A multi-level analysis of risk factors for Schistosoma japonicum infection in China

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• 作者：Jianzhou Yang ; Zhengyuan Zhao ; Yuesheng Li ; Daniel Krewski ; Shi Wu Wen
• 关键词：Schistosoma japonicum ; China ; Risk factors ; Multi-level model ; Logistic regression model
• 刊名：International Journal of Infectious Diseases
• 出版年：2009
• 出版时间：November 2009
• 年：2009
• 卷：13
• 期：6
• 页码：e407-e412
• 全文大小：120 K

文摘

Summary

Objective

The aim of this study was to explore the risk factors of schistosomiasis japonica in China, using a hierarchical multi-level model with individuals nested within villages.

Methods

A cross-sectional survey of schistosomiasis japonica was conducted in 16 villages in the Chinese province of Hunan. A multi-level modeling technique (HLM version 6.04) was used to assess risk factors of schistosomiasis. The results from this multi-level model were compared with those from a conventional single-level logistic regression model.

Results

A total of 10 245 individuals were enrolled in this study, of whom about 4.1 % were infected with Schistosoma japonicum. In the multi-level model analysis, individual level variables such as gender, age, and occupation, and village level variables such as type of S. japonicum endemic area, drinking water source, sewage treatment, June temperature, and April rainfall were associated with schistosomiasis japonica infection. Conventional single-level logistic regression analysis selected more independent variables, and had narrower confidence intervals around the corresponding regression coefficients. In particular, per capita income, precipitation in October, and density of infected snails were statistically significant in the conventional single-level logistic regression analysis but not in the multi-level model.

Conclusions

Multi-level modeling is a useful tool in the analysis of risk factors of schistosomiasis japonica. Because the multi-level model captures the hierarchical structure of the data, it may be considered a more appropriate analytical tool for data of this type. This technique may also be useful in the analysis of other infectious diseases with a similar hierarchical structure.