Page 234 - Special Topic Session (STS) - Volume 2

STS489 Glory A. et al.
small area levels. Understanding this dynamic in context will improve
identification of high risk groups for effective targeting of public health
interventions and resource allocation in resource limited settings.

Keywords
High blood pressure; Geo-additive models; CVD risk factors; Bayesian MCMC;
Space-time Modeling

1. Introduction
The burden of hypertension in Low and Middle-income countries in the
past two decades has become a serious cause for regional concern. High
blood pressure is a leading predictor of stroke and other cardiovascular
outcomes due to both chronic and communicable diseases across Sub-
Saharan Africa (Agyei-Mensah). Recent studies and reports have
demonstrated that these changes are, to a large extent, driven by lifestyle
changes such as increase in alcohol consumption and poor dietary choices,
as well as decline in physical activity over time due to rapid urbanization and
population growth (Aikins et al, 2010).
In South Africa, the burden of hypertension remains an important health
system challenge with an estimated prevalence burden of 77.9%, the highest
of any country in Sub-Saharan Africa (Lloyd-Sherlock et al, 2014). More so,
the evolving public health significance of geographic location continues to
shape patterns of health and disease outcomes across the region. Few
studies so far have attempted to study the role of geography (small area) as
a primary exposure risk in the observed prevalence patterns of hypertension
and other chronic diseases in Sub-Saharan Africa (Kandala et al, 2013;
Kandala and Stranges 2014; Weimman, 2016).
Two previous studies have examined the spatial variation in hypertension
in South African adult population (Kandala et al, 2013; Kandala et al, 2013).
While both studies provide important insight into the spatial epidemiology
of hypertension in South Africa, the South African demographic and health
(DHS) survey data analyzed was conducted in 1998, thereby leaving a
significant gap with regard to current situation since the turn of the
millennium. In this paper we aim to quantify the burden of hypertension from
2008 to 2017 in South African adult population by mapping geographical
variations in risk and to evaluate trend before and after the launch of the
National Strategic policy in 2012 to monitor cardiovascular morbidities
across the districts (DoHSA, 2013).

2. Methodology
Outcome variable: The primary outcome is the Bernoulli distribution of
hypertension (also known as raised blood pressure) in South Africa adult

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