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An analysis of recent stroke cases in South Africa: Trend, seasonality and predictors

机译:南非近期中风病例分析:趋势,季节性与预测因子

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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; p0.0001), diabetes (OR 14.53; 95% CI 13.36 - 15.79; p0.0001), female sex (OR 18.23; 95% CI 16.75 - 19.85; p0.0001), age 59 - 77 years (OR 1.37; 95% CI 1.24 - 1.50; p0.0001) and 78 - 98 years (OR 1.25; 95% CI 1.16 - 1.35; p0.0001) and white ethnic group (OR 2.00; 95% CI 1.86 - 2.15; p0.0001), 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.
机译:背景。南非(SA)由于社会渗目和生活方式而经历了流行病学转变。该过程导致非传染性疾病的增加,这反过来可能导致中风病例的上升。中风是SA中残疾​​的十大前进原因之一,每年〜25 000人死亡,9.5万年与残疾(YLD)居住。由于yld.bictives,这一巨大的行程妨碍了社会经济发展。探讨SA中风病例的季节性和趋势,并确定与中风相关的风险因素。方法。采用近期医院的数据(2014年1月 - 2017年12月包装)从SA私立医院(33%私有医院和67%的公众),绘制了14名645个疑似行程病例的样本。使用χ2检验和双变量分析评估疑似行程病例和潜在预测因子之间的关联。时间序列分析工具用于趋势和季节性组件包括时域和频域技术。使用泊松广告的线性模型,因为数据中没有固有的过分分散。多个逻辑回归分析用于评估几个预测器对中风病例的影响。结果。在14例645疑似卒中病例中,确认了51.5%。季节性分析给出了120例季节变化的近似季节性变化,中冬季最高的季节性峰值,夏季中季度最低的趋势。发现向上趋势和季节性参数都有统计学意义。与中风的增加的预测因素具有显着相关的中风的可能性是心脏问题(差异比(或)8.86; 95%置信区间(CI)8.23-9.55; p <0.0001),糖尿病(或14.53; 95%CI 136-15.79; P < 0.0001),女性(或18.23; 95%CI 16.75 - 19.85; P <0.0001),年龄59-77岁(或1.37; 95%CI 1.24 - 1.50; P <0.0001)和78 - 98年(或1.25;与相应的参考组相比,95%CI 1.16-1.35; P <0.0001)和白人民族(或2.00; 95%CI 1.86-2.15; P <0.0001)。通过泊松回归测量的中风病例的患病率比与逻辑回归结果一致。结论。 SA中卒中的趋势急剧应迫切被捕,考虑到相关的危险因素和季节性。

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