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Article(id=1190669166781804783, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1190669163988398295, articleNumber=null, orderNo=null, doi=10.11855/j.issn.0577-7402.1919.2025.0324, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1732204800000, receivedDateStr=2024-11-22, revisedDate=null, revisedDateStr=null, acceptedDate=1740067200000, acceptedDateStr=2025-02-21, onlineDate=1761807250923, onlineDateStr=2025-10-30, pubDate=1745769600000, pubDateStr=2025-04-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1761807250923, onlineIssueDateStr=2025-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1761807250923, creator=13701087609, updateTime=1761807250923, updator=13701087609, issue=Issue{id=1190669163988398295, tenantId=1146029695717560320, journalId=1189873630562394117, year='2025', volume='50', issue='4', pageStart='367', pageEnd='503', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1761807250258, creator=13701087609, updateTime=1761807667423, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1190670913772339410, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1190669163988398295, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1190670913772339411, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1190669163988398295, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=436, endPage=443, ext={EN=ArticleExt(id=1190669166995714290, articleId=1190669166781804783, tenantId=1146029695717560320, journalId=1189873630562394117, language=EN, title=Age-period-cohort model analysis and prediction of breast cancer incidence and mortality among Chinese women from 1992 to 2021, columnId=1190310109000602400, journalTitle=Medical Journal of Chinese People’s Liberation Army, columnName=Clinical Research, runingTitle=null, highlight=null, articleAbstract=

Objective To analyze the trends in incidence and mortality of breast cancer among Chinese women from 1992 to 2021, assess the impact of age, period, and cohort on its incidence and mortality rates, and predict future trends to provide a basis for developing effective intervention strategies. Methods Utilizing the 2021 Global Burden of Disease (GBD2021) database, the Joinpoint regression model was employed to analyze the trends in age-standardized incidence and mortality rates of breast cancer among Chinese women from 1992 to 2021. The age-period-cohort model was applied to estimate the age, period, and cohort effects on the incidence and mortality of breast cancer among Chinese women during the same period. The autoregressive integrated moving average (ARIMA) model was used to predict the age-standardized incidence and mortality rates of breast cancer among Chinese women from 2022 to 2026. A stratified analysis was conducted to explore the impact of different risk factors [including smoking, alcohol consumption, high body mass index (BMI), hyperglycemia, physical inactivity, and diet] on breast cancer mortality. Results From 1992 to 2021, the incidence and mortality rates of breast cancer among Chinese women showed an overall upward trend, with incidence rates rising from 15.95/100,000 in 1992 to 55.54/100,000 in 2021, and mortality rates increasing from 7.35/100,000 to 12.41/100,000. The age-standardized incidence rate also exhibited an upward trend, rising from 18.51/100,000 to 37.00/100,000, with an average annual percentage change (AAPC) of 2.43%. However, the age-standardized mortality rate showed an overall downward trend, decreasing from 9.05/100,000 to 8.24/100,000, with an AAPC of -0.35%. The APC model analysis revealed that the age, period, and cohort effects on incidence and mortality were statistically significant (P<0.001). Within the same birth cohort, breast cancer incidence increased in women aged 15-89 years but decreased in those≥90 years. Breast cancer mortality showed a steady increase with age. With the increase in years, the risk of breast cancer incidence gradually increased, reaching the highest between 2017 and 2021, with a relative risk (RR) value of 1.37. Conversely, the risk of breast cancer mortality decreased with the increase in years, with the lowest mortality between 2012 and 2016, and an RR value of 0.86. With the increase in the birth cohort year, the risk of breast cancer incidence gradually increased, while the risk of mortality gradually decreased. The ARIMA model prediction results showed that the age-standardized incidence rate of breast cancer among women would continue to rise from 2022 to 2026, reaching 40.25/100,000 by 2026, while the age-standardized mortality rate would tend to stabilize at 8.28/100,000 by 2026. Among the risk factors for breast cancer, diet was found to have the highest impact on breast cancer mortality. Conclusions The incidence rate of breast cancer among Chinese women continues to rise, indicating that the prevention and control situation remains severe. Future efforts should focus on developing precise screening programs for high-risk populations and optimizing early screening strategies and treatment resource allocation based on predicted trend.

