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Epidemiology of multimorbidity in childhood cancer survivors:
a matched cohort study of inpatient hospitalisations in Western
Australia
Tasnim Abdalla
1
✉, Jeneva L. Ohan
2
, Angela Ives
3
, Daniel White
4
, Catherine S. Choong
3,5
, Max Bulsara
6
and Jason D. Pole
7,8
© The Author(s) 2025
BACKGROUND: Childhood cancer survivors (CCS) experience an elevated burden of health complications, underscoring the
importance of understanding the patterns of multimorbidity to guide the management of survivors with complex medical needs.
METHODS: We examined the patterns of hospitalisations with multimorbidity in 5-year CCS (n=2938) and age- and sex-matched
non-cancer comparisons (n=24,792) using statewide records of inpatient admissions in Western Australia from 1987 to 2019.
RESULTS: Multimorbidity rates were higher for CCS (10.6, 95%CI 10.2–10.9) than for non-cancer comparisons (3.2, 95%CI 3.2–3.3).
CCS exhibited a significantly higher adjusted hazard ratio of multimorbidity, particularly when admitted for neoplasms (14.6, 95%CI
11.2–19.1), as well as blood (7.3, 95%CI 4.9–10.7), neurological and sensory (5.2, 95%CI 4.2–6.6), and cardiovascular (3.6, 95%CI
2.6–4.8) diseases. By the age of 55 years, chronic multimorbidity was more prevalent in survivors than in comparisons (14.5% vs.
5.3%). Psychiatric disorders were common comorbidities, particularly in those admitted for neurological and sensory (71.1%),
endocrine (61.5%), and digestive (59.3%) diseases. Multimorbidity during hospitalisation increased the length of hospital stay
(p< 0.05). Key condition clusters included (1) psychoactive substance dependence, alcohol misuse, and other mental disorders; (2)
hypertension, diabetes, kidney disease, and musculoskeletal diseases; (3) epilepsy, hypothyroidism, and other liver diseases; and (4)
hypertension, kidney disease, and other liver diseases.
CONCLUSIONS: These findings suggest that exposure to cancer in childhood elevates the risk of multimorbidity. The
reconfiguration of healthcare delivery to enhance personalised care and clinical integration is essential for effectively managing
multimorbidity in this population.
BJC Reports; https://doi.org/10.1038/s44276-024-00114-1
INTRODUCTION
The need for effective survivorship care for childhood cancer
survivors (CCS) has increased significantly over the last four
decades [1–5]. This is attributed to notable improvements in
survival rates for several types of children’s cancers [1,4,5], and
the mounting evidence demonstrating elevated morbidity [2] and
accelerated ageing [3,6,7] in the population of CCS. Survivorship
care requires efficient structuring to address the adverse effects of
cancer treatments on CCS (e.g., the elevated burden of
cardiomyopathy, neurocognitive deficits, and anxiety) and the
subsequent burden on healthcare services (e.g., the elevated risk
of repeated hospitalisations) [8]. CCS experience a significantly
higher cumulative burden of chronic health conditions (CHCs)
than the general population (p< 0.05) [9]. As the cancer survivor
population expands and ages, the need to mitigate the impact of
multiple chronic conditions on survivors through personalised
healthcare and optimal healthcare delivery will become increas-
ingly important [8,10].
In CCS, multimorbidity (two or more co-occurring conditions)
may develop due to clinical, behavioural, or social factors. These
factors include treatment-related complications [11], a higher
prevalence of comorbidities [12], side effects of drug-drug
interactions [13,14], social deprivation [13], obesity [15], and
physical inactivity [16]. Although the complexity of clinical
management increases in cancer survivors with multiple condi-
tions [11], existing clinical care focuses on managing single
conditions [17–19], which can lead to fragmented healthcare
provision and a greater illness burden [13,20]. Existing barriers,
such as the limited capacity of survivorship specialists [2],
inadequate care coordination [2,8], and suboptimal adherence
to the recommended follow-up care among survivors [21,22] can
further exacerbate the illness burden due to delayed and
inefficient management of complications. A recent systematic
review revealed a lack of comprehensive studies on the risk factors
for multimorbidity in cancer survivors [23]. This gap in
evidence can impede the development of clinical care
Received: 25 March 2024 Revised: 4 November 2024 Accepted: 6 December 2024
1
Faculty of Health and Medical Sciences, The University of Western Australia, Perth, Australia.
2
School of Psychological Science, The University of Western Australia, Perth,
Australia.
3
Medical School, The University of Western Australia, Perth, Australia.
4
Haematology Department, Women’s and Children’s Hospital, Adelaide, Australia.
5
Department of
Endocrinology, Perth Children’s Hospital, Perth, Australia.
6
Institute for Health Research, The University of Notre Dame Australia, Perth, Australia.
7
Centre for Health Services
Research, The University of Queensland, Brisbane, Australia.
8
Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.
✉email: tasnim.abdalla@research.uwa.edu.au
www.nature.com/bjcreports
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guidelines that can effectively address the management of
simultaneous conditions in this population.
Previous investigations of multimorbidity patterns in CCS have
primarily focused on CHCs [9,12,24–28]. An increased burden of
multimorbidity leading to increased utilisation of inpatient
[12,26,27] and emergency services [26,27] was observed in
survivors (aged <18) compared to their siblings and the general
population, particularly for general paediatric morbidity, neurologi-
cal deficits, psychiatric disorders, and endocrine conditions [26]. The
disproportionate difference observed in the younger population
continues into mid-adulthood [9,24,28], with evidence showing a
higher prevalence of any (37.6% versus 13.1%) [28] and severe/life-
threatening (22.5% versus 4.3%) CHCs [24], and a higher cumulative
count (an estimated average of 17.1) of CHCs by age 50 [9]. The
existing body of evidence needs to be expanded to identify clusters
of CHCs in adult CCS [26,27,29], provide longitudinal administrative
evidence on the prevalence of acute and chronic multiple
conditions [9,12,24,28] among the hospitalised population of
adult CCS [12], and examine the influence of co-occurring diagnoses
on repeated healthcare use and length of stay [12]. Additionally,
there is currently no data on the burden of multimorbidity in the
Australian CCS population, despite the increase in the number of
five-year prevalent cases by 80% between 1979 and 2018 [5].
We aimed to examine the overall and cause-specific multi-
morbidity in a hospitalised population of five-year CCS in Western
Australia (WA) and a matching non-cancer comparison group from
the general WA population using recurrent hospitalisation (and
community mental healthcare) records. We extended the com-
monly used operational definition of multimorbidity ‘≥two CHCs
in different organ systems in the same individual’[30]to‘≥two
conditions (acute or chronic)’at the time of hospitalisation.
