Closing the gap on causal processes of infection risk from cross-sectional data: structural equation models to understand infection and co-infection.
Closing the gap on causal processes of infection risk from cross-sectional data: structural equation models to understand infection and co-infection.
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DOI：
10.1186/s13071-015-1274-7
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发表时间：
2015-12-23
影响因子：
3.2
通讯作者：
VandeWoude S
中科院分区：
文献类型：
--
作者：
Carver S;Beatty JA;Troyer RM;Harris RL;Stutzman-Rodriguez K;Barrs VR;Chan CC;Tasker S;Lappin MR;VandeWoude S
疾病暴露风险的流行病学研究通常基于观察性、横截面数据，并使用统计方法作为正式因果过程和预测暴露风险的重要工具。然而，传统模型的一个公认的局限性是，推断的关系是相关的，不能轻易地区分疾病风险的直接和间接决定因素，并且通常是复杂相互关系的相当大的简化。当试图通过病原体促进来推断共同感染模式的因果关系时，这一点可能特别重要。我们基于我们对家猫中的猫伽马疱疹病毒（FcaGHV1）的研究系统，使用结构方程模型（SEM）描述横截面数据的分析，这是传统回归方法的当代进步。 SEM 强烈支持与 FcaGHV1 暴露和共同感染风险相关的潜在（宿主表型）变量，表明这些个体更有可能感染多种病原体。然而，也不同程度地检测到病原体协方差（潜在促进）的迹象：可能在 FcaGHV1、巴尔通体属和支原体属之间。我们的模型表明，多次暴露主要是由宿主表型特征驱动的，例如攻击性的雄性表型，其次是病原体与病原体之间的相互作用。本研究的结果证明了 SEM 在利用观测数据理解流行病学过程中的应用，并且可以更广泛地用作理解各种学科中复杂横截面信息的补充工具。
Epidemiological studies of disease exposure risk are frequently based on observational, cross-sectional data, and use statistical approaches as crucial tools for formalising causal processes and making predictions of exposure risks. However, an acknowledged limitation of traditional models is that the inferred relationships are correlational, cannot easily distinguish direct from indirect determinants of disease risk, and are often considerable simplifications of complex interrelationships. This may be particularly important when attempting to infer causality in patterns of co-infection through pathogen-facilitation. We describe analyses of cross-sectional data using structural equation models (SEMs), a contemporary advancement on traditional regression approaches, based on our study system of feline gammaherpesvirus (FcaGHV1) in domestic cats. SEMs strongly supported a latent (host phenotype) variable associated with FcaGHV1 exposure and co-infection risk, suggesting these individuals are simply more likely to become infected with multiple pathogens. However, indications of pathogen-covariance (potential facilitation) were also variably detected: potentially among FcaGHV1, Bartonella spp and Mycoplasma spp. Our models suggest multiple exposures are primarily driven by host phenotypic traits, such as aggressive male phenotypes, and secondarily by pathogen-pathogen interactions. The results of this study demonstrate the application of SEMs to understanding epidemiological processes using observational data, and could be used more widely as a complementary tool to understand complex cross-sectional information in a wide variety of disciplines.