Множественная выживаемость миеломы в Новом Южном Уэльсе, Австралия, в период лечения 2020.

Introduction

Multiple myeloma (MM), the second most common type of blood cancer, tends to be diagnosed at older ages, among men, or among people with a family history of MM 1 . Global MM trends have shown increasing incidence rates and decreasing mortality rates over the past several decades, with improving survival outcomes in high-income countries 2 – 5 . Similar trends have been observed in Australia 6 , which has one of the world’s highest MM incidence and mortality rates: 2,663 people were diagnosed with MM, and 1,180 MM deaths occurred in 2023 7 , 8 . Similarly, the 5-year survival increased from 27.3% in 1990–1994 to 57.6% in 2015–2019 8 . Survival outcomes are expected to continue to improve in Australia, thus leading in part to the projected doubling in 30-year prevalence from 2018 to 2043 6 .

Historically, chemotherapeutic drugs were the recommended treatment for MM 9 . Internationally, MM treatment advancements, which extend quality of life and prolong survival, have partially driven improvements in survival outcomes 9 , 10 . The introduction of the stem cell transplantation (SCT) in the 1990s marked one of the most important changes to MM treatment 11 . In Australia, SCT has traditionally been recommended for individuals younger than 65 years with acceptable levels of organ reserves and performance status 12 ; however, emerging research now indicates benefits for older individuals 13 . More recently, novel chemotherapeutic treatment agents have entered the market, including 2 nd generation protease inhibitors, immunomodulatory drugs, and chimeric antigen receptor T cell therapies from the mid-2000s 9 , 10 , 12 , 14 .

Reporting of the effects of treatment advancements on MM survival has been limited in the Australian context. One study has reported higher 5-year overall survival in people diagnosed with MM between 2002–2005 in the state of New South Wales (NSW) who received SCT compared with those who did not (62% vs . 54%, respectively) 15 . A more recent study in the state of Queensland has reported changes in survival according to diagnosis date over 3 study-defined treatment eras, and has found a significant improvement in 5-year relative survival, from 30% (1982–1995; chemotherapy regimens only) to 43% (1996–2007; introduction of SCT) to 53% (2008–2014; introduction of novel agents) 16 . However, large population-based studies reporting changes in MM survival over various periods of treatment advancements have not been replicated in other Australian regions.

Considering age at diagnosis, international studies have reported less pronounced improvements in MM survival for older age groups compared to younger age groups 3 , 17 , 18 . In contrast, a Queensland-based study has reported significant improvements in survival from 1982–1995 to 1996–2007 to 2008–2014 among older age groups 16 . Understanding age differences in MM survival in the Australian context is key to enabling comparisons across health systems and populations.

Our study was aimed at quantifying the temporal trends in population-wide survival in individuals diagnosed with MM in 3 treatment eras from 1985 to 2015 in NSW, Australia, and differences by age group.

Patients and methods

We performed a retrospective cohort analysis of individuals diagnosed with MM (ICD-O-3 morphology code 973, equivalent to ICD-10 code C90.0) who were residents of NSW, Australia, between 1985 and 2015, with mortality follow-up to December 2020, thus allowing for a minimum of 5 years of follow-up. Individuals were identified from the NSW Cancer Registry, to which reporting of cancer diagnosis is a statutory requirement.

Individuals’ vital status data were obtained through routine annual linkage of cancer records with death records (the NSW Registry of Births Deaths and Marriages and Australian National Death Index, and the Cause of Death Unit Record File) until December 31, 2020. Probabilistic linkage was performed by the Centre for Health Record Linkage through a privacy-preserving approach and a matching process known to be highly accurate (false-positive and false negative rates < 0.4%).

Individuals first diagnosed at death were excluded, as were very young (< 20 years) or old individuals (90 years or older). Individuals were categorized into 3 previously defined treatment eras according to their diagnosis date 16 : 1985–1995 (chemotherapy only), 1996–2007 (autologous stem cell transplant), and 2008–2015 (novel agents: proteasome inhibitors and immunomodulatory drugs). Additionally, individuals were categorized into 3 broad age groups according to ages at diagnosis of 20–69, 70–79, or 80–89 years.

Statistical analysis

The baseline characteristics in the 3 treatment eras were compared with chi-squared tests.

