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Epidemiology and Psychiatric
Sciences
cambridge.org/eps
Original Article
Cite this article: Yang L, Shaw A, Nyman TJ,
Hall BJ (2024) The prevalence of intimate
partner violence and risk factors for women
and men in China during the Shanghai 2022
lockdown. Epidemiology and Psychiatric
Sciences 33, e14 1–13. https://doi.org/
10.1017/S2045796024000155
Received: 26 October 2023
Revised: 28 January 2024
Accepted: 12 February 2024
Keywords:
COVID-19; gender; intimate partner violence;
lockdown
Corresponding author: Brian J. Hall;
Email: brianhall@nyu.edu
© The Author(s), 2024. Published by
Cambridge University Press. This is an Open
Access article, distributed under the terms of
the Creative Commons Attribution licence
(http://creativecommons.org/licenses/by/4.0),
which permits unrestricted re-use,
distribution and reproduction, provided the
original article is properly cited.
The prevalence of intimate partner violence
and risk factors for women and men in China
during the Shanghai 2022 lockdown
Liying Yang1,2, Amy Shaw3, Thomas J. Nyman2,4and Brian J. Hall2,4
1The School of Psychology and Cognitive Science, East China Normal University, Shanghai, China; 2Center for
Global Health Equity, NYU Shanghai, Shanghai, China; 3Department of Psychology, Faculty of Social Sciences,
University of Macau, Taipa, China and 4Faculty of Arts and Sciences, NYU Shanghai, Shanghai, China
Abstract
Aims. Intimate partner violence (IPV) is a global public health concern with negative eects
on individuals and families. e present study investigated the prevalence, risk factors and
gender disparities associated with IPV during the Shanghai 2022 Covid-19 lockdown – a public
health emergency which may have exacerbated IPV.
Methods. We estimated the total IPV prevalence and prevalence of physical, sexual and verbal
IPV by using an adapted version of the Extended-Hurt, Insult, reaten, Scream scale. is
cross-sectional study was carried out using a population quota-based sampling of Shanghai
residents across 16 districts during the 2022 Shanghai lockdown (N=2026; 1058 men and
968 women).
Results. We found a distinct gendered dynamic, where women reported a signicantly higher
prevalence of experienced IPV (27.1%, 95% condence interval [CI]: 23.1–31.4) compared
to men (19.8%, 95% CI: 16.1–24.0). Notably, the prevalence estimate mirrored the national
lifetime IPV prevalence for women but was over twice as high for men. In multivariable logis-
tic regression analyses, economic stress (income loss: adjusted OR [aOR] =2.42, 95% CI:
1.28–4.56; job loss: aOR =1.73, 95% CI: 1.02–2.92; nancial worry much more than usual:
aOR =1.89, 95% CI: 1.00–3.57) and household burden (one child at home: aOR =1.81, 95%
CI: 1.12–2.92; not enough food: aOR =1.67, 95% CI: 1.04–2.70) were associated with increased
odds of overall IPV victimization among women but not men. With regard to more serious
forms of IPV, job loss (aOR =2.27, 95% CI: 1.09–4.69) and household burden (two or more
children at home: aOR =2.95, 95% CI: 1.33–7.69) were ass ociated with increased odds of phys-
ical IPV against men. For women, a lack of household supplies was associated with increased
odds of physical IPV (water: aOR =3.33, 95% CI: 1.79–6.25; daily supplies: aOR =2.27, 95%
CI: 1.18–4.35). Lack of daily supplies (aOR =2.17, 95% CI: 1.03–4.55) and job loss (aOR =2.66,
95% CI: 1.16–6.12) were also associated with increased odds of sexual IPV.
Conclusions. Although a larger proportion of women reported IPV, men experienced greater
IPV during the lockdown than previously estimated before the pandemic. Economic stressors,
including job loss, and household burden were critical risk factors for serious forms of IPV.
Improving gender equality that my account for disparities in IPV in China is critically needed.
Policies that mitigate the impact of economic losses during crises can potentially reduce IPV.