Incorporating acute conditions can help capture the possible
cause-effect relationship between acute and chronic conditions
over time [30], providing a more comprehensive indication of the
illness burden. Chronic disease-specific analyses were also con-
ducted to examine the prevalence of common and severe
conditions, and identify frequently co-occurring condition clusters.
METHODS
Study design and setting
This is a retrospective examination of whole-population cancer, inpatient,
and community mental health service records linked using the WA Data
Linkage System, from 1982 to 2019. WA is the largest state in Australia by
area, with 2.8 million people in 2019 [31], representing 10.7% of Australia’s
population. Eighty per cent of the WA’s population resides in major urban
areas, and 3.2% identify as Indigenous Australians [32].
Study participants
The WA Cancer Registry (WACR) and the Perth Children's Hospital (PCH)
Oncology Dataset were used to extract records for children (aged <18)
diagnosed with cancer in WA from 1 January 1982 to 30 June 2014. The
WACR is a statutory data collection for all histologically and radiologically
confirmed neoplasms in WA. The PCH is the tertiary referral centre for all
paediatric and adolescent cancers diagnosed in WA. The cancer diagnostic
groups were coded and categorised according to the International
Classification of Childhood Cancer, Third Edition [33]. The five-year survival
status was determined using the death date extracted from the Death
Registrations. For each cancer case, we sampled up to 10 sex- and birth/
month-year-matched individuals with no history of childhood cancer and a
non-deceased status at the corresponding case’s cancer diagnosis date
from the WA Birth Registrations. The large non-cancer comparison group
enabled matching without replacement, which helped improve the
matching quality by increasing the set of possible matches [34].
Multimorbidity ascertainment
In this study, two analyses were performed to assess the burden of
multimorbidity among hospitalised participants. The individual instances
of inpatient admissions were examined to ascertain the burden of
multimorbidity. In the primary analysis, multimorbidity was defined as two
or more co-occurring acute or chronic conditions (hereafter referred to as
‘any multimorbidity’) in the same individual. Co-occurring acute or chronic
conditions were categorised into major diagnostic groups, including: A)
blood and immunology; B) cardiovascular; C) digestive; D) endocrine,
nutritional, and metabolic; E) genitourinary; F) infectious and parasitic;
G) musculoskeletal and connective; H) neoplasms; I) neurological and
sensory; J) respiratory; and K) mental and behavioural disorders. Physical
and mental health conditions were identified using the principal
diagnosis (i.e., condition chiefly responsible for an inpatient episode) and
21 additional diagnoses (i.e., conditions with significant influence on the
treatment of the current episode) fields of records in the Hospital
Morbidity Data Collection (HMDC). Since 1980, the HMDC has collected
mandatory reported data on all inpatient activities in public and private
hospitals in WA. All records (including day and obstetric admissions) were
examined for the presence of multimorbidity. The co-existing mental and
behavioural disorders were also identified using the Mental Health
Information Data Collection (MIND). Since 1966, the MIND has collected
mandatory reported data on contacts with specialised ambulatory
community-based services. Diagnostic conditions were coded using the
International Statistical Classification of Diseases and Related Health
Problems, Ninth Revision, Clinical Modification and Tenth Revision,
Australian Modification (Supplementary Table 1) [35,36].
In the secondary analysis, multimorbidity was defined as two or more
co-occurring CHCs in the same individual. CHCs were defined using
Tonelli’s validated algorithm for identifying multimorbidity in health
administrative data (Supplementary Table 2)[37,38]. This algorithm
identifies 30 CHCs with high to moderate validity (i.e., high: positive
predictive value (PPV) and ≥70 sensitivity; moderate: ≥70 PPV and <70%
sensitivity) [37,38]. It captures a reasonable number of core CHCs
identified in a previously validated algorithm by Barnett et al. [39] and a
recent systematic review [40]. Additional CHCs were defined based on
conditions published in an international Delphi consensus to measure
multimorbidity [41] and publications by the Australian Institute of Health
and Welfare (Supplementary Table 2) [42,43].
The prevalence of CHCs at the end of the study period was determined
based on the diagnoses recorded within five years of the primary cancer
diagnosis date (or assigned date in the matched comparisons) and
throughout the post-survival period. The prevalence of conditions with
potential for prolonged remission or cure at the end of the study was
assessed using specific observation windows: 24 months for chronic pain,
peptic ulcer disease, and severe constipation, and 12 months for depression
and anxiety. These observation windows were defined based on Tonelli’s
and Barnett’s validated algorithms for measuring multimorbidity.(33–35) The
baseline prevalence of chronic multimorbidity was determined using the
diagnostic information recorded within five years of the primary cancer
diagnosis date (or assigned date in the matched comparisons). Similar
lookback periods of 12 and 24 months were also applied to conditions with
the possibility of a cure to determine their presence at baseline
(Supplementary Table 2).
Statistical analysis
Descriptive statistics were reported as counts and percentages for
categorical variables and as the means (standard deviations [SDs]) or
medians (interquartile ranges [IQRs]) for continuous variables. The baseline
prevalence of chronic multimorbidity was calculated and reported as a
percentage with a 95% confidence interval (CI). Follow-up for multimorbid
conditions started five years after the initial cancer diagnosis date (index
date). In many cancer survivors, the risk of tumour recurrence decreases five
years after the primary diagnosis, enabling the identification of complica-
tions not attributed to the immediate toxic effects of anti-cancer treatment
[2,20]. In the event of a new cancer within five years of the index date, the
latest diagnosis date substituted the original index to ensure that acute
therapy-related toxicities were not captured. The matched comparisons
were assigned to the index date of the corresponding cancer case to ensure
that the follow-up period was comparable. The participants were followed
up until the date of administrative censoring (30 June 2019) or until the date
of death, whichever occurred first.
