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. 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. 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. 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. National Institute for Health Research.

中文翻译：

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用于评估集中专科癌症服务对出行时间、公平性和结果的预期影响的卫生服务规划：一项基于全国人口的建模研究


许多国家正在实行癌症专科服务的集中化，但在实施前往往没有评估潜在影响。我们开发了一个卫生服务规划模型，以直肠癌手术为例，可以估计不同集中化方案对出行时间、服务获取公平性、患者结果和医院工作量的预期影响。对于这项基于人群的建模研究，我们使用了从国家癌症登记和分析服务 (NCRAS) 常规收集的个体患者水平数据，并链接到 NHS 医院发病统计 (HES) 数据库，其中包含 11 888 名被诊断患有直肠癌的患者。 2016年4月1日至2018年12月31日期间患癌症，随后在英格兰163家提供直肠癌手术的国民医疗服务（NHS）医院接受了大直肠癌切除术。考虑了五种集中化方案： 关闭低流量中心（方案 A）；关闭非综合性癌症中心（情景 B）；关闭中心，导致患者净流失至其他中心（情景 C）；关闭符合情景 A、B 和 C 中所有三个标准的中心（情景 D）；关闭再入院率高的中心（情景 E）。 我们使用条件逻辑回归来预测受影响患者转移到每个剩余中心的概率以及旅行时间的预期变化，使用多级逻辑回归来预测 30 天紧急再入院率，并使用线性回归来分析预期额外旅行时间之间的关联针对中心关闭的患者和五种患者特征，包括年龄、性别、社会经济贫困、合并症和患者居住区的农村性（农村、城市[非伦敦]或伦敦）。我们还量化了额外的工作量，定义为重新分配到剩余中心的额外患者数量。在这 11888 名患者中，4130 名 (34·7%) 为女性，5249 名 (44·2%) 为 70 岁及以上，5005 名 (42·1%) 患有至少一种合并症。情况A导致163个直肠癌手术中心中的43个（26%）关闭，影响1599名（13·5%）患者；情景 B 导致 112 个中心（69%）关闭，影响 7029 名患者（59·1%）；情景 C 导致 56 个中心（34%）关闭，影响 3142 名患者（26·4%）；情景 D 导致 24 个中心（15%）关闭，影响 874 名患者（7·4%）；情景 E 导致 16 个 (10%) 中心关闭，影响 1000 名 (8·4%) 患者。对于每种情况，重新分配的患者的预计出行时间至少增加两倍，平均增加 23 分钟；然而，额外的出行时间并没有对弱势患者群体造成不成比例的影响。所有情景均导致 30 天再入院率显着降低（范围 4-48%）。 场景A 3 家医院，场景B 41 家医院，场景C 13 家医院，场景D 没有医院，场景E 2 家医院每年必须管理至少20 名额外患者。这种卫生服务规划模型可用于指导有关关闭中心的复杂决策并为缓解策略提供信息。该方法可以应用于不同国家或地区的癌症和其他复杂健康状况患者的医疗保健系统。国家健康研究所。