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Development of a prototype for high-frequency mental health surveillance in Germany: data infrastructure and statistical methods
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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10375021/ https://www.ncbi.nlm.nih.gov/pubmed/37521976 http://dx.doi.org/10.3389/fpubh.2023.1208515 |
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author | Junker, Stephan Damerow, Stefan Walther, Lena Mauz, Elvira |
author_facet | Junker, Stephan Damerow, Stefan Walther, Lena Mauz, Elvira |
author_sort | Junker, Stephan |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10375021 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103750212023-07-29 Development of a prototype for high-frequency mental health surveillance in Germany: data infrastructure and statistical methods Junker, Stephan Damerow, Stefan Walther, Lena Mauz, Elvira Front Public Health Public Health 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. Frontiers Media S.A. 2023-07-14 /pmc/articles/PMC10375021/ /pubmed/37521976 http://dx.doi.org/10.3389/fpubh.2023.1208515 Text en Copyright © 2023 Junker, Damerow, Walther and Mauz. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Public Health Junker, Stephan Damerow, Stefan Walther, Lena Mauz, Elvira Development of a prototype for high-frequency mental health surveillance in Germany: data infrastructure and statistical methods |
title | Development of a prototype for high-frequency mental health surveillance in Germany: data infrastructure and statistical methods |
title_full | Development of a prototype for high-frequency mental health surveillance in Germany: data infrastructure and statistical methods |
title_fullStr | Development of a prototype for high-frequency mental health surveillance in Germany: data infrastructure and statistical methods |
title_full_unstemmed | Development of a prototype for high-frequency mental health surveillance in Germany: data infrastructure and statistical methods |
title_short | Development of a prototype for high-frequency mental health surveillance in Germany: data infrastructure and statistical methods |
title_sort | development of a prototype for high-frequency mental health surveillance in germany: data infrastructure and statistical methods |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10375021/ https://www.ncbi.nlm.nih.gov/pubmed/37521976 http://dx.doi.org/10.3389/fpubh.2023.1208515 |
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