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Comorbidity in elderly cancer patients in relation to overall
and cancer-specific mortality
TL Jørgensen*
,1
, J Hallas
2
, S Friis
3
and J Herrstedt
1
1
Department of Oncology, Odense University Hospital, Strandboulevarden 29, 5000 Odense C, Denmark;
2
Institute of Public Health, IPH, Research Unit
of Clinical Pharmacology, University of Southern Denmark, Winsløwparken 19
2
, 5000 Odense C, Denmark;
3
Institute of Cancer Epidemiology, Danish
Cancer Society, Strandboulevarden 49, 2100 Copenhagen Ø, Denmark
BACKGROUND: Aims of this study were to describe the prevalence of comorbidity in newly diagnosed elderly cancer cases compared
with the background population and to describe its influence on overall and cancer mortality.
METHODS: Population-based study of all 70 þyear-olds in a Danish province diagnosed with breast, lung, colorectal, prostate, or
ovarian cancer from 1 January 1996 to 31 December 2006. Comorbidity was measured according to Charlson’s comorbidity index
(CCI). Prevalence of comorbidity in newly diagnosed cancer patients was compared with a control group by conditional logistic
regression, and influence of comorbidity on mortality was analysed by Cox proportional hazards method.
RESULTS: A total of 6325 incident cancer cases were identified. Elderly lung and colorectal cancer patients had significantly more
comorbidity than the background population. Severe comorbidity was associated with higher overall mortality in the lung, colorectal,
and prostate cancer patients, hazard ratios 1.51 (95% CI 1.24–1.83), 1.41 (95% CI 1.14–1.73), and 2.14 (95% CI 1.65–2.77),
respectively. Comorbidity did not affect cancer-specific mortality in general.
CONCLUSION: Colorectal and lung cancer was associated with increased comorbidity burden in the elderly compared with the
background population. Comorbidity was associated with increased overall mortality in elderly cancer patients but not consistently
with cancer-specific mortality.
British Journal of Cancer (2012) 106, 1353 – 1360. doi:10.1038/bjc.2012.46 www.bjcancer.com
Published online 21 February 2012
&2012 Cancer Research UK
Keywords: aged; elderly; comorbidity; survival
Because of the aging population, the number of elderly cancer
patients (X70 years) has increased rapidly during the last two
decades (Engholm et al, 2011). Several reports have shown a high
prevalence of comorbid conditions among elderly cancer patients
(Ogle et al, 2000; Koroukian et al, 2006; Wedding et al, 2007),
but few studies have compared the prevalence of comorbidity
in people with and without cancer. Furthermore, results have
been conflicting; some studies have reported a clear association
between a diagnosis of cancer and increased comorbidity
(Smith et al, 2008), whereas others have not (Zeber et al, 2008;
Driver et al, 2010).
A high level of comorbidity is associated with lower survival of
cancer patients (Yancik et al, 1998; Lund et al, 2008; Tetsche et al,
2008; Patniak et al, 2011). Obviously, this could be explained by
excess mortality related to the comorbid conditions. There are a
number of reasons why cancer-specific mortality may be elevated
as a consequence of non-malignant comorbidity: for example, due
to suboptimal antineoplastic treatment of these patients (Hurria
et al, 2003; Janssen-Heijnen et al, 2005; Koppie et al, 2008) and/or
to an increased treatment toxicity resulting in reduced treatment
adherence. Indeed, one study found all levels of comorbidity in
lung cancer patients to be associated with increased toxicity
and reduced total dose of chemotherapy, and comorbidity was
found to be predictive of a decrease in overall survival whereas age
itself was not (Asmis et al, 2008). Treatment toxicity could also
influence the prognosis. Furthermore, comorbidity has been
associated with delay of cancer diagnosis and hence more
advanced disease stage at diagnosis (Bjerager et al, 2006). Few
studies have focused on cancer-specific mortality and its relation
to comorbidity.
The aims of this study were to describe the prevalence of
comorbidity in newly diagnosed elderly cancer cases compared
with the background population and to describe the influence of
comorbidity on overall and cancer-specific mortality.
MATERIALS AND METHODS
For the description of comorbidity of cancer cases, we conducted a
population-based case–control study of all inhabitants, aged 70
years and more, of Funen County (population 480 000),
Denmark, who were diagnosed with breast, lung, colorectal,
prostate, or ovarian cancer from 1 January 1996 to 31 December
2006. The population of Funen constitutes B9% of the
Danish population, and validation studies have previously shown
that Funen is representative of the entire population of Denmark
(Gaist et al, 1997). The second part of the study, describing overall
and cancer-specific mortality according to level of comorbidity,
was designed as a retrospective cohort study using the cancer
subjects only.
