ITC Faculty Geo-Information Science and Earth Observation, University of Twente, Enschede, the Netherlands.
Department of Mathematics, University Jaume I, Castellón, Spain.
Stat Methods Med Res. 2024 Sep;33(9):1637-1659. doi: 10.1177/09622802241268488. Epub 2024 Aug 14.
Multivariate disease mapping is important for public health research, as it provides insights into spatial patterns of health outcomes. Geostatistical methods that are widely used for mapping spatially correlated health data encounter challenges when dealing with spatial count data. These include heterogeneity, zero-inflated distributions and unreliable estimation, and lead to difficulties when estimating spatial dependence and poor predictions. Variability in population sizes further complicates risk estimation from the counts. This study introduces multivariate Poisson cokriging for predicting and filtering out disease risk. Pairwise correlations between the target variable and multiple ancillary variables are included. By means of a simulation experiment and an application to human immunodeficiency virus incidence and sexually transmitted diseases data in Pennsylvania, we demonstrate accurate disease risk estimation that captures fine-scale variation. This method is compared with ordinary Poisson kriging in prediction and smoothing. Results of the simulation study show a reduction in the mean square prediction error when utilizing auxiliary correlated variables, with mean square prediction error values decreasing by up to 50%. This gain is further evident in the real data analysis, where Poisson cokriging yields a 74% drop in mean square prediction error relative to Poisson kriging, underscoring the value of incorporating secondary information. The findings of this work stress on the potential of Poisson cokriging in disease mapping and surveillance, offering richer risk predictions, better representation of spatial interdependencies, and identification of high-risk and low-risk areas.
多变量疾病制图对于公共卫生研究很重要，因为它可以深入了解健康结果的空间模式。用于绘制空间相关健康数据的地质统计方法在处理空间计数数据时会遇到挑战。这些挑战包括异质性、零膨胀分布和不可靠的估计，从而导致在估计空间依赖性和进行不良预测时出现困难。人口规模的变化进一步使从计数中估计风险变得复杂。本研究引入了多元泊松协克里金法来预测和过滤疾病风险。纳入了目标变量与多个辅助变量之间的成对相关性。通过模拟实验和宾夕法尼亚州人类免疫缺陷病毒发病率和性传播疾病数据的应用，我们展示了精确的疾病风险估计，能够捕捉到细微的变化。该方法与普通泊松克里金法在预测和平滑方面进行了比较。模拟研究的结果表明，利用辅助相关变量可以减少均方预测误差，均方预测误差值最多可降低 50%。在真实数据分析中，这种增益更为明显，泊松协克里金法相对于泊松克里金法可使均方预测误差降低 74%，这进一步凸显了纳入次要信息的价值。这项工作的结果强调了泊松协克里金在疾病制图和监测中的潜力，提供了更丰富的风险预测、更好地表示空间相互依存关系以及识别高风险和低风险区域。
