Comparing 911 and emergency hotline calls for domestic violence in seven cities: What happened when people started staying home due to COVID‐19?

Abstract

Research Summary : We examine changes in help‐seeking for domestic violence (DV) in seven U.S. cities during the COVID‐19 pandemic. Using Bayesian structural time‐series modeling with daily data to construct a synthetic counterfactual, we test whether calls to police and/or emergency hotlines varied in 2020 as people stayed home due to COVID‐19. Across this sample, we estimate there were approximately 1030 more calls to police and 1671 more calls to emergency hotlines than would have occurred absent the pandemic. Policy Implications : Interagency data sharing and analysis holds great promise for better understanding localized trends in DV in real time. Research‐practitioner partnerships can help DV coordinated community response teams (CCRTs) develop accessible and sustainable dashboards to visualize data and advance community transparency. As calls for drastic changes in policing are realized, prioritization of finite resources will become critical. Data‐driven decision‐making by CCRTs provides an opportunity to work within resource constraints without compromising the safety of DV victims.

The Supply and Demand Shifts in Policing at the Start of the Pandemic: A National Multi-Wave Survey of the Impacts of COVID-19 on American Law Enforcement

“…Getting people back out of their homes will not undo the abuse that may have already occurred during the stay-at-home periods. Formal and informal mechanisms for reporting family violence also shifted during COVID ( Richards et al, 2021 ). For example, school policies and laws have long facilitated child abuse reporting by third parties (e.g., teachers and counselors), but virtual education altered this dynamic.…”

Reporting to police by intimate partner violence victim-survivors during the COVID-19 pandemic

“…Researchers have attempted to overcome this issue by looking to other sources, such as calls to domestic violence hotlines, as alternative measures of help-seeking. These studies have tended to show large increases in calls early in the pandemic (Beigelman & Castelló, 2020; Perez-Vincent et al, 2020; Richards et al, 2021), though not exclusively (Sorenson et al, 2021). While there may not be the same barriers to reporting to hotlines as there are with police, they still represent measures of service usage, rather than the actual levels of IPV (Sorenson et al, 2021).…”

An introductory examination on the differences between frequentist and Bayesian multiple regression using real-world data on bias-based victimization among Latinx adults.

“…Also, to reiterate points made earlier, NHT methods of determining meaningful results is analogous to frequentist logic, as meaningfulness simply means that either the CI (for frequentist) or HDI (for Bayesian) does not include 0. While this is not considered a strictly Bayesian practice, it is often used by Bayesian studies, likely to be conceptually palatable by largely frequentist researchers (see e.g., Cerdá et al, 2010; Gruenewald et al, 2013; Hughes et al, 2022; Richards et al, 2021; Rickman et al, 2021; Yalch & Levendosky, 2018). Given that NHT is often used, we felt it important to show how results differ when using NHT and HDI-ROPE methods.…”