Breast cancer incidence and survival in Scotland by socio-economic deprivation and tumour subtype.

BACKGROUND: Women from socio-economically deprived areas are less likely to develop and then to survive breast cancer (BC). Whether associations between deprivation and BC incidence and survival differ by tumour molecular subtypes and mode of detection in Scotland are unknown. METHODS: Data consisted of 62,378 women diagnosed with invasive BC between 2000 and 2016 in Scotland. Incidence rates and time trends were calculated for oestrogen receptor positive (ER+) and negative (ER-) tumours and stratified by the Scottish Index of Multiple Deprivation (SIMD) quintiles and screening status. SIMD is an area-based measure derived across seven domains: income, employment, education, health, access to services, crime and housing. We calculated adjusted hazard ratios (aHR [95% confidence intervals]) for BC death by immunohistochemical surrogates of molecular subtypes for the most versus the least deprived quintile. We adjusted for mode of detection and other confounders. RESULTS: In Scotland, screen-detected ER+tumour incidence increased over time, particularly in the least deprived quintile [Average Annual Percentage Change (AAPC) = 2.9% with 95% CI from 1.2 to 4.7]. No marked differences were observed for non-screen-detected ER+tumours or ER- tumours by deprivation. BC mortality was higher in the most compared to the least deprived quintile irrespective of ER status (aHR = 1.29 [1.18, 1.41] for ER+ and 1.27 [1.09, 1.47] for ER- tumours). However, deprivation was associated with significantly higher mortality for luminal A and HER2-enriched tumours (aHR = 1.46 [1.13, 1.88] and 2.10 [1.23, 3.59] respectively) but weaker associations for luminal B and TNBC tumours that were not statistically significant. CONCLUSIONS: Deprivation is associated with differential BC incidence trends for screen-detected ER+tumours and with higher mortality for select tumour subtypes. Future efforts should evaluate factors that might be associated with reduced survival in deprived populations and monitor progress stratified by tumour subtypes and mode of detection.

Monitoring indirect impact of COVID-19 pandemic on services for cardiovascular diseases in the UK.

OBJECTIVE: To monitor hospital activity for presentation, diagnosis and treatment of cardiovascular diseases during the COVID-19) pandemic to inform on indirect effects. METHODS: Retrospective serial cross-sectional study in nine UK hospitals using hospital activity data from 28 October 2019 (pre-COVID-19) to 10 May 2020 (pre-easing of lockdown) and for the same weeks during 2018-2019. We analysed aggregate data for selected cardiovascular diseases before and during the epidemic. We produced an online visualisation tool to enable near real-time monitoring of trends. RESULTS: Across nine hospitals, total admissions and emergency department (ED) attendances decreased after lockdown (23 March 2020) by 57.9% (57.1%-58.6%) and 52.9% (52.2%-53.5%), respectively, compared with the previous year. Activity for cardiac, cerebrovascular and other vascular conditions started to decline 1-2 weeks before lockdown and fell by 31%-88% after lockdown, with the greatest reductions observed for coronary artery bypass grafts, carotid endarterectomy, aortic aneurysm repair and peripheral arterial disease procedures. Compared with before the first UK COVID-19 (31 January 2020), activity declined across diseases and specialties between the first case and lockdown (total ED attendances relative reduction (RR) 0.94, 0.93-0.95; total hospital admissions RR 0.96, 0.95-0.97) and after lockdown (attendances RR 0.63, 0.62-0.64; admissions RR 0.59, 0.57-0.60). There was limited recovery towards usual levels of some activities from mid-April 2020. CONCLUSIONS: Substantial reductions in total and cardiovascular activities are likely to contribute to a major burden of indirect effects of the pandemic, suggesting they should be monitored and mitigated urgently.

Assessment of automated image analysis of breast cancer tissue microarrays for epidemiologic studies.

BACKGROUND: A major challenge in studies of etiologic heterogeneity in breast cancer has been the limited throughput, accuracy, and reproducibility of measuring tissue markers. Computerized image analysis systems may help address these concerns, but published reports of their use are limited. We assessed agreement between automated and pathologist scores of a diverse set of immunohistochemical assays done on breast cancer tissue microarrays (TMA). METHODS: TMAs of 440 breast cancers previously stained for estrogen receptor (ER)-alpha, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), ER-beta, and aromatase were independently scored by two pathologists and three automated systems (TMALab II, TMAx, and Ariol). Agreement between automated and pathologist scores of negative/positive was measured using the area under the receiver operating characteristics curve (AUC) and weighted kappa statistics for categorical scores. We also investigated the correlation between immunohistochemical scores and mRNA expression levels. RESULTS: Agreement between pathologist and automated negative/positive and categorical scores was excellent for ER-alpha and PR (AUC range = 0.98-0.99; kappa range = 0.86-0.91). Lower levels of agreement were seen for ER-beta categorical scores (AUC = 0.99-1.0; kappa = 0.80-0.86) and both negative/positive and categorical scores for aromatase (AUC = 0.85-0.96; kappa = 0.41-0.67) and HER2 (AUC = 0.94-0.97; kappa = 0.53-0.72). For ER-alpha and PR, there was a strong correlation between mRNA levels and automated (rho = 0.67-0.74) and pathologist immunohistochemical scores (rho = 0.67-0.77). HER2 mRNA levels were more strongly correlated with pathologist (rho = 0.63) than automated immunohistochemical scores (rho = 0.41-0.49). CONCLUSIONS: Automated analysis of immunohistochemical markers is a promising approach for scoring large numbers of breast cancer tissues in epidemiologic investigations. This would facilitate studies of etiologic heterogeneity, which ultimately may allow improved risk prediction and better prevention approaches.