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Estimating excess mortality in people with cancer and multimorbidity in the
COVID-19 emergency
Preprint · April 2020
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Estimating excess mortality in people withcancer and multimorbidity in the COVID-19
emergencyAlvina G. Lai, Ph.D.1,2,�, Laura Pasea, Ph.D.1,2*, Amitava Banerjee, DPhil1,2,3*, Spiros Denaxas, Ph.D.1,2,6,7, Michail Katsoulis,
Ph.D.1,2, Wai Hoong Chang, MSc1,2, Bryan Williams, Ph.D.4,5,6, Deenan Pillay, Ph.D.8, Mahdad Noursadeghi, Ph.D.8, DavidLinch, FMedSci6,9, Derralynn Hughes, FRCPath10,11, Martin D. Forster, Ph.D.4,10, Clare Turnbull, Ph.D.12, Natalie K. Fitzpatrick,
MSc1,2, Kathryn Boyd, MD13, Graham R. Foster, Ph.D.14, DATA-CAN15, Matt Cooper, Ph.D.15, Monica Jones, PGDip15, KathyPritchard-Jones, FMedSci15,16,17,18, Richard Sullivan, Ph.D.19, Geoff Hall, Ph.D.15,20,21, Charlie Davie, FRCP11,15,16, Mark
Lawler, Ph.D.15,22, and Harry Hemingway, FMedSci1,2,6,�
1Institute of Health Informatics, University College London, London, UK2Health Data Research UK, University College London, London, UK
3Barts Health NHS Trust, The Royal London Hospital, London, UK4University College London Hospitals NHS Trust, London, UK
5Institute of Cardiovascular Science, University College London, London, UK6University College London Hospitals NIHR Biomedical Research Centre, London, UK
7The Alan Turing Institute, London, UK8Division of Infection and Immunity, University College London, London, UK
9Department of Hematology, University College London Cancer Institute, London, UK10University College London Cancer Institute, London, UK
11Royal Free NHS Foundation Trust, London, UK12Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
13Northern Ireland Cancer Network, UK14Barts Liver Centre, Blizard Institute, Queen Mary University of London, London, UK
15DATA-CAN, Health Data Research UK hub for cancer hosted by UCLPartners, London, UK16UCLPartners Academic Health Science Partnership, London UK
17Centre for Cancer Outcomes, University College London Hospitals NHS Foundation Trust, London, UK18UCL Great Ormond Street Institute for Child Health, University College London, London, UK
19Conflict and Health Research Group, Institute of Cancer Policy, King’s College London, London, UK20Leeds Institute of Medical Research, University of Leeds, Leeds, UK
21School of Medicine, University of Leeds, Leeds, UK22Patrick G Johnston Centre for Cancer Research, Queen’s University Belfast, UK
*Joint second authors
Background: Cancer and multiple non-cancer conditions areconsidered by the Centers for Disease Control and Prevention(CDC) as high risk conditions in the COVID-19 emergency.Professional societies have recommended changes in cancerservice provision to minimize COVID-19 risks to cancerpatients and health care workers. However, we do not knowthe extent to which cancer patients, in whom multi-morbidityis common, may be at higher overall risk of mortality asa net result of multiple factors including COVID-19 infec-tion, changes in health services, and socioeconomic factors.
Methods: We report multi-center, weekly cancer diagnosticreferrals and chemotherapy treatments until April 2020 inEngland and Northern Ireland. We analyzed population-based health records from 3,862,012 adults in England toestimate 1-year mortality in 24 cancer sites and 15 non-cancer comorbidity clusters (40 conditions) recognized byCDC as high-risk. We estimated overall (direct and indirect)effects of COVID-19 emergency on mortality under differ-ent Relative Impact of the Emergency (RIE) and differentProportions of the population Affected by the Emergency(PAE). We applied the same model to the US, using Surveil-lance, Epidemiology, and End Results (SEER) program data.
Results: Weekly data until April 2020 demonstrate significantfalls in admissions for chemotherapy (45-66% reduction)and urgent referrals for early cancer diagnosis (70-89%
reduction), compared to pre-emergency levels. Under con-servative assumptions of the emergency affecting only peoplewith newly diagnosed cancer (incident cases) at COVID-19 PAE of 40%, and an RIE of 1.5, the model estimated6,270 excess deaths at 1 year in England and 33,890 excessdeaths in the US. In England, the proportion of patientswith incident cancer with ≥1 comorbidity was 65.2%. Thenumber of comorbidities was strongly associated with can-cer mortality risk. Across a range of model assumptions,and across incident and prevalent cancer cases, 78% ofexcess deaths occur in cancer patients with ≥1 comorbidity.
Conclusion: We provide the first estimates of potential excessmortality among people with cancer and multimorbidity due tothe COVID-19 emergency and demonstrate dramatic changesin cancer services. To better inform prioritization of cancer careand guide policy change, there is an urgent need for weekly dataon cause-specific excess mortality, cancer diagnosis and treat-ment provision and better intelligence on the use of effectivetreatments for comorbidities.
