Hospitalizations and Cost of Inpatient Care for Physical Diseases in Survivors of Childhood Cancer in Western Australia: A Longitudinal Matched Cohort Study

The long-term health outcomes following exposure to cancer treatments are a serious concern for health professionals involved in treating childhood cancers (1, 2). Although diagnostic and therapeutic advances have led to marked improvements in life-years saved (3), adverse physical outcomes associated with a cancer diagnosis or exposure to treatment regimens have been well documented (4). Side-effects of cancer treatments can be latent, with some persisting or fully manifesting many years after treatment completion (4).

Population-based studies play a crucial role in identifying the long-term and late effects of cancer treatment in the population of childhood cancer survivors (CCS; ref. 5). Evidence from retrospective population-based studies shows that survivors experience an elevated risk of various chronic physical diseases (6), multimorbidity (7, 8), and recurrent diseases (9). Subsequent malignant neoplasms (SMN), cardiovascular, digestive and nervous system diseases are the most commonly reported physical diseases in CCS (10–12). Moreover, the examination of routinely collected hospital administrative databases revealed higher healthcare utilization among CCS compared with the general population. Factors such as chemotherapy, cranial irradiation, and younger age at cancer diagnosis have been identified as contributors to variations in healthcare utilization across different subgroups of CCS (13, 14). Although the utilization of inpatient care services among CCS has been reported in several studies, a common limitation of existing studies is the examination of survivors predominantly exposed to non-contemporary treatment regimens (14–16) and the lack of outcome data in older survivors (9, 17). In Australia, the burden of physical morbidity on CCS and the hospital-care system is unknown. Identifying at-risk groups and target periods for interventions is essential to improve the effectiveness of health system responses.

Furthermore, longitudinal data on the costs associated with hospital-based services for managing physical complications among CCS are currently lacking internationally, despite this information potentially providing guidance for financial long-term healthcare planning (18). In Australia, the Government (federal and state/territory) covers all costs of care for public patients at public hospitals and subsides some medical services provided at private hospitals through the universal publicly funded health insurance scheme (Medicare; ref. 19). Patients receiving care at private and public hospitals may also use private health insurance rebates and make out-of-pocket payments for certain services (19). Therefore, examining the financial implications of hospital care utilization can provide insights into the areas where policy interventions are needed to alleviate the burden on survivors and optimize healthcare resources.

Western Australia (WA) is home to over 80 hospitals, including 23 private hospitals (20, 21). We examined the longitudinal patterns of hospitalizations for physical diseases and associated inpatient-care costs in children diagnosed at age ≤18 years who completed cancer treatment and remained disease-free for at least five years (hereafter referred to as CCS). Survivors’ outcomes were compared with those observed in a matched comparison group from the general WA population.

We retrospectively examined whole-population cancer, inpatient and death records linked through the WA Data Linkage System using probabilistic matching with a clerical review, from 1982 to 2019. The automated probabilistic matching process links records based on a similarity weight score calculated using personal identifiers (22). The clerical review process involves a manual check of potential links with intermediate matching weight to determine the accuracy of the links (22).

The study was conducted in WA, Australia's largest state (approximately 2.5 million square kilometers) with 2.8 million people (23) representing 10.7% of Australia's population. 80% of WA's population resides in major urban areas and 3.3% identify as Indigenous Australians (22).

The WA Cancer Registry (WACR) and the Perth Children's Hospital (PCH) Oncology Dataset were used to extract records of children (≤18 years) diagnosed with cancer from 1982 to 2014. The WACR is a statutory repository that collects data on all histologically confirmed neoplasms in WA (24). Diagnostic details (including diagnosis date, site, and morphology) and demographic factors were retrieved. Diagnostic groups were defined according to the International Classification of Childhood Cancer, third edition (ICCC-3; ref. 25), and further classified into hematological (ICCC-3 codes I–II), central nervous system (CNS; III), and solid (IV–XI) tumors. The PCH Oncology Dataset was used to extract non-notifiable Langerhans Cell Histiocytosis (LCH) cases treated at the Department of Pediatric and Adolescent Oncology and Hematology. PCH is the tertiary referral center for all pediatric and adolescent cancers diagnosed in WA. Death records were extracted from the WA Death Registrations to exclude those who had survived less than five years post primary cancer diagnosis. For each identified CCS, up to 10 gender- and birth month/year-matched “comparisons” who did not have a cancer diagnosis record before age 18 years and were alive at the cancer diagnosis date of the corresponding CCS were sampled from the WA Birth Registrations.

