Emerging burden of post-cancer therapy complications on unplanned hospitalisation and costs among Australian cancer patients: a retrospective cohort study over 14 years.

Introduction

Cancer continues to be a major global health challenge 1 , contributing to the disease burden worldwide, including in Australia where it accounts for approximately 17% of the total disease burden in 2022 2 . In 2023, about 165,000 Bangladeshi were diagnosed with cancer, facing a mortality rate of 195 per 100,000 individuals 3 . These numbers are projected to rise dramatically by 2044, with incidence and mortality rates expected to by 51% and 36%, respectively. The economic impact of cancer is also substantial, with expenditures reaching AU$10.1 billion in 2016, representing 8.6% of total disease expenditures in Australia 4 . The cost associated with hospital admissions for cancer treatments, including systemic therapy and radiotherapy, significantly contributes to this financial burden.

Advances in cancer treatment over the last decade have improved patient outcomes significantly 5 . Around 323,000 cancer patients in Australia receive systemic therapy or radiotherapy annually, with around 160,000 patients receiving both 6 , 7 , which are essential components of cancer treatment protocols. These modalities not only enhance quality of life but also extend overall survival 8 . However, the effectiveness of these treatments varies based on the severity of the disease 9 , and is not without risks 10 , 11 . Notably, severe post-therapy complications often lead to unplanned hospitalisations, straining healthcare resources. A study highlighted that 90% of cancer patients hospitalised after emergency visits suffered from post-therapy complications, with a significant impact on hospital length of stay (LOS) and associated costs 12 .

Despite the benefits, systemic therapy and radiotherapy can lead to adverse effects 13 – 15 such as sepsis, neutropenia, and pneumonia 14 – 18 , necessitating hospital care 14 , particularly for older adults and advanced-stage patients 16 . These complications not only exacerbate patient suffering but also interrupt ongoing cancer treatment, potentially derailing therapeutic outcomes 13 , 19 . Additionally, these unplanned hospitalisations represent a heavy economic burden on patients, families 14 , and the healthcare system, with the primary treatment phase alone costing over $6 billion annually 20 .

The growing prevalence of cancer and the consequential increase in therapy-related complications underscore the need for comprehensive research to inform clinical and policy decisions. To our knowledge, there is currently no clear evidence-based research at a local or national level in Australia that examines the diversity of emergency admissions and inpatient care based on the cancer type and stage. In addition, evidence on post-therapy complications is limited in Australia which could help develop an oncology care model and reflect the reality of cancer care in the Australian healthcare system. To address this evidence gap, this study aimed to estimate the incidence of post-therapy complications due to systemic therapy and radiotherapy complications, measure their impact on hospitalisations, including LOS, and estimate that associated hospitalisation costs and factors associated hospitalisation among cancer patients in Victoria, Australia.

We hypothesise that different post-therapy complications occurring from systemic therapy and radiotherapy have significant impacts on healthcare systems, resulting in longer unplanned hospital LOS and higher hospitalisation medical costs, with some potential factors contributing more significantly than others.

Methods

Study design and setting

A retrospective cohort study was conducted in Victoria, Australia, to investigate the post-therapy complications of systemic therapy and radiotherapy among cancer patients over a 14-year period (2006–2020). This study analysed administrative health data of the admitted patients to comprehensively examine treatment-related complications, and hospitalisation events, and their associated medical costs from the Australian healthcare system perspective. Patient demographics, clinical characteristics, clinical and administrative details for admitted episode of care records were extracted from linked data source (i.e., Victorian Admitted Episodes Dataset, VAED) from 2006 to 2020. The VAED provides a comprehensive dataset of the causes, effects and nature of illness, and the usage of health services in Victoria.

Study population

The study population included patients at their first cancer diagnosis, even if they were diagnosed with multiple types of cancer. The study included patients who fulfilled the following criteria: (i) having a diagnosis of any cancer (only considered the first cancer diagnosis), (ii) have received systemic therapy and/or radiotherapy between 2006 and 2020, and (iii) have complete medical record data available, including ‘International Classification of Diseases (ICD) 10th version (ICD-10) codes or Australian Classification of Health Intervention (ACHI) codes for treatment. The records of cancer patients who could not meet these criteria were excluded from the analysis (Fig. 1 ). Systemic therapy and radiotherapy were defined based on available data (ACHI codes or ICD-10codes, see Appendix Table A1). In addition, we excluded patients who experienced post cancer-therapy related complications more than 5 years after the first date of therapy.

Fig. 1 Distribution of study participants.

Outcome measures

This study considered outcome measures: (i) post-therapy complication status, (ii) unplanned LOS in days due to post-therapy complications, and (iii) associated hospitalisation costs, valued in Australian dollars.

Hospital length of stay (LOS)

Unplanned hospital LOS for post-cancer treatment-related therapy was defined as the number of days from hospital admission to discharge. Therefore, the LOS was calculated as the discharge date minus the hospital admission date for both patients with treatment-related complications and without complications. We calculated a LOS for each admission. If a patient did not experience any cancer-therapy related complications, their hospital admission was solely for post-cancer treatment-related therapy. Then after, the comparative analysis of LOS between these two groups (complication vs. non-complication) were conducted. We have detailed the hospitalisation process in Fig. 1 .

Hospitalisation costs for post-therapy complications

From the perspective of the Australian healthcare system, the medical costs associated with hospitalisation are calculated based on a unit of measurement known as ‘National Weighted Activity Units (NWAUs)’, which are assigned to each inpatient hospital admission 21 . NWAUs are used as the standard unit of measurement of hospital activity in Australia, against which the ‘National Efficient Price (NEP)’ is applied, and ‘activity-based funding (ABF)’ is provided to hospitals. To calculate the total hospitalisation medical cost for each admission, the NWAU for that admission is multiplied by the NEP, which is currently AU$ 5,134 per national weighted activity unit for the fiscal year 2019-20, to determine the cost to the healthcare system. Admitted acute ABF costs were calculated based on the AR-DRG classification related to systemic therapy and radiotherapy-related complications. National cost weights as determined by the ‘Independent Hospital Pricing for each AR-DRG code’ were then applied to determine admitted acute hospital costs 22 . Each patient’s hospitalisation medical cost comprised the total cost of an admitted acute ABF AR-DRG presentation. Costs were reported as mean and rounded to the nearest whole Australian dollars (AU$), using the 2020 reference year 22 .

Measure of post-therapy complications Occurrence of systemic therapy and radiotherapy-related complications, which were defined according to the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification (ICD-10-AM) codes was the main exposure variables in the study (Appendix Table A2). We considered any post-therapy complications that emerged subsequent to the first administration date of systemic therapy or radiotherapy. The prevalence of treatment-related complications was calculated based on the most reported complications. In addition, the status of post-therapy complications was categorised as follows: 1 indicates patients who reported at least one therapy (i.e., systemic therapy and/or radiotherapy) related complication during the post-therapy period and reported on subsequent hospitalisation, while 0 indicates patients who did not report any such complication.

Covariates

Relevant covariates with a known or hypothesised association with the exposure variables or outcomes of interest (unplanned hospital LOS and hospitalisation medical costs) were identified from published literature 14 , 19 , 23 – 31 . Several covariates were included in regression models to answer each research question as outlined below. Sociodemographic factors were ascertained, including age (categorised as 18–25 years; 26–35 years; 36–45 years; 46–55 years; 56–65 years; >65 years), sex (male or female), Indigenous status, and year of cancer diagnosis.

Statistical considerations

In the descriptive analyses, patient’s characteristics were presented using frequencies, n (%) for categorical variables or mean (standard deviation, SD) for continuous data. A single imputation approach was applied to replace missing data with a single predicted value for estimating hospitalisation medical costs, such as the mean for a given case 32 . Continuous nature of data (i.e. unplanned hospital LOS in days and associated hospitalisation costs) was presented as mean with SD and mean differences between the two groups (with and without a diagnosis of post-therapy complications). The univariate analysis consisted of a non-parametric bootstrapping approach (with 1,000 replications) through a Generalised Linear Models (GLM) to assess the statistical significance of differences with 95% confidence intervals [CIs] in unplanned hospital LOS days [or hospitalisation medical costs]. For categorical data, the chi-square test was used to compare the post cancer therapy-related complications status (present or absent), by patients’ characteristics.

For the univariate and multivariable analyses, the estimation approach was based on the nature of the outcome variables in each model. Unadjusted and adjusted associations were estimated between unplanned hospital LOS (or hospitalisation medical costs) and the main exposure variables using the GLM with a log-link, gamma distribution, and robust estimates of variance. In addition, a logistic regression model was used to determine the association between post-therapy complications and patients’ characteristics. The reference group selected was the lower risk category of covariates. All statistical significance was considered as p-value < 0.05. All analyses were performed using the statistical software Stata/BE 18. This study has reported according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement for cohort studies 33 . All methods were carried out in accordance with relevant national guidelines and institutional regulations.