Correspondence: [email protected] | [email protected]
Introduction
The excess risk of death in people living with cancerduring the COVID-19 emergency may be due not only
Lai et al. | April 28, 2020 | 1–10
to COVID-19 infection, but also to the unintended healthconsequences of changes in health service provision, thephysical or psychological effects of social distancing, andeconomic upheaval. Recent national evidence suggests thatthere is an excess of deaths, both in those infected withSARS-CoV-2, and in those with no infection(1). Optimalcancer care must balance the need to protect patients fromCOVID-19 infection, with continued access to early diag-nosis and potentially curative treatment(2, 3). Professionalassociations in the US(4–6), UK(7–9) and Europe(9) haverecommended revising cancer care activities for triaging ofsystemic anti-cancer treatment, surgery and risk-adaptedradiotherapy(10). All elective surgery has been postponed inthe UK(8, 9). In the US, more than a quarter of patients withcancer reported a delay in their cancer treatment in April2020 because of COVID-19(4). Despite these recommen-dations for changes in healthcare services, there is a lack ofnear real-time data quantifying the extent of disruption dueto reconfiguration in service delivery for cancer patients.
Existing publications on cancer and COVID-19 have beenlimited to small case series(11, 12). The US Centersfor Disease Control and Prevention (CDC) and Pub-lic Health England (PHE) have identified patients withspecific malignant and non-malignant conditions as atgreater risk of developing severe illness from COVID-19exposure(13–15). Patients with cancer commonly have otherconditions considered to further increase their COVID-19risk; multimorbidity in cancer is an increasing clinicalconcern(16, 17). Oncologists, internists and family physi-cians need to balance the requirement to encourage peopleto socially isolate, with their need to access hospital servicesto ensure the most effective cancer care. There is a lackof evidence on pan-cancer estimation of mortality risksaccording to type and number of multimorbid conditions.
Our objectives were to: 1) quantify changes in cancer careactivities in near real-time from weekly multi-center hospitaldata; 2) estimate the prevalence, incidence and background(pre-COVID-19) 1-year mortality risk across 24 site-specificcancers; 3) estimate the prevalence of 15 CDC- and PHE-relevant co-occurring condition clusters (multimorbidity) bycancer site and their association with excess mortality and4) estimate overall excess deaths due to the COVID-19 pan-demic over a 1-year period, based on different Proportion ofthe population Affected by the Emergency (PAE) and Rela-tive Impact of the Emergency (RIE), using a previously pub-lished model(18).
MethodsWeekly information on cancer care. Through DATA-CAN, the UK National Health Data Research Hubfor Cancer(19), we obtained weekly returns for urgentcancer referrals for early diagnosis and chemother-apy attendances (from 2018 to most current data)from hospitals in Leeds, London and Northern Ireland.
Patient cohort. We used population-based electronic healthrecords in England from primary care data linked to theOffice for National Statistics(ONS) death registration, usingthe open-access CALIBER resource(20, 21). The studypopulation consisted of 3,862,012 adults aged ≥30 years,registered with a general practice from 1 January 1997 to1 January 2017 with at least one year of follow-up data.
Electronic health record phenotype definitions of diseasesand risk factors relevant to COVID-19 are available athttps://caliberresearch.org/portal and have previously beenvalidated(22–25). Further details are in supplementarymethods. In brief, phenotypes are based on hospital andprimary care information recorded in primary care, usingthe Read clinical terminology(version 2). We definednon-fatal, incident and prevalent cases of cancer across24 primary cancer sites according to previously validatedCALIBER electronic health record phenotypes, whichincluded: biliary tract, bladder, bone, brain, breast, cervix,colon-rectum, Hodgkin’s lymphoma, kidney, leukemia, liver,lung, melanoma, multiple myeloma, non-Hodgkin’s lym-phoma, esophagus, oropharynx, ovary, pancreas, prostate,stomach, testis, thyroid and uterus(26). We defined cancersas prevalent (diagnosed at any time prior to baseline) andincident if they occurred during follow-up. We defined15 comorbid conditions or condition clusters involving 40individual conditions defined by the Centers for DiseaseControl (CDC) or Public Health England (PHE) as associatedwith poor outcomes caused by severe COVID-19 infection.
Analyses. For full details of the analyses, see supplementarymethods. Briefly, we estimated incidence rates per 100,000person years and 1-year mortality in our study population.Estimates were used to calculate the excess deaths in canceralone and cancer plus comorbidities, based on three levelsof PAE (10%, 40% and 80%) and four RIE scenarios asso-ciated with COVID-19(1.2, 1.5, 2.0 and 3.0) (additional de-tails on plausible choice of PAE and RIE are presented inthe discussion). We presented all results in Figures 2, 5, S4and S8, but in order to represent a plausible, likely conser-vative, scenario for the current emergency, we chose to high-light data for a PAE of 40% and a RIE of 1.5, with a rangeof 1.2–2 in the results text section of the manuscript. Weapplied our model to US population using publicly availabledata from the US from the Surveillance, Epidemiology, andEnd Results (SEER) program on incidence and 1 year mor-tality (taken as 1 minus the reported survival(27)). We con-sidered mortality data from SEER for individuals aged 40-64,65-74 and 75+.
