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Spatial and spatiotemporal pattern of stroke relative risk in Ghana using Bayesian modelling approach
Abdul-Karim Iddrisu1, Sampson Appiah Takyi2, Joyseline Owusu Afriyie2
1Department of Statistics, University of Botswana, Botswana; Department of Mathematics and Statistics, University of Energy and Natural Resources, Ghana.
Summary
This study mapped stroke risk across Ghana, identifying high-risk regions and predictors. Findings can guide public health interventions and resource allocation for stroke prevention.
Area of Science:
- Public Health
- Epidemiology
- Spatial Analysis
Background:
- Stroke is a leading global cause of death and disability.
- Ghana faces a significant stroke burden with limited regional risk data.
- Understanding spatial and temporal stroke risk is crucial for targeted interventions.
Purpose of the Study:
- To analyze the spatial and spatiotemporal distribution of stroke relative risk in Ghana.
- To identify regions with elevated stroke risk.
- To determine predictors associated with stroke risk.
Main Methods:
- Bayesian spatial and spatiotemporal models were employed to estimate regional stroke risk.
- Machine learning algorithms (Random Forest, Gradient Boosting) identified stroke risk predictors.
- Annual stroke data (2018-2022) from Ghana Health Service (DHIMS2) were utilized.
Main Results:
- Stroke relative risk showed a significant decrease over the study period.
- Specific regions were identified as high-risk by spatial and spatiotemporal models.
- Gross national income was a significant predictor, decreasing stroke risk; temperature and diabetes prevalence were not statistically significant.
Conclusions:
- The study provides critical data for resource allocation and targeted stroke screening in high-risk regions.
- Improving diagnostic capacity and surveillance systems is recommended to reduce bias and enhance case ascertainment.
- Findings align with Ghana's NCD policies, supporting data-driven decision-making through routine model updates.
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