Hospital separation(s) (defined as a completed episode of inpatient care) for physical diseases was the primary outcome (hereafter referred to as hospitalizations). The Hospital Morbidity Data Collection (HMDC) was used to ascertain all hospitalizations from birth to June 2019. The HMDC collects data on all episodes of care from private and public hospitals in WA under the Health Services Act 2016 (26). In WA, the electronic collection of admitted activity was established in 1980 to capture clinical and demographic information of patients (27). Admission records from 1972 to 1979 were gradually added to this collection. HMDC contains 21 built-in data quality checks and periodic, comprehensive audits of assigned diagnosis codes (27). Socio-demographic, administrative (admission and separation dates, admission status, separation mode, and funding source), and diagnostic (principal, co-diagnosis and up to 20 additional diagnoses, and diagnosis-related group) details were extracted from HMDC. Admitting causes were defined using the principal diagnosis field (i.e., chiefly responsible for occasioning inpatient care) and categorized using the International Statistical Classification of Diseases and Related Health Problems (ICD), Ninth/Tenth Revision, Clinical/Australian Modification, depending on the hospitalization year. All diseases were examined, except the diagnoses listed in chapters XVI to XXII (Supplementary Table S1). Inter-hospital transfers within the same episode of care were collapsed into a single record to avoid double-counting hospitalizations.

Several priori factors were incorporated to adjust statistical models. The Index of Relative Socioeconomic Disadvantage (IRSD) was used to assign the participants into quintiles [the most disadvantaged (quintile 1) to the least disadvantaged (quintile 5)] based on the economic and social conditions of people and all households within an area (approximately 250 households within a Statistical Local Area 1; ref. 28). Residential remoteness was defined using the Australian Statistical Geographic Standard Remoteness Area, which categorizes WA into five areas (major city, inner regional, outer regional, remote, and very remote) depending on the distance to services (29). The IRSD and residential remoteness indicators were assigned at baseline using geocoding information from the index post-survival hospitalization record (or the latest pre-survival record if baseline data were missing). The Charlson comorbidity index score was calculated by assigning weights to mutually exclusive comorbid conditions (30) recorded in the additional diagnosis fields at the time of admission. In HMDC, additional diagnosis fields record conditions that co-exist with the principal diagnosis or arise during inpatient care, and have a significant influence on the treatments required (26). The matching variables birth month/year and gender (M/F) were added as covariates to account for any potential residual confounding (31). Indigenous status was defined using a validated algorithm applied to all available WA government administrative data collections to ascertain and assign the status (32). The funding source was used to assign participants into public (i.e., all hospital-care costs covered by government funding), private (i.e., medical care costs covered by private insurance and self-funding), and others (i.e., care covered by compensations and specific government departments). This field captures the source of funding selected by the patient to cover the expenses associated with their hospitalization (26). Patients who are entitled to free inpatient care at public hospitals have the option to choose between public or private funding.

The hospitalization cost was estimated using the national price weights of public hospitals for the Australian refined diagnosis-related groups (AR-DRG). The AR-DRG is a classification system that categorizes admitted patients according to the clinical complexity of their condition and the resources used, and is based on the patient's administrative characteristics and principal diagnosis and procedure codes (33). The price weight is the ratio of the average DRG cost relative to all DRGs. It is calculated annually to capture the average direct and overhead costs associated with hospital care. On the basis of the relevant financial year, the average dollar cost for a unit of weight was used to convert the weight assigned for a DRG into an Australian dollar value. The historical costs were converted into a common base year using the Australian consumer price index for June 2022 to adjust for the influence of inflation on the costs incurred in different periods (34). Hospitalization records between 2000 and June 2019 were examined for cost estimation, as price weights were unavailable before 2000. The costs were estimated in the 2022 Australian dollars and converted to United States dollars using the exchange rate of $0.68 from June 30, 2022 (35).

