Dataset Information

Depression and Anxiety on Twitter During the COVID-19 Stay-At-Home Period in 7 Major U.S. Cities.

ABSTRACT:

Introduction

Although surveys are a well-established instrument to capture the population prevalence of mental health at a moment in time, public Twitter is a continuously available data source that can provide a broader window into population mental health. We characterized the relationship between COVID-19 case counts, stay-at-home orders because of COVID-19, and anxiety and depression in 7 major U.S. cities utilizing Twitter data.

Methods

We collected 18 million Tweets from January to September 2019 (baseline) and 2020 from 7 U.S. cities with large populations and varied COVID-19 response protocols: Atlanta, Chicago, Houston, Los Angeles, Miami, New York, and Phoenix. We applied machine learning‒based language prediction models for depression and anxiety validated in previous work with Twitter data. As an alternative public big data source, we explored Google Trends data using search query frequencies. A qualitative evaluation of trends is presented.

Results

Twitter depression and anxiety scores were consistently elevated above their 2019 baselines across all the 7 locations. Twitter depression scores increased during the early phase of the pandemic, with a peak in early summer and a subsequent decline in late summer. The pattern of depression trends was aligned with national COVID-19 case trends rather than with trends in individual states. Anxiety was consistently and steadily elevated throughout the pandemic. Google search trends data showed noisy and inconsistent results.

Conclusions

Our study shows the feasibility of using Twitter to capture trends of depression and anxiety during the COVID-19 public health crisis and suggests that social media data can supplement survey data to monitor long-term mental health trends.

SUBMITTER: Levanti D

PROVIDER: S-EPMC9773738 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Depression and Anxiety on Twitter During the COVID-19 Stay-At-Home Period in 7 Major U.S. Cities.

Levanti Danielle D Monastero Rebecca N RN Zamani Mohammadzaman M Eichstaedt Johannes C JC Giorgi Salvatore S Schwartz H Andrew HA Meliker Jaymie R JR

AJPM focus 20221222 1

<h4>Introduction</h4>Although surveys are a well-established instrument to capture the population prevalence of mental health at a moment in time, public Twitter is a continuously available data source that can provide a broader window into population mental health. We characterized the relationship between COVID-19 case counts, stay-at-home orders because of COVID-19, and anxiety and depression in 7 major U.S. cities utilizing Twitter data.<h4>Methods</h4>We collected 18 million Tweets from Jan ...[more]

PMID: 36573174

Similar Datasets

Project description:IntroductionStay-at-home orders during the COVID-19 pandemic decreased population mobility to reduce SARS-CoV-2 infection rates. We empirically tested the hypothesis that this public health measure was associated with a higher likelihood of opioid- and stimulant-involved deaths occurring in homes located in Cook County, Illinois.MethodsThe stay-at-home period was from March 21, 2020 to May 30, 2020. We analyzed overdose data from the Cook County Medical Examiner's Office using a death location description from case investigations categorized as home, medical, motel, scene, and other. Two groups of decedents were defined as either having an opioid or stimulant listed in the primary cause of death field. We modeled a weekly time series to detect changes in deaths (number) and trends during segmented time periods. Chi-square or Fisher's exact and adjusted logistic regression was used for testing the differences between the stay-at-home and a 13-week preceding period.ResultsThere were 4,169 and 2,012 opioid- and stimulant-involved deaths, respectively, from 2018 to 2020. Both groups were demographically similar: 75% male, 52% White, and aged 45 years (mean). In the 13 weeks before stay-at-home orders, 51% of opioid-involved deaths occurred in homes, which increased to 59% (p<0.0001) during the 10 weeks of the order and decreased back to 51% in the 18 weeks after the order expired. For stimulant-involved deaths, 51% were residential immediately before the orders, with a nonsignificant increase to 52% during the stay-at-home period. Before the pandemic, there were 20 deaths/week, increasing to 37 deaths/week (p<0.0001) during stay-at-home enactment. Deaths involving fentanyl among the opioid-involved group increased from 76% to 89%, whereas those involving heroin decreased from 55% to 37%. The adjusted OR for opioid-involved fatal overdoses occurring at home during this period compared with that occurring the 13 weeks before was 1.37 (95% CI=1.05, 1.79).ConclusionsThe likelihood of a death occurring at home, especially for people using opioids, increased during the stay-at-home order period. Findings have implications for mitigating overdose risks during social isolation.