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目的 分析1992-2021年我国女性乳腺癌发病和死亡的变化趋势，评估年龄、时期和队列对其发病率和死亡率的影响并预测未来趋势，为制定有效的干预策略提供依据。方法 利用2021年全球疾病负担数据库(GBD2021)，采用Joinpoint回归模型分析1992-2021年我国女性乳腺癌标化发病率和标化死亡率的变化趋势，运用年龄-时期-队列模型估计1992-2021年我国女性乳腺癌发病和死亡的年龄效应、时期效应及队列效应，利用自回归积分滑动平均模型(ARIMA模型)预测2022-2026年我国女性乳腺癌标化发病率和标化死亡率。采用分层分析方法探讨不同危险因素[包括吸烟、饮酒、高体重指数(BMI)、高血糖、运动不足和饮食]对乳腺癌死亡率的影响。结果 1992-2021年我国女性乳腺癌发病率和死亡率整体呈现上升趋势，分别从1992年的15.95/10万和7.35/10万上升至2021年的55.54/10万和12.41/10万；标化发病率也呈现上升趋势，从18.51/10万上升至37.00/10万，平均年度变化百分比(AAPC)为2.43%，但标化死亡率整体呈下降趋势，从9.05/10万降至8.24/10万，AAPC为-0.35%。年龄-时期-队列模型分析结果显示，发病和死亡的年龄、时期及队列效应均具有统计学意义(P<0.001)，在同一出生队列中女性乳腺癌的发病率在15~89岁年龄组呈增高趋势，在≥90岁年龄组呈下降趋势；女性乳腺癌死亡率随年龄增长呈稳步增长的趋势。女性乳腺癌的发病风险逐年增高，2017-2021年达到最高，相对危险度(RR)值为1.37；女性乳腺癌的死亡风险逐年降低，其中2012-2016年死亡风险最低，RR值为0.86。随着出生队列年份的增加，女性乳腺癌发病风险逐渐增高，死亡风险逐渐降低。ARIMA模型预测结果显示，2022-2026年女性乳腺癌标化发病率持续上升，2026年将达到40.25/10万，而标化死亡率将趋于平稳，到2026年为8.28/10万。在乳腺癌危险因素中，饮食引起的乳腺癌死亡率最高。结论 我国女性乳腺癌发病率呈持续上升趋势，防控形势依然严峻；未来应重点制定高危人群的精准筛查方案，并基于趋势预测优化早期筛查策略及治疗资源配置。

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尚卿，硕士研究生，主治医师，主要从事普通外科疾病方面的研究

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尚卿，硕士研究生，主治医师，主要从事普通外科疾病方面的研究

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尚卿，硕士研究生，主治医师，主要从事普通外科疾病方面的研究

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UCL. 上控制限；LCL. 下控制限

, figureFileSmall=MdP2rG1An+wlKuMNtejoiw==, figureFileBig=1xFXELbi89cd4T+ywdJE3A==, tableContent=null), ArticleFig(id=1190669331160773207, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1190669166781804783, language=EN, label=Fig.5, caption=The effects of different risk factors, age groups, periods, and cohorts on breast cancer mortality rates, figureFileSmall=5w5pXuxhBB377oDqGlQ3jg==, figureFileBig=GxXFZ4qZN8wbn0IMmXBkTQ==, tableContent=null), ArticleFig(id=1190669331240464984, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1190669166781804783, language=CN, label=图5, caption=不同危险因素与不同年龄、时期和队列对乳腺癌死亡率的影响, figureFileSmall=5w5pXuxhBB377oDqGlQ3jg==, figureFileBig=GxXFZ4qZN8wbn0IMmXBkTQ==, tableContent=null), ArticleFig(id=1190669331324351065, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1190669166781804783, language=EN, label=Tab.1, caption=

Joinpoint trend analysis of breast cancer incidence and mortality in China women from 1992 to 2021

, figureFileSmall=null, figureFileBig=null, tableContent=
指标APC(95%CI)(%)tP
标化发病率
1992-2003年2.18(2.06~2.30)39.12<0.001
2003-2007年a3.73(3.05~4.42)11.73<0.001
2007-2011年1.85(1.11~2.59)5.34<0.001
2011-2015年0.64(-0.23~1.53)1.550.140
2015-2021年b3.62(3.16~4.08)17.08<0.001
标化死亡率
1992-2000年0.39(0.22~0.55)4.88<0.001
2000-2011年-1.11(-1.20~-1.02)-24.89<0.001
2011-2014年c-2.83(-4.20~-1.44)-4.22<0.001
2014-2021年1.11(0.84~1.39)8.48<0.001
), ArticleFig(id=1190669331391459930, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1190669166781804783, language=CN, label=表1, caption=

