# data[].attributes.headline
Mental Health Hub used in Germany (Organization)

# data[].attributes.subject
Mental Health Hub used in Germany (Organization)

# data[].attributes.attachment_link.description
Hub Communities: Developing solutions for global health challenges together

# data[].attributes.headline
Global Organization assisting in Mental Health

# data[].attributes.subject
Global Organization assisting in Mental Health

# data[].attributes.attachment_link.title
German Alliance for Global Health Research: Home

# data[].attributes.attachment_link.description
The German Alliance for Global Health Research (GLOHRA) is an association of researchers from publicly-funded research institutions in Germany. Sponsored by the German Federal Ministry for Education and Research (BMBF), GLOHRA aims to strengthen global health research in Germany.

# data[].attributes.headline
School's closing due to mental health issues (COVID-19) News Article

# data[].attributes.subject
School's closing due to mental health issues (COVID-19) News Article

# data[].attributes.attachment_link.readable_text
During the Coronavirus Disease 2019 (COVID-19) pandemic, youth mental health problems have increased manifold. The contribution of school closures to this problem has been heavily debated but is largely unknown.

A new Science Advances study aimed to address this issue by combining data on variation in school closures and reopening across states in Germany with high-frequency data from the largest crisis helpline and survey data on youth mental health.
Study: The youth mental health crisis: Quasi-experimental evidence on the role of school closures. Image Credit: eldarnurkovic/Shutterstock.com
Background

The pandemic and the related public health measures have massively affected people's lives and mental health. During childhood and adolescence, the negative psychological consequences were particularly pronounced.

Mental health problems among children and adolescents are at all-time highs, and recent research has suggested a doubling of anxiety and depression levels in this age group during the pandemic compared to pre-pandemic levels. Despite this, studies on the role played by the pandemic are rare and of correlational nature.

Most studies find it difficult to disentangle the effects of the pandemic itself and the multiple public health measures. An exception is a Swedish study that revealed a lowering in mental health care demand among students who switched to remote learning at the start of the pandemic.

Despite the improvement compared to prior research, this study did not clarify whether the reduction in healthcare demand stemmed from an actual change in mental health or hid the rising number of unnoticed mental health problems, conveying a false all-clear signal.

About this study

The current study analyzed the potential costs and risks of school closures. It relied on unique German survey data on adolescents' mental health, collected between the 26th of May and the 10th of June 2020.

This was supplemented with data between August 2015 and November 2017, i.e., before the pandemic, to serve as control. Additionally, high-frequency data from the largest German crisis helpline was obtained from January 2019 until December 2020.

To isolate the overall strain imposed by the various pandemic measures and the pandemic, quasi-experimental variation in the length of school closures was used. The variation resulted from state-specific regulations for the different grade levels and school tracks.

To provide causal estimates, all state-specific corona protection ordinances were processed. A dataset was compiled, which enabled the assignment of each individual in the dataset with the respective mandatory weeks of school access restriction and causal estimation of the effect on mental health.

Key findings

It was observed that school closures significantly contributed to the recently observed aggravation of youth mental health problems. The crisis helplines' data helped understand that the youth were mainly struggling with family problems. It was highlighted that adolescents who experienced longer school closures struggled with these problems more and for prolonged periods.

Analysis of subgroups revealed that younger children struggled most, while the effects weakened monotonically with age. 11-year-olds were the youngest in the sample and experienced marked losses in their quality of life and behavioral and emotional problems.

Effects faded out and lost precision by mid-adolescence. Regarding gender heterogeneity, boys were seen to cope worse than girls.

Further, to understand the effect of living conditions at home, the living space available per school-aged child was considered. It was observed that adolescents suffered most from the burden if they resided in homes with limited living space.

The results documented here likely reflect only the tip of the iceberg. Only the short-run effects of prolonged school closures were explored in this study. More specifically, the estimates only correspond to the impact of school closures lasting between 4.7 and 10.1 weeks.

Subsequently, 25 or more additional weeks of school closure followed in Germany. The effects documented here are not expected to accumulate weekly because many adolescents could have learned to live with the altered situation.

Conclusions

The current study revealed that prolonged school closure led to a significant deterioration in the quality of life of youth, which stemmed from the development of early signs of mental health problems.

Compared to girls, this effect was more prominent in boys and younger adolescents. School closures were a significant reason behind the youth's mental health deterioration during the first COVID-19 wave.

In the future, more research must be conducted to formulate a better coping strategy to protect students from the negative effects of school closure.

These studies must focus on understanding the nature of student-teacher relationships and the quality of homeschooling that can alleviate the adverse effects of school closures.

# data[].attributes.attachment_link.title
School closures largely explain the deterioration of youth mental health over the first pandemic wave

# data[].attributes.attachment_link.description
The contribution of school closures to youth mental health.

# data[].attributes.headline
More research on Mental Health relating to COVID-19

# data[].attributes.subject
More research on Mental Health relating to COVID-19

# data[].attributes.attachment_link.readable_text
Department of Epidemiology and Health Monitoring, Robert Koch Institute, Berlin, Germany

Institute of Medical Sociology and Rehabilitation Science, Charité-Universitätsmedizin, Berlin, Germany

Background: Times of crisis such as the COVID-19 pandemic are expected to compromise mental health. Despite a large number of studies, evidence on the development of mental health in general populations during the pandemic is inconclusive. One reason may be that representative data spanning the whole pandemic and allowing for comparisons to pre-pandemic data are scarce.

Methods: We analyzed representative data from telephone surveys of Germany's adults. Three mental health indicators were observed in ~1,000 and later up to 3,000 randomly sampled participants monthly until June 2022: symptoms of depression (observed since April 2019, PHQ-2), symptoms of anxiety (GAD-2), and self-rated mental health (latter two observed since March 2021). We produced time series graphs including estimated three-month moving means and proportions of positive screens (PHQ/GAD-2 score ≥ 3) and reports of very good/excellent mental health, as well as smoothing curves. We also compared time periods between years. Analyses were stratified by sex, age, and level of education.

Results: While mean depressive symptom scores declined from the first wave of the pandemic to summer 2020, they increased from October 2020 and remained consistently elevated throughout 2021 with another increase between 2021 and 2022. Correspondingly, the proportion of positive screens first decreased from 11.1% in spring/summer 2019 to 9.3% in the same period in 2020 and then rose to 13.1% in 2021 and to 16.9% in 2022. While depressive symptoms increased in all subgroups at different times, developments among women (earlier increase), the youngest (notable increase in 2021) and eldest adults, as well as the high level of education group (both latter groups: early, continuous increases) stand out. However, the social gradient in symptom levels between education groups remained unchanged. Symptoms of anxiety also increased while self-rated mental health decreased between 2021 and 2022.

Conclusion: Elevated symptom levels and reduced self-rated mental health at the end of our observation period in June 2022 call for further continuous mental health surveillance. Mental healthcare needs of the population should be monitored closely. Findings should serve to inform policymakers and clinicians of ongoing dynamics to guide health promotion, prevention, and care.

1. Introduction

Like most countries, Germany has been hit by multiple waves of rising COVID-19 incidence and mortality as well as NPIs in response, which might relate to mental health dynamics temporally. Taking various epidemiological, healthcare- and policy-related parameters into account, the course of the pandemic in Germany can retrospectively be divided into eight phases (see Figure 1) ( 20- 24). After the first confirmed SARS-CoV-2 infection on 27 th January, 2020, a nationwide first wave of infections followed (March to May 2020). NPIs were put in place, resulting in an extensive lockdown which comprised travel and contact restrictions (gatherings of more than 2 people not permitted), working from home, closed leisure facilities, childcare facilities, schools, shops and restaurants ( 25). A milder interim period of low case numbers referred to as a "summer plateau" ( 20, 23) followed from May to September 2020. From October 2020 to February 2021 a second, more severe wave with a peak in deaths (highest in the whole pandemic thus far) and hospitalizations (highest in this study's observation period) ( 26) unfolded, again met by several NPIs and the beginning of the vaccination campaign ( 24). A second "partial" shutdown from the beginning of November 2020 ( 27) was initially less restrictive than the first (e.g., contact with one other household, leisure time facilities closed, restaurants closed). Measures were intensified in mid-December, with closed shops, childcare facilities, and schools, and working from home where possible ( 28). This shutdown went on until March 2021, when a stepwise reopening was decided upon ( 29). After a brief period of declining case numbers, a third wave emerged from March to June 2021, albeit with fewer hospitalizations and far fewer deaths. During this time, NPIs varied substantially between federal states ( 30). Another short summer plateau in 2021 (June to July) was followed by a fourth wave of infections from August to December 2021 ( 21). With regard to COVID-19 incidence, the fourth wave was the most severe up to that point with a nearly 10-fold number of average cases per day compared to the first wave and a nonstop transition into wave five, which began in December 2021 ( 21- 23) and was characterized by the highly contagious omicron variant with even higher infection rates ( 22, 25). Data on case numbers shows the largest peak to date in spring 2022 ( 31), with hospitalizations between about 75 to 95% of those seen in winter 2020/2021 but deaths at only about a fifth to a quarter of this peak ( 29). NPIs were characterized by restrictions in or mandatory tests for access to shops and leisure facilities as well as general contact restrictions for those who were neither vaccinated nor recovered from COVID-19 from autumn 2021 ( 32) to spring 2022. From December 2021 to end of January 2022, contact restrictions for vaccinated and recovered individuals were also put in place ( 33). NPIs were eased between February and March 2022 ( 34) and largely lifted in most German federal states at the beginning of April 2022 ( 35). Russia's invasion of Ukraine on 24 th February, 2022, marks a further major event in this time and the beginning of another crisis on a global scale that might affect mental health dynamics.

Despite the well-founded expectation of a mental health crisis, evidence on changes in mental health of adults during COVID-19 pandemic is (still) inconclusive. Turning first to international research, reviews point to a broad heterogeneity in current findings. While some reviews conducted early in the pandemic conclude that there was an increase in depressive and anxiety symptoms ( 36, 37), others found quick subsequent decreases or stable symptoms in general populations ( 38). A later review and meta-analysis reports no changes ( 39). One review summarizes a most likely 'big picture' ( 38) emerging from heterogeneous findings: symptom increases compared to pre-pandemic data during first lockdowns followed by declines as restrictions are eased, but not down to pre-pandemic levels ( 40). As findings accumulate, inconsistencies are growing, while the trajectory of manifest mental disorders remains an open question ( 41). A recent umbrella review based on 81 systematic reviews on global mental health trends during the pandemic evaluates the current state of research as follows: "Despite high volumes of reviews, the diversity of findings and dearth of longitudinal studies within reviews means clear links between COVID-19 and mental health are not available, although existing evidence indicates probable associations" [( 42), p. 2].

The existing literature from Germany prohibits clear conclusions as well. In a rapid review including 68 records published until mid-2021, we found study outcomes to be associated with the suitability of the data used for assessing changes in the general population reliably with regard to sampling methods and comparability of observation periods ( 43). While studies with particularly suitable research designs showed mixed results on the overall development of mental health in Germany, studies with more bias-prone designs predominantly reported deteriorating mental health. Importantly, two thirds of the reviewed studies are based on data collected during the first wave and the summer plateau of 2020, when COVID-19 incidence was comparatively low in Germany ( 43). The few studies that address the later course of the pandemic find an elevated frequency of depressive symptoms in the first months of 2021 ( 44, 45) or of depressive and anxiety symptoms into later 2020 ( 45, 46) compared to pre-pandemic data and an increase in mental distress ( 47) but a decrease in depressive and anxiety symptoms ( 45) in the second wave compared to the first wave. Further results from representative surveys spanning the whole pandemic period and allowing for comparisons to pre-pandemic baseline data are needed in order to adequately assess the mental health impact of the pandemic in the general population in Germany.