Results

Characteristics of the study population

A total of 8,633 patients with cancer were included in this study (Table 1 ). The mean age of the patients was 59 years, and approximately 65% of them were 56 years or older, and 52% were male. Most patients (98%) were not of Aboriginal or Torres Strait background. About 92% of patients received systemic therapy and 4.33% of them received radiotherapy, whereas 4% of the participants received both systemic therapy and radiotherapy (Table 1 ). Between 2006 and 2020, there were 1,035,294 hospital admissions associated with these patients, out of which 1,004,036 were identified as being associated with post-therapy complications (Appendix Table A3).

Table 1 Patient’s background characteristics. Variables Number of patients n % Patient diagnosed with post-therapy complications No 2450 28.38 Yes 6183 71.62 Type of therapy Systemic therapy 7919 91.73 Radiation therapy 374 4.33 Systemic therapy and radiotherapy 340 4.00 Cancer group Oral 8 0.09 Bladder 499 5.78 Pancreatic cancer 2 0.002 Brain 56 0.65 Cervical cancer 15 0.17 Stomach cancer 76 0.88 Esophageal cancer 75 0.87 Gastric cardia 230 2.66 Colorectal cancer 42 0.49 Hodgkin lymphoma 336 3.89 Non-Hodgkin lymphoma 902 10.45 Multiple myeloma 794 9.20 Anal canal 454 5.26 Liver 261 3.02 Melanoma of the skin 211 2.44 Pancreas 216 2.50 Lung 790 9.15 Breast 1045 12.10 Ovary 57 0.66 Kidney 42 0.49 Thyroid 118 1.37 III-defined sites 2 0.02 Unspecified malignant neoplasm 765 8.86 Rectal cancer 98 1.14 Bone and bone marrow 200 2.32 Acute lymphoblastic leukemia 635 7.36 Others 704 8.15 Patient’s characteristics Mean age of patients in years, mean (SD) 59.94 14.75 Patients age group, n (%) 18–25 years 152 1.76 26–35 years 448 5.19 36–45 years 820 9.50 46–55 years 1471 17.04 56–65 years 2185 25.31 >65 years 3557 41.20 Gender Male 4515 52.30 Female 4118 47.70 Indigenous status Aboriginal or Torres Strait 585 6.78 Neither aboriginal nor Torres Strait 7908 91.60 Patient refused or unable asked 140 1.62 Year of cancer diagnosis 2006–2009 1764 20.43 2010–2012 2116 24.51 2013–2015 2465 28.55 2016–2018 1683 19.49 2019–2020 605 7.01 Total number of patients 8633 100

Diagnosis of post-therapy complications due to systemic therapy and radiotherapy

The most common complications were reported as follows: anemia at 26%, sepsis at 15%, nausea and vomiting at 14%, neutropenia at 11% and pneumonia at 9% (Fig. 2 ).

Fig. 2 Top reported post-cancer therapy–related complications.

A significant majority of hospital admissions (94%; n = 945,846) were associated with post-cancer therapy complications among patients undergoing systemic therapy (Appendix Table A3). Notably, patients who received both systemic therapy and radiotherapy exhibited an 8.24-fold increase in the odds of experiencing post-therapy-related complications (OR = 8.24, 95% CI: 7.48 to 9.08, p < 0.001) compared with those who received systemic therapy alone. The odds of having post-therapy complications varied significantly with cancer type. Patients with acute lymphoblastic leukemia (OR = 16.99, 15.42 to 18.71), multiple myeloma (OR = 9.72, 9.00 to 10.5), melanoma (OR = 3.62, 3.20 to 4.09) showed the highest odds of complications. Increased odds were also noted for cancers of the kidney (OR = 3.54, 2.85 to 4.40), Hodgkin lymphoma (OR = 2.97, 2.75 to 3.20), esophageal cancer (OR = 2.43, 1.96 to 3.10), non-Hodgkin lymphoma (OR = 2.19, 2.06 to 2.33), and stomach cancer (OR = 2.30, 1.81 to 2.91) (Appendix Table A3).

Approximately41% of female patients experienced at least one post-therapy complication. Compared to male patients, female patients had 16% lower odds of developing complications (OR = 0.84, 0.81 to 0.86). The odds of experiencing post-cancer therapy increased significantly with patient age ( p < 0.001 , Table 1 ). Patients aged 56 to 65 years had a 1.44-fold higher odd (OR = 1.44, 1.28 to 1.62), and or those older than 65 years had a 1.51-fold higher odd (OR = 1.99, 1.77 to 2.25) compared to were found to be 1.51 times more likely to experience post-therapy complications that the younger counterparts. Moreover, approximately 62% of post-therapy complications associated with this age group. There was a notable decline in the prevalence of post-therapy complications from 31% in 2006–2009 to 10% in 2016–2018.

Distribution of hospitalisation length of stay (LOS) in days and hospitalisation medical costs

Patients experiencing post-therapy complications had a significantly longer mean hospital stay, with 2.23 days per admission (Table 2 ), equating to 360 days per patient (Table 3 ), compared to those without complications, who had 0.51 days per admission and a total of 6.48 days per patient. This difference in LOS resulted in an additional 1.72 days per admission (95%CI: 1.68 to 1.76) and 354 days per patient (336 to 371).

Table 2 Comparison of average hospital length of stay per admission by post-therapy complications status among cancer patients. Variables No post-therapy complications At least one post-therapy complications Mean difference Mean LOS in days per admission SD Mean LOS in days per admission SD Mean difference of hospital LOS in days (95% CI) § p -value Type of therapy Systemic therapy 0.46 2.09 2.21 6.76 1.75 (1.73 to 1.77) < 0.001 Radiation therapy 4.59 11.64 3.09 7.53 -1.50 (-2.77 to -0.23) 0.021 Systemic therapy and radiotherapy 1.02 3.18 2.40 6.68 1.38 (1.12 to 1.65) < 0.001 Cancer group Oral na 7.12 24.77 Bladder 0.36 2.31 2.30 6.88 1.94 (1.84 to 2.04) < 0.001 Pancreatic cancer na 14.06 16.37 Brain 0.69 2.79 2.73 5.20 2.04 (1.53 to 2.55) < 0.001 Cervical cancer 1.05 4.77 2.50 4.42 1.45 (-0.11 to 3.01) 0.124 Stomach cancer 0.88 2.87 1.91 4.52 1.03 (0.34 to 1.72) < 0.001 Esophageal cancer 0.41 0.79 1.64 5.30 1.23 (1.01 to 1.45) < 0.001 Gastric Cardia 0.59 2.37 1.74 6.19 1.15 (0.97 to 1.33) < 0.001 Colorectal cancer 0.73 4.89 1.51 4.87 -0.78 (-0.17 to 1.73) 0.061 Hodgkin lymphoma 0.28 0.95 1.16 4.08 0.88 (0.81 to 0.95) < 0.001 Non-Hodgkin lymphoma 0.56 2.08 2.87 7.10 2.31 (2.21 to 2.41) < 0.001 Multiple myeloma 0.28 1.62 2.71 8.12 2.43 (2.32 to 2.54) < 0.001 Anal canal 0.46 2.05 1.46 4.77 1.00 (0.90 to 1.10) < 0.001 Liver 0.62 3.14 1.34 4.84 0.72 (0.52 to 0.92) < 0.001 Melanoma of the skin 0.99 3.20 1.61 4.99 0.62 (0.25 to 0.99) < 0.001 Pancreas 0.50 2.81 1.32 3.36 0.82 (0.61 to 1.03) < 0.001 Lung 0.67 3.54 1.70 5.18 1.03 (0.88 to 1.79) < 0.001 Breast 0.41 1.60 1.42 3.98 1.01 (0.96 to 1.06) < 0.001 Ovary 0.22 0.43 1.76 4.89 1.54 (1.38 to 1.71) < 0.001 Kidney 0.30 0.81 1.55 5.05 1.25 (1.04 to 1.46) 0.002 Thyroid 2.20 3.32 3.60 8.85 1.40 (0.72 to 2.08) 0.001 III-defined sites na 2.25 3.36 Unspecified malignant neoplasm 0.44 1.78 1.61 5.22 1.17 (1.09 to 1.24) < 0.001 Rectal cancer 0.40 1.55 1.60 5.34 1.20 (0.96 to 1.44) < 0.001 Bone and bone marrow 0.85 3.03 1.93 6.58 1.08 (0.83 to 1.32) < 0.001 Acute lymphoblastic leukemia 0.37 1.93 2.67 7.67 2.30 (2.13 to 2.47) < 0.001 Others 0.89 4.21 2.06 6.28 1.17 (0.99 to 1.35) < 0.001 Patient’s characteristics Patients age group 18–25 years 0.30 0.81 1.89 6.61 1.59 (1.52 to 1.67) < 0.001 26–35 years 0.42 1.67 2.18 5.69 1.76 (1.65 to 1.88) < 0.001 36–45 years 0.47 2.05 2.41 7.29 1.95 (1.86 to 2.03) < 0.001 46–55 years 0.46 1.94 2.54 7.98 2.07 (2.01 to 2.13) < 0.001 56–65 years 0.44 1.96 1.95 6.43 1.52 (1.48 to 1.55) < 0.001 >65 years 0.62 3.15 2.35 6.49 1.72 (1.67 to 1.77) < 0.001 Gender Male 0.54 2.53 2.29 6.89 1.72 (1.69 to 1.80) 0.001 Female 0.48 2.37 2.14 6.57 1.66 (1.60 to 1.72) < 0.001 Indigenous status Aboriginal or Torres Strait 0.57 1.79 1.95 5.82 1.38 (1.29 to 1.47) < 0.001 Neither Aboriginal nor Torres Strait 0.51 2.50 2.24 6.80 1.73 (1.69 to 1.76) < 0.001 Patient refused or unable to asked 0.18 0.89 2.69 8.50 2.50 (2.23 to 2.78) < 0.001 Year of cancer diagnosis 2006–2009 0.64 2.53 2.07 6.68 1.44 (1.36 to 1.51) < 0.001 2010–2012 0.52 2.90 1.78 5.36 1.26 (1.17 to 1.35) < 0.001 2013–2015 0.51 2.15 2.23 6.78 1.72 (1.78 to 1.66) < 0.001 2016–2018 0.45 2.43 3.74 9.62 3.29 (3.19 to 3.39) < 0.001 2019–2020 0.33 1.55 5.53 10.19 5.20 (5.02 to 5.37) < 0.001 Overall 0.51 2.44 2.23 6.76 1.72 (1.68 to 1.76) < 0.001 § Bootstrap confidence intervals with 1000 replications using generalised linear regression model, p-value = the probability value. na not available, SD standard deviation, LOS length of stay, CI confidence interval.