Survival was measured from the date of diagnosis to the date of death, 5 years after diagnosis, or the study end date (December 31, 2020), whichever came first. Those alive at the end of follow-up were censored. Relative survival, the ratio of the observed proportion surviving in a group of individuals with MM to the expected proportion that would have survived in a comparable group of individuals from the general population 19 , was calculated because this method is preferred for measuring cancer survival at a population level 20 and is robust to inaccuracies in causes of death in population-based data collection 21 . The cohort method was used, because this study was designed to assess temporal trends in survival 22 . Observed survival was estimated with the life table method 23 , and expected survival was calculated with the Ederer II method 24 , on the basis of NSW life tables stratified by gender, age, and calendar year.

To assess differences in survival over time, we conducted 2 types of analysis. First, we fitted a relative survival regression model for excess deaths from MM 25 , 26 . In this analysis, numbers of death were modeled as a function of year of follow-up, gender, age group, and treatment era through Poisson regression with the logarithm of the person-years at risk as the offset. This model quantified the extent to which the excess risk of death in each treatment era differed from that in the reference era (1985–1995), after controlling for the factors included in the model. Relative excess risks (RERs) and their 95% confidence intervals (CIs) were calculated with the estimated coefficients and standard errors from the Poisson model.

Second, we estimated cause-specific survival with Fine and Gray’s competing risks cumulative incidence function and subdistribution hazard model 27 to adjust for other factors, as recommend by Lau et al. 28 . In this analysis, MM, as the underlying cause of death, was treated as the primary event of interest, and death due to other causes was treated as a competing risk. Cox proportional hazards regression was used for fitting the subdistribution hazard model 28 , 29 . All significance tests with P -value < 0.01 were considered to indicate statistical significance. All analyses were conducted in SAS version 9.4.

This analysis received ethical approval for the Cancer Institute NSW’s Enduring Cancer Data Linkage (also known as CanDLe) initiative from the NSW Population and Health Services Research Ethics Committee (2019/ ETH12584 ). Data were stored in the Secure Unified Research Environment facility, a remote access computing environment to which authorized researchers were given encrypted access with strong authentication.

Results

A total of 11,591 individuals 20–89 years of age diagnosed with MM between 1985 and 2015 were included in the study. The median age at diagnosis of the cohort was 70 years, and the interquartile range was 61–78 years; 57.4% were men. The distribution by age group showed a shift toward older age at diagnosis in the more recent cohort, with an increasing proportion of individuals 80–89 years of age over the study period (15.3% in 1985–1995 vs . 20.5% in 2008–2015) ( Table 1 ).

Relative survival

Relative survival improved over the 36-year study period; the largest increase was observed in the third treatment era. The 5-year relative survival over the treatment eras increased from 31.0% in 1985–1995, to 41.9% in 1996–2007, and 56.1% in 2008–2015 ( Table 1 and Figure 1 ). The improvement in survival over time was observed for all age groups ( Figure 1 ). For individuals younger than 70 years at diagnosis, the 5-year relative survival nearly doubled, from 36.5% in 1985–1995 to 68.5% in 2008–2015 ( Figure 1 ). Less improvement was observed for those 70–79 years of age, whereas little improvement was found for those 80–89 years of age between the first 2 treatment eras. In contrast, significant improvements in relative survival were observed for individuals > 70 years of age in 2008–2015. Similar trends in relative survival were observed between men and women (data not shown).

In the multivariable analysis, RERs due to diagnosis of MM were significantly lower ( P < 0.0001) for individuals diagnosed in 1996–2007 (RER = 0.73, 95% CI: 0.69–0.78) and 2008–2015 (RER = 0.48, 95% CI: 0.45–0.52) than in 1985–1995 ( Table 2 ). Age at diagnosis was a significant prognostic factor: older individuals had higher excess death (RER = 1.71 and 2.76 for those 70–79 and 80–89 years of age, respectively) than younger individuals (< 70 years of age). RERs decreased with year of follow-up, thereby indicating a progressively lower excess risk of death for people with prevalent MM.

Cause-specific survival

Cumulative incidence curves of death due to MM by treatment era are presented in Figure 2 . The cumulative incidence significantly decreased over time (Gray’s test P < 0.0001), and the 5-year cumulative incidence of death due to MM was much higher among those diagnosed in 1985–1995 (0.58, 95% CI: 0.57–0.60) than in 1996–2007 (0.47, 95% CI: 0.46–0.49) or 2008–2015 (0.37, 95% CI: 0.36–0.39) ( Table 3 ).

