First steps in analysing NHS waiting times: avoiding the 'stationary and closed population' fallacy.
The aim of this paper is to demonstrate the effect of excluding incomplete observations and competing events when calculating cross-sectional measures of NHS waiting times, and to obtain a more accurate estimate of the 'time-to-admission' of those listed on NHS waiting lists using life-table methods. The official 'times-since-enrollment' of all elective 'admissions' in England, 1 July to 31 December 1994 inclusive, were extracted from Hospital Episode Statistics. The official 'times-to-census' of all those on a waiting list in England at 30 September 1994 were obtained from aggregated KH07 data. The percentage waiting at least three months, at least six months etc., was calculated separately for each data set and compared with a period life-table derived from the combined data. The cumulative likelihood of elective admission is markedly overestimated across the whole range of waiting times. The experience of those still waiting, those removed from the list, those suspended or deferred and those put to the back of the queue is not taken into account in the calculation of official waiting times. The Department of Health currently presents the 'time-since-enrollment' of those admitted as though it indicates how long all patients can expect to wait for admission. The consequent bias in published summary statistics incorrectly quantifies the real experience of patients. It is recommended that calculation of waiting times from KH07 census counts and Hospital Episode Statistics be reconsidered in the light of what patients, clinicians, managers and politicians need to know about treatment delay.
Duke Scholars
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- Waiting Lists
- United Kingdom
- Time Factors
- Statistics & Probability
- State Medicine
- Probability
- Likelihood Functions
- Life Tables
- Hospitalization
- Government Agencies
Citation
Published In
DOI
ISSN
Publication Date
Volume
Issue
Start / End Page
Location
Related Subject Headings
- Waiting Lists
- United Kingdom
- Time Factors
- Statistics & Probability
- State Medicine
- Probability
- Likelihood Functions
- Life Tables
- Hospitalization
- Government Agencies