摘要
目的 利用多种来源数据建立中国新生儿破伤风高危地区的筛选方法.方法 利用2010-2011年国家法定传染病上报系统、全国妇幼卫生信息年报系统等提供的数据,选择6个新生儿破伤风相关指标,采用综合加权方法计算每个地级市的高危评分,筛选出最高危的30个地市,并将该结果与新生儿破伤风发病监测系统的破伤风发病率排位结果和WHO评分方法的排位结果进行比较.结果 我国最高危的区域集中分布在西南部贫困的少数民族聚集区和东南部流动人口聚集区,高危加权综合评分的前30位中有8个地市的破伤风发病率排位在30位之后.高危加权综合评分与WHO评分方法的结果相比较,340个地市中,排位分组完全相同的为276个,一致性Kappa系数为0.56(P <0.01).两种排位结果的卡方列联系数为0.74(P <0.01),具有高度关联.但在两种方法的前36位中,有10个地市不相同.结论 本综合加权评分纳入了多个影响我国新生儿破伤风发病率的因素,并考虑各因素数值大小的作用,因而与WHO评分方法相比,这种方法对我国新生儿破伤风高危地区的筛查更接近真实情况.
Objectives To establish a method for screening neonatal tetanus (NT) in high risk areas in China using multi-sources data.Methods We adopted six NT-related indicators from National Notifiable Disease Report System (NNDRS) and National Maternal and Child Health Annual Report System,to calculate weighted high-risk score at prefecture level in 2010 and 2011.And we selected the top 30 high risk cities,and compared the scores with the actual NT incidence ranking and WHO scoring.Results The highest areas distributed in the Southwest of China with poor and minority population,and the Southeast part with high density of migrants.In the leading 30 prefectures with high score between the methods of weighted high-risk scoring and reported NT incidence ranking,there were 8 different.In comparison of the results of the methods of weighed high-risk scoring and WHO scoring,276 prefectures in 340 distributed were divided into the same ranking groups,with Kappa coefficient 0.56 (P < 0.01).The Chi-Square association coefficient was 0.74(P < 0.01),which showed a high correlation.But there were 10 different prefectures in the leading 36 prefectures between the two methods.Conclusion The weighted scoring method included several possible factors influencing NT incidence and took their weights into consideration.Thereby,compared with WHO scoring method,this method could be more appropriate for the reality in China.
出处
《中华预防医学杂志》
CAS
CSCD
北大核心
2013年第10期900-904,共5页
Chinese Journal of Preventive Medicine
基金
国际组织UNICEF基金(YH601-11)
关键词
婴儿
新生
破伤风
发病率
认证
加权
Infant,newborn
Tetanus
Incidence
Validation
Weighting