1992-2021年中国女性乳腺癌发病率和死亡率Joinpoint趋势分析

, figureFileSmall=null, figureFileBig=null, tableContent=
指标APC(95%CI)(%)tP
标化发病率
1992-2003年2.18(2.06~2.30)39.12<0.001
2003-2007年a3.73(3.05~4.42)11.73<0.001
2007-2011年1.85(1.11~2.59)5.34<0.001
2011-2015年0.64(-0.23~1.53)1.550.140
2015-2021年b3.62(3.16~4.08)17.08<0.001
标化死亡率
1992-2000年0.39(0.22~0.55)4.88<0.001
2000-2011年-1.11(-1.20~-1.02)-24.89<0.001
2011-2014年c-2.83(-4.20~-1.44)-4.22<0.001
2014-2021年1.11(0.84~1.39)8.48<0.001
), ArticleFig(id=1190669331483734619, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1190669166781804783, language=EN, label=Tab.2, caption=

Results of the Wald χ2 test for the age-period-cohort model of breast cancer incidence and mortality in China women from 1992 to 2021

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变量发病率死亡率
χ2Pχ2P
全局偏移=0397.259<0.001108.241<0.001
总年龄偏差=06361.367<0.0014941.338<0.001
总时期偏差=022.032<0.00160.449<0.001
总队列偏差=0101.227<0.001115.341<0.001
时期RR=1424.073<0.001164.007<0.001
队列RR=12052.260<0.001376.139<0.001
局部偏移=全局偏移94.397<0.001111.102<0.001
), ArticleFig(id=1190669331559232092, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1190669166781804783, language=CN, label=表2, caption=

1992-2021年中国女性乳腺癌发病率和死亡率的年龄-时期-队列模型Wald χ2检验结果

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变量发病率死亡率
χ2Pχ2P
全局偏移=0397.259<0.001108.241<0.001
总年龄偏差=06361.367<0.0014941.338<0.001
总时期偏差=022.032<0.00160.449<0.001
总队列偏差=0101.227<0.001115.341<0.001
时期RR=1424.073<0.001164.007<0.001
队列RR=12052.260<0.001376.139<0.001
局部偏移=全局偏移94.397<0.001111.102<0.001
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1992-2021年中国女性乳腺癌发病和死亡的年龄-时期-队列模型分析及预测
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尚卿 1 , 王海鹏 1 , 王静 2
解放军医学杂志 | 临床研究 2025,50(4): 436-443
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解放军医学杂志 | 临床研究 2025, 50(4): 436-443
1992-2021年中国女性乳腺癌发病和死亡的年龄-时期-队列模型分析及预测
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尚卿1, 王海鹏1, 王静2
作者信息
  • 1河南省新乡市中心医院普通外科，河南新乡 453000
  • 2河南省新乡市中心医院检验科，河南新乡 453000

    • 尚卿，硕士研究生，主治医师，主要从事普通外科疾病方面的研究

Age-period-cohort model analysis and prediction of breast cancer incidence and mortality among Chinese women from 1992 to 2021
Qing Shang1, Hai-Peng Wang1, Jing Wang2
Affiliations
  • 1Department of General Surgery, Xinxiang Central Hospital, Xinxiang, Henan 453000, China
  • 2Department of Clinical Laboratory, Xinxiang Central Hospital, Xinxiang, Henan 453000, China
出版时间: 2025-04-28 doi: 10.11855/j.issn.0577-7402.1919.2025.0324
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摘要
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目的 分析1992-2021年我国女性乳腺癌发病和死亡的变化趋势，评估年龄、时期和队列对其发病率和死亡率的影响并预测未来趋势，为制定有效的干预策略提供依据。方法 利用2021年全球疾病负担数据库(GBD2021)，采用Joinpoint回归模型分析1992-2021年我国女性乳腺癌标化发病率和标化死亡率的变化趋势，运用年龄-时期-队列模型估计1992-2021年我国女性乳腺癌发病和死亡的年龄效应、时期效应及队列效应，利用自回归积分滑动平均模型(ARIMA模型)预测2022-2026年我国女性乳腺癌标化发病率和标化死亡率。采用分层分析方法探讨不同危险因素[包括吸烟、饮酒、高体重指数(BMI)、高血糖、运动不足和饮食]对乳腺癌死亡率的影响。结果 1992-2021年我国女性乳腺癌发病率和死亡率整体呈现上升趋势，分别从1992年的15.95/10万和7.35/10万上升至2021年的55.54/10万和12.41/10万；标化发病率也呈现上升趋势，从18.51/10万上升至37.00/10万，平均年度变化百分比(AAPC)为2.43%，但标化死亡率整体呈下降趋势，从9.05/10万降至8.24/10万，AAPC为-0.35%。年龄-时期-队列模型分析结果显示，发病和死亡的年龄、时期及队列效应均具有统计学意义(P<0.001)，在同一出生队列中女性乳腺癌的发病率在15~89岁年龄组呈增高趋势，在≥90岁年龄组呈下降趋势；女性乳腺癌死亡率随年龄增长呈稳步增长的趋势。女性乳腺癌的发病风险逐年增高，2017-2021年达到最高，相对危险度(RR)值为1.37；女性乳腺癌的死亡风险逐年降低，其中2012-2016年死亡风险最低，RR值为0.86。随着出生队列年份的增加，女性乳腺癌发病风险逐渐增高，死亡风险逐渐降低。ARIMA模型预测结果显示，2022-2026年女性乳腺癌标化发病率持续上升，2026年将达到40.25/10万，而标化死亡率将趋于平稳，到2026年为8.28/10万。在乳腺癌危险因素中，饮食引起的乳腺癌死亡率最高。结论 我国女性乳腺癌发病率呈持续上升趋势，防控形势依然严峻；未来应重点制定高危人群的精准筛查方案，并基于趋势预测优化早期筛查策略及治疗资源配置。

