Background

Cataract has a strong association with the socio-economic, environmental, and occupation-related factors. Despite spatial heterogeneity, globally, it is the rural population, especially women, who are at higher risk compared to urban population and men. Therefore, there is a need to identify the common contributing factors of the disease across regions.

Objective and methods

The objectives of this study are to evaluate the application of the COM-Poisson (CMPm) GLM for accurate parameter extraction that helps in analyzing cataract incidence, determining the key contributing factors, and performing risk assessment for identifying the vulnerable group and villages for the study area.

Results and conclusions

We found that COM-Poisson (CMPm) regression model was more suitable to estimate the parameter coefficients and handle under-dispersed cataract count data for the given conditions. Due to the importance of soil as a factor and particularly “Inceptisol” type of soil-order, the relation between occupation of the local workforce and the time of the year on cataract disease was established. The relative risk assessments among villages in each hobli were evaluated and listed.

over/under-dispersion, count data, Poisson generalization, modeling.

Received: June 22, 2022; Accepted: August 20, 2022; Published: September 13, 2022

How to cite this article: Vyasa Rao Prasanna and Syed Ashfaq Ahmed, Impact of geological factor on cataract eye disease using COM-Poisson regression model, JP Journal of Biostatistics 21 (2022), 103-139. http://dx.doi.org/10.17654/0973514322023

This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License

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