ResultsReal-time weekly hospital data for urgent cancer re-ferrals and chemotherapy attendances in England andNorthern Ireland. We observed that a majority of patientswith cancer or suspected cancer are not accessing healthcareservices. Major declines in chemotherapy attendances (45%,66%, 66% and 63% reduction; average=60%) and urgent
2 Lai et al. | Excess deaths in cancer and multimorbidity
Royal Free (England)
25
50
75
100
125
Month
% R
elat
ive to
the
2019
ave
rage
for
eac
h ho
spita
l
Chemotherapy
Leeds (England)25
50
75
100
125
Urgent referral for early diagnosis
25
50
75
100
125
Five Northern Ireland H
SCs
28-Feb-2020
25
50
75
100
125
2018 2019 2020 2018 2019 2020J F M A M J J A S O N D J F M A M J J A S O N D J F M J F M A M J J A S O N D J F M A M J J A S O N D J F M
Christmas First case of COVID-19
UC
LH (England)
28-Feb-2020
11-Feb-202011-Feb-2020
11-Feb-202011-Feb-2020
Easter
29-Feb-202029-Feb-2020
Fig. 1. Weekly hospital data on changes in urgent referrals and chemotherapy clinic attendance from eight hospitals in the UK. The data for Northern Ireland includesfive Health and Social Care Trusts (HSCs) that cover all health service provision in Northern Ireland: Belfast HSC, Northern HSC, South Eastern HSC, Southern HSC andWestern HSC.
cancer referrals for early diagnosis (70%, 74%, 89% and71% reduction; average=76%) were observed in eight hospi-tals across the UK: England (Leeds Teaching Hospitals NHSTrust, Royal Free Hospital and University College LondonHospitals) and Northern Ireland (all five Health and SocialCare Trusts) respectively, compared to pre-emergency levels(Figure 1). These data signal the need to identify patient-specific risk factors (cancer and multimorbidity) to facilitateprioritization of service provision during the pandemic.
Prevalence, incidence and background (pre-COVID-19)1-year mortality for 24 cancers. The overall prevalence ofany of the 24 cancers was 3.1% (117,978/3,862,012) in CAL-IBER (England), (Table S1). Prevalence of the 24 cancers byage, sex and year is shown in Figure S1. The age-adjustedincidence rates of the 24 cancers estimated in CALIBERwas 635 per 100 000 (compared with estimates from In-ternational Agency for Research on Cancer (IARC, UK) of590 per 100 000) (Figure S2A). The 1-year mortality across
24 cancer sites was, as anticipated, higher among incidentcases of cancer, compared to prevalent cases (Figure S3).
Estimates of excess COVID-19-related deaths by cancersite in England. When considering incident cancers inEngland, at COVID-19 PAE of 40%, we estimated 2,509,6270 and 12,543 excess deaths at RIE of 1.2, 1.5, and 2respectively (Figure 2A). Higher numbers were seen amongprevalent cancers than incident cancers when estimatingabsolute excess deaths, since the number of prevalent caseswere higher than the number of incident cases observed over1 year, e.g., for PAE 40%, we observed 11,645 excess deaths(range 4,655-23,287) at RIE of 1.5 (range 1.2–2) (FigureS4). When considering both prevalent and incident cancerstogether at COVID-19 PAE of 40%, we estimated 17,915 ex-cess deaths (range 7,164-35,830) at RIE of 1.5 (range 1.2–2).
Estimates of excess COVID-19-related deaths by cancersite for incident cases in US. We found broadly comparable
Lai et al. | Excess deaths in cancer and multimorbidity 3
Excess deaths across all 24 cancers
6270 3137 1567 628
25083 12543 6270 2509
50163 25083 12543 5018
Proportion of the population who are affected (PAE) by the COVID-19 emergencyA
Relative impact of the emergency (RAE)
14
22
3
63
11
2
76
2
9
24
14
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10% 40% 80%
1.2
1.5 2 3
1.2
1.5 2 3
1.2
1.5 2 3
TestisCervix
ThyroidHodgkin's lymphoma
UterusBone
OvaryKidney
MelanomaMultiple myeloma
OropharynxBreast
ProstateBiliary tract
LiverStomachBladder
Non−Hodgkin's lymphomaLeukaemia
OesophagusPancreas
BrainColorectal
Lung
1
10
100
1000
10000Absolute excess England deaths
24
72
5
91
48
19
161
8
64
89
180
1030
15
32
87
87
29
296
7
110
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81
218
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17
276
10
26
93
120
362
24
453
241
94
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40
318
446
898
5148
74
162
437
435
144
1482
34
552
21
53
185
239
725
47
906
482
188
1607
80
635
893
1796
10296
148
324
874
871
287
2963
69
1104
42
106
371
96
290
19
363
193
75
643
32
254
357
718
4118
59
129
349
348
115
1185
27
442
17
42
148
239
725
47
906
482
188
1607
80
635
893
1796
10296
148
324
874
871
287
2963
69
1104
42
106
371
478
1450
95
1813
964
376
3214
161
1270
1786
3592
20591
296
647
1747
1741
574
5926
137
2208
84
212
741
956
2899
190
3626
1927
752
6429
322
2541
3571
7183
41182
593
1294
3494
3482
1148
11853
274
4415
167
423
1482
191
580
38
725