乳腺癌  /  发病率  /  死亡率  /  年龄-时期-队列模型  /  预测

Objective To analyze the trends in incidence and mortality of breast cancer among Chinese women from 1992 to 2021, assess the impact of age, period, and cohort on its incidence and mortality rates, and predict future trends to provide a basis for developing effective intervention strategies. Methods Utilizing the 2021 Global Burden of Disease (GBD2021) database, the Joinpoint regression model was employed to analyze the trends in age-standardized incidence and mortality rates of breast cancer among Chinese women from 1992 to 2021. The age-period-cohort model was applied to estimate the age, period, and cohort effects on the incidence and mortality of breast cancer among Chinese women during the same period. The autoregressive integrated moving average (ARIMA) model was used to predict the age-standardized incidence and mortality rates of breast cancer among Chinese women from 2022 to 2026. A stratified analysis was conducted to explore the impact of different risk factors [including smoking, alcohol consumption, high body mass index (BMI), hyperglycemia, physical inactivity, and diet] on breast cancer mortality. Results From 1992 to 2021, the incidence and mortality rates of breast cancer among Chinese women showed an overall upward trend, with incidence rates rising from 15.95/100,000 in 1992 to 55.54/100,000 in 2021, and mortality rates increasing from 7.35/100,000 to 12.41/100,000. The age-standardized incidence rate also exhibited an upward trend, rising from 18.51/100,000 to 37.00/100,000, with an average annual percentage change (AAPC) of 2.43%. However, the age-standardized mortality rate showed an overall downward trend, decreasing from 9.05/100,000 to 8.24/100,000, with an AAPC of -0.35%. The APC model analysis revealed that the age, period, and cohort effects on incidence and mortality were statistically significant (P<0.001). Within the same birth cohort, breast cancer incidence increased in women aged 15-89 years but decreased in those≥90 years. Breast cancer mortality showed a steady increase with age. With the increase in years, the risk of breast cancer incidence gradually increased, reaching the highest between 2017 and 2021, with a relative risk (RR) value of 1.37. Conversely, the risk of breast cancer mortality decreased with the increase in years, with the lowest mortality between 2012 and 2016, and an RR value of 0.86. With the increase in the birth cohort year, the risk of breast cancer incidence gradually increased, while the risk of mortality gradually decreased. The ARIMA model prediction results showed that the age-standardized incidence rate of breast cancer among women would continue to rise from 2022 to 2026, reaching 40.25/100,000 by 2026, while the age-standardized mortality rate would tend to stabilize at 8.28/100,000 by 2026. Among the risk factors for breast cancer, diet was found to have the highest impact on breast cancer mortality. Conclusions The incidence rate of breast cancer among Chinese women continues to rise, indicating that the prevention and control situation remains severe. Future efforts should focus on developing precise screening programs for high-risk populations and optimizing early screening strategies and treatment resource allocation based on predicted trend.

