Zhang, HaoyuYu, Xue QinDavid, MichaelByles, JulieYap, Mei LingSteinberg, JuliaRutherford, ClaudiaBanks, EmilyCanfell, KarenRahman, Md Mijanur2026-07-062026-07-060962-9343PubMed:41764673ORCID:/0000-0002-4617-1302/work/219176592https://hdl.handle.net/1885/733813035Purpose: This study aimed to identify dominant comorbidity patterns among women cancer survivors and examine how these patterns relate to health-related quality of life (HRQL). Methods: 1544 participants (born 1946–1951) from the Australian Longitudinal Study on Women’s Health diagnosed with cancer during the follow-up period from 1993 to 2019 were included. HRQL is measured with Short Form-36 included in the survey. Latent class analysis was applied to identify comorbidity patterns, and linear regression was used to assess their association with HRQL domains, adjusting for demographic factors. Results: Five distinct comorbidity classes were identified: relatively healthy (n = 880, 57%); hypertension and arthritis (n = 278, 18%); arthritis and osteoporosis (n = 139, 9%); respiratory conditions (n = 170, 11%); and complex multimorbidity (n = 93, 6%). Compared to the relatively healthy class, women in all other classes had significantly lower average HRQL (p < 0.01). For example, the classes’ adjusted mean score for general health domain varied: relatively healthy (mean = 70.8, reference), hypertension and arthritis (mean = 63.1, 95% CI = 59.9, 66.3), arthritis and osteoporosis (mean = 60.0, 95% CI = 55.8, 64.1), respiratory conditions (mean = 60.9, 95% CI = 57.2, 64.7), and complex multimorbidity (mean = 48.6, 95% CI = 43.4, 53.8). Women in the complex multimorbidity class had the lowest HRQL across all domains: physical functioning [adjusted mean difference from relatively healthy (AMD=− 22.2 and 95% CI − 27.4, − 17.0)], mental health (AMD=-11.4, 95% CI=− 15.4, -7.5). Conclusion: Comorbidity patterns varied substantially among women cancer survivors and were strongly associated with differences in HRQL. Survivors with complex multimorbidity experienced the greatest impairments. Incorporating comorbidity profiling into survivorship care may help identify high-risk groups and support targeted interventions to optimise quality of life.Open Access funding enabled and organized by CAUL and its Member Institutions. KC and EB are supported by National Health and Research Council of Australia Investigator Grants (NHMRC: APP1194679 and APP2017742, respectively). MLY is funded by a National Health and Medical Research Council Investigator Grant (APP 2018108). 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 wish to thank the staff of the data linkage units of the State and Territory health departments (WA, Victoria, SA-NT, NSW, QLD) for the linkage of the data. Further, we thank the data custodians for the provision of the following data: Inpatient hospital data (5 States and Territories), Emergency Department data (5 States and Territories), Midwives Notification System (5 States and Territories), COD URF: Australian Co-ordinating Registry, the State Registries of Births, Deaths and Marriages, the Coroners, the National Coronial Information System and the Victorian Department of Justice and Community Safety.13en©2026 The authorsCancer survivorsComorbidity patternsHealth-related quality of lifeShort form-36Women’s health202610.1007/s11136-026-04191-2105031621688