Table 3 Comparison of average hospital length of stay (in days) per patient by post-therapy complications status among cancer patients. Therapy and cancer group Per-patient mean hospital LOS (SD) No post-therapy complications Diagnosed at least post-therapy complications Mean difference Mean LOS in days (SD) Mean LOS in days (SD) Mean difference of LOS in days (95% CI) § p -value Type of therapy Systemic therapy 6.12 (10.63) 369.71 (1060.25) 363.6 (330.51 to 396.68) < 0.001 Radiation therapy 10.12 (19.55) 108.88 (219.48) 98.77 (71.46 to 126.07) < 0.001 Systemic therapy and radiotherapy 19.87 (17.96) 373.66 (783.4) 353.8 (248.76 to 458.84) < 0.001 Cancer group Oral na 311.15 (483.74) - - Bladder 6.07 (13.58) 323.3 (771.92) 317.24 (244.74 to 389.73) < 0.001 Pancreatic cancer na 63.27 (87.87) - - Brain 8.09 (14.63) 57.11 (70.19) 49.02 (22.56 to 75.48) < 0.001 Cervical cancer 6.1 (12.18) 262.5 (414.13) 256.4 (−29.99 to 542.79) 0.087 Stomach cancer 7.56 (6.86) 110.12 (141.72) 102.56 (74.46 to 130.66) < 0.001 Esophageal cancer 2.99 (3.18) 144.44 (196.98) 141.45 (67.62 to 215.28) < 0.001 Gastric Cardia 8.03 (11.44) 191.45 (412.42) 183.42 (122.76 to 244.09) < 0.001 Colorectal cancer 5.31 (13.61) 105.13 (115.79) 99.82 (49.68 to 149.97) < 0.001 Hodgkin lymphoma 3.42 (4.89) 340.38 (929.59) 336.95 (244.8 to 429.1) < 0.001 Non-Hodgkin lymphoma 4.85 (10.46) 448.94 (1075.73) 444.09 (381.83 to 506.36) < 0.001 Multiple myeloma 5.08 (8.95) 812.86 (1452.64) 807.78 (688.87 to 926.69) < 0.001 Anal canal 6.61 (9.45) 208.76 (494.82) 202.16 (169.89 to 234.43) < 0.001 Liver 6.71 (11.44) 127.08 (260.18) 120.37 (71.29 to 169.44) < 0.001 Melanoma of the skin 6.06 (12.39) 235.67 (688.49) 229.61 (127.77 to 331.45) < 0.001 Pancreas 8.21 (12.56) 100.62 (129.28) 92.41 (73.05 to 111.76) < 0.001 Lung 8.23 (13.94) 114.44 (212.59) 106.21 (92.86 to 119.56) < 0.001 Breast 5.93 (10.28) 99.48 (244.21) 93.55 (70.89 to 116.22) < 0.001 Ovary 1.84 (1.57) 239.66 (405.98) 237.82 (76.54 to 399.11) < 0.001 Kidney 3.7 (2.99) 257.76 (516.34) 254.06 (88.38 to 419.75) < 0.001 Thyroid 4.62 (6.91) 184.55 (467.05) 179.93 (−17.85 to 377.71) 0.076 III-defined sites na na - - Unspecified malignant neoplasm 7.75 (9.81) 130.49 (256.45) 122.73 (102.31 to 143.15) < 0.001 Rectal cancer 10.7 (14.12) 134.23 (202.2) 123.53 (56.63 to 190.43) < 0.001 Bone and bone marrow 8.75 (12.04) 173.28 (323.06) 164.53 (125.09 to 203.96) < 0.001 Acute lymphoblastic leukemia 5.05 (7.89) 946.04 (21.37) 940.99 (760.89 to 1121.09) < 0.001 Others 7.59 (15.58) 253.49 (669.49) 245.91 (194.99 to 296.83) < 0.001 Overall 6.48 (11.52) 360.12 (1029.53) 353.65 (336.28 to 371.02) < 0.001 § Bootstrap confidence intervals with 1000 replications using generalised linear regression model, p-value = the probability value. na not available, SD standard deviation, LOS length of stay, CI confidence interval.

Patients undergoing both systemic therapy and radiotherapy with complications also exhibited extended hospital LOS, with a mean difference of 1.38 days, resulting in 354 days per patient as opposed to 19.87 days per patients for those without complications. Male patients with complications had a longer LOS of 2.29 days compared to 0.54 days for those without, showing a significant difference of 1.72 days per admission ( p < 0.001).

Patients with post-therapy complications incurred substantially higher hospitalisation costs, averaging AU$11,418 per admission in stark contrast to AU$2,627 for those without complications. This difference led to an excess cost of AU$8,791 per admission (8,685 to 8,897; p < 0.001; Table 4 ).

