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How much of socioeconomic differences in survival in patients with breast cancer can be explained by differences in stage of diagnosis and treatment? Application of causal mediation analysis to routine data
Meeting Abstracts
How much of socioeconomic differences in survival in patients with breast cancer can be explained by differences in stage of diagnosis and treatment? Application of causal mediation analysis to routine data Ruoran Li, Rhian Daniel, Bernard Rachet
Abstract Background Substantial socioeconomic inequalities in breast cancer survival exist in England. Late presentation at more advanced stages and differential access to treatment are two main factors that might contribute to the survival differences. We aimed to adapt methods from the causal inference setting to examine what proportions of the differences in survival can be explained by these factors. Methods Information for 36 793 women diagnosed with breast cancer between 2000 and 2007 was gathered by the population-based Northern and Yorkshire Cancer Registry. Vital status was ascertained until the end of 2007, at which point 29 009 women were still alive. Information on surgical treatment was retrieved from the Hospital Episode Statistics (HES) dataset. Hierarchical matching was done using a unique identification number, date of birth, postcode, and sex. The OPCS-4 (Office of Population Censuses and Surveys Classification of Interventions and Procedures) codes from HES within 1 month before and 6 months after cancer diagnosis were dichotomised into major versus minor or no procedures using recommendations from a clinical reference group. Deprivation category, based on the indices of multiple deprivation (income domain), was allocated to each patient according to their area of residence at the time of diagnosis. G-computation procedures were used to estimate the proportion of the effect of deprivation on treatment mediated by stage, survival mediated by stage, and survival mediated by treatment. Single stochastic imputation was incorporated in the g-computation procedures to handle missing stage (8%).
Published Online November 29, 2013 Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK (R Li MPhil, R Daniel PhD, B Rachet MD) Correspondence to: Ms Ruoran Li, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK [email protected]
Findings Net survival differed between the most affluent and the most deprived patients by 3·6% at 1 year (97·2% vs 93·6%) and 10·0% at 5 years (85·9% vs 75·9%) after diagnosis. Adverse stage distribution was associated with more deprived patients (localised stage 43% [2622 of 6045] in most affluent, 38% [2861 of 7489] in most deprived; distant metastasis 4% [224 of 6045] in most affluent, 6% [484 of 7489] in most deprived; p<0·0001). The more advanced the stage at diagnosis, the less likely the patient received major surgical treatment (p<0·0001). The most deprived patients were 9% (95% CI 1–18) more likely to receive major surgery than the most affluent patients. However, if most deprived patients had the stage distribution of the most affluent, they would be 8% (95% CI 5–13) more likely to receive such treatment. This effect was not noted for the other deprivation categories. The most deprived patients were almost three times more likely to die within 6 months after diagnosis than the most affluent patients (odds ratio 2·77, 95% CI 2·17–3·53); a third of this was mediated by adverse stage distribution (proportion mediated 0·35, 95% CI 0·23–0·48) whereas none was mediated through differential surgical treatment (p>0·5). Interpretation Preliminary results elucidate how much effect different contributory factors have on the differential short-term cancer survival between deprivation groups in patients with breast cancer. Efforts to advance diagnoses are important, but would reduce the socioeconomic inequalities in cancer survival by only a third. These results are based on population-based data—ie, they include virtually all patients diagnosed with a breast cancer in Yorkshire and the northeast of England, including those who were diagnosed with advanced stage and those who did not receive optimum treatment. Our study is nevertheless observational. We used a causal approach in which age, year of diagnosis, and regions of England were adjusted. We did not have reliable information on comorbidity, another potential mediator on the causal pathway. Quantifying the proportions of the deprivation gap mediated via comorbidity, stage, and treatment separately informs us about the parts they played and, ultimately, what could be done to most effectively reduce the deprivation gap in cancer survival. Funding None. Contributors RL, RD, and BR designed the study, interpreted data, and drafted and reviewed the abstract. RL analysed the data. Conflicts of interest We declare that we have no conflicts of interest.
