Surveillance of waiting times for access to treatment: a registry-based computed approach in breast cancer care

Abstract

The current study set out to automatically generate waiting times for access to surgery, chemotherapy and radiotherapy, and to analyse their determinants for non-metastatic breast cancer patients. We used data from the Poitou-Charentes regional cancer registry of women diagnosed with stages I-III breast carcinoma between 2008 and 2010. Waiting times were automatically computed from a previously validated algorithm modelling the care trajectory and then compared with national guidelines. The population of this study included 1082 patients. The compliance with guidelines ranged from 52.4% (access to adjuvant chemotherapy) to 89.2% (access to adjuvant radiotherapy). Younger age, a higher TNM stage, a lower grade, having a triple negative tumour, being the subject of multidisciplinary meetings and being a patient at a public hospital were associated with longer waiting times. The main result was the significant heterogeneity between geographical areas of treatment for all waiting times studied. The original, reproducible use of a registry-based automated algorithm to generate waiting times will help to follow these indicators routinely and efficiently.

Aggressive primary treatments with favourable 5-year survival for screen-interval breast cancers

“…In the present study, the prognostic variables included age, tumour stage classified according to the TNM classification of malignant tumours, histological Scarff-Bloom-Richardson grade, oestrogen and progesterone receptor status, and human epidermal growth factor receptor 2 (HER-2) expression. Cancer treatments (neoadjuvant treatment, surgery, adjuvant chemotherapy, and radiotherapy) were also recorded [ 27 , 28 ]. Hormone therapy was not reported outside neo-adjuvant hormone therapy.…”

Treatment time interval in breast cancer: A population‐based study on the impact of type and number of cancer centres attended

“…Indeed, the European Society of Breast Cancer Specialists (i.e., the EUSOMA working group) have set the standard of having at least 80% of patients with treatment time interval less than 6 weeks for quality accreditation (Biganzoli et al, 2017 ). In practice, treatment time interval may depend on the patients' sociodemographic characteristics (Ayrault‐Piault et al, 2016 ; Nouws et al, 2019 ; Padilla‐Ruiz et al, 2021 ; Redaniel et al, 2013 ; Reeder‐Hayes et al, 2019 ; Robertson et al, 2004 ; Smith et al, 2013 ), the patients (Molinié et al, 2013 ; Padilla‐Ruiz et al, 2021 ) and the cancer clinical characteristics (Dong et al, 2020 ; Nouws et al, 2019 ; Quillet et al, 2016 ), as well as the cancer treatment (Bleicher, 2018 ; Prakash et al, 2021 ). Besides the patients and cancer features, studies addressing the influence of healthcare provider on the treatment time interval have shown disparities related to the type of the treatment facility (Ayrault‐Piault et al, 2016 ; Molinié et al, 2013 ; Quillet et al, 2016 ; Revaux et al, 2014 ; Robertson et al, 2004 ).…”

“…In practice, treatment time interval may depend on the patients' sociodemographic characteristics (Ayrault‐Piault et al, 2016 ; Nouws et al, 2019 ; Padilla‐Ruiz et al, 2021 ; Redaniel et al, 2013 ; Reeder‐Hayes et al, 2019 ; Robertson et al, 2004 ; Smith et al, 2013 ), the patients (Molinié et al, 2013 ; Padilla‐Ruiz et al, 2021 ) and the cancer clinical characteristics (Dong et al, 2020 ; Nouws et al, 2019 ; Quillet et al, 2016 ), as well as the cancer treatment (Bleicher, 2018 ; Prakash et al, 2021 ). Besides the patients and cancer features, studies addressing the influence of healthcare provider on the treatment time interval have shown disparities related to the type of the treatment facility (Ayrault‐Piault et al, 2016 ; Molinié et al, 2013 ; Quillet et al, 2016 ; Revaux et al, 2014 ; Robertson et al, 2004 ). However, although breast cancer management may rely on several healthcare providers, on possible different places, very few authors have gone beyond the first treatment centres or surgery centre to characterise the places where patients were treated.…”

“…, the patients (Molinié et al, 2013;Padilla-Ruiz et al, 2021) and the cancer clinical characteristics (Dong et al, 2020;Nouws et al, 2019;Quillet et al, 2016), as well as the cancer treatment (Bleicher, 2018;Prakash et al, 2021). Besides the patients and cancer features, studies addressing the influence of healthcare provider on the treatment time interval have shown disparities related to the type of the treatment facility (Ayrault-Piault et al, 2016;Molinié et al, 2013;Quillet et al, 2016;Revaux et al, 2014;Robertson et al, 2004).…”

The EU and cancer research

“…() have used high‐quality registry data to examine colorectal cancer trends in the South Australian population—an excellent example of registry‐driven cancer intelligence. Quillet, Defossez, and Ingrand () also use registry‐based information to examine waiting times for treatment in breast cancer patients. Treatment intervals can be associated with significant anxiety, and measuring them through routinely available data (provided such data are available and accurate) is becoming a key strategy in monitoring health system performance for cancer patients.…”