The dynamic nature of the COVID-19 pandemic and its high relevance to all areas of public health created specific informational needs with regard to the mental health of the population. Specifically, it calls for a public health surveillance approach ( 71) involving continuous observation and timely reporting of updated time trends as the basis for planning, implementing, and evaluating interventions to protect and promote the health of the population ( 72). Accordingly, public health authorities have set up ongoing population surveys in order to monitor mental health trends at high frequency and serve as an early warning system, for example in the US ( 73) and UK ( 74). In Germany, pre-pandemic mental health monitoring focused on the estimation of 12-month-prevalences of varying mental health indicators, based on health interview and health examination surveys conducted at perennial intervals [e.g., ( 75- 79)]. In 2019, the Federal Ministry of Health commissioned the Robert Koch Institute (RKI) to establish a national Mental Health Surveillance in order to provide systematic and continuous evidence on the mental health of the population. As a conceptual foundation, core indicators for public mental health were identified ( 80) and prioritized by national stakeholders ( 81), integrating international expertise ( 82). With the onset of COVID-19 pandemic, first indicators from the comprehensive set had already been implemented in the running field work of the survey "German Health Update (GEDA)" ( 78). As the pandemic progressed, further measures were added to GEDA as well as to "COVID-19 vaccination rate monitoring in Germany (COVIMO)" ( 83). This representative data from ~1,000 respondents per month, and, as of 2022, 3,000 per month for some indicators, makes tracking the development of several mental health indicators in the German population in high-frequency cross-sectional time series possible, addressing some of the above-mentioned research gaps.

In the present study we analyze month-by-month time series for symptoms of common mental disorders (depressive symptoms and anxiety symptoms) as well as an indicator of positive mental health (self-rated mental health) in order to address the following three research questions: (1) How did depressive symptoms develop between April 2019 and June 2022 in the adult population in Germany? (2) Did developments of depressive symptoms in the observation period differ by gender, age, and level of education? If so, did mental health differences between subgroups vary over time? (3) How did symptoms of anxiety disorders and self-rated mental health develop between March 2021 and June 2022? Importantly, we examine both mean depressive and anxiety symptom scores and proportions of the population screening positive for possible depressive or anxiety disorder. This allows us to distinguish between developments in symptom severity at the population level and changes in the percentage of the population with potentially clinically relevant symptom levels, both of which are important public health indicators ( 84).

2. Materials and methods

2.1. Data

2.1.1. Surveys

Figure 1 maps the data collection periods for the two surveys and three indicators used in this study onto the phases of the COVID-19 pandemic in Germany. At the start of the COVID-19 pandemic, the third survey wave of the European Health Interview Survey as part of the study "German Health Update" (GEDA 2019/2020-EHIS) for Germany ( 78) had been in the survey phase conducting telephone interviews since April 2019. This survey included the screening questionnaire PHQ-8 ( 85), which comprises its abbreviated version PHQ-2 ( 86), as a measure of depressive symptoms.

The survey was originally not designed for monthly reporting; however, slight adjustments of the sample weighting permitted first analyses of the development of various health indicators in the months preceding the pandemic as well as the first months of the pandemic ( 87). Given the new informational needs arising from the pandemic, the survey was continued until the beginning of January 2021. After the end of GEDA 2019/2020, short inventories assessing mental health indicators, including the PHQ-2 ( 86), the GAD-2 ( 88), and a self-rated mental health (SRMH) item ( 89) were integrated into a running population-based telephone survey from mid-March to mid-July 2021 with a one-month data collection gap from mid-May to mid-June. This survey, the "COVID-19 vaccination rate monitoring in Germany (COVIMO)," was designed to be sampled on a monthly basis ( 83). From July 2021 until December 2021 and February until June 2022 (with a data gap in January 2022), continuous monthly interviews were carried out within the frameworks of GEDA 2021 and GEDA 2022 ( 90), respectively (see Figure 1).

2.1.2. Participants

Across the entire survey period, 45,102 participants aged 18 or older were included in the analyses. The distributions by gender, age, and level of education in the different surveys are shown in Table 1, number of monthly cases are shown in Supplementary Figure A1. The GEDA-EHIS 2019, GEDA 2020, 2021, and COVIMO studies surveyed ~1,000 participants per month. GEDA 2022 provided data for 3,000 participants per month. To reduce data gaps, the second half of each month was combined with the first half of the following month across the observation period.

2.2. Indicators of mental health status

2.2.1. Depressive symptoms

Depressive symptoms were observed prior to and during the pandemic using monthly data from beginning of April 2019 until mid-June 2022 (see Figure 1). There were four short data gaps (the largest between January and mid-March 2021). The indicator was measured with the established ultra-brief screening instrument "Patient Health Questionnaire-2" (PHQ-2) ( 86), which has been found to perform well as a screening tool for depressive disorders in the German general population ( 92). The PHQ-2 captures the frequencies of two core symptoms of depressive disorders, asking, "Over the last 2 weeks, how often have you been bothered by the following problems?": (1) "little interest and pleasure in doing things" (2) "feeling down, depressed or hopeless" (possible responses: 0 = "not at all," 1 = "several days," 2 = "more than half the days," 3 = "nearly every day"). The total score of the PHQ-2 ranges from 0 to 6 ("no symptoms" to "severe symptoms"). According to scoring recommendations ( 92), scores ≥ 3 represent a positive screen for possible depressive disorder and indicate a potential need for further diagnostic assessment. In our analytical sample, the internal consistency of the PHQ-2 is α = 0.73 [standardized alpha coefficient as recommended for two items ( 93), unstandardized α = 0.72], slightly higher than in a comparable German sample ( 45). Two measures are reported in the current study: (1) the mean depressive symptom score, which tracks changes in the mean severity of symptoms in the population ( 73); (2) the proportion of the adult population screening positive for possible depressive disorder.

2.2.2. Symptoms of anxiety

Symptoms of anxiety were observed monthly from mid-March 2021 to mid-June 2022 (see Figure 1) with two short data gaps (mid-May 2021 to mid-June 2021 and January 2022). The indicator was measured with the established ultra-brief screening instrument "Generalized Anxiety Disorder-2" (GAD-2), which has been found to perform well as a screening tool for anxiety disorders in the German general population ( 88). The GAD-2 captures the frequency of two core symptoms of anxiety disorders, asking, "Over the last 2 weeks, how often have you been bothered by the following problems?": (1) "feeling nervous, anxious or on edge" (2) "not being able to stop or control worrying" (possible responses: 0 = "not at all," 1 = "several days," 2 = "more than half the days," 3 = "nearly every day"). The total score of the GAD-2 ranges from 0 to 6 (no symptoms to severe symptoms). Scores ≥ 3 represent a positive screen for possible anxiety disorder, including generalized anxiety disorder, panic disorder, social anxiety disorder, and posttraumatic stress disorder ( 88). In our analytical sample, the internal consistency of the GAD-2 is α = 0.67 (standardized alpha unstandardized α = 0.66), almost the same value as in a comparable German sample ( 45). Just as with depressive symptoms, two measures are reported: (1) the mean anxiety symptom score and (2) the proportion of the adult population screening positive for possible anxiety disorder.

2.2.3. Self-rated mental health

SRMH was observed monthly from mid-March 2021 to mid-June 2022 (see Figure 1) with two short data gaps (mid-May 2021 to mid-June 2021 and January 2022). It was measured using the question: "How would you describe your overall mental health?" (possible responses: 5 = "excellent," 4 = "very good," 3 = "good," 2 = "fair," 1 = "poor"). The single item is an established way to measure SRMH in population based surveys ( 89). SRMH has been found to represent a dimension of mental health that is qualitatively distinct from psychopathology ( 94). Here and elsewhere ( 58) it is employed as a measure of positive mental health. Two measures are reported: (1) population mean SRMH score; (2) the proportion of the adult population rating their mental health as "very good" or "excellent," following previous categorization to identify the presence of positive mental health ( 58).

2.3. Sociodemographic variables used to measure mental health inequalities

Results are presented separately for women and men. For this purpose, respondents' information on the sex noted in their birth certificate was used. Information on gender could not be used in the present analyses, since the data for the evaluations are adjusted to the marginal distributions of the official reference statistics [source: Microcensus ( 95)], which lacks information on gender identity.

Four age groups were formed to capture young adulthood, different stages of middle age, and the ages of an increased risk of severe COVID-19 infection: 18-29, 30-44, 45-64, and 65 years and older.

Educational levels according to the CASMIN classification ("Comparative Analyses of Social Mobility in Industrial Nations") were used as an indicator of socioeconomic status ( 96). Three groups with low, medium, and high levels of education are distinguished on the basis of school and vocational qualifications.

2.4. Statistical analysis

All analyses were conducted in R version 4.1.2 and Stata /SE 17.0.

2.4.1. Estimation of moving three-month averages and smoothing curves

In order to assess mental health developments over time in the general population and by subgroup, we calculated time series of estimates along with smoothing curves to be represented graphically [for details, see ( 91)]. Our aim was to achieve high temporal resolution whilst working with sample size restrictions and also to smooth random fluctuations. The estimation procedure described below also ensures that possible fluctuations in distributions of sex, age, and level of education in the sample over time are corrected for and that stratified results are standardized for the other main sociodemographic characteristics.

For each of the three mental health indicators, linear and logistic regressions were used to predict a time series of means and proportions for the adult population in Germany. To handle low cell counts and reduce volatility over time, we estimated centered moving averages rather than monthly averages ( 97) using weighted data from three-month windows. Some three-month windows only included data from 2 months due to data gaps. The three-month windows move in steps of 1 month. The models for each three-month window regress the mental health indicators on sex, age group, and level of education, and interactions between them. While the linear models include all possible interaction terms, only all possible two-way interactions of the covariates but not the three-way interactions were included in the logistic regression models to avoid problems resulting from empty cells. Nonetheless, there were some empty cells around data gaps, where estimates are based on data from two rather than 3 months, resulting in estimation gaps in the time series of categorical PHQ-2 and GAD-2 estimates.

These regression models are the foundation for standardization for sex, age, and level of education between the three-month windows, which ensures that different distributions of these characteristics between them do not influence the results. For standardization, we calculated averaged predictions in a two-step process. First, we used the models to perform predictions on a standard population. To calculate arithmetic means of mental health indicator scores, we used the model estimates from the linear regressions and predicted the expected values of the indicator in question. To calculate proportions for categorical indicator outcomes, we predicted the expected probabilities. In a second step we averaged over all of the predictions. The standard population was calculated using data from the Microcensus 2018 ( 95), which approximates Germany's population in 2018.

The calculation of estimates for time series stratified by sex, age, and level of education was similar to the procedure described above. However, in order to exclude different distributions of the respective other two characteristics in different time periods as explanatory factors for temporal developments, stratified results by age group, sex, or level of education were standardized by the remaining two characteristics in the prediction step. For example, the results stratified by age group were standardized for sex and education. This was achieved by making predictions for every subgroup as if all observations in the standard population belonged to this subgroup. The standardization between subgroups means that the subgroup-specific estimates are not representative for the population subgroup. The mathematical and methodological foundations for model-based predictions and standardization can be found elsewhere ( 98- 100).

In order to improve results interpretation by making trends more visible, we additionally estimated smoothed curves using a general additive model ( 101) with a smoothing spline ( 102, 103) and curve by factor interaction ( 104). Values were predicted on the same standard population. The spline was fitted on weekly observations to maximize temporal resolution given sample size. To avoid over- or underfitting, the smoothing parameter was estimated using restricted maximum likelihood. However, we found that for our shorter time series, the curves based on weekly estimates were less smooth than the three-monthly predictions. Therefore, we only used this procedure for the longer time series.

Missing values in the dependent variables were excluded on a case-by-case basis. Observations without information on sex or age were not included in the survey and were treated as non-responses. Missings in education were imputed in accordance with the weighting procedure ( 66) by assigning the most frequent value, a medium level of education.

The initial interpretation of the times series was descriptive by visual inspection. Conservative criteria such as confidence interval comparisons were not used to evaluate developments over time at this stage because the first aim was to explore the overall trajectory. In addition to visual inspection, we carried out statistical comparisons between different time periods (see section 2.4.2).