Descriptive statistics of the study population are presented as counts and percentages for categorical variables and mean (standard deviation, SD) for continuous variables.

Follow-up for hospitalizations commenced at the point of cancer “survivorship,” being five years after the index childhood cancer diagnosis (index date). For many cancers, the risk of recurrence substantially decreases in those who have survived five years past the diagnosis and treatment (36). In the event of a new cancer diagnosis within five years of the index date, the new date substituted the original index date for the start of follow-up to ensure that acute treatment effects were not captured. Comparisons were assigned the index date of the corresponding cancer case to ensure the follow-up times were comparable. The hospitalization rate per 100 person-years (with 95% confidence intervals, CI), was quantified using all recorded events divided by the person-time at risk. The hospitalization duration was excluded from the denominator to calculate the time at risk of a hospitalization accurately. The participants were followed until June 30, 2019 or the date of death, whichever occurred first.

The Andersen–Gill (AG) model for recurrent events was used to analyze the risk of hospitalizations among survivors and comparisons, assuming a correlation between event times (37). The model estimated the hazard ratios (HR with 95% CI), adjusted for age (and age-squared term), gender, diagnosis decade, residential remoteness, IRSD, comorbidity score, funding source, and Indigenous status. All HRs reported in the study were estimated using the AG model. The cumulative burden of hospitalizations for any disease was determined using the mean cumulative count method, which estimated the total burden of hospitalizations accounting for competing risk due to death (38). The changes in cumulative count were represented graphically by age and time since diagnosis, in all participants and in sub-groups defined by index cancer diagnosis decades (1980s, 1990s, 2000s, and the 2010s) to assess the influence of temporal changes in treatment protocols on the count. The duration of hospitalization was quantified as the time difference between admission and separation, with one day assigned for a patient discharged on the same day. The total length of stay was calculated by summing the number of days for all stays. The difference in the incidence rate of same-day and overnight hospitalizations between CCS and comparisons was reported as an incidence rate difference (IRD) with a 95% CI. IRD was computed by dividing all events by the total person-time at risk. Generalized linear models (GLM) with Gamma family and log link (34) were used to calculate the annual mean total cost of publicly funded and privately funded hospitalizations, accounting for year, total annual hospitalizations, gender, age, cancer diagnosis decade, Indigenous status, and comorbidity score. The non-parametric bootstrapped method was used to calculate the mean difference (with 95% CI) in the total cost of hospitalizations for all-cause physical disease between cancer survivors and comparisons. The percentage change (APC) in the annual mean cost was estimated using GLM.

All statistical tests were two-sided and assessed at the P < 0.05 level. Data analyses were performed using SPSS 26 (IBM Corporation), Stata-MP 17 (College Station), and R 4.1.2 (R Foundation for Statistical Computing). The study was approved by the Human Research Ethics Committees at the WA Department of Health, Child and Adolescent Health Service, and the University of WA (references: RGS0000001488; RA/4/20/5340). The study was conducted in accordance with the ethical guidelines of the Australian National Health and Medical Research Council.

The study participants comprised 2,938 CCS and 24,792 comparisons. The baseline characteristics of participants are presented in Table 1. At the start of follow-up, the mean age was 13.9 years (SD 5.9) in survivors and 13.7 years (SD 5.9) in comparisons. At the end of follow-up, the mean age was 27.1 years (SD 10.6) in survivors and 26.9 years (SD 10.5) in comparisons. At the start of follow-up, there were significant baseline differences (P < 0.05) between survivors and comparisons in residential remoteness, Indigenous status, and physical comorbidity. Most survivors (n = 2,909, 99.0%) had a single childhood cancer diagnosis. The most common index cancer diagnoses were leukemia (21.1%), other epithelial and skin carcinomas (19.6%), and CNS tumors (14.2%). The follow-up extended for 32.5 years, totaling 38,630 person-years in survivors and 327,075 person-years in comparisons. 2,794 (95.1%) survivors and 24,615 (99.3%) comparisons were alive at the study exit.

