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估算中国女性乳腺癌残疾调整生命年和筛查影响:应用局部权重的探索性基于患病率分析。

Estimating disability-adjusted life years for breast cancer and the impact of screening in female populations in China, 2015-2030: an exploratory prevalence-based analysis applying local weights.

机构信息

Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan South Lane, Chaoyang District, Beijing, 100021, China.

National Central Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.

出版信息

Popul Health Metr. 2022 Oct 7;20(1):19. doi: 10.1186/s12963-022-00296-1.

DOI:10.1186/s12963-022-00296-1
PMID:36207752
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9547451/
Abstract

BACKGROUND

Most cancer disability-adjusted life year (DALY) studies worldwide have used broad, generic disability weights (DWs); however, differences exist among populations and types of cancers. Using breast cancer as example, this study aimed to estimate the population-level DALYs in females in China and the impact of screening as well as applying local DWs.

METHODS

Using multisource data, a prevalence-based model was constructed. (1) Overall years lived with disability (YLDs) were estimated by using numbers of prevalence cases, stage-specific proportions, and local DWs for breast cancer. Numbers of females and new breast cancer cases as well as local survival rates were used to calculate the number of prevalence cases. (2) Years of life lost (YLLs) were estimated using breast cancer mortality rates, female numbers and standard life expectancies. (3) The prevalence of and mortality due to breast cancer and associated DALYs from 2020 to 2030 were predicted using Joinpoint regression. (4) Assumptions considered for screening predictions included expanding coverage, reducing mortality due to breast cancer and improving early-stage proportion for breast cancer.

RESULTS

In Chinese females, the estimated number of breast cancer DALYs was 2251.5 thousand (of 17.3% were YLDs) in 2015, which is predicted to increase by 26.7% (60.3% among those aged ≥ 65 years) in 2030 (2852.8 thousand) if the screening coverage (25.7%) stays unchanged. However, if the coverage can be achieved to 40.7% in 2030 (deduced from the "Healthy China Initiative"), DALYs would decrease by 1.5% among the screened age groups. Sensitivity analyses found that using local DWs would change the base-case values by ~ 10%.

CONCLUSION

Estimates of DALYs due to breast cancer in China were lower (with a higher proportion of YLDs) than Global Burden of Disease Study numbers (2527.0 thousand, 8.2% were YLDs), suggesting the importance of the application of population-specific DWs. If the screening coverage remains unchanged, breast cancer-caused DALYs would continue to increase, especially among elderly individuals.

摘要

背景

全球大多数癌症伤残调整生命年(DALY)研究都使用了广泛的通用失能权重(DWs);然而,不同人群和癌症类型之间存在差异。以乳腺癌为例,本研究旨在估计中国女性人群的乳腺癌 DALY 以及筛查的影响,并应用当地 DWs。

方法

本研究使用多源数据构建了一个基于患病率的模型。(1)通过乳腺癌的患病例数、分期特异性比例和当地 DWs 来估计总体失能生命年(YLDs)。利用女性人数和新乳腺癌病例数以及当地生存率来计算患病例数。(2)通过乳腺癌死亡率、女性人数和标准预期寿命来估计死亡生命年(YLLs)。(3)使用 Joinpoint 回归预测 2020 年至 2030 年乳腺癌的患病率和死亡率以及相关 DALY。(4)筛查预测中考虑的假设包括扩大覆盖范围、降低乳腺癌死亡率以及提高乳腺癌早期阶段比例。

结果

在 2015 年,中国女性乳腺癌 DALY 估计数为 2251.5 千(其中 17.3%为 YLDs),预计到 2030 年将增加 26.7%(其中 60.3%为≥65 岁人群),如果筛查覆盖率(25.7%)保持不变。然而,如果到 2030 年覆盖率能够达到 40.7%(根据“健康中国行动”推断),筛查年龄组的 DALY 将减少 1.5%。敏感性分析发现,使用当地 DWs 将改变基本情况值约 10%。

结论

中国乳腺癌 DALY 的估计值低于全球疾病负担研究的数值(2527.0 千,8.2%为 YLDs),这表明应用人群特异性 DWs 的重要性。如果筛查覆盖率保持不变,乳腺癌引起的 DALY 将继续增加,尤其是在老年人中。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d81/9547451/701fe910706f/12963_2022_296_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d81/9547451/0210f10baada/12963_2022_296_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d81/9547451/c8403dc80870/12963_2022_296_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d81/9547451/79cc5a48014f/12963_2022_296_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d81/9547451/701fe910706f/12963_2022_296_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d81/9547451/0210f10baada/12963_2022_296_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d81/9547451/c8403dc80870/12963_2022_296_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d81/9547451/79cc5a48014f/12963_2022_296_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d81/9547451/701fe910706f/12963_2022_296_Fig4_HTML.jpg

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