385
150
1286
64
508
714
1437
8236
119
259
699
696
230
2371
55
883
33
85
296
478
1450
95
1813
964
376
3214
161
1270
1786
3592
20591
296
647
1747
1741
574
5926
137
2208
84
212
741
956
2899
190
3626
1927
752
6429
322
2541
3571
7183
41182
593
1294
3494
3482
1148
11853
274
4415
167
423
1482
1912
5798
379
7251
3854
1504
12858
643
5082
7142
14366
82365
1186
2589
6989
6965
2296
23706
549
8830
334
846
2965
10% 40% 80%
1.2
1.5 2 3
1.2
1.5 2 3
1.2
1.5 2 3
Testis
Bone
Prostate
Hodgkins lymphoma
Thyroid
Melanoma
Cervix
Biliary tract
Ovary
Multiple myeloma
Uterus
Breast
Kidney
Bladder
Oesophagus
Non−Hodgkins lymphoma
Leukaemia
Brain
Stomach
Colon
Liver
Pancreas
Lung
10
100
1000
10000
Absolute excess US deathsage 40-64
Proportion of the population who are affected (PAE) by the COVID-19 emergency
Excess deaths across all 24 cancers
25053 12527 6263 2506
100203 50103 25053 10019
200409 100203 50103 20040
Relative impact of the emergency (RAE)
8
26
3
48
16
11
71
6
22
43
54
315
5
13
37
24
16
101
2
34
6
9
14
19
64
7
121
39
27
177
15
56
107
135
788
14
32
92
61
40
252
5
85
15
23
35
38
128
14
242
79
55
354
31
112
214
269
1576
27
65
184
122
79
503
10
171
30
47
71
76
255
28
484
158
110
708
61
223
427
539
3152
55
130
368
243
158
1006
20
341
59
94
142
30
102
11
194
63
44
283
24
89
171
216
1261
22
52
147
97
63
402
8
136
24
37
57
76
255
28
484
158
110
708
61
223
427
539
3152
55
130
368
243
158
1006
20
341
59
94
142
151
510
56
968
315
220
1416
122
446
855
1078
6304
109
259
736
486
316
2012
40
682
119
187
283
302
1021
111
1935
630
439
2831
244
892
1710
2155
12608
218
518
1472
973
632
4025
81
1365
238
374
566
60
204
22
387
126
88
566
49
178
342
431
2522
44
104
294
195
126
805
16
273
48
75
113
151
510
56
968
315
220
1416
122
446
855
1078
6304
109
259
736
486
316
2012
40
682
119
187
283
302
1021
111
1935
630
439
2831
244
892
1710
2155
12608
218
518
1472
973
632
4025
81
1365
238
374
566
605
2042
222
3870
1261
878
5662
488
1784
3419
4310
25216
437
1037
2944
1946
1264
8050
162
2730
475
749
1133
10% 40% 80%
1.2
1.5 2 3
1.2
1.5 2 3
1.2
1.5 2 3
Prostate
Bone
Melanoma
Testis
Hodgkins lymphoma
Biliary tract
Thyroid
Cervix
Multiple myeloma
Uterus
Breast
Ovary
Kidney
Oesophagus
Bladder
Stomach
Non−Hodgkins lymphoma
Leukaemia
Brain
Liver
Colon
Pancreas
Lung
10
100
1000
10000
Absolute excess US deathsage 65-74
Proportion of the population who are affected (PAE) by the COVID-19 emergency
Excess deaths across all 24 cancers
8837 4421 2209 884
35340 17670 8837 3533
70684 35340 17670 7068
Relative impact of the emergency (RAE)
B C
Fig. 2. Estimated number of excess deaths at 1 year due to the COVID-19 emergency by cancer site for incident cases (A) scaled up to the population of England aged30+ consisting of 35 million individuals using England (CALIBER) mortality estimates, (B) scaled up to the population of US aged 40-64 consisting of 208 million individualsusing US (SEER) mortality estimates for this age range and (C) scaled up to the population of US aged 65-74 consisting of 61 million individuals using US (SEER) mortalityestimates for this age range.
1-year mortality among incident cancers across cancer sitesin England and the US (Figure S2B). For the US, based onSEER incidence and 1-year mortality for individuals aged40-64, at COVID-19 PAE of 40%, we estimated 10,019,25,053 and 50,103 excess deaths at RIE of 1.2, 1.5 and2 respectively, when extrapolating to the US populationof this age group(208,284,801 individuals) (Figure 2B).Using mortality estimates for individuals aged 65-74, atCOVID-19 PAE of 40%, we estimated 3,533, 8,837 and17,679 excess deaths at RIE of 1.2, 1.5 and 2 respectively,when extrapolating to the US population of this age group(61,346,445 individuals) (Figure 2C). For a PAE of 40% andRIE of 1.5 (range 1.2–2), we estimated 33,890 excess deaths(range 13,552–67,782) in individuals older than age 40.
Proportion of 15 comorbidity clusters relevant to COVID-
19 risk. Across all cancers, the proportions of 0, 1, 2 and 3+comorbidities were 51.8%, 25.2%, 14.2% and 8.8% respec-tively for prevalent cancers. Comorbidities were commonin people with cancer; e.g., hypertension (34,696 [19.0%]),CVD (23,532 [12.9%]), CKD (9,530 [5.2%]) and obesity(9,491 [5.2%]) (Figure 3). Prevalence of comorbidities insome cancers differed from the condition’s prevalence in theoverall population. For example, COPD (3.0%) compared toCVD (12.9%) is a relatively uncommon comorbidity in pa-tients with cancer, except for lung cancer where patients hada higher than background COPD prevalence (25.7%) (Figure3). Conversely, a ‘metabolic phenotype’ was particularlycommon in uterine cancer, where a relatively large propor-tion of patients had hypertension (48.8%), CVD (26.2%),obesity (14.1%) and type-2 diabetes (9.3%) (Figure 3).