A total of 8,669 hospitalizations were observed in CCS from 1987 to 2019, of which 70.9% were elective. The average number of hospitalizations per unique participant was 4.4 in survivors and 2.1 in comparisons. The proportion of survivors admitted at least once was higher than the comparisons (59.3% vs. 43.5%). The IRD per 100 person-years between CCS and comparisons was 7.1 (95% CI, 6.8–7.5) in same-day patient and 6.3 (95% CI, 6.0–6.6) in overnight patient stays (P < 0.05). Longer lengths of hospital stays were observed in survivors (mean 2.8, SD 6.4) than in comparisons (mean 1.8, SD 3.8; P < 0.05).

The overall hospitalization rate per 100 person-years was 22.4 (95% CI, 20.4–24.8) in survivors and 9.0 (95% CI, 8.9–9.1) in comparisons (Table 2). When examined by the index cancer diagnosis, the rate/100 person-years was highest in survivors of malignant bone tumors (39.2; 95% CI, 36.2–42.6), followed by CNS tumors (30.2; 95% CI, 28.7–31.7) and leukemia (28.0; 95% CI, 26.9–29.2). Other CCS clinical subgroups with highest inpatient encounters included those diagnosed in 2010–2014 (34.0; 95% CI, 25.7–46.1), those exposed to ≥1 childhood cancer (31.7; 95% CI, 25.6–39.4), those diagnosed with cancer at age 5–9 years (29.2; 95% CI, 21.9–39.8), and those with >1 comorbid condition (145.0; 95% CI, 138.0–152.3). Sociodemographic subgroups with highest inpatient encounters included females (24.2; 95% CI, 21.7–27.1), those from the lowest socioeconomically background (25.7; 95% CI, 21.0–31.8) and metropolitan areas (23.8; 95% CI, 21.1–27.0), Indigenous Australians (25.2; 95% CI, 22.7–28.0), those aged <10 years (26.7; 95% CI, 21.5–33.4), and those with Medicare (35.4; 95% CI, 34.5–36.4). Compared with the matched comparisons, elevated hospitalization rates were observed among survivors across all sociodemographic characteristics, funding sources, and number of comorbidities.

Relative to comparisons, and when controlling for other factors, a higher risk of hospitalizations for any physical disease was seen in CCS (HR, 2.0; 95% CI, 1.8–2.2; Table 3). When analyzed according to the admitting cause, all chronic diseases, except respiratory diseases, exhibited a significantly higher risk of hospitalizations in CCS compared with the comparison group (P < 0.05). The hospitalization risk was highest for malignant neoplasms (HR, 15.0; 95% CI, 11.3–19.8) and blood disorders (HR, 6.9; 95% CI, 2.6–18.2). Other physical diseases with a significant difference in risk included nervous system (HR, 2.6; 95% CI, 2.0–3.3), endocrine (HR, 2.3; 95% CI, 1.7–3.0), and circulatory (HR, 2.2; 95% CI, 1.6–2.9) diseases. The hospitalization risk for SMN was particularly high across all three major cancer diagnostic groups (hematological cancers: 42.1; 95% CI, 25.4–69.8; CNS tumors: 24.4; 95% CI, 12.0–49.4; and solid tumors: 7.9; 95% CI, 5.1–12.2; Supplementary Table S2). In hematological CCS, the comparative risk of hospitalization was highest for blood diseases (HR, 49.2; 95% CI, 19.1–126.3; P < 0.05). In CNS tumor CCS, the comparative risk was second highest for the nervous system and sense organ diseases (HR, 7.1; 95% CI, 3.9–12.7). In solid tumor CCS, the comparative risk was second highest for the nervous system and sense organ diseases (HR, 2.6; 95% CI, 1.8–3.8).

