Models to Assess Impact of Centralization of Specialist Cancer Services in England

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In a study reported in The Lancet Oncology, Aggarwal et al identified the impact of several models of centralization of specialist cancer services in England, using rectal cancer surgery as an example. As stated by the investigators, “Centralization of specialist cancer services is occurring in many countries, often without evaluating the potential impact before implementation.”

Study Details

In the population-based modeling study, individual patient-level data from the National Cancer Registration and Analysis Service linked to the National Health Service (NHS) Hospital Episode Statistics database was obtained for 11,888 patients diagnosed with rectal cancer between April 2016 and December 2018 who subsequently underwent major rectal cancer resection in any of 163 NHS hospitals providing rectal cancer surgery in England.

Five centralization scenarios were investigated:

  • Closure of lower-volume centers (scenario A)
  • Closure of noncomprehensive cancer centers (scenario B)
  • Closure of centers with a net loss of patients to other centers (scenario C)
  • Closure of centers meeting all three criteria in scenarios A, B, and C (scenario D)
  • Closure of centers with high readmission rates (scenario E).

Key Findings

Scenario A (closure of lower-volume centers) resulted in closure of 43 (26%) of the 163 rectal cancer surgery centers, affecting 1,599 patients (13.5%).

Scenario B (closure of noncomprehensive cancer centers) resulted in closure of 112 centers (69%), affecting 7,029 patients (59.1%).

Scenario C (closure of centers with a net loss of patients to other centers) resulted in closure of 56 centers (34%), affecting 3,142 patients (26.4%).

Scenario D (closure of centers meeting combined criteria of scenarios A, B, and C) resulted in closure of 24 centers (15%), affecting 874 patients (7.4%).

Scenario E (closure of centers with high readmission rates) resulted in closure of 16 centers (10%), affecting 1,000 patients (8.4%).

Across scenarios, there was at least a doubling of predicted patient travel time (mean increase = 23 minutes); however, there was no evidence that increased travel time disproportionately affected vulnerable patient groups, including those with comorbidities, those living in more socioeconomically deprived areas, and older patients.   

All scenarios were predicted to result in reductions in 30-day readmission rates, with estimated reductions of 4%, 5%, 7%, 13%, and 48% for scenarios A, B, C, D, and E, respectively.

Workload increase measured by the threshold of needing to manage ≥ 20 additional patients annually was found for 3 hospitals in scenario A, 41 in scenario B, 13 in scenario C, none in scenario D, and 2 in scenario E.

The investigators concluded, “This health service planning model can be used to guide complex decisions about the closure of centers 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 conditions.”

Ajay Aggarwal, PhD, of the Department of Health Services Research and Policy, London School of Hygiene & Tropical Medicine, is the corresponding author for The Lancet Oncology article.

Disclosure: The study was funded by the National Institute for Health Research. For full disclosures of the study authors, visit

The content in this post has not been reviewed by the American Society of Clinical Oncology, Inc. (ASCO®) and does not necessarily reflect the ideas and opinions of ASCO®.