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www.thelancet.com/oncology Vol 22 November 2021
1507
Articles
Lancet Oncol 2021; 22: 1507–17
Published Online
October 5, 2021
https://doi.org/10.1016/
S1470-2045(21)00493-9
*Collaborating authors are listed
in the appendix
Correspondence to:
Mr Aneel Bhangu, NIHR Global
Health Research Unit on Global
Surgery, Institute of Translational
Medicine, University of
Birmingham, Birmingham, UK
a.a.bhangu@bham.ac.uk
or
Mr James Glasbey, NIHR Global
Health Research Unit on Global
Surgery, Institute of Translational
Medicine, University of
Birmingham, Birmingham, UK
j.glasbey@bham.ac.uk
See Online for appendix
Effect of COVID-19 pandemic lockdowns on planned cancer
surgery for 15 tumour types in 61 countries:
an international, prospective, cohort study
COVIDSurg Collaborative*
Summary
Background Surgery is the main modality of cure for solid cancers and was prioritised to continue during COVID-19
outbreaks. This study aimed to identify immediate areas for system strengthening by comparing the delivery of
elective cancer surgery during the COVID-19 pandemic in periods of lockdown versus light restrictions.
Methods This international, prospective, cohort study enrolled 20 006 adult (≥18 years) patients from 466 hospitals in
61 countries with 15 cancer types, who had a decision for curative surgery during the COVID-19 pandemic and were
followed up until the point of surgery or cessation of follow-up (Aug 31, 2020). Average national Oxford COVID-19
Stringency Index scores were calculated to define the government response to COVID-19 for each patient for the
period they awaited surgery, and classified into light restrictions (index <20), moderate lockdowns (20–60), and full
lockdowns (>60). The primary outcome was the non-operation rate (defined as the proportion of patients who did not
undergo planned surgery). Cox proportional-hazards regression models were used to explore the associations between
lockdowns and non-operation. Intervals from diagnosis to surgery were compared across COVID-19 government
response index groups. This study was registered at ClinicalTrials.gov, NCT04384926.
Findings Of eligible patients awaiting surgery, 2003 (10·0%) of 20 006 did not receive surgery after a median follow-up
of 23 weeks (IQR 16–30), all of whom had a COVID-19-related reason given for non-operation. Light restrictions were
associated with a 0·6% non-operation rate (26 of 4521), moderate lockdowns with a 5·5% rate (201 of 3646; adjusted
hazard ratio [HR] 0·81, 95% CI 0·77–0·84; p<0·0001), and full lockdowns with a 15·0% rate (1775 of 11 827; HR 0·51,
0·50–0·53; p<0·0001). In sensitivity analyses, including adjustment for SARS-CoV-2 case notification rates, moderate
lockdowns (HR 0·84, 95% CI 0·80–0·88; p<0·001), and full lockdowns (0·57, 0·54–0·60; p<0·001), remained
independently associated with non-operation. Surgery beyond 12 weeks from diagnosis in patients without
neoadjuvant therapy increased during lockdowns (374 [9·1%] of 4521 in light restrictions, 317 [10·4%] of 3646 in
moderate lockdowns, 2001 [23·8%] of 11 827 in full lockdowns), although there were no dierences in resectability
rates observed with longer delays.
Interpretation Cancer surgery systems worldwide were fragile to lockdowns, with one in seven patients who were in
regions with full lockdowns not undergoing planned surgery and experiencing longer preoperative delays. Although
short-term oncological outcomes were not compromised in those selected for surgery, delays and non-operations
might lead to long-term reductions in survival. During current and future periods of societal restriction, the resilience
of elective surgery systems requires strengthening, which might include protected elective surgical pathways and long-
term investment in surge capacity for acute care during public health emergencies to protect elective sta and services.
Funding National Institute for Health Research Global Health Research Unit, Association of Coloproctology of Great
Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper
Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European
Society of Coloproctology, Medtronic, Sarcoma UK, The Urology Foundation, Vascular Society for Great Britain and
Ireland, and Yorkshire Cancer Research.
Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND
4.0 license.
Introduction
During the COVID-19 pandemic, government restrictions
have aimed to control community SARS-CoV-2 trans-
mission and included reducing population movement,
closing public facilities, and restricting gatherings.1
Restrictions have varied worldwide in stringency, with the
most severe leading to so-called lockdowns.2 Although
public and media attention has largely focussed on the
economic impact of lockdowns, the broader eects on
general health are poorly understood.3 Lockdowns might
have had collateral eects beyond controlling community
SARS-CoV-2 rates alone, due to changes in both public
behaviour and health system performance.4 These
might have disproportionate eects on vulnerable and
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marginalised communities.5,6 Understanding these eects
will justify expenditure on targeted system strengthening,
as further societal restrictions are predicted at a global
level.
In the first COVID-19 waves (ie, the 12 weeks of peak
disruption), at least 21 million elective operations were
cancelled globally, partly due to concerns over post-
operative SARS-CoV-2 infection and partly due to capacity
issues within hospitals.7,8 There was, however, general
guidance from health ministries and national surgical
associations that time-dependent surgery should
continue.9 This included curative cancer surgery, which
is a priority among the oncology com munity and a high-
value topic for society.10,11 Surgery remains the primary
method of cure for most solid cancers.
Since surgical databases and cancer registries do not
capture prospective decision making, they lack fidelity to
detect patients who did not undergo planned surgery. In
the case of curative surgery, these are the patients who
might have suered the most harm.12,13 Resection
margins alone are an inadequate marker of success, as
selection bias in patients who are able to undergo surgery
risks underestimating harm from treatment delays, and
neglects whole-system eects. We planned the
prospective COVIDSurg Cancer study to address these
areas and provide an accurate, whole-system analysis of
the impact of COVID-19 on planned cancer surgery.
Understanding any harms could allow for immediate
local and national policy changes, in preparation for
future societal restrictions.