Table 4 Distribution and comparison of per admission hospitalisation medical costs (AU$) by post-therapy complications status among cancer patients. Variables Patients experienced no post-therapy complications Patients experienced at least post-therapy complications Difference Mean costs per admission (AU$) SD Mean costs per admission (AU$) SD Mean difference of costs (95% CI) § p -value Type of therapy Systemic therapy 2,371 10,744 11,335 34,628 8964 (8881 to 9047) < 0.001 Radiation therapy 23,187 59,072 15,776 38,425 -7411 (-13543 to -1279) 0.018 Systemic therapy and radiotherapy 5,281 16,187 12,279 34,081 6998 (5873 to 8124) < 0.001 Cancer group Oral na 36,577 127,192 na Bladder 1,840 11,876 11,803 35,197 9963 (9540 to 10386) < 0.001 Pancreatic cancer na 72,184 84,047 na Brain 3,551 14,317 14,022 26,721 10,472 (7862 to 13081) < 0.001 Cervical cancer 5,371 24,513 12,701 22,338 7329 (-1549 to 16207) 0.106 Stomach cancer 4,495 14,740 9,820 23,171 5325 (1739 to 8912) 0.004 Esophageal cancer 2,194 4,098 8,428 27,124 6234 (5218 to 7250) < 0.001 Gastric Cardia 3,060 12,138 8,935 31,675 5875 (5013 to 6737) < 0.001 Colorectal cancer 3,774 24,984 7,769 24,905 3995 (938 to 7052) 0.010 Hodgkin lymphoma 1,442 4,892 5,960 20,930 4518 (4165 to 4871) < 0.001 Non-Hodgkin lymphoma 2,868 10,655 14,704 36,372 11,836 (11255 to 12417) < 0.001 Multiple myeloma 1,429 8,309 13,885 41,649 12,455 (11777 to 13134) < 0.001 Anal canal 2,397 10,523 7,480 24,439 5083 (4488 to 5679) < 0.001 Liver 3,180 16,125 6,886 24,792 3705 (2729 to 4681) < 0.001 Melanoma of the skin 5,128 16,350 8,248 25,562 3120 (1450 to 4790) < 0.001 Pancreas 2,576 14,408 6,783 17,227 4208 (3068 to 5347) < 0.001 Lung 3,463 18,130 8,743 26,493 5279 (4599 to 5960) < 0.001 Breast 2,093 8,205 7,316 20,358 5223 (4887 to 5558) < 0.001 Ovary 1,151 2,224 9,016 25,095 7865 (7175 to 8555) < 0.001 Kidney 1,535 4,162 7,940 25,746 6405 (5247 to 7564) < 0.001 Thyroid 11,296 17,060 18,470 45,438 7174 (4576 to 9771) < 0.001 III-defined sites na na na Unspecified malignant neoplasm 2,279 9,135 8,277 26,738 5998 (5662 to 6333) < 0.001 Rectal cancer 2,053 7,945 8,234 27,361 6181 (5313 to 7049) < 0.001 Bone and bone marrow 4,380 15,523 9,914 33,712 5534 (4657 to 6411) < 0.001 Acute lymphoblastic leukemia 1,890 9,895 13,693 39,341 11,803 (11263 to 12343) < 0.001 Others 4,593 21,563 10,547 32,186 5954 (4692 to 7216) < 0.001 Patient’s characteristics Patients age group 18–25 years 1,541 4,164 9,711 33,903 8170 (7260 to 9081) < 0.001 26–35 years 2,180 8,548 11,206 29,177 9026 (8391 to 9662) < 0.001 36–45 years 2,405 10,533 12,371 37,386 9966 (9534 to 10397) < 0.001 46–55 years 2,394 9,964 12,999 40,886 10,605 (10236 to 10974) < 0.001 56–65 years 2,251 10,075 10,014 32,933 7763 (7506 to 8020) < 0.001 >65 years 3,211 16,168 12,022 33,245 8811 (8487 to 9135) < 0.001 Gender Male 2,806 12,955 11,729 35,302 8923 (8749 to 9096) < 0.001 Female 2,487 12,160 10,976 33,663 8489 (8286 to 8692) < 0.001 Indigenous status Aboriginal or Torres Strait 2,918 9,201 9,984 29,790 7066 (6578 to 7554) < 0.001 Neither Aboriginal nor Torres Strait 2,656 12,828 11,497 34,865 8841 (8698 to 8984) < 0.001 Patient refused or unable to ask 955 4,579 13,802 43,631 12,847 (11499 to 14195) < 0.001 Year of cancer diagnosis 2006–2009 3,280 12,971 10,642 34,254 7363 (6981 to 7745) < 0.001 2010–2012 2,673 14,867 9,127 27,507 6454 (6144 to 6764) < 0.001 2013–2015 2,608 11,015 11,419 34,737 8811 (8518 to 9104) < 0.001 2016–2018 2,347 12,473 19,048 49,044 16,701 (16199 to 17204) < 0.001 2019–2020 1,733 7,942 27,930 51,807 26,197 (25478 to 26916) < 0.001 Overall 2,627 12,515 11,418 34,637 8791 (8685 to 8897) < 0.001 § Bootstrap confidence intervals with 1000 replications using generalised linear regression model, p-value = the probability value. na not available, SD standard deviation, LOS length of stay, CI confidence interval.

Per-patient costs for unplanned hospitalisation due to complications were dramatically higher at $1.82 million compared to AU$33,599 for those without complications, with a confidence interval ranging from AU$1.71 million to AU$1.94 million (Table 5 ). Cost disparities were also notable in patients receiving systemic therapy alone or in combination with radiotherapy, with those having complications incurring AU$11,335 and AU$12,279 per admission, respectively, significantly higher than those without complications (AU$2,371 and AU$5,281 per admission, respectively). Male patients with post-therapy complications faced an average hospitalisation cost of AU$11,729 per admission, significantly higher by AU$2,806 than those without complications (AU$ 8923 per admission). This trend was similar among female patients. Over time, hospitalisation costs have increased notably, with expenses for patients with complications tripling from AU$10,642 per admission in 2006–2009 to AU$27,930 per admission in 2019–2020 (Table 4 ).

Table 5 Distribution and comparison of per patient hospitalisation medical costs (AU$) by post-therapy complications status among cancer patients. Therapy and cancer group Per-patient mean hospital costs in AU$ (SD) No post-therapy complications Diagnosed at least post-therapy complications Difference Mean costs in AU$ (SD) Mean costs in AU$ (SD) Mean difference of costs in AU$ (95% CI) § p -value Type of therapy Systemic therapy 31,692 (54804) 1,904,443 (5457699) 1,872,751 (1701884 to 2043617) < 0.001 Radiation therapy 52,570 (100713) 563,621 (1138623) 511,050 (369222 to 652878.5) < 0.001 Systemic therapy and radiotherapy 105,876 (92881) 1,938,516 (4037320) 1,832,640 (1289555 to 2375724) < 0.001 Cancer group Oral na 1,597,451 (2483528) - Bladder 31,446 (70040) 1,670,388 (3973787) 1,638,943 (1263786 to 2014099) < 0.001 Pancreatic cancer na 324,828 (451099) - Brain 42,032 (75163) 293,600 (360193) 251,567 (115655 to 387479) < 0.001 Cervical cancer 31,360 (62543) 1,396,021 (2241343) 1,364,661 (−184807 to 2914128) 0.084 Stomach cancer 38,822 (35232) 567,747 (726597) 528,924 (385774 to 672075) < 0.001 Esophageal cancer 16,129 (16125) 746,692 (1010441) 730,563 (352396 to 1108730) < 0.001 Gastric Cardia 41,754 (60749) 992,009 (2128121) 950,255 (637872 to 1262639) < 0.001 Colorectal cancer 27,268 (69874) 544,484 (600855) 517,216 (255136 to 779295) < 0.001 Hodgkin lymphoma 17,703 (25205) 1,757,482 (4783267) 1,739,779 (1264646 to 2214913) < 0.001 Non-Hodgkin lymphoma 25,142 (54219) 2,311,476 (5534690) 2,286,334 (1965442 to 2607227) < 0.001 Multiple myeloma 26,317 (46264) 4,181,140 (7464618) 4,154,824 (35439904 to 4765743) < 0.001 Anal canal 34,322 (49013) 1,080,488 (2566112) 1,046,166 (878955.2 to 1213376) < 0.001 Liver 34,441 (58711) 661,040 (1359791) 626,600 (373493 to 879707) < 0.001 Melanoma of the skin 31,870 (63635) 1,217,749 (3568219) 1,185,879 (655485 to 1716273) < 0.001 Pancreas 42,953 (64603) 519,467 (664065) 476,514 (375426 to 577603) < 0.001 Lung 42,891 (71657) 591,954 (1093989) 549,063 (480476 to 617650) < 0.001 Breast 30,742 (52955) 517,964 (1272254) 487,222 (369801 to 604643) < 0.001 Ovary 9465 (8054) 1,234,330 (2082324) 1,224,865 (394806.9 to 2054924) < 0.001 Kidney 18,984 (15365) 1,338,223 (2665533) 1,319,239 (456601 to 2181877) < 0.001 Thyroid 23,740 (35493) 947,493 (2397821) 923,752 (−91648 to 1939153) 0.075 III-defined sites na na - Unspecified malignant neoplasm 40,276 (50704) 672,776 (1317938) 632,500 (527713 to 737287) < 0.001 Rectal cancer 54,924 (72478) 690,164 (1037722) 635,240 (291668 to 978812) < 0.001 Bone and bone marrow 44,906 (61836) 893,316 (1658943) 848,410 (646756 to 1050063) < 0.001 Acute lymphoblastic leukemia 25,949 (40547) 4,868,592 (1100037) 4,842,643 (3914453 to 5770832) < 0.001 Others 39,309 (80345) 1,306,230 (3441043) 1,266,921 (1006059 to 1527783) < 0.001 Overall 33,599 (59464) 1,855,879 (5299788) 1,822,280 (1705155 to 1939405) < 0.001 § Bootstrap confidence intervals with 1000 replications using generalised linear regression model, p-value = the probability value. na not available, SD standard deviation, LOS length of stay, CI confidence interval.

