The role of comorbidities on the uptake of systemic treatment and 3-year survival in older cancer patients.

BACKGROUND: Older patients are notably absent from clinical trials. Thus, observational studies are the primary avenue for understanding the role of comorbidity in cancer care and survival. We examined the impact of comorbidity on systemic treatment initiation and 3-year survival in a cohort of older cancer patients. PATIENTS AND METHODS: Our cohort comprised 2753 Australian veterans aged ≥65 years with full health coverage and a cancer registry notification for colorectal (CRC), breast, prostate or non-small-cell lung cancer (NSCLC). We established comorbidities based on drugs prescribed in the 6 months prior to cancer diagnosis. RESULTS: Patients with higher comorbidity burden were more likely to receive systemic treatment for prostate cancer [adjusted odds ratio 1.21, 95% confidence interval (CI) 1.05-1.39] but less likely for NSCLC (0.63, 95% CI 0.45-0.86). After adjusting for receipt of treatment, increased comorbidity resulted in shorter survival for CRC [adjusted hazard ratio (aHR) 1.16, 95% CI 1.07-1.26] and breast cancer (aHR 1.23, 95% CI 1.02-1.48). However, we did not demonstrate significant improvements in 3-year survival for patients receiving systemic treatment. CONCLUSION: Comorbidity influences systemic treatment uptake and adversely affects survival, with impact dependent upon comorbidity and cancer type. Clinical trials should be undertaken in older patients to better understand the risks and benefits of cancer treatments.

Parity: A key measure of confounding in data-linkage studies of outcomes after medically assisted reproduction.

Parity is a potential confounder of the association between medically assisted reproduction (MAR) and health outcomes. This concept paper describes a population-based record linkage study design for selecting MAR-unexposed women matched to the parity of MAR-exposed women, at the time of the first exposure to MAR. Women exposed to MAR were identified from claims for government subsidies for relevant procedures and prescription medicines, linked to perinatal records. Women unexposed to MAR were identified from linked perinatal and death records, matched to exposed women by age, rurality, age of first child (if any) and parity at the date of first MAR. The availability of a longitudinal, whole-of-population dataset ("population spine") based on enrolments in Australia's universal health insurance scheme was a critical design element. The example application examines cancer risk in women after exposure to MAR. Parity is a confounder in this setting because it is associated with MAR and hormone-sensitive cancers.