Methods
Study design and participants
This international, multicentre, prospective cohort study
included adult patients (≥18 years) with a diagnosis of a
surgically curable cancer during the COVID-19
pandemic. The study was conducted in accordance with a
preregistered protocol (NCT04384926). Local principal
investigators were responsible for obtaining clinical
audit, institutional review board, or ethical approval in
line with local and national regulations. In most settings,
a waiver of individual patient consent was obtained. In
other countries, formal written or verbal consent was
required based on recommendations of local ethics and
governance committees. Data were collected online and
stored on a secure server running the Research Electronic
Data Capture (REDCap) web application.14
Any hospital worldwide that performed elective cancer
surgery in an area aected by the COVID-19 pandemic
was eligible to participate. Patients listed for surgery to
cure a solid cancer were included in each centre for
3 months from local emergence of COVID-19, defined on
a centre-by-centre basis as the date where first notification
of SARS-CoV-2 cases occurred in the local area (between
Jan 21 and April 14, 2020). Participating centres identified
all patients with a decision for surgery (or would have
had a decision for surgery under normal, prepandemic
circumstances) from multidisciplinary team meetings,
tumour board, outpatient clinics, or local equivalents.
Previous international outcomes studies from our group
have shown that this method achieves greater than
Research in context
Evidence before this study
Guidance from health ministries and national surgical
associations prioritised time-dependent cancer surgery to
continue during societal restrictions related to COVID-19.
We searched PubMed and Embase on Feb 12, 2021, without date
limits, for prospective, multicountry studies describing non-
operation rates for patients planned to undergo elective surgery
during national or regional COVID-19 lockdowns using primary
data. We used the search terms “COVID-19”, “SARS-CoV-2”,
“coronavirus”, “lockdown” and “pandemic”, in combination with
“surgery” and “non-operation”, “cancellation”, “postponement”
or “delays”, and applied no language restrictions. Several
modelling studies estimated total elective surgeries cancelled due
to COVID-19, but no primary studies described the impact of
lockdowns on non-operation rates for patients due to undergo
curative cancer surgery.
Added value of this study
There is limited evidence of the collateral effects of COVID-19
pandemic lockdowns outside of modelling studies. Uniquely,
this international study prospectively enrolled patients with a
decision for curative surgery awaiting surgery during the
SARS-CoV-2 pandemic and tracked their care pathways
prospectively. It included data from the 15 most common solid
cancer types across all country-income settings, providing wide
generalisability to global policy. The analysis allowed a direct
comparison of full and moderate lockdowns to light
restrictions, accounting for their dynamic nature, where
different patients from the same country were exposed to
different lockdown states.
Implications of all the available evidence
This study has direct policy, organisational, and clinical
implications. It has revealed the fragility of elective cancer surgery
systems to lockdowns, particularly health systems in lower-
middle-income countries. This study demonstrates the need for
system strengthening in elective surgery across all settings to
mitigate against impending COVID-19 lockdowns and future
pandemics. This should include both global reorganisation to
provide protected COVID-19-free elective surgical pathways
(and staffing) that sustainably allow safe surgery to continue,
and improved surge capacity for acute care during public health
emergencies. The potential long-term effects for patients who
underwent delayed surgery may require closer follow-up for
metastatic disease. This study could inform policy makers’
planning regarding the collateral effects of societal restrictions.
For the protocol see https://
globalsurg.org/cancercovidsurg/
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v www.thelancet.com/oncology Vol 22 November 2021
1509
95% case ascertainment and greater than 98% data
accuracy during external validation.15 If a specialty within
a hospital was unable to confirm consecutive enrolment,
their data were excluded from analysis. Patients’ care
pathways were followed up until the point of surgery or
until cessation of follow-up at Aug 31, 2020. This date
was selected to ensure all patients had a minimum of
12-weeks follow-up. Where a patient underwent surgery,
outcome data was collected up to 30 days after surgery.
Where patients remained non-operated, their last known
status was recorded.
The 15 most common solid cancer types were included
in this study, including colorectal, oesophageal, gastric,
head and neck (oral, oropharyngeal, laryngeal, hypo-
pharyngeal, salivary, thyroid, paranasal sinus, skin),
thoracic (lung, pleural, mediastinal, chest wall), liver,
pancreatic, prostate, bladder, renal and upper urinary
tract urothelial, gynaeco logical (uterine, ovarian, cervical,
vulval, vaginal), breast, soft-tissue sarcoma, bony sarcoma,
and intracranial malignancies. Participating centres could
contribute data for either single or multiple cancers. Early
cancers that were planned to be managed with endo-
scopic surgery alone (eg, transurethral resection of
bladder tumour, transanal endoscopic micro surgery)
were excluded. Patients who were suspected to have an
operable cancer, but were later identified to have a
non-cancerous condition (eg, on postoperative histo-
pathology), or were treated as benign and unexpectedly
identified to be malignant on postoperative histopathology
were also excluded.
Definition of lockdowns
We used the Oxford COVID-19 Stringency Index to
define each country’s national government response to
COVID-19. This index is a composite of 19 indicators
including measures and behavioural interventions
related to containment and closure, economic response,
and health systems. Each indicator is scored using an
ordinal scale (0 to 2, 3, 4, or 5), with an overall score
calculated by adding together individual indicator scores
(appendix p 56). Total scores can range from 0 (no
restrictions) to 100 (most stringent restrictions). The
index has been previously validated by demonstrating
associations with population SARS-CoV-2 infection rates
and mobile phone mobility data.1
The average national Oxford COVID-19 Stringency
Index scores were calculated for each patient for the
period they waited for surgery. To define cutos that were
reflective of real-world policy, we sampled reported
lockdown dates from a sample of high-income countries,
upper-middle-income countries (UMICs), and lower-
middle-income countries (LMICs; appendix p 55). Dates
were taken from national policy, media, and press
sources. On the date of transition into lockdown, the
point estimate for the COVID-19 Stringency Index score
was extracted (appendix p 55). This point estimate was
used to classify patients into three stringency groups:
light restrictions (index <20), moderate lockdowns
(20–60), and full lockdowns (>60). These groups allowed
a direct com parison of full and moderate lockdowns to
light restrictions, accounting for their dynamic nature,
whereby dierent patients from the same country were
exposed to dierent lockdown states. For each patient, a
median average score while waiting for surgery and the
number of weeks in full lockdown were calculated
and used in analyses. Full details are given in the
appendix (pp 2, 57–58).
