Trajectories of health-related quality of life after cancer diagnosis in a cohort of Australian women: A longitudinal study

Trajectories of health-related quality of life after cancer diagnosis in a cohort of Australian women: A longitudinal study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Trajectories of health-related quality of life after cancer diagnosis in a cohort of Australian women: A longitudinal study Md Mijanur Rahman, Julia Steinberg, Michael David, Xue Qin Yu, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7181864/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Mar, 2026 Read the published version in Health and Quality of Life Outcomes → Version 1 posted 14 You are reading this latest preprint version Abstract Background With increasing cancer survival rates, optimising health-related quality of life (HRQL) has become a major priority. However, longitudinal assessments of HRQL outcomes in people with cancer are limited. We aimed to examine the changes in HRQL after a cancer diagnosis and identify the long-term trajectories of HRQL outcomes and associated factors. Methods This study included 1414 women diagnosed with primary invasive cancer from 1996 to 2019 and 2828 women without cancer from a large cohort (born in 1946-51) of the Australian Longitudinal Study on Women’s Health, linked to the Australian Cancer Database. Generalised linear models were used to estimate changes in HRQL outcomes, adjusting for sociodemographic factors and other health conditions. Group-based multitrajectory modelling was applied to identify HRQL trajectories over time. Results In the short-term (≤ 3 years), we found a significant decline in the adjusted mean difference (AMD) across all HRQL domains at post-cancer versus pre-cancer survey, with the largest decline in general health (AMD − 10.3, 95%CI: -11.43, -9.18). The corresponding changes within the same period among women without cancer were not significant at p < 0.05, except for physical functioning. In the long-term (≤ 15 years), four HRQL trajectory groups were identified, including very low HRQL trajectory (n = 184, 13%), moderate HRQL trajectory (n = 355, 25%), medium-high HRQL trajectory (n = 532, 38%) and high HRQL trajectory (n = 343, 24%). In the control sample, a greater proportion of women belonged to the high HRQL trajectory group (29% versus 24%). Cancer survivors in the very low or moderate HRQL trajectory groups had significantly lower HRQL scores than the corresponding trajectory groups in the control group ( p < 0.05). Compared with the high HRQL trajectory group, the very low HRQL trajectory group experienced more difficulties in managing their available income (60% versus 22%, p < 0.01) and had ≥ 2 comorbidities (42% versus 9%, p < 0.01). Conclusions Our findings suggest the importance of measuring HRQL soon after diagnosis as a baseline measure, and considering both baseline and ongoing HRQL when guiding supportive care for women cancer survivors. Additionally, targeted initiatives that prevent and manage comorbidities and financial hardship in those with low HRQL at baseline are critical for equitable care. Health-related quality of life women cancer survivors pre- and post-cancer adjusted mean differences and trajectory group Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The population of cancer survivors is growing rapidly worldwide, with approximately 54 million people living with a cancer following diagnosis within five years [ 1 ]. While the prognosis of many cancers has substantially improved in recent years, stressors related to diagnosis and treatment and their late effects, such as psychosocial problems, insomnia, or other symptoms, may persist years after treatment [ 2 ]. Additionally, many survivors experience other ongoing challenges, including fear of recurrence, the need (or desire) to resume roles or daily activities, and uncertainty about current and future health conditions. These challenges can have long-lasting negative effects on survivors’ health and emotional well-being [ 3 ]. Enhancing health-related quality of life (HRQL) after a cancer diagnosis has become a major priority and is one of the key goals of cancer treatment and survivorship care. However, studies examining longitudinal changes in HRQL outcomes after a cancer diagnosis are limited [ 4 ]. HRQL has been widely used as an important outcome in observational studies, interventions, and surveillance of health and well-being, both in oncology and public health research [ 5 – 7 ]. An assessment of HRQL provides outcome measures in a range of areas, including physical, social and role functioning, mental and general health, pain and vitality. Previous studies among selected cancer populations reported that people with cancer are more likely to experience poorer HRQL across several domains than the general population [ 8 , 9 ]. While a diagnosis of cancer and the adverse effects associated with cancer treatment can contribute to poorer HRQL outcomes, several sociodemographic factors [ 10 ], behavioural characteristics such as physical inactivity [ 11 ], and clinical factors such as comorbidities [ 12 ] are also associated with poorer HRQL outcomes. Most previous HRQL studies in oncology have focused primarily on disease- and treatment-related variables and prognostic factors [ 13 ], and assessing post-treatment or comparing pre- and post-treatment HRQL [ 14 , 15 ]. Although these studies provided valuable insights into treatment effects and key determinants of HRQL, establishing baseline data following cancer diagnosis and comparing them with those of cancer-free controls is essential to fully understand the combined impact of diagnosis and treatment in cancer patients. Furthermore, with increasing cancer survival, longitudinal assessment of patient-reported outcome data and identification of groups of survivors who experience long-term poorer HRQL outcomes are crucial for targeting interventions and designing supportive survivorship care to improve survivorship outcomes. This study aims to quantify the short-term changes in HRQL outcomes following a cancer diagnosis and to identify the long-term HRQL trajectories over time, along with the associated factors. Methods Data source and sample This study was based on a large cohort of women (born 1946-51) who participated in the Australian Longitudinal Study on Women’s Health (ALSWH) between 1996 and 2022. The ALSWH survey data were linked with a range of administrative health data, including the Australian Cancer Database (ACD), from which cancer incidence information was ascertained in this study. Women in the cohort were recruited randomly via the Medicare Australia database, with oversampling from remote and regional areas to better represent the similar age groups of the entire Australian population. In 1996 (cohort aged 45–50), 13,714 women completed a self-reported postal questionnaire, yielding a response rate of 52–56%. They were first followed up in 1998 and every three years thereafter, with 7159 women completing the 9th follow-up in 2022 when they were aged 71–76. Details about the ALSWH cohort, survey waves and attrition rates have been published elsewhere [ 16 , 17 ]. Among the 12,956 women whose information was linked to the ACD data, 2756 women were diagnosed with primary invasive cancer until 2019 as of the latest available data. After applying the exclusion criteria [diagnosed with cancer before 1996 (409), died within one year after diagnosis (233), did not complete the survey after cancer diagnosis (483) and had missing information in HRQL questions (207)], the sample of cancer survivors included 1414 women, who completed at least one survey before cancer diagnosis and one after. The control sample consisted of 2828 women from the same birth cohort (1945-50) who had no history of cancer diagnosis until 2019. They were selected by matching survey completion, which was parallel to the survey that women first completed after cancer diagnosis, with a 1:2 case-control ratio, i.e., twice the number of women with incident cancer who completed the survey questionnaire in each survey. This survey-based matching strategy ensured that women in the control sample were comparable to those in the sample of cancer survivors in terms of age and timing of survey completion, thereby aligning exposure periods and age distributions between groups across all follow-up waves. A detailed derivation of the sample is provided in Supplementary Fig. 1. The research ethics committees of the University of Newcastle and the University of Queensland approved the ALSWH study; access to the survey data for the current study was approved by the ALSWH data access committee. Additional approvals to access the ACD data were obtained from each state or territory. The data linkage between the ALSWH survey and the ACD database was conducted by the Australian Institute of Health and Welfare. Exposure variable The diagnosis of cancer was the primary exposure variable, ascertained from the linked ACD data using International Classification of Diseases for Oncology (ICD-O-3) (Supplementary Table 1). The cancer cases were categorised into six major sites: breast, melanoma of the skin, colorectal, female genital organs (cervix, uterus, ovary, vulva and vagina), and blood and lymphatic systems (Hodgkin/non-Hodgkin lymphoma, immunoproliferative, myeloma and leukemia). All remaining cancers were considered ‘other cancers’. The cancer stage at diagnosis is not available in the ACD data, as the state/territory registry does not routinely record this information. Outcome variables The HRQL domains were the primary outcome variables and were assessed using the Medical Outcomes Study (MOS) Short Form-36 (SF-36) self-reported 36-item questionnaire at each survey. Th SF-36 questionnaire measures HRQL across eight domains/domains, including physical functioning, social functioning, mental health, general health, vitality, bodily pain, role emotional and role physical.[ 18 ] Raw scores were calculated as the sum of scale items in each domain and transformed to 0-100 using the formula: (Row score-minimum possible raw score)/possible raw score range*100, with higher scores indicating better outcomes). Two domains, including the role emotional and role physical domains, were not included in the current analysis because of the non-normality of the derived scores [ 19 ]. Sociodemographic and health factors Sociodemographic factors included age at cancer diagnosis, area of residence, marital status, country of birth, educational qualifications, and ability to manage available income. A detailed categorisation of these variables is provided in Table 1 . Health factors included: physician diagnosis/treatment for any major health condition other than cancer, including diabetes, arthritis, osteoporosis, heart disease, stroke, hypertension, asthma, kidney disease, low iron level, and bronchitis. The number of other health conditions was categorised as ‘no other conditions’, ‘1–2 other conditions', and ‘>2 other conditions'. Statistical analysis Initially, we explored the characteristics of women with and without cancer using frequencies and percentages for categorical variables and medians and interquartile ranges for continuous variables. Generalised linear models (GLM) with generalised estimating equations using an exchangeable working correlation structure were applied to estimate the adjusted mean difference (AMD) and 95% confidence intervals (CIs) for HRQL scores at the post-cancer survey compared to the pre-diagnosis survey, which examined the overall effect of time with regard to the exposure status. For each domain of the HRQL, the models were initially performed for all cancers combined, and then stratified by cancer site, adjusting for age at cancer diagnosis, time since cancer diagnosis, area of residence, marital status, country of birth, educational qualifications, ability to manage available income and number of other health conditions. To compare the sample of cancer survivors with the non-cancer control group, a generalised estimating equations (GEE) model was also performed on HRQL domain scores between the two subsequent surveys (matched with pre- and post-cancer surveys) to assess whether the control group generally experienced similar changes across HRQL domains over time. To examine the long-term changes in HRQL outcomes, we followed up cancer survivors for up to 15 years (five three-year surveys) since their cancer diagnosis. The baseline survey was the first survey they completed after cancer diagnosis, and the subsequent survey measures (administered every three years) were follow-ups, with 465 women completing the fourth follow-up (Supplementary Fig. 1). Similarly, women in the control group were also followed up in the same surveys, matching with the surveys completed by women with cancer. We applied group-based multitrajectory modelling (GBMT) to identify distinct trajectory groups based on the changes in the HRQL domain scores over time, separately for the samples of cancer survivors and the control group.