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PP59 Intervention-generated inequalities in lung cancer care: cohort study using linked cancer registry, Hospital Episode Statistics, and audit data
  1. LF Forrest1,2,
  2. M White1,2,
  3. G Rubin1,3,
  4. J Adams1,2
  1. 1Fuse, UKCRC Centre for Translational Research in Public Health, Newcastle Upon Tyne, UK
  2. 2Institute of Health and Society, Newcastle University, Newcastle Upon Tyne, UK
  3. 3Wolfson Research Institute, Durham University, Stockton on Tees, UK


Background In the UK, lung cancer is the second most incident cancer and the leading cause of cancer mortality. Survival is socio-economically patterned. In England, there are target waiting times for urgent referral (14 days) and treatment intervals (31 days from diagnosis) for cancer. It has been suggested that socio-economic inequalities in receipt of, and time to, treatment may contribute to inequalities in cancer survival. Unintended variations in outcome that result from the way that interventions are organised and delivered have been described as intervention-generated inequalities. Northern and Yorkshire Cancer Registry and Information Centre (NYCRIS), Hospital Episode Statistics (HES) and lung cancer audit (LUCADA) data were linked to investigate socio-economic inequalities in receipt of, and time to, lung cancer treatment and any impact on survival.

Methods NYCRIS data for 28,733 lung cancer patients diagnosed in 2006–10 were analysed. Socio-economic position (SEP) was measured using the income domain of the Index of Multiple Deprivation. Stage was recorded in LUCADA for 27% (n = 7769) and co-morbidity score was potentially available for 65% (n = 18,650) in HES. Logistic regression was used to examine the likelihood of receipt of treatment and of receiving timely referral and treatment within target, by SEP. Cox regression models were used to calculate hazard ratios (HRs) for all-cause mortality, in the full cohort and in the subset with stage recorded.

Results Socio-economic inequalities in receipt of lung cancer surgery and chemotherapy, and in the referral interval to first hospital appointment, were found. Patients treated within target times had lower likelihood of survival. Socio-economic inequalities in survival were found in a multivariable analysis adjusted for age, sex, histology, year, timely referral, stage, performance status and co-morbidity, with those in the most deprived socio-economic group having significantly higher risk of death (HR=1.11, 95% CI 1.07 to 1.16). When receipt of treatment was included in the analysis the association no longer remained significant (HR=1.02, 95% CI 0.98 to 1.06). Addition of timeliness of treatment did not alter the conclusion.

Discussion Socio-economic inequalities in survival from lung cancer were statistically explained by socio-economic inequalities in receipt of treatment, but not by inequalities in timeliness of referral and treatment, in the full cohort. High levels of missing stage and performance status data were a limitation. However, similar patterns of survival were found in the staged subset. Further research is required to determine the unexplained socio-economic variance in treatment rates. Interventions to increase treatment rates in more deprived groups may reduce survival inequalities.

  • socio-economic inequalities

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