ABSTRACT By contrasting the Great Depression and the Coronacrisis, we demonstrate that narrative economics (Shiller, 2017) is key in the analysis of economic fluctuations. We note the importance of the populist narrative to understand the economic and health outcomes of the Coronacrisis in Mexico and highlight the role of the predominance of different economic paradigms in economic policy decision-making. We suggest that, just as in 1929, by following orthodox primary fiscal balance sheet policies at the cost of contracting government investment, the Mexican economy will undergo a long and painful recovery process compared to its global peers.

This is the README file for the scripts of the preprint "Self-Perceived Loneliness and Depression During the COVID-19 Pandemic: a Two-Wave Replication Study" by Carollo et al. (2022)

Access the pre-print here: https://ucl.scienceopen.com/document/read?vid=0769d88b-e572-48eb-9a71-23ea1d32cecf

Abstract: Background: The global COVID-19 pandemic has forced countries to impose strict lockdown restrictions and mandatory stay-at-home orders with varying impacts on individual’s health. Combining a data-driven machine learning paradigm and a statistical approach, our previous paper documented a U-shaped pattern in levels of self-perceived loneliness in both the UK and Greek populations during the first lockdown (17 April to 17 July 2020). The current paper aimed to test the robustness of these results by focusing on data from the first and second lockdown waves in the UK. Methods: We tested a) the impact of the chosen model on the identification of the most time-sensitive variable in the period spent in lockdown. Two new machine learning models - namely, support vector regressor (SVR) and multiple linear regressor (MLR) were adopted to identify the most time-sensitive variable in the UK dataset from wave 1 (n = 435). In the second part of the study, we tested b) whether the pattern of self-perceived loneliness found in the first UK national lockdown was generalizable to the second wave of UK lockdown (17 October 2020 to 31 January 2021). To do so, data from wave 2 of the UK lockdown (n = 263) was used to conduct a graphical and statistical inspection of the week-by-week distribution of self-perceived loneliness scores. Results: In both SVR and MLR models, depressive symptoms resulted to be the most time-sensitive variable during the lockdown period. Statistical analysis of depressive symptoms by week of lockdown resulted in a U-shaped pattern between week 3 to 7 of wave 1 of the UK national lockdown. Furthermore, despite the sample size by week in wave 2 was too small for having a meaningful statistical insight, a qualitative and descriptive approach was adopted and a graphical U-shaped distribution between week 3 and 9 of lockdown was observed. Conclusions: Consistent with past studies, study findings suggest that self-perceived loneliness and depressive symptoms may be two of the most relevant symptoms to address when imposing lockdown restrictions.

In particular, the folder includes the scripts for the pre-processing, training, and post-processing phases of the research.

==== PRE-PROCESSING WAVE 1 DATASET ==== - "01_preprocessingWave1.py": this file include the pre-processing of the variables of interest for wave 1 data; - "02_participantsexcludedWave1.py": this file include the script adopted to implement the exclusion criteria of the study for wave 1 data; - "03_countryselectionWave1.py": this file include the script to select the UK dataset for wave 1.

==== PRE-PROCESSING WAVE 2 DATASET ==== - "04_preprocessingWave1.py": this file include the pre-processing of the variables of interest for wave 2 data; - "05_participantsexcludedWave1.py": this file include the script adopted to implement the exclusion criteria of the study for wave 2 data; - "06_countryselectionWave1.py": this file include the script to select the UK dataset for wave 2.

==== TRAINING ==== - "07_MLR.py": this file includes the script to run the multiple regression model; - "08_SVM.py": this file includes the script to run the support vector regression model.

==== POST-PROCESSING: STATISTICAL ANALYSIS ==== - "09_KruskalWallisTests.py": this file includes the script to run the multipair and the pairwise Kruskal-Wallis tests.