Introduction
Intimate partner violence (IPV) refers to actual or threatened behaviours by a current or
former intimate partner that cause physical, sexual or psychological harm, including acts
of physical aggression, sexual/reproductive coercion, and psychological abuse and control-
ling behaviours (World Health Organization, 2010). e impact of IPV on the health and
well-being of individuals and families is oen devastating, and IPV is recognized as a global
public health problem (Hou et al.,2010; World Health Organization, 2022). In the context
of mainland China, studies reported a gender imbalance in experiencing IPV. According to
Hu et al. (2021), in mainland China, the lifetime prevalence of experiencing IPV victim-
ization is 25% for women and 8% for men. A similar gender imbalance of IPV victimiza-
tion has also been observed globally (Ansara and Hindin, 2010; Archer, 2002; Gass et al.,
2021; Heath et al.,2013; Oram et al.,2022) and, moreover, research shows that globally
women are overrepresented in terms of severe injuries and deaths due to IPV (World Health
Organization, 2010). Nonetheless, both men and women may act as either perpetrators or vic-
tims. Available IPV research from Western countries found support for both gender symmetry
(mostly concerning yelling, shouting and less severe forms of physical violence; Archer, 2006;
Straus, 2011) and asymmetry (mainly involving severe aggression such as kicking, choking,
https://doi.org/10.1017/S2045796024000155 Published online by Cambridge University Press
2 Yang et al.
restricting physical freedom or sexual violence; Archer, 2006;
Hamberger and Larsen, 2015). erefore, in examining the preva-
lence of gender-based IPV and its associated factors, the nature or
types of violence and sample characteristics need to be taken into
account.
Complicating the matter further, stressful events such as the
outbreaks of public health emergencies have been linked to
increases in IPV, possibly due to the added stress, anger, frustration
and isolation in crisis situations (Bowles, 2020; Smyth et al.,2021).
Stress theory postulates that IPV is a way for perpetrators to release
their stress and heightened pressure during crises oen leads indi-
viduals to commit IPV (Farrington, 1986). For example, violence
against women and girls increased when the Ebola virus epidemic
hit West Africa in 2014–2016 (Usta et al.,2021; Yasmin, 2016).
In the recent coronavirus outbreak (COVID-19), fear of COVID
infection, stay-at-home policies and job loss/unemployment have
increased household stress worldwide (Barrett, 2020; Li et al.,2023;
Su et al.,2022; U.S. Bureau of Labor Statistics, 2020), triggering
interpersonal conict at home and potentially IPV. In China, the
police department of Jianli County in Hubei Province received 162
domestic violence complaints in January 2020 – more than three
times higher than the 47 cases reported during the same month in
the previous year (Zhang, 2020). In the U.S., the number of IPV sur-
vivors reaching out increased during COVID-19 (Lee, 2020) and in
a study (Peitzmeier et al.,2021) where the results did not reveal an
increase in the overall prevalence of IPV, the IPV severity increased
and novel cases of IPV occurred in relationships that had not been
abusive prior to COVID.
It is worth noting that not all data indicate an increase in
IPV during COVID-19. For instance, the New South Wales crime
statistics suggested no change in domestic violence gures from
March 2019 to March 2020 (Freeman, 2020). Data from the
International Sexual Health and Reproductive health study which
comprises 15,336 participants in 30 countries even showed some-
what reduced IPV during COVID-19 in 2020 (Campbell et al.,
2021). However, these results should be interpreted with caution, as
researchers in the eld have warned that rates of IPV probably did
not decrease during the pandemic, but rather that IPV survivors
were unable to report IPV victimization safely while being con-
ned to homes together with abusers (Evans et al.,2020; Peitzmeier
et al.,2021).
In addition to concerns about a global surge in IPV during pub-
lic health crises, advocates especially expressed fears of exacerbated
vulnerability to IPV for women (Evans et al.,2020; Guterres, 2020;
Peitzmeier et al.,2021). Admittedly, IPV research (mostly based on
data in Western and more developed economies) has revealed that
both men and women could act as perpetrators or victims (Archer,
2006; Hamberger and Larsen, 2015; Straus, 2011), but across the
world, a higher lifetime prevalence of experiencing IPV has been
reported for women, who also tend to be overrepresented in terms
of severe injuries and deaths resulting from IPV (World Health
Organization, 2010). In mainland China, the lifetime prevalence
of IPV victimization for women is three times the estimate for
men (Hu et al.,2021). In the COVID-19 era, however, whether the
pandemic has disproportionately impacted women in experienc-
ing IPV remains open to empirical examination, and if so, what
risk factors may be associated with the increases in IPV for men
and women need to be investigated.
To address these key research questions, we conducted an
online cross-sectional survey study during the 2022 Shanghai lock-
down. In March 2022, the second wave of the pandemic broke
out in Shanghai, prompting the government to implement a city-
wide lockdown, requiring all residents to stay at home to control
the spread of the virus (Hall et al.,2023). e societal shutdown
resulted in many individuals losing their sources of income, caus-
ing signicant economic pressure which likely increased the risk
of IPV victimization for both men and women (Barrett, 2020; Li
et al.,2023). Additionally, stay-at-home orders might have lead
to additional stressors that could trigger more conicts at home
and potential IPV (Evans et al.,2020; Guterres, 2020; Peitzmeier
et al.,2021). Regarding gender (a)symmetry in experiencing IPV
and being adversely impacted by the COVID-related lockdown,
although the nationwide data suggested a higher lifetime preva-
lence for women (Hu et al.,2021), this might not apply to Shanghai
(one of the most economically and socially developed cities in
China) where there are even 5% more women with college degrees
or higher levels of education in the workforce than men (Lu, 2015).
