Department of Mathematics and Statistics, University of Guelph, Stone Road, Guelph, N1G 2W1, Canada.
Department of Production Animal Health, University of Calgary, University Drive NW, Calgary, T2N 1N4, Canada.
BMC Public Health. 2019 Sep 5;19(1):1232. doi: 10.1186/s12889-019-7521-7.
School absenteeism data have been collected daily by the public health unit in Wellington-Dufferin-Guelph, Ontario since 2008. To date, a threshold-based approach has been implemented to raise alerts for community-wide and within-school illness outbreaks. We investigate several statistical modelling approaches to using school absenteeism for influenza surveillance at the regional level, and compare their performances using two metrics.
Daily absenteeism percentages from elementary and secondary schools, and report dates for influenza cases, were obtained from Wellington-Dufferin-Guelph Public Health. Several absenteeism data aggregations were explored, including using the average across all schools or only using schools of one type. A 10% absence threshold, exponentially weighted moving average model, logistic regression with and without seasonality terms, day of week indicators, and random intercepts for school year, and generalized estimating equations were used as epidemic detection methods for seasonal influenza. In the regression models, absenteeism data with various lags were used as predictor variables, and missing values in the datasets used for parameter estimation were handled either by deletion or linear interpolation. The epidemic detection methods were compared using a false alarm rate (FAR) as well as a metric for alarm timeliness.
All model-based epidemic detection methods were found to decrease the FAR when compared to the 10% absence threshold. Regression models outperformed the exponentially weighted moving average model and including seasonality terms and a random intercept for school year generally resulted in fewer false alarms. The best-performing model, a seasonal logistic regression model with random intercept for school year and a day of week indicator where parameters were estimated using absenteeism data that had missing values linearly interpolated, produced a FAR of 0.299, compared to the pre-existing threshold method which at best gave a FAR of 0.827.
School absenteeism can be a useful tool for alerting public health to upcoming influenza epidemics in Wellington-Dufferin-Guelph. Logistic regression with seasonality terms and a random intercept for school year was effective at maximizing true alarms while minimizing false alarms on historical data from this region.
自 2008 年以来，安大略省惠灵顿-杜弗林-格伦公共卫生部门每天都会收集学校缺课数据。迄今为止，已经实施了基于阈值的方法来对社区范围内和校内疾病爆发发出警报。我们研究了几种使用学校缺课数据进行区域流感监测的统计建模方法，并使用两个指标比较了它们的性能。
从惠灵顿-杜弗林-格伦公共卫生部门获取了小学和中学的每日缺课率百分比和流感病例报告日期。探索了几种缺课数据汇总方法，包括使用所有学校的平均值或仅使用一种类型的学校。使用 10%缺勤阈值、指数加权移动平均模型、具有和不具有季节性项的逻辑回归、星期几指标以及学年的随机截距和广义估计方程作为季节性流感的流行检测方法。在回归模型中，将具有各种滞后的缺课数据用作预测变量，并且在用于参数估计的数据集存在缺失值时，通过删除或线性插值来处理。使用误报率 (FAR) 和警报及时性指标比较了流行检测方法。
与 10%缺勤阈值相比，所有基于模型的流行检测方法都发现降低了 FAR。回归模型优于指数加权移动平均模型，并且通常包含季节性项和学年的随机截距，会产生更少的误报。表现最好的模型是具有季节性逻辑回归模型和学年随机截距以及星期几指标的模型，其中使用具有线性插值缺失值的缺课数据估计参数，产生的 FAR 为 0.299，而之前的阈值方法最好只能产生 0.827 的 FAR。
学校缺课可以成为惠灵顿-杜弗林-格伦公共卫生部门预警即将发生的流感流行的有用工具。具有季节性项和学年随机截距的逻辑回归在最大化真实警报的同时最小化历史数据的误报方面非常有效。