breast cancer  /  incidence  /  mortality  /  age-period-cohort model  /  prediction
尚卿, 王海鹏, 王静. 1992-2021年中国女性乳腺癌发病和死亡的年龄-时期-队列模型分析及预测. 解放军医学杂志, 2025 , 50 (4) : 436 -443 . DOI: 10.11855/j.issn.0577-7402.1919.2025.0324
Qing Shang, Hai-Peng Wang, Jing Wang. Age-period-cohort model analysis and prediction of breast cancer incidence and mortality among Chinese women from 1992 to 2021[J]. Medical Journal of Chinese People’s Liberation Army, 2025 , 50 (4) : 436 -443 . DOI: 10.11855/j.issn.0577-7402.1919.2025.0324
正文
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2020年全球癌症统计数据显示，乳腺癌为最常见的恶性肿瘤之一，也是全球癌症相关死亡的第五大原因[1]。据估算，2020年我国有416 371名女性被诊断为乳腺癌，占全球乳腺癌新发病例总数的18%[2-3]。随着城市化进程的加快，我国的疾病负担从传染性疾病转移到非传染性疾病(包括所有癌症，特别是乳腺癌)[4-6]。由于乳腺癌的发病率一直居高不下，给我国带来了沉重的疾病负担。目前已经确定了乳腺癌的各种危险因素(包括遗传因素和非遗传因素)，如高体重指数(BMI)、社会经济地位低下、妊娠和激素替代疗法等[7-8]。其相关危险因素因地区和时间不同而有所差异。目前，绝大多数乳腺癌相关研究集中在发病机制和治疗方案方面[9]，而流行病学研究有助于揭示乳腺癌对人类健康的负担，并可为提高国家公共卫生水平、合理配置医疗资源、制定卫生战略提供依据。本研究通过年龄-时期-队列模型系统地分析1992-2021年不同因素对我国乳腺癌流行趋势的影响，旨在为预测乳腺癌未来的发病和死亡趋势提供参考。
1 资料与方法
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1.1 资料来源
本研究数据来源于2021年全球疾病负担数据库(Global Burden of Disease，GBD2021)，该数据库旨在评估全球范围内359种疾病、伤害、死亡及其他危险因素对健康造成的负担[10]。GBD纳入的中国地区数据主要来自国家疾病监测系统、国家疾病预防控制中心死因登记报告信息系统、国家妇幼卫生监测网等[11]。本研究从GBD2021数据库中提取中国女性乳腺癌发病和死亡的相关数据，数据筛选标准如下：地区选择“China”，指标选择“Incidence”“Deaths”，疾病为“Breast Cancer”，时间为“1992-2021”，性别为“Female”、年龄组选择15~19、20~24、......、90~94、95岁及以上。
1.2 研究方法
1.2.1 Joinpoint回归模型
Joinpoint回归模型基于时间序列数据的分段线性回归分析时间变化趋势并发现具有统计学意义的转折点，通过计算年度变化百分比(annual percent change，APC)和平均年度变化百分比(average annual percent change，AAPC)分析多个阶段性变化情况，模型选择通常基于最小化贝叶斯信息准则(Bayesian information criterion，BIC)或赤池信息准则(Akaike information criterion，AIC)，以平衡模型的复杂性和拟合效果。本研究利用Joinpoint回归模型对1992-2021年中国女性乳腺癌标化发病率和标化死亡率的变化趋势进行分析，APC、AAPC>0且P<0.05表示该时间段内观测值整体呈上升趋势，APC、AAPC<0且P<0.05表示该时间段内观测值整体呈下降趋势[12]。
1.2.2 年龄-时期-队列模型
