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蒋希宏, 杨骥. 猩红热流行中“W-S现象”论证及预测[J]. 中国公共卫生, 2019, 35(2): 109-111. DOI: 10.11847/zgggws1119925
引用本文: 蒋希宏, 杨骥. 猩红热流行中“W-S现象”论证及预测[J]. 中国公共卫生, 2019, 35(2): 109-111. DOI: 10.11847/zgggws1119925
Xi-hong JIANG, Ji YANG. Winter-spring phenomenon in scarlet fever epidemic: theoretic verification and prediction[J]. Chinese Journal of Public Health, 2019, 35(2): 109-111. DOI: 10.11847/zgggws1119925
Citation: Xi-hong JIANG, Ji YANG. Winter-spring phenomenon in scarlet fever epidemic: theoretic verification and prediction[J]. Chinese Journal of Public Health, 2019, 35(2): 109-111. DOI: 10.11847/zgggws1119925

猩红热流行中“W-S现象”论证及预测

Winter-spring phenomenon in scarlet fever epidemic: theoretic verification and prediction

  • 摘要:
      目的  利用猩红热流行存在的季节性规律,探索一种简单可靠的流行趋势和发病率预测模型。
      方法  使用2010 — 2017年全国猩红热月发病率资料论证“W-S现象”并根据该理论建立预测模型。
      结果  各省级行政区“W-S现象”的春季高峰预测结果符合率为97.53 %(158/162),春季高峰差值(Y)与冬季高峰差值(X)呈100 %正相关。相关系数r = 0.21~0.98。85.19 %(23/27)省份呈中等程度以上相关(r > 0.5),59.26 %(16/27)省份呈高度相关(r > 0.8);66.67 %(18/27)的回归模型有统计学意义(P < 0.05),r = 0.81~0.98;预测省级行政区的发病率公式为:Y = 0.045 + 1.014X。
      结论  猩红热的流行中存在非常典型的“W-S现象”。

     

    Abstract:
      Objective  To explore a simple and reliable model for the prediction of incidence rate and trend of scarlet fever epidemic based on the seasonal regularity in prevalence of the disease.
      Methods  We collected data on monthly incidence of scarlet fever in 27 province-level regions across China between 2010 and 2017 via China Disease Prevention and Control Information System and analyzed the data for theoretic verification of winter-spring phenomenon in scarlet fever epidemics and to construct a model for the predication of the scarlet fever epidemic based on the winter-spring phenomenon theory.
      Results  The coincidence rate was 97.53% between the predicted peak scarlet fever incidence rates and the 162 observed rates in spring season in province-level regions during the period. For the province-level regional data, the difference (expressed as Y in prediction formula) in the peak incidence rate between two spring season epidemics of consecutive years were correlated positively with that (expressed as X) in the peak incidence rate between two winter season epidemics, with the correlation coefficients ranging from 0.21 to 0.98; moderate positive correlation (r > 0.5) between the Y and the X mentioned above was observed for 85.19% (23 of 27 provinces) of the data and high positive correlation (r > 0.8) was observed for 59.26% (16 of 27 provinces) of the data. Statistically significant regression models were established for 66.67% (18 of 27 provinces) of the data (all P < 0.05), with the correlation coefficients of from 0.81 to 0.98. A formula for predicting the incidence rate of scarlet fever in province-level regions was constructed: Y = 0.045 + 1.014X.
      Conclusion  Typical winter-spring phenomenon exists in the prevalence of scarlet fever in China.

     

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