Factors associated with length of stay (LOS) in days per admission and hospitalisation medical costs

Table 6 presents the results of two different models for analysing hospitalisation LOS and the associated costs per admission. Potential variables were adjusted based on qualified variables from the unadjusted explorations (Appendix Table A4). The regression analysis revealed that patients who had complications after therapy had a significantly longer hospital LOS (β = 1.30, 1.25 to 1.35) and higher medical costs (β = 1.29, 1.26 to 1.33). However, the results varied depending on the patient’s characteristics and of the specific cancer diagnosis. For instance, patients aged 65 and above had a significantly longer hospital LOS per admission (β = 0.37, 0.33 to 0.40) and higher hospitalisation medical costs (β = 0.36, 0.34 to 0.39) compared with younger patients aged 18 to 25 years.

Table 6 Association of post-therapy complications with length of stay (LOS) and hospitalisation medical costs on unplanned hospitalisation from an Australian healthcare system perspective. Variables Adjusted model for hospitalisation length of stay (LOS) Adjusted model for hospitalisation costs (AU$) β-coefficient (95% CI) p -value β-coefficient (95% CI) p -value Type of therapy Systemic therapy (= ref) Radiation therapy 0.58 (0.50 to 0.66) < 0.001 0.57 (0.51 to 0.65) < 0.001 Systemic therapy and radiotherapy 0.15 (0.12 to 0.18) < 0.001 0.14 (0.12 to 0.18) < 0.001 Patient experience of complications (ref = no) 1.30 (1.25 to 1.35) < 0.001 1.29 (1.26 to 1.33) < 0.001 Cancer group (ref = other type of cancer) Bladder −0.78 (−1.09 to −0.47) < 0.001 −0.76 (−1.14 to −0.44) < 0.001 Pancreatic cancer 0.29 (−0.47 to 1.06) 0.454 0.24 (−1.76 to 2.36) 0.778 Brain −0.65 (−1.03 to −0.27) 0.001 −0.65 (−1.05 to −0.25) 0.001 Cervical cancer −0.78 (−1.16 to −0.40) < 0.001 −0.80 (−1.21 to −0.39) < 0.001 Stomach cancer −0.89 (−1.21 to −0.58) < 0.001 −0.90 (−1.26 to −0.54) < 0.001 Esophageal cancer −1.09 (−1.42 to −0.77) < 0.001 −1.10 (−1.46 to −0.74) < 0.001 Gastric cardia −1.01 (−1.32 to −0.69) < 0.001 −1.01 (−1.36 to −0.66) < 0.001 Colorectal cancer −1.13 (−1.48 to −0.77) < 0.001 −1.13 (−1.50 to −0.75) < 0.001 Hodgkin lymphoma −1.29 (−1.60 to −0.98) < 0.001 −1.29 (−1.64 to −0.94) < 0.001 Non-Hodgkin lymphoma −0.40 (−0.71 to −0.09) 0.011 −0.41 (−0.76 to −0.06) 0.022 Multiple myeloma −0.53 (−0.84 to −0.22) 0.001 −0.53 (−0.88 to −0.18) 0.003 Anal canal −1.12 (−1.43 to −0.81) < 0.001 −1.13 (−1.48 to −0.78) < 0.001 Liver −1.19 (−1.50 to −0.88) < 0.001 −1.20 (−1.55 to −0.85) < 0.001 Melanoma of the skin −1.04 (−1.35 to −0.73) < 0.001 −1.04 (−1.39 to −0.69) < 0.001 Pancreas −1.29 (−1.60 to −0.97) < 0.001 −1.29 (−1.64 to −0.94) < 0.001 Lung −1.04 (−1.35 to −0.73) < 0.001 −1.05 (−1.40 to −0.70) < 0.001 Breast −1.13 (−1.44 to −0.82) < 0.001 −1.14 (−1.49 to −0.79) < 0.001 Ovary −0.89 (−1.21 to −0.57) < 0.001 −0.89 (−1.25 to −0.53) < 0.001 Kidney −1.31 (−1.63 to −0.99) < 0.001 −1.31 (−1.67 to −0.96) < 0.001 Thyroid −0.42 (−0.75 to −0.09) 0.013 −0.42 (−0.80 to −0.05) 0.028 III-defined sites −0.61 (−0.93 to −0.29) < 0.001 −0.62 (−1.00 to −0.24) 0.001 Unspecified malignant neoplasm −1.00 (−1.31 to −0.69) < 0.001 −1.00 (−1.35 to −0.65) < 0.001 Rectal cancer −1.05 (−1.38 to −0.73) < 0.001 −1.06 (−1.42 to −0.70) < 0.001 Bone and bone marrow −0.86 (−1.17 to −0.54) < 0.001 −0.86 (−1.22 to −0.51) < 0.001 Acute lymphoblastic leukemia −0.47 (−0.78 to −0.16) 0.003 −0.48 (−0.83 to −0.13) 0.007 Patient’s characteristics Patients age group (ref = 18–25 years) 26–35 years 0.21 (0.17 to 0.25) < 0.001 0.20 (0.17 to 0.24) < 0.001 36–45 years 0.29 (0.26 to 0.33) < 0.001 0.26 (0.26 to 0.33) < 0.001 46–55 years 0.34 (0.30 to 0.37) < 0.001 0.33 (0.30 to 0.37) < 0.001 56–65 years 0.12 (0.08 to 0.15) < 0.001 0.12 (0.09 to 0.15) < 0.001 >65 years 0.37 (0.33 to 0.40) < 0.001 0.36 (0.34 to 0.40) < 0.001 Gender Female (ref = male) 0.001 (−0.02 to 0.01) 0.539 0.003 (−0.02 to 0.01) 0.565 Indigenous status Aboriginal or Torres Strait (= ref) reference reference Neither Aboriginal nor Torres Strait 0.11 (0.08 to 0.13) < 0.001 0.09 (0.08 to 0.14) < 0.001 Patient refused or unable asked 0.17 (0.08 to 0.26) < 0.001 0.16 (0.09 to 0.25) < 0.001 Year of diagnosis (ref = 2006–2009) 2010–2012 −0.09 (−0.10 to −0.07) < 0.001 −0.08 (−0.10 to −0.07) < 0.001 2013–2015 0.12 (0.10 to 0.14) < 0.001 0.11 (0.10 to 0.14) < 0.001 2016–2018 0.53 (0.51 to 0.55) < 0.001 0.52 (0.50 to 0.55) < 0.001 2019–2020 0.78 (0.75 to 0.81) < 0.001 0.75 (0.72 to 0.82) < 0.001 A generalised linear regression model was used to estimate the regression coefficient. ref = reference group.

Discussion

This study provides a comprehensive analysis of post-therapy complications among cancer patients, highlighting significant increases in hospitalisation length of stay (LOS) and medical costs. Notably, 72% of patients developed complications post-systemic and/or radiation therapy, predominantly manifesting as anemia, sepsis, nausea and vomiting, and neutropenia. The types of cancer most frequently associated with these complications were acute lymphoblastic leukemia, multiple myeloma, melanoma, kidney, Hodgkin lymphoma, esophageal cancer, non-Hodgkin lymphoma, and stomach among the participants. Patients experiencing post-therapy complications had an average increase in hospital LOS by 1.72 additional days, reflecting unplanned extensions beyond their initial treatment plans. These patients also faced significantly higher hospitalisation costs, with additional expenses averaging AU$8,791 compared to those without complications.

Our analysis revealed that patients who underwent both systemic therapy and radiotherapy had an 8.24 times higher chance of experiencing post-therapy complications compared with those who only received systemic therapy. These findings are consistent with previous studies that also showed a correlation between receiving cancer therapies and post-therapy complications 34 – 37 . This increased risk might be attributable to the cumulative toxic effects of multiple therapies on patient health, as suggested in other studies 34 – 37 . This comparative insight is crucial for healthcare providers in strategising care to mitigate these risks, particularly in treatments involving multiple therapy modalities 38 .

This study highlighted the significance of post-therapy and revealed that patients who experienced complications, such as prolonged following therapy, had significantly longer unplanned hospital stays and compared with those who did not experience any complications. This finding is consistent with published data that indicates patients with post-therapeutic complications have longer hospital stays and higher treatment costs 12 , 14 , 18 , 19 , 39 . These results reinforce the critical need for effective monitoring and management of patients during and after cancer treatment to mitigate the risks associated with post-therapy complications. Such complications not only impose a substantial economic burden on patients and the healthcare system but also potentially delay ongoing cancer treatment 40 , adversely affecting patient outcomes. It is imperative that healthcare providers implement robust strategies for early identification and management of potential complications. Assessing factors such as performance status, organ function, comorbidity, and patient frailty can guide clinicians in tailoring treatment plans that minimise the risk of adverse effects 18 , 34 , 41 . Moreover, proper communication with patients about the potential for hospitalisations and other treatment-related adverse events is essential. Providing detailed information helps patients make informed decisions about their treatment options, balancing the benefits against the risks. This approach can significantly improve the quality of care, reduce the impact on healthcare resources, and enhance the overall quality of life for patients and their caregivers.