4 Lai et al. | Excess deaths in cancer and multimorbidity
Stomach Testis Thyroid Uterus
Oesophagus Oropharynx Ovary Pancreas Prostate
Liver Lung Melanoma Multiple myeloma Non−Hodgkin’s lymphoma
Cervix Colorectal Hodgkin’s lymphoma Kidney Leukaemia
Biliary tract Bladder Bone Brain Breast
CVD
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)
N = 114 N = 4,983 N = 505 N = 925 N = 28,652
N = 25,515 N = 10,027 N = 2,327 N = 1,093 N = 3,745
N = 138 N = 2,338 N = 10,164 N = 1,109 N = 4,622
N = 770 N = 1,775 N = 2,130 N = 283 N = 10,244
N = 695 N = 2,236 N = 953 N = 2,306
Age = 71.5 Age = 71.3 Age = 51.9 Age = 55.5 Age = 61.9
Age = 41.6 Age = 70.0 Age = 44.4 Age = 66.3 Age = 65.9
Age = 69.3 Age = 71.4 Age = 50.0 Age = 70.3 Age = 63.6
Age = 71.0 Age = 62.9 Age = 62.2 Age = 72.0 Age = 73.3
Age = 72.9 Age = 37.7 Age = 51.4 Age = 65.6
Fig. 3. Proportion of patients with any of the 15 comorbidity clusters by cancer site for prevalent cases (N=117,978) in a population of 3,862,012 adults in England (CALIBER).Age indicates mean age at diagnosis.
Number of comorbid conditions and 1-year mortality inincident cancers. The proportions of individuals with 0,1, 2 and 3+ comorbidities for incident cancers were 34.8%,25.6%, 20.5% and 19.2% respectively (Figure 4A). We foundin incident cases that multimorbidity (≥3 vs 0 conditions)was associated with a further increase in 1-year mortality.Cancers of the pancreas (65.7% vs. 80.1%), biliary tract(58.6% vs. 64.8%), lung (51.9% vs. 60.9%), brain (46.4%vs. 80.9%) and stomach (42.4% vs. 48.3%) exhibited themost pronounced effects (Figure 4B, Figure S6). Consistentfindings were found for prevalent cancer cases (Figure S5,Figure S7)
Excess 1-year COVID-19-related deaths by cancer siteand number of comorbid conditions in England. For inci-dent cancers, when considering COVID-19 PAE of 40% andRIE of 1.5, we observed 1,210, 1,509, 1,572 and 1,983 excessdeaths in individuals with 0, 1, 2 and 3+ non-cancer comor-bidities; 5,064 (80.7%) of these deaths occur in patients with≥1 comorbidities (Figure 5). When considering COVID-19PAE of 40% and RIE of 1.5 for prevalent cancers, we ob-served 2,724, 3,480, 2,955 and 2,558 excess deaths in indi-viduals with 0, 1, 2 and 3+ non-cancer comorbidities; 8,993(76.8%) of these deaths occur in patients with ≥1 comor-bidities (Figure S8). When considering both prevalent and
Lai et al. | Excess deaths in cancer and multimorbidity 5
A B
%
0 1 2 3+
TestisCervixBreast
MelanomaProstate
Hodgkin's lymphomaThyroidUterus
BladderNon−Hodgkin's lymphoma
OropharynxMultiple myeloma
LeukaemiaOvary
ColorectalKidney
BoneOesophagus
StomachBrainLiver
Biliary tractLung
Pancreas
0
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TestisCervixBreast
MelanomaProstate
Hodgkin's lymphomaThyroidUterus
BladderNon−Hodgkin's lymphoma
OropharynxMultiple myeloma
LeukaemiaOvary
ColorectalKidney
BoneOesophagus
StomachBrainLiver
Biliary tractLung
Pancreas
Number of COVID-19 relevant comorbidities
20
40
60
80
Number of comorbidities Mortality
%
Number of COVID-19 relevant comorbidities
Fig. 4. Comorbidity clusters relevant for COVID-19 risk in incident cases in England (CALIBER). (A) Proportion of individuals with 0, 1, 2 and 3+ comorbidities by cancer site.(B) 1-year mortality in individuals with 0, 1, 2 and 3+ comorbidities by cancer site.
incident cancers together at COVID-19 PAE of 40%, we es-timated 17,991 excess deaths at RIE of 1.5; 78.1% of thesedeaths occur in patients with ≥1 comorbidities.
DiscussionThis is the first study demonstrating profound recentchanges in cancer care delivery in multiple centers andthe first study modelling overall excess deaths acrossincident and prevalent cases of cancer (24 sites) wherecomorbidities were present in 78.1% of excess deaths.
Near real-time intelligence. There has been a lack of em-pirical insights and cancer-specific models to counterbalanceinfectious disease modeling in informing evolving policyresponses to the emergency. We propose that weekly na-tional indicators and warnings for vulnerable patient groupssuch as cancer patients with multimorbidity are essential.For example, weekly cause-specific mortality reportingof registered deaths and linking of those death records toprimary and secondary care records for cancer and otherservices would allow rapid ‘root cause analysis’ of the extentto which service changes have a net positive or adverse con-tribution to excess mortality and other non-fatal outcomes.
Changes in cancer services in response to the emergency.We demonstrated major changes in the delivery of cancerservices in the UK, which are also likely to impact on cancerpatient survival. Two metrics were chosen to reflect bothactive treatment for patients diagnosed with cancer and theurgent referral of patients who may have a new diagnosisof cancer. The large declines observed in chemotherapyattendance may reflect workforce/capacity or resources beingredirected to care for infected patients (e.g., to intensive care)and the desire of clinicians and patients to minimize the risksof COVID-19 infection(8). We note the importance of con-sidering multimorbidity in chemotherapy prioritization, as
the risk associated with COVID-19 increases in patients withmultimorbidity. However, some of these patients may benefitfrom chemotherapy avoidance to mitigate immunosuppres-sion. Additionally, we observed large declines in urgentreferrals for early cancer diagnosis. Delays in urgent cancerreferrals may be caused by patients struggling to secureappointments due to reprioritized health systems, or patientsdeciding not to seek care due to perceived risk of COVID-19infection(17). An unintended consequence of these servicechanges may be excess deaths via increases in emergencyadmissions, cancer stage shift at presentation and theundermining of curative/life prolonging surgical resection.