erefore, this study aims to examine the prevalence and vari-
ous types of IPV and the associated risk factors for men and women
during the 2022 Shanghai lockdown, as well as to explore the gen-
der (a)symmetry of IPV victimization in this population. Earlier
studies on IPV during COVID-19 mainly based their estimates
of IPV on suboptimal proxy measures such as ocial crime or
helpline data (e.g., Ceroni et al.,2023; Freeman, 2020; Graham-
Harrison, 2020), which could have led to underestimated IPV rates
due to the limited reporting channels during lockdowns. In the
present study, we use the more direct self-report survey approach
to collect these data. To our knowledge, this is the very rst study
that directly examined the prevalence, severity or types, and cor-
relates of IPV for men and women during the 2022 Shanghai
lockdown.
Methods
Participants
Data were drawn from a cross-sectional online survey conducted
in Shanghai between April 29 and 1 June 2022 (from the middle to
the end of the lockdown period). A total of 3230 participants were
recruited via purposive sampling to reach a geographic target sam-
ple of 200 residents in each of the 16 Shanghai districts (Hall et al.,
2023). Participants (1058 men and 968 women) who self-reported
being married or cohabitating during the lockdown were included
in our data analysis. We developed the survey on Wenjuanxing, a
Chinese online questionnaire platform. e IP address function
of Wenjuanxing locates participants’ network addresses automat-
ically. ese addresses were used to conrm that participants were
in Shanghai at the time of the survey. Digital informed consent was
obtained before study perception. Each participant who completed
the survey was oered 6 Chinese yuan (∼$1 USD) (see the process
of participant selection in Figure 1). e study was approved by the
NYU Shanghai Institutional Review Board.
Measures and instruments
Lockdown-related stressors
Key economic and household stressors were assessed. First, we
assessed nancial stressors caused by the lockdown, including
income loss (yes/no), job loss (yes/no), nancial worries (not at all,
not more than usual, more than usual and much more than usual)
and job worry (yes/no). We assessed household burden, including
https://doi.org/10.1017/S2045796024000155 Published online by Cambridge University Press
Epidemiology and Psychiatric Sciences 3
Figure 1. Flow diagram for study participants.
the number of children home during the lockdown, and whether
the family was prepared to undergo the lockdown (‘At the begin-
ning of the lockdown, did your family have sucient stock of the
following items (food, water and daily supplies) for at least 1 week?’
(yes/no)).
Intimate partner violence
Participants completed an adapted version of the Extended-Hurt,
Insult, reaten, Scream (E-HITS) screening tool to assess IPV
exposure. e E-HITS has demonstrated reliability and validity in
assessing IPV in healthcare settings (Chan et al.,2010; Goldstein
et al.,2023). Participants were asked, ‘How oen did a partner (1)
physically hurt you? (2) insult you or talk down to you? (3) threaten
you with harm? (4) scream or curse at you? and (5) force you to
have sexual activities?’ during lockdown. Responses were scored
on a 5-point Likert scale (1 =never to 5 =frequently). Total scores
were calculated from the sum of item ratings (range, 5–25), with a
score of ≥7 dened as IPV exposure. Following Relyea et al. (2020),
any answer of two (rarely) or higher on any item was coded as
experienced that subtype of IPV.
Study covariates
Study covariates included age group (women: 18–24, 25–34, 35–44,
≥45; men: 18–24, 25–34, 35–44, 45–54, ≥55), educational attain-
ment (less than high school, high school, college graduate or
more), household income in Chinese Yuan (<4000, 4001–8000,
8001–15,000, 15,001–30,000, 30,000 or higher), employment status
(employed, part-time, unemployed), previous psychiatric diagno-
sis by a mental health professional (yes/no) and migration status.
In China, the hukou is a household registration that is bound to a
particular rural or urban location. People with a Shanghai hukou
can enjoy preferential policies, like local healthcare and educational
resources in the city. ere are two ways to obtain a Shanghai
hukou, being born in Shanghai or migrating to the city and meet-
ing certain occupational or other criteria. Four kinds of migration
status were investigated in this study: Shanghai local, migrant with
hukou, migrant without hukou and temporary migrants who do
not intend to permanently migrant to Shanghai.