Definition of SARS-CoV-2 rates
The case notification rate was calculated at an individual
patient level as a median average between the date of
local emergence of COVID-19 and the date of surgery or
cessation of follow-up via the Our World in Data portal.16
A high COVID-19 burden area was classified as a median
of at least 25 cases per 100 000 per 14 days, representing
WHO recommendations at the time of the study (ie, in
keeping with first pandemic wave levels). Case rates were
used for exploratory analyses only, and stratified by World
Bank income tertile (appendix p 58).17,18
Other definitions
The World Bank index (2019/20 update) was used to
classify countries and patients into three groups based
on Gross National Income per capita (US$) calculated
using the Atlas method: high-income countries, UMICs,
and LMICs (including patients from both low-income
countries and LMICs). Data on baseline patient status
was collected for the purpose of adjustment for case-mix
in exploring associations between lockdowns and
surgical capacity (appendix p 59).
Patients were classified into three groups according to
their neoadjuvant treatment group: (1) no neoadjuvant
therapy (ie, straight to surgery); (2) neoadjuvant therapy,
standard care (where the treating clinician administered
neoad juvant treatment in accordance with their usual
care); (3) neoadjuvant therapy, COVID-19 decision
(where the treating clinician administered a neoadjuvant
treatment where this would not typically be indicated). To
estimate the impact of lockdown on treatment delays, the
relationship between lockdowns and the interval from
diagnosis to decision for surgery to surgery was
measured. The interval from date of diagnosis to the date
of surgery was calculated in whole weeks to identify
points of system friction (appendix p 60).
Outcomes
A resilient elective surgical care system is defined as a
hospital or network of hospitals that is able to maintain
both its capacity and safety during public health crises.19
As a measure of the ability of surgical systems to
maintain their capacity, the primary outcome measure
was the non-operation rate. This non-operation rate was
defined as an eligible patient (ie, with a plan to undergo
surgery) not undergoing their planned operation during
Articles
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the study window. Patients were classified as being
operated if they underwent surgery, regardless of whether
there was a change to the planned urgency (eg, from
elective to emergency) or intent (eg, from curative
to palliative). Patients who died or progressed to
unresectable disease before surgery were classified as
non-operated, so these did not act as competing risks.
For patients who did not receive their surgery as
planned during the follow-up window, the treating
clinical team selected one or more reasons that the
patient had not had surgery. These reasons included
those explicitly COVID-19 related (eg, decision to delay
surgery due to patient risk during COVID-19), and those
non-COVID-19 related (eg, delay due to other unrelated
medical or surgical condition). More than one reason for
non-operation could be selected for each patient,
representing the complexity of decision making (full
details are given in the appendix p 61).
Secondary outcome measures related to safety of surgery
were presented for patients who underwent surgery during
the follow-up period: (1) resection margin status for those
selected for surgery; (2) resectable disease at the time of
surgery; (3) preoperative cancer compli cation requiring
emergency surgery; (3) 30-day post operative SARS-CoV-2
infection rate; (4) 30-day post operative mortality rate;
(5) new detection of metastatic disease up to a maximum
of 30 days after surgery. As neoadjuvant therapy has a
complex interplay with treatment interval, an a-priori
decision was made to only include patients who went
straight to surgery (no neoadjuvant therapy) in exploration
of the eects of treatment delay on secondary outcomes
(appendix p 62).
Statistical analysis
The full statistical method is presented in the
appendix (p 63). Cox proportional hazards regression
modelling was used to explore associations between
lockdowns and the primary outcome, presented as
adjusted hazard ratios (HRs) and 95% CIs. Operation
was included as the outcome event, and no censoring
was performed for death or progression to unresectable
disease to deal with competing risks, given individuals
had the same follow-up time (ie, describing sub-
distribution rather than cause-specific hazards). An
α level was set at 0·05 (5%) for interpretation of
significance. Several preplanned sensitivity analyses
were conducted for the primary analysis to examine
robustness of findings; namely, (1) including elective
operations only in the definition of the primary outcome;
(2) accounting for an interaction eect between World
Bank income group and COVID-19 stringency index
group; and (3) accounting for local SARS-CoV-2 case
notification rates, stratified by World Bank income group.
Two further sensitivity analyses were performed to
Figure 1: Flowchart of included patients
*Found clinically, radiologically, or during surgery.
27 700 patients identified from multidisciplinary team meeting (or equivalent)
15 622 no neo-
adjuvant
therapy
4727 <4 weeks from diagnosis to
surgery
173 unresectable disease*
1736 neoadjuvant
therapy
(standard care)
5597 5–8 weeks from diagnosis to
surgery
87 unresectable disease*
2601 9–12 weeks from diagnosis to
surgery
46 unresectable disease*
2697 >12 weeks from diagnosis to
surgery
52 unresectable disease*
18 003 operated 2003 non-operated
645 neoadjuvant
therapy
(COVID-19
decision)
1353 no neoadjuvant
therapy
164 neoadjuvant
therapy
(standard care)
486 neoadjuvant
therapy
(COVID-19
decision)
20 006 adult patients with decision for curative cancer surgery (61 countries, 466 centres)
7694 excluded
764 surgery planned with palliative intent
2466 diagnosis made after surgery
840 diagnosis made after decision for surgery
1859 carcinoma in situ (Tis/Ta)
222 planned redo resection
883 intracranial tumours
660 local resection only
Articles
v www.thelancet.com/oncology Vol 22 November 2021
1511
ensure that dierences in cancer case-mix across income
settings were not responsible for residual confounding;
these included, (1) cancer location removed from the
model; and (2) patients older than 50 years only.
