Correcting for day of the week and public holiday effects: improving a national daily syndromic surveillance service for detecting public health threats.
Correcting for day of the week and public holiday effects: improving a national daily syndromic surveillance service for detecting public health threats.
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DOI：
10.1186/s12889-017-4372-y
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发表时间：
2017-05-19
影响因子：
4.5
通讯作者：
Smith GE
中科院分区：
文献类型：
--
作者：
Buckingham-Jeffery E;Morbey R;House T;Elliot AJ;Harcourt S;Smith GE
由于服务提供和患者行为每天都在变化，用于公共卫生监测的医疗保健数据可能会显示出一周中的大部分影响。公共假日的影响使这些经常性的影响更加复杂。实时综合征监测需要每天分析一系列医疗保健数据源，包括家庭医生咨询（在英国称为全科医生或GP）。在综合征全科医生监测数据分析过程中，如果不对这种报告偏倚进行调整，可能会导致误解，包括误报或延迟发现疫情。从每日时间序列数据中去除一周中某一天的影响的最简单的平滑方法是7天移动平均线。英国公共卫生部制定了工作日移动平均线，试图从每日GP数据中消除公共假日的影响。然而，这两种方法都没有充分考虑到一周中的一天和公共假日的影响。扩展工作日移动平均线被开发出来。这是另一种数据驱动的方法，用于将平滑趋势曲线添加到每日医疗保健数据的时间序列图中，旨在考虑公众假期和一周中的某一天的影响。它基于这样的假设，即寻求医疗保健服务的人数是疾病水平/严重程度和患者每天寻求医疗保健的能力或愿望的组合。通过应用英国公共卫生管理的全科医生小时内综合征监测系统的两个综合征指标的数据，将延长工作日移动平均值与七天和工作日移动平均值进行了比较。延长的工作日移动平均线成功地平滑了综合症医疗数据，并考虑了一周中的一天和公共假日的影响。相比之下，七日和工作日移动平均线无法解释所有这些影响，这导致了误导性的平滑曲线。这项研究的结果使人们有可能确定趋势和异常活动的综合征监测数据从GP服务的实时独立的影响所造成的一周中的一天和公共假日，从而改善公共卫生行动从这些数据的分析。
As service provision and patient behaviour varies by day, healthcare data used for public health surveillance can exhibit large day of the week effects. These regular effects are further complicated by the impact of public holidays. Real-time syndromic surveillance requires the daily analysis of a range of healthcare data sources, including family doctor consultations (called general practitioners, or GPs, in the UK). Failure to adjust for such reporting biases during analysis of syndromic GP surveillance data could lead to misinterpretations including false alarms or delays in the detection of outbreaks. The simplest smoothing method to remove a day of the week effect from daily time series data is a 7-day moving average. Public Health England developed the working day moving average in an attempt also to remove public holiday effects from daily GP data. However, neither of these methods adequately account for the combination of day of the week and public holiday effects. The extended working day moving average was developed. This is a further data-driven method for adding a smooth trend curve to a time series graph of daily healthcare data, that aims to take both public holiday and day of the week effects into account. It is based on the assumption that the number of people seeking healthcare services is a combination of illness levels/severity and the ability or desire of patients to seek healthcare each day. The extended working day moving average was compared to the seven-day and working day moving averages through application to data from two syndromic indicators from the GP in-hours syndromic surveillance system managed by Public Health England. The extended working day moving average successfully smoothed the syndromic healthcare data by taking into account the combined day of the week and public holiday effects. In comparison, the seven-day and working day moving averages were unable to account for all these effects, which led to misleading smoothing curves. The results from this study make it possible to identify trends and unusual activity in syndromic surveillance data from GP services in real-time independently of the effects caused by day of the week and public holidays, thereby improving the public health action resulting from the analysis of these data.