Received 5 December 2011; revised 16 January 2012; accepted 22
January 2012; published online 21 February 2012
*Correspondence: Dr TL Jørgensen;
E-mail: trine.joergensen@ouh.regionsyddanmark.dk
British Journal of Cancer (2012) 106, 1353 – 1360
&
2012 Cancer Research UK All rights reserved 0007 – 0920/12
www.bjcancer.com
Epidemiology
Data sources
We obtained data from four registers: The Danish Cancer Register
(DCR), the Funen County Patient Administrative System (FPAS),
Odense Pharmacoepidemiologic Database (OPED), and the Danish
Causes of Death Register (DCDR). Linkage between these registers
was carried out using the personal identification number (PIN); a
unique identifier of all Danish citizens assigned by the Central
Population Register since 1968. The PIN includes date of birth and
gender, and allows an accurate linkage between population-based
registers (Pedersen et al, 2006).
The DCR has recorded incident cases of cancer in Denmark
since 1943 and has been shown to have accurate and almost
complete capture of cancer cases (Storm et al, 1997). The cancer
diagnoses in the DCR are coded by the 10th revision of the
International Classification of Diseases (ICD-10). Among other
variables, it includes the PIN, date of cancer diagnosis, method of
verification, diagnosis, histological classification, and date of
death.
The FPAS has recorded all discharge diagnoses of non-
psychiatric hospital admissions in Funen since 1973. From 1995,
all outpatient contacts have been recorded as well. Diagnoses
in the FPAS are coded by the ICD-8 until December 1993, hereafter
by the ICD-10. Secondary care, inpatient or outpatient, is
provided almost exclusively by the national health services,
and, thus, this register covers virtually all hospital contacts in
Funen County.
The prescription database OPED has collected data on all
reimbursed prescriptions in Funen since 1990. The national health
services partly reimburse most prescription drugs for all Danish
inhabitants independently of private insurances. Drugs are
classified according to the Anatomic Therapeutic Chemical (ATC)
classification system, developed by the WHO (WHO, 2003). Each
record in OPED contains the PIN of the patient, the date of
purchase, the pharmacy, the prescriber, and a full account of what
has been dispensed, including the brand name, the ATC code, dose
unit, and quantity. The prescribed daily dose and the indication for
prescribing are not recorded in the database. Odense Pharmacoe-
pidemiologic Database also contains a demographic module which
holds information on residency, migrations, and death of all
citizens of Funen (Hallas and Nissen, 1994; Hallas, 2001).
Since 1943, the DCDR has recorded data on death certificates of
all the Danish citizens. The register holds information on the PIN,
date of death, main cause of death, and on up to four contributory
causes of death, coded according to the ICD-10 since 1994 (Juel
and Helweg-Larsen, 1999).
Cancer cases and controls
We identified 6325 cases with a first time diagnosis of breast,
lung, colorectal, prostate, or ovarian cancer during the period 1
January 1996 to 31 December 2006. Breast, lung, colorectal, and
prostate cancer were selected since these cancer types constitute
the four most frequent cancer types in Denmark. Ovary cancer
was included since a future nationwide study of this cancer
was planned simultaneously with the present study. Cases were
assigned with an index date, which was the date of cancer
diagnosis.
Controls were extracted from OPED’s demographic module
by use of a risk set sampling technique (Langholz and Goldstein,
1996). For each case, we randomly selected four controls among
all residents of Funen who matched the case by birth year and
gender, and who did not have a diagnosis of cancer at the time
the corresponding case was diagnosed (index date). One 103-year-
old colorectal cancer case only had three eligible controls
and, thus, our final control/case ratio deviated slightly from
4 : 1. Controls were eligible as cases at a later point in the study
period.
Comorbidity
Comorbidity was described according to Charlson’s comorbidity
index (CCI) (Charlson et al, 1987). The CCI is a weighted index that
takes into account both the number and the seriousness of
comorbid diseases, and it was originally validated in the breast
cancer patients. The index is based on 19 chronic conditions, each
with an assigned weight from 1–6 according to the relative risk of
dying within 1 year. Four of the 19 conditions described in the CCI
are related to malignant disease and these were excluded from our
analyses to allow a balanced comparison between subjects with and
without cancer diagnoses. The CCI has previously been adapted for
use with ICD-10 administrative data (Nuttall et al, 2006), and the
index has been validated specifically in elderly cancer patients
(Extermann, 2000). We divided the CCI score (CCIS) into three
groups: CCIS 0 ¼no comorbidity, CCIS 1– 2 ¼low to moderate
comorbidity, and CCIS X3¼severe comorbidity.
