四川省石渠县棘球蚴病流行的影响因素分析

doi:10.12140/j.issn.1000-7423.2019.04.010

摘要/Abstract

摘要：

目的探索人群棘球蚴病流行的主要影响因素,研究棘球蚴病的流行与多种影响因素的关系,为开展棘球蚴病防治工作和制定适合本地区的控制策略和措施提供依据。方法从2015年11月-2017年6月在四川省石渠县开展的棘球蚴病流行情况调查的数据中提取对棘球蚴病流行可能有影响的因素作为自变量,以人群患病率作为因变量,进行直线回归分析,筛选出有统计学意义的因素,进行多重线性回归分析,建立多重线性回归模型。结果单因素线性回归分析结果显示,户均犬数、犬感染率、户均羊数、户均牛数、牛患病率、年户均自宰牛羊数、小型哺乳类动物密度、防治棘球蚴病知识知晓率均是人群患病的影响因素（P < 0.05）,回归系数分别为 5.278、0.818、0.183、0.274、0.234、3.035、0.038、-0.539,其中户均犬数的影响最大,户均犬数每增加1条犬,人群患病率增加5.278倍。多重线性回归分析结果显示,防治知识知晓率（X9）、户均犬数（X1）、犬感染率（X2）和小型哺乳类动物密度（X7）对人群患病率的影响有统计学意义（P < 0.05）,多重线性回归模型方程为Y = 11.213 - 0.146X9 + 2.184X1 + 0.231X2 + 0.009X7。其中,户均犬数对人群患病率的影响最大。结论户均犬数、犬感染率、小型哺乳类动物密度、防治棘球蚴病知识知晓率等因素与人群患棘球蚴病有关。

关键词: 棘球蚴病, 影响因素, 多重线性回归模型

Abstract:

Objective To analyze the main influencing and risk factors related to the epidemic of human echinococcosis and their impacts on the prevalence of echinococcosis as a basis for providing a strategy for the better control of echinococcosis in the region. Methods The data of an epidemiological survey on echinococcosis in Shiqu, Sichuan, from November 2015 to June 2017 were collected to identify the factors related to the endemic of echinococcosis as independent variables, and the population prevalence of echinococcosis as dependent variables. Linear regression analysis was conducted to identify the factors with statistical significance related to the endemic of echinococcosis. A multiple linear regression model was established to analyze the factors that significantly effect the prevalence of echinococcosis in the region. Results The results of single factor linear regression analysis showed that the average number of dog per household, the infection rate of dogs, the average number of sheep per household, the average number of cattle per household, the prevalence rate of cattle, the average slaughtered number of cattle and sheep per household, the density of small mammals, and people’s awareness for the knowledge to prevent and control echinococcosis were all the influencing and risk factors (with b = 5.278, 0.818, 0.183, 0.274, 0.234, 3.035, 0.038, -0.539, respectively, P < 0.05). The average number of dog per household has the greatest impact. The prevalence rate of the population could increases by 5.278 times if one dog would be increased in each household. The results of multiple linear regression analysis showed that the knowledge awareness(X9), the average number of dog per household (X1), the infection rate of dogs (X2) and the density of small mammals (X7) had significant effect on the prevalence of echinococcosis in local population(P < 0.05). The equation of multiple linear regression model was Y = 11.213 - 0.146X9 + 2.184X1 + 0.231X2 + 0.009X7. Among them, the average number of dogs per household has the greatest impact. Conclusion The effective and risk factors such as the number of dog per household, the rate of canine infection, the density of small mammals, and the awareness rate of prevention and treatment of echinococcosis are related to the prevalence of echinococcosis in the endemic area.

Key words: Echinococcosis, Risk factors, Multiple linear regression model

中图分类号:

R532.32

何伟, 王谦, 黄燕, 喻文杰, 张光葭, 廖沙, 王奇, 杨柳, 陈凡, 李汭芮, 钟波. 四川省石渠县棘球蚴病流行的影响因素分析[J]. 中国寄生虫学与寄生虫病杂志, 2019, 37(4): 428-432.

Wei HE, Qian WANG, Yan HUANG, Wen-jie YU, Guang-jia ZHANG, Sha LIAO, Qi WANG, Liu YANG, Fan CHEN, Rui-rui LI, Bo ZHONG. Risk factors of echinococcosis in Shiqu County, Sichuan Province[J]. Chinese Journal of Parasitology and Parasitic Diseases, 2019, 37(4): 428-432.

