Clinical characteristics and predictors of disease progression in parkinson's disease in a cohort of patients in the ashanti region, Ghana.

Abstract

Background: Parkinson's disease (PD) is a progressive neurodegenerative disorder with motor and non-motor symptoms. Sub-Saharan Africa (SSA) with its rapidly rising ageing population is experiencing a surge in the burden of PD in clinical care. Delayed diagnosis and treatment initiation, coupled with socioeconomic and healthcare barriers, may uniquely contribute to the population of PD with advanced clinical disease in SSA. This study aimed to clinically characterize Ghanaian PD patients, and identify predictors of mid-to-advanced disease.

Methods: A hospital-based cross-sectional study was conducted at Komfo Anokye Teaching Hospital, Ghana (May 2023 - March 2024). Consecutive adults meeting UK Brain Bank criteria for PD diagnosis were neurologist-confirmed and evaluated using structured interviews including Hoehn and Yahr (H&Y) staging, and MDS-UPDRS. Data were analyzed using Graph Pad Prism, with logistic regression identifying predictors of mid-to-advanced PD defined by H&Y stages 3-5.

Results: Of 161 participants (mean age 64.0 ± 9.1 years; 114 males), 98 (60.9%) were in early-stage PD (H&Y stages 1-2). Mid-to-advanced stages (H&Y stages 3-5) had higher motor scores (19.9 vs 15.4; p = 0.003) but lower non-motor scores (p < 0.001). Medication use was universal in mid-to-advanced stages vs 42.9% in early stages (p = 0.001). Male patients, those who had lost employment due to PD, and those with moderate UPDRS severity were more likely to have mid-to-advanced disease, whereas higher income and urinary symptoms were less likely.

Conclusion: Mixes of socio-demographic and clinical features are associated with mid-to-advanced stages of PD among this Ghanaian sample of PD patients. Integrating socioeconomic determinants into prognostic models may enable earlier identification of patients with mid-to-advanced PD and guide targeted interventions such as social support systems to improve their quality of life in resource-limited settings.