The estimated hazard ratios (HRs) by treatment era are reported in Table 3 . After adjustment for gender and age at diagnosis, treatment era was a significant prognostic factor ( P < 0.0001): HRs were significantly diminished in the later 2 eras (HR = 0.87, 95% CI: 0.81–0.92 for 1996–2007 and HR = 0.69, 95% CI: 0.64–0.73 for 2008–2015).

Discussion

Our study is the largest population-based analysis of MM survival in Australia to date, involving more than 11,000 individuals with MM. We observed a shift toward older age at diagnosis over time and an improvement in relative survival over the treatment eras, from 31.0% in 1985–1995 to 56.1% in 2008–2015, similarly to previous studies 16 , 18 . In the analysis adjusting for competing risk, the cumulative incidence of MM mortality was higher for those diagnosed in the 1985–1995 era than the later 2 eras. Over the 36-year study period, the excess mortality increased with increasing age at diagnosis, and the adjusted HR indicated that treatment era was a significant prognostic factor for MM.

Treatment era

Treatment options have been an important focus of MM management, given that MM is considered an incurable disease, and appropriate treatment can prolong life 10 . The treatment advancements in Australia from 1985 to 2020 have been previously outlined 12 , 30 . Briefly, the progression from chemotherapy alone to SCT to novel agents has introduced more options for the increasing number of individuals diagnosed and living with MM 6 , 30 . From the 1990s, SCT was heralded as a breakthrough in MM treatment, with evidence of improved 5-year overall survival in individuals with MM who received SCT compared with those who did not receive SCT in Australia 15 ; however, improvements were generally seen in younger individuals (under 70 years of age) 16 . In our study, relative survival increased in the 1996–2007 era, aligning with the availability of SCT. The improvement in relative survival continued in the 2008–2015 treatment era, thus reflecting the introduction of novel agents in Australia. Evidence has indicated that SCT, compared with conventional therapies, has resulted in improvements in overall survival 31 , 32 . Although not directly comparable, a Chinese hospital-based study 32 has found that people with MM who received SCT had significantly higher survival than those who did not receive SCT (similarly to our comparison of the first 2 treatment eras). In the same study, novel-agent-based regimens were associated with improved survival for people with MM 32 , similarly to our observations in the first and third treatment eras in this study. The use of combinations of novel agents or younger age at treatment (< 70 years) have also demonstrated more pronounced improvements in overall survival 33 , 34 . The improvements in 5-year relative survival across the treatment eras observed in our study were similar to those in a study by Harwood et al. 16 reporting rates of 30% (1982–1995), 43% (1996–2007), and 53% (2008–2014).

Age group

Our findings showed improvements in relative survival across treatment eras by age group and gender. In older age groups (≥ 70 years), survival improved across the treatment eras, from 26% to 48% in people 70–79 years of age and, to a lesser extent, from 19% to 34% in people ≥ 80 years of age. Although many studies have reported improved survival outcomes over time for all age groups, the increase has commonly been less pronounced in older age groups compared to in younger age groups 17 , 18 , 35 , as observed in those > 70 years of age between 1985–1995 and 1996–2007. An improvement was also seen in Harwood et al. 16 but was less pronounced in people ≥ 80 years of age, in whom the relative survival was 23% in 2008–2014. More recently, improvements in all age groups have become more evident 3 , 16 – 18 , 35 . The observation of less pronounced improvements in survival among older age groups until more recently is attributable to the lack of treatment options before the introduction of novel agents, given that Australian guidelines regarding SCT eligibility for people > 65 years of age have only recently changed, and that poorer disease biology and/or other comorbidities are associated with older age 12 , 15 , 36 .

Strengths, limitations, and suggestions for future research

This study has several notable strengths. First the study included a substantially larger sample than previous Australian studies. Moreover, it captured all individuals with MM in the chosen population (NSW), unlike randomized controlled trials or other commonly used treatment efficacy study designs, which tend to involve strict recruitment selection criteria. Furthermore, we used triangulation within methods to increase study validity, and we observed a temporal survival improvement with 2 different analytical approaches: relative and cause-specific survival.

