Models for assessing the impact of centralization of specialist cancer services in England
By Matthew Stenger
Published: 08/12/2022 10:49:00
In a study published 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 the investigators stated, “Centralization of specialist oncology services is occurring in many countries, often without assessing the potential impact prior to implementation.”
In the population-based modeling study, individual patient data from the National Cancer Registration and Analysis Service linked to the National Health Service (NHS) Hospital Episode Statistics database were obtained for 11,888 patients diagnosed with rectal cancer between April 2016 and December 2018 who then underwent major rectal cancer resection at one of 163 NHS hospitals performing rectal cancer surgery in England.
Five centralization scenarios were studied:
- Closure of low-volume centers (scenario A)
- Closure of incomplete cancer centers (scenario B)
- Closure of centers with a net loss of patients to other centers (scenario C)
- Closure of centers meeting the three criteria of scenarios A, B and C (scenario D)
- Closure of centers with high readmission rates (scenario E).
Scenario A (closing low-volume centers) resulted in the closure of 43 (26%) of the 163 rectal cancer surgery centers, affecting 1,599 patients (13.5%).
Scenario B (closure of incomplete cancer centers) led to the closure of 112 centers (69%), affecting 7,029 patients (59.1%).
Scenario C (closure of centers with net loss of patients to other centers) led to the closure of 56 centers (34%), affecting 3,142 patients (26.4%).
Scenario D (closure of centers meeting the combined criteria of scenarios A, B and C) led to the closure of 24 centers (15%), affecting 874 patients (7.4%).
Scenario E (closure of centers with high readmission rates) led to the closure of 16 centers (10%), affecting 1,000 patients (8.4%).
In all scenarios, expected patient travel time at least doubled (average 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 socio-economically deprived areas, and patients aged.
All scenarios were expected 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.
The increase in workload measured by the threshold of needing to manage ≥ 20 additional patients per year was observed for 3 hospitals in scenario A, 41 in scenario B, 13 in scenario C, none in scenario D and 2 in scenario E.
Investigators concluded, “This health service planning model can be used to guide complex decisions about facility closures and inform mitigation strategies. The approach could be applied in different national or regional health care systems for patients with cancer and other complex health conditions.
Ajay Aggarwal, PhDof the Department of Health Services Research and Policy, London School of Hygiene & Tropical Medicineis the corresponding author for The Lancet Oncology article.
Disclosure: The study was funded by the National Institute for Health Research. For full disclosures from the study authors, visit thelancet.com.
The content of this article has not been reviewed by the American Society of Clinical Oncology, Inc. (ASCO®) and does not necessarily reflect the views and opinions of ASCO®.