Using micro-data on six surveys–the Gallup World Poll 2005–2023, the U.S. Behavioral Risk Factor Surveillance System, 1993–2022, Eurobarometer 1991–2022, the UK Covid Social Survey Panel, 2020–2022, the European Social Survey 2002–2020 and the IPSOS Happiness Survey 2018–2023 –we show individuals’ reports of subjective wellbeing in Europe declined in the Great Recession of 2008/9 and during the Covid pandemic of 2020–2021 on most measures. They also declined in four countries bordering Ukraine after the Russian invasion in 2022. However, the movements are not large and are not apparent everywhere. We also used data from the European Commission’s Business and Consumer Surveys on people’s expectations of life in general, their financial situation and the economic and employment situation in the country. All of these dropped markedly in the Great Recession and during Covid, but bounced back quickly, as did firms’ expectations of the economy and the labor market. Neither the annual data from the United Nation’s Human Development Index (HDI) nor data used in the World Happiness Report from the Gallup World Poll shifted much in response to negative shocks. The HDI has been rising in the last decade reflecting overall improvements in economic and social wellbeing, captured in part by real earnings growth, although it fell slightly after 2020 as life expectancy dipped. This secular improvement is mirrored in life satisfaction which has been rising in the last decade. However, so too have negative affect in Europe and despair in the United States.

This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in A program for strengthening the Federal Reserve’s ability to fight the next recession, PIIE Working Paper 20-5.

If you use the data, please cite as: Reifschneider, David, and David Wilcox. (2020). A program for strengthening the Federal Reserve’s ability to fight the next recession. PIIE Working Paper 20-5. Peterson Institute for International Economics.

This dataset is made available under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). See LICENSE.pdf for details.

Dataset description

Parquet file, with:

35694 rows

154 columns

The file is indexed on [participant]_[month], such that 34_12 means month 12 from participant 34. All participant IDs have been replaced with randomly generated integers and the conversion table deleted.

Column names and explanations are included as a separate tab-delimited file. Detailed descriptions of feature engineering are available from the linked publications.

File contains aggregated, derived feature matrix describing person-generated health data (PGHD) captured as part of the DiSCover Project (https://clinicaltrials.gov/ct2/show/NCT03421223). This matrix focuses on individual changes in depression status over time, as measured by PHQ-9.

The DiSCover Project is a 1-year long longitudinal study consisting of 10,036 individuals in the United States, who wore consumer-grade wearable devices throughout the study and completed monthly surveys about their mental health and/or lifestyle changes, between January 2018 and January 2020.

The data subset used in this work comprises the following:

Wearable PGHD: step and sleep data from the participants’ consumer-grade wearable devices (Fitbit) worn throughout the study

Screener survey: prior to the study, participants self-reported socio-demographic information, as well as comorbidities

Lifestyle and medication changes (LMC) survey: every month, participants were requested to complete a brief survey reporting changes in their lifestyle and medication over the past month

Patient Health Questionnaire (PHQ-9) score: every 3 months, participants were requested to complete the PHQ-9, a 9-item questionnaire that has proven to be reliable and valid to measure depression severity

From these input sources we define a range of input features, both static (defined once, remain constant for all samples from a given participant throughout the study, e.g. demographic features) and dynamic (varying with time for a given participant, e.g. behavioral features derived from consumer-grade wearables).

The dataset contains a total of 35,694 rows for each month of data collection from the participants. We can generate 3-month long, non-overlapping, independent samples to capture changes in depression status over time with PGHD. We use the notation ‘SM0’ (sample month 0), ‘SM1’, ‘SM2’ and ‘SM3’ to refer to relative time points within each sample. Each 3-month sample consists of: PHQ-9 survey responses at SM0 and SM3, one set of screener survey responses, LMC survey responses at SM3 (as well as SM1, SM2, if available), and wearable PGHD for SM3 (and SM1, SM2, if available). The wearable PGHD includes data collected from 8 to 14 days prior to the PHQ-9 label generation date at SM3. Doing this generates a total of 10,866 samples from 4,036 unique participants.

Objectives. This study investigated the association between unemployment and depressive symptoms and major depression disorder worldwide using a systematic review and meta-analysis. Methods. Search time was limited to all articles published in English until December 2020. In the association between unemployment and depression, first, the results of qualified studies were extracted and, then, the results of each study were pooled with each other using the random effects method. Results. The prevalence of depression in the unemployed is 21%, 95% confidence interval (CI) [18, 24%]. This prevalence for depression symptoms is 24%, 95% CI [20, 28%] and for major depressive disorder is 16%, 95% CI [9–24%]. The association between unemployment and depressive symptoms was odds ratio (OR) 2.06, 95% CI [1.85, 2.30] and the association for major depressive disorder was OR 1.88, 95% CI [1.57, 2.25]. The association between unemployment and depression in men was OR 2.27, 95% CI [1.76, 2.93] and in women was OR 1.62, 95% CI [1.40, 1.87]. Conclusions. What is clear from the present study is that unemployment can lead to a higher prevalence of depressive symptoms and major depressive disorder, thereby undermining the mental health of the unemployed.