Although robust, our study is not without limitations. Given that comprehensive treatment information was not available, the exact treatment regimens in the cohort are unknown. The treatment eras were used as proxies for the treatment provided, in line with previous studies 16 . Furthermore, individuals were categorized into treatment eras according to their diagnosis date; however, although the date of diagnosis determined the number and choice of treatment options available to an individual, it did not indicate that the individual actually received the newest type of treatment available or even any treatment at all. For example, an individual diagnosed with MM between 2008 and 2014 (the “novel agent” treatment era) might have received SCT; however, the 5-year survival outcomes were calculated as part of the third treatment era. This approach might have overestimated the survival benefits attributed to treatment available at the time, because the survival improvements might potentially have been due to treatment regimens from earlier eras or other unknown lifestyle or biological factors. Although our relative survival analysis considered this possibility by using calendar-specific lifetables from the general population, such biases cannot be completely eliminated when long-term historical data are used.

As costly new MM therapies emerge, demonstrating the efficacy of MM treatments in terms of not only survival but also quality of life outcomes will become increasingly important, to ensure that treatments extend life without sacrificing quality of life. This aspect represents an important gap in the current MM literature: recent Australian research has reported that MM is associated with the highest levels of psychological distress, disability, and pain among all cancer types, as well as the lowest likelihood of continuing in the workforce, thus resulting in high indirect costs 37 – 39 .

Conclusions

Our study demonstrated improvements in survival from 1985 to 2020 in a cohort of more than 11,500 people with MM in Australia. MM treatment advancements over multiple decades in Australia corresponded to the survival improvements identified, thus probably reflecting the effects of the introduction of SCT and novel agents (proteasome inhibitors and immunomodulatory drugs) into routine clinical practice in Australia. Although, in more recent years, older age groups have started to experience significant improvements in survival with treatment advancements, MM remains more commonly diagnosed at older than younger ages, when survival improvements are poorer and treatment options are unclear, owing to a lack of evidence. Given the increasing prevalence of MM in Australia, the aging population and late-age at diagnosis, the effects of MM treatment on survival and quality of life, particularly in older age, warrants further attention.

Acknowledgements

This research was conducted on data from the Cancer Institute NSW’s Enduring Cancer Data Linkage initiative, led by the Cancer Institute NSW and supported by the NSW Ministry of Health. Record linkage was provided by the Centre for Health Record Linkage. The Cause of Death Unit Record File was provided by the Australian Coordinating Registry on behalf of the NSW Registry of Births, Deaths and Marriages; the NSW Coroner; and the National Coronial Information System. We thank the NSW Cancer Registry; the State and Territory Registries of Births, Deaths and Marriages; the State Coroners; and the National Coronial Information System for enabling use of data for this publication.

Conflict of interest statement

No potential conflicts of interest are disclosed.

Author contributions

Conceptualization, and Writing—original draft, and preparation of the Introduction and Discussion: Eleonora Feletto.

Conceptualization, Methodology, Statistical analysis, and Writing—original draft, and preparation of the Methods and Results; had full access to all data in the study, and takes responsibility for the integrity of the data and the accuracy of the data analysis: Xue Qin Yu.

Writing—Critical revision of the manuscript for important intellectual content, and review and editing: Eleonora Feletto, Qingwei Luo, Anna Kelly, Marianne Weber, David Goldsbury, Katherine Barron, Karen Canfell, Xue Qin Yu.

Data availability statement

The data cannot be made available by the authors. These third party data are not owned or collected by the authors, and on-provision by the authors is not permitted by the relevant data custodians (NSW Ministry of Health), as it would compromise the participants confidentiality and privacy. The data contain potentially identifying and sensitive patient information. However, the data are available from the data custodians for approved research projects. Data access enquiries can be made to Cancer Institute NSW ( https://www.cancer.nsw.gov.au/research-and-data/cancer-data-and-statistics/data-available-on-request/candle-program ). Other researchers may be able to access these data through the same process followed by the authors.

References

1. Padala SA Barsouk A Barsouk A Rawla P Vakiti A Kolhe R Epidemiology, staging, and management of multiple myeloma Med Sci (Basel) 2021 9 3 33498356 10.3390/medsci9010003 PMC7838784

2. Eisfeld C Kajüter H Möller L Wellmann I Shumilov E Stang A Time trends in survival and causes of death in multiple myeloma: a population-based study from Germany BMC Cancer 2023 23 317 37024813 10.1186/s12885-023-10787-5 PMC10080943

3. Thorsteinsdottir S Dickman PW Landgren O Blimark C Hultcrantz M Turesson I Dramatically improved survival in multiple myeloma patients in the recent decade: results from a Swedish population-based study Haematologica 2018 103 e412 5 29567776 10.3324/haematol.2017.183475 PMC6119139