The overall rate of recurrent hospitalisations with any multimorbidity
(per 100 person-years) was quantified using all records with a multi-
morbidity indicator divided by the total person-time at risk. The cause-
specific rate per 1000 person-years was quantified using all records with
indicators for multimorbidity and the specific cause being examined. The
risk of recurrent hospitalisations for cause-specific multimorbidity in
T. Abdalla et al.
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survivors compared with the non-cancer group was estimated using the
Andersen-Gill model for recurrent events (an extension of the Cox model)
[44]. The regression model accounted for the confounding effects of
several demographic and clinical covariates, including diagnosis decade
(categorised into the 1980s, 1990s, 2000s, and the 2010s), cancer diagnosis
age (continuous and categorised into the age groups <5, 5–9, 10–14, and
15–18 years), sex, the Index of Relative Socio-economic Disadvantage, IRSD
(ranging from the most (quintile 1) to the least (quintile 5) disadvantaged),
and the Remoteness Areas (RA) derived from the Accessibility/Remoteness
Index of Australia (categorised into a major city, inner regional, outer
regional, remote, and very remote). Residential remoteness and socio-
economic status indicators were assigned at the start of the follow-up
period and carried forward. Age was added linearly with the age-squared
term to account for the nonlinear effect of age. The cause-specific adjusted
hazard ratio (aHR) was reported. A sensitivity analysis was performed to
examine the aHR for multimorbidity in participants with no history of a
new cancer or a recurrence of the primary cancer during the post-survival
period. The overall and cause-specific rates of recurrent hospitalisations
with chronic multimorbidity (per 1000 person-years) were quantified. The
difference in the length of hospital stays (LoS) between patients admitted
with and without any comorbidities was estimated using negative
binomial regression models, adjusted for the presence of multiple
diagnosis codes (yes/no), sex, age (and age squared term), RA, IRSD,
diagnosis age, diagnosis decade and Indigenous status. The mean LoS (in
days) was reported by the primary admitting cause.
Hierarchical clustering analysis (‘unsupervised classification’)[45] was
conducted to examine the presence of non-random clusters of CHCs in
survivors. CHCs were identified using all the diagnostic information
recorded within five years of the primary cancer diagnosis and across the
post-survival period. The conditions were coded as dichotomous variables
(1=condition present, 0=condition absent). Before clustering, the den-
drogram and Hopkins statistic (which measures the clustering tendency
within the dataset, with values close to 1 indicating a high clustering
tendency and values close to 0 indicating a random distribution of data
points) [46] were examined to assess the clustering tendency of the data.
The Hopkin statistic (H=0.7) indicated the presence of a moderate
clustering tendency within the data. Conditions with a < 5 prevalence were
excluded from the clustering analysis. The Jaccard distance method was
used to assess the dissimilarity between pairs of conditions. Ward’s
minimum variance method was selected to minimise the total within-
cluster variance by progressively joining the clusters with the smallest
within-cluster distance [45]. The conditions with the highest prevalence
within the dominant clusters were reported.
The prevalence of CHCs [37,38] within subgroups stratified by socio-
demographic and clinical factors by study exit was also reported. The
temporal change in the cumulative prevalence of chronic multimorbidity
(defined as the presence of two or more co-occurring non-cancer diseases,
identified using Tonelli’s algorithm) [37,38] in survivors with no history of
multiple CHCs during the transition to adult healthcare services was
examined at ages 18–29, 30–39, and 40–55 years. The analyses were
conducted using SPSS 29 (IBM Corporation, New York), StataNow-18
BE (College Station, Texas) and R 4.1.2 (R Foundation for Statistical
Computing, Vienna), with a two-sided p-value < 0.05 indicating statistical
significance.
Ethics approval
The Human Research Ethics Committees at the WA Department of Health,
Child and Adolescent Health Service, and the University of WA approved
access to the de-identified health data through a consent waiver according
to their respective guidelines (References: RGS0000001488; RA/4/20/5340).
RESULTS
The study cohort consisted of 2,938 CCS and 24,792 non-cancer
comparisons. The baseline characteristics of the study participants
are summarised in Supplementary Table 3. The follow-up period
spanned 32.5 years, totalling 38,630 person-years in survivors and
327,075 person-years in comparisons. The duration of follow-up
was similar for both groups, with a median of 12.0 years (range,
<1–32.4 years). The mean age at the end of follow-up was 27.1 (SD
10.6) years in survivors and 26.9 (SD 10.5) years in comparisons,
with the follow-up extending to early adulthood in both
participant groups (range, 5–55 years). For survivors, the median
time since cancer diagnosis was 13.3 (range, 5.0–37.5) years, and
the most common childhood cancer diagnoses were leukaemia
(21.1%), other epithelial and skin carcinomas (19.6%), and CNS
tumours (14.2%).
Chronic multimorbidity prevalence at study entry
At baseline, survivors had a significantly higher prevalence of chronic
multimorbidity (9.1%, 95%CI 8.0–10.1%) compared to the non-cancer
group (0.8%, 95%CI 0.7–1.0%), p < 0.05 (Supplementary Table 3).
Among survivors, the most common non-cancer CHCs were back
problems (3.2%, 95%CI 2.5–43.8%), chronic kidney disease (3.0%, 95%
CI 2.4–3.6%), hypertension (2.8%, 95%CI 2.2–3.4%), other liver
diseases (2.6%, 95%CI 2.0–3.2%), and peripheral neuropathy (1.7%,
95%CI 1.2–2.1%). Baseline chronic multimorbidity was more prevalent
among survivors diagnosed at age 5–9 years (11.2%, 95%CI
8.5–13.9%) than among those diagnosed between the ages
of 15–18 years (7.6%, 95%CI 5.7–9.5%) and among survivors of
haematological cancers (14.6%, 95%CI 12.3–16.8%) compared to
survivors of CNS tumours (8.9%, 95%CI 6.1–11.6%) and solid tumours
(7.5%, 95%CI 6.0–9.0%). It was also more prevalent in male (9.3%, 95%
CI 7.8–10.8%) than in female (8.8%, 95%CI 7.3–10.2%) survivors.
Coexistence of acute and chronic conditions
During the study period, 31.3% of the survivors and 17.2% of
the comparisons were admitted to the hospital with multiple
conditions. The overall rate of any multimorbidity per 100 person-
years was 10.6 (95%CI 10.2–10.9) in survivors and 3.2 (95%CI
3.2–3.3) in comparisons (Table 1). Among the survivors, the rate/
100 person-years was highest in those diagnosed with cancer at
age 5–9 years (12.5, 95%CI 11.7–13.3), those diagnosed in
1982–1989 (11.3, 95%CI 10.7–11.9), those from low socio-
economic status (13.7, 95%CI 12.9–14.6), and those of female sex
(12.2, 95%CI 11.7–12.7) (Table 1). Within the major cancer
diagnostic categories, the rate of any multimorbidity per 100
person-years was highest in CNS tumour survivors (16.3, 95%CI
15.2–17.4), followed by haematological (12.2, 95%CI 11.6–12.9),
and solid tumour survivors (8.3, 95%CI 8.0–8.7). In CNS tumour
survivors, the rate was highest in survivors diagnosed with cancer
at age <5 years (22.2, 95%CI 19.9–24.7), and those from low socio-
economic status (29.8, 95%CI 26.6–33.4) (Supplementary Table 4).
