2003—2018年间中国女性宫颈癌发病与死亡趋势研究

张仲华; 刘晨瑛; 任会叶; 梁少辉

doi:10.16462/j.cnki.zhjbkz.2022.01.003

2003—2018年间中国女性宫颈癌发病与死亡趋势研究

doi: 10.16462/j.cnki.zhjbkz.2022.01.003

1.
710049 西安，西安科技大学理学院数学教研室
2.
710043 西安，西安曲江妇产医院妇产科

基金项目:

国家自然科学基金 11201277

详细信息

通讯作者:
刘晨瑛，E-mail: liuchenying0420@sina.com

中图分类号: R19;R737.33
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- 被引次数: 0
出版历程
- 收稿日期: 2021-03-23
- 修回日期: 2021-07-07
- 网络出版日期: 2022-01-16
- 刊出日期: 2022-01-10

Analysis and prediction of the incidence and mortality trends of cervical cancer in Chinese women from 2003 to 2018

1.
Department of Mathematics, School of Science, Xi'an University of Science and Technology, Xi'an 710049, China
2.
Department of Obstetrics and Gynecology, Xi'an Qujiang Obstetrics and Gynecology Hospital, Xi'an 710043, China

Funds:

National Natural Science Foundation of China 11201277

More Information

Corresponding author: LIU Chen-ying, E-mail: liuchenying0420@sina.com

摘要

摘要: 目的研究2003—2018年中国20~79岁女性宫颈癌发病率和死亡率的变化趋势，对未来五年宫颈癌发病及死亡率的趋势进行预测。方法收集我国2003—2018年20~79岁女性宫颈癌的发病和死亡数据，利用联结点回归模型分析趋势变化规律，进一步利用年龄-时期-队列模型探讨年龄、时期和队列因素对宫颈癌发病和死亡率的影响。分别建立自回归滑动平均混合模型(autoregressive integrated moving average model, ARIMA)、灰色模型(grey model, GM)(1，1)和误逆差传播(back propagation, BP)神经网络模型对发病率和死亡率进行拟合，选取预测精度高的模型预测未来五年宫颈癌的发病率和死亡率。结果 2003—2018年间女性宫颈癌的发病率具有2个转折点，发病趋势先快速上升随后下降；死亡率具有1个转折点，趋势是先下降再上升。总体上看，宫颈癌的发生风险随着年龄的增长而增大，在55~＜60岁达到峰值后缓慢下降。死亡风险从年龄上看不断上升，时期效应随着时期的推进而增大，队列效应则不断减弱。通过对比发现BP神经网络模型拟合的效果较好。结论 2003—2018年间中国女性宫颈癌的发病率和死亡率整体上呈现下降的趋势，受年龄影响较大而受时期和队列的影响较小，未来五年发病率和死亡率将呈下降趋势。因此，应加强女性宫颈癌筛查和HPV疫苗接种工作，做好防控措施。
- 宫颈癌 /
- 趋势 /
- APC模型 /
- 联结点回归模型 /
- 预测
Abstract: Objective To study the change tendency of the incidence and mortality of the females with cervical cancer in China from 2003 to 2018, and to predict their trends in the next five years. Methods The data between 2003 and 2018 on the incidence and mortality of female cervical cancer cases aged 20-79 years old in China was collected. Then the Joinpoint regression model was used to analyze the regularity of the incidence and mortality on the base of the data, and the age-period-cohort (APC) model was further used to explore the influences of age, period and cohort on the numbers of the incidence and the mortality of females with cervical cancer. The autoregressive integrated moving average model (ARIMA), grey model (GM) (1, 1) and back propagation (BP) neural network model were developed to fit the incidence and mortality, and the model with the high-precision prediction was selected to foresee the incidence and mortality in the next five years. Results From 2003 to 2018, the incidence of female cervical cancer shows two turning points, with a rapid increase and then a decline; The mortality has a turning point, and decreases first and then increases. In general, the risk of cervical cancer cases increases with age, and slowly decreases after reaching a peak in their 55- < 60 years old. The risk of mortality keeps rising constantly with respect to age, the period effect increases with period evolving and the cohort effect decreases constantly. The fitting results of different models illustrate that the BP neural network model has better effect. Conclusions From 2003 to 2018, the incidence rate and the mortality rate of female cervical cancer cases decrease as a whole, and are more affected by age, but less affected by period and cohort. It is predicted that they will decline in the next five years. Therefore, it is necessity for women to strengthen the screening of cervical cancer and take HPV vaccination.
- Cervical cancer /
- Trend /
- APC model /
- Joinpoint regression model /
- Prediction

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图 1 女性宫颈癌年龄段发病率趋势

Figure 1. Tendency of incidence of female cervical cancer cases with different ages

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图 2 女性宫颈癌年龄段死亡率趋势

Figure 2. Tendency of mortality of female cervical cancer cases with different ages

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图 3 女性宫颈癌发病率的时期趋势

Figure 3. Tendency of incidence of female cervical cancer cases with different periods

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图 4 女性宫颈癌死亡率的时期趋势

Figure 4. Tendency of mortality of female cervical cancer cases with different periods

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图 5 女性宫颈癌发病率的队列趋势

Figure 5. Tendency of incidence of female cervical cancer cases with different cohorts

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图 6 女性宫颈癌死亡率的队列趋势

Figure 6. Tendency of mortality of female cervical cancer cases with different cohorts

