Adult mortality remains a neglected public issue in Sub-Sahara Africa (SSA), with most policy instruments concentrated on child and maternal health. Lack of vital registration system in SSA, further, has made it impossible to accurately estimate adult mortality. However, interest to better understand the epidemiology of HIV/AIDS, which has a greater impact on adults, has rejuvenated research in adult mortality. Understanding the hazard of and factors associated with adult mortality is crucial towards designing programmes and interventions aiming at improving the well-being of adults. The main objective of the study was to apply an event history discrete time survival analysis approach to elucidate effects of socio-economic factors on adult mortality in Namibia. Specifically I simultaneously estimated the effects of socio-economic determinants on adult mortality in Namibia; as well as investigated geographical effects of location on adult mortality in Namibia, using spatial frailty models).
The study used adult mortality data of 25,854 individuals aged 15 years and above, from the 2006/07 Namibia Demographic and Health Survey. The socio-economic factors used for the study included the type of residence (urban/rural); region; age of household head; sex of household head; marital status; education; nearest health facility; means to the nearest health; time to the nearest health facility and wealth index.
The explanatory analysis was carried out using the Kaplan Meier curves, with the log rank test used to assess significance. Further geo-additive survival models were carried out using the Bayesian framework for joint modelling of fixed, non-linear and spatial frailties. The proposed Bayesian model assumed the following prior distributions: for the fixed effects I assigned diffuse priors, while the baseline was fitted using penalized random walks priors. For unstructured random effects and the structured spatial effects, the exchangeable normal prior and conditional autoregressive prior respectively were assumed.
Results from the best model, which adjusted for the baseline, unstructured random effects, spatially structured effects as well as the fixed effects at a constituency level, showed that the overall baseline hazard of adult mortality declined constantly from age 15 up to age 40 years. The hazard of mortality subsequently increased from age 60 years. Lack of resources to improve the health, wellbeing and living standards of adult and old age people may be responsible for the increase in hazard of mortality from age 60 years.
Further, results show a clear disadvantage for adults in rural areas; for those not married as well as for those of low wealth ranking particularly the poorest, for those in female headed households. Furthermore, in terms of health factor, the result shows that adult seeking health care from hospital, as well as traveling to nearest health facility within minutes and accessing the nearest health facility by means of a car provided an advantage of better survival at old age.
In terms of the geographic effects, the hazard map, fitted at a constituency level, shows that that there was high hazard of an adult dying in the North Eastern part of the country while in the North Western and Central East there was a reduced risk in the hazard of an adult mortality.
The unstructured random effects, again fitted at a constituency level, indicated that there was spatial variation in the hazard of adult mortality at a constituency level with constituencies for Caprivi and Erongo regions in the lower hazard, while constituencies for Oshikoto and Otjozondjupa region were at the higher hazard of adult mortality.
It is hoped that this study particularly the spatial analysis section will help health planners, policy makers to identifying specific areas with high hazard of adult mortality in order to design, evaluate programmes and develop strategies aiming at improving the health and well-being of adults. Moreover, if the country is to achieve national development goals such as Vision 2030, Millennium Development Goals (MDGs) and National Development Programme 4 (NDP4), then efforts should be made to support adults in areas with high hazard of mortality while at the same time considering the impacts of socio-economic factors, since adults form part of the economic and productive age group for a population.
SSA, R (2021). An Event History Analysis Of Socio-Economic Determinants Of Adult Mortality In Namibia. Afribary.com: Retrieved May 13, 2021, from https://afribary.com/works/an-event-history-analysis-of-socio-economic-determinants-of-adult-mortality-in-namibia
Research, SSA. "An Event History Analysis Of Socio-Economic Determinants Of Adult Mortality In Namibia" Afribary.com. Afribary.com, 28 Apr. 2021, https://afribary.com/works/an-event-history-analysis-of-socio-economic-determinants-of-adult-mortality-in-namibia . Accessed 13 May. 2021.
Research, SSA. "An Event History Analysis Of Socio-Economic Determinants Of Adult Mortality In Namibia". Afribary.com, Afribary.com, 28 Apr. 2021. Web. 13 May. 2021. < https://afribary.com/works/an-event-history-analysis-of-socio-economic-determinants-of-adult-mortality-in-namibia >.
Research, SSA. "An Event History Analysis Of Socio-Economic Determinants Of Adult Mortality In Namibia" Afribary.com (2021). Accessed May 13, 2021. https://afribary.com/works/an-event-history-analysis-of-socio-economic-determinants-of-adult-mortality-in-namibia