The adjusted HR (aHR) for any multimorbidity in survivors versus
the comparisons was significantly higher (p< 0.05) for all major
admitting causes. The aHR was highest for neoplasms (14.6, 95%CI
11.2–19.1), blood (7.3, 95%CI 4.9–10.7), neurological and sensory
(5.2, 95%CI 4.2–6.6), cardiovascular (3.6, 95%CI 2.6–4.8), and
endocrine (3.0, 95%CI 2.4–3.6) diseases (Table 2). The most
prevalent diagnoses within each major diagnostic category are
presented in Supplementary Table 5. Overall, the sensitivity
analysis indicated a lower aHR for any multimorbidity in
participants with no history of a cancer diagnosis in the post-
survival period. However, the variability in the risk between
survivors and their comparisons remained significantly higher
across all diagnostic categories (Supplementary Table 6). The
prevalence of comorbidity pairs stratified by major physical
diagnostic groups and mental disorders is presented in Fig. 1.
Mental disorders were prevalent among admitted survivors,
irrespective of the principal cause of admission. Co-occurring
mental disorders were most prevalent in patients admitted for
neurological and sensory (71.1%), endocrine (61.5%), and
digestive (59.3%) diseases. Several comorbid disorders were
particularly prevalent in admitted patients, including endocrine
diseases in patients admitted for infectious and parasitic
diseases (42.6%), genitourinary and musculoskeletal diseases in
patients admitted for blood diseases (40.0% and 38.7%,
respectively), and digestive diseases in patients admitted for
endocrine diseases (34.2%).
The average LoS was longer among survivors admitted with
any multimorbidity than among those admitted with a single
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Table 1. The number and rate of any multimorbidity in hospitalised 5-year childhood cancer survivors compared to age- and sex-matched comparisons, categorised by clinical and
sociodemographic characteristics, Western Australia, 1987 to 2019.
Characteristics Cancer survivors (N=2938) Matched comparisons (N=24,792)
Persons N(%) Total events
N
a
Events n(%)
b
Rate/100 PY (95%
CI)
Persons N(%) Total events
N
a
Events n(%)
b
Rate/100 PY (95%
CI)
Total 921 (31.3)
c
15,562 4077 (26.2) 10.6 (10.2–10.9) 4261 (17.2)
c
58,014 10,601 (18.3) 3.2 (3.2–3.3)
Sex
Male 396 (43.0) 6410 1754 (27.4) 9.0 (8.5–9.4) 1721 (40.4) 20,736 4125 (19.9) 2.5 (2.4–2.5)
Female 525 (57.0) 9152 2323 (25.4) 12.2 (11.7–12.7) 2540 (59.6) 37,278 6476 (17.4) 4.1 (4.0–4.2)
Attained age (years)
d
<25 years 574 (62.3) 7973 2453 (30.8) 11.2 (10.8–11.6) 2118 (49.7) 26,548 3979 (15.0) 2.1 (2.0–2.2)
>25 years 482 (52.3) 7589 1624 (21.4) 9.7 (9.3–10.2) 2629 (61.7) 31,466 6622 (21.0) 4.8 (4.7–4.9)
Socio-economic quintile
e
0–20% (most disadvantaged) 209 (22.7) 3633 1091 (30.0) 13.7 (12.9–14.6) 858 (20.1) 11,043 2359 (21.4) 3.8 (3.7–4.0)
20–40% 185 (20.1) 2488 658 (26.4) 8.9 (8.3–9.7) 933 (21.9) 12,691 2237 (17.6) 3.6 (3.4–3.7)
40–60% 174 (18.9) 3473 609 (17.5) 8.7 (8.0–9.4) 830 (19.5) 11,243 2095 (18.6) 3.2 (3.1–3.4)
60–80% 185 (20.1) 2743 804 (29.3) 11.5 (10.7–12.3) 809 (19.0) 11,332 1938 (17.1) 3.0 (2.9–3.2)
80–100% (least
disadvantaged)
157 (17.0) 3007 836 (27.8) 10.6 (9.9–11.3) 756 (17.7) 10,626 1708 (16.1) 2.5 (2.4–2.7)
Missing 11 (1.2) 218 79 (36.2) 5.7 (4.3–7.0) 75 (1.8) 1079 264 (24.5) 3.6 (3.2–4.0)
Residential remoteness
f
Major city 652 (70.8) 11,745 3114 (26.5) 11.7 (11.3–12.1) 2903 (68.1) 39,102 6763 (17.3) 3.4 (3.3–3.4)
Inner regional 89 (9.7) 1428 357 (25.0) 8.6 (7.8–9.6) 467 (11.0) 6412 1114 (17.4) 3.2 (3.0–3.4)
Outer regional 96 (10.4) 1278 333 (26.1) 9.5 (8.5–10.6) 449 (10.5) 6118 1179 (19.3) 3.5 (3.3–3.7)
Remote 48 (5.2) 607 131 (21.6) 6.7 (5.7–8.0) 248 (5.8) 3942 879 (22.3) 4.2 (4.0–4.5)
Very remote 28 (3.0) 354 109 (30.8) 10.9 (9.1–13.3) 191 (4.5) 2403 661 (27.5) 6.1 (5.6–6.5)
Missing 8 (0.9) 150 33 (22.0) 2.4 (1.7–3.4) <5 37 5 (13.5) —
Cancer diagnosis group
g
Haematological cancers 267 (28.6) 4822 1475 (30.6) 12.2 (11.6–12.9) ————
Central nervous system
tumours
185 (20.1) 2361 878 (37.2) 16.3 (15.2–17.4) ————
Solid tumours 456 (49.5) 8251 1691 (20.5) 8.3 (8.0–8.7) ————
Cancer diagnosis age
h
<5 years 271 (29.4) 4340 1295 (29.8) 10.5 (9.9–11.1) 1122 (26.3) 14,467 2383 (16.5) 2.2 (2.1–2.2)
5–9 years 171 (18.6) 2976 888 (29.8) 12.5 (11.7–13.3) 722 (16.9) 10,169 2097 (20.6) 3.4 (3.3–3.6)
10–14 years 229 (24.9) 3478 908 (26.1) 9.7 (9.1–10.4) 1101 (25.8) 15,610 2725 (17.5) 3.5 (3.4–3.6)
15 - <18 years 250 (27.1) 4768 986 (20.7) 10.0 (9.4–10.7) 1316 (30.9) 17,768 3396 (19.1) 4.3 (4.2–4.5)
Cancer diagnosis decade
i
1982–1989 259 (28.1) 6003 1436 (23.9) 11.3 (10.7–11.9) 1267 (29.7) 19,147 3822 (20.0) 3.7 (3.6–3.9)
1990–1999 315 (34.2) 5263 1377 (26.2) 9.9 (9.4–10.5) 1524 (35.8) 21,485 3835 (17.8) 3.2 (3.1–3.3)
2000 - 2014 347 (37.7) 4296 1264 (29.4) 10.5 (10.1–11.1) 1470 (34.5) 17,382 2944 (16.9) 2.8 (2.7–2.9)
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condition (Supplementary Table 7). The mean difference in LoS
was significantly greater (p< 0.05) across all diagnostic cate-
gories, except for blood diseases (p> 0.05). Comorbidities
managed during inpatient care for mental disorders (10.2,
SD 8.3), neoplasms (5.9, SD 6.3), and infectious and parasitic
diseases (4.5, SD 5.8) had the greatest impact on the duration of
hospital stay.