2.4.2. Statistical time period comparisons between survey years

For the longer depressive symptoms time series, we conducted statistical comparisons between the three survey years for two periods of months: (1) mid-March to mid-September [calendar week (CW) 11-37] 2019, 2020, 2021, mid-March to mid-June (CW 11-24) 2022, and (2) mid-September to end of December 2019, 2020, 2021 (CW 38-52). For the shorter time series of SRMH and anxiety symptoms, mid-March to mid-September 2021 (CW 11-37) and mid-March to mid-June (CW 11-24) 2022 were compared. These time periods were chosen based on (1) the declaration of a pandemic on March 11 from WHO ( 1), (2) data gaps for the initial months of each year, and (3) the turning point in the development of depressive symptoms at the end of the summer of 2020, as shown in Figure 3. Small gaps in some of these time periods could not be avoided; for example, there was no data from mid-March to the beginning of April 2019. We tested for two kinds of trends. First, we tested for differences in mean depressive or anxiety symptom score and proportions at or above PHQ-2/ GAD-2 cutoff between corresponding time periods across years for the overall population as well as within the different subgroups. For SRMH we tested for differences in mean and proportions with very good or excellent SRMH. Second, we tested for possible changes in differences in means or proportions between subgroups over the specified time periods.

To conduct these comparisons, we again used linear and logistic regression models to produce averaged predictions as described above. However, here we calculated estimates for the defined time periods by including a set of dummy variables indicating these periods in the survey year. Furthermore, all possible interactions of these dummies with age group, sex, and level of education were included. The specification of the linear and the logistic regression models again differed with regard to level of interaction. In the logistic regression only, three-way interactions were included, whereas the linear model also included four-way interactions. After model estimation, the standard population was used for prediction of the means of the specified time periods. Contrasts between the time periods and the differences between the subgroups between the time periods were estimated. We used Stata's "margins, contrast" command ( 105) for estimation and statistical testing using Wald tests, applying a significance level of ( p < 0.05).

Before running these contrasts, we conducted joint tests or omnibus tests in order to control for multiple comparisons and reduce the likelihood of false significant results by using protected tests ( 106). We only performed pairwise comparisons between time periods if the hypothesis that all possible differences were zero could be rejected. To assess the permissibility of pairwise comparisons within subgroups, we jointly tested if the differences within all subgroups defined by sex, age group or level of education, respectively, were zero. To address the question of whether differences between subgroups changed over time, we conducted joint tests including all possible differences, i.e., a test for interaction of time and sociodemographic characteristic.

3. Results

Results of joint tests for differences between time periods in the general population were significant across indicators with the exception of proportions of positive PHQ-2 screen comparisons between years for the September-December time period (see Supplementary Table A1). Joint tests for differences in symptoms of depression in this same time period (CW 38-52) between years stratified by sex, age, and level of education were not significant for mean PHQ-2 scores, and only significant for age for proportion of positive screens. Because none of the joint tests for interactions between time periods and sociodemographic characteristics except for the interaction between mean anxiety score and age yielded significant results, we did not examine the question of changes in differences further. Results for individual pairwise comparisons are reported below only in case of significant joint test results.

3.1. Time trends of depressive symptoms in the adult general population

Figure 2. Time trends in depressive symptoms (PHQ-2). Time series starting from estimate centered on April/May 2019 and ending on estimate centered on April/May 2022. Calculation of three-month moving estimates and smoothing curves shown in (A, C) detailed in section 2.4.1 in Methods. Calculation of estimates for CW11-37 (mid-March to mid-September; CW11-24 in 2022) and CW38-CW52 (mid-September to end December) as well as p-values for comparisons between time periods shown in (B, D) detailed in section 2.4.2 in Methods. Gaps in the time series shown in (A, C) are due to data gaps. Larger gaps arise in the time series for proportion of positive screens (C) due to empty cells (absence of positive screens within certain sex, age, and level of education interaction cells in the regression model). p-values for comparisons between CW38-52 time period between years not shown for proportion of positive screens (D) due to non-significant joint tests, see Supplementary Table A1.

Mean depressive symptom scores as well as the proportion of the population with a positive screen decreased during the first wave of the pandemic and the first summer plateau (March-September) in 2020, resulting in lower levels than in the same period in 2019.

After the first summer plateau, the time series are characterized by two increases: Both PHQ-2 measures (means and positive screens) first increased between the beginning of the second wave in autumn 2020 and the beginning of the third wave in spring 2021. They reached relatively steady levels above those of 2019 from spring 2021 ( p-values for comparisons of CW 38-52 period cannot be reported for proportions of positive screens due to non-significant joint tests). Both then showed further increases from late 2021 to early 2022 and remained elevated until the end of the observation period. March-June 2022 depressive symptom levels were significantly higher than in March-September in all three previous years.

This overall trajectory manifests in the following development of time period estimates for the spring/summer months over the 4 years observed ( Tables 2, 4): The mean depressive symptom score in the population first decreased from 0.96 in 2019 to 0.86 in 2020 and then increased to 1.11 in 2021 and to 1.26 in 2022. The proportion of the population with a positive screen first decreased from 11.1% in 2019 to 9.3% in 2020. It then increased to 13.1% in 2021 and 16.9% in 2022.

3.2. Time trends of depressive symptoms stratified by sociodemographic characteristics

Figure 3. Time trends in depressive symptoms (PHQ-2) by subgroups. Time series starting from estimate centered on April/May 2019 and ending on estimate centered on April/May 2022. Calculation of three-month moving estimates and smoothing curves detailed in section 2.4.1 in Methods. Estimates for each sociodemographic characteristic subgroup are standardized for the respective other two characteristics (e.g., estimates for women standardized for age and level of education). Gaps in the time series are due to data gaps. Larger gaps arise in the time series for proportion of positive screens (B, D, F) due to empty cells (absence of positive screens within certain sex, age, and level of education interaction cells in the regression model). Supplementary Figure A2 shows these time series in a separate plot for each age group.

3.2.1. Time trends of depressive symptoms by sex

Time series plots suggest that throughout most of the observation period, mean depressive symptom scores were higher in women than in men ( Figure 3A). Likewise, percentages of positive screens for possible depressive disorder appear to be higher in women than in men for the most part, although the overlap was greater than for mean scores ( Figure 3B). The overall shape of the plotted time series stratified by sex roughly matches that of the whole population: initial symptom declines followed by two increases. However, women experienced less relief in the early phases of the pandemic and earlier symptom increases in the later phases:

Declines in both mean symptom scores and proportion of positive screens in the early phases of the pandemic are seen in both sexes, but limited to the summer plateau in women. Statistical comparisons between spring/summer 2019 and 2020 only show significant declines in men ( Tables 2, 4). The plotted time series suggest two increases in both measures in both sexes, the first between autumn 2020 and spring 2021, the second at the end of 2021/beginning of 2022. Statistical comparisons reveal significant increases above 2019 levels in spring/summer 2021 for women but not for men ( p-values for CW 38-52 cannot be reported due to non-significant joint tests). Men's depressive symptom levels surpass 2019 levels for the first time in 2022, also rising significantly above 2021 levels. In women, this second visible symptom level increase does not result in levels significantly above 2021 levels.

Over the course of the observation period, mean scores and the percentage of positive screens among women standardized by age and level of education increased by 0.33 points (from 1.00) and 5.9 percentage points (from 11%) between spring/summer 2019 and spring/summer 2022; among men, by 0.26 points (from 0.93) and 5.5 percentage points (from 11.5%) ( Tables 2, 4).

3.2.2. Time trends of depressive symptoms by age

While no declines in depressive symptom levels are visible among those aged 65+ for the early stages of the pandemic, plotted time series show decreasing means and proportions of positive screens from the beginning of the outbreak among the middle age groups and among the youngest in summer 2020. However, these declines resulted in a significant difference between means in spring/summer 2019 and 2020 only for those aged 45-64 years ( Table 2).

At different times between the second wave and the end of the observation period, every age group then showed increases in depressive symptom levels beyond pre-pandemic levels:

Among 18- to 29-years-olds, means and positive screens rose very markedly compared to other groups from autumn 2021 to the end of the year. The standardized proportion of positive screens reached 18.6% in September-December 2021, representing an 8 percentage point-increase from the same period in 2019, and means rose from 1.01 to 1.57. However, statistical uncertainty is fairly high in this group, and this increase did not reach significance compared to pre-pandemic levels ( Table 5; p-values for means cannot be reported due to non-significant joint tests). Following the sharp increase at the end of 2021, symptoms returned to lower levels numerically but not statistically significantly above 2019 levels in 2022.

30- to 44-year-olds showed a temporary increase in both PHQ-2 measures in spring/summer 2021. Standardized mean symptom scores increased significantly from 2019 in this time and markedly compared to other groups (from 0.89 in 2019 to 1.21 in 2021, Table 2). The temporary increase of 3.7 percentage points (from 11.5% in 2019) in the standardized proportion of positive screens did not reach statistical significance ( Table 4). Mean symptom scores (but not proportions) again significantly surpassed 2019 levels in spring/summer 2022, but not 2021 levels. However, this increase does not stand out in magnitude.

45- to 64-year-olds exhibited an increase in depressive symptom levels above pre-pandemic levels later than the other age groups. The time series graphs show increases from about mid-2021. Both PHQ-2 measures significantly surpassed 2019 levels for the first time in 2022 ( Tables 2, 4), also surpassing 2021 levels, resulting in a 5.9 percentage point increase (from 12.7%) in the standardized proportion of positive screens between spring/summer 2019 and the same period in 2022.

3.2.3. Time trends of depressive symptoms by level of education

A social gradient is apparent throughout the observation period, with higher mean scores and proportions of positive screens for possible depression in those with the lowest levels of education, followed by the middle and high-level groups (all standardized by sex and age; Figures 3E, F). Just like with stratification by sex, the overall shape of the plotted time series stratified by level of education roughly matches that of the whole population: initial symptom declines followed by two increases.

However, the declines in means and positive screens in the first pandemic spring and summer result in statistically significant differences between 2019 and 2020 only in the middle level of education group ( Tables 2, 4).

Smoothing curves suggest that the subsequent increases between autumn 2020 and spring 2021 as well as end of 2021 to beginning of 2022 amount to a particularly steady increasing trend in the high level of education group. Indeed, statistical time period comparisons show significant increases beyond 2019 levels in spring/summer 2021 in the high level group but not in the other two groups ( p-values for CW 38-52 cannot be reported due to non-significant joint tests). Comparisons between 2022 and corresponding 2019 and 2021 time windows show significant increases compared to both years in both the high and medium level of education groups. In the low level of education group, means also increased significantly beyond 2019 (but not 2021) levels in 2022, and a p-value of 0.057 suggests a possible increase in the proportion of positive screens as well.

Looking at the whole observation period, the standardized proportions of those with a positive screen rose by 5.2 percentage points (from 5.5%) in the high level of education group, 4.6 (from 10.6%) in the middle group, and 4.8 percentage points (from 16.2%) in the low level of education group (not significant) between spring/summer 2019 and 2022 ( Table 4).

3.3. Time trends of symptoms of anxiety and self-rated mental health

Symptoms of anxiety were observed from March 2021 to June 2022 and overall increased in this time ( Figure 4, Table 6). Looking only at 2021, the moving averages suggest a possible increase in mean anxiety score in the population from spring into autumn, flattening out by the end of the year. This development is hardly reflected in the proportion of those exceeding the cut-off value for possible anxiety disorder. However, empty cells (no positive screens within certain sex, age, and level of education interaction cells in the regression model) around the two data gaps made it impossible to calculate the first and last estimates of 2021, as well as a CW 38-52 estimate, for the categorical outcome ( Figure 4, Table 6).

Figure 4. Time trends in anxiety symptoms and SRMH in the total population. Time series starting from estimate centered on March/April 2021 and ending on estimate centered on April/May 2022. Calculation of 3-month moving estimates detailed in section 2.4.1 in Methods. Gaps in the time series in (B) (including two missing estimates at the start of the time series) are due to empty cells (absence of positive screens within certain sex, age, and level of education interaction cells in the regression model) arising from data gaps. "Proportion with positive mental health" (D) is the proportion who rated their mental health as "very good" or "excellent".