[ 20 ] A censored normal distribution was considered for each domain score, and GBMT models were performed for two to six trajectory groups, using linear, quadratic, and cubic forms. The optimum number of trajectory groups was selected based on the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values. Additionally, trajectory group size (at least 10% of the sample) and the average posterior probability for participants belonging to a certain trajectory group at least 0.70 were considered [ 21 , 22 ]. Detailed information about GBMT and the model selection procedure is provided in Appendix 1). The differences between trajectory groups by sociodemographic factors and health characteristics were presented via cross-tabulation and tested using the Chi-square tests. The mean scores of the HRQL domains before and after cancer diagnosis, as well as the baseline mean scores for women with and without cancer, were compared across trajectory groups using paired t-tests and two-sample t-tests, respectively. All analyses were performed in SAS 9.4 (SAS Institute, Inc., Cary, NC) and Stata version 18 [ 23 ]. Results Of 1414 women included in the cancer case group, 46% (n = 631) were diagnosed with breast cancer, followed by melanoma of the skin (16%, n = 219) and colorectal cancer (10%, n = 139; Table 1 ). The median age at cancer diagnosis was 61 years (IQR: 55–66), and the median time to complete the first survey since cancer diagnosis was 1.42 years (0.70–2.30). The characteristics of the sample of cancer survivors and cancer free controls were almost similar at the survey completed before cancer diagnosis – the majority of women resided outside of major cities (62% versus 63%), were born in Australia (78% vs 76%), were married or de facto (73% versus 71%), had formal education (87% versus 85%), had no difficulties in managing available income (65% versus 65%) and had at least one other health condition (69% versus 65%). Table 1 Distribution of the sample by sociodemographic and health factors in the post-cancer survey. Characteristics Women with cancer n † =1414(%) Women without cancer ¥ n = 2828(%) Area of residence Major cities 537(38.0) 1052(37.2) Inner regional 569(40.2) 1099(38.9) Remote/outer regional 308(21.8) 677(23.9) Country of birth Australia 1098(78.2) 2179(77.8) Other English-speaking countries 188(13.4) 392(14) All other countries 118(8.4) 230(8.2) Marital status Married/De facto 1030(72.8) 2011(71.1) Never married/divorced/ separated/widowed 384(27.2) 817(28.9) Education No formal 186(13.2) 429(15.2) 12 years of education 519(36.7) 1071(37.9) Managing available income Difficult or impossible 483(35) 901(35.4) Easy or not too bad 899(65.1) 1645(64.6) Other health condition ¶ 0 427(30.2) 987(34.9) 1–2 713(50.4) 1363(48.2) > 2 274(19.4) 478(16.9) Cancer site Breast 631(44.6) Melanoma of skin 219(15.5) Colorectal 139(9.8) Female genital organs ‡ 120(8.5) Blood and lymphatic system § 95(6.7) Other 210(14.9) Age at diagnosis (Median, IQR) 61 (55–66) - Time since diagnosis (Median, IQR) 1.42 (0.7–2.3) † Only the complete cases are reported. So, the frequencies in some variables may not add up to the total sample size of n = 1414 and 2828. ¥ Randomly selected from different surveys with two times the number of cancer cases registered during the survey interval. ‡ included the cervix, uterus, Ovary, vulva, and vagina. § included Hodgkin/non-Hodgkin lymphoma, Immunoproliferative, myeloma and Leukaemia. ¶ health conditions included Arthritis/rheumatism, diabetes, heart disease, high blood pressure/hypertension, stroke, thrombosis, low iron, asthma, Bronchitis/emphysema, and osteoporosis. IQR: Interquartile Range Figure 1 presents the AMDs and 95% CIs for changes in the HRQL domain scores at the post- versus pre-cancer survey (completed within 3 years) and the corresponding matched surveys for women without cancer. There were significant decreases in scores across all domains, with the largest in general health (AMD= -10.31, 95%CI: -11.43, -9.18) followed by social functioning (AMD= -8.11, 95%CI: -9.68, -6.55) and physical functioning (AMD= -7.40, 95%CI: -8.40, -6.28). In the non-cancer control group, only physical functioning scores (AMD= -1.55, 95%CI: -2.16, -0.94) decreased significantly, whereas changes in other domain scores were not significant at p < 0.05. In terms of cancer site, physical functioning scores significantly decreased for all major cancer sites except for melanoma (Supplementary Fig. 2). While general health and social functioning scores declined substantially for all cancer sites, changes in mental health scores were not associated with any specific cancer site (p < 0.05). The changes in bodily pain and vitality scores were significant for the breast, colorectal, blood and lymphatic systems (p < 0.05), but not significant for melanoma of the skin and female genital cancers. The GBMT model in the sample of cancer survivors identified four distinct HRQL trajectory groups based on patterns of changes in the HRQL outcomes across six domains (Fig. 2 ). The optimum number of groups in the GBMT model was selected based on the fit indices (Supplementary Table 2). Additionally, the average posterior probability for those assigned to a trajectory group with the maximum posterior probability rule was > 0.90 in the four-group model, indicating that participants were appropriately grouped into the trajectory groups. Based on the HRQL outcomes, the trajectory groups were named as very low HRQL trajectory (Group 1, n = 184, 13%), moderate HRQL trajectory (Group 2, n = 355, 25%), medium-high HRQL trajectory (Group 3, n = 532, 38%) and high HRQL trajectory (Group 4, n = 343, 24%). Although the groups had distinct domain score levels, all the trajectory groups exhibited similar patterns of change over time, with a moderate increase in the average score at the initial follow-up (early post-treatment period) and a nearly plateauing trend until the end of follow-up (up to 15 years since cancer diagnosis). However, the trajectory groups were quite different in terms of the domains’ mean scores at the survey before and after cancer diagnosis (Fig. 3 ). For example, the mean scores across the HRQL domains significantly declined in Group 1 at the post-cancer survey ( p < 0.01), whereas no significant changes were observed in Group 4. The sizes of the corresponding trajectory groups observed in the control sample were moderately different (Supplementary Fig. 3), with a lower proportion of women in trajectory Group 1 (11% versus 13%) and a greater proportion in trajectory Group 4 (29% versus 24%). The baseline mean scores across domains were significantly lower among women cancer survivors who belonged to trajectory Group 1 and Group 2 than those who belonged to the respective trajectory groups in the control sample (Fig. 4 ). However, the corresponding differences between the samples of cancer survivors and controls were not statistically significant at p < 0.05 for those who belonged to trajectory Group 3 and Group 4. Table 2 Health-related quality of life trajectory groups by sociodemographic and health factors. Characteristics Very Low HRQL (Group 1) n = 175( %) Low- moderate HRQL (Group 2) n = 362 (%) Moderate-High HRQL (Group 3) n = 540 (%) High HRQL (Group 4) n = 337 (%) P -value £ Area of residence Major cities 64(35) 145(41) 204(38) 131(39) Inner regional 78(42) 143(40) 219(41) 132(39) p < 0.01 Outer regional/remote 42(23) 69(19) 115(21) 72(21) Marital status Married/de facto 121(66) 238(67) 398(74) 251(75) p < 0.01 Single/widow/separated 63(34) 119(33) 140(26) 84(25) Education No formal 39(21) 53(15) 60(11) 34(10) Higher school or lower 99(54) 185(52) 276(51) 149(44) p Higher school € 46(25) 119(33) 202(38) 152(45) Managing income Difficult or impossible 110(60) 168(47) 172(32) 74(22) p < 0.01 Easy or not too bad 74(40) 189(53) 366(68) 261(78) Body Mass Index Standard or Underweight ¥ 45(26) 1116(34) 194(38) 164(51) Overweigth 49(28) 101(29) 177(35) 103(32) p < 0.01 Obese 78(45) 127(37) 142(28) 55(17) Smoking status Never smoked 95(52) 208(59) 346(64) 222(67) Past smoker 63(34) 109(31) 164(31) 98(30) p < 0.01 Current smoker 25(14) 38(11) 27(5) 12(4) Exercise status Sedentary or low 141(81) 186(55) 204(40) 99(30) p < 0.01 Moderate or high 156(89) 274(81) 387(75) 259(80) No of chronic conditions 0 25(14) 74(21) 169(31) 122(36) 1–2 82(45) 192(54) 283(53) 184(55) p 2 77(42) 91(25) 86(16) 29(9) Cancer type Breast 76(41) 160(45) 238(44) 157(47) p < 0.01 Melanoma 16(9) 42(12) 93(17) 68(20) Colorectal 20(11) 36(10) 54(10) 29(9) Female genital 13(7) 31(9) 54(10) 22(7) Blood and lymphatic 20(11) 30(8) 28(5) 17(5) Other 39(21) 58(16) 71(13) 42(13) Median age at cancer diagnosis (IQR) 59 (53–63) 57 (52–63) 57 (50–62) 58(53–63) p = 0.56 € Trade/apprentice/diploma/certificate/university or higher qualifications ¥ Underweight women (n = 24) were merged with standard weight to suppress them as part of data protection rules. £ All p-values were from Chi-square tests except for the median age at cancer diagnosis by Kruskal-Wallis test. The trajectory groups differed significantly in terms of demographic factors and clinical characteristics (Table 2 ). Over two-thirds (67%) of survivors in Group 4 were those with breast or melanoma cancer, whereas 50% were in the very low HRQL group. The proportion of survivors who had no formal education was almost double in Group 1 (21%) with those in Group 4 (10%). Similarly, the proportion of survivors who reported ‘difficult or impossible in managing on the available income’ was noticeably higher in Group 1 (60%) than in Group 4 (22%). The corresponding percentages for the control groups were 57% and 26%, respectively (Supplementary Table 3). Discussion In this longitudinal cohort study, we found a substantial decline in HRQL outcomes across all domains at the post-cancer compared to the pre-cancer survey, completed within three years. Women who had lower HRQL outcomes at pre-cancer experienced the most significant decline, with the largest in general health, followed by social and physical functioning. However, the corresponding changes were not significant among those who had high HRQL before cancer diagnosis. Similarly, among cancer-free controls, there were no significant changes within the same period, except for physical functioning, which decreased significantly, consistent with the findings of a previous study on this cohort [ 24 ], reporting its link with ageing and age-associated non-cancer morbidities. While very few previous studies focused on pre- and post-cancer changes in HRQL across several domains and compared with cancer-free controls [ 25 , 26 ], our findings are consistent with several studies reporting that people with cancer are likely to have poorer HRQL attributable to the diagnosis of the disease and treatment side effects.