QUANTITATIVE MODELING OF THE EPIDEMIOLOGICAL BURDEN OF NON-COMMUNICABLE DISEASES IN GHANA: A TIME-DEPENDENT BIOSTATISTICAL ANALYSIS

Non-communicable diseases (NCDs) are a mounting public health concern in Ghana, accounting for over 45% of adult hospital admissions and a 14.7% increase in mortality from 2020 to 2024. The growing burden-coupled with inadequate surveillance and regional healthcare disparities-necessitates a data-driven, time-sensitive approach to disease modeling and intervention planning. This study aimed to quantify and predict the epidemiological burden of NCDs in Ghana using time-dependent biostatistical models, addressing gaps in previous research that lacked longitudinal scope. A quantitative methodology was employed, analyzing secondary data from over 3.1 million cases across five years using linear mixed models, Cox proportional hazards models, time-series regression, and Bayesian methods. Major findings include a statistically significant rise in NCD incidence from 150,000 to 190,000 cases annually, with time-series regression confirming a strong fit (R² = 0.83). Age, gender, and urban residence were significant predictors (β = 0.25, 0.15, 0.30 respectively), while health intervention coverage was inversely correlated with mortality (β = 0.18, p = 0.005), and socioeconomic status had a protective effect (β = -0.20, p = 0.010). The best predictive model-linear mixed-achieved an R² of 0.85 with a strong overall correlation coefficient of r = -0.91 between screening and mortality. These results highlight the critical role of predictive modeling in NCD mitigation, urging policymakers to expand screening to ≥70% coverage, strengthen regional healthcare infrastructure, and integrate real-time analytics in national health strategy. Recommendations include institutionalizing predictive systems, targeting high-risk demographics, and adopting digital health tools for localized intervention.