Modeling impact of the emergency: the ‘untold toll’. Wemodeled a range of estimates of PAE because the true valueis not known. PAE is a summary measure of exposure to theadverse health consequences of the emergency, combiningfour parameters; the proportion of the population with1) ill-health in those infected, 2) net adverse health dueto changes in health services designed to protect cancerpatients from infection, 3) net adverse health consequencesof physical distancing, 4) adverse health consequences ofeconomic downturn. Since empirical estimates of each ofthese four parameters for PAE are not yet available, we chosea PAE range of 10%-80%, with 40% as a plausible estimate.
RIE is a summary measure of the combined impact onmortality of infection, health service change, physicaldistancing and economic downturn. We modeled a rangeof estimates of RIE. The only direct evidence we have todate of the magnitude of the RIE comes from national deathreporting in England(1). Here, an RIE of 1.5 is consistentwith ONS estimates of excess deaths during the COVID-19emergency in England, which reported almost 8,000 moredeaths in the week of 10th April 2020, than the 5-yearaverage of 10,520(1). A higher RIE is possible, giventhe following evidence: 1) patients with cancer may have
6 Lai et al. | Excess deaths in cancer and multimorbidity
Proportion of the population who are affected (PAE) by the COVID-19 emergency
10% 40% 80%
01
23+
1.2
1.5 2 3
1.2
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1.2
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TestisCervix
ThyroidHodgkin's lymphoma
UterusBone
OvaryKidney
MelanomaOropharynx
Multiple myelomaBreast
ProstateBiliary tract
LiverStomachBladder
Non−Hodgkin's lymphomaLeukaemia
OesophagusPancreas
BrainColorectal
Lung
TestisCervix
ThyroidHodgkin's lymphoma
UterusBone
OvaryKidney
MelanomaOropharynx
Multiple myelomaBreast
ProstateBiliary tract
LiverStomachBladder
Non−Hodgkin's lymphomaLeukaemia
OesophagusPancreas
BrainColorectal
Lung1
10
100
1000
Absolute excess England deaths
10% 40% 80%
10% 40% 80% 10% 40% 80%
1.2
1.5 2 3
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1.5 2 3
1.2
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1.2
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1.2
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1.2
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1.2
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1.2
1.5 2 3
Number of individuals with 0 comorbidity45569 (34.8%)
Number of individuals with 2 comorbidities26876 (20.5%)
Number of individuals with 1 comorbidity33543 (25.6%)
Number of individuals with 3+ comorbidities25108 (19.2%)
Excess deaths across all 24 cancers
1210 608 303 120
4838 2419 1210 482
9677 4838 2419 965
Excess deaths across all 24 cancers
1509 754 375 150
6022 3013 1509 602
12049 6022 3013 1203
Excess deaths across all 24 cancers
1572 787 393 157
6294 3146 1572 629
12586 6294 3146 1258
Num
ber o
f com
orbi
ditie
s
Relative impact of the emergency (RAE)
Num
ber o
f com
orbi
ditie
s
Excess deaths across all 24 cancers
1983 993 495 201
7928 3963 1983 792
15859 7928 3963 1585
Fig. 5. Estimated number of excess deaths at 1 year due to the COVID-19 emergency by cancer site and number of non-cancer comorbidities for incident cases, scaled upto the population of England aged 30+ consisting of 35 million individuals.
twice the odds of developing COVID-19 infection (oddsratio=2.31) compared to the rest of the population(28),2) COVID-19 causes patients with cancer to deterioratemore rapidly (hazard ratio=3.5) compared to those withoutcancer(11) and 3) COVID-19 case fatality rates are higherin patients with comorbid conditions including CVD, di-abetes, hypertension and cancer(29). For cancer patientsof working age, unemployment may affect mortality, aswe have previously demonstrated across 75 countries(30).Social isolation is also known to represent a mortality riskin cancer(31, 32). Our findings relate to predicted excessmortality in the next 12 months. However, the socio-economic effects on health from the current epidemic arelikely to last for a considerable period beyond one year(33),meaning our figures are likely a significant underestimate.
Excess deaths. Based on the available evidence, we estimatean excess of 6,270 excess deaths at 1 year in patients withincident cancers in England. The recorded underlyingcause of these excess deaths may be cancer, COVID-19,or comorbidity (such as myocardial infarction), and it is
likely that in the COVID-19 emergency, there may bechanges in cause-of-death recording. This is why our modeluses death from any cause – for which there can be nochanges in recording - and which is relevant to multimorbidcancer patients. Our conservative estimate nonethelessrepresents a significant additional burden of 20%, the totalnumber of cancer deaths annually among incident casesin England is 31,354(34). We present estimates of excessdeaths in incident cases, most of whom will be under activesurgical, adjuvant or palliative treatments. We also demon-strate excess deaths in those with prevalent cancers, manyof whom have survived the initial high-risk period; thesedata are particularly relevant to ongoing patient management.