年龄-时期-队列模型以Poisson分布为基础，从年龄、时期、队列3个维度对目标变量进行分解，以探讨目标变量的年龄、时期和出生队列效应对疾病负担的影响，该模型目前广泛应用于人口学、流行病学及社会学等领域，特别是慢性病的发病率和死亡率趋势研究[13]。本研究将年龄段划分从15岁以上以5岁为一个划分阶段，共分为17个年龄组；时期分为6组(1992-1996年、1997-2001年、2002-2006年、2007-2011年、2012-2016年、2017-2021年)；1992-2021年5年为一个划分节点，将出生队列分为22组(1893-1897年、1898-1902年、1903-1907年、1908-1912年、1913-1917年、1918-1922年、1923-1927年、1928-1932年、1933-1937年、1938-1942年、1943-1947年、1948-1952年、1953-1957年、1958-1962年、1963-1967年、1968-1972年、1973-1977年、1978-1982年、1983-1987年、1988-1992年、1993-1997年、1998-2002年)，出生队列=时期–年龄。本研究采用国际癌症研究机构提供的年龄-时期-队列模型网页分析工具对年龄、时期和出生队列效应进行分析并利用Graphpad Prism 9.0软件绘制统计图，年龄-时期-队列模型通过线性模型来描述疾病发病率和死亡率的对数变化，并分解为3个因素的加法效应[14]。年龄效应指某种现象或疾病随年龄增长而变化的趋势；时期效应反映某种现象或疾病在特定时间段内的变化，可能受环境、社会、经济、医疗技术等因素的影响；队列效应指出生于相同时间段的一组人群所经历的独特环境或经历对其健康状况的影响[15]。年龄-时期-队列模型输出参数包括净漂移值(net drift)、局部漂移(local drift)、年龄偏差系数、时期(队列)偏差系数、纵向年龄曲线和时期(队列)率比。
1.2.3 自回归积分滑动平均模型(autoregressive integrated moving average model，ARIMA模型)分析
在ARIMA(p，d，q)模型中，p表示自回归项的阶数，d表示差分次数，q表示移动平均项的阶数[16]。在SPSS 23.0软件中，使用时间序列分析模块中的ARIMA模型功能进行模型选择与拟合。通过分析自相关函数(ACF)和偏自相关函数(PACF)图，初步识别模型的阶数。利用SPSS的自动模型选择功能，确定最佳模型配置。模型参数通过最大似然估计法进行估计。模型拟合后，生成残差ACF和PACF图，检查残差是否为白噪声[17]。利用拟合的ARIMA模型，对2022-2026年中国女性乳腺癌标化发病率和标化死亡率进行预测。为了评估预测的准确性，计算均方误差和平均绝对百分比误差。
1.2.4 分层分析
采用分层分析的方法探讨不同危险因素(包括吸烟、饮酒、高BMI、高血糖、运动不足和饮食)对乳腺癌死亡率的影响。通过描述性统计方法，分别计算各分层中的乳腺癌死亡率，在此基础上，考虑不同因素与年龄、时期和队列之间的交互作用，探讨这些交互作用对乳腺癌死亡率的联合影响。
1.3 统计学处理
采用Excel 2016建立数据库进行一般描述性分析，包括发病人数、发病率、标化发病率、死亡人数、死亡率和标化死亡率。应用Joinpoint 4.9.1.0软件计算APC和AAPC，分别对乳腺癌发病和死亡的变化趋势进行分析。使用GraphPad Prism 9.0及国际癌症研究机构提供的年龄-时期-队列模型网页工具进行年龄-时期-队列分析。采用SPSS 23.0软件中的ARIMA模型对乳腺癌标化发病率和标化死亡率进行预测，检验水准α=0.05。
2 结果
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2.1 1992-2021年中国女性乳腺癌发病和死亡情况
1992-2021年中国女性乳腺癌的发病人数和死亡人数呈持续上升趋势，发病人数从1992年的9.30万例上升至2021年的38.58万例，死亡人数则从4.29万例上升至8.81万例；发病率和死亡率整体也呈上升趋势，发病率从1992年的15.95/10万上升至2021年的55.54/10万，死亡率从7.35/10万上升至12.41/10万；标化发病率呈上升趋势(1992年为18.51/10万，2021年为37.00/10万)，但标化死亡率整体呈下降趋势(1992年为9.05/10万，2021年为8.24/10万)(图1)。
2.2 1992-2021年中国女性乳腺癌发病率和死亡率的变化趋势
Joinpoint回归模型分析结果显示，1992-2021年中国女性乳腺癌标化发病率呈逐年上升趋势(AAPC=2.43%，t=24.45，P<0.001)，其中2003-2007年和2015-2021年增长趋势最为明显，APC分别为3.73%(95%CI 3.05%~4.42%)和3.62%(95%CI 3.16%~4.08%)。1992-2021年标化死亡率呈逐年下降趋势(AAPC=-0.35%，t=-4.25，P<0.001)，其中2011-2014年和2000-2011年下降趋势最为明显，APC分别为-2.83%(95%CI -4.20%~‍-1.44%)和-1.11%(95%CI -1.20%~-1.02%)(表1)。