Table 6. Anxiety symptoms and self-rated mental health in different time periods: mean values, percentages and p-values for statistical comparisons.

SRMH was observed from March 2021 to June 2022 and overall declined in this time ( Figure 4, Table 6): Mean SRMH declined steadily between spring and autumn 2021 and then remained at a fairly constant level for the rest of the observation period. The percentage of those with very good or excellent SRMH, on the other hand, continued to decline until the end of 2021 and then increased slightly in 2022. The overall declines in both means and percentages in the course of the observation period were confirmed by statistical comparisons between spring/summer 2021 (mean: 3.44; percentage very good or excellent SRMH: 44.3%) and spring/summer 2022 (mean: 3.35 and percentage: 39.6%). Declines between 2021 and 2022 were found in both females and males, in those aged 45 and older, and in those with a low or high level of education ( Supplementary Figures A5, A6, Supplementary Table A3).

4. Discussion

4.1. Summary

The present study investigated how depressive symptoms developed month-by-month between April 2019 and June 2022 in the adult population in Germany and whether trajectories differ by sex, age, and level of education. Moreover, it explored how symptoms of anxiety and SRMH developed in the shorter time window of March 2021 to June 2022. We found:

(1) Mean population depressive symptom scores as well as proportions of the population screening positive for possible depressive disorder showed a decline in the first wave of the pandemic and into the first summer plateau compared to the same months the year prior. Percentages of positive screens declined from about 11% in spring/summer 2019 to 9% in 2020. During the second wave starting in October 2020, this proportion as well as mean scores increased and remained consistently elevated throughout most of 2021, even during the summer months. Late 2021 until early spring 2022 saw another increase in both measures and sustained higher levels until the end of the observation period. By spring/summer 2021, the prevalence of those screening positive increased to 13%, between March and June 2022, it reached ~17%.

(2) The observed overall trends in the development of depressive symptoms are, for the most part, evident across the examined subgroups. However, declines and increases are more pronounced in some groups than in others and vary in time course. The reduction in depressive symptoms in 2020 is particularly pronounced in men, among the two middle age groups, and the middle level of education group. Increases in depressive symptoms from autumn 2020 onward were found in all groups. However, numerically striking or statistically significant increases compared to pre-pandemic periods in 2019 were reached at different times. Women showed earlier symptom level increases than men, the youngest experienced particularly marked increases in 2021, and the eldest adults as well as the high level of education group stand out for earlier and more continuous increases than found in their respective comparison groups. The social gradient in symptom levels by level of education remained unchanged by these developments. No significant interactions between sociodemographic characteristic and time period, i.e., no evidence for changes in differences between subgroups, were found in the observation period.

(3) In keeping with these developments in depressive symptoms, SRMH decreased and anxiety symptoms increased between spring/summer 2021 and 2022. While both symptoms of depression and anxiety showed marked increases between the final estimates of 2021 and the first estimates of 2022 and remained elevated, SRMH showed no marked changes at this time.

4.2. Reduction in symptoms of depression in the first phases of the pandemic

Contrary to warnings of a potential mental health crisis at the start of the pandemic ( 2- 5), our depressive symptom time series using the PHQ-2 show an initial reduction in both mean depressive symptom scores and proportions of individuals with a positive screen among adults in Germany during a first wave of infections. This first wave was mild in Germany compared to some other countries ( 107), and in the first pandemic summer, restrictions were eased and case numbers very low ( 20, 25). Analyses using the longer PHQ-8 in the same GEDA-EHIS data also showed a temporary reduction in symptoms of depression in the population between April 2020 and August 2020 in a month-by-month time series of the proportion of positive screens ( 108).

Our analyses do not permit conclusions about causal associations between pandemic developments and mental health developments, much less on possible reasons for any putative associations between the two. However, the context within which mental health developments take place and their temporal coincidence with societal developments warrant discussion. Possible benefits of a general and novel deceleration of life during lockdown in the relatively mild first wave and relief from a relatively quick return to near-normalcy in the first pandemic summer could be taken into consideration as potential factors playing into the dynamics we find. Benefits of deceleration as a potential explanation is supported by the fact that analyses based on the same data examining all depressive symptoms included in the PHQ-8 found a particular reduction in fatigue, loss of energy, and concentration difficulties, which are all closely linked to chronic stress ( 87, 108).

Stratification by subgroups shows that while there is evidence of symptom reduction in the first pandemic summer in all groups except adults aged 65 years and older, lower symptom levels in the first wave and statistically significant reductions in spring/summer 2020 compared with spring/summer 2019 were found in groups that may have experienced a particular deceleration of life: the middle-aged, who are typically particularly busy with the demands of paid and unpaid work, and men, who, for example, took on less additional childcare than women when childcare facilities closed, particularly in high-income countries ( 53). The middle level of education group and, somewhat less markedly, the low level of education group also exhibit this pattern. Several workplace-related factors may have played a mediating role in a possible association between educational attainment and mental health, e.g., significantly reduced working hours with or without financial compensation vs. increased working hours or job loss and working from home ( 46).

4.3. Declines in mental health from the second wave onward

4.3.1. Declines in mental health from the second wave onward in the general population

A significantly higher proportion of positive PHQ-2 screens ( 45) and mean symptoms scores ( 59) in early 2021 compared to 2019 were also found in the German representative panel study (the "Socio-Economic Panel"). However, in contrast to our finding that symptoms of depression first increased in the second wave following an initial decline in the first pandemic months, scores and percentages were actually found to be lower in January/February 2021 than in April through June 2020 in the SOEP. Despite this discrepancy, the January/February 2021 proportion of positive screens in this other study is 12%, very similar to the September-December 2020 levels (12.1%) in our study (we do not have data from January and February 2021).

While we have no data on symptoms of anxiety and SRMH from before the pandemic or in its early stages, our findings of a potential increase in symptoms of anxiety and a clear decrease in SRMH between March 2021 and the end of 2021 are in keeping with the picture of worsening mental health following the onset of the second wave.

These changes occurred in the context of a second wave of infections much larger than the first, followed very quickly by a third wave and a fourth, very severe wave with only short periods of lower infection rates in between. Although vaccinations began at the end of 2020, measures to slow transmission were in place for much of this time, mortality rates were high, and hospitals were reported to have come dangerously close to their limits ( 20, 21, 23- 25). While, again, our results do not allow for conclusions on causal relationships between infection rates, mortality, NPIs, or other pandemic factors and mental health, associations between mean PHQ-4 scores and "pandemic intensity" have been reported in a meta-analysis ( 117).

The sheer increased duration of the cumulative pandemic stressors may also explain potential pandemic-related changes in mental health later on (author?) ( 19). A resilient response is more likely in the face of brief stressors than in the face of more chronic stress ( 18). In general, longer-term experiences of lack of control and helplessness threaten mental health and may be particularly related to depressive symptoms ( 16). Reductions in protective factors such as social contact, leisure activities ( 19), and access to the full spectrum of health services ( 16) may also grow more harmful with longer durations. Finally, most mental disorders take time to develop and manifest with a prodromal phase ( 118, 119). While our study does not address the prevalence of any mental disorders, the individual symptoms that comprise these disorders might be subject to the same dynamics.

Our findings of a further increase in symptoms of depression and symptoms of anxiety between late 2021 and early 2022 resulting in by far the highest levels in our over three-year observation period lend further support to the assumption of a potential build-up of pressure on mental health. The pandemic context at this time was a fifth wave of infections driven by the omicron variant immediately following the fourth wave and reaching the largest ever peak in new infections in April 2022 ( 21, 22), and yet a suspension of most NPIs from the beginning of April ( 35). Another major and acute population-wide stressor in the final 4 months of our observation period was the war in the Ukraine beginning on February 24 th, 2022. It is of note that symptoms of depression and anxiety increased rather markedly in the January/February-centered estimate of 2022, which includes data until mid-March 2022, suggesting potential mental health impacts of the war ( 120) and emerging economic developments ( 11, 18). The fact that subsequent estimates remained elevated raises the possibility of developments beyond a short-lived reaction to a discrete event.

The absence of a decline in SRMH between late 2021 and early 2022 following its decline within 2021 suggests that increases in depressive and anxiety symptoms did not translate to people reporting worse overall mental health within this specific time window. Importantly, the temporal reference frame of these measures is very different: while the PHQ-2 and GAD-2 ask about the previous 2 weeks, SRMH has no reference time. Perhaps SRMH shows different dynamics in this particular time window as a more global and less acute measure. Also, mental health problems are known not to necessarily translate into poor SRMH in general ( 121). The present observation period is too short for conclusions about differences in dynamics between these indicators, but this would be interesting to analyze in longer time series.

4.3.2. Declines in mental health from the second wave onward by subgroup

Turning to subgroups, stratification by sex, age, and level of education in our uniquely long and continuous time series revealed increases in depressive symptom levels at different times after the onset of the second wave of infections in all groups. Increases in symptom levels were more pronounced in women than in men until the end of 2021. This finding is as expected based on previous literature [e.g., 41, 52, 53, 109] and considering factors such as a greater burden from increased care work among women ( 53) and increases in domestic violence ( 14, 16, 53). In 2022, however, men also showed a significant increase above 2019 levels, as well as above 2020 and 2021 levels (and, just like women, also increases in anxiety and declines in SRMH). This later increase may be due to new stressors or simply a delay in negative mental health developments.

While the sexes and the level of education groups all showed relatively similar overall trajectories, age groups differ in the shape of their time series after the onset of the second wave, suggesting that stressors and protective factors may differ by age in particular. In keeping with previous findings of mental health vulnerabilities among young adults in the pandemic ( 41, 49, 54- 57), the youngest age group stands out in our study for its steep increase in depressive symptoms (and also symptoms of anxiety) at the end of 2021. Vulnerabilities in this group could be related to the transitional nature of young adulthood, the particularly great importance of social contact with peers when leaving the parental home ( 19, 51), and an overall greater disruption of life in this group ( 58) during the pandemic. On the other end of the age spectrum, those aged 65+ years stand out for early significant increases beyond pre-pandemic levels and a particularly constant trend of increase throughout the observation period from 9% positive screens in spring/summer 2019 to 18% in 2022 (standardized estimates). While most studies highlight risks among younger adults, a German study using primary care data found early increases of mental health diagnoses among those aged 80 and over ( 122), consistent with our results. A greater risk of severe disease and death from COVID-19 ( 16) may have resulted in greater stress and isolation throughout the pandemic ( 19) in this age group, with less relief from temporary suspensions of NPIs. While 45-64-year-olds do show worsening mental health, but only late 2021/early 2022, 30-44-years-olds stand out for somewhat steadier levels across indicators (except for one temporary increase in depressive symptoms), suggesting they may have been more resilient in the observation period.

4.4. Strengths and limitations

4.4.1. Strengths

Three features of the study should be highlighted as strengths: (1) Continuous, representative data spanning 1 year before the outbreak of COVID-19 and over 2 years of the pandemic: While most of the existing literature on mental health developments during the COVID-19 pandemic in Germany covers limited time periods and focuses on the early phases of the pandemic only, we present results on the whole course of the pandemic until June 2022, including pre-pandemic data for depressive symptoms. (2) Development of a method for assessing trends at higher temporal resolution: A method for deriving robust month-by-month results from relatively small samples was developed. Using graphical representations of monthly moving estimates, multiple adjustments of the sample, and smoothing spline curves, we were able to produce graphic time series for the visual identification of trends which were nearly all verified by statistical time period comparisons. This demonstrates the feasibility of this approach to high-frequency mental health surveillance. (3) Examining developments over time both in mean scores and using scale cutoffs: The relevance of population means for public mental health in connection to Geoffrey Rose's ideas about prevention and health promotion at the population level has been previously discussed ( 84). Changes in the population symptom level are of interest irrespective of whether they result in more positive screens. The additional examination of positive screens permits conclusions on whether changes manifest in increases or reductions in cases of potential immediate clinical significance.