[ 27 , 28 ] As pre-cancer data on HRQL outcomes are rarely available in practice, developing post-cancer baseline measures and conducting ongoing longitudinal assessments are crucial for informed treatment decisions and guiding long-term supportive care. However, ongoing assessments of HRQL outcomes remain difficult to implement in clinical practice, especially in low-resource settings, underscoring the need for innovative context-sensitive implementation approaches that can be adopted across diverse healthcare settings. One key finding is the identification of four trajectories of long-term HRQL outcomes based on up to 15 years of follow-up data. The trajectory groups were significantly different in terms of baseline domain scores (Group 1 had very low scores across all domains whereas Group 4 had very high scores), which consistently continued over the follow-up period with moderate variations or almost plateaued in some domains. While the trend in HRQL domains is expected to improve over time with recovery from many cancers, this may be partly offset by the declining trend in HRQL domains associated with the prospective aging of the cohort, as reflected in the trajectory groups observed for the control sample [ 29 ]. Our findings are consistent with a recent longitudinal study reporting that self-reported quality of life scores decreased for people undergoing breast cancer treatment, but improved after treatment and remained stable ten years after cancer diagnosis, with global quality of life comparable to that of the control population [ 4 ]. However, the trajectory groups observed in our study were based on the all-cancer combined, with 46% being breast cancer survivors. Survivors in Group 1 experienced the largest decline in HRQL within the first few years following diagnosis, while they already had low scores before cancer diagnosis. HRQL outcomes worsen further over time likely due to the high burden of comorbidities, ongoing treatment effects, and financial toxicity stemming from treatment costs and time away from employment [ 3 , 5 , 22 , 30 ]. On the other hand, survivors in Group 4 did not experience noticeable changes in HRQL outcomes after their cancer diagnosis and remained consistently high throughout the follow-up period. This finding is consistent with a previous study reporting changes in physical functioning and psychological distress in people with common cancers who have not received treatment recently, are comparable to those reported in people without cancer [ 31 ]. This may partly be explained by the lower prevalence of comorbidities and obesity, and the better ability to manage financial toxicity. Additionally, the differences in HRQL outcomes between the two trajectory groups may also be explained by the differences in cancer stages and types of cancer diagnosed, with over two-thirds having breast cancer or melanoma in Group 4, compared to almost half in Group 1. We found that a diagnosis of melanoma was not associated with changes in HRQL across several domains, including social functioning, mental health, bodily pain, and vitality, which is consistent with the findings of cancer-free controls. For cancer types associated with relatively poorer survival, for example, colorectal cancer and those diagnosed with blood & lymphatic system, we observed worse HRQL outcomes across all domains, as reflected in Group 1, which included a higher proportion of survivors with these cancer types. While an advanced stage at diagnosis is associated with poorer HRQL [ 27 ], we could not explore this in the current study as cancer staging information is not available in the ACD data. Consistent with previous studies reporting factors associated with poorer HRQL outcomes among cancer survivors [ 3 , 5 , 10 , 32 ], we found that the survivors in Group 1 and Group 2 were more likely to have low education levels, not to be married or in a de facto relationship, have financial difficulties in managing available income, have obesity, be currently smoking, and have > 2 other comorbidities, compared to those in Group 3 and Group 4. The variations were wider in the sample of cancer survivors than in the control group, especially in managing available income and the number of comorbidities. Financial hardship and the number of comorbidities might have worsened over time due to financial toxicity and the late effects of post-cancer treatment [ 3 ]. Additionally, the substantially lower scores across all domains of the HRQL at the first post-cancer survey compared with the matched survey for the control group reflect the impact of cancer diagnosis in the first few years following diagnosis. One key strength of our study is the use of survey data from a large longitudinal cohort linked to administrative data, including the Australian Cancer Database. However, our study has several limitations. First, HRQL outcomes were measured using the self-reported SF-36 questionnaire, which is not specifically designed for cancer survivors. This tool may not be sensitive enough to measure cancer and treatment-specific effects on HRQL. However, previous studies reported that the SF-36 questionnaire provides a reliable and valid indication of general health in breast cancer survivors and for generic HRQL [ 33 , 34 ]. Second, the sample of cancer survivors may have been biased toward healthy inclusion, as we included only those who completed at least one survey before and after diagnosis and were largely living in the community and could complete the postal questionnaire. Cancer survivors who are at the end of life or severely ill are likely to be underrepresented. The findings may not be generalisable to men or those in hospice or hospital settings. Previous studies on this cohort reported that those who did not complete the survey questionnaire were likely to have worse health and die prematurely [ 35 ]. Third, the sample of cancer survivors was developed over time, from 1996 to 2019. Therefore, with changing cancer treatment regimens during this period, the HRQL outcomes for those who underwent older treatment regimens might have been poorer than those who underwent treatment more recently. Further studies with cancer-specific clinical factors are warranted to understand the potential treatment-related differences in HRQL domains across cancer sites. Conclusion This is the first cohort study to provide a comprehensive outlook of both short- and long-term changes in HRQL outcomes across several domains by comparing pre- and post-cancer measures with cancer-free controls, and identifying trajectory groups over time. The post-cancer worsening HRQL outcomes, which are likely to persist long-term, underscore the importance of early assessment of these outcomes after cancer diagnosis and continuing longitudinal assessment over time, especially for those with comorbidities and who have experienced financial difficulties. Where feasible, capturing retrospective pre-cancer HRQL outcomes via recall-based tools may enhance understanding, although such approaches require rigorous validation and warrant further study. Finally, identifying individuals with lower HRQL soon after cancer diagnosis can inform targeted supportive care interventions, including psychosocial support and financial assistance to improve survivorship outcomes. Abbreviations HRQL: Health-related quality of life ALSWH: Australian Longitudinal Study on Women’s Health GBMT: Group-based multi-trajectory AMD: Adjusted mean difference CI: Confidence interval Declarations Acknowledgement The research on which this report is based was conducted as part of the Australian Longitudinal Study on Women's Health by the University of Queensland and the University of Newcastle. We are grateful to the Australian Government Department of Health for funding and to the women who provided the survey data. We also acknowledge the Australian Capital Territory, New South Wales, Northern Territory, Queensland, South Australia, Tasmanian, Victorian, and Western Australian Cancer Registries for providing data, and the Australian Institute of Health and Welfare (AIHW) as the integrating authority. Funding MLY , KC and EB are supported by National Health and Research Council of Australia Investigator Grants (NHMRC; APP 2018108, APP1194679 and APP2017742, respectively). JS is supported by a Cancer Institute NSW Career Development Fellowship (2022/CDF1154) KC is co-PI of an investigator-initiated trial of cervical screening, ‘Compass’, run by the ACPCC, a government-funded not-for-profit charity. Compass receives infrastructure support from the Australian government and the ACPCC has received equipment and a funding contribution from Roche and Microbix. She is also co-Lead on the Elimination Partnership in the Pacific for Cervical Cancer (EPICC) which has received support from the Australian government, the Minderoo Foundation and equipment donations from Cepheid Inc. Ethics declaration This study was conducted as part of the Australian Longitudinal Study on Women’s Health, which was approved by the ethics committees of the University of Newcastle (H-2011-0371) and the University of Queensland (2012/HE000132). Access to the survey data was approved by the ALSWH data access committee. Approval for accessing the linked Australian Cancer Database was obtained from the Australian Institute of Health and Welfare HREC (EC2020/3/1115). Written informed consent was obtained from individual participants. Study participants were informed of their freedom to opt out of the study at any time without having to justify their decision. The study was conducted in accordance with the Declaration of Helsinki. Consent for publication Not applicable Data availability This data analysis was conducted under conditions approved by the relevant ethics committee(s). As a condition of approval, data are not sharable. Access to data by other individuals or agencies would require appropriate ethical approvals to be in place. Conflict of interest The authors declare no conflict of interest. Authors’ contribution MMR, JS and JB conceptualised the study. All authors contributed to the study design. MMR conducted the data analysis and wrote the main manuscript. KC and MLY provided supervision in data analysis and manuscript preparation. All authors read, reviewed and approved the final manuscript. References Bray, F., et al., Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2024. 74 (3): p. 229-263. Schmidt, M.E., J. Wiskemann, and K. Steindorf, Quality of life, problems, and needs of disease-free breast cancer survivors 5 years after diagnosis. Qual Life Res, 2018. 27 (8): p. 2077-2086. Manne, S., et al., Factors associated with health-related quality of life in a cohort of cancer survivors in New Jersey. BMC Cancer, 2023. 23 (1): p. 664. Gao, Y., et al., Longitudinal changes of health-related quality of life over 10 years in breast cancer patients treated with radiotherapy following breast-conserving surgery. Qual Life Res, 2023. 32 (9): p. 2639-2652. Han, X., et al., Factors Associated With Health-Related Quality of Life Among Cancer Survivors in the United States. JNCI Cancer Spectr, 2021. 5 (1). Cella, D. and A.A. Stone, Health-related quality of life measurement in oncology: advances and opportunities. Am Psychol, 2015. 70 (2): p. 175-85. Kaplan, R.M. and R.D. Hays, Health-Related Quality of Life Measurement in Public Health. Annu Rev Public Health, 2022. 43 : p. 355-373. Joshy, G., et al., 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 medicine, 2020. 18 : p. 1-15. Rahman, M.M., et al., Association of optimism and social support with health-related quality of life among Australian women cancer survivors - A cohort study. Asia Pac J Clin Oncol, 2024. Shapiro, C.L., Cancer Survivorship. N Engl J Med, 2018. 379 (25): p. 2438-2450. Bours, M.J., et al., Candidate Predictors of Health-Related Quality of Life of Colorectal Cancer Survivors: A Systematic Review. Oncologist, 2016. 21 (4): p. 433-52. Fu, M.R., et al., Comorbidities and Quality of Life among Breast Cancer Survivors: A Prospective Study. J Pers Med, 2015. 5 (3): p. 229-42. Gustavsson-Lilius, M., J. Julkunen, and P. Hietanen, Quality of life in cancer patients: The role of optimism, hopelessness, and partner support. Qual Life Res, 2007. 16 (1): p. 75-87. Agarwal, S.K., et al., Prospective evaluation of the quality of life of oral tongue cancer patients before and after the treatment. Ann Palliat Med, 2014. 3 (4): p. 238-43. Hassel, A.J., et al., Oral health-related quality of life and depression/anxiety in long-term recurrence-free patients after treatment for advanced oral squamous cell cancer. J Craniomaxillofac Surg, 2012. 40 (4): p. e99-102. Dobson, A.J., et al., Cohort profile update: Australian longitudinal study on women’s health. International Journal of Epidemiology, 2015. 44 (5): p. 1547-1547f. Loxton, D., et al., Online and offline recruitment of young women for a longitudinal health survey: findings from the Australian Longitudinal Study on Women’s Health 1989-95 cohort. Journal of medical Internet research, 2015. 17 (5). Ware, J.E., et al., SF-36 health survey. Manual and interpretation guide, 1993. 2 . Australian Longitudinal Study on Women's Health, The SF-36. 2006. Nagin, D.S., et al., Group-based multi-trajectory modeling. Stat Methods Med Res, 2018. 27 (7): p. 2015-2023. Nagin, D.S., Group-based modeling of development. 2005: Harvard university press. Park, J.H., et al., Trajectories of health-related quality of life in breast cancer patients. Support Care Cancer, 2020. 28 (7): p. 3381-3389. Jones, B.L. and D.S. Nagin, A note on a Stata plugin for estimating group-based trajectory models. Sociological Methods & Research, 2013. 42 (4): p. 608-613. Leigh, L., J.E. Byles, and G.D. Mishra, Change in physical function among women as they age: findings from the Australian Longitudinal Study on Women's Health. Qual Life Res, 2017. 26 (4): p. 981-991. Schwartz, R.M., et al., Change in Quality of Life after a Cancer Diagnosis among a Nationally Representative Cohort of Older Adults in the US. Cancer Invest, 2019. 37 (7): p. 299-310. Trentham-Dietz, A., et al., Health-related quality of life before and after a breast cancer diagnosis. Breast Cancer Res Treat, 2008. 109 (2): p. 379-87. Dixit, J., et al., Health-related quality of life and its determinants among cancer patients: evidence from 12,148 patients of Indian database. Health Qual Life Outcomes, 2024. 