Disparities in the treatment and outcome of cancer are of global concern. Lung cancer mortality rates have been shown to be higher in lower socioeconomic groups and for those who live regionally and remotely. Disparities in outcome may be due to variations in stage at diagnosis or evidence-based treatment. However, the relationships between socioeconomic status (SES), geographic locality, and evidence-based treatment and time to evidence-based treatment, are yet to be fully understood.
To investigate associations between SES, geographic locality and evidence-based treatment and time from diagnosis to evidence-based treatment.
A retrospective cohort analysis of the NSW Lung Cancer Patterns of Care study dataset. This includes information on demographics, treatment and outcome for people with lung cancer, registered in the NSW Central Cancer Registry between November 01 2001 and December 31 2002. Nine guidelines from the 2004 Australian Clinical Practice Guidelines for the Prevention, Diagnosis and Management of Lung Cancer were used to determine numerators and denominators for evidence-based treatment analysis. Logistic regression was used to identify predictors of evidence-based treatment. Cox-proportional hazards regression was used to identify predictors in time to evidence-based treatment.
For 1,214 eligible patients, evidence-based treatment ranged from 4% to 79%, depending upon the guideline. Remoteness was independently associated with lower rates of evidence-based treatment for surgical management of those with stage I and stage II NSCLC (p=0.00), lobectomy for those with operable NSCLC (p=0.02), and longer waiting times between diagnosis and treatment (p=0.01). SES was associated with lower evidence-based chemotherapy for advantaged patients with stage IV NSCLC (p=0.02).
These results suggest that geographic locality and SES play independent roles in evidence-based treatment uptake. Exploring ways in which people living outside of major cities can better receive timely diagnosis and evidence-based treatment is important in reducing disparity gaps.