# Socio-environmental predictors of diabetes incidence disparities in Tanzania mainland: a comparison of regression models for count data

## TL;DR

This study explores how socio-environmental factors influence diabetes cases in Tanzania using statistical models to identify disparities and effective analysis methods.

## Contribution

The study introduces the generalized Poisson model as a superior method for analyzing diabetes incidence disparities in Tanzania.

## Key findings

- Diabetes incidence is unevenly distributed, with higher cases in northern and urban areas.
- Generalized Poisson model outperformed negative binomial and Poisson models in analyzing diabetes data.
- Population, GDP, and hospital numbers are significant factors associated with diabetes counts.

## Abstract

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.

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.

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.

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.