A secondary analysis was used to explore the incremental
eect of weeks in lockdown on a patient’s likelihood of
non-operation. Intervals from diagnosis to surgery were
compared across COVID-19 government response index
groups. We only analysed the interval between diagnosis
and surgery for patients who did not receive neo-adjuvant
therapy to avoid confounding due to legitimate delays to
surgery in patients who receive neo-adjuvant therapy. All
analyses were carried out using R, version 3.1.1 (packages
finalfit, tidyverse, ggsurvplot).
Role of the funding source
The funders of the study had no role in study design,
data collection, data analysis, data interpretation, or
writing of the report.
Results
20 006 patients were eligible for inclusion in 466 hospitals
in 61 countries (figure 1). Of these patients, 1891 (9·5%)
were from 17 UMICs and 2249 (11·2%) were from
12 LMICs. A wide range of patients, tumours, and
operations were included. The most common tumour
types included were breast (n=3896; 19·5%), head and
neck (n=3517; 17·6%), colon (n=3428; 17·1%), and
gynaecological (n=2169; 10·8%). Distribution of patients
and cancers across income groups and countries is
shown in the appendix (pp 8–9).
Of patients planned for cancer surgery during the
COVID-19 pandemic, 4521 (22·6%) of 20 006 were
awaiting surgery during a period of light restrictions,
3646 (18·2%) during moderate lockdowns, and
11 827 (59·1%) during full lockdowns (n=12 missing data;
appendix p 4). The proportion of patients awaiting
surgery in full lockdowns was higher in areas with high
than low community SARS-CoV-2 case notification rates
and in UMICs and LMICs than in high-income countries
(appendix p 10). Patients awaiting surgery during light
restrictions had a lower mean number of weeks in full
lockdown (2·4 weeks [SD 1·7]) compared with patients
awaiting surgery during moderate (5·5 weeks [2·9]) or
full lockdowns (12·7 weeks [5·4]; p<0·0001, from one-
way ANOVA).
Most patients (16 975 [84·8%] of 20 006) had a plan to
progress straight to surgery (no neoadjuvant therapy),
with 1900 (9·5%) receiving standard care neoadjuvant
therapy, and 1131 (5·7%) receiving a COVID-19 decision
for neoadjuvant therapy. During full lockdowns, patients
were more likely to have a COVID-19 decision for
neoadjuvant therapy than during moderate lockdowns or
light restrictions (appendix p 3).
During the COVID-19 pandemic, 2003 (10·0%) of
20 006 patients did not undergo their planned surgery by
the end of follow-up (figure 2; appendix p 12). Patients
awaiting surgery during periods of light restrictions had
a lower rate of non-operation (26 [0·6%] of 4521) than
those in moderate lockdowns (201 [5·5%] of 3646) or full
lockdowns (1775 [15·0%] of 11 827; appendix p 5).
Figure 2: Effects of lockdowns on surgical capacity
(A) Differences in resilience of surgical systems across income settings by COVID-19 stringency index group.
Percentages represent proportion operated by group. (B) Kaplan-Meier plot demonstrating proportion of patients
remaining non-operated over time from cancer diagnosis grouped by COVID-19 stringency index group.
Plot censored at 28 weeks maximum follow-up from cancer diagnosis. Shading represents this represents the
95% CI, using the statistical package ggsurvplot.
+
+
+
+
+
+
0
Proportion of patients (%)
High income
25
75
50
100
Number at risk
Light restrictions
Moderate lockdown
Full lockdown
0 4 8 12 16 20 24 28
4520
3633
11678
3080
2576
9266
1214
1257
6572
581
720
4752
330
454
3274
195
276
2044
86
170
1132
2
46
423
Time since diagnosis (weeks)
0
25
50
75
100
Proportion non-operated (%)
95·8
4·2
86·3
13·7
99·5
0·5
0
Proportion of patients (%)
Upper-middle income
25
75
50
100
85·5
14·5
99·6
0·4
89·5
10·5
0
Proportion of patients (%)
Lower-middle income
25
75
50
100
88·5
11·5
98·5
1·5
75·8
24·2
A
B
Operated
No Yes
Light restrictions
Moderate lockdown
Full lockdown
p<0·0001
+
+
++
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
++++++++++++++++++
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+++++++++++++++
Full lockdownModerate lockdownLight restrictions
Articles
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www.thelancet.com/oncology Vol 22 November 2021
Figure 3: Multivariable Cox
proportional hazards model
of factors associated with
non-operation during
COVID-19
19 832 in dataframe,
19 066 in model, 766 missing.
17 597 (91·8%) of
19 066 patients included in
this model were operated by
the end of follow-up. Missing
data are described in the
appendix (p 10), as well as the
full model (p 12).
ASA=American Society of
Anesthesiologists Physical
Status Classification System.
ECOG=Eastern Cooperative
Oncology Group. RCRI=Revised
Cardiac Risk Index.
