associated with and have an impact on diabetes prevalence in Africa. Countries with elevated risk factor rates also tended to have high diabetes prevalence. Egypt, Seychelles, Mauritius, Libya, Comoros, Tunisia, Sudan, Djibouti, South Sudan, Gabon, Equatorial Guineas, Algeria and South Africa were identified as the top countries with high diabetes prevalence. The high prevalence in these countries can be attributed to their high GDPs, physician densities, population ages, and population growth factors. The countries with low prevalence rate on the continent are Benin (0.8%), Gambia, Burkina Faso, Niger, Mali, Senegal, Sierra Leon, Guinea, Guinea Bissau, Cabo Verde, Ghana, Liberia, Mauritania, Nigeria, Sao Tome and Principe, and Kenya, all having less than 2.3% diabetes prevalence rates. This can be attributed to their relatively low rates of the risk factors GDP, Urban population growth and physician density.
Our study has several limitations. First, the diabetes prevalence estimates were modelled from survey data, and we did not account for the survey sampling uncertainty or the biases and limitations of the survey. Second, we did not consider changes over time and therefore do not know how rapidly the diabetes prevalence in our spatial clusters could change. Third, we did not account for the movement of people between countries in our estimates of prevalence of diabetes. It is not known what percentage of the residents developed diabetes while residing in another county. Fourth, the Moran I for the regions was not included due to small sample size, as the number of countries in those regions were included in the data and not local counties or municipalities level.
From the LISA results, outlier countries were identified, that is, countries with high diabetes prevalence, but surrounded by countries with low diabetes (South Africa, Mali, Sierra Leone, Congo Republic, Congo DRC, Ethiopia, Uganda, Madagascar). Countries with low diabetes but surrounded with countries with high diabetes (Cote d’Ivoire, Liberia, Senegal, Chad, Eritrea, Tanzania, Lesotho, Libya, Swaziland) were also identified. This pattern should be given a close attention as it has implications on the spread of diabetes on the continent. As mentioned in the literature review (Motala, 2002; Ogurtsova et. al, 2015; Peer et. al, 2014), it has been discovered that a good percentage of these countries are high-income countries and the most populous ones. However, risk factors are not limited to high income or populous countries as Non-communicable disease (NCD) burden is common to developing countries. As positioned by the International Diabetes Federation (IDF), in efforts towards halting the rise of diabetes, a global network for diabetes awareness at the country and continental levels can be created and facilitated through joint policies and collaborative efforts (Day, 2016). The result of LISA and cluster analysis from this study could be used as a tool or instrument in mapping the joint policies and collaborative effort in the fight against diabetes instead of individual country fighting the disease in silos. Likewise, countries in this region need to improve on non-communicable disease surveillance and monitor policies can be formed with best evidence.
Efficient and continuous program for prevention and awareness need to be put in place.
In conclusion the use of cluster analysis and LISA map helps in strengthening the results, due to the heterogeneity of the population and diabetes in Africa, the cluster analysis brings together the homogenous groups of countries in the same clusters, that is countries with similar pattern in terms of the level of their prevalence and risk factors can come together to form joint policies. On the similar pattern, the hot spots countries from LISA maps can be further identified and re- grouped from their various clusters and then given more attention and urgent action in order to reduce diabetes burden in the continent.
To achieve one of the study's objectives of comparing classical and Bayesian statistics, we fitted several models such as linear regression, Poisson, and negative binomial from the classical perspective, and their respective Bayesian models from the Bayesian perspective. The Bayesian linear, Poisson, and negative binomial models were fitted using the MCMC estimation method. The regression analysis showed that GDP, physician density, urbanization, and population age were
significant across the models, with population age and physician density having positive significance in all the regression analyses. The differences between the classical and Bayesian approaches were observed, as well as differences in the mean and the parameter of estimate. We can conclude that the use of Bayesian techniques did not make a significant difference in the result due to the use of a non-informative prior. Using information from previous years can help strengthen the use of Bayesian techniques.
Overall, the highest prevalence is seen within the North, Central, East and South regions, while the West Africa region having has the prevalence. The high prevalence rate within the North, Central, East, and South regions could be attributed to increasing urbanization, demographic changes, and increases in population within the older age groups with the associated changes in levels of risk factors such as obesity, physical inactivity, alcohol consumption, and tobacco smoking. Countries in Sub- Saharan Africa, especially South Africa, are currently known to be undergoing a wide range of epidemiological transitions, with an increasing burden of non-communicable disease (Azevedo and Alla, 2008; Mbanya et al., 2010; Sobngwi et al., 2012) However, the determinant factors for low diabetes prevalence in the West Africa region could be the low rates of obesity, physical inactivity, and urbanization in most of the countries as compared with the other regions. Another contributing factor of low diabetes prevalence in this region could be the climate condition (Adeghate et al., 2006;
Fisch et al., 1987; Vandenheede et al., 2012; Aikins, 2005; Tishkoff and Williams, 2002).
In determinng the relationship between diabetes and the socio economic risk factors, OLS and GWR was compared and the strenght of GWR was established. The improved model from local GWR shows that Obesity,Urban population growth, age dependency ratio, physician density , HDI and GNI are significant variables. However,there are also limitations to our findings. The local R2s accounted for 100% of country-level diabetes prevalence. The residual parameters was not tested for spatial correlation because it was not part of the study objective. A further analysis could have been done by mapping the residuals to visualised the variables that are significants and then identify which country they are most significant. The primary strength of this study is the use of GWR in the analysis of the spatial distribution and correlates of diabetes prevalence. Siordia and colleagues (7) introduced the concept of spatial.