Incidence trends and age-period-cohort analysis of leishmaniasis in China from 1990 to 2019

doi:10.3969/j.issn.1672-2302.2024.02.003

Abstract

Abstract:

Objective To analyze the change trend of leishmaniasis incidence in China from 1990 to 2019, and investigate the influence of age, period and cohort factors on the incidence of leishmaniasis for reference to develop the prevention and control policy for this prevalence in China. Methods By the 2019 Global Burden of Disease Database as information source, the data on the incidence, crude incidence and standardized incidence of leishmaniasis in China from 1990 to 2019 were extracted. The Joinpoint regression model was used to analyze the change trend of leishmaniasis standardized incidence in China from 1990 to 2019, and the annual percentage change (APC) and average annual percentage change (AAPC) were calculated. An age-period-cohort model was used to analyze the effects of age, period and cohort factors on the incidence of leishmaniasis in China. Results From 1990 to 2019, the incidence of leishmaniasis in China was decreased from 4 487 to 904 cases, with a decrease of 79.85%. The crude incidence and the standard incidence were declined from 0.38/100 000 to 0.06/100 000 and from 0.37/100 000 to 0.08/100 000, with a drop of 84.21% and 78.38%, respectively. The Joinpoint regression model showed that the standardized incidence of leishmaniasis in the whole population, men and women in China from 1990 to 2019 represented a decreasing trend, and AAPC was -5.00%, -5.06% and -4.95%, respectively, with statistically significant differences (t=-90.65, -90.70, -90.82, all P<0.01). The results of age-period-cohort model analysis suggested that the risk of leishmaniasis in the Chinese population from 1990 to 2019 was decreased with age (RR: 5.24 to 0.23) and period (RR: 2.14 to 0.67), and the risk of leishmaniasis was lessened with later birth cohort (RR: 7.86 to 0.12). Conclusion The low prevalence of leishmaniasis in China from 1990 to 2019 indicates that the prevention and control of leishmaniasis in China has achieved remarkable results, yet it is still necessary to strengthen the intervention of the vector and the protection of high-risk populations to reduce the burden of leishmaniasis in the key region in China.

Key words: Leishmaniasis, Incidence rate, Joinpoint regression, Age-period-cohort model, China

CLC Number:

R531.6，R181.3

BAI Jinshu, QIN Liyan, SHI Guangzhong, WANG Feng, WANG Fushuang, LI Rui, ZHAO Jiangshan. Incidence trends and age-period-cohort analysis of leishmaniasis in China from 1990 to 2019[J]. Journal of Tropical Diseases and Parasitology, 2024, 22(2): 76-82.