22 (1): p. 26. Rahman, M.M., et al., Association of optimism and social support with health-related quality of life among Australian women cancer survivors - A cohort study. Asia Pac J Clin Oncol, 2025. 21 (2): p. 221-231. Noto, S., Perspectives on Aging and Quality of Life. Healthcare (Basel), 2023. 11 (15). Andreu, Y., et al., Exploring the independent association of employment status to cancer survivors' health-related quality of life. Health Qual Life Outcomes, 2023. 21 (1): p. 44. Zhang, Y., et al., Physical disability and psychological distress before and after a diagnosis of cancer: evidence on multiple cancer types from a large Australian cohort study, compared to people without a cancer diagnosis. BMC Med, 2025. 23 (1): p. 290. Rodriguez, J.L., et al., Factors Associated with Health-Related Quality of Life Among Colorectal Cancer Survivors. Am J Prev Med, 2015. 49 (6 Suppl 5): p. S518-27. Butterworth, P. and T. Crosier, The validity of the SF-36 in an Australian National Household Survey: demonstrating the applicability of the Household Income and Labour Dynamics in Australia (HILDA) Survey to examination of health inequalities. BMC Public Health, 2004. 4 : p. 44. Treanor, C. and M. Donnelly, A methodological review of the Short Form Health Survey 36 (SF-36) and its derivatives among breast cancer survivors. Quality of Life Research, 2015. 24 : p. 339-362. Rahman, M.M., et al., Onset and progression of chronic disease and disability in a large cohort of older Australian women. Maturitas, 2022. 158 : p. 25-33. Supplementary Material Supplementary figures and tables are not available with this version. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 12 Mar, 2026 Read the published version in Health and Quality of Life Outcomes → Version 1 posted Editorial decision: Revision requested 19 Nov, 2025 Reviews received at journal 15 Nov, 2025 Reviewers agreed at journal 01 Nov, 2025 Reviews received at journal 29 Oct, 2025 Reviewers agreed at journal 08 Oct, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviews received at journal 18 Aug, 2025 Reviews received at journal 17 Aug, 2025 Reviewers agreed at journal 30 Jul, 2025 Reviewers agreed at journal 28 Jul, 2025 Reviewers invited by journal 28 Jul, 2025 Editor assigned by journal 23 Jul, 2025 Submission checks completed at journal 23 Jul, 2025 First submitted to journal 21 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7181864","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":492268936,"identity":"4d30e52b-5226-40d1-bab2-75dd6a4d8e5b","order_by":0,"name":"Md Mijanur 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02:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7181864/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7181864/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12955-026-02508-w","type":"published","date":"2026-03-12T15:57:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88468198,"identity":"815fd5c7-02d6-4412-8327-5d58f378f3a9","added_by":"auto","created_at":"2025-08-06 18:07:14","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":588839,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in health-related quality of life outcomes for women with and without cancer.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7181864/v1/084420bac7691bdb7da3af23.jpeg"},{"id":88467849,"identity":"5f106ee9-cc8d-465a-b8ea-860ce846d9e3","added_by":"auto","created_at":"2025-08-06 17:59:14","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":571539,"visible":true,"origin":"","legend":"\u003cp\u003eHealth-related quality of life outcomes over time since cancer diagnosis by trajectory groups.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7181864/v1/86063d84114d982fe9a07cdf.jpeg"},{"id":88467846,"identity":"6b159dfe-90ea-4c02-818a-0afafa1829da","added_by":"auto","created_at":"2025-08-06 17:59:14","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":487102,"visible":true,"origin":"","legend":"\u003cp\u003eHealth-related quality of life outcomes at pre- and post-cancer survey by trajectory groups.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7181864/v1/4b584a1ee40e59980ebb6a1c.jpeg"},{"id":88467850,"identity":"16ea60e7-4d05-4752-8018-ed70e80ad0a3","added_by":"auto","created_at":"2025-08-06 17:59:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":14319,"visible":true,"origin":"","legend":"\u003cp\u003eHealth-related quality of life trajectory groups at baseline for women with and without cancer\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7181864/v1/ad97ce6b52262a5de23c1582.png"},{"id":104739551,"identity":"2ef43034-abcf-454a-9581-d286c84c3589","added_by":"auto","created_at":"2026-03-16 16:09:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2722523,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7181864/v1/11540f23-8e30-4504-bd42-31aa42b5534e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Trajectories of health-related quality of life after cancer diagnosis in a cohort of Australian women: A longitudinal study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe population of cancer survivors is growing rapidly worldwide, with approximately 54\u0026nbsp;million people living with a cancer following diagnosis within five years [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. While the prognosis of many cancers has substantially improved in recent years, stressors related to diagnosis and treatment and their late effects, such as psychosocial problems, insomnia, or other symptoms, may persist years after treatment [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Additionally, many survivors experience other ongoing challenges, including fear of recurrence, the need (or desire) to resume roles or daily activities, and uncertainty about current and future health conditions. These challenges can have long-lasting negative effects on survivors’ health and emotional well-being [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Enhancing health-related quality of life (HRQL) after a cancer diagnosis has become a major priority and is one of the key goals of cancer treatment and survivorship care. However, studies examining longitudinal changes in HRQL outcomes after a cancer diagnosis are limited [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHRQL has been widely used as an important outcome in observational studies, interventions, and surveillance of health and well-being, both in oncology and public health research [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e–\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. An assessment of HRQL provides outcome measures in a range of areas, including physical, social and role functioning, mental and general health, pain and vitality. Previous studies among selected cancer populations reported that people with cancer are more likely to experience poorer HRQL across several domains than the general population [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. While a diagnosis of cancer and the adverse effects associated with cancer treatment can contribute to poorer HRQL outcomes, several sociodemographic factors [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], behavioural characteristics such as physical inactivity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and clinical factors such as comorbidities [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] are also associated with poorer HRQL outcomes. Most previous HRQL studies in oncology have focused primarily on disease- and treatment-related variables and prognostic factors [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and assessing post-treatment or comparing pre- and post-treatment HRQL [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Although these studies provided valuable insights into treatment effects and key determinants of HRQL, establishing baseline data following cancer diagnosis and comparing them with those of cancer-free controls is essential to fully understand the combined impact of diagnosis and treatment in cancer patients.\u003c/p\u003e\u003cp\u003eFurthermore, with increasing cancer survival, longitudinal assessment of patient-reported outcome data and identification of groups of survivors who experience long-term poorer HRQL outcomes are crucial for targeting interventions and designing supportive survivorship care to improve survivorship outcomes. This study aims to quantify the short-term changes in HRQL outcomes following a cancer diagnosis and to identify the long-term HRQL trajectories over time, along with the associated factors.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eData source and sample\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study was based on a large cohort of women (born 1946-51) who participated in the Australian Longitudinal Study on Women’s Health (ALSWH) between 1996 and 2022. The ALSWH survey data were linked with a range of administrative health data, including the Australian Cancer Database (ACD), from which cancer incidence information was ascertained in this study. Women in the cohort were recruited randomly via the Medicare Australia database, with oversampling from remote and regional areas to better represent the similar age groups of the entire Australian population. In 1996 (cohort aged 45–50), 13,714 women completed a self-reported postal questionnaire, yielding a response rate of 52–56%. They were first followed up in 1998 and every three years thereafter, with 7159 women completing the 9th follow-up in 2022 when they were aged 71–76. Details about the ALSWH cohort, survey waves and attrition rates have been published elsewhere [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAmong the 12,956 women whose information was linked to the ACD data, 2756 women were diagnosed with primary invasive cancer until 2019 as of the latest available data. After applying the exclusion criteria [diagnosed with cancer before 1996 (409), died within one year after diagnosis (233), did not complete the survey after cancer diagnosis (483) and had missing information in HRQL questions (207)], the sample of cancer survivors included 1414 women, who completed at least one survey before cancer diagnosis and one after. The control sample consisted of 2828 women from the same birth cohort (1945-50) who had no history of cancer diagnosis until 2019. They were selected by matching survey completion, which was parallel to the survey that women first completed after cancer diagnosis, with a 1:2 case-control ratio, i.e., twice the number of women with incident cancer who completed the survey questionnaire in each survey. This survey-based matching strategy ensured that women in the control sample were comparable to those in the sample of cancer survivors in terms of age and timing of survey completion, thereby aligning exposure periods and age distributions between groups across all follow-up waves. A detailed derivation of the sample is provided in Supplementary Fig.\u0026nbsp;1.\u003c/p\u003e\u003cp\u003eThe research ethics committees of the University of Newcastle and the University of Queensland approved the ALSWH study; access to the survey data for the current study was approved by the ALSWH data access committee. Additional approvals to access the ACD data were obtained from each state or territory. The data linkage between the ALSWH survey and the ACD database was conducted by the Australian Institute of Health and Welfare.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExposure variable\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe diagnosis of cancer was the primary exposure variable, ascertained from the linked ACD data using International Classification of Diseases for Oncology (ICD-O-3) (Supplementary Table\u0026nbsp;1). The cancer cases were categorised into six major sites: breast, melanoma of the skin, colorectal, female genital organs (cervix, uterus, ovary, vulva and vagina), and blood and lymphatic systems (Hodgkin/non-Hodgkin lymphoma, immunoproliferative, myeloma and leukemia). All remaining cancers were considered ‘other cancers’. The cancer stage at diagnosis is not available in the ACD data, as the state/territory registry does not routinely record this information.\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutcome variables\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe HRQL domains were the primary outcome variables and were assessed using the Medical Outcomes Study (MOS) Short Form-36 (SF-36) self-reported 36-item questionnaire at each survey. Th SF-36 questionnaire measures HRQL across eight domains/domains, including physical functioning, social functioning, mental health, general health, vitality, bodily pain, role emotional and role physical.