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Mortality due to cancer treatment delay: systematic review and meta-analysis

BMJ 2020; 371 doi: https://doi.org/10.1136/bmj.m4087 (Published 04 November 2020) Cite this as: BMJ 2020;371:m4087

Linked Editorial

Prioritising research into cancer treatment delays

Linked Opinion

Counting the invisible costs of covid-19: the cancer pandemic

Rapid Response:

Waiting time paradox in the effects of time to diagnosis or treatment on mortality

Dear Editor:

We read with great interest the systematic review and meta-analysis conducted by Hanna et al. quantifying the mortality risk due to cancer treatment delay based on 34 studies for seven common cancers [1]. Their work indicates that even prior to the Covid-19 pandemic, a four-week delay in commencing cancer treatment could impact survival, raising concerns of how delays caused by the present Covid-19 pandemic may lead to worsening cancer outcomes [2-5].

Cancer treatment delays have been frequently studied and the authors are to be commended for considering the ‘waiting time paradox’ in the analysis [1]. The waiting time paradox, the effect that patients with more severe symptoms and a poor prognosis are referred early, is an important source of bias in studies investigating the impact of diagnostic and treatment delays on cancer survival [6-8]. Such an effect may be more evident in research on cancers that lack early symptoms and/or signs (like pancreatic or lung cancer), as mixed associations (positive, no and negative associations) with survival have been reported [6,8-11].

The waiting time paradox particularly is present in studies investigating diagnostic intervals (like time from symptom onset to diagnosis), but is still relevant in studies addressing the time from diagnosis to the first cancer treatment as studied by Hanna et al. [1]. For example, sicker patients may receive emergency treatment shortly after diagnosis and die more quickly after cancer treatment. There is potentially an additional effect relating to the optimal timing of subsequent treatment. Patients who receive the next treatment before complete recovery from surgery may have a higher mortality risk [12].

Hanna et al. attempted to address the issue of the waiting time paradox by including only studies which conducted specific sub-analyses and/or had inclusion/exclusion criteria for cancer patients with short intervals (e.g., “less than four weeks”) or poor outcomes (e.g., “death within four to eight weeks of diagnosis”) [1].

We suggest that such an approach is imperfect for primary studies examining the effects of time to diagnosis or treatment on mortality. Instead, more recent analytic approaches such as restricted cubic spline regression may be better as it accounts for non-linear associations between time and mortality [12,13]. In this manner, all patients with different intervals are analysed as a whole and the effect of the waiting time paradox can be identified when the mortality risk presents a U-shape association. The issue remains though of how best to account for the waiting time paradox in meta-analyses. The approach applied by Hanna et al. was pragmatic but probably does not account for it fully. Meta-regression approaches should be developed that address the waiting time paradox and which might better identify optimal times to diagnosis and treatment of cancer.

Reference
1.Hanna TP, King WD, Thibodeau S, et al. Mortality due to cancer treatment delay: systematic review and meta-analysis. BMJ 2020;371:m4087.
2.Helsper CW, Campbell C, Emery J, et al. Cancer has not gone away: A primary care perspective to support a balanced approach for timely cancer diagnosis during COVID‐19. Eur J Cancer Care 2020;e13290.
3.Dinmohamed AG, Visser O, Verhoeven RHA, et al. Fewer cancer diagnoses during the COVID-19 epidemic in the Netherlands. Lancet Oncol 2020;21:750-751.
4.Sud A, Torr B, Jones ME, et al. Effect of delays in the 2-week-wait cancer referral pathway during the COVID-19 pandemic on cancer survival in the UK: a modelling study. Lancet Oncol 2020;21:1035-1044.
5.Maringe C, Spicer J, Morris M, et al. The impact of the COVID-19 pandemic on cancer deaths due to delays in diagnosis in England, UK: a national, population-based, modelling study. Lancet Oncol 2020;21:1023-1034.
6.Neal RD, Tharmanathan P, France B, et al. Is increased time to diagnosis and treatment in symptomatic cancer associated with poorer outcomes? Systematic review. Br J Cancer 2015;112 Suppl 1:S92-107.
7.Weller D, Vedsted P, Rubin G, et al. The Aarhus statement: improving design and reporting of studies on early cancer diagnosis. Br J Cancer 2012;106:1262-7.
8.Jacobsen MM, Silverstein SC, Quinn M, et al. Timeliness of access to lung cancer diagnosis and treatment: A scoping literature review. Lung Cancer 2017;112:156-164.
9.Jensen AR, Mainz J, Overgaard J. Impact of delay on diagnosis and treatment of primary lung cancer. Acta Oncol 2002;41:147-52.
10.Olsson JK, Schultz EM, Gould MK. Timeliness of care in patients with lung cancer: A systematic review. Thorax 2009;64:749-56.
11.Vinas F, Ben Hassen I, Jabot L, Monnet I, Chouaid C. Delays for diagnosis and treatment of lung cancers: A systematic review. Clin Respir J 2016;10:267-71.
12.Salazar MC, Rosen JE, Wang Z, et al. Association of Delayed Adjuvant Chemotherapy With Survival After Lung Cancer Surgery. JAMA Oncol 2017;3:610-619.
13.Herndon JE, Harrell Jr FE. The restricted cubic spline hazard model. Communications in Statistics-Theory and Methods 1990;19:639-663.

Competing interests: No competing interests

11 December 2020
Jianrong Zhang
PhD Student
Rebecca Bergin, Allison Drosdowsky, Xiaofei Wang, Maarten J. IJzerman, Jon D. Emery
Centre for Cancer Research & Department of General Practice, University of Melbourne, Melbourne, Victoria 3000, Australia