Socio-environmental predictors of diabetes incidence disparities in Tanzania mainland: a comparison of regression models for count data
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
Springer Science and Business Media LLC
Abstract
Abstract
Background
Diabetes is one of the top four non-communicable diseases that cause death and illness to many people around the world. This study aims to use an efficient count data model to estimate socio-environmental factors associated with diabetes incidences in Tanzania mainland, addressing lack of evidence on the efficient count data model for estimating factors associated with disease incidences disparities.
Methods
This study analyzed diabetes counts in 184 Tanzania mainland councils collected in 2020. The study applied generalized Poisson, negative binomial, and Poisson count data models and evaluated their adequacy using information criteria and Pearson chi-square values.
Results
The data were over-dispersed, as evidenced by the mean and variance values and the positively skewed histograms. The results revealed uneven distribution of diabetes incidence across geographical locations, with northern and urban councils having more cases. Factors like population, GDP, and hospital numbers were associated with diabetes counts. The GP model performed better than NB and Poisson models.
Conclusion
The occurrence of diabetes can be attributed to geographical locations. To address this public health issue, environmental interventions can be implemented. Additionally, the generalized Poisson model is an effective tool for analyzing health information system count data across different population subgroups.
Description
Abstract. Full text available at https://link.springer.com/article/10.1186/s12874-024-02166-w
Keywords
Diabetes Incidence, Count Data Models, Generalized Poisson Model, Geographical Distribution, Socio-Environmental Factors
Citation
Mbwambo, S. H., Mbago, M. C., & Rao, G. S. (2024). Socio-environmental predictors of diabetes incidence disparities in Tanzania mainland: a comparison of regression models for count data. BMC Medical Research Methodology, 24(1), 75.