Socio-economic predictors of chronic diseases in Ghana are not well understood and their influence has been relatively overlooked. This paper seeks to examine the influence of socio-economic predictors of chronic diseases in Ghanaians three different age groups. The data employed in the study were drawn from Global Ageing and Adult Health survey conducted in Ghana by SAGE and was based on the design for the World Health Survey. The survey was conducted in 2007 and collected data on socio-economic characteristics and other variables of the individuals interviewed. The overall results suggest that chronic diseases in relatively older Ghanaians reflects social and economic exposures with the differentials observed only partially explained by current social and economic conditions. Our results were by and large very much expected from the current medical knowledge available.

The article here investigated the impact of Preventive Health Behaviors and Risk Factors as measures of Health Status of Ghanaians. We carry out a cross-sectional analysis of 5573 adults who participated and had indicated that they needed to state their health description in the three years prior to the phase 2007 World Health Organization, a study on Global Ageing and Adult health (SAGE) conducted in Ghana. The ordinal logistic regression model was employed for analysis using R. The results suggest that, there is incontrovertible evidence showing a strong relationship between preventive health behaviors and health status of Ghanaians. Again, the lifestyle of Ghanaians clearly manifests in their positive correlation with the good and moderate health state due to the high percentage (38.96% and 39.04%) respectively. The outcome points to a potential link with the Ghanaian social and health policies.

As the concept of methodology has advanced, varied methods of estimating residuals have been developed including regression method, Bartlett’s method and Anderson-Rubin’s method. The study utilized estimation maximization approach together with other methods of estimating residuals under the structural equation model. The results showed that the strength of the existing methods in structural equation modelling are the weaknesses of the estimation maximization method, and vice versa. It was, therefore, found that from the comparative model fit information that the Bartlett’s based method gave better residual parameter estimates compared to the Regression based and the Anderson Rubin based methods. However, the estimation maximization method gave better residual parameter estimates than the other three existing methods; the Regression, Bartlett’s and the Anderson Rubin based methods.