Model application in the US. Using SEER estimatesof incidence and 1-year mortality, we modeled excessdeaths in the US. For example, with an RIE 1.5 andPAE 40% we estimate 33,890 excess 1-year deaths.Like other cancer registries, SEER does not obtaininformation on the range of (COVID-19 relevant) co-morbidities. We found good agreement between Eng-
Lai et al. | Excess deaths in cancer and multimorbidity 7
land and US estimates for incidence and mortality(35).
Importance of multimorbidity. We show that most pa-tients with cancer have non-cancer comorbid conditionswhich confer additional mortality risk; many of thesecomorbidities are treatable by non-cancer services. TheCOVID-19 emergency has prompted new questions aboutwhich cancer patients are most vulnerable and how bestto mitigate risk. Effective management of hypertension,for example, may avert death and major adverse events,but it is possible that blood pressure control may worsenduring the emergency and contribute to excess deaths.
Policy implications. Our study can inform policy in fourareas. First, there is a need to mobilize access to real-timenational data on mortality (to determine which diseasecombinations pose the greatest risk) and on cancer healthservices activity (to monitor the effects of system changeduring the emergency on care delivery and future healthoutcomes). This health services intelligence should includeboth data from cancer services and from those services(e.g. internists and family physicians) who manage thetreatable comorbidities of cancer patients. Second, thetriaging of chemotherapy prioritization would usefullyincorporate patient-specific risk/benefit assessments toinclude multimorbidity, particularly in situations wherechemotherapy outweighs the benefits of non-treatment andsafety issues(17) related to COVID-19. Third, there is aneed to enable access to life-saving early diagnosis andto apply a triage system to prioritize urgent referrals forcitizens with worrying symptoms, especially those withcomorbidities, as multimorbidity may mask an underlyingcancer. Fourth, the policy of ‘shielding’ vulnerable patientsunder active treatment for cancer, or with one of a range ofother non-malignant conditions has been implemented in1.5 million individuals in England, involving home deliveryof food parcels and medicines for an initial period of 12weeks. We propose that estimates of background (preCOVID-19) 1-year mortality risks provides a transparentrationale, readily executable in health records, for iden-tifying priority patient groups including cancer patientswith multimorbidities to receive shielding interventions.
Limitations. This study has important limitations. First,there is a lack of near real-time clinical data in multipledigitally-mature hospitals with large numbers of COVID-19infected patients. Second, the health records we usedmay have missed cases of cancer and underestimatedincidence(36); if so, our estimates of excess deaths maybe conservative. The National Health Service has na-tional linked hospital admissions and cancer registrationdata with information on stage and details of surgical,chemotherapeutic and radiotherapy treatment of cancer.However, information governance for such data can takemonths to secure, making data-enabled research and time-sensitive responsive service improvement difficult. Third,we did not have access to multimorbidity data for the
US. Fourth, we did not have access to data on children.
ConclusionOur data have highlighted how cancer patients with multi-morbidity are a particularly at-risk group during the currentpandemic. In order to ensure effective cancer policy andavoid excess deaths, both during and after the COVID-19emergency, it is critical to ensure near-real time reporting ofcause-specific excess mortality, urgent cancer referrals andtreatment statistics, so as to inform the most optimal deliveryof care in this extremely vulnerable group of patients.
Authors’ contributionResearch question: AL, HH. Funding: AL, AB, HH,DATA-CAN. Study design and analysis plan: AL, LP, AB,MK, WHC, HH. Preparation of data, including electronichealth record phenotyping in the CALIBER open portal:AL, LP, SD. Provision of weekly hospital data: GH, KPJ,MDF, DH, ML, KB, CD. Statistical analysis: AL, LP, MK,WHC. Drafting initial versions of manuscript: AL, HH.Drafting final versions of manuscript: AL, ML, HH. Crit-ical review of early and final versions of manuscript: Allauthors.
Declaration of interestsML has received honoraria from Pfizer, EMD Serono andRoche for presentations unrelated to this research. ML hasreceived an unrestricted educational grant from Pfizer for re-search unrelated to the research presented in this paper. MDFhas received research funding from AstraZeneca, BoehringerIngelheim, Merck and MSD and honoraria from Achilles,AstraZeneca, Bayer, Boehringer Ingelheim, Bristol-MeyersSquibb, Celgene, Guardant Health, Merck, MSD, Nanobi-otix, Novartis, Pharmamar, Roche and Takeda for advisoryroles or presentations unrelated to this research. GRF re-ceives funding from companies that manufacture drugs forhepatitis C virus (AbbVie, Gilead, MSD) and consult forGSK and Arbutus in areas unrelated to this research.
AcknowledgementsWe thank Tony Hagger, Shiva Thapa, Mohammed Emran,Cara Anderson, Louise Herron, Philip Melling and Lee Cog-ger for their help on collating data on urgent cancer referralsand chemotherapy attendances. We thank Charles Swantonfor his valuable comments on the manuscript. This work usesdata provided by patients and collected by the NHS as part oftheir care and support.