Coexistence of chronic conditions
The overall rate of recurrent hospitalisations with chronic multi-
morbidity was higher in survivors than in comparisons (rate/1000
PY 16.2, 95%CI 15.0–17.5 vs. 8.6, 95%CI 8.3–9.0) (Supplementary
Table 8). Among survivors, the rate of hospitalisation (per 1000 PY)
was highest for psychiatric disorders (7.3, 95%CI 6.4–8.2), followed
by gastro-intestinal (4.0, 95%CI 3.4–4.6) and kidney disease (3.4,
95%CI 2.8–4.0). By age 55, 14.5% (95%CI 13.2–15.7%) of survivors
were admitted to the hospital with chronic multimorbidity
(Supplementary Table 9), compared with 5.3% (95%CI 5.0–5.6%)
of comparisons (data not shown in the table). The characteristics
associated with a higher prevalence of multimorbidity included
CNS tumour diagnosis (22.1%, 95%CI 18.1–26.0), cancer diagnosis
in the 1980s (20.6%, 95%CI 17.0–24.1), high socio-economic
disadvantage (18.6%, 95%CI 15.3–21.8%), and female sex (16.3%,
95%CI 14.4–18.2%) (Supplementary Table 9).
In survivors with chronic multimorbidity, the most common
diagnoses included back problems (32.2%, 95%CI 27.8–36.7%),
other liver diseases (27.3%, 95%CI 23.1–31.5%), kidney disease
(26.8%, 95%CI 22.6–31.0%), hypertension (24.7%, 95%CI
20.6–28.8%), and psychoactive substance dependence (19.1%,
95%CI 15.3–22.8%) (Fig. 2). Hierarchical clustering analysis
revealed four key clusters: 1) psychoactive substance dependence,
alcohol misuse, and other mental and behavioural disorders; 2)
hypertension, diabetes, kidney disease, and musculoskeletal
diseases; 3) epilepsy, hypothyroidism, and other liver diseases;
and 4) hypertension, kidney disease, and other liver diseases
(Table 3). Following the transition to adult healthcare services, the
prevalence of CHC multimorbidity increased with age in
both survivors and comparisons (Supplementary Fig. 1); however,
the prevalence was consistently higher (p< 0.05) in survivors
across the three examined age groups <29 (14.8 vs. 5.5%), 30–39
(21.2 vs. 10.2%), and 40+(16.6 vs. 8.6%).
DISCUSSION
This study investigated longitudinal hospitalisation (and commu-
nity mental health service contact) records of five-year CCS and an
aged- and sex-matched non-cancer comparison group to assess
the burden of multimorbidity and the clustering of multiple
conditions in survivors. In this investigation, the operational
definition of chronic multimorbidity was extended to capture
acute conditions, which provided novel insight into the burden
compared with previous studies [9,12,15,24,26,27]. This
mechanistic understanding of multimorbidity is needed, as
conditions with the possibility of complete remission or with
life-long implications can negatively impact survivors’quality of
life and functional capacity [47]. Our findings showed a higher rate
of recurrent hospitalisations with any multimorbidity among
survivors compared to comparisons. The cause-specific adjusted
risk of any multimorbidity was more than 2-fold higher in survivors
than in comparisons across all examined diagnostic categories,
indicating heterogeneity in outcomes due to the biological
differences underlying cancer types [9], variability in therapeutic
intensity [9], and socio-behavioural factors [11]. An increased risk
across the diagnostic categories was observed in survivors
with and without a history of recurrence or new neoplasm during
the post-survival period. The duration of hospitalisation was
significantly longer in survivors admitted with multiple conditions
than in those admitted with a single condition. Comorbidities
Table 1. continued
Characteristics Cancer survivors (N=2938) Matched comparisons (N=24,792)
Persons N(%) Total events
N
a
Events n(%)
b
Rate/100 PY (95%
CI)
Persons N(%) Total events
N
a
Events n(%)
b
Rate/100 PY (95%
CI)
Childhood cancer diagnosis times
j
Single exposure 910 (98.9) 15,370 4050 (26.4) 10.6 (10.2–10.9) ————
Multiple exposures 10 (1.1) 192 27 (14.1) 10.3 (7.1–15.1) ————
a
Total number of hospitalisations in survivors and the matched comparisons from January 1987 to June 2019.
b
Hospitalisations with ≥2 chronic or acute conditions were classified by the International Statistical Classification of Diseases and Related Health Problems, Ninth Revision, Clinical Modification and Tenth
Revision, Australian Modification codes. These codes included chapters for blood, cardiovascular, digestive, endocrine, genitourinary, infectious and parasitic, musculoskeletal, neoplasms, neurological, sensory,
and respiratory diseases, as well as mental and behavioural disorders.
c
The proportion of participants from the total cohort admitted to the hospital with two or more chronic or acute conditions. PY, person-years (i.e., total duration each participant was at risk of hospitalisation with
any multimorbidity). CI, confidence intervals.
d
Age at the time of hospitalisation.
e
Classified according to the Index of Relative Socio-economic Disadvantage.
f
Classified according to the Remoteness Areas derived from the Accessibility/Remoteness Index of Australia.
g
Cancer diagnoses were coded and grouped according to the International Classification of Childhood Cancer ( Third Edition). Multimorbidity records of survivors with a history of unknown tumour type or
Langerhans Cell Histiocytosis were not included.
h
Age of matched comparison at the time of cancer diagnosis in the corresponding case.
i
Calendar period at the time of cancer diagnosis in the corresponding case.
j
Number of cancer diagnoses before age 18 years.