2.3 1992-2021年中国女性乳腺癌发病和死亡的年龄-时期-队列模型分析
年龄-时期-队列模型分析结果显示，1992-2021年中国女性乳腺癌发病率和死亡率的全局偏移、总年龄偏差、总时期偏差、总队列偏差、时期相对危险度(RR)值、队列RR值及局部偏移差异均有统计学意义(P<0.001)，表明其变化趋势为年龄、时期、队列三方面共同影响的结果(表2)。
年龄-时期-队列效应见图2。随着年龄的增长，在同一出生队列中，女性乳腺癌的发病率在15~89岁年龄段呈增高趋势，尤其在25~69岁年龄组增幅较为明显。从25~29岁年龄组的2.25/10万上升至65~69岁年龄组的91.91/10万，而在≥90岁年龄组则呈下降趋势，其中85~89岁年龄组的发病率最高，为125.87/10万；女性乳腺癌死亡率随年龄增长呈稳步增高趋势，在≥95岁年龄组达到最高，为70.72/10万。女性乳腺癌的发病风险逐年增高，1992-1996年最低，2017-2021年达到最高，以2002-2006为参考时期，6个时期的RR值分别为0.81、0.89、1.00、1.14、1.20和1.37；女性乳腺癌的死亡风险逐年降低，6个时期的RR值分别为1.04、1.04、1.00、0.94、0.86和0.89，其中2012-2016年死亡风险最低，50~59岁年龄组发病率和死亡率随年份的增加明显增高。随着出生队列年份的增加，女性乳腺癌发病风险随之增高，在1912年最低，为0.54，2002年达到最高，为3.42，死亡风险随出生年份增加逐渐降低，1897年最高，为1.55，2002年最低，为0.50。时期趋势如图3所示。随着时期的推移，乳腺癌发病率在45~69岁年龄组呈显著增长趋势，在40~44岁年龄组呈先上升后下降的趋势，其余所有年龄组均呈缓慢增长趋势；死亡率在50~69岁年龄组增长趋势显著。乳腺癌年龄发病率随出生队列的推移，除40~44 岁年龄组呈先上升后下降的趋势外，其余所有年龄组均呈增长趋势；死亡率在15~44岁年龄组呈波动下降的趋势，在>45岁年龄组呈波动增长的趋势。
2.4 2022-2026年乳腺癌标化发病率和标化死亡率预测
ARIMA模型预测结果显示，2022-2026年女性乳腺癌标化发病率将延续前30年的趋势持续上升，于2026年达到40.25/10万，而标化死亡率将趋于平稳，到2026年为8.28/10万(图4)。
2.5 危险因素分层分析
在6种危险因素(吸烟、饮酒、高BMI、高血糖、运动不足和饮食)中，饮食对乳腺癌死亡率的影响最大，其次是高BMI和高血糖。在95岁及以上年龄组中，饮食因素导致的乳腺癌死亡率最高，达11.5/10万；在2017-2021年，由饮食因素引起的死亡率为2.0/10万；而在1893-1897年出生的人群中，饮食因素导致的乳腺癌死亡率最高，达14.4/10万(图5)。
3 讨论
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本研究分析了1992-2021年中国乳腺癌负担的变化趋势，结果显示，乳腺癌仍是我国的重要疾病负担，2021年乳腺癌发病例数为38.58万例，死亡例数为8.81万例，乳腺癌的绝对负担将持续增加至2026年。
1992-2021年，我国女性乳腺癌的发病人数和死亡人数均呈显著上升趋势，可能与多个因素有关，包括人口老龄化、城市化进程、生活方式的改变(如饮食结构的变化、缺乏体力活动、肥胖率上升)以及筛查率的提高等[18]。尽管总体发病率在增高，但随着医学技术的进步以及治疗手段的改进，乳腺癌的早期发现比例及治疗效果逐步提高，导致死亡率的上升幅度低于发病率[19]。标化发病率在研究期间持续增高，而标化死亡率总体呈下降趋势。标化发病率的增高可能反映了乳腺癌在不同年龄组中的普遍性增高，尤其是在年轻女性中，其受环境、遗传等多种因素的影响逐渐显现[20]。标化死亡率的下降可能是治疗效果改善、早期筛查推广以及健康教育普及的结果，尤其是抗肿瘤药物的进展、新型治疗方案的应用和患者健康管理意识的增强，这些对降低死亡率起到了重要作用[21]。但在2014-2021年标化死亡率增高，这段时间乳腺癌早期筛查普及，发现了更多早期和中期患者，因此患者数量的增加直接影响了死亡率。