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How much of socioeconomic differences in survival in patients with breast cancer can be explained by differences in stage of diagnosis and treatment? Application of causal mediation analysis to routine data Ruoran Li, Rhian Daniel, Bernard Rachet
Abstract Background Substantial socioeconomic inequalities in breast cancer survival exist in England. Late presentation at more advanced stages and differential access to treatment are two main factors that might contribute to the survival differences. We aimed to adapt methods from the causal inference setting to examine what proportions of the differences in survival can be explained by these factors. Methods Information for 36 793 women diagnosed with breast cancer between 2000 and 2007 was gathered by the population-based Northern and Yorkshire Cancer Registry. Vital status was ascertained until the end of 2007, at which point 29 009 women were still alive. Information on surgical treatment was retrieved from the Hospital Episode Statistics (HES) dataset. Hierarchical matching was done using a unique identification number, date of birth, postcode, and sex. The OPCS-4 (Office of Population Censuses and Surveys Classification of Interventions and Procedures) codes from HES within 1 month before and 6 months after cancer diagnosis were dichotomised into major versus minor or no procedures using recommendations from a clinical reference group. Deprivation category, based on the indices of multiple deprivation (income domain), was allocated to each patient according to their area of residence at the time of diagnosis. G-computation procedures were used to estimate the proportion of the effect of deprivation on treatment mediated by stage, survival mediated by stage, and survival mediated by treatment. Single stochastic imputation was incorporated in the g-computation procedures to handle missing stage (8%).
Published Online November 29, 2013 Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK (R Li MPhil, R Daniel PhD, B Rachet MD) Correspondence to: Ms Ruoran Li, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK [email protected]
Findings Net survival differed between the most affluent and the most deprived patients by 3·6% at 1 year (97·2% vs 93·6%) and 10·0% at 5 years (85·9% vs 75·9%) after diagnosis. Adverse stage distribution was associated with more deprived patients (localised stage 43% [2622 of 6045] in most affluent, 38% [2861 of 7489] in most deprived; distant metastasis 4% [224 of 6045] in most affluent, 6% [484 of 7489] in most deprived; p<0·0001). The more advanced the stage at diagnosis, the less likely the patient received major surgical treatment (p<0·0001). The most deprived patients were 9% (95% CI 1–18) more likely to receive major surgery than the most affluent patients. However, if most deprived patients had the stage distribution of the most affluent, they would be 8% (95% CI 5–13) more likely to receive such treatment. This effect was not noted for the other deprivation categories. The most deprived patients were almost three times more likely to die within 6 months after diagnosis than the most affluent patients (odds ratio 2·77, 95% CI 2·17–3·53); a third of this was mediated by adverse stage distribution (proportion mediated 0·35, 95% CI 0·23–0·48) whereas none was mediated through differential surgical treatment (p>0·5). Interpretation Preliminary results elucidate how much effect different contributory factors have on the differential short-term cancer survival between deprivation groups in patients with breast cancer. Efforts to advance diagnoses are important, but would reduce the socioeconomic inequalities in cancer survival by only a third. These results are based on population-based data—ie, they include virtually all patients diagnosed with a breast cancer in Yorkshire and the northeast of England, including those who were diagnosed with advanced stage and those who did not receive optimum treatment. Our study is nevertheless observational. We used a causal approach in which age, year of diagnosis, and regions of England were adjusted. We did not have reliable information on comorbidity, another potential mediator on the causal pathway. Quantifying the proportions of the deprivation gap mediated via comorbidity, stage, and treatment separately informs us about the parts they played and, ultimately, what could be done to most effectively reduce the deprivation gap in cancer survival. Funding None. Contributors RL, RD, and BR designed the study, interpreted data, and drafted and reviewed the abstract. RL analysed the data. Conflicts of interest We declare that we have no conflicts of interest.
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61