T. Abdalla et al.
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Table 2. Number and rate of any multimorbidity in hospitalised 5-year childhood cancer survivors and age- and sex-matched comparisons, by diagnostic categories, Western Australia, 1987–2019.
Diagnostic
Categories
Cancer survivors Matched comparisons Multimorbidity
Hazard ratio
a
(95%CI)
Persons Events Multimorbidity
Rate/1000 PY
b
(95%CI)
Persons Events Multimorbidity
Rate/1000 PY
b
(95%CI)
Total N≥2
conditions
n(%)
Total
events
N
c
Events n
(%)
d
Total N≥2
conditions
n(%)
Total
events
N
₤
Events n
(%)
a
Blood diseases 252 200 (79.4) 1226 762 (62.2) 19.7 (18.4–21.2) 870 544 (62.5) 1783 911 (51.1) 2.8 (2.6–3.0) 7.3 (4.9–10.7)
Cardiovascular
diseases
286 245 (85.7) 589 487 (82.7) 12.6 (11.5–13.8) 1111 724 (65.2) 1770 1205 (68.1) 3.7 (3.5–3.9) 3.6 (2.6–4.8)
Digestive diseases 992 441 (44.5) 2175 905 (41.6) 23.4 (21.9–25.0) 6455 1987 (30.8) 10,561 3233 (30.6) 9.9 (9.5–10.2) 2.2 (1.9–2.7)
Endocrine
diseases
470 370 (78.7) 1190 896 (75.3) 23.2 (21.7–24.8) 1779 1359 (76.4) 3688 2760 (74.8) 8.4 (8.1–8.8) 3.0 (2.4–3.6)
Genitourinary
diseases
522 302 (57.9) 1202 608 (50.6) 15.7 (14.5–17.0) 2600 1222 (47.0) 5297 2282 (43.1) 7.0 (6.7–7.3) 2.3 (1.6–3.2)
Infectious and
parasitic diseases
443 354 (79.9) 834 695 (83.3) 18.0 (16.7–19.4) 2384 1667 (69.9) 3602 2584 (71.7) 7.9 (7.6–8.2) 2.5 (2.1–2.9)
Musculoskeletal
diseases
429 223 (52.0) 932 466 (50.0) 12.1 (11.0–13.2) 2412 852 (35.3) 4228 1364 (32.3) 4.2 (4.0–4.4) 3.0 (2.3–3.9)
Neoplasms 609 411 (67.5) 4027 1910 (47.4) 49.4 (47.3–51.7) 1283 602 (46.9) 2849 1095 (38.4) 3.3 (3.2–3.6) 14.6 (11.2–19.1)
Neurological and
sensory diseases
493 348 (70.6) 1563 1009 (64.6) 26.1 (24.6–27.8) 1792 924 (51.6) 3445 1686 (48.9) 5.2 (4.9–5.4) 5.2 (4.2–6.6)
Respiratory
infections and
diseases
430 281 (65.3) 773 531 (68.7) 13.7 (12.6–15.0) 2464 1127 (45.7) 3641 1694 (46.5) 5.2 (4.9–5.4) 2.8 (2.2–3.5)
Mental diseases 772 451 (58.4) 2664 2106 (79.1) 54.5 (52.2–56.9) 4101 2144 (52.3) 11,451 6279 (54.8) 19.2 (18.7–19.7) 2.9 (2.4–3.6)
a
Hazard ratio adjusted for sex, age (and age-squared term), cancer diagnosis age (or equivalent age in comparisons), cancer diagnosis decade (or equivalent calendar date in comparisons), residential
remoteness, socioeconomic status, and Indigenous status.
b
PY, person-years (i.e., total span of observation).
c
Sum of hospitalisations from January 1987 to June 2019.
d
Hospitalisations with ≥2 chronic or acute conditions were classified by the International Statistical Classification of Diseases and Related Health Problems, Ninth Revision, Clinical Modification and Tenth
Revision, Australian Modification codes. These codes included chapters for blood, cardiovascular, digestive, endocrine, genitourinary, infectious and parasitic, musculoskeletal, neoplasms, neurological, sensory,
and respiratory diseases, as well as mental and behavioural disorders. CI, confidence intervals.
T. Abdalla et al.
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managed during hospitalisation for a mental disorder or a
neoplasm had the most significant impact on the length of stay.
The analyses identified characteristics with an elevated burden of
multimorbidity (including cancer diagnosis at age 5-9, exposure to
oncologic treatments in the 1980s, a history of CNS tumour and
haematological cancer diagnosis, female sex, and low social
disadvantage) that have been previously identified as predispos-
ing factors for increased morbidity [8,11,48] and could benefit
from risk-stratified interventions [11,17].
Examination of conditions comorbid with the principal admitting
cause showed a high prevalence of mental disorders, particularly in
survivors admitted for neurological, sensory, endocrine, and
digestive disorders. The adverse effects of accumulating physical
health conditions on psychological functioning can explain this
finding [48]. Evidence from the general adult population has
identified an association between psychological distress in patients
with unmanaged multimorbidity and fragmented healthcare
delivery [49,50]. This study also identified a higher prevalence of
comorbidity pairs, suggesting potential disease associations pre-
viously documented in the literature. Notable associations include
an elevated risk of pathological changes in the endocrine system
due to infections [51]; the influence of imbalances in endocrine
hormones on the functionality of the gastrointestinal tract [52]; and
elevated genitourinary and musculoskeletal [53,54]morbidity
following haematological diseases [54] and haematopoietic cell
transplantation [17].
In the chronic condition-specific analyses, a higher prevalence
of multimorbidity was observed in survivors than in comparisons.
Similar findings were previously reported in five-year CCS aged
<18 years (cumulative incidence 5.3 vs. 1.3%) [12] and 18–48 years
(relative risk 4.9, 95%CI 4.4–5.5) [28] in comparison with the
general population [12] and siblings [28]. These disproportionate
differences in prevalence were also observed at baseline, high-
lighting the consistent pattern of elevated illness among survivors
at a relatively young age [12]. Young adult survivors contributed
more to the burden of multimorbidity on hospital services, which
can be explained by the higher absolute number of survivors aged
<30 years. In addition, the higher burden of illness in younger
survivors can be a consequence of childhood cancer treatments
(such as chest and alkylating agents), which have been linked to
accelerated epigenetic ageing and an earlier development of age-
related CHCs (such as hypertension and cardiovascular disease)
[55]. Compared with those in the general population and siblings,
the manifestation of CHCs in survivors can be more severe,
irrespective of primary cancer diagnosis [56,57]. Following the
transition to adult healthcare services, hospitalised survivors
without a history of chronic multimorbidity exhibited a higher
cumulative prevalence of multiple conditions across various age
groups compared to their counterparts. This trend can be
attributed to the latent health vulnerabilities that increase
over time [58] as survivors age [59].
