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

• Published date: 01 August 2021
• Author: Tara N. Richards,Justin Nix,Scott M. Mourtgos,Ian T. Adams
• Date: 01 August 2021
• DOI: http://doi.org/10.1111/1745-9133.12564

Received:  March  Revised:  September  Accepted:  October 

DOI: ./- .

SPECIAL ISSUE ARTICLE

THE IMPACT OF COVID-19 ON CRIME AND JUSTICE

Comparing 911 and emergency hotline calls for

domestic violence in seven cities: What

happened when people started staying home

due to COVID-19?

Tara N. Richards1Justin Nix1Scott M. Mourtgos2

Ian T. Adams2

University of Nebraska Omaha, Omaha,

Nebraska, USA

University of Utah, Salt Lake City, Utah,

USA

Correspondence

TaraN. Richards, University of Nebraska

Omaha, Dodge Street,  CPACS,

Omaha,NE , USA.

Email:tararichards@unomaha.edu

JustinNix, University of Nebraska Omaha,

Dodge Street,  CPACS, Omaha, NE

,USA.

Email:jnix@unomaha.edu

ScottMourtgos, University of Utah, 

SouthCentral Campus Drive, Carolyn and

KemGardner Commons, Suite , Salt

LakeCity, Utah, , USA.

Email:scott.mourtgos@utah.edu

IanAdams, University of Utah,  South

CentralCampus Drive, Carolyn and Kem

GardnerCommons, Suite , Salt Lake

City,Utah, , USA.

Email:ian.adams@utah.edu

Abstract

Research Summary: We examine changes in help-

seeking for domestic violence (DV) in seven U.S. cities

during the COVID- pandemic. Using Bayesian struc-

tural time-series modeling with daily data to construct a

synthetic counterfactual, we test whether calls to police

and/or emergency hotlines varied in  as people

stayed home due to COVID-. Across this sample, we

estimate there were approximately  more calls to

police and  more calls to emergency hotlines than

would have occurred absent the pandemic.

Policy Implications: Interagency data sharing and

analysis holds great promise for better understand-

ing localized trends in DV in real time. Research-

practitioner partnerships can help DV coordinated com-

munity 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 providesan opportunity to work within resource

constraints without compromising the safety of DV

victims.

Criminology & Public Policy. ;:–. ©  American Society of Criminology 573wileyonlinelibrary.com/journal/capp

574 RICHARDS  .

KEYWORDS

COVID-, domestic violence, police, victims

Since the onset of the COVID- pandemic, there has been significant discussion regarding the

impact of stay-at-home orders on the prevalence of domestic violence (DV). The pandemic fos-

tered increases in a range of stressors such as unemployment, financial instability (Center on

Budget and Policy Priorities, ), and parental stress (Brown et al., ; Lee et al., ), all of

which are associated with DV (Anderberg et al., ; Azier,; C. G. Moore et al., ). Con-

currently, alcohol consumption in the home increased (Chalfin et al., ), which can catalyze

violence between family members (Bushman, ; Foran & O’Leary, ; Livingston, ).

With nonessential businesses shut down, schools and churches closed, and citizens’ movement

limited, victims and their children were separated from support systems and confined with their

abusers. Further, many victim advocacy agencies reduced their capacity or moved their services

online. Given fears about COVID transmission, victims’ ability to seek safety with family and

friends was also likely limited. Taken together, scholars and practitioners suggested that DVinci-

dents would increase significantly in both frequency and severity, while the United Nations rec-

ognized DV as a “shadow pandemic” across the globe (UN Women,).

Months into the pandemic, a limited but rapidly growing body of research provided empirical

evidence that DV-related calls for service to police in the United States did increase directly after

stay-at-home orders (e.g., Piquero et al., ), but longer-term studies also showed that trends

in calls often normalized quickly after these rapid escalations (Leslie & Wilson, ; McCray &

Sanga, ). Taken together, a meta-analysis of  U.S. studies estimated an % increase in DV

during the pandemic (Piquero et al., ). In addition, at least one study demonstrated localized

differences in trends for DV calls for service across different jurisdictions (Nix & Richards, ).

Further, evidence showed that increases in calls for DVservice were concentrated among house-

holds who had not previously called police for DV service (Leslie & Wilson, ;McCray & Sanga

), suggesting potential pandemic-related changes in DV victimization, DVreporting, or both,

which requires further examination.

While these studies showed changes in police calls for DV service during COVID-, prior

research has consistently demonstrated that most DV victims do not call police after incidents

of violence (Morgan & Truman, ). Victims of DV describe a range of barriers to reporting to

police, including concerns that they will not be believed or that nothing will be done, fears of

retaliation by the perpetrator, and a relianceon the perpetrator for material resources (e.g., hous-

ing and financial support), among others (Robinson et al., ). The pandemic-related economic

downturn has likely increased concerns regarding negative consequences of reporting to police,

and victims may be inclined to access support and resources from other sources such as emer-

gency hotlines (Sorenson et al., ). As such, victim reports to emergency DV hotlines provides

an important source of data regarding incidents of DV during COVID-.

This study adds to the limited research on the impact of COVID- on DV by examining DV

calls for service to both police and victim service organizations’ emergency DV hotlines in seven

U.S. cities from January ,  to October , .Using Bayesian Structural Time Series (BSTS)

modeling, we first examine the observed trend in DV calls for service topolice and emergency hot-

lines during the entire study period. Using the resulting BSTS models, we estimate the expected

trend (i.e., counterfactual) in DV calls for service to police and emergency hotlines during the