Oral Presentation 2025 Joint Meeting of the COSA ASM and IPOS Congress

Comorbidity Patterns and Health-Related Quality of Life in a Cohort of Australian Women Cancer Survivors (126276)

MD Mijanur Rahman 1 , Mei Ling Yap 1 , Haoyu Zhang 2 3 , XUE Yap 3 , Michael David 3 , Julia Steinburg 3 , Julie Byles 4 , Claudia Rutherford 5 , Karen Canfell 2 , Emily Banks 6
  1. CCORE, Ingham Institute, School of Clinical Medicine, UNSW Sydney, Sydney, NSW, Australia
  2. Sydney School of Public Health, , University of Sydney, Sydney, Australia, Sydney, NSW, Australia
  3. The Daffodil Centre, a joint venture between University of Sydney and Cancer Council NSW, University of Sydney, Sydney, NSW, Australia
  4. Centre for Women’s Health Research, University of Newcastle, Newcastle, Australia, Newcastle, NSW, Australia
  5. 8. Cancer Care Research Unit (CCRU), Susan Wakil School of Nursing and Midwifery, Faculty of Medicine and Health, , The University of Sydney, Sydney, NSW, Australia
  6. 9. National Centre for Epidemiology and Population Health, , Australian National University, Canberra, Australia, Canberra, ACT, Australia

Background: Comorbidity is common among people with cancer and is associated with poorer cancer outcomes. 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: This study included 1544 participants (born 1946-1951) of the Australian Longitudinal Study on Women’s Health diagnosed with cancer during the follow-up period from 1993 to 2019, identified using the Australian Cancer database. HRQL were measured using the SF-36 questionnaire.  Latent class analysis was applied to identify comorbidity patterns, and multiple linear regression was used to assess their association with HRQL domains.

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%). Each class represents a distinct pattern of comorbidities, as reflected in their descriptive names. The classes were significantly different in terms of demographic and behavioural characteristics (p<0.01). For example, half (50.2%) of women in the hypertension and arthritis class were obese compared to one-fifth (20.2%) of the relatively healthy class. The HRQL score varied significantly across the classes (p < 0.01), with women in the complex multimorbidity class having the worst scores across all domains. For example, compared to women in the relatively healthy class, the adjusted mean differece (AMD) of the general health domain for those in the hypertension and arthritis (AMD=-7.7, 95%CI=-10.9, -4.5), arthritis and osteoporosis (AMD=10.8,95%CI=-15.0, -6.7), respiratory conditions (AMD=-9.9, 95%CI=-13.6, -6.1), and complex multimorbidity (AMD=-22.2, 95%CI=-27.4, -17.0).

 

Conclusion: Comorbidity pattern varies substantively among women cancer survivors. Those with the greatest and most complex comorbidities reported the lowest HRQL scores across different domains. Our findings provided evidence on the relationship between distinct comorbidity profiles and HRQL for cancer survivors, informing comprehensive survivorship care, including comorbidity management.