部队人群中甲、乙型肝炎感染率的催化模型分析

马静; 张习坦; 窦凤琴; 向启贵; 王宝贵

文章摘要

马静,张习坦,窦凤琴,向启贵,王宝贵.部队人群中甲、乙型肝炎感染率的催化模型分析[J].中华流行病学杂志,1989,10(3):138-142

部队人群中甲、乙型肝炎感染率的催化模型分析

Catalytic Model Analysis of HAV and HBV Infection Rate In a PLA Population

收稿日期: 出版日期:2021-06-01

DOI：

中文关键词: 数学模型催化模型 HAV和HBV感染率

英文关键词: mathematical model Catalytic model HAV and HBV infection rate

基金项目:

作者	单位
马静	军事医学科学院微生物流行病研究所
张习坦	军事医学科学院微生物流行病研究所
窦凤琴	军事医学科学院微生物流行病研究所
向启贵	中国人民解放军38650部队卫生科
王宝贵	中国人民解放军38650部队卫生科

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中文摘要:

应用催化模型拟合部队人群中甲、乙型肝炎感染率的横断面调查资料,并用随后一年半时间的随访结果加以验证,结果尚称满意。所计算的人群平均感染力(r值)可以基本反映相对固定人群中甲、乙型肝炎的感染动态,可以用于甲、乙型肝炎感染在人群中动态的预测。本文通过对某航校2053人1983年12月普查结果进行分析,用简单催化模型拟合HAV感染率,计算结果:ŷ=0.9849(1-e^-0.1117t),r (感染力)=0.1117,与横断面调查结果符合良好,R²=0.9758;与随访一年半HAV新感染密度111.8/1000人年十分相近。用可逆催化模型拟合HBV感染率,计算结果为:ŷ=0.6736(1-e^-0.06t),r (感染力)=(a+b)=0.06,其中a (阳转率)=0.041,b (阴转率)=0.019,拟合结果与实际调查结果十分相近,R²=0.9487。经过一年半随访,看到HBV感染标记在相对固定人群中确呈双向改变,其阳转率大于阴转率(77.08/1000人年对10.11/1000人年),与计算的a>b总趋势相符。人群HBV感染是受多因素影响的复杂过程,所用于拟合的公式需进一步改进。

英文摘要:

Mathematical model can be used to express the quantitative relationship between the factors of epidemiologic processes. In this paper, we tried to analyze the data of HAV and HBV infection rates obtained in a cross-section study in a Navy aviation school in December 1983, using simple and reversible catalytic models.It was shown that the expected HAV prevalence rate was not significantly different from the observed HAV infection rate and the observed incidence density of HAV new infection (111.8/1000 person-year) found during follow-up period of one and-a-half years was nearly identical to the theoretical value(111.7/100 person-year). The result of reversible model fitted the HBV prevalence rate well, not significantly different from the data of the crosssection study, but the revalue was a little different from that observed in the follow-up period.
The results suggest that mathematical model is very useful in the analysis of e HAV and HBV prevalence rates in a fixed population. The catalytic model can be used to predict the average infection rates. To make the models fitting better, a long-time follow-up study or multivariable analysis is required and the formula needs further improvement.

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