4. Fonseca R Abouzaid S Bonafede M Cai Q Parikh K Cosler L Trends in overall survival and costs of multiple myeloma, 2000-2014 Leukemia 2017 31 1915 21 28008176 10.1038/leu.2016.380 PMC5596206

5. Cowan AJ Allen C Barac A Basaleem H Bensenor I Curado MP Global burden of multiple myeloma: a systematic analysis for the Global Burden of Disease study 2016 JAMA Oncol 2018 4 1221 7 29800065 10.1001/jamaoncol.2018.2128 PMC6143021

6. Luo Q Jenkin D Weber M Steinberg J White K Irving A Multiple myeloma incidence, mortality and prevalence estimates for Australia, 1982 to 2043 Med J Aust 2024 Online First 15 July 10.5694/mja2.52366 39003689

7. Huang J Chan SC Lok V Zhang L Lucero-Prisno DE Xu W The epidemiological landscape of multiple myeloma: a global cancer registry estimate of disease burden, risk factors, and temporal trends Lancet Haematol 2022 9 e670 7 35843248 10.1016/S2352-3026(22)00165-X

8. Australian Institute of Health and Welfare Cancer data in Australia Canberra AIHW 2023 Report No.: CAN 122. Available from: https://www.aihw.gov.au/reports/cancer/cancer-data-in-australia/contents/about

9. Kyle RA Rajkumar SV An overview of the progress in the treatment of multiple myeloma Expert Rev Hematol 2014 7 5 7 24433105 10.1586/17474086.2014.870030

10. Legarda MA Cejalvo MJ de la Rubia J Recent advances in the treatment of patients with multiple myeloma Cancers 2020 12 3576 33265952 10.3390/cancers12123576 PMC7761116

11. Al Hamed R Bazarbachi AH Malard F Harousseau JL Mohty M Current status of autologous stem cell transplantation for multiple myeloma Blood Cancer J 2019 9 1 10 10.1038/s41408-019-0205-9 PMC6453900 30962422

12. Myeloma Australia’s Medical and Scientific Advisory Group Clinical practice guideline multiple myeloma Myeloma Australia 2022

13. Sharma M Zhang MJ Zhong X Abidi MH Akpek G Bacher U Older patients with myeloma derive similar benefit from autologous transplantation Biol Blood Marrow Transplant 2014 20 1796 803 25046833 10.1016/j.bbmt.2014.07.013 PMC4194262

14. Australian Government Department of Health and Aged Care The pharmaceutical benefits scheme (PBS) 2022 Available from: https://www.pbs.gov.au/pbs/home

15. Nivison-Smith I Simpson JM Dodds AJ Ma DDF Szer J Bradstock KF A population-based analysis of the effect of autologous hematopoietic cell transplant in the treatment of multiple myeloma Leuk Lymphoma 2013 54 1671 6 23286335 10.3109/10428194.2013.763396

16. Harwood M Dunn N Moore J Mollee P Hapgood G Trends in myeloma relative survival in Queensland by treatment era, age, place of residence, and socioeconomic status Leuk Lymphoma 2020 61 721 7 31724464 10.1080/10428194.2019.1688322

17. Andres M Feller A Arndt V Trends of incidence, mortality, and survival of multiple myeloma in Switzerland between 1994 and 2013 Cancer Epidemiol 2018 53 105 10 29414629 10.1016/j.canep.2018.01.015

18. Costa LJ Brill IK Omel J Godby K Kumar SK Brown EE Recent trends in multiple myeloma incidence and survival by age, race, and ethnicity in the United States Blood Adv 2017 1 282 7 29296944 10.1182/bloodadvances.2016002493 PMC5727774

19. Ederer F Axtell LM Cutler SJ The relative survival rate: a statistical methodology Natl Cancer Inst Monogr 1961 6 101 21 13889176

20. Mariotto AB Noone AM Howlader N Cho H Keel GE Garshell J Cancer survival: an overview of measures, uses, and interpretation J Natl Cancer Inst Monogr 2014 2014 145 86 25417231 10.1093/jncimonographs/lgu024 PMC4829054

21. Percy C Stanek E Gloeckler L Accuracy of cancer death certificates and its effect on cancer mortality statistics Am J Public Health 1981 71 242 50 7468855 10.2105/ajph.71.3.242 PMC1619811

