调查研究

黔东南少数民族地区常见儿童先心病流行病学调查及延迟就医影响因素

  • 杨秀华 ,
  • 杨勇灵 ,
  • 张镇 ,
  • 龙建军 ,
  • 程韬 ,
  • 陈健 ,
  • 田存浩
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  • 贵州省黔东南苗族侗族自治州人民医院 (贵州 凯里 556000 )

收稿日期: 2023-08-01

  网络出版日期: 2024-03-06

基金资助

贵州省卫生健康委科学技术基金项目(gzwkj2021-206)

Epidemiological study on common congenital heart disease in children in ethnic minority areas in southeastern Guizhou and influencing factors of delayed medical treatment

  • Xiuhua YANG ,
  • Yongling YANG ,
  • Zhen ZHANG ,
  • Jianjun LONG ,
  • Tao CHENG ,
  • Jian CHEN ,
  • Cunhao TIAN
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  • People′s Hospital of Qiandongnan Miao and Dong Autonomous Prefecture,Rongjiang 556000,China

Received date: 2023-08-01

  Online published: 2024-03-06

摘要

目的 探究黔东南少数民族地区常见儿童先心病(CHD)流行病学调查及延迟就医影响因素。 方法 抽取2019年1月至2022年7月黔东南苗族侗族自治州部分家庭3个月至14岁儿童共18 850例;其中CHD患儿105例,纳入训练集;根据是否延迟就医,分为延迟组(80例)和未延迟组(25例);另选2022年7-12月部分家庭CHD患儿(35例)纳入验证集。收集研究对象的一般资料,对比分析延迟组和未延迟组患儿的一般资料。多因素logistic回归分析危险因素,构建风险评分模型并评价。 结果 18 850例儿童CHD患病率为5.57‰(105/18 850),其中黎平县患病率最高,而黄平县患病率最低;CHD类型中继发孔型房间隔缺损患儿比例最高,主动脉瓣畸形最低。CHD复杂情况中单一型患儿比例最高,合并3种及以上最低。CHD患儿中,延迟就医率为76.19%(80/105);中位延迟就医时间为12个月,平均(18.78 ± 4.77)个月。多因素logistic回归分析结果显示,心脏杂音2 ~ 3级、监护人中小学教育程度、家庭人均收入< 2 000元、监护人经常饮酒,均是延迟就医的独立危险因素(P < 0.05),医疗费用商业结算是独立保护因素(P < 0.05)。风险评分模型将患儿分为3组,即低(≤ 80分)、中(> 80分且≤ 134分)和高风险(>134分);模型评价结果显示,其精准度较高,且有效性较好,安全可靠,实用性强。 结论 黎平县患病率最高,继发孔型房间隔缺损患儿比例最高,单一型患儿比例最高。大部CHD患儿存在延迟就医。心脏杂音、监护人教育程度、家庭人均收入、监护人饮酒情况、医疗费用结算方式,均是延迟就医的独立影响因素。

本文引用格式

杨秀华 , 杨勇灵 , 张镇 , 龙建军 , 程韬 , 陈健 , 田存浩 . 黔东南少数民族地区常见儿童先心病流行病学调查及延迟就医影响因素[J]. 实用医学杂志, 2024 , 40(2) : 253 -260 . DOI: 10.3969/j.issn.1006-5725.2024.02.022

Abstract

Objective To conduct an epidemiological survey of common congenital heart disease (CHD) among children in ethnic minority areas in southeastern Guizhou and to explore the influencing factors of delayed medical treatment. Methods From January 2019 to July 2022, 18 850 children aged 3 months to 14 years in Qiandongnan Miao and Dong Autonomous Prefecture were selected; 105 children with CHD were included in the training set, and they were divided into delayed group (80 cases) and non-delayed group (25 cases) according to whether or not to delay medical treatment. In addition, children with CHD (35 cases) from July 2022 to December 2022 were included in the validation set. The general data of the subjects in the two groups were compared and analyzed. Multivariate logistic regression was performed and risk scoring model was constructed. Results The prevalence of CHD in 18 850 children was 5.57‰ (105/18 850), with the highest prevalence in Liping County, and the lowest in Huangping County. The proportion of children with secondary atrial septal defect was the highest, and that of the aortic valve malformation was the lowest. Among the complex cases of CHD, the proportion of children with single type was the highest, and that of children with three or more types were the lowest. Among children with CHD, the rate of delayed medical treatment was 76.19% (80/105). The median delay in medical treatment was 12 months, with an average of (18.78 ± 4.77) months. Multifactor logistic regression analysis showed that heart murmur (level 2 ~ 3), less-educated (primary and secondary school) guardian, family per capita income < 2 000 yuan, and frequent drinking of the guardian were independent risk factors for delayed medical treatment (P < 0.05), and commercial settlement of medical expenses was independent protective factor (P < 0.05). Risk scoring model divided the children into three groups: low risk (≤ 80 points), medium risk (> 80 points and ≤ 134 points) and high (> 134 points) risk group. The evaluation of the model show that it was accurate, effective, safe, and reliable. Conclusion The highest prevalence is observed in Liping County. The proportion of children with secondary atrial septal defect and the proportion of children with single type are the highest. Delayed medical treatment is found in most of the children with CHD. Cardiac murmur, education background of the guardian, per capita family income, guardian alcohol consumption, and medical expense settlement method are all independent influencing factors for delayed medical treatment.

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