Spatial and spatio-temporal modeling and mapping of self-reported health among individuals between the ages of 15-49 years in South Africa.
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Date
2019
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Abstract
Self-reported health has been commonly used as a measure of individuals health in public
health studies. Health presents a complete physical, emotional, and social well-being. It
also plays an important role in the development of the country, economically and socially.
Poor health still remains a serious problem and it is linked to high burden of diseases in the
world. As part of the Healthy People 2020 and Sustainable Development Goals (SDGs)
in Sub-Saharan African (SSA), the goals of improving health has not been achieved.
Hence, further investigation of the
influential factors on health is relevant to improving
health inequalities in SSA countries. Disease mapping provides a robust tool to assess
geographical variation of disease and has been used in epidemiology and public health
studies. The aim of this research is to use two distinct response outcome variables to investigate
factors and geographical variations that are associated with self-reported health
in South Africa. To accomplish the former and the latter, this research uses data from the
National Income Dynamics Study (NIDS). The NIDS datasets are longitudinal data collected
every two years from 2008. In this research, several structured additive regression
(STAR) models were utilized within a Bayesian methodology, particularly the Bayesian
hierarchical models. Models reviewed included Bayesian spatial and spatio-temporal cumulative
logit models and logistic regression models, the primary interest was on the
conditional autoregressive (CAR) models. Furthermore, the nonlinear effects of individuals
age and body mass index (BMI) were part of the research interest. Two applications
are discussed; one for the cumulative logit models for the ordinal response, the other for
the logistic regression models of the binary response. In the case of the ordinal response,
inference was based on the empirical Bayes approach, while for the binary case, a fully
Bayesian procedure was used. Similar results were obtained between the two approaches.
Findings reveal that age, gender, household income, education, exercising level, alcohol
consumption level, smoking, employment, nutrition status, TB, and depression were associated
with self-reported health. The BMI was found to have a nonlinear relationship with
self-reported health. Also, the findings show that age has a positive linear effect on selfreported
health. In addition, the findings reveal significant spatial variation, with higher
poor health prevalence in the Siyanda, John Taoli Gaetsewe, Ngaka Modiri Molema, Dr
Ruth Segomotsi Mompati, Dr Kenneth Kaunda, Frances Baard, Lejweleputswa, Xhariep,
Thabo Mofutsanyane, Fezile Dabi, Mangaung, Chris Hani, Umgungundlovu, Sisonke, Zululand,
Umkhanyakude and Gert Sibande districts. Nevertheless, low poor health prevalence
was recorded in the West Coast, Cape Winelands, Overberg, Eden, Central Karoo,
Uthungulu, iLembe, and eThekwini districts. Interventions to improve individuals health
should include addressing of gender inequalities, education, and income inequalities but
altogether with employment status and healthy living lifestyle, in particular, targeting
districts identified to have highest poor health prevalence.
Description
Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.