Graph and download economic data for Dates of U.S. recessions as inferred by GDP-based recession indicator (JHDUSRGDPBR) from Q4 1967 to Q1 2025 about recession indicators, GDP, and USA.

As the COVID-19 pandemic restricted individuals to their houses for a substantial amount of time, people took to the virtual world to stay connected with their peers, family and friends. Likewise, news channels and other forms of electronic media also witnessed a steep rise in viewership all across the globe. That being said, social media has led to adverse impacts on the mental health of individuals through addiction, stress, anxiety, depression and post-traumatic stress syndromes.

The primary objective of this data is to analyze both the positive and negative effects of social media usage on individuals during an unprecedented global lockdown. Existing literature has found significant connections between the use of social media and mental health during extensive periods of lockdown (Swarnam. S., 2021; Pragholapat, A., 2020., Hong, W. et al., 2020). This dataset is used to understand the extent of depression and anxiety experienced by persons restricted to stay-at-home confinements ...

(Note: Part of the content of this post was adapted from the original DIRECT Psychoradiology paper (https://academic.oup.com/psyrad/article/2/1/32/6604754) and REST-meta-MDD PNAS paper (http://www.pnas.org/cgi/doi/10.1073/pnas.1900390116) under CC BY-NC-ND license.)Major Depressive Disorder (MDD) is the second leading cause of health burden worldwide (1). Unfortunately, objective biomarkers to assist in diagnosis are still lacking, and current first-line treatments are only modestly effective (2, 3), reflecting our incomplete understanding of the pathophysiology of MDD. Characterizing the neurobiological basis of MDD promises to support developing more effective diagnostic approaches and treatments.An increasingly used approach to reveal neurobiological substrates of clinical conditions is termed resting-state functional magnetic resonance imaging (R-fMRI) (4). Despite intensive efforts to characterize the pathophysiology of MDD with R-fMRI, clinical imaging markers of diagnosis and predictors of treatment outcomes have yet to be identified. Previous reports have been inconsistent, sometimes contradictory, impeding the endeavor to translate them into clinical practice (5). One reason for inconsistent results is low statistical power from small sample size studies (6). Low-powered studies are more prone to produce false positive results, reducing the reproducibility of findings in a given field (7, 8). Of note, one recent study demonstrate that sample size of thousands of subjects may be needed to identify reproducible brain-wide association findings (9), calling for larger datasets to boost effect size. Another reason could be the high analytic flexibility (10). Recently, Botvinik-Nezer and colleagues (11) demonstrated the divergence in results when independent research teams applied different workflows to process an identical fMRI dataset, highlighting the effects of “researcher degrees of freedom” (i.e., heterogeneity in (pre-)processing methods) in producing disparate fMRI findings.To address these critical issues, we initiated the Depression Imaging REsearch ConsorTium (DIRECT) in 2017. Through a series of meetings, a group of 17 participating hospitals in China agreed to establish the first project of the DIRECT consortium, the REST-meta-MDD Project, and share 25 study cohorts, including R-fMRI data from 1300 MDD patients and 1128 normal controls. Based on prior work, a standardized preprocessing pipeline adapted from Data Processing Assistant for Resting-State fMRI (DPARSF) (12, 13) was implemented at each local participating site to minimize heterogeneity in preprocessing methods. R-fMRI metrics can be vulnerable to physiological confounds such as head motion (14, 15). Based on our previous work examination of head motion impact on R-fMRI FC connectomes (16) and other recent benchmarking studies (15, 17), DPARSF implements a regression model (Friston-24 model) on the participant-level and group-level correction for mean frame displacements (FD) as the default setting.In the REST-meta-MDD Project of the DIRECT consortium, 25 research groups from 17 hospitals in China agreed to share final R-fMRI indices from patients with MDD and matched normal controls (see Supplementary Table; henceforth “site” refers to each cohort for convenience) from studies approved by local Institutional Review Boards. The consortium contributed 2428 previously collected datasets (1300 MDDs and 1128 NCs). On average, each site contributed 52.0±52.4 patients with MDD (range 13-282) and 45.1±46.9 NCs (range 6-251). Most MDD patients were female (826 vs. 474 males), as expected. The 562 patients with first episode MDD included 318 first episode drug-naïve (FEDN) MDD and 160 scanned while receiving antidepressants (medication status unavailable for 84). Of 282 with recurrent MDD, 121 were scanned while receiving antidepressants and 76 were not being treated with medication (medication status unavailable for 85). Episodicity (first or recurrent) and medication status were unavailable for 456 patients.To improve transparency and reproducibility, our analysis code has been openly shared at https://github.com/Chaogan-Yan/PaperScripts/tree/master/Yan_2019_PNAS. In