Diagnoses Disease Clinical Trial
Official title:
Socioeconomic Inequalities in the Diagnosis and Treatment of Colon and Ovarian Cancer in England Between 2016-2017: A Data Linkage Study Linking National Cancer Registration and Analysis Service, Hospital Episode Statistics and NHS Digital Datasets
This study is a population-based, patient-level analysis of colon and ovarian cancer diagnoses in England over a 2-year period using a dataset created by linking NCRAS and NHS digital datasets. Our analyses will look into inequalities in the diagnostic and treatment pathway, and inequalities in treatment received, for those diagnosed with colon and ovarian cancer between 2016-2017.
Colon and ovarian cancer survival in England lags behind other comparable countries. There is also evidence of a deprivation gradient in survival in England, where those from the most deprived areas are more likely to experience a worse survival with the disease. Inequalities in the diagnostic and treatment pathways, and in treatments received, may contribute to this deprivation gap in survival. This study will look at the secondary care diagnostic interval and the treatment interval to assess for any socioeconomic inequalities in both these intervals. This study will also look at anticancer treatments received (surgery, chemotherapy and the combination of both treatment modalities) to evaluate for any socioeconomic inequalities in treatments received. Important variables will be adjusted for in statistical modelling, including age, co-morbidities, ethnicity and stage of disease To evaluate for inequalities in treatments received and diagnostic and treatment intervals the study will use data from the cancer registry, Systemic Anticancer Therapy (SACT), Hospital Episodes Statistics, Diagnostic Imaging Dataset and Cancer Waiting Times datasets. These datasets will be linked by the National Cancer Registration and Analysis Service, using a unique identifier. We will receive a pseudo-anonymised dataset, allowing for patient-level analyses but without identifying individual patients. ;
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