Figures/Tables 9

Table 1

Table 2

Figure 1

Figure 2

Table 3

Figure 3

Figure 4

Figure 5

Figure 6

References 27

[1]	Alvar J, Yactayo S, Bern C. Leishmaniasis and poverty[J]. Trends Parasitol, 2006, 22(12):552-557. doi: 10.1016/j.pt.2006.09.004 pmid: 17023215
[2]	Bates PA. Transmission of Leishmania metacyclic promastigotes by phlebotomine sand flies[J]. Int J Parasitol, 2007, 37(10):1097-1106. doi: 10.1016/j.ijpara.2007.04.003 pmid: 17517415
[3]	World Health Organization. Control of the Leishmaniasis[R]. Geneva:WHO, 2010,5-9.
[4]	Burza S, Croft SL, Boelaert M. Leishmaniasis[J]. Lancet, 2018, 392(10151):951-970. doi: S0140-6736(18)31204-2 pmid: 30126638
[5]	Luo ZW, Zhou ZB, Hao YW, et al. Establishment of an indicator framework for the transmission risk of the mountain-type zoonotic visceral leishmaniasis based on the Delphi-entropy weight method[J]. Infect Dis Poverty, 2022, 11(1):122. doi: 10.1186/s40249-022-01045-0 pmid: 36482475
[6]	Postigo JAR, Jain S, Madjou S, et al. Global leishmaniasis surveillance: 2021, assessing the impact of the COVID-19 pandemic[EB/OL]. (2022-11-11)[2023-08-01]. https://www.who.int/publications/i/item/who-wer9745-575-590.
[7]	汤林华, 许隆祺, 陈颖丹. 中国寄生虫病防治与研究:上册[M]. 北京: 北京科学技术出版社, 2012:8-9.
[8]	罗卓韦, 周正斌, 公衍峰, 等. 我国内脏利什曼病的流行现状和防控挑战[J]. 中国寄生虫学与寄生虫病杂志, 2022, 40(2):146-152. doi: 10.12140/j.issn.1000-7423.2022.02.003
[9]	GBD2019 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019[J]. Lancet,2020, 396(10258):1223-1249.
[10]	GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019[J]. Lancet,2020, 396(10258):1204-1222.
[11]	Zhang J, Ma B, Han X, et al. Global,regional, and national burdens of HIV and other sexually transmitted infections in adolescents and young adults aged 10-24 years from 1990 to 2019: a trend analysis based on the Global Burden of Disease Study 2019[J]. Lancet Child Adolesc Health, 2022, 6(11):763-776. doi: 10.1016/S2352-4642(22)00219-X pmid: 36108664
[12]	Qiu HB, Cao SM, Xu RH. Cancer incidence, mortality,and burden in China: a time-trend analysis and comparison with the United States and United Kingdom based on the global epidemiological data released in 2020[J]. Cancer Commun (Lond), 2021, 41(10):1037-1048.
[13]	Dong Z, Wang QQ, Yu SC, et al. Age-period-cohort analysis of pulmonary tuberculosis reported incidence, China, 2006-2020[J]. Infect Dis Poverty, 2022, 11(1):85. doi: 10.1186/s40249-022-01009-4 pmid: 35902982
[14]	Rosenberg PS, Check DP, Anderson WF. A web tool for age-period-cohort analysis of cancer incidence and mortality rates[J]. Cancer Epidemiol Biomarkers Prev, 2014, 23(11):2296-2302.
[15]	Zou ZY, Cini K, Dong B, et al. Time trends in cardiovascular disease mortality across the BRICS: an age-period-cohort analysis of key nations with emerging economies using the global burden of disease study 2017[J]. Circulation, 2020, 141(10):790-799. doi: 10.1161/CIRCULATIONAHA.119.042864 pmid: 31941371
[16]	Guan Z, Chen C, Huang CY, et al. Epidemiological features and spatial-temporal distribution of visceral leishmaniasis in mainland China: a population-based surveillance study from 2004 to 2019[J]. Parasit Vectors, 2021, 14(1):517. doi: 10.1186/s13071-021-05002-y pmid: 34620225
[17]	Hao YW, Hu XK, Gong YF, et al. Spatio-temporal clustering of Mountain-type Zoonotic Visceral Leishmaniasis in China between 2015 and 2019[J]. PLoS Negl Trop Dis, 2021, 15(3):e0009152.
[18]	Li YY, Luo ZW, Hao YW, et al. Epidemiological features and spatial-temporal clustering of visceral leishmaniasis in mainland China from 2019 to 2021[J]. Front Microbiol, 2022, 13:959901.
[19]	周正斌, 潘改芹, 李元元, 等. 2021年我国内脏利什曼病疫情分析[J]. 中国寄生虫学与寄生虫病杂志, 2023, 41(2):149-155. doi: 10.12140/j.issn.1000-7423.2023.02.004
[20]	吴文婷, 王小梅, 黎新宇, 等. 2011—2021年北京市利什曼病流行情况[J]. 寄生虫与医学昆虫学报, 2022, 29(1):9-13.
[21]	郝瑜婉, 田添, 朱泽林, 等. 我国输入性利什曼病传播风险矩阵评估研究[J]. 中国血吸虫病防治杂志, 2018, 30(4):428-432.
[22]	周正斌, 李元元, 张仪, 等. 2015—2018年我国内脏利什曼病疫情分析[J]. 中国寄生虫学与寄生虫病杂志, 2020, 38(3):339-345. doi: 10.12140/j.issn.1000-7423.2020.03.013
[23]	周正斌, 李元元, 张仪, 等. 2019年我国内脏利什曼病疫情分析[J]. 中国寄生虫学与寄生虫病杂志, 2020, 38(5):602-607. doi: 10.12140/j.issn.1000-7423.2020.05.012
[24]	王兆俊, 熊光华, 管立人. 新中国黑热病流行病学与防治成就[J]. 中华流行病学杂志, 2000, 21(1):51-54.
[25]	吴忠道. 我国人体寄生虫病与少见或罕见寄生虫病[J]. 热带病与寄生虫学, 2022, 20(3):121-125.
[26]	Lun ZR, Wu MS, Chen YF, et al. Visceral leishmaniasis in China:an endemic disease under control[J]. Clin Microbiol Rev, 2015, 28(4):987-1004.
[27]	张红卫, 刘颖, 杨成运, 等. 以全健康理念推进我国内脏利什曼病从控制走向消除[J]. 热带病与寄生虫学, 2022, 20(4):181-184.