The mean cumulative count of hospitalizations for any physical disease showed differences between CCS and comparisons, which persisted with the increase in the time since diagnosis (Fig. 1A) and with increasing age (Fig. 1B). The mean cumulative count of hospitalizations was higher in survivors than the comparisons, with an increase in the time since diagnosis (Supplementary Fig. S1) and age (Supplementary Fig. S2), irrespective of the cancer diagnosis decade.

Medicare was the primary funding scheme for hospitalizations (survivors 63.9%, comparisons 50.0%), followed by private insurance (survivors 33.3%, comparisons 43.4%) and self-funding (survivors 1.2%, comparisons 2.9%). Most admissions occurred in public settings (survivors 72%, comparisons 60%, approximately). Overall, 596 and 685 unique DRGs for physical diseases were extracted from the morbidity records of survivors and comparisons, respectively. Overall, the APC in the mean total cost of hospitalizations for any disease significantly increased by 14.5% in survivors and by 42.0% in comparisons, P < 0.05. The overall hospitalization cost was higher in survivors, as indicated by the significant difference in the mean total costs of publicly funded ($11,483; 95% CI, $8,796–$14,170; P < 0.05) and privately funded ($7,546; 95% CI, $4,860–$10,231) hospitalizations. Among publicly and privately funded hospitalizations, the adjusted annual mean cost of hospitalizations per individual for any disease was higher in survivors than in comparisons (Table 4; Supplementary Table S3). Among survivors, the adjusted annual mean cost per individual was highest for subsequent neoplasms (Public funding $24,723; 95% CI, $20,151–$29,295); private insurance/self-funded $18,519; 95% CI, $14,206–$22,832; Table 4; Supplementary Table S3).

Delivering healthcare that supports the long-term health needs of those with a history of childhood cancer is widely recognized as a critical component of effective survivorship care, as endorsed in international (5, 39) and national guidelines (40, 41). To date, there is no longitudinal data on the burden of physical diseases among CCS in Australia. This retrospective matched study examined hospitalizations for physical diseases in CCS over a follow-up period of 32 years and estimated the cost of inpatient care over 20 years.

We identified elevated rates and risks of hospitalizations in CCS compared with the matched population. This is consistent with evidence from CCS cohorts in the United States, United Kingdom, Canada, Norway, Finland, Denmark, Sweden, and Iceland; refs. 42–45). The evidence generated from international CCS studies has informed the formulation of recommendations (5, 46–48) centered on long-term surveillance and care for adverse effects in CCS. Here, we provide further evidence that supports existing recommendations by examining stratified outcomes in survivors exposed to traditional and more contemporary treatments compared with several previous studies (14–16, 43, 49, 50). Although considerable heterogeneity in study designs complicates more specific comparisons, some moderate differences in the magnitude of risk were noted (14, 51, 52). A Washington-based study conducted during a comparable diagnosis period (1974–2014) has identified a 2.7-fold-increased risk of hospitalization in survivors versus comparisons (51). The lower estimate observed in our study (HR, 2.0) may be partially explained by factors, including the exclusion of non-principal hospitalizations, model adjustment for additional covariates, hospital admission policy differences (53), and health care accessibility (54).