22. Brenner H Gefeller O Hakulinen T Period analysis for “up-to-date” cancer survival data: theory, empirical evaluation, computational realisation and applications Eur J Cancer 2004 40 326 35 14746849 10.1016/j.ejca.2003.10.013

23. Chiang CL Introduction to stochastic processes in biostatistics 99 th ed John Wiley & Sons 1968

24. Ederer F Heise H Instructions to IMB 650 programmers in processing survival computations. Methodological note No. 10, end results evaluation section Bethesda National Cancer Institute 1959

25. Dickman PW Sloggett A Hills M Hakulinen T Regression models for relative survival Stat Med 2004 23 51 64 14695639 10.1002/sim.1597

26. Yu XQ O’Connell DL Gibberd RW Coates AS Armstrong BK Trends in survival and excess risk of death after diagnosis of cancer in 1980-1996 in New South Wales, Australia Int J Cancer 2006 119 894 900 16550595 10.1002/ijc.21909

27. Fine JP Gray RJ A proportional hazards model for the subdistribution of a competing risk J Am Stat Assoc 1999 94 496 509

28. Lau B Cole SR Gange SJ Competing risk regression models for epidemiologic data Am J Epidemiol 2009 170 244 56 19494242 10.1093/aje/kwp107 PMC2732996

29. So Y Lin G Johnston G Using the PHREG procedure to analyze competing-risks data Washington, DC SAS Institute 2014

30. Joshua DE Bryant C Dix C Gibson J Ho J Biology and therapy of multiple myeloma Med J Australia 2019 210 375 80 31012120 10.5694/mja2.50129

31. Puertas B González-Calle V Sobejano-Fuertes E Escalante F Queizán JA Bárez A Novel agents as main drivers for continued improvement in survival in multiple myeloma Cancers (Basel) 2023 15 1558 36900349 10.3390/cancers15051558 PMC10000382

32. Fan H Wang W Zhang Y Wang J Cheng T Qiu L Current treatment paradigm and survival outcomes among patients with newly diagnosed multiple myeloma in China: a retrospective multicenter study Cancer Biol Med 2023 20 77 87 36647781 10.20892/j.issn.2095-3941.2022.0612 PMC9843447

33. Kastritis E Zervas K Symeonidis A Terpos E Delimbassi S Anagnostopoulos N Improved survival of patients with multiple myeloma after the introduction of novel agents and the applicability of the International Staging System (ISS): an analysis of the Greek Myeloma Study Group (GMSG) Leukemia 2009 23 1152 7 19225533 10.1038/leu.2008.402

34. Kumar SK Rajkumar SV Dispenzieri A Lacy MQ Hayman SR Buadi FK Improved survival in multiple myeloma and the impact of novel therapies Blood 2008 111 2516 20 17975015 10.1182/blood-2007-10-116129 PMC2254544

35. Chan HSH Milne RJ Impact of age, sex, ethnicity, socio-economic deprivation and novel pharmaceuticals on the overall survival of patients with multiple myeloma in New Zealand Br J Haematol 2020 188 692 700 31584720 10.1111/bjh.16238

36. Wildes TM Rosko A Tuchman SA Multiple myeloma in the older adult: better prospects, more challenges J Clin Oncol 2014 32 2531 40 25071143 10.1200/JCO.2014.55.1028 PMC4129503

37. Joshy G Khalatbari-Soltani S Soga K Butow P Laidsaar-Powell R Koczwara B Pain and its interference with daily living in relation to cancer: a comparative population-based study of 16,053 cancer survivors and 106,345 people without cancer BMC Cancer 2023 23 774 37700229 10.1186/s12885-023-11214-5 PMC10498633

38. Joshy G Thandrayen J Koczwara B Butow P Laidsaar-Powell R Rankin N Disability, psychological distress and quality of life in relation to cancer diagnosis and cancer type: population-based Australian study of 22,505 cancer survivors and 244,000 people without cancer BMC Med 2020 18 372 33256726 10.1186/s12916-020-01830-4 PMC7708114

39. Thandrayen J Joshy G Stubbs J Bailey L Butow P Koczwara B Workforce participation in relation to cancer diagnosis, type and stage: Australian population-based study of 163,556 middle-aged people J Cancer Surviv 2022 16 461 73 34008147 10.1007/s11764-021-01041-7 PMC8964624