As some patients were diagnosed and treated for type 2 diabetes
(diabetes mellitus, DM) solely in primary care, we classified all
users of antidiabetics (ATC codes A10A and A10B) as having a
diagnosis of DM, which yielded additional 367 patients. Likewise,
users of drugs for obstructive airway diseases, ATC code R03 (but
not beta-adrenergics alone), were encoded as having chronic
pulmonary disease (CPD) even if they did not have a CPD
diagnosis in the FPAS (yielding additional 986 patients). Finally,
we encoded all subjects as having dementia if they had the
diagnosis in FPAS or if they were using anti-dementia drugs, ATC
code N06D (24 additional patients). As we have earlier found that
drug use among elderly cancer patients increases significantly
during the past 6 months before diagnosis (Jorgensen et al, 2012),
we used the period of 6 – 10 months before index date to assess
drug use, defined as using one or more prescription medication
during this period.
Death and censoring
We used the primary cause of death from the DCDR as outcome of
death. The diagnoses were divided into two subgroups; death from
the primary cancer and death from other causes. A total of 1416
cases had a malignant cause of death diagnosis other than the
original cancer diagnosis. These cases were controlled manually
(TLJ), and 26 were found to have died from a secondary malignant
disease. In 96 observations, the dates differed between the DCR
and the DCDR, and in 79 cases, the date of death was only recorded
in one of the registers. We believe it is unlikely that study subjects
would be registered with a date of death in either register if they
had not indeed died and these subjects were treated in the analyses
as such. However, for 42 cases with a date of death only from the
DCR, we had no information on the cause of death. Twenty-nine of
these cases were not censored before death because of the end of
follow-up or emigration and were therefore excluded from the
mortality analyses Further, we also excluded 38 cases aged 70 years
or more who were registered with a cancer diagnosis at the date of
death only. After these exclusions, 6287 study subjects were
available for analyses of overall mortality, and 6258 for cancer-
specific mortality.
Analysis
We used conditional logistic regression to compute odds ratios
(ORs) for the association between low – moderate and severe
comorbidity and the five cancer groups. The reference for all of
these analyses was no comorbidity.
We analysed the trend in comorbidity for the study period by a
logistic regression model with calendar year and case status as
explanatory variables. The model was validated by a goodness-of-
fit test, comparing the observed proportions with the correspond-
ing predictions from the model. The difference was significant, and
Comorbidity in elderly cancer patients
TL Jørgensen et al
1354
British Journal of Cancer (2012) 106(7), 13 53 – 1360 &2012 Cancer Research UK
Epidemiology
an interaction variable between cases status and calendar year was
included in the model.
Kaplan–Meier survival analysis by CCIS 0, 1–2, and 3 þwas
calculated and plotted for each cancer site. Follow-up started on
the date of diagnosis and continued until death, emigration, or 31
December 2008, whichever occurred first. In the analyses of
cancer-specific mortality, deaths from other causes than the
primary cancer were treated as censoring events. To estimate the
association between comorbidity and overall and cancer-specific
mortality, we used Cox proportional hazards analysis, and the
hazard ratios (HRs) calculated were adjusted for age, gender
(where relevant), and year of diagnosis. We speculated that the
effect of age might be quadratic, not linear, and constructed a
variable that equalled the squared value of age. This variable was
only significant in analyses of prostate cancer cases, and is
included only for this site. The proportional hazards assumption
was checked graphically using the Nelson–Aalen estimator and
analytically using Schoenfeld residuals.
All statistical analyses were performed using Stata version 11
(StataCorp. LP, College Station, TX, USA).
The study was approved by the Danish Data Protection Agency.
RESULTS
In the study period, 6325 cases of breast, lung, colorectal, prostate,
and ovarian cancer were identified. Median age was 78 years, range
70–103. The three most common comorbidities among cases
and controls were CPD, DM, and congestive heart failure. Further
characteristics of cases and the 25 299 controls are displayed
in Table 1.
Overall, comorbidity at index date increased modestly during
the study period 1996–2006. Overall, OR for having at least one
comorbidity was 1.05 (95% CI 1.03 – 1.07) per year. The increase
was similar among elderly cases and controls, OR 1.13 (95% CI
0.90–1.43). The prevalence of comorbidity in the two groups
according to study year is shown in Figure 1.
