Department of Epidemiology and Health Monitoring, Robert Koch Institute, Berlin, Germany.
Front Public Health. 2023 Jul 14;11:1208515. doi: 10.3389/fpubh.2023.1208515. eCollection 2023.
In the course of the COVID-19 pandemic and the implementation of associated non-pharmaceutical containment measures, the need for continuous monitoring of the mental health of populations became apparent. When the pandemic hit Germany, a nationwide Mental Health Surveillance (MHS) was in conceptual development at Germany's governmental public health institute, the Robert Koch Institute. To meet the need for high-frequency reporting on population mental health we developed a prototype that provides monthly estimates of several mental health indicators with smoothing splines. We used data from the telephone surveys German Health Update (GEDA) and COVID-19 vaccination rate monitoring in Germany (COVIMO). This paper provides a description of the highly automated data pipeline that produces time series data for graphical representations, including details on data collection, data preparation, calculation of estimates, and output creation. Furthermore, statistical methods used in the weighting algorithm, model estimations for moving three-month predictions as well as smoothing techniques are described and discussed. Generalized additive modelling with smoothing splines best meets the desired criteria with regard to identifying general time trends. We show that the prototype is suitable for a population-based high-frequency mental health surveillance that is fast, flexible, and able to identify variation in the data over time. The automated and standardized data pipeline can also easily be applied to other health topics or other surveys and survey types. It is highly suitable as a data processing tool for the efficient continuous health surveillance required in fast-moving times of crisis such as the Covid-19 pandemic.
在 COVID-19 大流行期间以及实施相关非药物控制措施的过程中，人们明显需要对人群的心理健康进行持续监测。当大流行袭击德国时，德国政府公共卫生研究所罗伯特科赫研究所正在概念上开发一项全国性的心理健康监测（MHS）。为了满足对人群心理健康高频报告的需求，我们开发了一个原型，该原型使用平滑样条提供了几个心理健康指标的月度估计值。我们使用了来自电话调查德国健康更新（GEDA）和德国 COVID-19 疫苗接种率监测（COVIMO）的数据。本文提供了一个高度自动化的数据管道的描述，该数据管道为图形表示生成时间序列数据，包括有关数据收集、数据准备、估计计算和输出创建的详细信息。此外，还描述和讨论了用于加权算法、移动三个月预测模型估计以及平滑技术的统计方法。具有平滑样条的广义加性建模最符合关于识别一般时间趋势的期望标准。我们表明，该原型适用于基于人群的高频心理健康监测，具有快速、灵活和能够识别数据随时间变化的能力。自动化和标准化的数据管道也可以轻松应用于其他健康主题或其他调查和调查类型。它非常适合作为数据处理工具，用于在 COVID-19 大流行等快速变化的危机时期进行高效的连续健康监测。