图/表 4

表1

表2

表3

表4

参考文献 24

[1]	Eckert J, Gemmell MA, Meslin FX, et al. WHO/OIE manual on echinococcosis in humans and animals: a public health problem of global concern[M]. Geneva: World Organization for Animal Health, 2001.
[2]	李雍龙. 人体寄生虫学[M]. 北京: 人民卫生出版社, 2008: 144-150.
[3]	温浩, 徐明谦. 实用棘球蚴病学[M]. 北京: 科学出版社, 2007: 6-27.
[4]	汪天平, 毛佐华. 人兽共患寄生虫病学[M]. 北京: 人民卫生出版社, 2009: 58-59.
[5]	肖宁, 邱加闽, Nakao M, 等. 青藏高原东部地区发现的新种: 石渠棘球绦虫的生物学特征[J]. 中国寄生虫学与寄生虫病杂志, 2008, 26(4): 307-312.
[6]	Miterpáková M, Hurníková Z, Antolová D, et al. Endoparasites of red fox (Vulpes vulpes) in the Slovak Republic with the emphasis on zoonotic species Echinococcus multilocularis and Trichinella spp[J]. Helminthologia (Bratislava), 2009, 46(2):73-79.
[7]	Lavikainen A, Lehtinen MJ, Laaksonen S, et al. Molecular characterization of Echinococcus isolates of cervid origin from Finland and Sweden[J]. Parasitology, 2006, 133(5): 565-570.
[8]	Le TH, Pearson MS, Blair D, et al. Complete mitochondrial genomes confirm the distinctiveness of the horse-dog and sheep-dog strains of Echinococcus granulosus[J]. Parasitology, 2002, 124(1): 97-112.
[9]	Capuano F, Rinaldi L, Maurelli MP, et al. Cystic echinococcosis in water buffaloes: epidemiological survey and molecular evidence of ovine (G1) and buffalo (G3) strains[J]. Vet Parasitol, 2006, 137(3): 262-268.
[10]	Camposbueno A, LópezAbente G, AndrésCercadillo AM. Risk factors for Echinococcus granulosus infection: a case-control study[J]. Am J Trop Med Hyg, 2000, 62(3): 329-334.
[11]	何伟, 尚婧晔, 喻文杰, 等. 四川省石渠县包虫病流行病学现状调查[J]. 预防医学情报杂志, 2017, 33(9): 850-854.
[12]	易德友, 王谦, 黄燕, 等. 石渠县包虫病患者血清学检测结果与分析[J]. 中国病原生物学杂志, 2008, 3(8): 621-622.
[13]	喻文杰, 王谦, 廖沙, 等. 2017年四川省石渠县包虫病患病情况现状调查[J]. 预防医学情报杂志, 2018, 34(5): 545-549.
[14]	邱加闽, 陈兴旺, 觉呷, 等. 四川石渠县人畜包虫病流行病学调查[J]. 中国人兽共患病学报, 1988, 4(4): 38-39.
[15]	肖宁, 周章俊, 陈兴旺, 等. 四川省藏区防治棘球蚴病健康教育效果评价[J]. 中国寄生虫学与寄生虫病杂志, 2012, 30(1): 6-11.
[16]	王谦, 邱加闽, Schantz P, 等. 四川省西部藏区家庭饲养牲畜人群棘球蚴病风险因素的调查[J]. 中国寄生虫学与寄生虫病杂志, 2001, 19(2): 93-96.
[17]	张静宵, 王虎. 青海省动物棘球蚴病及棘球绦虫感染的流行病学调查[J]. 中国寄生虫学与寄生虫病杂志, 2007, 25(4): 350-352.
[18]	诸欣平, 苏川. 人体寄生虫学[M]. 北京: 人民卫生出版社, 2013.
[19]	王虎. 青海省人体棘球蚴病风险因素分析[J]. 中国病原生物学杂志, 2004, 17(4): 214-216.
[20]	史大中, 李文科, 包根书. 甘肃泡型包虫病流行与人行为危险因素[J]. 中国公共卫生, 2003, 19(8): 973-974.
[21]	朱曜宇, 伍卫平, 官亚宜, 等. 四川省道孚县棘球蚴病防治中犬驱虫项目投入与需要分析[J]. 中国寄生虫学与寄生虫病杂志, 2018, 36(1): 87-92.
[22]	温浩, 初向东, 王桂芝, 等. 新疆和布克赛尔蒙古自治县囊型包虫病危险因素分析[J]. 中华流行病学杂志, 2010, 31(3): 297-299.
[23]	黄燕, David HD, 杨文, 等. 四川省藏族牧区家犬棘球绦虫病流行病学调查研究(英文)[J]. 中国寄生虫学与寄生虫病杂志, 2008, 26(4): 245-252.
[24]	韩帅, 余晴, 杨诗杰, 等. 棘球蚴病野外犬科类传染源网格化干预与评价[J]. 中国寄生虫学与寄生虫病杂志, 2018, 36(5): 73-76.