Favours
operation
Favours
non-operation
n/N HR (95% CI) p value
COVID-19 stringency index
Light restrictions
Moderate lockdown
Full lockdown
World bank index
High income
Upper-middle income
Lower-middle income
Age, years
<50
50−59
60−69
70−79
≥80
Sex
Female
Male
ASA grade
1−2
3−5
ECOG performance status score
0
1
≥2
Current smoker
No
Yes
Pre-existing respiratory condition
No
Yes
RCRI
0
1
2
≥3
Disease stage
Early disease
Advanced or nodal disease
Cancer location
Head or neck
Colon
Rectal
Gastric
Oesophageal
Lung
Liver
Pancreatic
Sarcoma
Prostate
Kidney or upper tract urothelial
Bladder
Gynaecological
Breast
4371/4391
3364/3509
9862/11 166
14 138/15 115
1687/1801
1772/2150
3228/3527
3636/3916
4742/5166
4333/4639
1658/1818
10 175/10 776
7422/8290
12 549/13 565
5048/5501
10 469/11 201
5195/5672
1933/2193
15 555/16 802
2042/2264
15 598/16911
1999/2155
5413/5682
8992/9839
2531/2810
661/735
9670/10453
7927/8613
3142/3469
3208/3359
1309/1462
644/712
324/435
1047/1159
696/759
628/741
377/413
427/504
363/422
104/139
1884/2048
3135/3444
Ref
0·81 (0·77–0·84)
0·51 (0·50–0·53)
Ref
0·97 (0·92–1·02)
0·83 (0·78–0·87)
Ref
1·10 (1·05–1·15)
1·01 (1·04–1·14)
1·17 (1·11–1·23)
1·06 (0·99–1·14)
Ref
0·99 (0·95–1·02)
Ref
0·99 (0·95–1·03)
Ref
0·96 (0·92–0·99)
0·89 (0·84–0·94)
Ref
1·03 (0·99–1·08)
Ref
0·98 (0·94–1·03)
Ref
1·01 (0·95–1·08)
0·96 (0·89–1·03)
0·90 (0·81–0·10)
Ref
0·96 (0·93–0·10)
Ref
1·16 (1·08–1·24)
0·59 (0·54–0·64)
0·67 (0·61–0·74)
0·32 (0·28–0·36)
0·82 (0·76–0·90)
0·63 (0·57–0·69)
0·73 (0·66–0·81)
0·67 (0·59–0·75)
0·44 (0·40–0·50)
0·65 (0·57–0·73)
0·51 (0·42–0·62)
0·86 (0·79–0·93)
1·00 (0·94–1·05)
p<0·0001
p<0·0001
p=0·2202
p<0·0001
p=0·0002
p=0·0005
p<0·0001
p=0·0772
p=0·4569
p=0·5327
p=0·0198
p<0·0001
p=0·1712
p=0·4716
p=0·7788
p=0·2449
p=0·0438
p=0·0181
p<0·0001
p<0·0001
p<0·0001
p<0·0001
p<0·0001
p<0·0001
p<0·0001
p<0·0001
p<0·0001
p<0·0001
p<0·0001
p=0·0001
p=0·8835
1·00·6 1·2
0·4 0·8
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1775 (88·7%) of 2003 patients who remained non-
operated were in regions with full lockdowns
(appendix p 11). The baseline rate of non-operation was
low across all income settings during periods of light
restrictions (22 [0·5%] of 4089 in high-income countries;
one [0·4%] of 228 in UMICs; three [1·5%] of
204 in LMICs), and high during periods of full lock-
down (1188 [13·7%] of 8644 in high-income countries;
139 [10·5%] of 1329 in UMICs; 448 [24·2%] of
1854 in LMICs). At 12 weeks after diagnosis, 581 [12·8%]
of 4520 patients under light restrictions remained non-
operated, 720 (19·9%) of 3622 patients during moderate
lockdowns, and 4752 (40·7%) of 11 678 patients during
full lockdowns (figure 2). After multivariable adjustment,
both moderate (HR 0·81, 95% CI 0·77–0·84; p<0·0001)
and full lock downs (HR 0·51, 0·50–0·53; p<0·0001)
were associated with a lower likelihood of a patient
receiving their planned cancer surgery (figure 3;
appendix p 10). This was con sistent across planned
sensitivity analyses (appendix pp 13–16). The overall level
of missingness was low (<1%) for all variables included
in the models.
Being in an LMIC, increasing frailty (Eastern
Cooperative Oncology Group 1 or ≥2), comorbidity
(Revised Cardiac Risk Index ≥3), and having locally
advanced or nodal disease, or both, were all independently
associated with increased likelihood of non-operation.
There was significant variability in the likelihood of
non-operation by cancer site. Where the primary
outcome definition was revised to include elective
surgery only, both UMICs and LMICs were observed to
have a higher adjusted non-operation rates than high-
income countries (appendix p 13). The eect of lockdown
on non-operation diered by income group, with LMICs
broadly less likely to operate at a given level of lockdown
compared with the high-income group (appendix p 15).
Patients waiting for surgery in LMICs during full
lockdowns were most likely to remain non-operated
compared with patients in LMICs during light
restrictions (HR 0·41, 95% CI 0·38–0·44; p<0·0001;
appendix p 16). In the secondary analysis, waiting for
surgery for 5–6 weeks or more in full lockdown was
associated with a reduced likelihood of a patient
undergoing their cancer operation compared with
0 weeks in full lockdown (5–6 weeks in full lockdown
HR 0·86, 95% CI 0·80–0·93, p<0·0001; appendix p 6).
Patients younger than 50 years were less likely than
patients 50 years and older to receive their planned
surgery across several sensitivity analyses. Patients
planned to have surgery aged younger than 50 years
were more commonly from LMICs than UMICs
or high-income countries commonly from LMICs
(appendix pp 18–19). In a sensitivity analysis including
only patients older than 50 years, the eect of lockdowns
on surgical capacity remained consistent with the
primary result (ie, moderate and full lockdowns were
associated with a significant increase in the odds of
non-operation compared with light restrictions;
appendix p 20).
Increasing SARS-CoV-2 case notification rates were
associated with increasing non-operation rates (appendix
p 21). Both moderate and full lockdowns were consistently
associated with an increased likelihood of non-operation,
even after adjustment for local SARS-CoV-2 rates
(appendix p 22). The largest magnitude of eect was seen
when transitioning from light restrictions or moderate
lockdowns to full lockdowns across all income and
SARS-CoV-2 case notification rate groups. LMICs were
particularly fragile to increasing SARS-CoV-2 rates and
full lockdowns, with a non-operation rate of 381 (58·7%)
of 649 (appendix p 21).
