MSc(Med)Population Based Field Epidemiology, Faculty of Health Sciences, University of the Witwatersrand, 2009 / Introduction: In Africa, elderly adult mortality, just like many issues affecting the old has not been adequately addressed by research. This study explored the relationship between socioeconomic status (SES) and elderly adult mortality in an economically deprived region of rural Ghana. Methods: Data from the Navrongo DSS was used for the analysis. SES was determined from the asset data using principal component analysis. A total of 15030 adults aged over 60 years were included in the study, out of which 1315 died. We investigated the above relationship using Cox proportional hazards regression methods while controlling for other variables. Results: Socioeconomic status (SES) was found not to be a determinant of elderly mortality. Compared to the lowest SES quintile, the adjusted hazards ratios were, 0.94 (95%CI: 0.79–1.12) for second quintile, 0.91 (95%CI: 0.76–1.08) for third quintile, 0.89 (95%CI: 0.75–1.07) for fourth quintile and 1.02 (95%CI: 0.86–1.21) for the highest income quintile. However, living without a spouse [HR=1.98, 95%CI: 1.74–2.25], being male [HR=1.80, 95%CI: 1.59–2.04] and age [HR=1.05, 95%CI: 1.04– 1.05] were significant factors for elderly adult mortality. Conclusion: These results indicate that companionship, social and family ties in the health of the elderly adults are of more importance than the socioeconomic status of the household. Efforts should therefore be made to support the elderly, such as stipend for the elderly adults, especially those living alone; lowering the provision of free medical care in public hospitals to cover people over the age of 60 and not just 70 year olds and above as is currently done; encourage family care for the elderly relatives through provision of an elderly caretaker allowance among others.

MSc (Med), Popualtion-Based Field Epidemiology, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand / Background Improving the health of the poor and reducing health inequalities between the poor and non-poor has become central goals of international organizations like the World Bank and WHO as well as, national governments in the contexts of their domestic policies and development assistance programmes. There are also unquantified and poorly understood inequalities in access to health services within and between various population groups. Little is known about the factors that determine these inequalities and the mechanisms through which they operate in various sub-groups. Objectives The aim of the study was first to describe under-five mortality trend according to wealth index; second to describe risk factors for under five mortality; and finally to investigate the relationship between socio-economic and demographic factors and under five mortality during the period 2001 to 2006. Methods The study involved all children born in 2001-2006. A total of 22,422 children younger than 5 years were found in 21,494 households yielding 36603.13 Person-Years Observed (PYOs) up to 31st December 2006. Household wealth index was constructed by use of Principal Component Analysis (PCA), as a proxy measure of each household SES. From this index households were categorized into five quintiles (i.e., poorest, poorer, poor, less poor and least poor). Life table estimates were used to estimate mortality rates per 1000 PYO for infants (0-1), childhood (1-5) and underfives children. Health inequality was measured by poorest to least poor mortality rate ratio and by computing mortality concentration indices. Trend test chi-square was used to determine significance in gradient of mortality rates across wealth index quintiles. Risk factors of child mortality were assessed by the use of Cox proportional hazard regression taking into account potential confounders. v Results The result indicates unexpected low mortality rate for infant (33.4 per 1,000 PYO, 95% CI (30.4 – 35.6)) and childhood (15.0 per 1,000 PYO, 95% CI (13.9 – 16.3)). Under-five mortality rate was 18.2 per 1,000 PYO (95% CI (75.6 – 108.0)). The poorest to least poor ratios were 1.1, 1.5 and 1.5 for infants, childhood, and under-five year olds respectively, indicating that children in the poorest quintile were more likely to die as compared to those in the least poor household. Computed values for concentration indices were negative (infant C= -0.02, children C= -0.09 and underfive C= -0.04) indicating a disproportionate concentration of under-five mortality among the poor. The mortality rates trend test chi-square across wealth index quintiles were significant for both childhood (P=0.004) and under-five year old children (P<0.005) but not for infants (P=0.134). In univariate Cox proportional hazard regression, children in the least poor households were shown to have a 35% reduced risks of dying as compared to children in the poorest category [crude H.R =0.65, P=0.001, 95% C.I (0.50 – 0.84)]. The results showed that for under five children, a boy is 1.15 times more likely to die as compared to a girl [crude H.R =1.14, P=0.038, 95% C.I (1.00 - 1.31)]. Second born had a 18% reduced risk of dying as compared to first born [crude H.R =0.82, P=0.048, 95% C.I (0.67 – 0.99)]. After controlling for potential confounders, the adjusted hazard ratio for wealth index decreased slightly. The estimated hazard for wealth index in the univariate was 0.65 while in the multivariate modeling the estimated hazard ratio is 0.60 in the first model. Conclusion The study shows that household socio-economic inequality is associated with underfive mortality in the Navrongo DSS area. The findings suggest that reductions in infant, childhood, and under five mortalities are mainly conditional in health and education interventions as well as socioeconomic position of households. The findings further call for more pragmatic strategies or approaches for reducing health inequalities. These could include reforms in the health sector to provide more equitable resource allocation. Improvement in the quality of the health services offered to the poor and redesigning interventions and their delivery to ensure they are more inclined to the poor.