Hierarchical clustering analysis revealed moderate and clinically
meaningful clustering of some chronic conditions within indivi-
dual survivors [45]. This moderate clustering across the cohort of
survivors reflects individual-level variability in the conditions they
experience. The prevalence of conditions with unshared patho-
physiology has been previously reported in survivors and can be
attributed to differences in risk factors, treatment exposures and
pathophysiological pathways [60]. The co-occurrence of psychoac-
tive substance abuse, alcohol misuse, and other mental disorders
highlights the importance of monitoring and educating survivors
about risky behaviours. Early interventions targeting these
Principal
disease/disorder
% of principal hospitalisations with a comorbidity
Comorbidity
Blood
Cardiovascular
Digestive
Endocrine
Genitourinary
Infection
Musculoskeletal
Neurological
Respiratory
Mental disorders
Blood NA
NA
NA
NA
17.6
42.6
11. 8
13.2
31.9
NA
NA
NA
NA
NA
NA19.5 9.87.520.35.341.425.69.01.5
5.1
1.1
4.6
9.6
6.1
3.4
7.4 16.3
12.0
15.5
10.6
9.2
4.9
11.6 10.9
4.5
8.6
21.3
12.8
34.2
30.6
14.5 18.7 40.0
13.5
5.2
12.8
13.8
8.6
4.5
9.4 17.4 11.6 16.7 45.7
71.1
46.12.6
25.0
10.65.3 1.1 46.8
51.44.76.82.0
0.0 9.4 3.4 61.5
59.3
6.713.71.9
7.2 13.5 17.1 45.9
28.01.31.338.7
18.4
10.27.1
23.0
27.0
8.5
8.1
3.6
28.0
33.3
14.8
2.7
4.5
%
%
%
%
%
%
%
%
%
%
Cardiovascular
Digestive
Endocrine
Genitourinary
Infection
Musculoskeletal
Neurological
Respiratory
Mental disorders
Prevalence (%)
60
40
20
0
Fig. 1 Prevalence of comorbidity and any multimorbidity in hospitalised 5-year childhood cancer survivors, by the principal cause of
hospitalisation, Western Australia, 1987–2019.
§
Mental disorders were identified using the principal diagnosis and 21 additional diagnoses
fields in the hospitalisation records, as well as records of contact with community-based mental health services in the same year of
hospitalisation (only patients with clinically relevant symptoms or diagnoses were considered).
T. Abdalla et al.
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BJC Reports
behaviours in adolescent survivors can help mitigate the socio-
economic impacts of substance misuse, prevent associated
psychiatric and neurotoxic effects [61,62] and reduce the risk of
drug addiction problems in adulthood [63]. The interplay of
predisposing risk factors can contribute to the clustering of
hypertension, diabetes, and kidney and musculoskeletal diseases.
The effect of cranial radiation on the hypothalamic-pituitary axis
(HPA) has been linked to endocrine complications (e.g., diabetes)
[64] and to subsequent obesity and overweight in CCS [17]. The
effects of total body and abdominal irradiation on the HPA,
adipose and pancreatic tissues are also linked to high relative fat
mass [65]. These cardiometabolic risk factors can, in turn,
exacerbate the risk of cardiovascular diseases, including hyperten-
sion [66]. Exposure to cancer treatments (including alkylating
agents, total body irradiation or abdomen radiation) [17] can also
contribute to nephrotoxicity and decline in kidney function [67],
which can lead to or be exacerbated by hypertension [67,68]. The
presence of musculoskeletal diseases in this cluster can be
attributed to metabolic impairments that may lead to impaired
bone growth in CCS [54]. The clustering of hypothyroidism, liver
disease and epilepsy can potentially occur in the same survivor as
a consequence of therapeutic exposure [28]. The diagnosis of
epilepsy and hypothyroidism in CNS tumour survivors can result
from surgical resection near or within the hypothalamic-pituitary
region [69] and CNS-directed treatment [70]. Additionally, CCS
have an increased risk of metabolic syndrome [71,72], which can
lead to hepatic abnormalities, including non-alcoholic fatty liver
disease [71] and cellular liver injury [73].
Policy implications. The findings reported in this study highlight
the importance of continuity of care and periodic surveillance to
prevent the progression of symptoms and acute conditions to
chronic diseases. Evidence shows that integrating care (functional,
organisational, and clinical) can help reduce the burden of illness
[13,74] and prevent unnecessary hospitalisations. Managing
survivors according to evidence-based guidelines [17,75]within
survivorship clinics that enable access to specialised expertise and
patient-centred approaches (including access to self-management
tools) can help address the diverse multimorbid conditions observed
Back problems
Prevalence (%)
Prevalence (95% Cl)Chronic health conditions
32.2 (27.8-36.7)
27.3 (23.1-31.5)
26.8 (22.6-31.0)
24.7 (20.6-28.8)
19.1 (15.3-22.8)
18.6 (14.9-22.3)
16.0 (12.5-19.5)
13.4 (10.2-16.7)
10.6 (7.7-13.5)
10.6 (7.7-13.5)
10.1 (7.3-13.0)
9.4 (6.6-12.2)
9.4 (6.6-12.2)
8.7 (6.0-11.4)
8.7 (6.0-11.4)
6.8 (4.4-9.2)
6.8 (4.4-9.2)
6.8 (4.4-9.2)
6.6 (4.2-8.9)
6.1 (3.8-8.4)
5.6 (3.5-7.8)
5.2 (3.1-7.3)
5.2 (3.1-7.3)
2.3 (0.9-3.8)
Other liver diseases
Chronic kidney diseases
Hypertension
Psychoactive substance dependence
Epilepsy
Asthma
Alcohol misuse
Stroke and TIA
Sinusitis
Diabetes
Other gastrointestinal diseases
Hypothyroidism
Peripheral neuropathy
Other musculoskeletal diseases
Other mental and behavioural diseases
30 402010
0
Other cardiovascular diseases
Depression
Severe constipation
Heart failure
Anxiety
Other respiratory diseases
Cirrhosis
Other endocrine diseases
Fig. 2 Prevalence of chronic health conditions in 5-year childhood cancer survivors with multimorbidity at study exit, Western Australia, June 2019.