Treatment-related complications, including anemia, sepsis, and neutropenia that occur after systemic and radiotherapy, have been found to be associated with longer hospitalisation stay per admission. Cancer patients undergoing active treatment are often immunocompromised, which makes them more susceptible to sepsis leading to mortality. Previous studies show that cancer patients have a higher incidence of severe sepsis, which results in increased hospitalisation and mortality 14 , 18 . For example, cancer patients with severe sepsis stayed in hospital for almost three times as long as cancer patients without severe sepsis 14 , 18 . Anemia is also prevalent among cancer patients and can lead to longer hospitalisation in the oncology unit 31 , 42 . These occurrences can adversely impact overall survivorship, quality of life, and increase length of hospital stay 43 . Neutropenia is another severe complication of chemotherapy among cancer patients. Patients who experience neutropenia mostly have longer hospitalisation periods 44 . Another study found that neutropenia is a life-threatening toxic effect of chemotherapy that can be responsible for longer hospital LOS 39 . However, the toxicity profile of each cancer treatment modality varies according to dosages, treatment duration, and patient characteristics. These findings highlight the need to manage these complications effectively with supportive measures at home or by increasing outpatient monitoring. Furthermore, it underlines the importance of preventing hospitalisation due to therapy-related complications to optimise patient outcomes. While the reasons for prolonged LOS without complications were not directly examined in this study. However, previous studies indicated that factors including pain, infection and dyspnea can influence hospitalisation 14 , 24 , 31 , 41 , 45 .

Study strengths and limitations

The main strength of this study was the use of a large cohort of cancer patients from Victoria, Australia. Data was extracted from administrative linked data sources, including the Victorian Admitted Episodes Dataset, for the period between 2006 and 2020. These administrative datasets are based on hospital data compiled by individual public and private hospitals through two hospital networks in Victoria, Australia. This study conducted a 14-year longitudinal analysis among medically diagnosed cancer patients, allowing the identification of trends and changes in the burden of post-therapy complications over time. By capturing associations, this analysis aids in understanding how therapy practices and patient health outcomes have evolved. It is important to note that while this study identifies associations, it does not establish causality between treatment practices and patient outcomes. This study adopted a comprehensive approach by investigating complications from several aspects, including clinical characteristics, hospitalisation, and healthcare system costs from the Australian healthcare system perspective. This multifaceted exploration improves the understanding of the complexities surrounding post-therapy complications.

This study has some limitations such as data was collected from only two hospital networks in metropolitan Victoria. However, it is important to note that the results may not be fully representative of the entire state and might be biased towards a particular population. To ensure accurate and reliable results, a more comprehensive study would be required, which would involve collecting data from a state-wide cancer registry and including data from all state hospitals. By doing so, we can ensure that our findings are based on extensive and diverse data, and that we can make confident and reliable conclusions. The study was conducted from the Australian healthcare system perspective and assessed the direct medical costs of unplanned hospitalisation due to post-therapy complications in cancer patients. However, patients who experience post-therapy complications may also incur greater indirect costs, such as psychological stress, reduced productivity in daily activities, and increased time off from work and additional care burdens on their family members. In order to perform rigorous explorations for the economic analysis of post-therapy complications, healthcare utilisation data, including Medicare claims through the Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Schedule (PBS) claims data, is required. Unfortunately, we were unable to capture all aspects of healthcare use and assess the long-term economic impact of these cancer patients due to a lack of data access and funding. The substantial group size imbalance, with the majority of patients receiving systemic therapy only, may have impacted the precision of estimates, and this limitation should be considered when interpreting the findings. We acknowledge that the stages of cancer among patients may significantly influence the choice of treatment modalities and subsequent risk of complications, which may not be fully captured by the analysis of this specific subgroup within our study. Moreover, the study considered post-therapy complications according to the study design and research questions, while cancer patients may also be diagnosed with non-cancer chronic comorbid conditions or diseases, which may contribute to a higher disease burden and hospitalisation events. However, we acknowledge that the inclusion of performance status, comorbidity indices, frailty, and organ function could enrich the analysis, providing a more nuanced understanding of risk factors associated with post-therapy complications and their economic impact. The absence of these variables in our analysis is indeed a limitation, as it could affect the depth of risk stratification and potentially influence the interpretation of our findings. Therefore, future research is required using a similar study design that captures all relevant aspects of healthcare use in an economic assessment to provide robust evidence for policymakers and stakeholders. In addition, we recommend that future clinical studies with larger subsets of patients receiving combined therapies be conducted to validate and expand upon these findings.

Implications and key recommendations

The research findings have several policy implications for the Australian healthcare system, addressing various aspects of cancer care and post-therapy complications. This study proposes some key recommendations:

Develop risk stratification models to accurately identify patients at a higher risk of post-therapy complications. This should focus on factors such as age, gender, health conditions, and specific cancer types. Implement targeted interventions for these high-risk groups to ensure early detection, monitoring, and effective management of complications. Given the observed higher risk of complications among patients receiving both systemic therapy and radiotherapy—despite the small size of this subgroup—, it is prudent to advocate, for a more cautious approach. We recommend the implication of tailored treatment protocols for these patients, which include enhanced monitoring and coordinated care. These protocols should focus on active surveillance, early detection, and prompt management of potential complications. This approach may help mitigate the risks associated with combined treatment modalities and improve patient outcomes. Establish strategies for continuous monitoring and enhancement of the quality of cancer care. Consistently assess the occurrence of post-therapy complications, evaluate the impact on hospitalisation, and adjust healthcare practices accordingly. This could involve regular assessments and incorporating lessons learned into clinical guidelines.

Conclusion

This study provides evidence of the economic impact of post-therapy complications and associated unplanned hospitalisation on cancer patients and the healthcare system. The research offers a compilation of the most reported complications, highlighting the prevalence of anemia, sepsis, nausea and vomiting, neutropenia, pneumonia, and acute kidney failure. This information is essential for adopting or introducing interventions that may mitigate and effectively manage these specific complications in cancer care. The study also revealed that post-therapy complications significantly increase unplanned hospitalisation LOS and associated hospitalisation medical costs. This underscores the importance of effective management of post-therapy complications to optimise the use of healthcare resources.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Author contributions

All authors critically reviewed earlier versions of the draft and approved the final manuscript. RAM and OW conceived the paper. RAM, OW, KA, JG, MS, PKD, and MPM developed the analysis plan. RAM did the analysis and wrote the initial draft. RAM, MS, PKD, MPM, KA, JG, AMNR, MGH, SKM, KNK, SHC and OW contributed to the write up and editing.

Data availability

The data underlying this article will be shared upon reasonable request to the corresponding author.

Declarations

Competing interests

The authors declare no competing interests.

Ethical approval

To ensure compliance with ethical standards and participant confidentiality, we obtained ethical approval from the ‘Royal Melbourne Hospital Human Research Ethics Committee’ (approval # 202201/1). The data were de-identified to maintain anonymity prior to analysis. Due to the retrospective nature of the study, Royal Melbourne Hospital Human Research Ethics Committee waived the need of obtaining informed consent. All methods were performed in accordance with the relevant guidelines and regulations.

References

1. Sung H Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer J. Clin. 2021 71 209 249 10.3322/caac.21660 33538338 Sung, H. et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer. J. Clin. 71 , 209–249 (2021). 10.3322/caac.21660 33538338

2. Australian Institute of Health and Welfare and Australian Government. Australian Burden of Disease Study 2023. Cat. No: BOD 39, Australian Government (2023).

3. Australian Institute of Health and Welfare and Australian Government. Cancer data in Australia. Overview of cancer in Australia. 2023. https://www.aihw.gov.au/reports/cancer/cancer-data-in-australia/contents/overview-of-cancer-in-australia-2023 (Accessed 23 November 2023) (2023).

4. Australian Institute of Health and Welfare. Health System Expenditure on cancer and Other Neoplasms in Australia, 2015–16. Cancer Series no. 131. Cat. no. CAN 142 (AIHW, 2021).

