An analysis of recent stroke cases in South Africa: Trend, seasonality and predictors
Background. South Africa (SA) is experiencing an epidemiological transition as a result of sociodemographic and lifestyle changes. This process is leading to an increase in non-communicable diseases, which in turn may result in an upswing of stroke cases. Stroke is among the top 10 leading causes of disability in SA, and accounts for ~25 000 deaths annually and 95 000 years lived with disability (YLD). This huge burden of stroke hampers socioeconomic development as a result of YLD.
Objectives. To investigate the seasonality and trend of stroke cases in SA, and determine the risk factors associated with stroke.
Methods. Using recent hospital-based data (January 2014 - December 2017 inclusive) from SA private and public hospitals (33% private and 67% public), a sample of 14 645 suspected stroke cases was drawn. Associations between suspected stroke cases and potential predictors were assessed using χ2 tests and bivariate analysis. Time series analysis tools for trend and seasonality components included both time domain and frequency domain techniques. A Poisson generalised linear model was used, as there was no over-dispersion inherent in the data. Multiple logistic regression analysis was used to assess the effect of several predictors on stroke cases.
Results. Of the 14 645 suspected cases of stroke, 51.5% were confirmed. Seasonality analysis gave an approximate seasonal change of 120 cases, the highest seasonal peak occurring in mid-winter and the lowest dip in mid-summer. Both upward trend and seasonality parameters were found to be statistically significant. Predictors significantly associated with an increased likelihood of stroke were heart problems (odds ratio (OR) 8.86; 95% confidence interval (CI) 8.23 - 9.55; p<0.0001), diabetes (OR 14.53; 95% CI 13.36 - 15.79; p<0.0001), female sex (OR 18.23; 95% CI 16.75 - 19.85; p<0.0001), age 59 - 77 years (OR 1.37; 95% CI 1.24 - 1.50; p<0.0001) and 78 - 98 years (OR 1.25; 95% CI 1.16 - 1.35; p<0.0001) and white ethnic group (OR 2.00; 95% CI 1.86 - 2.15; p<0.0001), compared with the respective reference groups. The prevalence ratios of stroke cases as measured by Poisson regression were in agreement with logistic regression results.
Conclusions. The increasing trend of stroke in SA should be arrested urgently, taking into account both the associated risk factors and seasonality.
E Ranganai, Department of Statistics, College of Science, Engineering and Technology, School of Science, University of South Africa, Roodepoort, South Africa
L Matizirofa, Department of Statistics, Faculty of Science, University of Johannesburg, South Africa
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Date published: 2020-01-29
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