Development of a prototype for high-frequency mental health surveillance in Germany
Data infrastructure and statistical methods
Bibliographic Data
| ID | 22084994 |
|---|---|
| Authors | Stephan Junker (0000-0001-8612-9347, Robert Koch Institute), Stefan Damerow (0000-0002-7265-1123, Robert Koch Institute, corresponding author), Lena Walther (0000-0002-2703-5022, Robert Koch Institute), Elvira Mauz (0000-0003-1988-9789, Robert Koch Institute) |
| Year | 2023 |
| Volume | 11 |
| Pages | 1208515-1208515 |
| Publication date | 2023-07-14 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2023.1208515 |
| PMID | 37521976 |
| OpenAlex | W4384338452 |
| Language | EN |
| Citations received | 1 |
| References cited | 36 |
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
Ggplot2
Generalized Additive Models
Regression Modeling Strategies
Generalized Additive Models
Thin Plate Regression Splines
Predictive Margins with Survey Data
Anxiety Disorders in Primary Care
Detecting and monitoring depression with a two-item questionnaire (PHQ-2)
Single item measures of self-rated mental health
Varying-Coefficient Models
Estimating predicted probabilities from logistic regression
Fast Stable Restricted Maximum Likelihood and Marginal Likelihood Estimation of Semiparametric Generalized Linear Models
Multidisciplinary research priorities for the Covid-19 pandemic
Time trends in mental health indicators in Germany's adult population before and during the Covid-19 pandemic
The Patient Health Questionnaire-2
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,33 |
| Citation span | 2023 - 2023 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |