Health service planning to assess the expected impact of centralising specialist cancer services on travel times, equity, and outcomes: a national population-based modelling study

Ajay Aggarwal*, Lu Han, Stephanie van der Geest, Daniel Lewis, Yolande Lievens, Josep Borras, David Jayne, Richard Sullivan, Marco Varkevisser, Jan van der Meulen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


Background: Centralisation of specialist cancer services is occurring in many countries, often without evaluating the potential impact before implementation. We developed a health service planning model that can estimate the expected impacts of different centralisation scenarios on travel time, equity in access to services, patient outcomes, and hospital workload, using rectal cancer surgery as an example. Methods: For this population-based modelling study, we used routinely collected individual patient-level data from the National Cancer Registration and Analysis Service (NCRAS) and linked to the NHS Hospital Episode Statistics (HES) database for 11 888 patients who had been diagnosed with rectal cancer between April 1, 2016, and Dec 31, 2018, and who subsequently underwent a major rectal cancer resection in 163 National Health Service (NHS) hospitals providing rectal cancer surgery in England. Five centralisation scenarios were considered: closure of lower-volume centres (scenario A); closure of non-comprehensive cancer centres (scenario B); closure of centres with a net loss of patients to other centres (scenario C); closure of centres meeting all three criteria in scenarios A, B, and C (scenario D); and closure of centres with high readmission rates (scenario E). We used conditional logistic regression to predict probabilities of affected patients moving to each of the remaining centres and the expected changes in travel time, multilevel logistic regression to predict 30-day emergency readmission rates, and linear regression to analyse associations between the expected extra travel time for patients whose centre is closed and five patient characteristics, including age, sex, socioeconomic deprivation, comorbidity, and rurality of the patients' residential areas (rural, urban [non-London], or London). We also quantified additional workload, defined as the number of extra patients reallocated to remaining centres. Findings: Of the 11 888 patients, 4130 (34·7%) were women, 5249 (44·2%) were aged 70 years and older, and 5005 (42·1%) had at least one comorbidity. Scenario A resulted in closures of 43 (26%) of the 163 rectal cancer surgery centres, affecting 1599 (13·5%) patients; scenario B resulted in closures of 112 (69%) centres, affecting 7029 (59·1%) patients; scenario C resulted in closures of 56 (34%) centres, affecting 3142 (26·4%) patients; scenario D resulted in closures of 24 (15%) centres, affecting 874 (7·4%) patients; and scenario E resulted in closures of 16 (10%) centres, affecting 1000 (8·4%) patients. For each scenario, there was at least a two-times increase in predicted travel time for re-allocated patients with a mean increase in travel time of 23 min; however, the extra travel time did not disproportionately affect vulnerable patient groups. All scenarios resulted in significant reductions in 30-day readmission rates (range 4–48%). Three hospitals in scenario A, 41 hospitals in in scenario B, 13 hospitals in scenario C, no hospitals in scenario D, and two hospitals in scenario E had to manage at least 20 extra patients annually. Interpretation: This health service planning model can be used to to guide complex decisions about the closure of centres and inform mitigation strategies. The approach could be applied across different country or regional health-care systems for patients with cancer and other complex health conditons. Funding: National Institute for Health Research.

Original languageEnglish
Pages (from-to)1211-1220
Number of pages10
JournalThe Lancet Oncology
Issue number9
Publication statusPublished - Sept 2022


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