ORs of all comorbidities according to cancer site are shown in
Figures (2A– D). Regardless of cancer site, moderate–severe renal
disease was more prevalent in cases than in controls, ORs were
3.00 (95% CI 1.04– 8.65), 2.09 (95% CI 1.04– 4.19), 2.24 (95% CI
1.16–4.31), and 2.53 (95% CI 1.23 –5.20) for breast, lung, colorectal,
and prostate cancer, respectively. There were too few diagnoses of
moderate to severe renal disease among ovarian cancer cases and
controls to provide a valid estimate (data not shown).
Prevalence of morbidity and ORs for the association between
CCIS and cancer status according to cancer site are shown in
Table 2. Lung cancer cases had more comorbidity than controls in
all age groups. Cases had more of all comorbidities, except diabetes
and dementia, which appeared less frequently than in controls,
although the results were not statistically significant (Figure 2B).
Cases with colorectal cancer also had more comorbidity than
controls, especially ulcer disease, vascular disease, and DM
(Figure 2C). Prostate cancer cases especially had more ulcer and
connective tissue disease (Figure 2D).
Cumulative incidence proportions of deaths at 3 months, 1 year,
and 5 years, and HRs are shown according to cancer site and
comorbidity status for overall and cancer-specific mortality in
Tables 3 and 4. Corresponding Kaplan–Meier survival analyses
are shown if Figures 3 and 4. Breast cancer cases with CCIS 1 – 2
had higher overall and cancer-specific mortality than cases with
CCIS 0 and CCIS 3 þ. For lung, colorectal and prostate cancer
cases, severe comorbidity was associated with increased overall
mortality: HR for CCIS 3 þwas 1.51 (95% CI 1.24–1.83), 1.41
(95% CI 1.14–1.73), and 2.14 (95% CI 1.65–2.77), respectively. For
cancer-specific mortality, this association was only seen in lung
cases, HR 1.29 (95% CI 1.03 – 1.60). For ovarian cancer cases CCIS
was not associated with increased mortality in general.
DISCUSSION
This population-based study demonstrated an association with
frequencies of low–moderate and severe comorbidity and
diagnoses of lung and colorectal cancer among elderly individuals.
Moderate to severe renal disease was associated with diagnoses of
several cancer types. Comorbidity was associated with increased
overall mortality across most cancer sites. For cancer-specific
mortality, however, the association was only clearly apparent
among lung cancer patients.
End-stage renal disease has previously been associated with an
increased occurrence of several cancer diseases (Sutherland et al, 1977;
Table 1 Characteristics of cases and controls
Cases Controls
Median age (IQR)
All 78 (74 – 83) 78 (74 – 83)
Breast cancer (n¼1106) 79 (74 – 85) —
Lung cancer (n¼1719) 76 (73 – 80) —
Colorectal cancer (n¼2040) 79 (74 – 84) —
Prostate cancer (n¼1231) 78 (74 – 83) —
Ovarian cancer (n¼229) 77 (74 – 82) —
CCIS N(%) N(%)
CCIS 0 5192 (82.1) 21 868 (86.4)
CCIS 1 – 2 779 (12.3) 2428 (9.6)
CCIS 3+ 354 (5.6) 1003 (4.0)
History of
Ischemic heart disease 127 (2.01) 373 (1.47)
Congestive heart failure 199 (3.15) 548 (2.17)
Periferal vascular disease 147 (2.32) 352 (1.39)
Cerebrovascular disease 172 (2.72) 541 (2.14)
Dementia 54 (0.85) 294 (1.16)
Chronic pulmonary disease 778 (12.3) 2035 (8.04)
Connective tissue disease 46 (0.73) 136 (0.54)
Ulcer disease 137 (2.17) 312 (1.23)
Mild liver disease 12 (0.19) 42 (0.17)
DM 405 (6.40) 1352 (5.34)
Hemiplegia 7 (0.11) 7 (0.03)
Moderate to severe renal disease 44 (0.70) 76 (0.30)
DM with end organ damage 162 (2.56) 513 (2.03)
Moderate to severe liver disease 1 (0.02) 10 (0.04)
Abbreviations: CCIS ¼Charlson comorbidity index score; DM ¼diabetes mellitus;
IQR ¼interquartile range.
5
10
15
20
25
%
1996 1998 2000 2002 2004 2006
Year
ControlsCases
Figure 1 Proportion of cases and controls with CCIS X1 according to
study year.
Comorbidity in elderly cancer patients
TL Jørgensen et al
1355
British Journal of Cancer (2012) 106(7), 1353– 1360&2012 Cancer Research UK
Epidemiology
Inamoto et al, 1991; Maisonneuve et al, 1999). The reason for this
association is not clear, but chronic infections, a compromised
immune system, and altered DNA repair have been suggested as
possible risk factors (Vamvakas et al, 1998).
