The association between exposure to PM <sub>2.5</sub> components from coal combustion and mortality in female breast cancer patients

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Abstract

PM _2.5 components may promote the development of breast cancer and increase the risk of mortality. This study aims to investigate the associations between long-term exposure to PM _2.5 components and multiple causes of mortality among women with breast cancer living in Inner Mongolia, China. We constructed an Inner Mongolia cohort of 33 952 breast cancer patients from 2012 to 2021 using data from the Inner Mongolia Regional Health Information Platform. We assessed each patient’s exposure to PM _2.5 components using the Tracking Air Pollution in China database. Cox regression models were used to estimate adjusted hazard ratios and 95% confidence intervals (95% CIs). A total of 3295 deaths were identified. For each interquartile increase in concentration in the 5 years before diagnosis, the all-cause mortality increased significantly by 5% (HR: 1.05, 95%CI: 1.00–1.10) for black carbon and by 4% (HR: 1.04, 95%CI: 1.00–1.09) for sulfate (SO ₄ ²⁻), and decreased by 7% (HR: 0.93, 95%CI: 0.88–0.98) for nitrate (NO ₃ ⁻). An association between organic matter and an increased all-cause mortality was also observed. Similar results were found for associations with risk of death from breast cancer-specific causes, cardio-cerebrovascular disease (CCVD) causes, and respiratory causes. Stronger associations were observed in older age groups and in Han Chinese patients. Our results showed that long-term exposure to black carbon, organic matter, and SO ₄ ²⁻ were more responsible for the increased risk of death from all causes, breast cancer-specific causes, CCVD causes, and respiratory causes. This suggests that more effective measures to control coal combustion emissions in Inner Mongolia are urgently needed. The elderly and Han Chinese populations may be at high risk.

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Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

Hyuna Sung, Jacques Ferlay, Rebecca Siegel … (2021)

This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.

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A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation

Mary Charlson, Peter Pompei, Kathy Ales … (1987)

The objective of this study was to develop a prospectively applicable method for classifying comorbid conditions which might alter the risk of mortality for use in longitudinal studies. A weighted index that takes into account the number and the seriousness of comorbid disease was developed in a cohort of 559 medical patients. The 1-yr mortality rates for the different scores were: "0", 12% (181); "1-2", 26% (225); "3-4", 52% (71); and "greater than or equal to 5", 85% (82). The index was tested for its ability to predict risk of death from comorbid disease in the second cohort of 685 patients during a 10-yr follow-up. The percent of patients who died of comorbid disease for the different scores were: "0", 8% (588); "1", 25% (54); "2", 48% (25); "greater than or equal to 3", 59% (18). With each increased level of the comorbidity index, there were stepwise increases in the cumulative mortality attributable to comorbid disease (log rank chi 2 = 165; p less than 0.0001). In this longer follow-up, age was also a predictor of mortality (p less than 0.001). The new index performed similarly to a previous system devised by Kaplan and Feinstein. The method of classifying comorbidity provides a simple, readily applicable and valid method of estimating risk of death from comorbid disease for use in longitudinal studies. Further work in larger populations is still required to refine the approach because the number of patients with any given condition in this study was relatively small.

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Sensitivity Analysis in Observational Research: Introducing the E-Value.

Peng Ding, Tyler J. VanderWeele (2017)

Sensitivity analysis is useful in assessing how robust an association is to potential unmeasured or uncontrolled confounding. This article introduces a new measure called the "E-value," which is related to the evidence for causality in observational studies that are potentially subject to confounding. The E-value is defined as the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment-outcome association, conditional on the measured covariates. A large E-value implies that considerable unmeasured confounding would be needed to explain away an effect estimate. A small E-value implies little unmeasured confounding would be needed to explain away an effect estimate. The authors propose that in all observational studies intended to produce evidence for causality, the E-value be reported or some other sensitivity analysis be used. They suggest calculating the E-value for both the observed association estimate (after adjustments for measured confounders) and the limit of the confidence interval closest to the null. If this were to become standard practice, the ability of the scientific community to assess evidence from observational studies would improve considerably, and ultimately, science would be strengthened.