4.4.2. Limitations

Limitations in the interpretation and evaluation of our findings include: (1) Time periods of observation and comparison: Because the time series on anxiety symptoms and SRMH span only about 16 calendar months during the pandemic and include no pre-pandemic data, observed developments cannot be contextualized temporarily and are more difficult to interpret than the longer time series for depressive symptoms (11 months pre-pandemic, 27 months during the pandemic). However, even for depressive symptoms, 11 months of pre-pandemic data are not sufficient to control for seasonal trends and long-term secular trends. By providing more context, longer time series would also facilitate our understanding of how meaningful the observed magnitudes of change are. (2) Gaps in data collection: Data collection was interrupted four times for depressive symptoms and twice for anxiety symptoms and SRMH. Also, for some months the number of observations was low. These monthly periods with fewer than 1,000 observations were minimized by using months ranging from the middle of one calendar month to the following calendar month. Additionally, predictions on a three-month window were still made when only 2 months were included. Thus, results assigned to the central months might be biased toward the first or the third months in the window or just averages of the first and the third month. Also, one gap in the GEDA study was filled with data from the COVIMO study, which had a comparable design but a different overall framing and focus. However, we checked for and did not find systematic, study-related differences. (3) Representativity of the sample for the general population and statistical power for subgroups: The response to population-based telephone surveys typically varies systematically by sociodemographic factors ( 125). In particular, younger individuals and those with lower levels of education are underrepresented in our study. We used weighting factors to account for population structure, but the small number of cases in these groups may mean that possible changes over time within a group and differences from other groups might not be detected. In order to reliably achieve statistical significance in the subgroup analyses, larger sample sizes within certain subgroups would be required. Mentally ill and especially severely mentally ill individuals may also be less likely to participate ( 126, 127), a bias for which we cannot correct. Similarly, we cannot rule out the possibility that willingness to participate in a survey conducted by a governmental public health institute during the pandemic was related to subjective pandemic-related psychological distress. (4) Measurement and scaling: Using short versions of screeners to measure depressive and anxiety symptoms results in a restricted range of scores compared to the full questionnaires. This might have decreased the likelihood of detecting changes compared to the respective long versions with more items. Additionally, the PHQ-2 and the GAD-2 as well as its long versions PHQ-8, PHQ-9, and GAD-7 measure the severity of depressive and anxiety symptoms on an ordinal scale. However, validation supports the interpretation as a metric scale ( 86, 128). According to this assumption, distributions of PHQ-based measures are commonly described by means of sum scores [e.g., 46, 57, 73, 114]. Furthermore, information based on self-report can be subject to recall-bias and social desirability ( 129). While telephone surveys have the advantage of not limiting the sample to those who are able to complete a survey online or via an app, social desirability may represent more of a confound with this survey mode ( 130).

4.5. Conclusion and implications

The main implications of our findings derive from the observation of a two-stage substantial decline in mental health in later phases of the pandemic. While the clinical significance of the changes observed in population mean depressive symptom scores is unclear, the increase in depressive symptoms cannot solely be attributed to elevated symptom levels below the clinical screening threshold. Instead, it resulted in an increase in the proportion of the population screening positive for possible depressive disorder by ~5-6% points when comparing estimates for CW 11-37 in 2019 with almost the same weeks (CW 11-24) in 2022. Our findings of increasing symptoms of anxiety and decreasing SRMH between 2021 and 2022 are consistent with this picture of a deterioration of mental health in the population. Continued surveillance will show whether this deterioration was temporary or part of a more sustained development. Research using more extensive screening instruments and diagnostic tools as well as research looking at trajectories of mental healthcare needs is also required for a full assessment of longer-term changes and their clinical meaning. However, our results as they stand call for vigilance with regard to possible changes in mental healthcare needs ranging from an increased need for diagnostic clarification and sub-clinical prevention measures to a greater need for secondary prevention. In addition, they point to a great need for mental health promotion and health in all policies approaches ( 131).

Evidence on vulnerable groups can provide guidance in the allocation of measures of mental health promotion and prevention. Overall, none of the examined sociodemographic groups prove to be consistently resilient. An effective public health response thus faces the challenge of addressing the entire population and cannot target clearly identifiable risk groups. However, in keeping with many other studies, a particular focus on women and young adults, but also the eldest adults, may be warranted. The final months in our time series, which saw the introduction of a new major societal-level stressor, indicates that mental health developments of men and adults in later middle age should also be observed closely. They show that vulnerabilities may be subject to change over time, demanding continued observation and reporting to increase awareness and flexibility in public health policy and mental health practitioners. As was the case before the pandemic, there is still a high need for mental health support for individuals of low socioeconomic status. Despite our finding of particularly early and continued increases in depressive symptoms among the high level of education group, the social gradient of lower mental health in the low level of education groups clearly persists across our time series.

Particularly with regard to current circumstances, mental health trends in the population should be observed and evaluated continuously and systematically. Further temporal dynamics in mental health seem very likely in view of a wide range of potential contributing factors and ongoing crises. These include the continued dynamic development of the pandemic and public health measures in response ( 132), the risk of chronification of stress reactions due to the persistence of stressors or loss of resources ( 16, 19), and the emergence of further mental health risk factors such as a long-term economic recession ( 11, 18) as well as other crises not related to the pandemic. It is possible that recent events such as the war in the Ukraine may have contributed to 2022 mental health declines ( 120). The exacerbation of the global climate crisis ( 133, 134) represents another major ongoing contextual factor. All of these crises taken together might also contribute to increased experiences of multiple disasters, which can exert a specific impact on public health ( 135). Fundamentally, the psychological impact of crises is likely to vary over time. For the pandemic, we can assume overlapping effects of immediate fear, followed by responses to adversities, consequences of insufficient mental health support, and long-term implications of recession or uncertainty ( 110). Because mental disorders frequently develop over a longer period of time during which multiple stressors exceed individual resources and interact with individual vulnerability, the possibility of delayed and substantial rises in the prevalence of mental disorders cannot be ruled out.

A continuation of mental health surveillance made possible by uninterrupted data collection is also needed in less tumultuous times to safeguard crisis preparedness. Our results show that mental health trends in the general population can change suddenly, supporting the utility of an early warning system. Sufficiently long time series of mental health indicators are required in order for high-frequency surveillance to help inform public health policy by identifying changes, assessing their significance and relevance against the backdrop of previous dynamics, and evaluating the impact of public health interventions effectively. In addition to this fundamental need for continuous mental health data, future studies should expand findings to the whole life span by including the observation of children and adolescents. Moreover, they should go beyond the use of screening instruments to measure symptoms in assessing the prevalence of mental disorders and include longitudinal designs in order to better understand mechanisms of vulnerability and resilience in the face of individual as well as collective determinants of mental health.

Data availability statement

The data set cannot be made publicly available because informed consent from study participants did not cover public deposition of data. The population-based data from the German health monitoring program that was used in this study is available from the Robert Koch Institute (RKI) but restrictions apply to the availability of this data, which was used under license for the current study. A minimal data set is archived in the Health Monitoring Research Data Centre at the RKI and can be accessed by all interested researchers. On-site access to the data set is possible at the Secure Data Centre of the RKI's Health Monitoring Research Data Centre. Requests should be submitted to the Health Monitoring Research Data Centre, Robert Koch Institute, Berlin, Germany (email: fdz@rki.de).

Ethics statement

GEDA and COVIMO are subject to strict compliance with the data protection provisions set out in the EU General Data Protection Regulation (GDPR) and the Federal Data Protection Act (BDSG). Participation in the study was voluntary. The participants were informed about the aims and contents of the study and about data protection. Informed consent was obtained verbally. In the case of GEDA 2019/2020, the Ethics Committee of the Charité-Universitätsmedizin Berlin assessed the ethics of the study and approved the implementation of the study (application number EA2/070/19).

Author contributions

EM and LW wrote most sections of the manuscript, integrating drafts for specific sections from co-authors. EM developed the main conceptual ideas and coordinated the research process as a senior researcher. EM, LW, SJ, SD, CK, and SM developed the specific research questions and discussed the analytical approach. SJ performed the statistical analyses, worked with SD to develop the statistical methods, and produced the results graphs. Together they drafted the analysis section of the manuscript. LW and EM interpreted the results. CK primarily contributed to the interpretation of educational disparities, assisted by SM as senior researcher in the area of health inequalities. JT, as project leader of the Mental Health Surveillance, contributed to all content discussions and by drafting some sections of the Introduction and Discussion. SE contributed information on the existing relevant literature for Germany. DP and SS contributed to the manuscript with comments and suggestions, as did HH as the head of the Unit of Mental Health at the RKI. All authors gave crucial input on drafts of the manuscript and read and approved the final version.

Funding

The "MHS-Establishment of a National Mental Health Surveillance at Robert Koch-Institute" project is funded by the Federal Ministry of Health (Grant Number: ZMI5-2519FSB402). CK collaboration on social inequalities in mental health during the pandemic was funded by the German Research Foundation (Project Number: 458531028).

Acknowledgments

We would like to thank all colleagues in Unit 21: Epidemiological Data Center of the Department RKI who provides us with quality-assured data on a monthly basis. In particular, Jennifer Allen as project manager of the GEDA surveys has collaborated continuously.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The handling editor WR declared a shared affiliation with the author SS at the time of review.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2023.1065938/full#supplementary-material

Abbreviations

COVIMO, COVID-19 vaccination rate monitoring in Germany; CW, calendar week; GAD-2, Generalized Anxiety Disorder Questionnaire (2 items); GEDA, German Health Update; EHIS, European Health Interview Survey; PHQ-2/8/9, Patient Health Questionnaire (2/8/9 items); RKI, Robert Koch Institute; SES, Socio-economic status; SRMH, Self-rated mental health.

References

# data[].attributes.attachment_link.title
Time trends in mental health indicators in Germany's adult population before and during the COVID-19 pandemic

# data[].attributes.attachment_link.description
BackgroundTimes of crisis such as the COVID-19 pandemic are expected to compromise mental health. Despite a large number of studies, evidence on the development of mental health in general populations during the pandemic is inconclusive. One reason may be that representative data spanning the whole pandemic and allowing for comparisons to pre-pandemic data are scarce.MethodsWe analyzed representative data from telephone surveys of Germany's adults.

# data[].attributes.headline
Peer Reviewed Article on the state of Mental Health on EU

# data[].attributes.subject
Peer Reviewed Article on the state of Mental Health on EU

# data[].attributes.headline
Mental Health tools to understand Mental Health in the population

# data[].attributes.subject
Mental Health tools to understand Mental Health in the population

# data[].attributes.attachment_link.readable_text
Department of Epidemiology and Health Monitoring, Robert Koch Institute, Berlin, Germany

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.

1. Introduction

The COVID-19 pandemic and non-pharmaceutical interventions to reduce transmission of the virus as well as the societal discourse on the pandemic had far-reaching impacts on populations worldwide. Questions about the potential consequences for mental health arose from the beginning of the outbreak ( 1- 3). As the pandemic unfolded, it became clear that it was a long-term stressor with many phases and sometimes rapid changes in circumstances ( 4). Therefore, ongoing and high-frequency monitoring of mental health and other outcomes was called for, and several public health institutions began tracking mental health indicators at regular intervals, including in the US ( 5), France ( 6), and England ( 7). Importantly, this type of continuous, temporally finer-grained surveillance comes with particular requirements for data processing, estimate calculation, and output for interpretation.

A nationwide Mental Health Surveillance (MHS) was in conceptual development at the Robert Koch Institute when the pandemic began in Germany ( 8). In line with the established concept of public health surveillance ( 9), the aim of the MHS is to regularly and systematically quantify core mental health indicators in order to provide information on the development of population mental health as a foundation for public health action. While this surveillance system was not yet in operation at the start of the pandemic and not initially conceived for high-frequency updates on mental health, we implemented a strategy for mental health monitoring using monthly data from a series of population-based telephone surveys of adults in Germany to meet new information needs arising in the pandemic. The particular aim of this high-frequency surveillance approach is to provide information on possible changes in population mental health almost as they unfold in order to enable policymakers and health care practitioners to respond swiftly for optimal health promotion and prevention, particularly in times of crisis.