addition, we would like to share the R-fMRI indices of the 1300 MDD patients and 1128 NCs through the R-fMRI Maps Project (http://rfmri.org/REST-meta-MDD). These data derivatives will allow replication, secondary analyses and discovery efforts while protecting participant privacy and confidentiality.According to the agreement of the REST-meta-MDD consortium, there would be 2 phases for sharing the brain imaging data and phenotypic data of the 1300 MDD patients and 1128 NCs. 1) Phase 1: coordinated sharing, before January 1, 2020. To reduce conflict of the researchers, the consortium will review and coordinate the proposals submitted by interested researchers. The interested researchers first send a letter of intent to rfmrilab@gmail.com. Then the consortium will send all the approved proposals to the applicant. The applicant should submit a new innovative proposal while avoiding conflict with approved proposals. This proposal would be reviewed and approved by the consortium if no conflict. Once approved, this proposal would enter the pool of approved proposals and prevent future conflict. 2) Phase 2: unrestricted sharing, after January 1, 2020. The researchers can perform any analyses of interest while not violating ethics.The REST-meta-MDD data entered unrestricted sharing phase since January 1, 2020. The researchers can perform any analyses of interest while not violating ethics. Please visit Psychological Science Data Bank to download the data, and then sign the Data Use Agreement and email the scanned signed copy to rfmrilab@gmail.com to get unzip password and phenotypic information. ACKNOWLEDGEMENTSThis work was supported by the National Key R&D Program of China (2017YFC1309902), the National Natural Science Foundation of China (81671774, 81630031, 81471740 and 81371488), the Hundred Talents Program and the 13th Five-year Informatization Plan (XXH13505) of Chinese Academy of Sciences, Beijing Municipal Science & Technology Commission (Z161100000216152, Z171100000117016, Z161100002616023 and Z171100000117012), Department of Science and Technology, Zhejiang Province (2015C03037) and the National Basic Research (973) Program (2015CB351702). REFERENCES1. A. J. Ferrari et al., Burden of Depressive Disorders by Country, Sex, Age, and Year: Findings from the Global Burden of Disease Study 2010. PLOS Medicine 10, e1001547 (2013).2. L. M. Williams et al., International Study to Predict Optimized Treatment for Depression (iSPOT-D), a randomized clinical trial: rationale and protocol. Trials 12, 4 (2011).3. S. J. Borowsky et al., Who is at risk of nondetection of mental health problems in primary care? J Gen Intern Med 15, 381-388 (2000).4. B. B. Biswal, Resting state fMRI: a personal history. Neuroimage 62, 938-944 (2012).5. C. G. Yan et al., Reduced default mode network functional connectivity in patients with recurrent major depressive disorder. Proc Natl Acad Sci U S A 116, 9078-9083 (2019).6. K. S. Button et al., Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci 14, 365-376 (2013).7. J. P. A. Ioannidis, Why Most Published Research Findings Are False. PLOS Medicine 2, e124 (2005).8. R. A. Poldrack et al., Scanning the horizon: towards transparent and reproducible neuroimaging research. Nat Rev Neurosci 10.1038/nrn.2016.167 (2017).9. S. Marek et al., Reproducible brain-wide association studies require thousands of individuals. Nature 603, 654-660 (2022).10. J. Carp, On the Plurality of (Methodological) Worlds: Estimating the Analytic Flexibility of fMRI Experiments. Frontiers in Neuroscience 6, 149 (2012).11. R. Botvinik-Nezer et al., Variability in the analysis of a single neuroimaging dataset by many teams. Nature 10.1038/s41586-020-2314-9 (2020).12. C.-G. Yan, X.-D. Wang, X.-N. Zuo, Y.-F. Zang, DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging. Neuroinformatics 14, 339-351 (2016).13. C.-G. Yan, Y.-F. Zang, DPARSF: A MATLAB Toolbox for "Pipeline" Data Analysis of Resting-State fMRI. Frontiers in systems neuroscience 4, 13 (2010).14. R. Ciric et al., Mitigating head motion artifact in functional connectivity MRI. Nature protocols 13, 2801-2826 (2018).15. R. Ciric et al., Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity. NeuroImage 154, 174-187 (2017).16. C.-G. Yan et al., A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. NeuroImage 76, 183-201 (2013).17. L. Parkes, B. Fulcher, M. Yücel, A. Fornito, An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI. NeuroImage 171, 415-436 (2018).18. L. Wang et al., Interhemispheric functional connectivity and its relationships with clinical characteristics in major depressive disorder: a resting state fMRI study. PLoS One 8, e60191 (2013).19. L. Wang et al., The effects of antidepressant treatment on resting-state functional brain networks in patients with major depressive disorder. Hum Brain Mapp 36, 768-778 (2015).20. Y. Liu et al., Regional homogeneity associated with overgeneral autobiographical memory of first-episode treatment-naive patients with major depressive disorder in the orbitofrontal cortex: A resting-state fMRI study. J Affect Disord 209, 163-168 (2017).21. X. Zhu et al., Evidence of a dissociation pattern in resting-state default mode network connectivity in first-episode, treatment-naive major depression patients. Biological psychiatry 71, 611-617 (2012).22. W. Guo et al., Abnormal default-mode