By the end of the follow-up (median 23 weeks,
IQR 16–30), 2003 patients had not undergone planned
surgery. 453 (22·6%) of 2003 patients had been formally
re-staged. Detection of new metastatic disease is shown
in the appendix (p 26).
Of non-operated patients for whom data were available
(n=2001; two missing data), all had at least one COVID-
19-related reason provided for non-operation (table 1);
most commonly this involved a team decision to delay
surgery during COVID-19 due to individual patient risk
(1456 [72·8%] of 2001). 533 (26·6%) of 2001 patients were
provided an alternative treatment modality as a result of
COVID-19. 306 (15·3%) patients had at least one non-
COVID-19-related reason provided for non-operation.
All patients
COVID-19 related
Multidisciplinary team decision to delay surgery due to patient risk during COVID-19 1456 (72·8%)
Change to alternative treatment modality because of COVID-19 533 (26·6%)
Patient choice to avoid surgery during COVID-19 pandemic 460 (23·0%)
Ongoing neoadjuvant therapy (COVID decision) 378 (18·9%)
No bed, critical care bed, or operating room space available due to COVID-19 299 (14·9%)
Change of recommendations in society guidelines related to COVID-19 220 (11·0%)
Patient unable to travel to hospital related to COVID-19 140 (7·0%)
Collateral impact on supporting services causing delay 24 (1·2%)
Patient delayed due to SARS-CoV-2 infection 23 (1·1%)
Died of COVID-19 while waiting for surgery 14 (0·6%)
Total 2001 (100·0%)
Non-COVID-19 related
Progression to unresectable disease 179 (8·9%)
Delay due to other unrelated medical or surgical condition 59 (2·9%)
Died unrelated to COVID-19 while waiting for surgery 34 (1·7%)
Patient unable to afford surgery 24 (1·2%)
Patient choice to avoid surgery unrelated to COVID-19 35 (1·7%)
Total 306 (15·3%)
We anticipated that decisions to delay or cancel surgery during COVID-19 would be complex. Therefore, selecting more
than one reason for non-operation during the follow-up window for each patient was permitted. One patient could
have both one or more COVID-19-related and non-COVID-19-related reasons selected. Where it was unclear whether a
reason was directly COVID-related (eg, disease progression) this was classified as not COVID-19-related. Two patients
(0·1%) had no reasons given for non-operation during the follow-up window selected (missing data). Proportions are
therefore expressed as a percentage of 2001 non-operated patients and with data available.
Table 1: Reasons that patients did not received planned surgery
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179 (8·9%) patients progressed to unresectable disease.
48 (2·4%) patients died before their planned surgery
(14 due to COVID-19-related complications, and 34 due to
non-COVID-19-related causes).
Delays from diagnosis to operation were observed
during full lockdowns for operated patients (n=18 003)
across all neoadjuvant treatment groups (figure 4). In
patients who went straight to surgery (no neoadjuvant
therapy; 15 622 [86·3%] of 18 003), full lockdown was
associated with 2001 [23·8%] of 11 827 patients not
receiving surgery within 12 weeks of diagnosis compared
with 317 (10·4%) of 3646 patients during moderate
lockdowns, and 374 (9·1%) of 4521 patients under light
restrictions (appendix p 25). For these patients, each
additional week in lockdown was associated with treat-
ment delay (p<0·0001; figure 4). Increasing SARS-CoV-2
case notifi cation rates were also associated with increased
delays beyond 12 weeks across income groups, with the
longest delays observed in UMICs and LMICs during
periods with high SARS-CoV-2 rates (appendix p 23). The
point of system friction was dierent across dierent
income groups (appendix p 7). Full lockdown was asso-
ciated with an increased interval from decision to surgery
across all settings compared with both light restrictions
and moderate lockdown (figure 4; appendix p 24).
In patients who went straight to surgery (n=15 622),
postoperative histopathological and clinical outcomes
during light restrictions, moderate lockdowns, and full
lockdowns were similar (table 2). Characteristics and
outcomes of patients by interval from diagnosis to
operation are in the appendix (pp 25–26). Variation in
outcomes by income group are shown in the appendix
(p 27).
Discussion
The design of this study allowed a holistic overview of
dierent health systems’ surgical capacity and outcomes
during lockdowns. The analysis allowed a direct
comparison during full and moderate lockdowns to
periods with light restrictions, taking account of the
dynamic nature of government policies, where dierent
patients from the same country were exposed to dierent
lockdown states. During full lockdowns, one in seven
patients did not receive their planned operation, all of
whom had a pandemic-related reason for non-operation.
This finding was robust, and consistent in sensitivity
analyses. In a secondary analysis, awaiting surgery in a
full lockdown for greater than 6 weeks was associated
with an increased likelihood of non-operation. These
data reveal the fragility of elective cancer surgery to
lockdowns, which was independent of both local
SARS-CoV-2 rates and case-mix. Patients with cancer in
LMICs, of increasing frailty, or with advanced disease
were most vulnerable to lockdown eects. Capacity for
major elective cancer should be part of every country’s
strategy to address whole-population health needs and
prevent further collateral harm.
Identifying at-risk groups allows targeted system
strengthening during both COVID-19 lockdowns and
future pandemics. Firstly, vulnerable patient groups
(eg, those with a poorer performance score, more cardiac
comorbidities, or advanced cancers) were all less likely to
receive surgery. Secondly, certain operation types that
Figure 4: Lockdown and delay to surgery
(A) Delay from diagnosis to surgery during lockdowns (according to COVID-19 stringency index group) by
neoadjuvant therapy group. Percentages represent proportion of operated patients who were in each interval from
diagnosis to operation group. (B) Weeks in full lockdown and interval from cancer diagnosis to operation.
Plot displays patients who went straight to surgery (no neoadjuvant therapy only). Full lockdown defined as a
COVID-19 stringency index score of more than 60. Plotted line represents a smoothed conditional mean from a
fitted generalised additive model. The shaded area denotes bounds of the 95% CI.