变量分类 Variable classification	变量名称 Variable name	变量类型 Variable type	赋值编码 Assignment coding
因变量 Dependent variable	人群患病率 Population prevalence	连续型变量 Continuous variables	Y
自变量 Independent variable	户均犬数 Average dog number per household	连续型变量 Continuous variables	X1
自变量 Independent variable	犬感染率Canine infection rate	连续型变量 Continuous variables	X2
自变量 Independent variable	户均羊数 Average number of sheep per household	连续型变量 Continuous variables	X3
自变量 Independent variable	户均牛数 Average number of cattle per household	连续型变量 Continuous variables	X4
自变量 Independent variable	牛患病率 Prevalence rate of cattle	连续型变量 Continuous variables	X5
自变量 Independent variable	户均年自宰牛羊数 Number of cattle and sheep slaughtered per household per year	连续型变量 Continuous variables	X6
自变量 Independent variable	小型哺乳类动物密度 Density of small mammals	连续型变量 Continuous variables	X7
自变量 Independent variable	小型哺乳类动物感染率 Infection rate in small mammals	连续型变量 Continuous variables	X8
自变量 Independent variable	人群防治棘球蚴病知识知晓率 Awareness rate of prevention and control knowledge	连续型变量 Continuous variables	X9

指标 Index	极小值Minimum value	极大值Maximum value	均值 Mean value	标准差Standard deviation	K-S检验Kolmogorov-Smirnov test
Y	1.330	14.780	6.793	4.356	0.054
X1	0.850	3.440	1.726	0.764	0.151
X2	0.000	19.120	6.250	4.615	0.200
X3	84.000	136.000	108.434	16.042	0.148
X4	18.500	49.650	32.933	9.953	0.200
X5	23.330	63.330	43.506	13.304	0.200
X6	0.720	3.600	2.060	0.914	0.200
X7	18.000	328.000	153.261	95.666	0.200
X8	0.000	68.000	14.841	15.558	0.083
X9	62.140	89.890	75.460	7.561	0.200

自变量 Independent variable	回归系数 Regression coefficient	标准误 Standard error	标准偏回归系数 Standard partial regression coefficient	t	P	回归系数的95%置信区间 95% confidence interval of regression coefficient
自变量 Independent variable	回归系数 Regression coefficient	标准误 Standard error	标准偏回归系数 Standard partial regression coefficient	t	P	下限Lower limit	上限Upper limit
常量Constant X1	-2.318	0.883		-2.624	0.016	-4.155	-0.481
常量Constant X1	5.278	0.470	0.926	11.241	< 0.001	4.302	6.255
常量Constant	1.678	0.792		2.120	0.046	0.032	3.324
X2	0.818	0.103	0.867	7.971	< 0.001	0.605	1.032
常量Constant	-13.044	4.799		-2.718	0.013	-23.024	-3.065
X3	0.183	0.044	0.674	4.177	< 0.001	0.092	0.274
常量Constant	-2.215	2.561		-0.865	0.397	-7.541	3.111
X4	0.274	0.075	0.625	3.668	0.001	0.118	0.429
常量Constant	-3.390	2.269		-1.494	0.150	-8.110	1.329
X5	0.234	0.050	0.715	4.684	< 0.001	0.130	0.338
常量Constant	0.540	1.801		0.300	0.767	-3.205	4.285
X6	3.035	0.802	0.637	3.786	0.001	1.368	4.702
常量Constant	0.918	0.963		0.953	0.351	-1.085	2.922
X7	0.038	0.005	0.842	7.144	< 0.001	0.027	0.049
常量Constant	47.490	3.353		14.164	< 0.001	40.517	54.463
X9	-0.539	0.044	-0.936	-12.196	< 0.001	-0.631	-0.447

模型Model	回归系数 Regression coefficient	标准误 Standard error	标准偏回归系数 Standard partial regression coefficient	t	P	回归系数的95%置信区间 95% confidence interval of regression coefficient
模型Model	回归系数 Regression coefficient	标准误 Standard error	标准偏回归系数 Standard partial regression coefficient	t	P	下限Lower limit	上限Upper limit
常量Constant	11.213	5.420	-	2.069	0.053	-0.174	22.600
X9	-0.146	0.061	-0.253	-2.384	0.028	-0.274	-0.017
X1	2.184	0.451	0.383	4.838	< 0.001	1.236	3.132
X2	0.231	0.075	0.244	3.064	0.007	0.073	0.389
X7	0.009	0.003	0.197	2.772	0.013	0.002	0.016