UK undergraduates completed the 21 item Depression Anxiety Stress Scale (DASS-21) in the autumn of 2020, 2019, 2018 and 2017. Overall, we had 763 participants. We compared depression, anxiety and stress subscale scores as well as scores on each question of the DASS-21 across the four years.Asian and European studies suggest that the Covid-19 pandemic is worsening university student mental health. We aimed to investigate whether this was also the case in the UK. UK undergraduate students completed the 21 item Depression Anxiety and Stress Scale in the autumn of either 2020, 2019, 2018 or 2017. Data was collected as part of other behavioural psychology experiments conducted in the School of Psychology at the University of Birmingham.

2020 was the first time in the history of El Salvador that mental health was assessed on a national scale. The pandemic has increased depression and anxiety symptoms in populations such as the US (Twenge & Joiner, 2020) and UK (Smith et al., 2020). Adolescents may have been particularly affected by the pandemic because of the drastic changes in their life at an age when social interactions are highly important for development (Burns et al., 2002). Studies on mental health or wellbeing are mostly done with WEIRD populations. Large-scale studies are less common in other populations as they take high amounts of resources to implement. Previous studies have shown that family support is a protective factor associated with resilience in youths exposed to adversity, (Nearchou et al., 2020) and that family support is inversely associated with symptoms for depression at the begging of adolescence (Needham, 2008). In adolescents with diabetes, it has been shown that family support is a predictor of diabetes self-care, including following a dietary plan (Skinner & Hampson, 1998) and that poor health habits overall are predictors of mental disorders (Hoare et al., 2020). At the same time higher social support is associated with lower depression and anxiety and overall higher well-being (Khalid, 2014). Studies with young people have shown varied results regarding gender and social support and depression (Needham, 2008; Rueger et al., 2016), but girls who show lower levels of parental support show higher levels of depression (Needham, 2008); at the same time being a girl is associated with both depression and anxiety in comparison to their male counterparts (Mazza et al., 2020). This dataset provides variables that reflect the impact of the pandemic on the participants social relationships. Family and social impact will be used instead of family and social support, higher family or social impact represent a more adaptive functioning during the pandemic in either group. The analysis of this dataset will give an opportunity to test for these associations during an extraordinary event such as the pandemic which significantly modified how people interact and support each other and thus have repercussions on their mental health.

By April 2026, it is projected that there is a probability of ***** percent that the United States will fall into another economic recession. This reflects a significant decrease from the projection of the preceding month.

