School of Mathematics, Statistics and Mechanics, Beijing University of Technology.
Department of Mathematics and Statistics, Institute of Business Management, Karachi.
Geospat Health. 2024 Sep 3;19(2). doi: 10.4081/gh.2024.1313.
Spatial cluster analyses of health events are useful for enabling targeted interventions. Spatial scan statistic is the stateof- the-art method for this kind of analysis and the Poisson Generalized Linear Model (GLM) approach to the spatial scan statistic can be used for count data for spatial cluster detection with covariate adjustment. However, its use for modelling is limited due to data over-dispersion. A Generalized Linear Mixed Model (GLMM) has recently been proposed for modelling this kind of over-dispersion by incorporating random effects to model area-specific intrinsic variation not explained by other covariates in the model. However, these random effects may exhibit a geographical correlation, which may lead to a potential spatial cluster being undetected. To handle the over-dispersion in the count data, this study aimed to evaluate the performance of a negative binomial- GLM in spatial scan statistic on real-world data of low birth weights in Khyber-Pakhtunkhwa Province, Pakistan, 2019. The results were compared with the Poisson-GLM and GLMM, showing that the negative binomial-GLM is an ideal choice for spatial scan statistic in the presence of over-dispersed data. With a covariate (maternal anaemia) adjustment, the negative binomial-GLMbased spatial scan statistic detected one significant cluster covering Dir lower district. Without the covariate adjustment, it detected two clusters, each covering one district. The district of Peshawar was seen as the most likely cluster and Battagram as the secondary cluster. However, none of the clusters were detected by GLMM spatial scan statistic, which might be due to the spatial correlation of the random effects in GLMM.
空间聚集分析在医学领域中非常有用，可以为有针对性的干预措施提供支持。空间扫描统计是进行这种分析的最先进方法，泊松广义线性模型（GLM）方法可用于调整协变量的计数数据，以进行空间聚类检测。然而，由于数据过度离散，其用于建模的应用受到限制。最近提出了广义线性混合模型（GLMM），通过在模型中纳入随机效应来模拟区域特定的内在变异，从而对这种过度离散进行建模，而这些内在变异无法用模型中的其他协变量来解释。然而，这些随机效应可能表现出地理相关性，这可能导致潜在的空间聚类未被检测到。为了处理计数数据中的过度离散，本研究旨在评估负二项式-GLM 在空间扫描统计中的表现，以分析 2019 年巴基斯坦开伯尔-普赫图赫瓦省的低出生体重真实数据。结果与泊松-GLM 和 GLMM 进行了比较，结果表明，在存在过度离散数据的情况下，负二项式-GLM 是空间扫描统计的理想选择。通过协变量（产妇贫血）调整，基于负二项式-GLM 的空间扫描统计检测到一个显著的聚类，覆盖了迪尔低地区。没有协变量调整时，它检测到两个聚类，每个聚类覆盖一个区。白沙瓦区被视为最有可能的聚类，而巴塔哥尔区则是次要聚类。然而，GLMM 空间扫描统计并没有检测到任何聚类，这可能是由于 GLMM 中随机效应的空间相关性。
