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癌症差异:预测、COVID-19 以及基于情景的诊断延迟影响

Cancer disparities: Projection, COVID-19, and scenario-based diagnosis delay impact.

作者信息

Arik Ayse, Cairns Andrew J G, Streftaris George

机构信息

School of Risk and Actuarial Studies, University of New South Wales, Sydney, New South Wales, Australia.

School of Mathematical and Computer Sciences, Heriot-Watt University, and Maxwell Institute for Mathematical Sciences, Edinburgh, United Kingdom.

出版信息

PLoS One. 2025 Sep 2;20(9):e0330752. doi: 10.1371/journal.pone.0330752. eCollection 2025.

Abstract

There has been limited research on how disparities in cancer mortality may evolve in the future, although relevant socio-economic and regional disparities in cancer risk are well-documented. We studied future trends in breast cancer (BC) and lung cancer (LC) mortality up to 2036 across affluent and deprived communities in nine regions of England, motivated by the distinct socio-economic patterns and burden of these cancer types. We used cancer death registrations from the Office for National Statistics on population and deaths in nine regions of England by underlying cause of death from 2001 to 2018, stratified by sex, 5-year age group, and income deprivation. We applied a gender- and cause-specific Bayesian hierarchical model to obtain robust estimates of cancer mortality by age group, gender, deprivation quintile, and region, up to 2036. In these models, we also used a data-driven proxy for age-at-diagnosis as an additional risk factor, and non-smoker prevalence rates as a proxy for smoking. We found that if pre-COVID conditions and trends remained the same, socio-economic disparities in LC would persist during our projection period. LC mortality rates for women in 2036 were found to be around 60% lower in the least deprived areas of London, as compared to the most deprived in the same region, with the disparities being even higher in northern regions and among men. Using data from the period 2011-2018, our model estimated 2% fewer LC deaths than those registered during the pandemic years (2020-2022) across England (and 4% fewer for men). Scenarios linked to delays in LC diagnosis led to stark differences in future excess mortality - significantly higher excesses in the northern regions compared to the southern regions, and in the most deprived areas compared to the least deprived areas. Additionally, our findings show that if pre-COVID conditions and trends remained unchanged, BC mortality would continue to decline up to 2036, with comparable rates in the regions of England. During the pandemic years, BC deaths were estimated to decline by 1% across England compared to the pre-pandemic trends (2001-2018). However, our analysis shows 10% to 13% increase in BC deaths for women aged 80+ in the same years. Cancer disparities are predicted to persist in the future unless targeted interventions are implemented. Our results underscore the notable impact of delays in cancer diagnosis on cancer mortality and related inequalities. Future research that models different causes of death while adjusting model outputs for competing risk factors might be beneficial. Further models with individual-level socio-economic risk factors would also be useful.

摘要

尽管癌症风险方面相关的社会经济和地区差异已有充分记录,但关于癌症死亡率差异在未来可能如何演变的研究却很有限。受这些癌症类型独特的社会经济模式和负担的影响,我们研究了截至2036年英格兰九个地区富裕和贫困社区的乳腺癌(BC)和肺癌(LC)死亡率的未来趋势。我们使用了英国国家统计局关于2001年至2018年英格兰九个地区按潜在死因、性别、5岁年龄组和收入剥夺分层的人口和死亡的癌症死亡登记数据。我们应用了一个性别和病因特定的贝叶斯分层模型,以获得截至2036年按年龄组、性别、剥夺五分位数和地区划分的癌症死亡率的稳健估计值。在这些模型中,我们还使用了一个数据驱动的诊断年龄代理作为额外的风险因素,并使用非吸烟者患病率作为吸烟的代理。我们发现,如果新冠疫情前的状况和趋势保持不变,在我们的预测期内,肺癌的社会经济差异将持续存在。与同一地区最贫困地区相比,2036年伦敦最不贫困地区女性的肺癌死亡率预计低约60%,北部地区以及男性中的差异甚至更大。利用2011 - 2018年期间的数据,我们的模型估计,英格兰在大流行年份(2020 - 2022年)登记的肺癌死亡人数比模型估计的多2%(男性多4%)。与肺癌诊断延迟相关的情景导致未来超额死亡率存在显著差异——北部地区的超额死亡率明显高于南部地区,最贫困地区高于最不贫困地区。此外,我们的研究结果表明,如果新冠疫情前的状况和趋势保持不变,到2036年乳腺癌死亡率将继续下降,英格兰各地区的下降速度相当。在大流行年份,与疫情前趋势(2001 - 2018年)相比,英格兰乳腺癌死亡人数估计下降了1%。然而,我们的分析显示,同年80岁及以上女性的乳腺癌死亡人数增加了10%至13%。除非实施有针对性的干预措施,否则癌症差异预计在未来仍将持续。我们的结果强调了癌症诊断延迟对癌症死亡率和相关不平等现象的显著影响。未来在调整模型输出以考虑竞争风险因素的同时对不同死因进行建模的研究可能会有所帮助。纳入个体层面社会经济风险因素的进一步模型也将很有用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4c30/12404486/8e13413c6d1d/pone.0330752.g001.jpg

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