The New York State Departments of Environmental Conservation and Health are concerned about groundwater contamination in the carbonate-bedrock aquifers with the potential to host karst features throughout New York State, especially relating to the unintended introduction of chemical or agricultural contamination into these aquifers. USGS Scientific Investigations Report, SIR 2020-5030 (Kappel and others, 2020), provides local and State regulators and the public the information needed to determine the extent of carbonate bedrock in New York, the associated environmental impacts of karst, and the means to protect New York’s karst water resources. The four geodatabases presented in this data release were compiled in support of SIR 2020-5030. Closed depression-focused recharge is one potential pathway for aquifer contamination. A closed depression is any enclosed area that has no surface drainage outlet and from which water escapes only by evaporation or subsurface drainage. On a topographic map a closed depression is typically represented by a hachured contour line forming a closed loop. The map representation applies to closed depressions of both natural and anthropogenic origin. Closed depressions formed by natural processes need not be karst in origin to represent a source of focused-recharge. Three of the four geodatabases in this data release form a comprehensive inventory of all closed depressions, natural and anthropogenic, within the State which are proximal to carbonate, evaporite, or marble units and that have the potential for developing karst features. The fourth geodatabase in this data release contains a digital representation of the study area boundary adopted for the GIS analyses. The three closed depression inventory geodatabases were compiled in the following order: 1) Digital Contour Database of Closed Depressions, 2) Digital Raster Graphic Database of Closed Depressions, and 3) LiDAR Database of Closed Depressions. There is no duplication of features among these three geodatabases. Additionally, the closed depressions inventoried for this data release, were compared with closed depressions mapped in other published geospatial data to eliminate duplication with those datasets. The datasets referenced were the New York State Department of Environmental Conservation Mining Database and the National Hydrography Dataset waterbody features. The Digital Contour Database of Closed Depressions contains features derived from data associated with U.S. Geological Survey Scientific Investigations Report 2012–5167. The source data is a statewide contour dataset that was generated from the National Elevation Dataset (NED) and the National Hydrography Dataset (NHD) in a fully automated process. Closed depressions included in the Digital Raster Graphic Database of Closed Depressions were digitized from an assemblage of approximately 650 Digital Raster Graphic (DRG) images of scanned U.S. Geological Survey 1:24,000-scale topographic maps. A DRG is a scanned image of a U.S. Geological Survey topographic map that can be added as a background layer in a GIS. The LiDAR Database of Closed Depressions contains features generated from high-resolution LiDAR-derived bare-earth DEMs obtained from the New York State Office of Information Technology Services. At the time of analysis (2017) LiDAR data existed for approximately 65 percent of the study area. The DEMs were processed to identify depressions with an area of at least 4,047 square meters (1-acre) and a depth of at least 1-meter. These threshold values are greater than what is typically used for lidar-based sinkhole identification studies. For the purpose of this study, the use of lidar was primarily intended to identify closed depressions that were not represented in the Digital Raster Graphic Database, in the same manner that the DRG images were used to identify closed depressions not represented in the Digital Contour Database. For that reason, the threshold values were based on random sampling of DRG-derived closed depressions within the study area and represent the approximate mean geometric characteristics of the closed depressions sampled. For ongoing and planned larger-scale county-based assessments in New York, the thresholds will be reduced to 10- and 30-centimeters depth and 100 square meters.

This dataset contains county-level information for U.S. counties from 2020 to 2022, aiming to explore the potential relationship between COVID-19 vaccination coverage and the prevalence of severe depression. It integrates multiple data sources, including public health statistics, socioeconomic indicators, environmental variables, and demographic characteristics. The dataset is structured to support spatial, temporal, and statistical analysis.Key Variables Include:Mental Health: Severe depression rates per 100,000 population for 2021 and 2022COVID-19 Metrics: Case rates per 100,000 (2021, 2022), and vaccination rates (2-dose complete, 5+ population)Socioeconomic Data: Unemployment rates, median household income, percent of adults with bachelor's degree or higherEnvironmental Factors: Average daily sunlight (KJ/m²), cooling degree daysDemographics: Population size, gender distribution, age distribution, urbanization rateHealth Behavior Indicators: Rates of smoking, obesity, physical inactivity, and excessive drinkingLog-transformed versions of several variables are also included to support regression modeling and machine learning tasks.Purpose:The dataset is curated for research that investigates the interplay between COVID-19 vaccination campaigns and mental health outcomes, with potential applications in spatial epidemiology, public health policy, and social determinants of health research.Temporal Coverage: 2020–2022Geographic Scope: U.S. counties (N ≈ 3,000+)Data Format: XlsxSuggested Citation: Wencong Cui, Yuqing Wang, "COVID-19 Vaccination and Depression: U.S. County-Level Dataset (2020–2022)", Figshare, 2025. DOI: 10.6084/m9.figshare.29451644

