Impact of cancer diagnosis on life expectancy by area-level socioeconomic groups in New South Wales, Australia: a population-based study

Abstract

Objective: Improvement in cancer survival over recent decades has not been accompanied by a narrowing of socioeconomic disparities. This study aimed to quantify the loss of life expectancy (LOLE) resulting from a cancer diagnosis and examine disparities in LOLE based on area-level socioeconomic status (SES). Methods: Data were collected for all people between 50 and 89 years of age who were diagnosed with cancer, registered in the NSW Cancer Registry between 2001 and 2019, and underwent mortality follow-up evaluations until December 2020. Flexible parametric survival models were fitted to estimate the LOLE by gender and area-level SES for 12 common cancers. Results: Of 422,680 people with cancer, 24% and 18% lived in the most and least disadvantaged areas, respectively. Patients from the most disadvantaged areas had a significantly greater average LOLE than patients from the least disadvantaged areas for cancers with high survival rates, including prostate [2.9 years (95% CI: 2.5–3.2 years) vs. 1.6 years (95% CI: 1.3–1.9 years)] and breast cancer [1.6 years (95% CI: 1.4–1.8 years) vs. 1.2 years (95% CI: 1.0–1.4 years)]. The highest average LOLE occurred in males residing in the most disadvantaged areas with pancreatic [16.5 years (95% CI: 16.1–16.8 years) vs. 16.2 years (95% CI: 15.7–16.7 years)] and liver cancer [15.5 years (95% CI: 15.0–16.0 years) vs. 14.7 years (95% CI: 14.0–15.5 years)]. Females residing in the least disadvantaged areas with thyroid cancer [0.9 years (95% CI: 0.4–1.4 years) vs. 0.6 years (95% CI: 0.2–1.0 years)] or melanoma [0.9 years (95% CI: 0.8–1.1 years) vs. 0.7 years (95% CI: 0.5–0.8 years)] had the lowest average LOLE. Conclusions: Patients from the most disadvantaged areas had the highest LOLE with SES-based differences greatest for patients diagnosed with cancer at an early stage or cancers with higher survival rates, suggesting the need to prioritise early detection and reduce treatment-related barriers and survivorship challenges to improve life expectancy.

Keywords

• cancer diagnosis
• life expectancy
• loss of life expectancy
• area-level socioeconomic status
• flexible parametric model