Breast cancer cases suffered marginally more from DM than
controls, and as DM is a known risk factor for the development of
breast cancer (Larsson et al, 2007), this association may have a
causal component. Among breast cancer cases, we found the
highest overall and cancer-specific mortality among those with
CCIS 1–2, which is in contrast with other studies (Land et al,
2011). Sample size was low, however, with only 43 cases having
CCIS 3 þ, so this result might be a chance finding.
Lung cancer cases had significantly more comorbidity than
controls. Lung cancer is mainly caused by cigarette smoking and
lung cancer patients have a high comorbidity burden caused by
other smoking-related diseases such as CPD and cardio-vascular
disease (Janssen-Heijnen et al, 2005). Smoking was thus an
important cause of the comorbidity –lung cancer association.
Comorbidity influenced both overall and cancer-specific mortality.
Two explanations of this pattern may be offered. First, comorbidity
has been found to cause delay of diagnosis of lung cancer (Bjerager
et al, 2006) and advanced stage of lung cancer at diagnosis is
strongly related to increased mortality. Second, the high pre-
valence of comorbidity in these patients might result in inelig-
ibility for surgery, radiotherapy, and chemotherapy. Indeed, lung
cancer patients with two or more comorbid conditions have been
reported to be less likely to receive active treatment for their
disease (Blanco et al, 2008).
The prevalences of vascular disease, CPD, ulcer disease, and DM
were higher in colorectal cancer cases than in controls. Life-style
factors such as poor diet, obesity, and low physical activity level
are risk factors for colorectal cancer as well as of conditions like
DM and vascular disease (Libutti et al, 2011). Other studies have
reported similar results as ours and also found a high prevalence of
ulcer and CPD (Yancik et al, 1996; Gross et al, 2006; Smith et al,
2008), whereas some studies have not found any significant
Breast cancer OR C*/ ctr**
Ischemic heart disease
Congestive heart disease
1.0 9/38
1.4 24/68
Periferal vascular disease 1.0 12/47
Cerebrovascular disease
Connective tissue disease
Dementia
1.0 21/85
0.7 11/64
Chronic pulmonary disease 1.1 81/293
0.3 2/29
Ulcer disease 0.8 11/55
Mild liver disease 2.0 2/4
Diabetes mellitus (DM) 1.3 70/226
Renal disease 3.0 6/8
DM with end-organ disease 1.0 22/88
95% CI
0.0 0.2 1.0 5.0 25.0
Lung cancer OR C/ctr
Ischemic heart disease
Congestive heart disease
Connective tissue disease
2.3 55/97
2.1 73/142
Periferal vascular disease 2.6 63/100
Cerebrovascular disease
Dementia
1.6 55/141
0.6 10/70
Chronic pulmonary disease 2.9 339/540
1.8 14/31
Ulcer disease 2.3 50/89
Mild liver disease 2.5 5/8
Diabetes mellitus (DM) 1.1 100/362
Renal disease 2.1 12/23
DM with end-organ disease 1.4 46/131
95% CI
0.3 0.5 1.0 2.0 4.0 8.0
Colorectal cancer OR C/ctr
Ischemic heart disease 1.0 30/124
1.3 56/171
Periferal vascular disease 1.5 42/115
Cerebrovascular disease 1.4 61/173
0.9 22/100
Chronic pulmonary disease 1.3 218/672
1.6 19/49
Ulcer disease 2.0 46/91
Mild liver disease 0.6 3/21
Diabetes mellitus (DM) 1.3 146/453
Renal disease 2.2 14/25
DM with end-organ disease 1.4 55/157
95% CI
0.1
Prostate cancer OR C/ctr
Ischemic heart disease 1.3 33/105
1.2 45/149
Periferal vascular disease 1.4 28/83
Cerebrovascular disease 1.0 33/128
0.8 9/47
Chronic pulmonary disease 1.1 126/460
2.1 11/21
Ulcer disease 1.7 29/69
Mild liver disease 1.0 2/8
Diabetes mellitus (DM) 1.1 75/269
Renal disease 2.5 12/19
DM with end-organ disease 1.0 32/123
95% CI
0.1 0.3 0.5 1.0 2.0 4.0 8.0
Congestive heart disease
Dementia
Connective tissue disease
Congestive heart disease
Dementia
Connective tissue disease
0.5 – 2.0
0.9 – 2.3
0.5 – 1.9
0.6 – 1.6
0.4 – 1.3
0.9 – 1.5
0.1 – 1.2
0.4 – 1.5
0.4 – 10.9
1.0 – 1.6
1.0 – 8.7
0.6 – 1.6
1.6 – 3.2
1.6 – 2.8
1.9 – 3.6
1.2 – 2.2
0.3 – 1.1
2.5 – 3.3
1.0 – 3.4
1.6 – 3.2
0.8 – 7.6
0.9 – 1.4
1.0 – 4.2
1.0 – 2.0
8.0
4.0
2.01.00.50.3
0.7 – 1.5
1.0 – 1.8
1.0 – 2.1
1.1 – 1.9
0.6 – 1.4
1.1 – 1.6
0.9 – 2.6
1.4 – 2.9
0.2 – 1.9
1.1 – 1.6
1.2 – 4.3
1.0–1.9
0.9 – 1.9
0.9 – 1.7
0.9 – 2.1
0.7 – 1.5
0.4 – 1.6
0.9 – 1.4
1.0 – 4.4
1.1 – 2.6
0.2 – 4.7
0.9 – 1.5
1.2 – 5.2
0.7 – 1.5
*C = cases, n **ctr = controls
Figure 2 Forest plot of ORs associating Charlson comorbidity items with a diagnosis of cancer according to cancer site.