5. Elmore LW Blueprint for cancer research: critical gaps and opportunities Cancer J. Clin. 2021 71 107 139 10.3322/caac.21652 33326126 Elmore, L. W. et al. Blueprint for cancer research: critical gaps and opportunities. Cancer J. Clin. 71 , 107–139 (2021). 10.3322/caac.21652 33326126

6. Tervonen HE Creighton N Zhao GW Ng M Currow DC Capture of systemic anticancer therapy use by routinely collected health datasets Public. Health Res. Pract. 2020 30 3 6 10.17061/phrp3012004 32152620 Tervonen, H. E., Creighton, N., Zhao, G. W., Ng, M. & Currow, D. C. Capture of systemic anticancer therapy use by routinely collected health datasets. Public Health Res. Pract. 30 , 3–6 (2020). 10.17061/phrp3012004 32152620

7. Australian Institute of Health and Welfare. Radiotherapy in Australia 2018–19 (2020).

8. de Boer SM Adjuvant chemoradiotherapy versus radiotherapy alone in women with high-risk endometrial cancer (PORTEC-3): patterns of recurrence and post-hoc survival analysis of a randomised phase 3 trial Lancet Oncol. 2019 20 1273 1285 10.1016/S1470-2045(19)30395-X 31345626 PMC6722042 de Boer, S. M. et al. Adjuvant chemoradiotherapy versus radiotherapy alone in women with high-risk endometrial cancer (PORTEC-3): patterns of recurrence and post-hoc survival analysis of a randomised phase 3 trial. Lancet Oncol. 20 , 1273–1285 (2019). 31345626 10.1016/S1470-2045(19)30395-X PMC6722042

9. Shahjalal M Assessing health related quality of life among cancer survivors during systemic and radiation therapy in Bangladesh: a cancer specific exploration BMC Cancer 2023 23 1 13 10.1186/s12885-023-11670-z 38062438 PMC10704718 Shahjalal, M. et al. Assessing health related quality of life among cancer survivors during systemic and radiation therapy in Bangladesh: a cancer specific exploration. BMC Cancer . 23 , 1–13 (2023). 38062438 10.1186/s12885-023-11670-z PMC10704718

10. Toale KM Johnson TN Ma MQ Todd KH Thomas CR Chemotherapy-Induced Toxicities Oncologic Emergency Medicine: Principles and Practice 2016 Geneva, Switzerland Springer 381 406 Toale, K. M., Johnson, T. N. & Ma, M. Q. Chemotherapy-Induced Toxicities. In: (eds Todd, K. H. & Thomas, C. R.) Oncologic Emergency Medicine: Principles and Practice. Geneva, Switzerland: Springer; 381–406. (2016).

11. FitzGerald TJ Todd KH Thomas CR Treatment toxicity: Radiation Oncologic Emergency Medicine: Principles and Practice 2021 Cham Springer 663 677 FitzGerald, T. J. et al. Treatment toxicity: Radiation. In: (eds Todd, K. H. & Thomas, C. R.) Oncologic Emergency Medicine: Principles and Practice. Springer, Cham; 663–677. (2021).

12. Adelson, K. B., Dest, V., Velji, S., Lisitano, R. & Lilenbaum, R. Emergency department (ED) utilization and hospital admission rates among oncology patients at a large academic center and the need for improved urgent care access. J. Clin. Oncol. 32 (30_suppl), 19 (2014).

13. Brooks GA A clinical prediction model to assess risk for chemotherapy- related hospitalization in patients initiating palliative chemotherapy JAMA Oncol. 2016 1 441 447 10.1001/jamaoncol.2015.0828 PMC4576342 26181251 Brooks, G. A. et al. A clinical prediction model to assess risk for chemotherapy- related hospitalization in patients initiating palliative chemotherapy. JAMA Oncol. 1 , 441–447 (2016). 10.1001/jamaoncol.2015.0828 PMC4576342 26181251

14. Jairam V Treatment-related complications of systemic therapy and radiotherapy JAMA Oncol. 2019 5 1028 1035 10.1001/jamaoncol.2019.0086 30946433 PMC6583836 Jairam, V. et al. Treatment-related complications of systemic therapy and radiotherapy. JAMA Oncol. 5 , 1028–1035 (2019). 30946433 10.1001/jamaoncol.2019.0086 PMC6583836

15. Hong AM The benefit for radiotherapy at specialised sarcoma centres: a systematic review and clinical practice guidelines from the Australia and New Zealand Sarcoma Association Radiother. Oncol. 2022 177 158 162 10.1016/j.radonc.2022.10.037 36336110 Hong, A. M. et al. The benefit for radiotherapy at specialised sarcoma centres: a systematic review and clinical practice guidelines from the Australia and New Zealand Sarcoma Association. Radiother. Oncol. 177 , 158–162 (2022). 36336110 10.1016/j.radonc.2022.10.037

16. Manzano JGM Luo R Elting LS George M Suarez-Almazor ME Patterns and predictors of unplanned hospitalization in a population-based cohort of elderly patients with GI cancer J. Clin. Oncol. 2014 32 3527 3533 10.1200/JCO.2014.55.3131 25287830 PMC4209103 Manzano, J. G. M., Luo, R., Elting, L. S., George, M. & Suarez-Almazor, M. E. Patterns and predictors of unplanned hospitalization in a population-based cohort of elderly patients with GI cancer. J. Clin. Oncol. 32 , 3527–3533 (2014). 25287830 10.1200/JCO.2014.55.3131 PMC4209103

17. Rocha PHP Adverse radiation therapy effects in the treatment of head and neck tumors Radiographics 2022 42 806 821 10.1148/rg.210150 35302867 Rocha, P. H. P. et al. Adverse radiation therapy effects in the treatment of head and neck tumors. Radiographics 42 , 806–821 (2022). 35302867 10.1148/rg.210150

18. Williams MD Hospitalized cancer patients with severe sepsis: analysis of incidence, mortality, and associated costs of care Crit. Care 2004 8 1 8 10.1186/cc2893 15469571 PMC1065011 Williams, M. D. et al. Hospitalized cancer patients with severe sepsis: analysis of incidence, mortality, and associated costs of care. Crit. Care . 8 , 1–8 (2004). 15469571 10.1186/cc2893 PMC1065011

19. O’Neill CB Hospitalizations in older adults with advanced cancer: the role of chemotherapy J. Oncol. Pract. 2016 12 e138 e148 10.1200/JOP.2015.004812 PMC5702789 26869655 O’Neill, C. B. et al. Hospitalizations in older adults with advanced cancer: the role of chemotherapy. J. Oncol. Pract. 12 , e138–e148 (2016). 10.1200/JOP.2015.004812 PMC5702789 26869655

20. Goldsbury DE Health services costs for cancer care in Australia: estimates from the 45 and up study PLoS One 2018 13 e0201552 10.1371/journal.pone.0201552 30059534 PMC6066250 Goldsbury, D. E. et al. Health services costs for cancer care in Australia: estimates from the 45 and up study. PLoS One . 13 , e0201552 (2018). 30059534 10.1371/journal.pone.0201552 PMC6066250

21. IHPA. National Weighted Activity Unit (NWAU) calculators 2019-20. Independent Hospital Pricing Authority (IHPA)-National Efficient Price Determination. (2020).

22. IHPA. National efficient price determination 2018-19. Independent Hospital Pricing Authority (IHPA). (2018).

23. Andrianopoulos, N., Jolley, D., Evans, S. M., Brand, C. A. & Cameron, P. A. Application of variable life adjusted displays (VLAD) on victorian admitted episodes dataset (VAED). BMC Health Serv. Res. ; 12 . (2012). 10.1186/1472-6963-12-278 PMC3576249 22925089

24. Rivera DR Trends in adult cancer-related emergency department utilization: an analysis of data from the nationwide emergency department sample JAMA Oncol. 2017 3 e172450 10.1001/jamaoncol.2017.2450 28859189 PMC5824316 Rivera, D. R. et al. Trends in adult cancer-related emergency department utilization: an analysis of data from the nationwide emergency department sample. JAMA Oncol. 3 , e172450 (2017). 28859189 10.1001/jamaoncol.2017.2450 PMC5824316

25. Berecki-Gisolf J Fernando T D’Elia A Trends in mortality outcomes of hospital-admitted injury in Victoria, Australia 2001–2021 Sci. Rep. 2023 13 1 9 10.1038/s41598-023-34114-x 37138036 PMC10156905 Berecki-Gisolf, J., Fernando, T. & D’Elia, A. Trends in mortality outcomes of hospital-admitted injury in Victoria, Australia 2001–2021. Sci. Rep. 13 , 1–9 (2023). 37138036 10.1038/s41598-023-34114-x PMC10156905

26. Dulisse B A retrospective study of the clinical and economic burden during hospitalizations among cancer patients with febrile neutropenia J. Med. Econ. 2013 16 720 735 10.3111/13696998.2013.782034 23452298 Dulisse, B. et al. A retrospective study of the clinical and economic burden during hospitalizations among cancer patients with febrile neutropenia. J. Med. Econ. 16 , 720–735 (2013). 23452298 10.3111/13696998.2013.782034