本研究年龄-时期-队列模型分析结果显示，发病和死亡的全局偏移显著，表明在研究期间呈现出明显的趋势性变化，可能与长期的环境、生活方式、公共卫生政策或医疗水平的改善相关。乳腺癌发病率和死亡率受年龄、时期和出生队列3个维度的共同影响。年龄效应显示，乳腺癌的发病率和死亡率随年龄增加而增高，尤其是>85岁的老年女性发病率和死亡率最高，提示老龄化对乳腺癌流行的推动作用较强。因此，建议针对高风险年龄组制定更为精细化的筛查策略，如提高筛查频次和采用更精准的筛查技术(乳腺超声、乳腺MRI等)[22]。本研究发现，乳腺癌发病率从25~29岁年龄组的2.25/10万迅速上升到65~69岁年龄组的91.91/10万，随后增长速度较慢，因此，女性乳腺癌筛查指南建议45~69岁的女性应接受筛查，每1~2年进行一次乳腺超声检查[23]。时期效应则表明，2017-2021年发病风险达到最高(RR=1.37)，这可能与医学技术的进步、健康教育和早期筛查的推广密切相关。部分人群生活方式的变化导致高脂肪、高糖、高蛋白和低膳食纤维类饮食增多，而体力活动减少[15]。肥胖、缺乏运动、压力以及不规律的工作和休息等不健康行为可增加乳腺癌的发病和死亡风险。此外，近年来女性生殖模式和态度的变化，包括生育晚、子女少和母乳喂养时间短，也增加了乳腺癌的发病风险[24]。而2012-2016年乳腺癌的死亡风险明显下降(RR=0.86)，表明近年来乳腺癌的治疗效果有所提升，但随着发病率的上升，优化治疗资源配置成为亟待解决的问题。因此，在不同发病阶段，可根据患者的年龄、疾病进展以及治疗可达性，采用个性化的治疗方案。早期乳腺癌患者可能更多依赖乳腺切除术、局部治疗和放疗，而对于晚期或转移性乳腺癌患者，则应更多考虑靶向治疗、免疫治疗等前沿治疗手段。队列效应分析进一步显示，较晚出生的女性面临更高的乳腺癌发病风险，但死亡风险逐步降低，这可能与现代生活方式的变化、环境污染增加以及健康管理措施的强化有关。
本研究采用ARIMA模型预测发现，2022-2026年中国女性乳腺癌的标化发病率将继续增高，并在2026年达到40.25/10万，而标化死亡率将趋于平稳，到2026年为8.28/10万，表明未来乳腺癌的防治依然面临挑战。鉴于发病率的增高趋势，建议进一步加强乳腺癌的预防与早期筛查，尤其是对高危人群进行重点干预。同时，应持续推进乳腺癌治疗技术的发展和普及，确保更多患者能够接受有效的治疗，以稳定控制并进一步降低死亡率。此外，应加强公共健康教育，提高女性对乳腺癌的认知，倡导健康的生活方式，以降低乳腺癌的发病风险。政府和卫生部门也应制定并落实相关政策，以应对乳腺癌负担的持续增加。
在不同的危险因素中，饮食因素在乳腺癌死亡率中的作用明显高于其他危险因素，尤其在老年群体中表现突出。≥95岁年龄组由饮食因素引起的乳腺癌死亡率高达11.5/10万，反映了随着年龄增长，个体对不良饮食习惯的累积效应更加显著。此外，2017-2021年由饮食引起的死亡率为2.0/10万，显示了近年来饮食因素对乳腺癌死亡率的持续影响，这可能与现代饮食习惯的变化有关，如高脂肪、低纤维饮食以及加工食品的普及[25]。因此，可通过媒体、社区活动和健康讲座等途径提高公众对健康饮食与乳腺癌风险之间关系的认知。
本研究存在一定的局限性：(1)研究数据来源于2021年GBD数据库，后者依赖于模型估计，并非真实的观测数据，故难以避免估计结果存在偏差的可能性；(2)由于数据有限，未进行城乡差异的分析，且年龄、时期及出生队列以5岁(年)进行划分，可能某些变化未被发现；(3)数据来源主要依赖于各省、区的统计报告，可能存在偏远地区数据报告不全或质量不足的问题，建议未来的研究可考虑整合省级的乳腺癌数据，加强与地方疾病控制中心、医院及其他公共卫生机构的合作，进一步完善数据收集工作。（4）由于数据库中饮食风险因素的聚合性质，未来研究需要通过个体化膳食评估方法进一步解析不同饮食成分与乳腺癌风险的关联。
综上所述，本研究结果显示，1992-2021我国乳腺癌发病率和死亡率呈增高趋势，防控形势依然严峻。应加强我国乳腺癌的防治工作，呼吁女性在成年后定期接受乳房触诊和目视检查。为了延长乳腺癌患者的寿命，提高其生活质量，有必要根据乳腺癌的类型、患者的健康状况以及自身需求，制定个性化的治疗方案。总之，我国女性乳腺癌患者的疾病负担是当前和未来需要解决的一大问题。
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2025年第50卷第4期
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doi: 10.11855/j.issn.0577-7402.1919.2025.0324
  • 接收时间：2024-11-22
  • 首发时间：2025-10-30
  • 出版时间：2025-04-28
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  • 收稿日期：2024-11-22
  • 录用日期：2025-02-21
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    1河南省新乡市中心医院普通外科，河南新乡 453000
    2河南省新乡市中心医院检验科，河南新乡 453000
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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