Table 2 Comorbidity and OR for the association between CCI score and cancer status according to cancer site
CCIS 0 CCIS 1 – 2 CCIS 3+
Cancer site Cases n(%) Controls n(%) OR Cases n(%) Controls n(%) OR (95% CI) Cases n(%) Controls n(n%) OR (95% CI)
Breast 964 (87.2) 3886 (87.9) Ref. 99 (9.0) 380 (8.6) 1.06 (0.84 – 1.34) 43 (3.9) 157 (3.5) 1.11 (0.78 – 1.57)
Lung 1299 (75.6) 5959 (92.2) Ref. 303 (17.6) 667 (9.7) 2.07 (1.78 – 2.40) 117 (6.8) 250 (3.6) 2.12 (1.67 – 2.68)
Colon/rectum 1690 (82.8) 7020 (86.0) Ref. 234 (11.5) 808 (9.9) 1.19 (1.02 – 1.39) 116 (5.7) 332 (4.1) 1.50 (1.20–1.87)
Prostate 1037 (84.2) 4199 (85.3) Ref. 123 (10.0) 490 (10.0) 1.03 (0.83 – 1.27) 71 (5.8) 235 (4.8) 1.20 (0.91–1.59)
Ovary 202 (88.2) 804 (87.8) Ref. 20 (8.7) 83 (9.1) 0.98 (0.58 – 1.64) 7 (3.1) 29 (3.2) 0.93 (0.40 – 2.15)
Abbreviations: CCI ¼Charlson’s comorbidity index; CCIS ¼Charlson comorbidity index score; CI ¼confidence interval; OR ¼odds ratio; Ref. ¼reference.
Comorbidity in elderly cancer patients
TL Jørgensen et al
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British Journal of Cancer (2012) 106(7), 13 53 – 1360 &2012 Cancer Research UK
Epidemiology
differences between elderly colorectal cancer patients and controls
in the comorbidity burden (Driver et al, 2010). Comorbidity had a
negative impact on overall but not on cancer-specific mortality
which is in accordance with the findings of most other studies
(Yancik et al, 1998; Janssen-Heijnen et al, 2005; Asmis et al, 2008;
Zeber et al, 2008). However, Koukoran et al found that
comorbidity was not associated with overall but with an increased
cancer-specific mortality. Our findings suggest that elderly color-
ectal cancer patients do not receive inferior antineoplastic
treatment resulting in early death from colorectal cancer. Rather,
they die from their comorbid conditions.
Prostate cancer cases experienced more connective tissue and
ulcer disease than controls. This has not been described elsewhere
and should be confirmed by other studies. It might be speculated
that a diagnosis of connective tissue disease is likely to be
associated with the use of non-steroidal anti-inflammatory drugs
(NSAIDs) that are associated with an increased risk of ulcer
disease (Hallas et al, 1995). Sixty-seven percent of prostate cancer
Table 3 Overall mortality at 3 months, 1 and 5 years and HR according to cancer site and comorbidity level
Cancer site, CCIS n3 Months, % (95% CI) 1 Year, % (95% CI) 5 Years, % (95% CI) HR (95% CI)
Breast 1100
CCIS ¼0 959 9 (8 – 11) 17 (15 – 20) 48 (45 – 52) 1.00 (Ref.)
CCIS ¼1 – 2 98 11 (6 – 19) 18 (12 – 28) 61 (61 – 71) 1.40 (1.10 – 1.79)
CCISX3 43 14 (7– 28) 21 (11 – 36) 48 (34 – 65) 0.93 (0.61 – 1.43)
Lung 1702
CCIS ¼0 1287 38 (36– 41) 73 (71 – 75) 94 (93 – 95) 1.00 (Ref.)