27. Haffty BG Long-term results of hypofractionated radiation therapy for breast cancer Breast Dis. 2010 21 267 268 10.1056/NEJMoa0906260 20147717 Haffty, B. G. Long-term results of hypofractionated radiation therapy for breast cancer. Breast Dis. 21 , 267–268 (2010). 10.1056/NEJMoa0906260 20147717

28. Albano D Imaging side effects and complications of chemotherapy and radiation therapy: a pictorial review from head to toe Insights into Imaging 2021 12 1 28 10.1186/s13244-021-01017-2 34114094 PMC8192650 Albano, D. et al. Imaging side effects and complications of chemotherapy and radiation therapy: a pictorial review from head to toe. Insights into Imaging . 12 , 1–28 (2021). 34114094 10.1186/s13244-021-01017-2 PMC8192650

29. Chin, V. et al. Chemotherapy and radiotherapy for advanced pancreatic cancer. Cochrane Database of Systematic Reviews. CD011044:1–168. (2018). 10.1002/14651858.CD011044.pub2 PMC6494171 29557103

30. Minami-Shimmyo Y Risk factors for treatment-related death associated with chemotherapy and thoracic radiotherapy for lung cancer J. Thorac. Oncol. 2012 7 177 182 10.1097/JTO.0b013e31823c4c07 22134071 Minami-Shimmyo, Y. et al. Risk factors for treatment-related death associated with chemotherapy and thoracic radiotherapy for lung cancer. J. Thorac. Oncol. 7 , 177–182 (2012). 22134071 10.1097/JTO.0b013e31823c4c07

31. Saxena A Hospitalization rates for complications due to systemic therapy in the United States Sci. Rep. 2021 11 1 7 10.1038/s41598-021-86911-x 33795827 PMC8016938 Saxena, A. et al. Hospitalization rates for complications due to systemic therapy in the United States. Sci. Rep. 11 , 1–7 (2021). 33795827 10.1038/s41598-021-86911-x PMC8016938

32. Gabrio A Mason AJ Baio G Handling missing data in within-trial cost-effectiveness analysis: a review with future recommendations PharmacoEconomics - Open. 2017 1 79 97 10.1007/s41669-017-0015-6 29442336 PMC5691848 Gabrio, A., Mason, A. J. & Baio, G. Handling missing data in within-trial cost-effectiveness analysis: a review with future recommendations. PharmacoEconomics - Open. 1 , 79–97 (2017). 29442336 10.1007/s41669-017-0015-6 PMC5691848

33. von Elm E The strengthening the reporting of Observational studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies J. Clin. Epidemiol. 2008 61 344 349 10.1016/j.jclinepi.2007.11.008 18313558 von Elm, E. et al. The strengthening the reporting of Observational studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J. Clin. Epidemiol. 61 , 344–349 (2008). 18313558 10.1016/j.jclinepi.2007.11.008

34. Rabello LSCF Clinical outcomes and microbiological characteristics of severe pneumonia in cancer patients: a prospective cohort study PLoS ONE 2015 10 1 13 10.1371/journal.pone.0120544 PMC4372450 25803690 Rabello, L. S. C. F. et al. Clinical outcomes and microbiological characteristics of severe pneumonia in cancer patients: a prospective cohort study. PLoS ONE . 10 , 1–13 (2015). 10.1371/journal.pone.0120544 PMC4372450 25803690

35. Zhou XH Melfi CA Hui SL Methods for comparison of cost data Ann. Intern. Med. 1997 127 752 756 10.7326/0003-4819-127-8_Part_2-199710151-00063 9382393 Zhou, X. H., Melfi, C. A. & Hui, S. L. Methods for comparison of cost data. Ann. Intern. Med. 127 , 752–756 (1997). 9382393 10.7326/0003-4819-127-8_part_2-199710151-00063

36. Olson R Population-based comparison of two feeding tube approaches for head and neck cancer patients receiving concurrent systemic-radiation therapy: is a prophylactic feeding tube approach harmful or helpful? Support. Care Cancer 2013 21 3433 3439 10.1007/s00520-013-1936-y 23949840 Olson, R. et al. Population-based comparison of two feeding tube approaches for head and neck cancer patients receiving concurrent systemic-radiation therapy: is a prophylactic feeding tube approach harmful or helpful? Support. Care Cancer . 21 , 3433–3439 (2013). 23949840 10.1007/s00520-013-1936-y

37. Mohan G P AHT Narayanasamy JJAMSDK Vellingiri A Recent advances in radiotherapy and its associated side effects in cancer—a review Scandinavian J. Rheumatol. Supplement 2019 80 1 10 Mohan, G., P, A. H. T., Narayanasamy, J. J. A. M. S. D. K. & Vellingiri, A. Recent advances in radiotherapy and its associated side effects in cancer—a review. Scandinavian J. Rheumatol. Supplement . 80 , 1–10 (2019).

38. Warde P Combined androgen deprivation therapy and radiation therapy for locally advanced prostate cancer: a randomised, phase 3 trial Lancet 2011 378 2104 2111 10.1016/S0140-6736(11)61095-7 22056152 PMC3243932 Warde, P. et al. Combined androgen deprivation therapy and radiation therapy for locally advanced prostate cancer: a randomised, phase 3 trial. Lancet 378 , 2104–2111 (2011). 22056152 10.1016/S0140-6736(11)61095-7 PMC3243932

39. Caggiano V Weiss RV Rickert TS Linde-Zwirble WT Incidence, cost, and mortality of neutropenia hospitalization associated with chemotherapy Cancer 2005 103 1916 1924 10.1002/cncr.20983 15751024 Caggiano, V., Weiss, R. V., Rickert, T. S. & Linde-Zwirble, W. T. Incidence, cost, and mortality of neutropenia hospitalization associated with chemotherapy. Cancer 103 , 1916–1924 (2005). 15751024 10.1002/cncr.20983

40. Abdelfatah E Ramos-Santillan V Cherkassky L Cianchetti K Mann G High risk, high reward: Frailty in colorectal cancer surgery is associated with worse postoperative outcomes but equivalent long-term oncologic outcomes Sci. Rep. 2023 30 2035 2045 10.1245/s10434-022-12970-7 36648616 Abdelfatah, E., Ramos-Santillan, V., Cherkassky, L., Cianchetti, K. & Mann, G. High risk, high reward: Frailty in colorectal cancer surgery is associated with worse postoperative outcomes but equivalent long-term oncologic outcomes. Sci. Rep. 30 , 2035–2045 (2023). 10.1245/s10434-022-12970-7 36648616

41. Hassett MJ Chemotherapy-related hospitalization among community cancer center patients Oncologist 2011 16 378 387 10.1634/theoncologist.2010-0354 21349949 PMC3228109 Hassett, M. J. et al. Chemotherapy-related hospitalization among community cancer center patients. Oncologist 16 , 378–387 (2011). 21349949 10.1634/theoncologist.2010-0354 PMC3228109

42. Seshadri T The Australian cancer anaemia survey: a snapshot of anaemia in adult patients with cancer Med. J. Aust. 2005 182 453 457 10.5694/j.1326-5377.2005.tb06784.x 15865588 Seshadri, T. et al. The Australian cancer anaemia survey: a snapshot of anaemia in adult patients with cancer. Med. J. Aust. 182 , 453–457 (2005). 15865588 10.5694/j.1326-5377.2005.tb06784.x

43. Doni L The impact of anemia on quality of life and hospitalisation in elderly cancer patients undergoing chemotherapy Crit. Rev. Oncol. Hematol. 2011 77 70 77 10.1016/j.critrevonc.2010.04.002 20483635 Doni, L. et al. The impact of anemia on quality of life and hospitalisation in elderly cancer patients undergoing chemotherapy. Crit. Rev. Oncol. Hematol. 77 , 70–77 (2011). 20483635 10.1016/j.critrevonc.2010.04.002

44. Nguyen M Jacobson T Torres J Wann A Potential reduction of hospital stay length with outpatient management of low-risk febrile neutropenia in a regional cancer center Cancer Rep. 2021 4 2 5 10.1002/cnr2.1345 PMC8222550 33635593 Nguyen, M., Jacobson, T., Torres, J. & Wann, A. Potential reduction of hospital stay length with outpatient management of low-risk febrile neutropenia in a regional cancer center. Cancer Rep. 4 , 2–5 (2021). 10.1002/cnr2.1345 PMC8222550 33635593

45. Mahumud RA Systemic therapy and radiotherapy related complications and subsequent hospitalisation rates: a systematic review BMC Cancer 2024 24 1 826 10.1186/s12885-024-12560-8 38987752 PMC11238411 Mahumud, R. A. et al. Systemic therapy and radiotherapy related complications and subsequent hospitalisation rates: a systematic review. BMC Cancer . 24 (1), 826 (2024). 38987752 10.1186/s12885-024-12560-8 PMC11238411