年份	发病数（例）			粗发病率（/10万）			标化发病率（/10万）
年份	男性	女性	全人群	男性	女性	全人群	男性	女性	全人群
1990	3 159	1 328	4 487	0.52	0.23	0.38	0.51	0.23	0.37
1991	2 983	1 253	4 236	0.48	0.22	0.35	0.48	0.22	0.35
1992	2 809	1 178	3 987	0.45	0.20	0.33	0.45	0.21	0.33
1993	2 636	1 104	3 740	0.42	0.19	0.31	0.41	0.19	0.30
1994	2 464	1 030	3 494	0.38	0.17	0.28	0.39	0.18	0.28
1995	2 294	956	3 250	0.35	0.16	0.26	0.36	0.17	0.26
1996	2 115	879	2 994	0.32	0.14	0.24	0.33	0.15	0.24
1997	1 925	798	2 723	0.29	0.13	0.21	0.30	0.14	0.22
1998	1 741	720	2 461	0.26	0.12	0.19	0.28	0.13	0.20
1999	1 580	651	2 231	0.24	0.10	0.17	0.26	0.12	0.19
2000	1 456	599	2 055	0.22	0.10	0.16	0.24	0.11	0.18
2001	1 356	557	1 913	0.20	0.09	0.15	0.23	0.11	0.17
2002	1 262	518	1 780	0.19	0.08	0.14	0.21	0.10	0.16
2003	1 181	485	1 666	0.17	0.08	0.13	0.20	0.09	0.15
2004	1 122	461	1 583	0.16	0.07	0.12	0.19	0.09	0.14
2005	1 093	450	1 543	0.16	0.07	0.12	0.19	0.09	0.14
2006	1 090	449	1 539	0.16	0.07	0.11	0.19	0.09	0.14
2007	1 095	452	1 547	0.16	0.07	0.11	0.19	0.09	0.14
2008	1 104	457	1 561	0.16	0.07	0.12	0.20	0.09	0.15
2009	1 110	461	1 571	0.16	0.07	0.12	0.20	0.09	0.15
2010	1 108	461	1 569	0.16	0.07	0.12	0.20	0.09	0.15
2011	1 085	453	1 538	0.15	0.07	0.11	0.20	0.09	0.15
2012	1 038	436	1 474	0.15	0.06	0.11	0.19	0.09	0.14
2013	979	413	1 392	0.14	0.06	0.10	0.18	0.08	0.13
2014	917	388	1 305	0.13	0.06	0.09	0.17	0.08	0.12
2015	860	366	1 226	0.12	0.05	0.09	0.16	0.07	0.12
2016	811	348	1 159	0.11	0.05	0.08	0.15	0.07	0.11
2017	759	327	1 086	0.11	0.05	0.08	0.14	0.07	0.10
2018	699	303	1 002	0.10	0.04	0.07	0.13	0.06	0.09
2019	630	274	904	0.09	0.04	0.06	0.11	0.05	0.08

性别	年份	APC（95%CI）（%）	t值	P值	AAPC（95%CI）（%）	t值	P值
总体	1990—1995	-6.68（-6.92，-6.43）	-56.82	<0.01	-5.00（-5.11，-4.90）	-90.65	<0.01
	1995—2000	-7.90（-8.18，-7.61）	-57.46	<0.01
	2000—2004	-5.19（-5.56，-4.81）	-29.36	<0.01
	2004—2011	0.76（0.63，0.89）	12.73	<0.01
	2011—2017	-5.93（-6.11，-5.75）	-68.84	<0.01
	2017—2019	-9.71（-10.60，-8.82）	-22.46	<0.01
男性	1990—1995	-6.79（-7.04，-6.54）	-57.06	<0.01	-5.06（-5.17，-4.96）	-90.70	<0.01
	1995—2000	-8.00（-8.28，-7.71）	-57.27	<0.01
	2000—2004	-5.23（-5.60，-4.85）	-29.35	<0.01
	2004—2011	0.73（0.60，0.86）	12.13	<0.01
	2011—2017	-5.97（-6.16，-5.79）	-68.89	<0.01
	2017—2019	-9.74（-10.63，-8.83）	-22.16	<0.01
女性	1990—1995	-6.59（-6.84，-6.34）	-55.80	<0.01	-4.95（-5.06，-4.85）	-90.82	<0.01
	1995—2000	-7.80（-8.08，-7.52）	-57.55	<0.01
	2000—2004	-5.20（-5.56，-4.83）	-29.88	<0.01
	2004—2011	0.75（0.63，0.88）	12.94	<0.01
	2011—2017	-5.86（-6.04，-5.69）	-70.02	<0.01
	2017—2019	-9.63（-10.51，-8.75）	-22.44	<0.01

零假设	χ²值	P值
净漂移=0	608.21	<0.01
总年龄偏差=0	789.41	<0.01
总时期偏差=0	143.49	<0.01
总队列偏差=0	41.15	<0.01
全时期RR=1.00	951.57	<0.01
全队列RR=1.00	1 344.09	<0.01
所有局部漂移=净漂移	36.62	<0.01