Funding statementWe acknowledge Health Data Research UK (HDR UK) sup-port for the HDR UK substantive sites involved in this re-search (HDR London, HDR Wales and Northern Ireland) andDATA-CAN. DATA-CAN is part of the Digital Innovation
8 Lai et al. | Excess deaths in cancer and multimorbidity
Hub Programme, delivered by HDR UK and funded by UKResearch and Innovation through the government’s IndustrialStrategy Challenge Fund (ISCF). AGL is supported by fund-ing from the Wellcome Trust, National Institute for HealthResearch (NIHR) University College London Hospitals andNIHR Great Ormond Street Hospital Biomedical ResearchCenters. AB is supported by research funding from NIHR,British Medical Association, Astra-Zeneca and UK Researchand Innovation. KPJ is supported by the NIHR Great Or-mond Street Hospital Biomedical Research Centre. CD isfunded by UCLPartners. HH is an NIHR Senior Investiga-tor and is funded by the NIHR University College LondonHospitals Biomedical Research Centre, supported by HealthData Research UK (grant No. LOND1), which is funded bythe UK Medical Research Council, Engineering and PhysicalSciences Research Council, Economic and Social ResearchCouncil, Department of Health and Social Care (England),Chief Scientist Office of the Scottish Government Healthand Social Care Directorates, Health and Social Care Re-search and Development Division (Welsh Government), Pub-lic Health Agency (Northern Ireland), British Heart Foun-dation, Wellcome Trust, The BigData@Heart Consortium,funded by the Innovative Medicines Initiative-2 Joint Under-taking under grant agreement No. 116074.
Supplementary methodsThis study was performed as part of the CALIBERprogram (https://caliberresearch.org/portal), which is aresearch resource consisting of anonymized, coded vari-ables extracted from linked electronic health records,methods, tools and specialized infrastructure. Ethicalapproval was granted (20_074R2) by the MHRA (UK)Independent Scientific Advisory Committee of the ClinicalPractice Research Data Link under Section 251 (NHSSocial Care Act 2006). The interpretation and conclu-sions contained in this study are those of the authors’ alone.
15 comorbid conditions or condition clusters involv-ing 40 individual conditions defined by the Centersfor Disease Control (CDC) or Public Health England(PHE) as associated risk factors for poor outcomes causedby severe COVID-19 infection. Both lists from CDCand PHE included chronic respiratory disease; chronicheart disease; immunocompromised; HIV or use of cor-ticosteroids; obesity; diabetes; chronic kidney disease;chronic liver disease. The PHE list included additionalcondition clusters (chronic neurological disorders; splenicdisorders). We have performed analyses for all the aboveconditions and have additionally considered hypertension,Crohn’s disease, cystic fibrosis and rheumatoid arthritis.
Given that condition clusters such as (i) chronic heartdisease would involve a range of conditions, we have derivedcomposite variables to include 15 conditions considered ascardiovascular disease (CVD) that included acute myocar-dial infarction, unstable angina, chronic stable angina, heartfailure, cardiac arrest or sudden coronary death, transient
ischemic attack, intracerebral hemorrhage, subarachnoidhemorrhage, ischemic stroke, abdominal aortic aneurysm,peripheral arterial disease, atrial fibrillation, congenitalheart disease, hypertrophic and dilated cardiomyopathyand valve disease (multiple, mitral and aortic)(23). Wealso considered (ii) Hypertension, defined as ≥140 mmHgsystolic blood pressure (or ≥150 mmHg for people aged≥60 years without diabetes and chronic kidney disease)and/or ≥90 mmHg diastolic blood pressure(22), (iii) type2 diabetes, (iv) obesity, defined as a body mass index of≥30kg/m2, (v) chronic kidney disease (CKD), (vi) chronicobstructive pulmonary disease (COPD)(25), (vii) patientson immunosuppressive drugs (not cancer chemotherapy),(viii) patients with HIV or corticosteroid prescription, (ix)chronic neurological disorders, defined as a composite ofParkinson’s disease, motor neuron disease, learning disabil-ity and cerebral palsy, (x) multiple sclerosis separately, (xi)splenic disorders, defined as a composite of splenomegaly,splenectomy and hyposplenism, (xii) chronic liver diseases,defined as a composite of chronic viral hepatitis B or C,primary biliary cholangitis, liver fibrosis, liver cirrhosisand non-alcoholic fatty liver disease, (xiii) Crohn’s dis-ease, (xiv) cystic fibrosis and (xv) rheumatoid arthritis(24).
Analyses. In CALIBER (England), we estimated the fre-quency (%) of comorbid conditions in prevalent cancers (di-agnosed at or any time prior to baseline) and incident can-cers as occurring any time over follow-up. We estimatedincidence rates for as the number of new cancers by can-cer site per 100,000 person years. We compared CALIBERincidence rates for England with those from the UK fromthe International Agency for Research on Cancer (IARC).In CALIBER, we generated Kaplan-Meier estimates on 1-year mortality for incident cancers observed from 2012-2016(by cancer cites and by number of comorbid conditions) andprevalent cancers (by cancer sites and by number of comor-bid conditions). We only considered non-fatal incident cases,i.e., alive for at least 30 days following cancer diagnosis to ac-count for potential time lag in death recording. Excess deathsfor incident cancers were estimated based on 1-year mortal-ity and 1-year incidence rates, scaled up to a population of35,407,313 individuals aged 30 and above in England basedon population estimates from 2018(37). We used publiclyavailable data from the US from the Surveillance, Epidemi-ology, and End Results (SEER) program to estimate 1 yearmortality as 1 minus the reported survival(27). We consid-ered mortality data from SEER for individuals aged 40-64,65-74 and 75+. We applied our model (RIE and PAE) toSEER estimates of incidence and 1-year mortality (accord-ing to the corresponding age groups) scaled up to the USpopulation of individuals aged 40-64 (208,284,801 individu-als) and 65-74 (61,346,445 individuals) based on populationestimates from 2018(38).
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10 Lai et al. | Excess deaths in cancer and multimorbidity
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