Development of a prototype for high-frequency mental health surveillance in Germany

Data infrastructure and statistical methods

Datos Bibliográficos

ID22084994
AutoresStephan Junker (0000-0001-8612-9347, Robert Koch Institute), Stefan Damerow (0000-0002-7265-1123, Robert Koch Institute, autor de correspondencia), Lena Walther (0000-0002-2703-5022, Robert Koch Institute), Elvira Mauz (0000-0003-1988-9789, Robert Koch Institute)
Año2023
Volumen11
Páginas1208515-1208515
Fecha de publicación2023-07-14
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaFrontiers in Public Health (JOURNAL)
Identificadores de la revistaISSN: 2296-2565 • E-ISSN: 2296-2565
EditorialFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2023.1208515
PMID37521976
OpenAlexW4384338452
IdiomaEN
Citas recibidas1
Referencias citadas36

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

Data mining · Data science · Environmental health · Mental health · Operations research · Population · Public health · Public health surveillance · Smoothing · Weighting · Computer Science · COVID-19 epidemiological studies · Engineering · Health and Medical Studies · Medicine · Mental Health Research Topics