The first survey that we used for high-frequency monitoring was the third wave of the European Health Interview Survey conducted as part of the study "German Health Update" (GEDA 2019/2020-EHIS) for Germany, which began data collection almost exactly 1 year before the outbreak of the pandemic ( 10). The study continued until January 2021, which was beyond the official end of GEDA 2019/2020 - EHIS. Even though the survey was not designed for monthly reporting, adjustments to sample weighting allowed for the calculation of monthly representative time-varying estimates of various health indicators, including symptoms of depression, as shown in previous reports by researchers at Robert Koch Institute ( 11, 12). Subsequently, data for high-frequency mental health monitoring has come from the studies "COVID-19 vaccination rate monitoring in Germany (COVIMO)" ( 13) as well as GEDA 2021 and GEDA 2022 ( 14), all of which were designed for monthly reporting given pandemic-related monitoring needs.

Data on several mental health indicators from these surveys was used to build a prototype for mental health surveillance of the adult population in Germany on the basis of graphically represented, continuously updated time series of monthly estimates. The prototype was developed to meet the following criteria:

1. Output should be updated to include the most recent available data as fast as possible, requiring a highly automated data processing and estimate calculation pipeline.

2. The results should be generalizable to the adult population in Germany.

3. It should be possible to analyze developments over time adjusted for demographic changes with regard to sex, age, and level of education.

4. It should be possible to compare sociodemographic subgroups by sex, age, and level of education, standardized and unstandardized for the respective other two characteristics.

5. The results should be as temporally fine-grained as possible.

6. Although our prototype's objective is to identify changes, it should not be overly responsive to minor and random fluctuations, as this would complicate the graphical interpretation. In other words, it necessitates the use of a technique for smoothing short-term fluctuations.

7. The results need to be in a format suitable for graphical presentation, for example via a dashboard.

In this paper, we describe the prototype comprising an automated data pipeline from data collection, data preparation, and calculation of estimates to output creation. Furthermore, the statistical methods used are described. These include a weighting algorithm, a linear and a logistic regression model used to make predictions on a standard population for a moving three-month window, and a generalized additive model ( 15) with smoothing splines employed to make predictions on a standard population for a weekly interval.

2. Methods

2.1. Data and software

The data used for the analyses are from telephone health surveys conducted on behalf of the Federal Ministry of Health (BMG) as part of the nationwide health monitoring program German Health Update (GEDA) ( 10, 14, 16, 17) and COVID-19 vaccination rate monitoring in Germany (COVIMO) ( 18). The number of observations per monthly period ranging from the middle of one month to the middle of the following month between April 2019 and April 2022 are shown in Figure 1.

Figure 1. Observations per one-month period ranging from middle of the month to middle of the following month.

SAS SE ® software, version 17.1 is used for data management and data cleaning procedures as well as to perform adjustment weighting. All other data processing and some of the analysis steps are carried out in R version 4.1.2. ( 19), RStudio 2022.02.1.461 ( 20). Several packages available for R are used. For data preparation and pipeline programming, dplyr 1.0.7 ( 21), rlang 0.4.12 ( 22), readstata13 0.10.0 ( 23), ISOweek 0.6-2 ( 24), and stringr 1.4.0 ( 25) are used. RStata ( 26) is used to transfer data back and forth between R and Stata. For analysis and prediction of the smoothing splines, mgcv 1.8.39 is used. The tool rmarkdown 2.11 ( 27- 29) is used to structure analyses and graphs. We create the graphs with ggplot2 ( 30). Stata 17 ( 31) is used to estimate marginal predictions with confidence intervals on the basis of linear and logistic regression models.

2.2. Data pipeline

In order to meet the first criterion of promptly updating the output as soon as new data becomes available, we implemented a highly automated data pipeline. It consists of two parts, the data collection and quality assurance by the Epidemiological Data and Survey Center of Department 2 (EDC) of the Robert Koch Institute and external contractors as well as the data preparation and analysis for the purpose of mental health surveillance. In this section the work of the EDC is briefly summarized while the automated process for the MHS is laid out in more detail. At the end of the data pipeline estimation results are stored in a table format or as lists of tables, ensuring broad compatibility for further applications (criterion 7).

For the first part of the data pipeline, an external market and social research institute (USUMA GmbH) is contracted to conduct the telephone surveys, and provides the data to the EDC of the Robert Koch Institute. Figure 2 shows a simplified version of the data pipeline starting at the EDC.

Figure 2. Data pipeline.

Before the data is made available for analysis, the EDC performs standardized data editing and data quality assurance on the raw survey data. For example, implausible data is deleted or corrected, cases are cleaned, and new variables are generated ( 10). This only applies to data from the GEDA study, however. Because the COVIMO study was not integrated into the EDC's data editing and data quality assurance procedures, these were performed by the authors. Because the study is conducted using computer-assisted telephone interviews which already include filtering of questions and value range checks, data inconsistencies are rare and data editing is limited to very few cases. Weighting factors are provided by the EDC for both data sources. The data is then made available in Stata and SAS format.

After the data has been made available by the EDC, it is prepared for the specific calculations performed within the MHS. Because the aim is to update the results as fast as possible on the basis of continuously collected data, data preparation needs to be mostly automatic and flexible. Thus, several functions and scripts were developed in R to perform the following tasks: First, an object (tibble) is created encompassing all necessary metadata for the survey data files. The metadata contains information on the location of the data and information for data processing, including an identifier specifying the function used for further processing of this data file. There are automatic checks for new data or data updates in predefined folders. When new data becomes available in one of these folders, a new row is automatically added. If neither updated data nor new data is found, the data processing stops. If, however, one of the two criteria are met, the data files are prepared separately from each other by the specified function, a new metadata file is created and saved, and a list of data frames containing the data from the different surveys and survey waves is created and later unpacked to a single data frame. Data is imported in the form of Stata files. Predefined value labels are omitted as special characters or extensive value labels are error-prone in R.

To prepare the MHS survey data, data editing is done in two stages. In the first instance of data editing, variables are coded into a standard format within individual data frames including data from only one data file, as mentioned above. Individual data frames are used because different data sources require different forms of preparation. For example, some variables are already generated in one data file but not in the other, so this has to be done retroactively only for these data files. Also, time identifiers for the interview date differ in format between the data files. The newly created time identifiers include a standardized string variable identifying the calendar week of the interview following ISO-norm 8601 ( 32) and the interview month, here operationalized as middle of one month to middle of the following month (see section 2.3.2).

The second stage of data preparation begins after all data files are combined into one: this stage of data preparation includes the naming of the different levels of categorical variables and, most importantly, the creation of additional time variables. Weekly and monthly identifiers are further processed to include information on the whole time series from start to finish, including all data gaps, as date, ordered factor, and numeric variables counting the weeks and months from the start of the time series.

The next step of the data pipeline covers the calculation of adjusted predictions for each three-month period (statistical details section 2.3.2) and a smoothed curve on a weekly basis (statistical details section 2.3.3). As Stata's margin function is commonly used to estimate marginal predictions with confidence intervals, the calculation of the adjusted predictions is performed in Stata 17.1. For this purpose, every data frame contained in the abovementioned list is used to perform Stata calculations. We use the package Rstata ( 26) to facilitate communication and translation between R and Stata. This package enables initiation of Stata and the transfer of data frames from R to Stata. The created Stata files are then read back into R using the package readStata13. The calculation is performed with Stata do-files for linear and logistic regressions, including the calculations of margins, confidence intervals, and finally, the saving as a temporary data file. The required definitions of control variables, dependent variables, weights, and data used for predictions are managed with Stata macros which are specified in another do-file created with R. The Stata results are stored as a data frame including the predicted means and proportions (with confidence intervals) of the respective mental health indicator and the related time-identifying variables and stratification variables/levels. The estimates for the smoothed curves, on the other hand, are calculated in R because the smoothing algorithm used is included in the R package mgcv ( 33) and not implemented in Stata. The data frame including all data from the individual data files is used for this calculation.

2.3. Statistical methods

This section details methods used for data weighting and output calculation within the semi-automated data pipeline described above. In what follows, the rationale behind these methods is briefly outlined. Subsequent sections describe different methodological steps and decisions in greater depth.

Criteria 2-5 specified above require that the prototype produce a temporally fine-grained output, representative estimates which can be compared over time, and standardized as well as unstandardized estimates for comparisons between the subgroups of interest. With the estimation of monthly weights by the EDC detailed in 2.3.1 below, fluctuations in participation between groups defined by regions, sex, level of education, and age over time can be addressed. Weighting also ensures representativity by approximating the German population and correcting for different probabilities by design.

The criterion of maximum temporal resolution and limitations of our dataset informed our first decision about the output calculation (see section 2.3.2): While producing a new estimate monthly would be desirable, our data includes too few observations in one-month periods for a direct estimation of monthly means and proportions: with a small monthly n, cell counts can simply be too low, and time series risk becoming noisy with random fluctuations. Data gaps and months with a particularly small number of observations pose added challenges. To base calculations on larger subsamples and thereby reduce the risk of random fluctuations, we opted to calculate estimates for moving three-month windows. This technique provides more observations for estimation, smooths time series by reducing random fluctuations, and still produces new estimates for every month.

Standardization between subgroups (criterion 4) as well as ensuring representativity and comparability (criteria 2 & 3) over time given the specific time windows used in analyses necessitates the use of regression modelling (see section 2.3.2) rather than a straightforward calculation of means and proportions. For standardization between the subgroups defined by one characteristic - for example sex - by the remaining two, we used predictions on a standard population (see section 2.3.4). Regression models including the sociodemographic characteristics as independent variables provided the foundation for standardization ( 34, 35). This procedure also ensures standardization across time when the official population distributions are changed within the EDC's weighting process. It acts as a secondary safeguard to address changes in distribution between the monthly samples, particularly when weights cannot be estimated for the exact time periods we used.

Sample means are sensitive to outliers and can be very sensitive to short-term fluctuations depending on sample size. For better separation of signal from noise (criterion 5) we considered several additional, less sensitive smoothing techniques and chose a thin plate smoothing spline because we found this method to strike a good balance between best fit and smoothing (see section 2.3.3). These choices and the methods are explained in more detail in the following sections.

2.3.1. Data weighting

Data weighting is necessary to meet criterion 2 ("The results should be generalizable to the adult population in Germany"). The EDC's data weighting procedure considers two aspects of sampling bias: (1) different selection probabilities of participants (design weighting) and (2) different likelihoods of participaton within different population subgroups (adjustment weighting).

Despite random sampling in the sense that random phone numbers were dialed, study participants have different probabilities of having been selected into the samples at hand (selection probability). The telephone studies used here recruit participants via a dual-frame approach, using landline phone numbers and mobile phone numbers. The probability of drawing a specific phone number is a major part of the overall selection probability of each respective individual survey participant. In the mobile phone frame, the individual selection probability also depends on how many mobile phone numbers each participant has. In addition, the number of persons using the phone number called for recruitment plays into selection probability. The selection probability in the landline frame is also dependent on the probability of selecting a specific individual from each contacted household. This, in turn, is determined by household composition and household size. Design weighting compensates for the different selection probabilities of participants, in that persons with a lower selection probability represent more people from the population than persons with a higher selection probability. Due to data policy and data privacy reasons, information on sampling frame and household composition are not available to the EDC. The contracted market and social research institute therefore provides design weights calculated as described in Häder et al. ( 36). The weights are calculated independently for the GEDA and COVIMO studies.

Table 1. Adjustment levels.

It is possible that the number of observations in an adjustment level is small or zero. The calculation cannot handle empty adjustment cells because there must always be at least one observation to which the distribution can be fitted. In addition, an insufficient number of observations can lead to extreme values for the weighting factors. If the number of observations in an adjustment level for a time point is less than ten, a check is run as to whether the weighting factor values are zero or greater than a factor of ten. A weighting factor of zero cannot be used in analyses, since corresponding participants are thus practically excluded. On the other hand, participants with large weighting factors have excessive impact on the analyses. Another problem with a small number of observations in an adjustment cell is an endless iteration process in the raking procedure, which is here limited to 300 iterations. To address these problems arising from insufficient or zero observations in an adjustment cell, extreme values in the weighting factor, or an exceeded iteration limit, age groups are combined for the survey time point and adjustment level in question in order to increase the number of observations.