Table 3. Most common clusters of conditions in 5-year childhood cancer survivors with chronic multimorbidity, Western Australia, 1987-2019.
Cluster group The most common conditions in the cluster group
Mental disorders Psychoactive substance dependence, alcohol misuse, other mental and
behavioural disorders
Cardiovascular, endocrine, renal and musculoskeletal diseases Hypertension, diabetes, kidney disease, and musculoskeletal diseases
Endocrine and neurological diseases Epilepsy, hypothyroidism, and other liver diseases
Renal, cardiovascular and endocrine diseases Kidney disease, hypertension, and other liver diseases
Clusters of chronic health conditions were identified using hierarchical cluster analysis, based on the Jaccard distancing and Ward’s minimum variance
methods. The analysis included conditions with a prevalence of more than 5% in 5-year childhood cancer survivors with chronic multimorbidity at study exit
(N=424 individuals).
T. Abdalla et al.
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in CCS. However, the availability of survivorship clinics in overbooked
haematology/oncology centres can challenge the provision of
recommended follow-up care, highlighting the importance of
general practitioners’involvement in the longitudinal monitoring
of treatment outcomes [19]. The development and implementation
of avenues (e.g., secure online patient portals) [76] that enhance
shared care between specialised expertise within survivorship clinics
and general practitioners is needed [20,77]. A pre-planned multi-
disciplinary approach needs to be organised around the prevalent
clusters of conditions [30] to help reduce the complications arising
from the interactions between these conditions and their treatments.
The higher prevalence of chronic multimorbidity at baseline and in
those aged <30 years emphasises the importance of personalised
and holistic healthcare management during and immediately
following treatment completion. This approach can ensure early
management of existing and newly developed conditions that may
impact the course and outcomes of the illness burden in survivors
[74,78]. Furthermore, enabling access to integrated and personalised
management during the transition to adult healthcare services is
necessary to address the lack of comprehensive transition protocols
[79,80] and ensure continuity of care [60].
Future research. An investigation of the total multimorbidity
burden using linked hospitalisation, emergency, primary care, and
pharmaceutical health records is warranted. Utilisation of different
research methods to understand behavioural and social risk factors
[81,82] is also needed to inform the development of targeted
interventions, and reduce the risk of premature death caused by
preventable chronic conditions [83]. A review of the availability and
effectiveness of the interventions that survivors and caregivers could
use to manage multiple conditions is necessary.
This study had notable strengths. First, the mandatory and
comprehensive collection of clinically confirmed cancer diagnoses
and deaths ensures accurate and complete identification of CCS.
Second, the mandatory collection of all admitted activities in
public and private hospitals has enabled accurate and complete
ascertainment of the inpatient services utilised by the participants.
This provides the opportunity to assess how accumulated CHCs
can impact the need for hospital services throughout the
survivorship period. Third, the 21 additional diagnosis fields have
consistently been used to capture conditions that significantly
impact the treatments received and resources utilised during
inpatient care, which allowed for the examination of severe
comorbidities that can have a significant impact on the patient at
the time of hospitalisation. Fourth, accessing records of out-
patient mental health events provided the opportunity to
ascertain psychiatric comorbidities managed at the community
level. Fifth, the inclusion of sex- and age-matched comparisons
from the general WA population allowed for a more accurate
assessment of the potential impacts of childhood cancer on multiple
disease burden by providing a control group for comparison. This
study had five main limitations. First, comprehensive indicators of
ethnicity, social factors (such as employment status and educational
level), health-related behaviours, functional deficits, disability, and
frailty are not routinely collected in hospitalisation records, limiting
the possibility of examining their impact on multimorbidity patterns.
Second, the lack of treatment elements and cancer diagnosis stage
data prevented the examination of their attribution to comorbidity
and total multimorbidity. Third, the lack of access to primary care
contacts, outpatient visits, and pharmaceutical dispensing data
prevented a more comprehensive quantification of the scale and
impact of multimorbidity on survivors. Fourth, the absence of an
internationally accepted definition and approach to measure multi-
morbidity [41] can reduce the comparability of findings. Fifth, the
associations between condition severity and observation time [84]
and the higher scores of severe CHC observed in survivors [9,56]can
potentially increase the likelihood of identifying multimorbidity in
CCS than in the comparisons due to the documented higher severity
of their conditions [57].
In summary, this study revealed a higher burden of multi-
morbidity in hospitalised CCS compared with the matched non-
cancer group, which extended from childhood to middle adult-
hood. The observed morbidity was heterogeneous, reflecting the
necessity to ensure holistic management that incorporates disease-
oriented specialists, and primary care is available for survivors with
an increased prevalence of and susceptibility to multiple conditions.
Collaborative international research focusing on the effects of
treatment modality, diagnosis type, and diagnosis stage on the risk
of developing common multimorbid conditions could help guide
the development ofprevention strategies for conditions with similar
aetiologies. Additionally, it is essential to understand how various
clusters of chronic conditions can impact the physical and
psychological functioning of survivors.
DATA AVAILABILITY
The datasets generated and/or analysed during the current study are not publicly
available due to the terms of the ethics approval granted by the WA Health Central
Human Research Ethics Committee (HREC) and data disclosure policies of the Data
Providers. The datasets may be available from the corresponding author upon
request and subject to approval from the HREC and relevant custodians.
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ACKNOWLEDGEMENTS
We thank the WA Data Linkage Branch, and the data custodians of the WA Registries
of Births, Deaths and Marriages, WA Cancer Registry, Oncology Dataset, Hospital
Morbidity Data Collection, and Mental Health Information Data Collection, for their
assistance with the data extraction and linkage. We acknowledge Dr. Thomas
Walwyn’s contributions to the classification of cancer diagnosis groups. We thank Mr
Marty Firth for his assistance with the statistical methods.
AUTHOR CONTRIBUTIONS
TA, AI, JO, and JP designed the study. TA analysed the data and wrote the original
draft. All authors (TA, AI, JO, JP, DW, CC, and MB) interpreted the data, revised the
drafts, and approved the submitted copy.
FUNDING
This study was supported by the Clinical Implementation Unit, WA Cancer and
Palliative Care Network, and the University of WA. TA was supported by the
Australian Government Research Training Program Scholarship. The corresponding
author had full access to all the study data. The sponsors had no role in the study
design, data analysis, data interpretation, or the writing of the manuscript.
COMPETING INTERESTS
The authors declare no competing interests.
ADDITIONAL INFORMATION
Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s44276-024-00114-1.
Correspondence and requests for materials should be addressed to Tasnim Abdalla.
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