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图 7 BP神经网络拟合及预测结果

Figure 7. The fitting and forecasting results of BP neural network model

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表 1 中国女性宫颈癌发病率、死亡率APC模型分析结果

Table 1. APC model results of the incidence and mortality of female cervical cancer in China

变量	发病率				死亡率
变量	β	s_x	Z值	P值	β	s_x	Z值	P值
年龄(岁)
20~＜25	-1.86	0.43	-4.34	＜0.001	-2.55	1.22	-2.10	0.036
25~＜30	-0.71	0.22	-3.22	0.001	-1.51	0.64	-2.36	0.018
30~＜35	-0.07	0.17	-0.40	0.689	-0.75	0.48	-1.59	0.113
35~＜40	0.15	0.15	0.95	0.344	-0.27	0.40	-0.68	0.496
40~＜45	0.33	0.15	2.25	0.025	0.16	0.34	0.47	0.638
45~＜50	0.39	0.14	2.83	0.005	0.44	0.29	1.53	0.126
50~＜55	0.42	0.13	3.24	0.001	0.69	0.24	2.93	0.003
55~＜60	0.44	0.12	3.64	＜0.001	0.70	0.20	3.56	＜0.001
60~＜65	0.32	0.12	2.77	0.006	0.69	0.17	4.16	＜0.001
65~＜70	0.26	0.11	2.33	0.020	0.78	0.16	5.00	＜0.001
70~＜75	0.18	0.12	1.48	0.139	0.80	0.18	4.47	＜0.001
75~79	0.17	0.14	1.22	0.224	0.82	0.23	3.62	＜0.001
时期(年)
2003	-0.16	0.07	-2.42	0.015	-0.25	0.13	-1.99	0.047
2008	-0.13	0.06	-2.11	0.035	-0.18	0.09	-2.04	0.042
2013	0.08	0.06	1.40	0.162	0.12	0.09	1.32	0.186
2018	0.21	0.06	3.48	＜0.001	0.32	0.11	2.75	0.006
队列(年)
1928—1932	0.42	0.23	1.79	0.073	0.92	0.34	2.68	0.007
1933—1937	0.35	0.17	2.04	0.041	0.81	0.26	3.05	0.002
1938—1942	0.27	0.14	1.95	0.051	0.66	0.22	3.07	0.002
1943—1947	0.18	0.12	1.44	0.149	0.50	0.19	2.67	0.008
1948—1952	0.23	0.13	1.84	0.065	0.47	0.20	2.36	0.018
1953—1957	0.21	0.13	1.53	0.125	0.34	0.23	1.50	0.135
1958—1962	0.10	0.15	0.66	0.511	0.13	0.28	0.47	0.639
1963—1967	0.10	0.16	0.62	0.534	0.09	0.32	0.29	0.769
1968—1972	0.16	0.16	1.02	0.309	-0.01	0.37	-0.02	0.980
1973—1977	-0.01	0.17	-0.04	0.966	-0.26	0.42	-0.60	0.547
1978—1982	-0.08	0.18	-0.41	0.681	-0.40	0.48	-0.83	0.405
1983—1987	-0.15	0.20	-0.74	0.461	-0.51	0.53	-0.95	0.345
1988—1992	-0.38	0.25	-1.52	0.127	-0.72	0.68	-1.05	0.295
1993—1997	-0.80	0.41	-1.98	0.048	-1.07	1.11	-0.97	0.334
1998—2002	-0.60	0.87	-0.69	0.490	-0.98	2.38	-0.41	0.681
b	2.64	0.07	36.28	＜0.001	1.61	0.18	8.75	＜0.001
偏差	1.90				1.05
自由度	20				20
AIC	5.74				4.77
BIC	-75.52				-75.57

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表 2 标化发病率及标化死亡率模型拟合值

Table 2. Fitting values of the age-adjusted incidence and mortality

年份(年)	标化发病率 (/10万)	ARIMA(0, 1, 0)	GM(1, 1)	BP神经网络	标化死亡率 (/10万)	ARIMA(0, 1, 0)	GM(1, 1)	BP神经网络
2003	11.26	—	—	—	4.88	—	—	—
2004	11.45	11.42	10.91	—	4.81	4.76	4.48	—
2005	11.37	11.61	11.10	—	4.91	4.71	4.54	—
2006	11.23	11.52	11.30	—	4.49	4.84	4.60	—
2007	11.25	11.39	11.51	—	4.56	4.45	4.66	—
2008	11.28	11.41	11.72	11.46	4.65	4.54	4.72	4.38
2009	11.55	11.44	11.93	11.49	4.66	4.66	4.78	4.61
2010	11.85	11.71	12.14	11.92	4.53	4.70	4.84	4.33
2011	12.16	12.01	12.36	12.26	4.49	4.60	4.90	4.97
2012	12.51	12.32	12.58	12.57	4.81	4.59	4.97	4.98
2013	12.98	12.67	12.81	12.97	5.30	4.94	5.03	5.02
2014	13.55	13.14	13.04	13.49	4.95	5.45	5.10	5.08
2015	13.88	13.70	13.28	13.90	5.16	5.13	5.17	4.83
2016	13.87	14.03	13.52	14.20	5.28	5.37	5.23	5.15
2017	13.31	14.03	13.76	13.45	5.00	5.52	5.30	4.96
2018	13.73	13.47	14.01	13.65	5.89	5.27	5.37	5.91
MAE	—	0.22	0.31	0.10	—	0.08	0.07	0.04
MSE	—	0.08	0.13	0.02	—	0.21	0.20	0.19
MAPE(%)	—	2.45	1.71	0.79	—	4.27	4.12	3.96

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