Objectives: To assess the association between students’ financial loss and depressive symptoms during the first wave of the coronavirus disease 2019 (COVID-19) pandemic and whether this association varied by countries having different levels of lockdown measures.Methods: This cross-sectional survey, conducted in spring 2020, included 91,871 students from 23 countries. Depressive symptoms were measured using the shortened Center for Epidemiological Studies Depression Scale and information on lockdowns retrieved from the COVID-19 government response tracker. The association between financial loss and depressive symptoms was investigated estimating prevalence ratios (PR) with multilevel Poisson models.Results: Some 13% of students suffered financial loss during the lockdown and 52% had a relatively high depression score, with large between-countries differences. Minimally and maximally adjusted models showed a 35% (PR = 1.35, 95% Confidence Interval (CI) = 1.29–1.42) and 31% (PR = 1.31, 95% CI = 1.26–1.37) higher prevalence of depressive symptoms in students who lost economic resources compared to students with stable economic resources. No substantial differences in the association were found across countries.Conclusion: Depressive symptoms were more frequent among students who suffered financial loss during the pandemic. Policy makers should consider this issue in the implementation of COVID-19 mitigating measures.

In 2020, global gross domestic product declined by 6.7 percent as a result of the coronavirus (COVID-19) pandemic outbreak. In Latin America, overall GDP loss amounted to 8.5 percent.

Indicators from the Opinions and Lifestyle Survey (OPN) between 11 and 29 November 2020, measuring rates of some form of depression and/or anxiety in adults in Great Britain. Includes breakdowns by personal characteristics, impacts on life and well-being, loneliness and perceptions of when life will return to normal.

Objective: This study aims to investigate perinatal depression in women who gave birth during the COVID-19 pandemic in Wuhan, and to evaluate the effect of the pandemic on perinatal depression prevalence.Methods: A cross-sectional investigation was conducted into women hospitalized for delivery in Hubei Maternity and Child Healthcare Hospital from December 31, 2019 to March 22, 2020, a period which encompasses the entire time frame of the COVID-19 pandemic in Wuhan. The Edinburgh Postnatal Depression Scale (EPDS) was adopted to evaluate perinatal depression status. A Chi-square test and logistic regression model were utilized for data analysis.Results: A total of 2,883 participants were included, 33.71% of whom were found to suffer from depressive symptoms. In detail, 27.02%, 5.24%, and 1.46% were designated as having mild, moderate, and severe depressive symptoms, respectively. The perinatal depression prevalence increased as the COVID-19 pandemic worsened. Compared to the period from December 31, 2019 to January 12, 2020, perinatal depression risk significantly decreased within the 3 weeks of March 2–22, 2020 (1st week: OR = 0.39, 95% CI: 0.20, 0.78; 2nd week: OR = 0.35, 95% CI: 0.17, 0.73; and 3rd week: OR = 0.48, 95% CI: 0.25, 0.94); and the postnatal depression risk significantly rose within the four weeks of January 27-February 23, 2020 (1st week: OR = 1.78, 95% CI: 1.18, 2.68; 2nd week: OR = 2.03, 95% CI: 1.35, 3.04; 3rd week: OR = 1.48, 95% CI: 1.02, 2.14; and 4th week: OR = 1.73, 95% CI: 1.20, 2.48).Conclusion: The dynamic change of perinatal depression was associated with the progression of the COVID-19 pandemic among new mothers who were exposed to the pandemic. An elevated risk of postnatal depression was also observed during the COVID-19 pandemic.

Gender differences (GD) in mental health have come under renewed scrutiny during the COVID-19 pandemic. While rapidly emerging evidence indicates a deterioration of mental health in general, it remains unknown whether the pandemic will have an impact on GD in mental health. To this end, we investigate the association of the pandemic and its countermeasures affecting everyday life, labor, and households with changes in GD in aggression, anxiety, depression, and the somatic symptom burden. We analyze cross-sectional data from 10,979 individuals who live in Germany and who responded to the online survey “Life with Corona” between October 1, 2020 and February 28, 2021. We estimate interaction effects from generalized linear models. The analyses reveal no pre-existing GD in aggression but exposure to COVID-19 and COVID-19 countermeasures is associated with sharper increases in aggression in men than in women. GD in anxiety decreased among participants with children in the household (with men becoming more anxious). We also observe pre-existing and increasing GD with regards to the severity of depression, with women presenting a larger increase in symptoms during the hard lockdown or with increasing stringency. In contrast to anxiety, GD in depression increased among participants who lived without children (women > men), but decreased for individuals who lived with children; here, men converged to the levels of depression presented by women. Finally, GD in somatic symptoms decreased during the hard lockdown (but not with higher stringency), with men showing a sharper increase in symptoms, especially when they lived with children or alone. Taken together, the findings indicate an increase in GD in mental health as the pandemic unfolded in Germany, with rising female vulnerability to depression and increasing male aggression. The combination of these two trends further suggests a worrying mental health situation for singles and families. Our results have important policy implications for the German health system and public health policy. This public health challenge requires addressing the rising burden of pandemic-related mental health challenges and the distribution of this burden between women and men, within families and for individuals who live alone.

