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OP167 Socioeconomic inequalities in the diagnosis of ovarian cancer: a population-based English cancer registry study
  1. Benjamin Pickwell-Smith1,
  2. Lewis Paton2,
  3. Ireneous Soyiri1,
  4. Michael Lind1,
  5. Una Macleod1
  1. 1Hull York Medical School, University of Hull, Hull, UK
  2. 2Hull York Medical School, University of York, York, UK


Background Previous studies have shown that amongst women diagnosed with ovarian cancer in England, those who live in the most deprived areas systematically lose more years of life than those in the most affluent areas. Potential contributing factors include systematic differences in treatments received and delays in diagnosis and treatment between different socioeconomic groups. We examined socioeconomic inequalities in these measures by linking data from the Cancer Registry with other population-based datasets.

Methods The English Cancer Registry identified a retrospective cohort of patients diagnosed with ovarian cancer in England between 2016–2017. Cancer Registry data were linked to Hospital Episode Statistics, Cancer Pathway, Systematic Anti-Cancer Dataset and Diagnostic Imaging Datasets. Lasso logistic regression identified predictors of ovarian cancer treatment. Meanwhile, the secondary care diagnostic interval (SCDI) was used to calculate the time to diagnosis and treatment. Timeliness was examined using quantile regression. All analyses were conducted using STATA.

Results A total of 9,572 patients were included in the analyses. The median SCDI was 21 days for patients from the most and least deprived areas. However, patients from the most deprived area had a significantly longer time to diagnosis after adjusting for other factors (median difference 1.85 days [95% CI 0.51–3.20]).

The median treatment interval was 55 and 58 days for patients from the least and most deprived areas, respectively. The treatment interval was significantly longer for patients from the most, compared with the least deprived areas after adjusting for important factors (median difference 4.53 days [95% CI 2.46–6.61]). Area deprivation was also a predictor of receipt of surgery and chemotherapy.

Conclusion In this large cohort of women with ovarian cancer in England, the time to diagnosis and treatment was longer for those from the most deprived areas. This was despite adjusting for factors such as stage, age, ethnicity, and Charlson comorbidity score. Deprivation was also associated with a reduced likelihood of treatment. However, there was significant unexplained variation, and a limitation of this analysis was the measure of comorbidity. We used the Charlson comorbidity score, which may have under-reported the prevalence and severity of comorbidities. Further research is therefore needed to establish whether these variations can be explained by better capturing the presence of comorbid conditions. Reducing socioeconomic inequalities should also be a key policy target, with accurate and transparent reporting of inequalities across the cancer care continuum.

  • Ovarian Cancer
  • Socioeconomic Inequalities
  • Early Diagnosis

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