre- and post-procedure, including the possibility of exiting pre-procedure to non-elective NHS care or self-funded private care. We estimate the change in mean quality-adjusted life years (QALYs) over a lifetime horizon for 10 subgroups defined by sex and IMD quintile group. We simulate seven alternative waiting time scenarios ranging from 0 to 36 weeks. We include 18 weeks as a baseline waiting time consistent with current NHS policy.

We apply this framework to eight procedures: Cataract, Cholecystectomy, Coronary Artery Bypass Graft, Hernia, Hip Replacement, Hysterectomy, Knee Replacement and Percutaneous Coronary Intervention.

The model was populated with data from routinely collected datasets where possible (Hospital Episode Statistics, Patient Reported Outcome Measures, and Office for National Statistics Mortality records) and supplemented by the academic literature.

Results

We were able to develop and populate a Markov model which allows for comparison of the impact of waiting across procedures and population subgroups.

Our model estimates suggest hip and knee replacement provide the largest health gain from a 6 week reduction in waiting time (from 18 to 12 weeks). This is driven by a large change in health-related quality of life. However, this health improvement would also increase health inequality in the general population due to the high representation of less deprived quintiles amongst this patient population, and their greater capacity to benefit.

The key limitations relate to data availability, including not having access to data on all those who enter the waiting list. We are also only modelling waiting from the point the patient is added to the inpatient waiting list.

Discussion/Conclusion

The proposed framework was applied to eight different conditions which allowed for comparison of the impact of waiting times across these procedures. Data sources for the inputs varied across procedures leading to potential issues around comparability. Collecting additional routine data on those waiting for procedures would enable a much more accurate and comparable estimation of the health impact of waiting.