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] Raw scores were calculated as the sum of scale items in each domain and transformed to 0-100 using the formula: (Row score-minimum possible raw score)/possible raw score range*100, with higher scores indicating better outcomes). Two domains, including the role emotional and role physical domains, were not included in the current analysis because of the non-normality of the derived scores [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eSociodemographic and health factors\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSociodemographic factors included age at cancer diagnosis, area of residence, marital status, country of birth, educational qualifications, and ability to manage available income. A detailed categorisation of these variables is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Health factors included: physician diagnosis/treatment for any major health condition other than cancer, including diabetes, arthritis, osteoporosis, heart disease, stroke, hypertension, asthma, kidney disease, low iron level, and bronchitis. The number of other health conditions was categorised as ‘no other conditions’, ‘1–2 other conditions', and ‘\u0026gt;2 other conditions'.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eInitially, we explored the characteristics of women with and without cancer using frequencies and percentages for categorical variables and medians and interquartile ranges for continuous variables. Generalised linear models (GLM) with generalised estimating equations using an exchangeable working correlation structure were applied to estimate the adjusted mean difference (AMD) and 95% confidence intervals (CIs) for HRQL scores at the post-cancer survey compared to the pre-diagnosis survey, which examined the overall effect of time with regard to the exposure status. For each domain of the HRQL, the models were initially performed for all cancers combined, and then stratified by cancer site, adjusting for age at cancer diagnosis, time since cancer diagnosis, area of residence, marital status, country of birth, educational qualifications, ability to manage available income and number of other health conditions. To compare the sample of cancer survivors with the non-cancer control group, a generalised estimating equations (GEE) model was also performed on HRQL domain scores between the two subsequent surveys (matched with pre- and post-cancer surveys) to assess whether the control group generally experienced similar changes across HRQL domains over time.\u003c/p\u003e\u003cp\u003eTo examine the long-term changes in HRQL outcomes, we followed up cancer survivors for up to 15 years (five three-year surveys) since their cancer diagnosis. The baseline survey was the first survey they completed after cancer diagnosis, and the subsequent survey measures (administered every three years) were follow-ups, with 465 women completing the fourth follow-up (Supplementary Fig.\u0026nbsp;1). Similarly, women in the control group were also followed up in the same surveys, matching with the surveys completed by women with cancer. We applied group-based multitrajectory modelling (GBMT) to identify distinct trajectory groups based on the changes in the HRQL domain scores over time, separately for the samples of cancer survivors and the control group.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] A censored normal distribution was considered for each domain score, and GBMT models were performed for two to six trajectory groups, using linear, quadratic, and cubic forms.\u003c/p\u003e\u003cp\u003eThe optimum number of trajectory groups was selected based on the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values. Additionally, trajectory group size (at least 10% of the sample) and the average posterior probability for participants belonging to a certain trajectory group at least 0.70 were considered [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Detailed information about GBMT and the model selection procedure is provided in Appendix 1). The differences between trajectory groups by sociodemographic factors and health characteristics were presented via cross-tabulation and tested using the Chi-square tests. The mean scores of the HRQL domains before and after cancer diagnosis, as well as the baseline mean scores for women with and without cancer, were compared across trajectory groups using paired t-tests and two-sample t-tests, respectively. All analyses were performed in SAS 9.4 (SAS Institute, Inc., Cary, NC) and Stata version 18 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eOf 1414 women included in the cancer case group, 46% (n\u0026thinsp;=\u0026thinsp;631) were diagnosed with breast cancer, followed by melanoma of the skin (16%, n\u0026thinsp;=\u0026thinsp;219) and colorectal cancer (10%, n\u0026thinsp;=\u0026thinsp;139; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The median age at cancer diagnosis was 61 years (IQR: 55\u0026ndash;66), and the median time to complete the first survey since cancer diagnosis was 1.42 years (0.70\u0026ndash;2.30). The characteristics of the sample of cancer survivors and cancer free controls were almost similar at the survey completed before cancer diagnosis \u0026ndash; the majority of women resided outside of major cities (62% versus 63%), were born in Australia (78% vs 76%), were married or de facto (73% versus 71%), had formal education (87% versus 85%), had no difficulties in managing available income (65% versus 65%) and had at least one other health condition (69% versus 65%).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of the sample by sociodemographic and health factors in the post-cancer survey.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWomen with cancer\u003c/p\u003e\u003cp\u003en\u003csup\u003e\u0026dagger;\u003c/sup\u003e=1414(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWomen without cancer\u003csup\u003e\u0026yen;\u003c/sup\u003e\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;2828(%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArea of residence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMajor cities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e537(38.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1052(37.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInner regional\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e569(40.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1099(38.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRemote/outer regional\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e308(21.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e677(23.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCountry of birth\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAustralia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1098(78.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2179(77.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther English-speaking countries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e188(13.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e392(14)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAll other countries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e118(8.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e230(8.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried/De facto\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1030(72.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2011(71.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever married/divorced/\u003c/p\u003e\u003cp\u003eseparated/widowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e384(27.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e817(28.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo formal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e186(13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e429(15.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;=12 years of education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e709(50.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1328(47)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;12 years of education\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e519(36.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1071(37.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eManaging available income\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDifficult or impossible\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e483(35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e901(35.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEasy or not too bad\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e899(65.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1645(64.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOther health condition\u003c/b\u003e\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e427(30.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e987(34.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e713(50.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1363(48.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e274(19.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e478(16.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCancer site\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBreast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e631(44.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMelanoma of skin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e219(15.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eColorectal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139(9.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale genital organs\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e120(8.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood and lymphatic system\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95(6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e210(14.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge at diagnosis\u003c/b\u003e (Median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61 (55\u0026ndash;66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTime since diagnosis\u003c/b\u003e (Median, IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.42 (0.7\u0026ndash;2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003eOnly the complete cases are reported. So, the frequencies in some variables may not add up to the total sample size of n\u0026thinsp;=\u0026thinsp;1414 and 2828.\u003c/p\u003e\u003cp\u003e\u003csup\u003e\u0026yen;\u003c/sup\u003eRandomly selected from different surveys with two times the number of cancer cases registered during the survey interval.\u003c/p\u003e\u003cp\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003eincluded the cervix, uterus, Ovary, vulva, and vagina.\u003c/p\u003e\u003cp\u003e\u003csup\u003e\u0026sect;\u003c/sup\u003eincluded Hodgkin/non-Hodgkin lymphoma, Immunoproliferative, myeloma and Leukaemia.\u003c/p\u003e\u003cp\u003e\u003csup\u003e\u0026para;\u003c/sup\u003ehealth conditions included Arthritis/rheumatism, diabetes, heart disease, high blood pressure/hypertension, stroke, thrombosis, low iron, asthma, Bronchitis/emphysema, and osteoporosis.\u003c/p\u003e\u003cp\u003eIQR: Interquartile Range\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the AMDs and 95% CIs for changes in the HRQL domain scores at the post- versus pre-cancer survey (completed within 3 years) and the corresponding matched surveys for women without cancer. There were significant decreases in scores across all domains, with the largest in general health (AMD= -10.31, 95%CI: -11.43, -9.18) followed by social functioning (AMD= -8.11, 95%CI: -9.68, -6.55) and physical functioning (AMD= -7.40, 95%CI: -8.40, -6.28). In the non-cancer control group, only physical functioning scores (AMD= -1.55, 95%CI: -2.16, -0.94) decreased significantly, whereas changes in other domain scores were not significant at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn terms of cancer site, physical functioning scores significantly decreased for all major cancer sites except for melanoma (Supplementary Fig.\u0026nbsp;2). While general health and social functioning scores declined substantially for all cancer sites, changes in mental health scores were not associated with any specific cancer site (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The changes in bodily pain and vitality scores were significant for the breast, colorectal, blood and lymphatic systems (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but not significant for melanoma of the skin and female genital cancers.