Roquette Rita, Nunes Baltazar, Painho Marco
Nova IMS Information Management School, Lisbon, Portugal.
National Health Institute Doutor Ricardo Jorge, Avenida Padre Cruz, 1649-016, Lisbon, Portugal.
Popul Health Metr. 2018 Mar 27;16(1):6. doi: 10.1186/s12963-018-0164-6.
Knowledge regarding the geographical distribution of diseases is essential in public health in order to define strategies to improve the health of populations and quality of life. The present study aims to establish a methodology to choose a suitable geographic aggregation level of data and an appropriated method which allow us to analyze disease spatial patterns in mainland Portugal, avoiding the "small numbers problem." Malignant cancer mortality data for 2009-2013 was used as a case study.
To achieve our aims, we used official data regarding the mortality by all malignant cancer, between 2009 and 2013, and the mainland Portuguese resident population in 2011. Three different spatial aggregation levels were applied: Nomenclature of Territorial Units for Statistics, level III (28 areas), municipalities (278 areas), and parishes (4050 areas). Standardized Mortality Ratio (SMR) and relative risk (RR) were computed with Besag, York and Mollié model (BYM) for the evaluation of geographic patterns of mortality data. We also estimated Global Moran's I, Local Moran's I, and posterior probability (PP) for the spatial cluster analysis.
Our results show that the occurrence of lower and higher extreme values of the standardized mortality ratio tend to increase with the decrease of data spatial aggregation. In addition, the number of local clusters is higher at small spatial aggregation levels, although the area of each cluster is generally smaller. Regarding global clustering, data forms clusters at all considered levels. Relative risk (RR) computed by Besag, York and Mollié model, in turn, also shows different results at the municipalities and parishes levels. However, the difference is smaller than the difference obtained by SMR computation. This statement is supported by the coefficient variation values.
Our findings show that the choice of spatial data aggregation level has high importance in the research results, as different aggregation levels can lead to distinct results. In terms of the case study, we conclude that for the period of 2009-2013, cancer mortality in mainland Portugal formed clusters. The most suitable applicable spatial scale and method seemed to be at the municipalities level and Besag, York and Mollié model, respectively. However, further studies should be conducted in order to provide greater support to these results.
疾病地理分布的相关知识对公共卫生至关重要，以便制定改善人群健康和生活质量的策略。本研究旨在建立一种方法，以选择合适的数据地理汇总水平和恰当的方法，从而能够分析葡萄牙大陆的疾病空间模式，避免“小数量问题”。以2009 - 2013年恶性肿瘤死亡率数据作为案例研究。
为实现我们的目标，我们使用了2009年至2013年期间所有恶性肿瘤死亡率的官方数据以及2011年葡萄牙大陆常住人口数据。应用了三种不同的空间汇总水平：统计领土单位命名法第三级（28个区域）、市（278个区域）和教区（4050个区域）。使用贝萨格、约克和莫利模型（BYM）计算标准化死亡率（SMR）和相对风险（RR），以评估死亡率数据的地理模式。我们还估计了全局莫兰指数、局部莫兰指数和后验概率（PP）用于空间聚类分析。
我们的结果表明，标准化死亡率的较低和较高极值的出现往往随着数据空间汇总的减少而增加。此外，在小空间汇总水平上局部聚类的数量较多，尽管每个聚类的面积通常较小。关于全局聚类，数据在所有考虑的水平上都形成聚类。通过贝萨格、约克和莫利模型计算的相对风险（RR），在市和教区水平上也显示出不同的结果。然而，差异小于通过SMR计算获得的差异。变异系数值支持了这一说法。
我们的研究结果表明，空间数据汇总水平的选择对研究结果至关重要，因为不同的汇总水平可能导致不同的结果。就案例研究而言，我们得出结论，在2009 - 2013年期间，葡萄牙大陆的癌症死亡率形成了聚类。最合适的适用空间尺度和方法似乎分别是市水平和贝萨格、约克和莫利模型。然而，应进行进一步研究以更有力地支持这些结果。