In this project, we aimed to increase what is known about the negative effects of maternal depression and anxiety disorders (MDAD) on the mental health outcomes of children. Mental health is a topical area of research that is receiving increasing attention in the media and is one of five ESRC strategic priorities for investment. The main aim of the project was to help develop an understanding of how mental depression and anxiety disorders are transmitted from one generation to the next and ultimately help to design interventions better able to reduce the consequences of maternal mental health for children. We have used data from QResearch, a large consolidated database derived from anonymized health records from general practices in England matched with hospital administrative data, the Hospital Episode Statistics (HES). Further information is available under Related Resources.Problems relating to Maternal Depression and Anxiety Disorders (MDAD) are common and are known to affect child health and development. In the UK, the cost of perinatal mental health problems has been estimated at £8.1 billion for each birth cohort of children, and 72 percent of this cost is related to the direct impact on the children. The overarching aim of our proposed research is to examine the effect of MDAD on child health outcomes, with a special focus on the role that MDAD plays in the development of child depression and anxiety disorders (CDAD) in adolescence. In particular, this research will provide robust empirical evidence to understand how depression and anxiety disorders are transmitted from one generation to the next and to help design interventions aimed at reducing the negative consequences of poor maternal mental health for children. To achieve this aim, we will address the following research questions: 1) Are the negative effects of MDAD on children exclusively explained by genetic transmission and family background characteristics? Or are these negative effects also explained by changes in the child's home environment? If the transmission of mental and anxiety disorders is explained exclusively by genetic traits and family background characteristics, then interventions targeted at reducing the negative effect of MDAD on maternal behaviour, e.g. through cognitive behavioural therapy, would be ineffective. On the contrary, evidence on significant effects of MDAD after controlling for genetic and family background characteristics would suggest that MDAD can lead to changes in the child home environment, e.g. changes in maternal behaviour, harsher parenting style and lower time investments in the child, with negative consequences on children. 2) Do school policies and health practices have a role in attenuating the negative effect of maternal depression on children? We will answer this research question by focusing on whether starting school earlier harms or protects children who are exposed to MDAD, and on whether an early diagnosis of maternal depression can attenuate the negative effects suffered by children. We will develop and use state-of-the-art estimation methods in combination with a novel administrative dataset covering general practices and hospitals created by merging two population-based health databases from England - namely QResearch and Hospital Episode Statistics. Using this merged database, we will create a longitudinal household dataset that will allow us to study the mental health of mothers and their children at different stages of the children's lives up to adolescence. We are a multi-disciplinary team from the Universities of Oxford and York, consisting of experts in applied econometric methods, child and maternal mental health, psychology, general practice, and on the data that we plan to utilise. We will translate our research findings into advice for policy-makers to help them design new interventions aimed at achieving better outcomes for patients suffering from maternal mental health issues and their children. Our research will also have an impact on health practitioners, psychologists, academics and charities working with mothers and children. We will produce papers aimed at academics as well as non-technical outputs to engage with policy-makers and a non-academic audience. Furthermore, by sharing and explaining our data and estimation methods to academics, we will build capacity for further research based on large health datasets. The final central element of the project will be to build the capacity of early career researchers to undertake and lead large interdisciplinary projects. QResearch is a large, anonymised database of GP records from over 35 million patients with longitudinal data tracking back over 30 years & is linked to mortality, cancer registration & hospital data. In our analysis, we use individual-level information on general practice diagnostics, drug prescriptions, and maternity records from HES, which allows us to link children with their respective mothers. The QResearch linked database has high-quality data to support world-leading research to improve our understanding of disease and improve patient care. Our data includes all singletons born between 2002 and 2010.The mother-baby linkage in QResearch is done via maternal identifiers and year of birth.