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首页> 外文期刊>Emerging themes in epidemiology >From spatial ecology to spatial epidemiology: modeling spatial distributions of different cancer types with principal coordinates of neighbor matrices
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From spatial ecology to spatial epidemiology: modeling spatial distributions of different cancer types with principal coordinates of neighbor matrices

机译：从空间生态学到空间流行病学：使用相邻矩阵的主坐标对不同癌症类型的空间分布进行建模

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Background Epidemiology and ecology share many fundamental research questions. Here we describe how principal coordinates of neighbor matrices (PCNM), a method from spatial ecology, can be applied to spatial epidemiology. PCNM is based on geographical distances among sites and can be applied to any set of sites providing a good coverage of a study area. In the present study, PCNM eigenvectors corresponding to positive autocorrelation were used as explanatory variables in linear regressions to model incidences of eight most common cancer types in Finnish municipalities (n?=?320). The dataset was provided by the Finnish Cancer Registry and it included altogether 615,839 cases between 1953 and 2010. Results PCNM resulted in 165 vectors with a positive eigenvalue. The first PCNM vector corresponded to the wavelength of hundreds of kilometers as it contrasted two main subareas so that municipalities located in southwestern Finland had the highest positive site scores and those located in midwestern Finland had the highest negative scores in that vector. Correspondingly, the 165th PCNM vector indicated variation mainly between the two small municipalities located in South Finland. The vectors explained 13?-?58% of the spatial variation in cancer incidences. The number of outliers having standardized residual > |3| was very low, one to six per model, and even lower, zero to two per model, according to Chauvenet’s criterion. The spatial variation of prostate cancer was best captured (adjusted r2?=?0.579). Conclusions PCNM can act as a complementary method to causal modeling to achieve a better understanding of the spatial structure of both the response and explanatory variables, and to assess the spatial importance of unmeasured explanatory factors. PCNM vectors can be used as proxies for demographics and causative agents to deal with autocorrelation, multicollinearity, and confounding variables. PCNM may help to extend spatial epidemiology to areas with limited availability of registers, improve cost-effectiveness, and aid in identifying unknown causative agents, and predict future trends in disease distributions and incidences. A large advantage of using PCNM is that it can create statistically valid reflectors of real predictors for disease incidence models with only little resources and background information.
机译：背景流行病学和生态学共有许多基础研究问题。在这里，我们描述如何将邻居矩阵（PCNM）的主坐标（一种来自空间生态学的方法）应用于空间流行病学。 PCNM基于站点之间的地理距离，并且可以应用于提供良好研究区域覆盖的任何站点集。在本研究中，与正自相关相对应的PCNM特征向量被用作线性回归中的解释变量，以对芬兰市政当局中8种最常见的癌症类型的发病率进行建模（n = 320）。该数据集由芬兰癌症登记处提供，涵盖了1953年至2010年之间的615,839例病例。结果PCNM产生了165个特征值均为正的载体。第一个PCNM向量对应于数百公里的波长，因为它与两个主要子区域形成对比，因此位于芬兰西南部的市政当局在该向量中具有最高的阳性站点得分，而位于芬兰中西部的市政当局具有最高的阴性得分。相应地，第165个PCNM矢量表明主要在位于南芬兰的两个小城市之间存在变异。载体解释了癌症发病率的13％-〜58％的空间变化。标准化残差> | 3 |的离群数根据Chauvenet的标准，它非常低，每个模型一到六个，甚至更低，每个模型零到两个。最好地捕获了前列腺癌的空间变异（校正后的r2≤0.579）。结论PCNM可作为因果模型的补充方法，以更好地理解响应变量和解释变量的空间结构，并评估未测解释因子的空间重要性。 PCNM向量可用作人口统计和病因的代理，以处理自相关，多重共线性和混杂变量。 PCNM可能有助于将空间流行病学扩展到登记册有限的地区，提高成本效益，并帮助识别未知的病原体，并预测疾病分布和发病率的未来趋势。使用PCNM的一大优势在于，它只需很少的资源和背景信息，就可以为疾病发病率模型创建统计上有效的真实预测变量的反射器。

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