Missing values in any of the variables relevant for weighting are not permissible in the weighting procedure. Therefore, these must be assigned to a category or imputed. For education (ISCED), missing data are assigned to the middle category. Missing values in the self-declaration of federal state are imputed automatically, according to the distribution of federal states in Germany. Missing values in the political municipality size category are imputed based on the respective distribution in the federal state.

The adjustment weighting is performed with SAS SE software, version 17.1, using predefined syntax and macros for the raking procedure and distributional checks. It is performed after the data editing and quality assurance by the EDC.

2.3.2. Linear and logistic regressions for three-month windows

The time series that this prototype outputs encompass monthly estimates; specifically, monthly predicted mean values or proportions based on Stata's margins function for three-month windows. Periods of data collection within the surveys used for the prototype happen to begin roughly in the middle of the respective months. To optimize the number of cases per month, a monthly period is therefore defined as the middle of a month to the middle of the following month.

Linear and/or logistic regression models are fitted for each mental health indicator as the basis for prediction. In order to avoid bias due to changes in sociodemographic factors sex, age, and level of education between the monthly time periods and for the prediction of stratified values, the regression models include a list of covariates. For each individual i the linear regression model for metric mental health indicators is defined as:

and the logistic regression model for binary mental health indicators is defined as:

where is a vector of regression parameters and and are vectors of auxiliary covariates. The latter includes age groups, sex, and level of education as well as their interactions. The covariates of interaction differ between the linear and logistic regression models: the linear regression ( 1) includes all combinations of age group, sex, and education (), while the logistic regression ( 2) contains only two-way interactions () because otherwise, cells with zero observations are very likely to occur and prevent marginal prediction.

Some of the mental health indicators show a small prevalence of cases as defined by cutoffs, particularly within certain subgroups. For example, in one three-month period (centered on May/June 2021), 4% of men screened positive for possible anxiety disorder. Given the relatively small numbers of cases per survey month, this necessitated estimation for a three-month period rather than a one-month period in order to minimize random fluctuations (criterion 6). Following the procedure of centered moving averages ( 39), the observations from the previous and following month are defined as the period for the corresponding month. As the moving three-month windows overlap, regressions are separately fitted for each three-month window based exclusively on observations within it. Thus, for a survey period with M months, = 1 ... separate regressions model estimates will be derived. At the beginning and at the end of a survey or due to interruptions during the survey, it is not always possible to sum up three months for each estimate in the time series. In these cases, the estimation is calculated based on two months if observations of at least two of the three months are available. The model results are then used for average predictions on a standard population as described below.

2.3.3. Smoothing

The method of moving predictions is an intuitive way to smooth a time series (criterion 6). However, this smoothing technique is still very sensitive to outliers and might lead to overfitting the data and noisy time series from which the actual trajectory is difficult to detect ( 40). To prevent overfitting and thus misinterpretation due to random fluctuations, another smoothing technique is applied to our time series. Another disadvantage associated with the moving three-month windows is their limited utilization the available temporal information. The temporal information used is whether an observation falls within a given three-month window or not, disregarding its placement within that timeframe.

While sample size restricts the temporal resolution of the estimates, the data contains information down to the level of weeks. When time is not segmented into periods, but treated as a continuous variable, this information can be used despite small sample size at that level. In light of continuous data collection and the requirement of automatic output creation as well as its function in facilitating the interpretability of the time series output, the following criteria for smoothing were determined:

1. The smoothing technique needs to work automatically without manual specifications.

2. To ensure the accuracy of interpretation the smoothed curve should not introduce bias by either being overfitted to the data or oversmoothing and thus failing to capture important developments. Ideally, there is an objective criterion for optimization.

3. The smoothed curve has to be as stable as possible for time points in the past when new data is added but still be locally adaptive.

Several candidate smoothing techniques were considered on the available data: polynomials, restricted cubic regression splines, and smoothing splines. Polynomials did not fulfill the criteria because the curve is not smoothed locally. A polynomial function defines the shape of the curve globally. For example, a quadratic function will always produce an (inverted) "U"-shaped curve. Thus, an adaption of the curvature at some regions of the curve always necessitates a change in the whole curve. Also, this technique requires a decision about the ideal degrees of the polynomials with every new time point as well as checks for fit to data to prevent substantial changes in the curve for the previous point in the time series when new time points are added. A way to automate the determination of the ideal degree of the polynomial is to treat it as a tuning parameter and use cross validation: the model is fitted to one part of the data, and then the prediction is evaluated on another part of the data, for example using the root mean square error. This process is iterated for a predefined choice of values. However, this process is computationally time consuming and might still lead to substantial changes for the existing time series when a time point is added. Restricted cubic regression splines are locally adaptive as the function is fitted to different parts of the curve marked by knots. The regions before the first and after the last knot are restricted to linearity as the splines often show erratic behavior in these regions. The knots between which the function is fitted need to be defined and potentially manually adapted when new data points are added ( 41, 42). The necessity to make choices regarding the number of knots and their placement poses a disadvantage to a process that aims to be highly automated. One possibility, however, is to use a fixed number of knots with a default placement. Stone ( 43) showed that five knots should be sufficient for most scenarios a recommendation for default placement of knots is provided by Harrell ( 44).

As another method, we considered smoothing splines. Unlike cubic regressions they do not require knots; instead, a smoothing parameter controls the smoothness. We chose a smoothing spline with the basis function of a thin plate spline over other smoothing spline approaches because it is both theoretically well-founded and particularly suited to our needs given that the approximations developed by Simon Wood ( 42) made thin plate regressions computationally efficient so that they can also be used for large data sets. This technique has a very good level of accuracy, though the curves produced are not as smooth as other automatic smoothers ( 45). The smoothing parameter can be estimated automatically and simultaneously with the whole model by either using restricted maximum likelihood (REML) or generalized cross validation ( 46). This is a major advantage because it enables automatic estimation and avoids any manual presetting such as defining knots or degrees of polynomials. Thus, over-and underfitting to the data can be avoided without defining different choices of parameters for cross-validation. As generalized cross validation is prone to undersmoothing, we chose REML ( 46- 48).

Figure 3. Behavior of two candidate splines with 39 updates every 4 weeks beginning after 12 weeks.

To examine the overall behavior of the two techniques we also plotted the curve with the two different splines for the entire observation period (see Figure 4). Although the general trends remain consistent, the smoothing spline provides a more detailed view. This in itself is neither an advantage nor a disadvantage; however, as the smoothing spline technique provides a mechanism against under-and overfitting, it seems that the restricted cubic spline underfits or oversmooths the data. Thus, the smoothing splines meet our criteria best.

Figure 4. Comparison of two candidate splines across the entire observation period.

2.3.4. Prediction and standardization

In order to obtain standardized outcomes for the mental health indicators, the results are not estimated with the actual survey data, but with a standard population. This allows for direct standardization between time periods and between subgroups defined by sex, age, or level of education. For example, estimates for different age groups are adjusted for differences in sex and level of education (criteria 3 and 4). The estimates from the linear and logistic regression models as well as the general additive models for Gaussian and binomial distributions based on the survey data described above are used for these predictions on a standard population. The standard population is derived based on the latest available German microcensus data containing the population distribution by age group, sex, and education. The time series presented below were estimated using 2018 microncensus data ( 52).

This standard population is the basis for weekly and three-month predictions. The predictions are then averaged over the whole population as well as within each population subgroup by sex, age group, and level of education. Therefore, this method ensures standardization for age, sex and level of education over time. Consequently, older predications will undergo changes when the standard population is updated.

For direct standardization between the subgroups, predictive margins ( 35, 53) are calculated. The only difference to the procedure described above is that the standard population is used for each subgroup, treating all the rows as belonging to the subgroup in question. For example, predictions for the male population treat all individuals in the standard population as if they were male and vice versa for the female population ( 54). The Stata command "margins" is used for both the prediction with and without standardization between subgroups because the estimation of confidence intervals is already integrated into this command. With low or high prevalence, the confidence intervals estimated by the delta method may produce lower limits beyond zero and upper limits exceeding one. This is why resulting confidence intervals are constructed by means of a logit transform ( 55) using the undocumented command "coef_table, citype (logit)."

For the predictions of weekly values for the smoothed curve we wrote an R function using the following formula from Graubard and Korn ( 53):

where g(r, Z, ) is the predicted value which depends on the survey weighted estimator for the model parameters, Z i the S distinct values of the covariates for which the distribution in the external distribution is known, and r the value of the subgroup for which the mean or proportion is to be be predicted. When the prediction is standardized between subgroups, the weighting factor represents the number of individuals in the standard population with a distinct value of Z i. In case of an unstandardized prediction, the weighting factor only includes weights with distinct values of the covariates in subgroup r. For prediction of the whole population without stratification r can be removed from the formula.

As the calculation of confidence intervals of predictive margins with GAMs and survey design is beyond the scope of our project, only the point estimates are calculated.

2.3.5. Missings

Missings are handled differently depending on the type of variable. While only interviews with information on sex and age are included in the sample, level of education is imputed by assigning missing values to the middle category, in accordance with the weighting procedure described above. Observations with missing values in the mental health indicator are excluded from analysis. If patterns of missingness in these variables are dependent on age, sex, education, or their interactions, exclusion should not result in bias because the estimation controls for these factors.

3. Results

With the prototype described above, it is possible to provide automatic updates of time series with each survey wave and produce graphical representations of the development of mental health indicators for Germany's population [please (see 56) for results]. To generate monthly estimates, data from moving three-month windows is used for the calculation of a linear or logistic regression model adjusting for age group, sex, and level of education.

This three-month window moves along one month at a time for the calculation of the next three-month estimate until the last month with observations enters the window. If only one month is missing in the three-month window, calculations are still performed; if two months are missing, no calculations are performed and there is a gap in the time series. The visual output of these three-month average predictions is intuitively understandable to non-experts and has the benefit of providing discrete estimates for specific intervals. They also include confidence intervals, which provide important information for visual inspection.

Figure 5. Time trends of symptoms of depression (PHQ-2). (A) Population mean (range 0-6). (B) Proportion of population screening positive for possible depression (PHQ-2 > 2). Please note that these time series are presented here for the purposes of illustrating and discussing a methodological approach. Please see another study for the results of these analyses ( 56).

Figure 6. Time trends of symptoms of depression (population mean PHQ-2, range 0-6) stratified and standardized or unstandardized by sex (A, B), age group (C, D) and educational qualification (E, F), standardized (A, C, E) and unstandardized results (B, D, F). Please note that these time series are presented here for the purposes of illustrating and discussing a methodological approach. Please see another study for the results of these analyses ( 56).

This illustrates the need for a technique which provides more smoothing without overfitting. The addition of smoothing splines facilitates the differentiation between signal and noise to identify trends. An additional benefit is that all available temporal information is used to estimate the curve, providing information at a higher temporal resolution. However, with shorter time series such as the self-rated mental health time series [ Figures 7C, D, measured using a single item ( 58)], this additional temporal information smooths the time series less effectively than the moving three-month averaged predictions: the smoothing curves reflect fluctuations between monthly estimates, which the moving averages, by definition, are unable to show at the same temporal resolution. For shorter time series with low cell counts such as the time series for the screening instrument Generalized Anxiety Disorder-2 [GAD-2, ( 59)] ( Figures 7A, B), the general additive model fails to provide interpretable results for proportions; however, continued data collection may resolve this problem. Otherwise, the model specifications may need to be revised by reducing the number of interactions.

Figure 7. Time trends of symptoms of anxiety and self-rated mental health. (A, C) population mean GAD-2 scores (range 0-6) and mean SRMH scores (range 1-5), respectively. (B, D) show the proportion of the population screening positive for possible anxiety disorder at a GAD-2 score of >2 and those rating their own mental health "very good" or "excellent," respectively. These time series are presented here for the purposes of illustrating and discussing a methodological approach. Please see another study for the results of these analyses ( 56).

