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.

Domestic violence calls for police service in five US cities during the COVID-19 pandemic of 2020.

BACKGROUND: When COVID-19 stay-at-home orders were instituted, there were concerns that isolation may lead to increases in domestic violence (DV). Reports of increased rates of DV during the stay-at-home period have been suggestive of this but inconsistent across different locations. We sought to complement the existing studies by characterizing changes in DV trends in US cities of Chicago, Los Angeles (LA), New York City (NYC), Philadelphia, and Phoenix using police call volume data from January 1st, 2018, through Dec 31st, 2020. METHODS: The stay-at-home orders were generally instituted for most US states in the second half of March 2020. We used the call volume for the pre-COVID-19 period (Jan. 2018 to Feb. 2020) to model a forecast against the stay-at-home order period (Mar. - May 2020) and the period after lifting the order (June - Dec. 2020) using the interrupted autoregressive integrated moving average (ARIMA) time series model. RESULTS: During the stay-at-home order, increases in mean DV calls relative to pre-COVID-19 were observed in Chicago (47.8%), Phoenix (18.4%), NYC (3.5%), and LA (3.4%), but a decrease in Philadelphia (-4.9%). After lifting the stay-at-home order, changes in mean calls relative to pre-COVID-19 remained elevated in Chicago, slightly elevated in Phoenix, and returned to baseline in NYC and LA. CONCLUSION: Results suggest that the stay-at-home orders may have contributed to an increase in DV calls in some cities (Phoenix, and to a smaller extent LA, NYC), but the increase seen in Chicago (and to some extent Phoenix) persisted beyond the stay-at-home order and therefore may not be attributable to the stay-at-home orders. Additional studies are needed to help explain why the association between stay-at-home orders and DV police call volume seems to only appear in some locations.