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Modelling predictors of stroke disease in South Africa: Bayesian binary quantile regression approach

Show simple item record Matizirofa, Lyness 2021-08-20T14:17:12Z 2021-08-20T14:17:12Z 2020
dc.identifier.citation Int J Disabil Hum Dev 2020;19(2):173-185 en
dc.identifier.issn 2191-1231
dc.description.abstract Stroke is currently the second prevalent cause of death and disability worldwide. South Africa (SA) is experiencing an epidemiological transition due to socio-demographic and lifestyle changes leading to an increase of non-comm-unicable diseases, which in turn may result in an upswing of stroke cases. Modifiable predictors cause most strokes. The purpose of this paper is to address two important gaps in the stroke disease literature that is identifying and modelling predictors of stroke and estimating linear quantile models when predictors are measured with error. Methods: A hospital-based cross-sectional study design was used to model the predictors of stroke incidences in SA. We estimated posterior marginal by integrated nested Laplace approximations (INLA) for latent Gaussian models. The main objective of this study is to assess the effects of predictors of stroke for different quantiles for adults stroke patients and to estimate linear quantile regression models when predictors are measured with error. We used Bayesian quantile regression (BQR) methods. BQR was applied to stroke data collected between 2014 and 2018 in SA. The study considered lower, central and upper quantiles. Results: The study findings showed that stroke and modifiable risk factors were significantly associated with (p < 0.0001). The prevalence of stroke increased with cholesterol, hypertension, diabetes and heart-problem (OR 1.29, 1.33, 2.92 and 1.27) respectively. Modifiable and non-modifiable predictors had significant impact on stroke across quantiles. Conclusions: Most strokes were due to modifiable risk factors. Study findings showed significant impact of each predictor on stroke across quantiles. en
dc.language.iso en en
dc.publisher © Nova Science Publishers, Inc. en
dc.subject Stroke, Bayesian quantile regression, modifiable and non-modifiable predictors, South Africa en
dc.title Modelling predictors of stroke disease in South Africa: Bayesian binary quantile regression approach en
dc.type Article en
dc.description.department Statistics en

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