https://doi.org/10.4081/gh.2024.1313

Performance of a negative binomial-GLM in spatial scan statistic: a case study of low-birth weights in Pakistan

Vol. 19 No. 2 (2024)
Submitted: 12 May 2024
Accepted: 8 August 2024
Published: 3 September 2024
Spatial scan statistics, low birth weight, negative-binomial- GLM, Pakistan
Abstract Views: 104
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Authors

Sami Ullah
sami.khan3891@gmail.com
https://orcid.org/0000-0001-7371-7297
School of Mathematics, Statistics and Mechanics, Beijing University of Technology, China.
Mushtaq Ahmad Khan Barakzai
Department of Mathematics and Statistics, Institute of Business Management, Karachi, Pakistan.
Tianfa Xie
https://orcid.org/0000-0001-5817-0836
School of Mathematics, Statistics and Mechanics, Beijing University of Technology, China.

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.

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How to Cite

Ullah, S., Barakzai, M. A. K., & Xie, T. (2024). Performance of a negative binomial-GLM in spatial scan statistic: a case study of low-birth weights in Pakistan. Geospatial Health, 19(2). https://doi.org/10.4081/gh.2024.1313
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