CCIS ¼1 – 2 299 39 (34 – 45) 76 (71– 81) 95 (92 – 97) 1.12 (0.99 – 1.28)
CCISX3 116 49 (40 – 59) 85 (78 – 91) NA 1.51 (1.24 – 1.83)
Colorectal 2029
CCIS ¼0 1681 24 (22 – 26) 41 (39 – 43) 68 (66 – 70) 1.00 (Ref.)
CCIS ¼1 – 2 232 25 (20 – 31) 44 (38 – 51) 73 (66 – 79) 1.18 (1.00 – 1.38)
CCISX3 116 32 (24 – 41) 51 (42 – 60) 78 (70 – 86) 1.41 (1.14 – 1.73)
Prostate 1228
CCIS ¼0 1034 9 (7 – 10) 22 (20 – 25) 65 (62 – 68) 1.00 (Ref.)
CCIS ¼1 – 2 123 17 (11 – 25) 31 (24 – 40) 69 (60 – 78) 1.19 (0.96 – 1.47)
CCISX3 71 27 (18 – 39) 46 (36 – 59) 84 (74 – 92) 2.14 (1.65 – 2.77)
Ovary 228
CCIS ¼0 201 27 (22 – 34) 53 (47 – 60) 84 (78 – 89) 1.00 (Ref.)
CCIS ¼1 – 2 20 20 (8 – 45) 35 (18 – 60) 87 (67 – 97) 0.91 (0.56 – 1.50)
CCISX3 7 43 (16 – 83) 81 (44 – 99) NA 1.39 (0.57 – 3.42)
Abbreviations: CCIS ¼Charlson comorbidity index score; CI ¼confidence interval; HR ¼hazard ratio; NA ¼not applicable, OR ¼odds ratio; Ref. ¼reference.
Table 4 Cancer-specific mortality (95% CI) at 3 months, 1 and 5 years and HR according to cancer site and comorbidity level
Cancer site, CCIS n3 Months, % (95% CI) 1 Year, 5 (95% CI) 5 Years, 5 (95% CI) HR (95% CI)
Breast 1095
CCIS ¼0 954 6 (4 – 7) 11 (10 – 14) 31 (28 – 35) 1.00 (Ref.)
CCIS ¼1 – 2 98 8 (4 – 16) 13 (7 – 21) 32 (23 – 44) 1.18 (0.83 – 1.68)
CCISX3 43 5 (1 – 18) 10 (4 – 25) 16 (8 – 33) 0.48 (0.21 – 1.07)
Lung 1698
CCIS ¼0 1284 36 (33 – 39) 70 (68 – 73) 92 (90 – 93) 1.00 (Ref.)
CCIS ¼1 – 2 298 36 (31 – 42) 73 (68 – 78) 94 (90 – 96) 1.12 (0.98 – 1.29)
CCISX3 116 39 (31 – 50) 80 (71 – 88) NA 1.29 (1.03 – 1.60)
Colorectal 2019
CCIS ¼0 1675 21 (19 – 23) 36 (34 – 38) 58 (56 – 61) 1.00 (Ref.)
CCIS ¼1 – 2 229 23 (18 – 29) 39 (33 – 46) 59 (52 – 66) 1.12 (0.93 – 1.35)
CCISX3 115 20 (14 – 29) 36 (27 – 46) 58 (47 – 69) 1.00 (0.76 – 1.33)
Prostate 1219
CCIS ¼0 1028 6 (4 – 7) 16 (14 – 19) 52 (49 – 56) 1.00 (Ref.)
CCIS ¼1 – 2 123 10 (6 – 17) 19 (13 – 28) 49 (39 – 61) 0.89 (0.67 – 1.20)
CCISX3 68 16 (9 – 28) 27 (18 – 41) 51 (36 – 69) 1.32 (0.89 – 1.94)
Ovary 227
CCIS ¼0 200 24 (19 – 31) 51 (45 – 59) 82 (76 – 87) 1.00 (Ref.)
CCIS ¼1 – 2 20 20 (8 – 45) 35 (18 – 60) 86 (63 – 97) 0.88 (0.52 – 1.48)
CCISX3 7 43 (16 – 83) 81 (44 – 99) NA 1.53 (0.62 – 3.77)
Abbreviations: CCIS ¼Charlson comorbidity index score; CI ¼confidence interval; HR ¼hazard ratio; NA ¼not applicable, OR ¼odds ratio; Ref. ¼reference.