\u003c/p\u003e\u003cp\u003eThe GBMT model in the sample of cancer survivors identified four distinct HRQL trajectory groups based on patterns of changes in the HRQL outcomes across six domains (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The optimum number of groups in the GBMT model was selected based on the fit indices (Supplementary Table\u0026nbsp;2). Additionally, the average posterior probability for those assigned to a trajectory group with the maximum posterior probability rule was \u0026gt;\u0026thinsp;0.90 in the four-group model, indicating that participants were appropriately grouped into the trajectory groups.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBased on the HRQL outcomes, the trajectory groups were named as \u003cem\u003every low HRQL trajectory\u003c/em\u003e (Group 1, n\u0026thinsp;=\u0026thinsp;184, 13%), \u003cem\u003emoderate HRQL trajectory\u003c/em\u003e (Group 2, n\u0026thinsp;=\u0026thinsp;355, 25%), \u003cem\u003emedium-high HRQL trajectory\u003c/em\u003e (Group 3, n\u0026thinsp;=\u0026thinsp;532, 38%) and \u003cem\u003ehigh HRQL trajectory\u003c/em\u003e (Group 4, n\u0026thinsp;=\u0026thinsp;343, 24%). Although the groups had distinct domain score levels, all the trajectory groups exhibited similar patterns of change over time, with a moderate increase in the average score at the initial follow-up (early post-treatment period) and a nearly plateauing trend until the end of follow-up (up to 15 years since cancer diagnosis). However, the trajectory groups were quite different in terms of the domains\u0026rsquo; mean scores at the survey before and after cancer diagnosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For example, the mean scores across the HRQL domains significantly declined in Group 1 at the post-cancer survey (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), whereas no significant changes were observed in Group 4.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe sizes of the corresponding trajectory groups observed in the control sample were moderately different (Supplementary Fig.\u0026nbsp;3), with a lower proportion of women in trajectory Group 1 (11% versus 13%) and a greater proportion in trajectory Group 4 (29% versus 24%). The baseline mean scores across domains were significantly lower among women cancer survivors who belonged to trajectory Group 1 and Group 2 than those who belonged to the respective trajectory groups in the control sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, the corresponding differences between the samples of cancer survivors and controls were not statistically significant at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for those who belonged to trajectory Group 3 and Group 4.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHealth-related quality of life trajectory groups by sociodemographic and health factors.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVery Low HRQL (Group 1)\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;175( %)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow- moderate HRQL (Group 2) n\u0026thinsp;=\u0026thinsp;362 (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate-High HRQL (Group 3) n\u0026thinsp;=\u0026thinsp;540 (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh HRQL (Group 4) n\u0026thinsp;=\u0026thinsp;337 (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003csup\u003e\u0026pound;\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArea of residence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMajor cities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64(35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e145(41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e204(38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e131(39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInner regional\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78(42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e143(40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e219(41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e132(39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOuter regional/remote\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42(23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69(19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e115(21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e72(21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried/de facto\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e121(66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e238(67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e398(74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e251(75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle/widow/separated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63(34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e119(33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e140(26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e84(25)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo formal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39(21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53(15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60(11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34(10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigher school or lower\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e99(54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e185(52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e276(51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e149(44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;Higher school\u003csup\u003e\u0026euro;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46(25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e119(33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e202(38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e152(45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eManaging income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDifficult or impossible\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e110(60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e168(47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e172(32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e74(22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEasy or not too bad\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74(40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e189(53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e366(68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e261(78)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBody Mass Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStandard or Underweight\u003csup\u003e\u0026yen;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45(26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1116(34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e194(38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e164(51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverweigth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49(28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e101(29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e177(35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e103(32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78(45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e127(37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e142(28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e55(17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever smoked\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95(52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e208(59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e346(64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e222(67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePast smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63(34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e109(31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e164(31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e98(30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25(14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38(11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27(5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12(4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExercise status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSedentary or low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e141(81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e186(55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e204(40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e99(30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate or high\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e156(89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e274(81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e387(75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e259(80)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo of chronic conditions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25(14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74(21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e169(31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e122(36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82(45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e192(54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e283(53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e184(55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e77(42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e91(25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86(16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29(9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCancer type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBreast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76(41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e160(45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e238(44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e157(47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMelanoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16(9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42(12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93(17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e68(20)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eColorectal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20(11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36(10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54(10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29(9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale genital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13(7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31(9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54(10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22(7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood and lymphatic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20(11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30(8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28(5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17(5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39(21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58(16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71(13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42(13)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian age at cancer diagnosis (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59 (53\u0026ndash;63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57 (52\u0026ndash;63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57 (50\u0026ndash;62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58(53\u0026ndash;63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003e\u0026euro;\u003c/sup\u003eTrade/apprentice/diploma/certificate/university or higher qualifications\u003c/p\u003e\u003cp\u003e\u003csup\u003e\u0026yen;\u003c/sup\u003e Underweight women (n\u0026thinsp;=\u0026thinsp;24) were merged with standard weight to suppress them as part of data protection rules.\u003c/p\u003e\u003cp\u003e\u003csup\u003e\u0026pound;\u003c/sup\u003eAll p-values were from Chi-square tests except for the median age at cancer diagnosis by Kruskal-Wallis test.