Risk factors and classification of diabetes in South Africa.
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Date
2019
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Abstract
Diabetes prevalence has been seen to be on the increase in recent years, globally and
in South Africa. The number of people with diabetes globally has risen from 108
million in 1980 to 442 million in 2014. It was estimated that, of the 1.8 million people
between 20 and 79 years old with diabetes in South Africa in 2017, 84.8% were undiagnosed.
Diabetes was the 2nd leading underlying cause of death in South Africa in
2016. Identifying risk factors for diabetes will assist in raising public awareness and
assist public authorities to develop prevention programs. This study aimed to investigate
the prevalence and risk factors associated with diabetes in the South African
population aged 15 years and older, as well as explore various statistical methods of
classifying a person’s diabetic status.
This study made use of the South African Demographic Health Survey 2016 data
which involved a two-stage sampling design. The study participants included 6442
individuals aged 15 years and older. Of the individuals sampled, 11%, 67% and 22%
were found to be non-diabetic, pre-diabetic and diabetic, respectively. Classification
methods, namely, a decision tree, random forest and Bayesian neural network, were
used to assess classification of diabetic status based on the risk factors. Of the classification
methods, the Bayesian neural network gave the highest accuracy (75.9%).
These methods however, failed to account for the complex survey design and sampling
weights. In addition, these methods are not able to provide the estimated effect
that a risk factor has on the diabetic status.
Regression models were employed to identify the significant risk factors. Due to
the ordinal nature of diabetic status, initially the proportional odds model was fit.
However, the proportional odds assumption was found to be violated. A multinomial
generalized linear mixed model was fitted to account for the complexity of
the design. However, the model’s residuals were found to be spatially autocorrelated.
Accordingly, a spatial generalized additive mixed model, which accounts for
the complexity of the survey structure as well as incorporates nonlinear spatial effects,
was adopted. The highest accuracy from the regression models considered
was obtained from this adjusted surface correlation model (accuracy = 70.8%). Individuals
of the Black/African race were more likely to be diabetic (OR = 1.429; 95%
CI: 1.032-1.978) than other races. Individuals taking high blood pressure medication
were 1.444 times more likely to be diabetic than pre-diabetic (95% CI: 1.167-1.786)
compared to those not taking high blood pressure medication.
Description
Masters Degree. University of KwaZulu-Natal, Durban.