Project description:BACKGROUND:COVID-19 and home isolation has impacted quality of life, but the perceived impact on anxiety and sleep remains equivocal. The purpose of this study was to assess the impact of COVID-19 and stay-at-home orders on self-report anxiety and sleep quality, with a focus on sex differences. We hypothesized that the COVID-19 pandemic would be associated with increased anxiety and decreased sleep quality, with stronger associations in women. METHODS:One hundred three participants (61 female, 38 ± 1?years) reported perceived changes in anxiety and sleep quality due to stay-at-home orders during the COVID-19 pandemic and were administered the Spielberger State-Trait Anxiety Inventory (STAI), Pittsburgh Sleep Quality Index (PSQI), and Insomnia Severity Index (ISI). Chi-square and T test analyses were utilized to assess sex differences in reported anxiety and sleep. Analysis of covariance was used to compare the associations between reported impact of COVID-19 and anxiety/sleep parameters. RESULTS:Women (80.3%) reported higher prevalence of increased general anxiety due to COVID-19 when compared to men (50%; p = 0.001) and elevated STAI state anxiety compared to men (43 ± 1 vs. 38 ± 1?a.u., p = 0.007). Despite these differences in anxiety, the perceived impact of COVID-19 on PSQI was not different between sexes. However, when stratified by perceived changes in anxiety due to COVID-19, participants with higher anxiety responses to COVID-19 had higher ISI compared to those with no perceived changes in anxiety (9 ± 1 vs. 5 ± 1?a.u., p = 0.003). Additionally, participants who reported reduced sleep quality due to COVID-19 reported higher state anxiety (45 ± 1?a.u.) compared to those that perceived no change (36 ± 2?a.u., p = 0.002) or increased (36 ± 2?a.u., p < 0.001) sleep quality. CONCLUSION:COVID-19 and state-ordered home isolation was associated with higher anxiety and reduced sleep quality, with a stronger association in women with respect to anxiety.

Project description:BackgroundStay-at-home orders were one of the controversial interventions to curb the spread of COVID-19 in the United States. The stay-at-home orders, implemented in 51 states and territories between March 7 and June 30, 2020, impacted the lives of individuals and communities and accelerated the heavy usage of web-based social networking sites. Twitter sentiment analysis can provide valuable insight into public health emergency response measures and allow for better formulation and timing of future public health measures to be released in response to future public health emergencies.ObjectiveThis study evaluated how stay-at-home orders affect Twitter sentiment in the United States. Furthermore, this study aimed to understand the feedback on stay-at-home orders from groups with different circumstances and backgrounds. In addition, we particularly focused on vulnerable groups, including older people groups with underlying medical conditions, small and medium enterprises, and low-income groups.MethodsWe constructed a multiperiod difference-in-differences regression model based on the Twitter sentiment geographical index quantified from 7.4 billion geo-tagged tweets data to analyze the dynamics of sentiment feedback on stay-at-home orders across the United States. In addition, we used moderated effects analysis to assess differential feedback from vulnerable groups.ResultsWe combed through the implementation of stay-at-home orders, Twitter sentiment geographical index, and the number of confirmed cases and deaths in 51 US states and territories. We identified trend changes in public sentiment before and after the stay-at-home orders. Regression results showed that stay-at-home orders generated a positive response, contributing to a recovery in Twitter sentiment. However, vulnerable groups faced greater shocks and hardships during the COVID-19 pandemic. In addition, economic and demographic characteristics had a significant moderating effect.ConclusionsThis study showed a clear positive shift in public opinion about COVID-19, with this positive impact occurring primarily after stay-at-home orders. However, this positive sentiment is time-limited, with 14 days later allowing people to be more influenced by the status quo and trends, so feedback on the stay-at-home orders is no longer positively significant. In particular, negative sentiment is more likely to be generated in states with a large proportion of vulnerable groups, and the policy plays a limited role. The pandemic hit older people, those with underlying diseases, and small and medium enterprises directly but hurt states with cross-cutting economic situations and more complex demographics over time. Based on large-scale Twitter data, this sociological perspective allows us to monitor the evolution of public opinion more directly, assess the impact of social events on public opinion, and understand the heterogeneity in the face of pandemic shocks.