Comorbidity in elderly cancer patients
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cases used NSAIDS at diagnosis, compared with 43% of controls
(data not shown). Comorbidity influenced overall, but not cancer-
specific mortality in prostate cancer cases. This is in accordance
with the findings of other studies (Albertsen et al, 2011; Berglund
et al, 2011; Riihimaki et al, 2011) and indicates a non-inferior
antineoplastic treatment of patients with comorbidity.
Ovarian cancer cases generally died from their cancer disease;
overall and cancer-specific mortality was similar. Severe comor-
bidity seemed to be associated with higher mortality, although this
was based on small numbers. In a cohort study of 1995, women
with ovarian cancer, Tetsche et al (2008) found an association
between severe comorbidity and impaired survival over a 10-year
period. As in our study, the study lacked information on disease
stage. In contrast, Mass et al (2005) found treatment modality to
be the only parameter with a statistically significant prognostic
effect in a population-based study on 1116 ovarian cancer cases,
which included information on disease stage, treatment modality,
age, and comorbidity.
The strengths of our study are the population-based approach
with a large sample size, and use of valid registers with high
coverage, minimising the risk of selection bias. The DCR has been
found to cover 95–98% of all cancer diagnoses in Denmark
(Storm et al, 1997). Eighty-seven percent of the tumours are
verified histologically, and only 0.5% are registered solely on the
basis of death certificates (Danish Health Board, 2009). All
diagnoses in FPAS are transferred to the Danish National
Hospital Register (DNHR). The DNHR has been validated and
shown to possess a high degree of completeness and validity of
administrative data (Andersen et al, 1999). Furthermore,
incorrect or missing diagnoses in FPAS will most likely be
0.00
0.25
0.50
0.75
1.00
Fraction alive
0 2 4 6 8 10
Years
Breast cancer
0.00
0.25
0.50
0.75
1.00
0 2 4 6 8 10
Years
CCIS 0
CCIS 1−2
CCIS 3+
Lung cancer
0.00
0.25
0.50
0.75
1.00
0 2 4 6 8 10
Years
Colorectal cancer
Fraction alive
0 2 4 6 8 10
Years
Prostate cancer
0 2 4 6 8 10
Years
Ovarian cancer
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
Figure 3 Overall survival according to CCIS and cancer site.
0.00
0.25
0.50
0.75
1.00
Fraction alive
0 2 4 6 8 10
Years
Breast cancer
0 2 4 6 8 10
Years
CCIS 0
CCIS 1−2
CCIS 3+
Lung cancer
0 2 4 6 8 10
Years
Colorectal cancer
Fraction alive
0 2 4 6 8 10
Years
Prostate cancer
0 2 4 6 8 10
Years
Ovarian cancer
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
Figure 4 Cancer-specific survival according to CCIS and cancer site.
Comorbidity in elderly cancer patients
TL Jørgensen et al
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British Journal of Cancer (2012) 106(7), 13 53 – 1360 &2012 Cancer Research UK
Epidemiology
equally distributed among cases and controls, and thus result in a
non-differential misclassification.
Only few studies have validated the Danish Register of Causes of
Death (Pedersen et al, 2006). The causes of death are based on
death certificates, often filled by the patients’ general physicians.
One limitation is that it is possible that for deceases with a
previous diagnosis of cancer and an unclear cause of death, the
physician may apply the cancer disease as the cause of death. This
will result in artificially higher rates of cancer-specific death. In
our study, however, comorbidity was not associated with a higher
risk of death from cancer.
Limitations of our study are the lack of data on stage of disease
and cancer therapy. This might have confounded the effect of
comorbidity on cancer-specific mortality. We might have expected
patients with a high comorbidity burden to have undergone less
intensive treatment. However, we found no general effect of
comorbidity on cancer-specific mortality and we thus believe that
our results are valid.
In conclusion, diagnoses of colorectal and lung cancer were
associated with increased comorbidity burden compared with the
background population. Moderate to severe renal disease was
more prevalent in elderly cancer cases than in controls, regardless
of cancer site. Comorbidity was associated with increased overall
mortality of elderly cancer patients, but an association with
cancer-specific mortality was only seen for severe comorbidity in
lung cancer.
ACKNOWLEDGEMENTS
We would like to thank the Danish Cancer Society, the I.M.
Daehnfeldt Foundation, and The Danish Health Insurance
Foundation for supporting this study. The sponsors of the study
had no role in the study design, data collection, data analysis, data
interpretation, or writing of the report.
Conflict of interest
The authors declare no conflict of interest.
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Comorbidity in elderly cancer patients
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Epidemiology