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe trajectory groups differed significantly in terms of demographic factors and clinical characteristics (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Over two-thirds (67%) of survivors in Group 4 were those with breast or melanoma cancer, whereas 50% were in the very low HRQL group. The proportion of survivors who had no formal education was almost double in Group 1 (21%) with those in Group 4 (10%). Similarly, the proportion of survivors who reported \u0026lsquo;difficult or impossible in managing on the available income\u0026rsquo; was noticeably higher in Group 1 (60%) than in Group 4 (22%). The corresponding percentages for the control groups were 57% and 26%, respectively (Supplementary Table\u0026nbsp;3).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this longitudinal cohort study, we found a substantial decline in HRQL outcomes across all domains at the post-cancer compared to the pre-cancer survey, completed within three years. Women who had lower HRQL outcomes at pre-cancer experienced the most significant decline, with the largest in general health, followed by social and physical functioning. However, the corresponding changes were not significant among those who had high HRQL before cancer diagnosis. Similarly, among cancer-free controls, there were no significant changes within the same period, except for physical functioning, which decreased significantly, consistent with the findings of a previous study on this cohort [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], reporting its link with ageing and age-associated non-cancer morbidities. While very few previous studies focused on pre- and post-cancer changes in HRQL across several domains and compared with cancer-free controls [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], our findings are consistent with several studies reporting that people with cancer are likely to have poorer HRQL attributable to the diagnosis of the disease and treatment side effects.[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] As pre-cancer data on HRQL outcomes are rarely available in practice, developing post-cancer baseline measures and conducting ongoing longitudinal assessments are crucial for informed treatment decisions and guiding long-term supportive care. However, ongoing assessments of HRQL outcomes remain difficult to implement in clinical practice, especially in low-resource settings, underscoring the need for innovative context-sensitive implementation approaches that can be adopted across diverse healthcare settings.\u003c/p\u003e\u003cp\u003eOne key finding is the identification of four trajectories of long-term HRQL outcomes based on up to 15 years of follow-up data. The trajectory groups were significantly different in terms of baseline domain scores (Group 1 had very low scores across all domains whereas Group 4 had very high scores), which consistently continued over the follow-up period with moderate variations or almost plateaued in some domains. While the trend in HRQL domains is expected to improve over time with recovery from many cancers, this may be partly offset by the declining trend in HRQL domains associated with the prospective aging of the cohort, as reflected in the trajectory groups observed for the control sample [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Our findings are consistent with a recent longitudinal study reporting that self-reported quality of life scores decreased for people undergoing breast cancer treatment, but improved after treatment and remained stable ten years after cancer diagnosis, with global quality of life comparable to that of the control population [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, the trajectory groups observed in our study were based on the all-cancer combined, with 46% being breast cancer survivors.\u003c/p\u003e\u003cp\u003eSurvivors in Group 1 experienced the largest decline in HRQL within the first few years following diagnosis, while they already had low scores before cancer diagnosis. HRQL outcomes worsen further over time likely due to the high burden of comorbidities, ongoing treatment effects, and financial toxicity stemming from treatment costs and time away from employment [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. On the other hand, survivors in Group 4 did not experience noticeable changes in HRQL outcomes after their cancer diagnosis and remained consistently high throughout the follow-up period. This finding is consistent with a previous study reporting changes in physical functioning and psychological distress in people with common cancers who have not received treatment recently, are comparable to those reported in people without cancer [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This may partly be explained by the lower prevalence of comorbidities and obesity, and the better ability to manage financial toxicity. Additionally, the differences in HRQL outcomes between the two trajectory groups may also be explained by the differences in cancer stages and types of cancer diagnosed, with over two-thirds having breast cancer or melanoma in Group 4, compared to almost half in Group 1.\u003c/p\u003e\u003cp\u003eWe found that a diagnosis of melanoma was not associated with changes in HRQL across several domains, including social functioning, mental health, bodily pain, and vitality, which is consistent with the findings of cancer-free controls. For cancer types associated with relatively poorer survival, for example, colorectal cancer and those diagnosed with blood \u0026amp; lymphatic system, we observed worse HRQL outcomes across all domains, as reflected in Group 1, which included a higher proportion of survivors with these cancer types. While an advanced stage at diagnosis is associated with poorer HRQL [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], we could not explore this in the current study as cancer staging information is not available in the ACD data.\u003c/p\u003e\u003cp\u003eConsistent with previous studies reporting factors associated with poorer HRQL outcomes among cancer survivors [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], we found that the survivors in Group 1 and Group 2 were more likely to have low education levels, not to be married or in a de facto relationship, have financial difficulties in managing available income, have obesity, be currently smoking, and have \u0026gt;\u0026thinsp;2 other comorbidities, compared to those in Group 3 and Group 4. The variations were wider in the sample of cancer survivors than in the control group, especially in managing available income and the number of comorbidities. Financial hardship and the number of comorbidities might have worsened over time due to financial toxicity and the late effects of post-cancer treatment [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Additionally, the substantially lower scores across all domains of the HRQL at the first post-cancer survey compared with the matched survey for the control group reflect the impact of cancer diagnosis in the first few years following diagnosis.\u003c/p\u003e\u003cp\u003eOne key strength of our study is the use of survey data from a large longitudinal cohort linked to administrative data, including the Australian Cancer Database. However, our study has several limitations. First, HRQL outcomes were measured using the self-reported SF-36 questionnaire, which is not specifically designed for cancer survivors. This tool may not be sensitive enough to measure cancer and treatment-specific effects on HRQL. However, previous studies reported that the SF-36 questionnaire provides a reliable and valid indication of general health in breast cancer survivors and for generic HRQL [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Second, the sample of cancer survivors may have been biased toward healthy inclusion, as we included only those who completed at least one survey before and after diagnosis and were largely living in the community and could complete the postal questionnaire. Cancer survivors who are at the end of life or severely ill are likely to be underrepresented. The findings may not be generalisable to men or those in hospice or hospital settings. Previous studies on this cohort reported that those who did not complete the survey questionnaire were likely to have worse health and die prematurely [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Third, the sample of cancer survivors was developed over time, from 1996 to 2019. Therefore, with changing cancer treatment regimens during this period, the HRQL outcomes for those who underwent older treatment regimens might have been poorer than those who underwent treatment more recently. Further studies with cancer-specific clinical factors are warranted to understand the potential treatment-related differences in HRQL domains across cancer sites.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis is the first cohort study to provide a comprehensive outlook of both short- and long-term changes in HRQL outcomes across several domains by comparing pre- and post-cancer measures with cancer-free controls, and identifying trajectory groups over time. The post-cancer worsening HRQL outcomes, which are likely to persist long-term, underscore the importance of early assessment of these outcomes after cancer diagnosis and continuing longitudinal assessment over time, especially for those with comorbidities and who have experienced financial difficulties. Where feasible, capturing retrospective pre-cancer HRQL outcomes via recall-based tools may enhance understanding, although such approaches require rigorous validation and warrant further study. Finally, identifying individuals with lower HRQL soon after cancer diagnosis can inform targeted supportive care interventions, including psychosocial support and financial assistance to improve survivorship outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eHRQL: Health-related quality of life\u003c/p\u003e\n\u003cp\u003eALSWH: Australian Longitudinal Study on Women\u0026rsquo;s Health\u003c/p\u003e\n\u003cp\u003eGBMT: Group-based multi-trajectory\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAMD: Adjusted mean difference\u003c/p\u003e\n\u003cp\u003eCI: Confidence interval\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research on which this report is based was conducted as part of the Australian Longitudinal Study on Women's Health by the University of Queensland and the University of Newcastle. We are grateful to the Australian Government Department of Health for funding and to the women who provided the survey data. We also acknowledge the Australian Capital Territory, New South Wales, Northern Territory, Queensland, South Australia, Tasmanian, Victorian, and Western Australian Cancer Registries for providing data, and the Australian Institute of Health and Welfare (AIHW) as the integrating authority.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMLY\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eKC and EB are supported by National Health and Research Council of Australia Investigator Grants (NHMRC; APP 2018108, APP1194679 and APP2017742, respectively). JS is supported by a Cancer Institute NSW Career Development Fellowship (2022/CDF1154)\u0026nbsp;KC is co-PI of an investigator-initiated trial of cervical screening, ‘Compass’, run by the ACPCC, a government-funded not-for-profit charity. Compass receives infrastructure support from the Australian government and the ACPCC has received equipment and a funding contribution from Roche and Microbix. She is also co-Lead on the Elimination Partnership in the Pacific for Cervical Cancer (EPICC) which has received support from the Australian government, the Minderoo Foundation and equipment donations from Cepheid Inc.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted as part of the Australian Longitudinal Study on Women’s Health, which was approved by the ethics committees of the University of Newcastle (H-2011-0371) and the University of Queensland (2012/HE000132). Access to the survey data was approved by the ALSWH data access committee. Approval for accessing the linked Australian Cancer Database was obtained from the Australian Institute of Health and Welfare HREC (EC2020/3/1115). Written informed consent was obtained from individual participants. Study participants were informed of their freedom to opt out of the study at any time without having to justify their decision. The study was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis data analysis was conducted under conditions approved by the relevant ethics committee(s). As a condition of approval, data are not sharable. Access to data by other individuals or agencies would require appropriate ethical approvals to be in place.