0
Proportion of patients (%)
No neoadjuvant
therapy
25
75
50
100
05 10 15 20 25
Time in full lockdown, COVID-19 stringency index (weeks)
0
10
5
15
20
25
Time from diagnosis to operation (weeks)
n=42 n=39
n=35n=34
n=30
n=17
n=22
n=15
n=15
n=7
n=12
n=5
n=12
n=13
n=11
n=9 n=11
n=31
0
Proportion of patients (%)
Neoadjuvant therapy,
standard care
25
75
50
100
n=63 n=73
n=9
0·4
n=5
0
Proportion of patients (%)
Neoadjuvant therapy,
COVID-19 decision
25
75
50
100
n=23 n=27
n=22
n=45
n=5
n=27
n=36
n=14
n=23
n=10
A
B
Interval diagnosis to operation
0−4 weeks 5−8 weeks 9−12 weeks >12 weeks
Light restrictions Moderate lockdown Full lockdown
n=84
n=4
n=65
n=2
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require more intensive perioperative care, including
those for oesophageal and pancreatic cancer, were at
increased risk of cancellation. Thirdly, patients in LMICs
were less likely to undergo surgery during lockdowns
and SARS-CoV-2 surges, which included a high
proportion of young patients (<50 years). Protected
elective surgical capacity might include protected
COVID-19-free path ways (including dedicated surgical,
anaesthetic, and theatre sta) within larger hospitals, or
smaller bespoke elective surgery units that function as
part of cancer treatment networks.20 This also requires
long-term investment in surge capacity for the acute care
workforce and formal operational planning to manage
public health emergencies without major disruption to
elective care (further details are in the appendix p 64).
Together, protected elective surgical capacity might allow
essential elective surgery to continue despite external
system shocks.20
The least resilient systems were in LMIC settings,
exacerbating resource scarcity and capacity issues that
were present prepandemic in the management of
non-communicable diseases.11,21 Elective cancer surgery
systems in LMICs are typically under pressure from a high
burden of expedited and emergency presentations.11,15,21
This pressure was seen in our study, where the likelihood
of non-operation was higher in both UMICs and LMICs,
despite patients being younger and having fewer
comorbidities. These young patients were more frequently
aected by financial and geographical causes for non-
operation, revealing a particularly vulnerable group.
Measures to strengthen the security of global elective
cancer surgery must be implemented across all settings,
and as a priority in LMICs.6 Despite data demonstrating
the safety of neoadjuvant treatment during COVID-19, the
overall rate of neoadjuvant therapy as standard care or a
COVID-19 decision was low (15·2%). This low rate might
represent safety concerns or highlight capacity issues
elsewhere in the cancer care pathway.22 Developing robust
pathways from diagnosis through to definitive surgical
treatment, supported by public health teams and financial
protection mechanisms, will help to create both pandemic-
proof and more equitable systems.
Although we did not find an increase in the positive
resection margin rate or new metastatic disease
associated with increasing delays, these were highly
selected patients and with short-term follow-up only. The
high proportion of patients who did not receive planned
surgery reveals the true extent of potential harm. This
part of our analysis focussed on patients who were
treated without neoadjuvant therapy, who are likely to
represent the group at highest risk from unplanned
delays. Evidence from modelling studies and meta-
analyses suggest that 4-week incremental delays before
surgery are associated with increased rates of recurrence
and excess mortality.13 Taken together, patients who
experienced a delay to surgery during the COVID-19
pandemic might warrant strategies that support closer
follow-up for metastatic disease. It is possible that there
will be a reverse trend towards worsening cancer survival
rates over the next 5 years as a consequence of these
capacity issues, although the present study was not
designed to directly capture these long-term eects. We
acknowledge that for some cancer types, neoadjuvant
therapy has equivalent outcomes to the adjuvant
application of the same treatment and might be a
reasonable strategy to safely delay treatment where this is
required (eg, endocrine therapy for oestrogen-receptor-
positive breast cancers).23 The impact of changes to
neoadjuvant treatment pathways and both short-term
and long-term oncological outcomes requires further
exploration.
This study had several further limitations. First, eects
seen during lockdowns could be interpreted as normal
practice, which would have occurred outside of the
pandemic era. We dealt with this by including an internal
comparison (light restrictions), which is akin to normal
conditions and carried a non-operation rate of 0·5%. We
also collected clinicians’ reasons for non-operation, which
Light
restrictions
(n=4152)
Moderate
lockdown
(n=3057)
Full lockdown
(n=8402)
Total*
(N=15 622)
p value†
Margin status
R0 3471 (83·7%) 2619 (85·8%) 7238 (86·3%) 13 328 (85·5%) 0·0011
R1 381 (9·3%) 223 (7·4%) 581 (6·9%) 1185 (7·7%) ··
R2 79 (1·9%) 61 (2·0%) 157 (1·9%) 297 (1·9%) ··
Pathology unavailable 214 (5·2%) 147 (4·8%) 407 (4·8%) 768 (4·9%) ··
Missing 7 7 19 33 ··
Resectable disease at time of surgery
Resectable 4069 (98·0%) 2967 (97·1%) 8213 (97·8%) 15 249 (97·7%) 0·045
Unresectable 81 (2·0%) 90 (2·9%) 187 (2·2%) 358 (2·3%) ··
Unknown 2 0 2 4 ··
Pre-operative cancer-related complication requiring emergency surgery‡
Elective 4071 (98·2%) 2989 (97·8%) 8199 (97·8%) 15 259 (97·9%) 0·27
Emergency 74 (1·8%) 67 (2·2%) 185 (2·2%) 326 (2·1%) ··
30-day SARS-CoV-2 infection rate‡
No 4083 (98·3%) 3039 (99·4%) 8362 (99·5%) 15 484 (99·2%) <0·0001
Yes 69 (1·7%) 18 (0·6%) 40 (0·5%) 127 (0·8%) ··
30-day postoperative mortality rate‡
No 4080 (98·3%) 3016 (98·7%) 8307 (99·0%) 15 403 (98·8%) 0·0045
Yes 70 (1·7%) 41 (1·3%) 84 (1·0%) 195 (1·2%) ··
Missing 2 0 11 13 ··
New detection of metastatic disease§
No 2191 (98·3%) 1625 (98·3%) 4946 (98·2%) 8762 (98·2%) 0·87
Yes 38 (1·7%) 28 (1·7%) 93 (1·8%) 159 (1·8%) ··
Missing 7 5 15 27 ··
Data are n (%) or n. Patients with metastatic disease at baseline removed from denominator (N=8957). Percentages
presented by column total; missing data are excluded. R0=no microscopic or macroscopic disease. R1=microscopic
disease at the margin. R2=macroscopic disease at the margin. *11 missing this data point. †χ2 comparing light versus
moderate versus full lockdowns for each outcome. ‡Subgroups defined in the appendix (p 62). §Detailed data on
detection of new metastatic disease not collected for liver, pancreatic, breast, and gynaecological cancers.