The interpretation of time trends should be derived from both the three-month moving estimates and the smoothed curve using their respective strengths. For example, in Figure 5, the moving averages allow for a better visual detection of change points: While the curve shows a steady increase between July/August 2022 and end of 2022, the moving averages show that the development was actually more consistent until the end of the year and only increased then. The strength of the smoothing splines is the visual identification of trends when fluctuation is high ( Figure 6).

The predictions with a standard population ensure that the estimates can be directly standardized between the subgroups but also between the different time periods. Figure 6 shows the standardized and unstandardized stratified results. With our data, the differences are minimal; however, this might change with different data sources. While the unstandardized time series can be used to examine the development in real population subgroups, the standardized results can be used to better identify vulnerable groups. Both outputs provide important information for the planning of interventions for mental health promotion and prevention.

4. Discussion

The COVID-19 pandemic brought about a need for high-frequency monitoring of developments in the mental health of populations. Against this background, we expanded our range of strategies for a Mental Health Surveillance in Germany to include monthly reporting on a small set of indicators aiming to provide policymakers and practitioners in the health care system with high-frequency updates on developments in population mental health in order to enable swift public health action. We defined six criteria for the prototype presented in this paper and developed an infrastructure which creates new estimates as soon as new data becomes available (criterion 1). At present, data processing has to be initiated manually. However, the checks for new data could easily be automated using a task manager. With the programmed ggplot functions, graphical output can also be created automatically. The estimates are in a format and structure that can easily be fed into further applications such as databases and dashboards (criterion 7). The weighting procedure developed by the EDC allows for representative estimates for the adult population in Germany (criterion 2). As an additional safeguard against deviations between the sample distribution and the population distribution, the estimation procedure includes predictions based on the latest available estimates of the German population. This technique also allows for standardization between subgroups and over time (criteria 3 and 4). To ease graphical interpretation, the prototype produces two types of output. First, it calculates moving three-month predictions which can be considered as moving averages. Moving averages are intuitively understood and provide some smoothing of the data, but are still sensitive to short-term fluctuations. To facilitate the differentiation between signal and noise and make trends more visible, a smoothing curve is also calculated (criterion 6). The techniques developed also address our problem of few observations per month and temporal gaps in the data while optimizing temporal resolution (criterion 5). Our prototype therefore fulfills the criteria specified and is in use within the MHS ( 56).

The adaption of this prototype to other surveys is straightforward if the survey design remains the same over time and data has been adequately prepared for further processing. Basic data cleaning is not included in the data pipeline because the prototype was built for data from telephone surveys conducted by the EDC of the Robert Koch Institute, and initial processing of the raw data is handled within this established framework. However, the prototype does include individual scripts to homogenize the different surveys used (GEDA19/20, COVIMO, GEDA 2021, GEDA 2022). Thus, new scripts for other data sources can easily be added within the existing framework.

However, caution is warranted in the application of the prototype to multiple data sources. Minor changes in the main scripts suffice to adapt the analyses to changes in sampling designs (for example, multiple-stage sampling). However, the prototype cannot be as easily modified for other changes in survey design or data collection that might influence sample composition over time. In other words, the potential impacts of changes in data source on results should be carefully considered in applying this prototype.

Because the prototype performs prediction and standardization using a predefined marginal distribution of the German population, it includes a procedure that can correct for biases due to different study participation probabilities in certain population groups. As a consequence it can be used on unweighted data. The marginal distributions of the population used can easily be replaced by the latest or most appropriate available versions of public statistics. However, so far, the models employed for prediction are only adjusted for age group, sex, and level of education. If data is, for example, oversampled for people with a history of migration, the implemented procedure will not correct for this. Furthermore, it should be noted that a modification of the marginal distribution used for prediction and standardization will change results for the entire time series. While this ensures standardization between years, it should be noted that estimates are representative of the population only for the respective microcensus year. To achieve population representative estimates for all years in the time series, the prototype can be modified to standardize separately for each year using the respective microcensus datasets and not for the whole time series.

In the further development of the prototype, greater flexibility for the covariates should be an aim. For example, age or education groups have to be customizable for the expansion of this type of mental health surveillance for children and adolescents. This is also relevant for applications of the prototype to other health topics which might require stratification by different levels or different characteristics. A few adjustments were already made in the application of the prototype to the assessment of developments in child and adolescent mental health using data from the study "Kindergesundheit in Deutschland aktuell" [German Children's Health Update, ( 63)].

We encountered several difficulties owing to data transformation between SAS, STATA, and CSV throughout the calculation process. The implementation of STATA's margins command, including our specific use case, in R would reduce the variety of different data types to be processed This translation would need to cover the following three steps: first, the integration of probability weights for model estimation and for prediction; second, the calculation of confidence intervals for the means of the predictions; and third, the calculation the logit transformed confidence interval. A promising solution could be the R package marginal effects ( 64), which could potentially be integrated in the future.

As a smoothing technique to facilitate visual interpretation of the time series, we decided to use a general additive model with a smoothing thin plate spline. It produces curves that rapidly adapt to new trends emerging from new data, which also means that predictions for time periods further in the past do not change abruptly. Furthermore, the smoothing parameter of the spline can be estimated, resulting in a curve which neither over-nor underfits the data, whereas the cubic regression spline seems to excessively smooth and thus underfits. However, neither type of spline offers an advantage over the three-month predictions for short time series: they tend to be too sensitive to short-term changes in shorter time series to provide a sufficiently smoothed fit. We aim to further monitor the development of smoothing curves in these time series and define criteria for the length of time series at which the weekly smoothed predictions can be reported alongside the moving three-months average predictions.

A caveat in combining two representations of time series - a thin plate smoothing spline and three-month average estimates - is that there is more and potentially conflicting information to take into consideration in the process of visual interpretation. These two representations can come apart because the smoothing spline is estimated independently of average predictions for the three-month windows and estimated on weekly data in order to use all available temporal information and to achieve a curve without edges. This serves as a robustness check against overinterpretation of (random) changes in the three-month estimates. While the spline is sometimes inferior to the three-month moving averages when the goal is to visually detect change points, it is superior in separating signal from noise when there are large fluctuations in the estimated moving averages.

5. Conclusion

The high-frequency surveillance of health indicators using survey data can help enable rapid and flexible responses to ongoing changes in population health, particularly in times of crises. However, using survey data to assess health developments at frequent intervals poses unique data processing and analysis challenges. The prototype presented here was designed to overcome these challenges and to provide automatic monthly updates on multiple indicators on the basis of relatively small monthly samples. It will continue to serve as a basis for high-frequency surveillance of mental health within the MHS at the Robert Koch Institute (see 8) and may potentially be applied to high-frequency surveillance in other areas of health in the future. The prototype's output is highly suitable for publication and regular updates on a dashboard, which is the next aim within the MHS. The precondition, however, is ongoing continuous data collection. While there are numerous possibilities for the further development of the prototype, some of which have been addressed above, the described approach as it stands may be of use to other researchers in public health implementing a similar type of surveillance.

Data availability statement

The datasets presented in this article are not readily available because the population-based data from the German health monitoring program that were used for the development of the prototype presented here are available from the Robert Koch Institute (RKI) but restrictions apply to the availability of these data. The data sets cannot be made publicly available because informed consent from study participants did not cover public deposition of data. However, a minimal data set is archived in the Health Monitoring Research Data Centre at the RKI and can be accessed by all interested researchers. On-site access to the data set is possible at the Secure Data Centre of the RKI's Health Monitoring Research Data Centre. Requests to access the datasets should be directed to fdz@rki.de.

Ethics statement

The studies involving human participants were reviewed and approved by GEDA and COVIMO are subject to strict compliance with the data protection provisions set out in the EU General Data Protection Regulation (GDPR) and the Federal Data Protection Act (BDSG). Participation in the study was voluntary. The participants were informed about the aims and contents of the study and about data protection. Informed consent was obtained verbally. In the case of GEDA 2019/2020, the Ethics Committee of the Charité-Universitätsmedizin Berlin assessed the ethics of the study and approved the implementation of the study (application number EA2/070/19). Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Author contributions

SD and SJ developed the analysis methods in the paper. SJ mainly scripted the automatic pipeline while SD was in charge of the weighting procedure described in the paper. SD drafted the sections 3.2.1 and 3.2.2. SJ wrote the sections 2, 3.1, 3.2, 3.2.3, 3.2.4 and the Introduction. SD and SJ wrote section 3.2.5 together. Sections 1, 4 and 5 were written by LW, SD, and SJ. LW also edited and commented on all sections extensively. EM also commented on all sections and was in charge of the project. All authors contributed to the article and approved the submitted version.

Funding

The "MHS - Set-up of a National Mental Health Surveillance at Robert Koch Institute" project has been funded by the Federal Ministry of Health (Grant Number: Chapter 1504 Title 54401). The studies GEDA 2019/20, GEDA 2021, COVIMO an GEDA 2022 were funded by the Robert Koch Institute and the German Federal Health Ministry.

Acknowledgments

We would like to thank all colleagues of the Epidemiological Data Center (EDC) who provide us with quality-assured data on a monthly basis especially Jennifer Allen, Sabine Born and Ronny Kuhnert. Further we want to thank our colleagues from the statistics working group at our department of the Robert Koch Institute. Last but not least we thank Silke Beyhl for the design of Figure 2.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Abbreviations

COVIMO, COVID-19 Impfquoten Monitoring (COVID-19 vaccination rate monitoring); GAM, General Additive Model; GEDA, Gesundheit in Deutschland aktuell (German Health Update); MHS, Mental Health Surveillance.

Footnotes

References

10. Allen, J, Born, S, Damerow, S, Kuhnert, R, Lemcke, J, Müller, A, et al. German health update (GEDA 2019/2020-EHIS) - Background and methodology (2021) 6:66-79. doi: 10.25646/8559

CrossRef Full Text | Google Scholar

52. RDC of the Federal Statistical Office and Statistical Offices of the Federal States. doi: 10.21242/12211.2018.00.00.3.1.1

CrossRef Full Text | Google Scholar

# data[].attributes.attachment_link.title
Development of a prototype for high-frequency mental health surveillance in Germany: data infrastructure and statistical methods

# data[].attributes.attachment_link.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.

# data[].attributes.headline
Germany's Digital approach to provide Mental Health support (App Video)

# data[].attributes.subject
Germany's Digital approach to provide Mental Health support (App Video)

# data[].attributes.attachment_link.title
DiGA Fast Track - explained by Dr. Henrik Matthies, hih

# data[].attributes.headline
Impact of COVID-19 on Mental Health in EU/Germany

# data[].attributes.subject
Impact of COVID-19 on Mental Health in EU/Germany

# data[].attributes.attachment_link.title
Europe’s mental health in data: Which country is the most depressed?

# data[].attributes.attachment_link.description
The world is grappling with a mental health crisis. In Europe, antidepressant consumption has more than doubled in the last 20 years.

# data[].attributes.headline
Peer Support Education for Germans video

# data[].attributes.subject
Peer Support Education for Germans video

# data[].attributes.attachment_link.readable_text
Peer support is increasingly being recognised as a best practice intervention for rights-based, recovery-oriented mental health care around the world. However, much of the evidence for peer support comes from Anglophone countries in the Global North. In this two-part webinar, we will introduce the UPSIDES international research consortium on peer support to key stakeholders in German- and Hebrew-speaking countries, respectively. Speakers from both sites share their work on service user involvement in mental health care and the UPSIDES' recently launched, multi-site randomised controlled trial in Germany, Israel, India, Tanzania and Uganda. This is the second part of this webinar by UPSIDES partners in Germany (see Israel webinar here).

# data[].attributes.attachment_link.title
Peer-Begleitung in D-A-CH | Peer Support in Germany

# data[].attributes.attachment_link.description
Peer support is increasingly being recognised as a best practice intervention for rights-based, recovery-oriented mental health care around the world. However, much of the evidence for peer support comes from Anglophone countries in the Global North. In this two-part webinar, we will introduce the UPSIDES international research consortium on peer support to key stakeholders in German- and Hebrew-speaking countries, respectively.