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

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.

Green space justice amid COVID-19: Unequal access to public green space across American neighborhoods

Countries around the world have resorted to issuing stay-at-home orders to slow viral transmission since the COVID-19 pandemic. During the lockdown, access to public park plays a central role in the public health of surrounding communities. However, we know little about how such an unprecedented policy may exacerbate the preexisting unequal access to green space (i.e., green space justice). To address this research void, we used difference-in-difference models to examine socioeconomic disparities, urban-rural disparities, and mobility disparities in terms of public park access in the United States. Our national analysis using the weekly mobile phone movement data robustly suggests the following three key findings during COVID-19: (1) The elderly, non-college-educated people, poor people, and blacks are less likely to visit public parks frequently, while unemployed people appear to be the opposite. (2) Compared to rural areas, populations in urban neighborhoods appear to visit public parks more frequently and they generally go to larger parks to minimize the risk of infection. (3) Populations in neighborhoods with higher private vehicle ownership or those with a higher density of transit stops would more frequently visit and travel a longer distance to public parks during the stay-at-home order. Our results imply that conventional inequality in green space access may still exist and even become worse during COVID-19, which could negatively impact people's health during isolation. We suggest that special attention should be paid to park-poor neighborhoods during the pandemic and in the post-pandemic recovery phase.

State containment measures, living arrangements, and mental health of U.S. older adults during the COVID-19 pandemic

OBJECTIVES

Early in the coronavirus pandemic, U.S. states implemented several different types of containment measures to slow the disease's spread. Early evidence indicates containment measures were associated with changes in individuals' mental health. This study explores the associations between U.S. state containment measures and older adults' mental health and importantly, whether the associations vary by living arrangement and gender.

METHODS

The study analyzed national sample of adults aged 50 or older from 12 waves (April-July 2020) of the U.S. Household Pulse Survey (N = 394,934). State fixed-effects models linked four state containment measures (stay-at-home order, restaurant closure, bar closure, and movie theater closure) to levels of depression and anxiety across different types of living arrangements, net of controls. Men and women were analyzed separately.

RESULTS

Stay-at-home order and restaurant and bar closure, but not movie theater closure, were associated with higher levels of depression and anxiety in older adults. Living arrangements moderated the associations for women but not men. For women, compared to living alone, living with a spouse or intergenerational family was associated with higher levels of anxiety and depression during stay-at-home order and restaurant closure.

CONCLUSION

The associations between containment measures and mental health vary by type of living arrangement and were gendered, likely because household situations create different demands and supports that men and woman experience differently. Although containment measures are necessary to protect public health, paying attention to these underlying dynamics can inform policymakers' efforts to implement policies that balance harms and benefits for older adults.

Influence of a Stay-At-Home Order on Trauma Volume and Injury Patterns at a Level I Trauma Center in Ohio

BACKGROUND

Changes in injury patterns during the COVID pandemic have been reported in other states. The objective was to explore changes to trauma service volume and admission characteristics at a trauma center in northeast Ohio during a stay-at-home order (SAHO) and compare the 2020 data to historic trauma census data.

METHODS

Retrospective chart review of adult trauma patients admitted to a level I trauma center in northeast Ohio. Trauma admissions from January 21 to July 21, 2020 (COVID period) were compared to date-matched cohorts of trauma admissions from 2018 to 2019 (historic period). The COVID period was further categorized as pre-SAHO, active-SAHO, and post-SAHO.

RESULTS

The SAHO was associated with a reduction in trauma center admissions that increased after the SAHO (P = .0033). Only outdoor recreational vehicle (ORV) injuries (P = .0221) and self-inflicted hanging (P = .0028) mechanisms were increased during the COVID period and had substantial effect sizes. Glasgow Coma Scores were lower during the COVID period (P = .0286) with a negligible effect size. Violence-related injuries, injury severity, mortality, and admission characteristics including alcohol and drug testing and positivity were similar in the COVID and historic periods.

DISCUSSION

The SAHO resulted in a temporary decrease in trauma center admissions. Although ORV and hanging mechanisms were increased, other mechanisms such as alcohol and toxicology proportions, injury severity, length of stay, and mortality were unchanged.

An Examination of COVID-19 Mitigation Efficiency among 23 Countries

The purpose of this paper was to compare the relative efficiency of COVID-19 transmission mitigation among 23 selected countries, including 19 countries in the G20, two heavily infected countries (Iran and Spain), and two highly populous countries (Pakistan and Nigeria). The mitigation efficiency for each country was evaluated at each stage by using data envelopment analysis (DEA) tools and changes in mitigation efficiency were analyzed across stages. Pearson correlation tests were conducted between each change to examine the impact of efficiency ranks in the previous stage on subsequent stages. An indicator was developed to judge epidemic stability and was applied to practical cases involving lifting travel restrictions and restarting the economy in some countries. The results showed that Korea and Australia performed with the highest efficiency in preventing the diffusion of COVID-19 for the whole period covering 105 days since the first confirmed case, while the USA ranked at the bottom. China, Japan, Korea, and Australia were judged to have recovered from the attack of COVID-19 due to higher epidemic stability.

Weather and COVID-19 Deaths During the Stay-at-Home Order in the United States

OBJECTIVE

To estimate the association between weather and COVID-19 fatality rates during US stay-at-home orders.

METHODS

With a county-level longitudinal design, this study analyzed COVID-19 deaths from public health departments' daily reports and considered exposure as the 18 to 22 day-period before death. Models included state-level social distancing measures, Census Bureau demographics, daily weather information, and daily air pollution. The primary measures included minimum and maximum daily temperature, precipitation, ozone concentration, PM2.5 concentrations, and U.V. light index.

RESULTS

A 1 °F increase in the minimum temperature was associated with 1.9% (95% CI, 0.2% to 3.6%) increase in deaths 20 days later. An ozone concentration increase of 1 ppb (part per billion) decreased daily deaths by 2.0% (95% CI, 0.1% to 3.6%); ozone levels below 38 ppb negatively correlated with deaths.

CONCLUSIONS

Increased mobility may drive the observed association of minimum daily temperature on COVID-19 deaths.

Measuring voluntary and policy-induced social distancing behavior during the COVID-19 pandemic

Staying home and avoiding unnecessary contact is an important part of the effort to contain COVID-19 and limit deaths. Every state in the United States enacted policies to encourage distancing and some mandated staying home. Understanding how these policies interact with individuals' voluntary responses to the COVID-19 epidemic is a critical initial step in understanding the role of these nonpharmaceutical interventions in transmission dynamics and assessing policy impacts. We use variation in policy responses along with smart device data that measures the amount of time Americans stayed home to disentangle the extent that observed shifts in staying home behavior are induced by policy. We find evidence that stay-at-home orders and voluntary response to locally reported COVID-19 cases and deaths led to behavioral change. For the median county, which implemented a stay-at-home order with about two cases, we find that the response to stay-at-home orders increased time at home as if the county had experienced 29 additional local cases. However, the relative effect of stay-at-home orders was much greater in select counties. On the one hand, the mandate can be viewed as displacing a voluntary response to this rise in cases. On the other hand, policy accelerated the response, which likely helped reduce spread in the early phase of the pandemic. It is important to be able to attribute the relative role of self-interested behavior or policy mandates to understand the limits and opportunities for relying on voluntary behavior as opposed to imposing stay-at-home orders.