Table 2: Outcomes across COVID-19 stringency index groups for patients going straight to surgery
(no neoadjuvant therapy)
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were overwhelmingly COVID-19 related. Second, we used
the Oxford COVID-19 Stringency Index to define
lockdowns,1 calculated for each patient as the median
average during their wait for surgery. Although this index
has been validated, it is not yet widely used, and the
COVIDSurg Collaborative is an early adopter of this metric
for research purposes. This health policy measure
demonstrated association with patient level outcomes in
our dataset. However, we used an aggregate summary
statistic that did not reflect all changes in policy during the
study period. More work is required to understand the best
method to apply this measure in future epidemiological
studies. Third, as part of the exposure period to lockdowns
occurred after study entry (ie, decision for surgery), this
might have been subject to future information bias, where
patients remaining non-operated for a longer time might
have been more likely to await surgery during dierent
lockdown states, therefore have a central tendency in their
median average score.24 Fourth, this study required
prospective capture of team decision making, which might
have been subject to biases, although the scale and diversity
of the study mitigated against this. Fifth, definition of
SARS-CoV-2 rates is dependent on testing performed and
reported, so might vary at global scale.17,18 We present
exploratory analyses around SARS-CoV-2 rates stratified by
income setting and provide sensitivity analyses to
demonstrate that findings were robust. Sixth, we did not
present more detailed analyses of between-country or
within-country variation. Despite the large numbers in this
study, individual numbers per country were low enough to
risk type 1 error through multiple hypothesis testing.
Seventh, in this analysis we did not explore dierent
hospital types or delay in care for dierent cancers. There
might have been hospitals that shutdown completely and
did not take part in this study, meaning outcomes might
have been worse. There might be specialty specific findings
that allow future strategies to become stratified—eg,
patients with rectal and prostate cancer might benefit from
scaling up alternative neoadjuvant treatments; breast
and gynaecological cancer surgery might be amenable to
day case pathways; kidney, bladder, thoracic, and oeso-
phagogastric surgery might require the advanced support
of surgical units with critical care facilities; colon cancer
could be performed in standalone surgical units. Eighth,
we did not capture data on delays to diagnosis. Lockdowns
are a system-level issue and high friction in diagnostic
pathways was likely to have led to an increasing number of
tumours left undetected in the community.25 When
considering resilience of a complete elective surgery
system, there is a vital role of timely diagnostics in
preventing harm, which might be just as important as
delays between diagnosis and surgery. Finally, cancer care
is just one component of a functioning health system.
When making policy decisions about resourcing to
improve resilience, cancer must be balanced with other
high-burden conditions (eg, cardiovascular and cerebro-
vascular disease).26
At the time of publication, lockdowns of varying
magnitude remain in place across many countries
around the world, and further measures might be
imposed related to novel variants of concern and
variability in vaccine availability around the world
(appendix p 64); however, threats to stable elective
surgical systems are not limited to COVID-19; other viral
pandemics, seasonal pressures, and natural disasters all
aect surgical patients on an annual and recurring basis.
The lessons from this study might be used to inform
surgical system strengthening both during the COVID-19
pandemic and beyond.
Contributors
The writing group (JCG, AAde, AAdi, EA, APA, FA, JA, AMB, AC-C, JE,
ME, MF, CF, GG, DG, EAG, EH, PH, IL, SW, HL, SL, EL, GMAG, HM,
EJM, JM, DM, KM, MM, RM, DM, FN, FP, MP, PP, AR-DM, KR, ACR,
RKS, RS, JFFS, NS, GDS, RS, SS, ST, EHT, RV, DN, AAB) and the
statistical analysis group (JCG, KAM, DN, EH, AAB) contributed to
writing, data interpretation, and critical revision of the manuscript.
The writing group, operations committee, and dissemination committee
contributed to study conception, protocol development, study delivery,
and management. The collaborators contributed to data collection and
study governance across included sites. All members of the writing
group had full access to the data in the study, and JCG, KAM, DN, EH,
and AAB verified the underlying data in the study. AAB or JCG and the
writing committee had final responsibility for the decision to submit for
publication. Detailed role descriptions of all contributing collaborating
authors are shown in the appendix (pp 28–54).
Declaration of interests
All authors declare no competing interests.
Data sharing
Anonymised individual participant data will be made available upon
request to the corresponding authors after the date of publication, with
approval of the operations and dissemination committees, and
completion of a data sharing agreement.
Acknowledgments
This research was part-funded by the National Institute for Health
Research (NIHR; NIHR 16.136.79) using UK aid from the UK Government
to support global health research. RS receives funding from the Economic
and Social Research Council. JCG and AAB are funded by personal awards
from the NIHR Academy. The views expressed in this publication are
those of the authors and not necessarily those of the NIHR or the UK
Government.
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