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMMR, JS and JB conceptualised the study. All authors contributed to the study design. MMR conducted the data analysis and wrote the main manuscript. KC and MLY provided supervision in data analysis and manuscript preparation. All authors read, reviewed and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray, F., et al., Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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Asia Pac J Clin Oncol, 2024.\u003c/li\u003e\n\u003cli\u003eShapiro, C.L., Cancer Survivorship. N Engl J Med, 2018. \u003cstrong\u003e379\u003c/strong\u003e(25): p. 2438-2450.\u003c/li\u003e\n\u003cli\u003eBours, M.J., et al., Candidate Predictors of Health-Related Quality of Life of Colorectal Cancer Survivors: A Systematic Review. Oncologist, 2016. \u003cstrong\u003e21\u003c/strong\u003e(4): p. 433-52.\u003c/li\u003e\n\u003cli\u003eFu, M.R., et al., Comorbidities and Quality of Life among Breast Cancer Survivors: A Prospective Study. J Pers Med, 2015. \u003cstrong\u003e5\u003c/strong\u003e(3): p. 229-42.\u003c/li\u003e\n\u003cli\u003eGustavsson-Lilius, M., J. Julkunen, and P. Hietanen, Quality of life in cancer patients: The role of optimism, hopelessness, and partner support. Qual Life Res, 2007. \u003cstrong\u003e16\u003c/strong\u003e(1): p. 75-87.\u003c/li\u003e\n\u003cli\u003eAgarwal, S.K., et al., Prospective evaluation of the quality of life of oral tongue cancer patients before and after the treatment. Ann Palliat Med, 2014. \u003cstrong\u003e3\u003c/strong\u003e(4): p. 238-43.\u003c/li\u003e\n\u003cli\u003eHassel, A.J., et al., Oral health-related quality of life and depression/anxiety in long-term recurrence-free patients after treatment for advanced oral squamous cell cancer. J Craniomaxillofac Surg, 2012. \u003cstrong\u003e40\u003c/strong\u003e(4): p. e99-102.\u003c/li\u003e\n\u003cli\u003eDobson, A.J., et al., Cohort profile update: Australian longitudinal study on women\u0026rsquo;s health. International Journal of Epidemiology, 2015. \u003cstrong\u003e44\u003c/strong\u003e(5): p. 1547-1547f.\u003c/li\u003e\n\u003cli\u003eLoxton, D., et al., Online and offline recruitment of young women for a longitudinal health survey: findings from the Australian Longitudinal Study on Women\u0026rsquo;s Health 1989-95 cohort. Journal of medical Internet research, 2015. \u003cstrong\u003e17\u003c/strong\u003e(5).\u003c/li\u003e\n\u003cli\u003eWare, J.E., et al., SF-36 health survey. Manual and interpretation guide, 1993. \u003cstrong\u003e2\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eAustralian Longitudinal Study on Women\u0026apos;s Health, The SF-36. 2006.\u003c/li\u003e\n\u003cli\u003eNagin, D.S., et al., Group-based multi-trajectory modeling. Stat Methods Med Res, 2018. \u003cstrong\u003e27\u003c/strong\u003e(7): p. 2015-2023.\u003c/li\u003e\n\u003cli\u003eNagin, D.S., Group-based modeling of development. 2005: Harvard university press.\u003c/li\u003e\n\u003cli\u003ePark, J.H., et al., Trajectories of health-related quality of life in breast cancer patients. Support Care Cancer, 2020. \u003cstrong\u003e28\u003c/strong\u003e(7): p. 3381-3389.\u003c/li\u003e\n\u003cli\u003eJones, B.L. and D.S. Nagin, A note on a Stata plugin for estimating group-based trajectory models. Sociological Methods \u0026amp; Research, 2013. \u003cstrong\u003e42\u003c/strong\u003e(4): p. 608-613.\u003c/li\u003e\n\u003cli\u003eLeigh, L., J.E. Byles, and G.D. Mishra, Change in physical function among women as they age: findings from the Australian Longitudinal Study on Women\u0026apos;s Health. Qual Life Res, 2017. \u003cstrong\u003e26\u003c/strong\u003e(4): p. 981-991.\u003c/li\u003e\n\u003cli\u003eSchwartz, R.M., et al., Change in Quality of Life after a Cancer Diagnosis among a Nationally Representative Cohort of Older Adults in the US. Cancer Invest, 2019. \u003cstrong\u003e37\u003c/strong\u003e(7): p. 299-310.\u003c/li\u003e\n\u003cli\u003eTrentham-Dietz, A., et al., Health-related quality of life before and after a breast cancer diagnosis. Breast Cancer Res Treat, 2008. \u003cstrong\u003e109\u003c/strong\u003e(2): p. 379-87.\u003c/li\u003e\n\u003cli\u003eDixit, J., et al., Health-related quality of life and its determinants among cancer patients: evidence from 12,148 patients of Indian database. Health Qual Life Outcomes, 2024. \u003cstrong\u003e22\u003c/strong\u003e(1): p. 26.\u003c/li\u003e\n\u003cli\u003eRahman, M.M., et al., Association of optimism and social support with health-related quality of life among Australian women cancer survivors - A cohort study. Asia Pac J Clin Oncol, 2025. \u003cstrong\u003e21\u003c/strong\u003e(2): p. 221-231.\u003c/li\u003e\n\u003cli\u003eNoto, S., Perspectives on Aging and Quality of Life. Healthcare (Basel), 2023. \u003cstrong\u003e11\u003c/strong\u003e(15).\u003c/li\u003e\n\u003cli\u003eAndreu, Y., et al., Exploring the independent association of employment status to cancer survivors\u0026apos; health-related quality of life. Health Qual Life Outcomes, 2023. \u003cstrong\u003e21\u003c/strong\u003e(1): p. 44.\u003c/li\u003e\n\u003cli\u003eZhang, Y., et al., Physical disability and psychological distress before and after a diagnosis of cancer: evidence on multiple cancer types from a large Australian cohort study, compared to people without a cancer diagnosis. BMC Med, 2025. \u003cstrong\u003e23\u003c/strong\u003e(1): p. 290.\u003c/li\u003e\n\u003cli\u003eRodriguez, J.L., et al., Factors Associated with Health-Related Quality of Life Among Colorectal Cancer Survivors. Am J Prev Med, 2015. \u003cstrong\u003e49\u003c/strong\u003e(6 Suppl 5): p. S518-27.\u003c/li\u003e\n\u003cli\u003eButterworth, P. and T. Crosier, The validity of the SF-36 in an Australian National Household Survey: demonstrating the applicability of the Household Income and Labour Dynamics in Australia (HILDA) Survey to examination of health inequalities. BMC Public Health, 2004. \u003cstrong\u003e4\u003c/strong\u003e: p. 44.\u003c/li\u003e\n\u003cli\u003eTreanor, C. and M. Donnelly, A methodological review of the Short Form Health Survey 36 (SF-36) and its derivatives among breast cancer survivors. Quality of Life Research, 2015. \u003cstrong\u003e24\u003c/strong\u003e: p. 339-362.\u003c/li\u003e\n\u003cli\u003eRahman, M.M., et al., Onset and progression of chronic disease and disability in a large cohort of older Australian women. Maturitas, 2022. \u003cstrong\u003e158\u003c/strong\u003e: p. 25-33.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Supplementary Material","content":"\u003cp\u003eSupplementary figures and tables are not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"health-and-quality-of-life-outcomes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hqlo","sideBox":"Learn more about [Health and Quality of Life Outcomes](http://hqlo.biomedcentral.com)","snPcode":"12955","submissionUrl":"https://submission.nature.com/new-submission/12955/3","title":"Health and Quality of Life Outcomes","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Health-related quality of life, women cancer survivors, pre- and post-cancer, adjusted mean differences, and trajectory group","lastPublishedDoi":"10.21203/rs.3.rs-7181864/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7181864/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eWith increasing cancer survival rates, optimising health-related quality of life (HRQL) has become a major priority. However, longitudinal assessments of HRQL outcomes in people with cancer are limited. We aimed to examine the changes in HRQL after a cancer diagnosis and identify the long-term trajectories of HRQL outcomes and associated factors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis study included 1414 women diagnosed with primary invasive cancer from 1996 to 2019 and 2828 women without cancer from a large cohort (born in 1946-51) of the Australian Longitudinal Study on Women\u0026rsquo;s Health, linked to the Australian Cancer Database. Generalised linear models were used to estimate changes in HRQL outcomes, adjusting for sociodemographic factors and other health conditions. Group-based multitrajectory modelling was applied to identify HRQL trajectories over time.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIn the short-term (\u0026le;\u0026thinsp;3 years), we found a significant decline in the adjusted mean difference (AMD) across all HRQL domains at post-cancer versus pre-cancer survey, with the largest decline in general health (AMD \u0026minus;\u0026thinsp;10.3, 95%CI: -11.43, -9.18). The corresponding changes within the same period among women without cancer were not significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, except for physical functioning. In the long-term (\u0026le;\u0026thinsp;15 years), four HRQL trajectory groups were identified, including \u003cem\u003every low HRQL trajectory\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;184, 13%), \u003cem\u003emoderate HRQL trajectory\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;355, 25%), \u003cem\u003emedium-high HRQL trajectory\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;532, 38%) and \u003cem\u003ehigh HRQL trajectory\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;343, 24%). In the control sample, a greater proportion of women belonged to the high HRQL trajectory group (29% versus 24%). Cancer survivors in the very low or moderate HRQL trajectory groups had significantly lower HRQL scores than the corresponding trajectory groups in the control group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Compared with the high HRQL trajectory group, the very low HRQL trajectory group experienced more difficulties in managing their available income (60% versus 22%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and had\u0026thinsp;\u0026ge;\u0026thinsp;2 comorbidities (42% versus 9%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eOur findings suggest the importance of measuring HRQL soon after diagnosis as a baseline measure, and considering both baseline and ongoing HRQL when guiding supportive care for women cancer survivors. Additionally, targeted initiatives that prevent and manage comorbidities and financial hardship in those with low HRQL at baseline are critical for equitable care.\u003c/p\u003e","manuscriptTitle":"Trajectories of health-related quality of life after cancer diagnosis in a cohort of Australian women: A longitudinal study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-06 17:59:09","doi":"10.21203/rs.3.rs-7181864/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-19T15:31:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-15T11:19:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"45878267661964356224240040813262796399","date":"2025-11-01T12:08:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-29T20:56:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"146195161621642042720828013420080400191","date":"2025-10-08T19:37:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"285109981945276179009449770215676147639","date":"2025-09-08T09:15:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-18T10:44:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-17T15:28:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"74284393702411772914576476454315990452","date":"2025-07-30T09:34:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278774024346647081915844605727478743493","date":"2025-07-29T01:30:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-28T06:56:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-24T01:16:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-24T01:15:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Health and Quality of Life Outcomes","date":"2025-07-22T02:41:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"health-and-quality-of-life-outcomes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hqlo","sideBox":"Learn more about [Health and Quality of Life Outcomes](http://hqlo.biomedcentral.com)","snPcode":"12955","submissionUrl":"https://submission.nature.com/new-submission/12955/3","title":"Health and Quality of Life Outcomes","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bab3c76b-efc9-403e-b9a2-d1f7bb8935ad","owner":[],"postedDate":"August 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-16T16:04:45+00:00","versionOfRecord":{"articleIdentity":"rs-7181864","link":"https://doi.org/10.1186/s12955-026-02508-w","journal":{"identity":"health-and-quality-of-life-outcomes","isVorOnly":false,"title":"Health and Quality of Life Outcomes"},"publishedOn":"2026-03-12 15:57:58","publishedOnDateReadable":"March 12th, 2026"},"versionCreatedAt":"2025-08-06 17:59:09","video":"","vorDoi":"10.1186/s12955-026-02508-w","vorDoiUrl":"https://doi.org/10.1186/s12955-026-02508-w","workflowStages":[]},"version":"v1","identity":"rs-7181864","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7181864","identity":"rs-7181864","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}