The risk of hospitalizations was higher in survivors across all disease categories. Previous studies have reported supporting evidence of increased hospitalizations for digestive (45, 51, 55, 56), SMN (45, 51, 56), nervous system (45, 51, 56), blood (45, 51, 56), genitourinary (45, 51), musculoskeletal (45, 51), endocrine (45, 51, 56), circulatory (45, 51, 56), infections and parasitic (45, 51, 56), and respiratory diseases (43, 51, 56). In our cohort, the leading causes of hospitalization were SMN and blood diseases. In the subgroup analyses, a particularly elevated risk of SMN was observed among survivors of hematological, CNS, and solid tumors. A recent Australian study revealed that CCS diagnosed between 1983 and 2015 continue to experience a high incidence of new primacy cancers at 20–33 years after their initial diagnosis, particularly those exposed to chemotherapy and radiotherapy (57). These findings illustrate the importance of continued surveillance for SMN in survivors exposed to traditional (58, 59) and more contemporary treatments (57). Furthermore, we noted a link between the index tumor site and the site of physical complication with blood diseases highest in survivors of hematological cancers and nervous systems diseases highest in CNS and solid tumor survivors.

Using the mean cumulative count method to estimate the total burden of hospitalization, we identified a higher cumulative burden in survivors up to 30 years after the index cancer diagnosis. This finding suggests the need for long-term surveillance to provide early interventions for late effects and to prevent disease progression. In the British Columbia CCS cohort, a contradictory pattern, indicating a reduction in hospitalization with a longer time since cancer diagnosis was observed (43), possibly due to variations in study design and population characteristics, including the inclusion of only the first hospitalization diagnosis in a specific ICD chapter and the exclusion of survivors with inactive health insurance plan (43). In addition, our analysis showed an association between aging and hospitalization events, indicating a steady increase in the cumulative count of hospitalizations across the age spectrum. This finding aligns with previous research showing age-related physical morbidity in CCS (14, 60, 61). Aging in the general adult population is commonly associated with an increased risk of physical disease (62). However, among CCS, it has been observed that higher rates of frailty, similar to those seen in older adults, can occur at an earlier age (63). This early onset of frailty may be attributed to the long-term effects of cancer treatments (63). In this study, most survivors were younger than 50 years by the end of follow-up; thus, continued follow-up of CCS is required to identify and address their evolving healthcare needs as they age.

Our study's long-term follow-up period is characterized by change and modernization in cancer treatment protocols, which has likely influenced hospitalization patterns (14, 16, 43, 45, 52) due to a reduction in treatment-related adverse effects (64, 65). We noted a lower cumulative burden of hospitalization among survivors diagnosed in the 2000s, which can be linked to the administration of lower doses of chemotherapy and radiotherapy compared with the 1990s (65) and 1980s (64). However, despite these improvements in treatment protocols, our study revealed a higher cumulative burden across all diagnosis decades. This finding can be attributed to the continued use of therapeutic agents that can induce long-term damaging effects on the organs (65, 66).

We identified several subgroups of survivors with higher rates of hospitalization. Female survivors had a higher hospitalization rate than male survivors, confirming previous evidence suggesting the need to develop gender-specific therapy to address the disproportionate risk of adverse effects (67). Also, survivors of CNS and bone tumors were more likely to be hospitalized compared with survivors of other cancer types, likely reflecting the high therapeutic intensity (5). In addition, hospitalizations were more common in children under 10 years, likely due to their increased vulnerability to physical dysfunction (68). Long-term patient-centered care for young children and those exposed to intense cancer therapy can help ameliorate the prolonged impact of adverse-treatment outcomes. Moreover, survivors from the most deprived areas in WA were more likely to be hospitalized, which is consistent with the trend detected among CCS from the most deprived regions in England (8). Given that CCS are at higher risk of adverse socioeconomic outcomes (69), our result further emphasizes the importance of directing interventions to address the socioeconomic health inequities among survivors. Finally, we observed a higher hospitalization rate in survivors with multiple comorbidities, highlighting the need for patient-centered care plans that incorporate the needs of survivors with multiple chronic conditions.

This study represents the first attempt to estimate the burden of hospital care costs specifically related to physical diseases in CCS. The cost analysis accounted for the effects of gender, age, cancer diagnosis decade, Indigenous status, and comorbidity, which improved the estimated mean cost accuracy. The analysis revealed that all-cause hospitalizations in CCS resulted in greater expenditures on inpatient services compared with the general population. This finding highlights the economic value of government investment in establishing survivorship care services, which can address the potential expansion of demand for hospital care services. In addition, research efforts to develop effective models to prevent the documented loss of follow-up in survivors (70) are essential to ensure the provision of timely healthcare and less spending on tertiary care services.