Project description:The molecular etiology of invididual differences in complex behavior traits and susceptibility to psychiatric illness remains incomplete. Using an unbiased genetic approach in a mouse model, Quantitative Trait Loci (QTL) influencing anxiety-like behaviors and beta-carboline-induced seizure vulnerability have been mapped to the distal portion of mouse chromosome 10 and an interval specific congenic strain (ISCS; A.B6chr10; 66 cM to telomere) was developed. This A.B6chr10 strain facilitated defining the behavioral influences of this region as well as gene expression profiling to identify candidate gene(s) underlying this QTL. By microarray studies, an unsuspected E3 Ubiquitin Ligase, Ring Finger 41 (Rnf41 / Neuregulin Receptor Degrading Protein1; Nrdp1) was differentially expressed in the region of interest, comparing the hippocampi of A/J vs A.B6chr10 mice as well as A/J vs B6 mice. By RT-PCR, Rnf41 expression levels were significantly increased 1.5 and 1.3-fold in the hippocampi of C57BL6/J and A.B6chr10 mice compared to A/J mice, respectively. In addition, protein levels of Rnf41 were increased in hippocampi of B6 mice compared to A/J mice across postnatal development with a 5.5-fold difference at P56. Among LxS recombinant inbred mice (N=33), Rnf41 hippocampal mRNA expression levels were significantly correlated with open field behavior (r= .454, p=.0073). Re-analyzing a microarray database of human post-mortem prefrontal cortex (Brodmannâ??s Area 46/10), RNF41 mRNA expression levels were reduced significantly in patients with major depression and bipolar disorder compared to unaffected controls. Overall, Rnf41 is a pleiotropic candidate gene for anxiety-like behaviors, depression, and vulnerability to seizures. RNF41 and its binding partners provide novel etiological pathways for influencing behavior, highlighting a potential role for the ubiquitin proteasome system in psychiatric illness. Keywords: strain difference, genetic variation Microarray Affymetrix GeneChip: From three independent pools (3 hippocampi /pool) of total RNA for each line, probe was labeled, hybridized as one pool per GeneChip, using a total of six mouse MOE 430 arrays (Affymetrix, Santa Clara, CA). The sequential steps of reverse transcriptions, purification of ds cDNA, in vitro transcription and labeling of cRNA with biotin (Enzo BioArray High Yield RNA Transcript Labeling Kit), fragmentation of cRNA, hybridization to gene chips in a Affymetrix fluidics station 400 system, staining with streptavidin-phycoerythrin, washings, and scannings with a GeneArray Scanner were performed as described in the detailed protocols of the Affymetrix GeneChip Expression Analysis Technical Manual (Affymetrix, Santa Clara, CA) by the UTSW Microarray Core. The Affymetrix two GeneChip MOE 430 A and B set contains more than 45,000 probe sets, interrogating the expression level of 34,000 mouse gene transcripts. Illumina Microarray For the P0.5 microarray study, total RNA from a whole brain was individually used per Sentrix Mouse-6 BeadChip (Illumina, San Diego, CA), allowing individual samples to be evaluated and an increased sample size, namely six P0.5 mouse brains for each of three lines (A/J, B6, A.B6chr10). The Sentrix Mouse-6 BeadChip contains total 47,769 gene-specific, 70-mer oligonucleotide probes. Total RNA from each brain tissue of P0.5 mice was extracted in Trizol as above, reverse transcribed, labeled, and hybridized with a microarray according to the manufacturerâ??s detailed protocol (Illumina, San Diego, Ca.). Briefly, total RNA was reverse transcribed, then in vitro transcribed to cRNA and labeled with biotin-16-UTP. The labeled probe was hybridized to Illumina's Sentrix mouse-6 Expression BeadChips (Illumina, San Diego, CA), washed, and scanned with Illumina's BeadStation 500GX Genetic Analysis System in the UTSW Microarray core.

Dataset Information

Depression and Anxiety on Twitter During the COVID-19 Stay-At-Home Period in 7 Major U.S. Cities.

Introduction

Methods

Results

Conclusions

Publications

Depression and Anxiety on Twitter During the COVID-19 Stay-At-Home Period in 7 Major U.S. Cities.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets