article · Asian Journal of Research in Infectious Diseases
Tuberculosis presents a substantial public health challenge in agricultural communities in Benue State, Nigeria. An analysis of monthly tuberculosis mortality figures recorded by the Benue State Epidemiological Unit between January 2010 and December 2023 evaluated three statistical approaches: Poisson Regression, Negative Binomial Regression, and Generalized Poisson Regression. Because the epidemiological records exhibited over-dispersion, Negative Binomial Regression emerged as the best-fitting model across standard evaluative criteria, including the Akaike Information Criterion and Bayesian Information Criterion. The statistical evaluation showed that confirmed, active, and severe cases act as significant predictors of mortality. Conversely, a strong negative correlation was identified between patient recoveries and deaths. The findings indicate that prioritising appropriate analytical models alongside improved case management and data collection can assist targeted public health efforts for vulnerable farming populations.
Agricultural communities often face heightened vulnerability to severe infectious diseases like tuberculosis. Identifying which health metrics most accurately forecast mortality enables regional health authorities to monitor outbreaks more effectively. Demonstrating that increased recoveries directly correlate with reduced deaths reinforces the critical need for early case detection, treatment adherence, and dependable data reporting in rural healthcare systems.
The findings provide an applied statistical framework that public health agencies and healthcare software developers could integrate into regional epidemiological surveillance platforms. Based on retrospective analysis of public health data, the work represents applied, tested research that sits at an early stage relative to commercial software deployment, requiring validation within operational disease monitoring pipelines before serving as a real-time decision-support tool.
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Tuberculosis (TB) remains a major public health concern in many parts of the world, including Benue State, Nigeria, where agricultural communities are particularly vulnerable. This study aims to model the monthly mortality incidence of tuberculosis (TB) among farmers in Benue State, Nigeria, focusing on serologically confirmed, active, severe, recovered, and mortality cases using count data regression models. Three count data regression models: Poisson Regression (PR), Negative Binomial Regression (NBR), and Generalized Poisson Regression (GPR) were employed to predict TB-related mortality based on these variables. Secondary data from the Benue State Epidemiological Unit, Makurdi, spanning from January 2010 to December 2023, served as the basis for analysis. The study found the presence of over-dispersion in the Poisson Regression model which necessitated the use of NBR and GPR. Model performance was evaluated using -2 Log-Likelihood (-2 logL), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). Among the three competing models, NBR provided the best fit, with a -2 logL value of -901.92, an AIC of 1203.85, and a BIC of 1223.70, effectively addressing the over-dispersion in the data. The analysis identified confirmed, active, and severe TB cases as significant predictors of TB-related mortality in Benue State. Additionally, a strong negative and significant relationship was observed between recovered cases and mortality, indicating that an increase in recoveries correlates with a decline in TB-related deaths. The study recommends that policymakers and researchers should prioritize the Negative Binomial Regression model for TB analysis, enhance TB case management and treatment adherence, improve TB data collection, and design targeted interventions to reduce severe cases and increase recovery rates among vulnerable populations like farmers.
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DOI: 10.9734/ajrid/2025/v16i4435
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