There are four main strengths of this study. First, the use of whole-population high-quality cancer registration data enabled full coverage and accurate ascertainment of cancer cases. Second, the high-quality hospitalization records enabled a detailed investigation of hospitalization patterns and accurate ascertainment of comorbidities. Third, routinely collected administrative data address limitations inherent in studies using self-report, such as recall or reporting bias, and reduce selection and loss to follow-up bias. Finally, WA has a relatively small out-of-state migration (71) and is considered the most representative Australian state in terms of important socio-demographic and economic traits (72). This results in minimal chances of systematic error due to loss of follow-up and high generalizability of the findings. The study findings should be interpreted considering certain limitations. First, the lack of data on treatment elements and cancer diagnosis stage prevented the assessment of their attribution to the risk of illness. Second, the reported outcomes underestimate the true burden in the population of survivors, as data collected at the primary care and outpatient settings were not included. Third, the small number of events affected the precision of the adjusted effect estimates when examining the risk of less common diseases within the major cancer diagnostic groups. Fourth, granular ethnicity data were unavailable, hindering the examination of outcomes in racial/ethnic minorities within non-Indigenous participants. Fifth, estimating the cost of all privately funded hospitalizations using the price weights for public hospitals may have a moderate impact on the accuracy of the estimates. This is because the inpatient care setting was unknown for a proportion of admissions with private funding sources. Finally, the small proportion of survivors in late adulthood (1.7% of the total cohort ages ≥50 years) precluded detailed quantification of the burden of late effects in late adulthood.

In conclusion, our study contributes to the understanding of the burden of adverse physical effects on survivors and inpatient care services in an Australian context. We observed a high disease risk across all diagnostic categories examined. The cumulative burden of hospitalizations increased with age and time since cancer diagnosis, surpassing that of the matched comparisons. It is important to establish accessible clinics that regularly monitor survivors to mitigate the extended healthcare needed and the growing demand for limited healthcare resources. Our findings also demonstrated that hospitalization rates vary based on factors such as age at cancer diagnosis, gender, diagnosis type, diagnosis decade, number of childhood cancer diagnoses, physical multimorbidity, socioeconomic deprivation, residential remoteness, and Indigenous status. This finding can inform resource allocation and guide strategies for targeted interventions and prevention. It is crucial to prioritize the delivery of targeted and integrated healthcare services, as this can effectively alleviate the burden of hospitalization on survivors, their families, and the healthcare system.

No disclosures were reported.

T. Abdalla: Conceptualization, formal analysis, validation, investigation, visualization, methodology, writing–original draft, project administration, writing–review and editing. T. Walwyn: Conceptualization, supervision, funding acquisition, validation, investigation, methodology, writing–review and editing. D. White: Methodology, writing–review and editing. C.S. Choong: Conceptualization, writing–review and editing. M. Bulsara: Supervision, validation, investigation, visualization, methodology, writing–review and editing. D.B. Preen: Conceptualization, resources, software, supervision, funding acquisition, validation, investigation, visualization, methodology, project administration, writing–review and editing. J.L. Ohan: Conceptualization, supervision, funding acquisition, validation, investigation, visualization, methodology, writing–review and editing.

The WA Cancer and Palliative Care Network, the University of Western Australia, and the Australian Government Research Training Program supported this study. The funding bodies were not involved in this study's data interpretation and conclusions. We thank the WA Data Linkage Branch, and the data custodians of the WA Registry of Births, Deaths and Cancer, Hospital Morbidity Data Collection and Oncology Dataset, for their assistance with the study. We thank WA Data Linkage Branch for coordinating the data application. We thank Marty Firth for assistance with the data interpretation. We also thank the reviewers for their valuable comments and suggestions.

The publication costs of this article were defrayed in part by the payment of publication fees. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734.