Statistical analysis
All analyses were weighted to adjust for deviations between the
sample and the most recent Shanghai census. Weights were cal-
culated by utilizing logistic regression models to create an inverse
probability of sampling weights to account for the dierences in the
distribution of covariates (i.e., district and age) between the study
population and the 2020 Shanghai Census data (Cole and Stuart,
2010; Hall et al.,2023).
We estimated the prevalence of IPV and subtypes of IPV. Study
covariates were described using raw frequencies and weighted
percentages. Firstly, we used two-tailed χ2tests to estimate
the bivariable associations between demographic characteristics,
lockdown-related stressors and IPV victimization. en, bivariable
logistic regression analyses were conducted to explore the unad-
justed direct associations between demographic characteristics,
lockdown-related economic and household stressors and exposure
to IPV for men and women, respectively. Multivariable logistic
regression analyses then adjusted for demographic characteristics
(age, income, education, migrant status, past psychological diag-
nosis), regressing lockdown factors on IPV and IPV subtypes in
separate analyses. Analyses were conducted using svy commands
in Stata/MP 17.0, with statistical signicance at p<0.05. e 95%
condence intervals (CIs) for the prevalence and odds ratios were
calculated.
Results
Most men were aged 25–44 years (715/48.8%) (median age: 38,
IQR: 33–47, range: 19–88), and the majority of women were
25–34 years old (525/49.4%) (median age: 34, IQR: 30–39.5,
https://doi.org/10.1017/S2045796024000155 Published online by Cambridge University Press
4 Yang et al.
Table 1. Participant Characteristics
N (%)
Men Women
Overall 1058 (100) 968 (100)
Age
18−24 20 (1.1) 27 (2.6)
25−34 337 (23.2) 525 (49.4)
35−44 378 (25.6) 264 (24.6)
45−54/≥45 203 (14.6) 152 (23.4)
≥55 120 (35.5) –
Education
Less than high school 173 (18.1) 162 (14.1)
High school 264 (25.8) 196 (19.2)
College graduate or higher 621 (56.1) 610 (66.7)
Income
Less than 4000 59 (3.6) 90 (8.0)
4001−8000 274 (33.0) 231 (25.7)
8001−15000 361 (37.0) 267 (29.6)
15,001−30000 261 (19.0) 262 (25.1)
30000 or higher 103 (7.4) 118 (11.6)
Employment
Employed 887 (87.6) 792 (71.7)
Part–time 64 (4.1) 48 (5.2)
Unemployed 107 (8.3) 128 (11.7)
Migrant status
Shanghai local 340 (48.0) 275 (34.1)
Migrant with hukou 140 (9.9) 160 (16.2)
Migrant without hukou 344 (24.6) 291 (25.7)
Temporary 234 (17.5) 242 (24.0)
Past psychological diagnosis
No 1029 (98.6) 920 (79.4)
Yes 29 (1.4) 48 (20.6)
range: 19–86). Most men (621/56.1%) and women (610/66.7%)
reported a university degree, with a family income between 8000
and 15,000 Chinese yuan (men: 361/30.0%; women: 267/29.6%),
and most of them were employed (men: 887/87.6%; women:
792/71.7%). Nearly half the sample of men (587/42.1%) and
women (533/49.7%) reported being migrants without a Shanghai
household registration (i.e., hukou). Twenty-nine (1.4%) men and
forty-eight (20.6%) women reported a previous psychiatric diag-
nosis (see Table 1).
e prevalence of IPV experienced by men was signicantly
dierent by age, education, migrant status, past psychological
diagnosis, income loss, job loss and nance worry. For women,
they diered by migrant status, past psychological diagnosis,
income loss, job loss, nance worry and food preparation (see
Figure 2–Figure 5).
Nearly, 20% (19.8%, 95% CI: 16.1–24.0) men and 30% (27.1%,
95% CI: 23.1–31.4) women experienced IPV during lockdown.
Verbal violence was the most prevalent form of IPV for both
men and women (insulted you: women: 29.2%, 95% CI: 25.8–34.4;
men: 22.3%, 95% CI: 18.4–26.8; screamed or cursed at you:
women: 30.6%, 95% CI: 26.3–35.2; men: 23.8%, 95% CI: 19.8–28.4)
(see Table 2).
Correlates of overall IPV by sex
Table 3 reports the results of unadjusted binary logistic regression
analysis. e multivariable analyses suggested several correlates of
IPV aer adjusting for demographic characteristics (see Table 4).
For economic and household stress, women who experienced
income loss (adjusted OR [aOR] =2.42, 95% CI: 1.28–4.56), job
loss (aOR =1.73, 95% CI: 1.02–2.92) and worried about nances
much more than usual (aOR =1.89, 95% CI: 1.00–3.57) compared
to not experiencing these losses during lockdown had increased
odds of IPV. Women who had one child at home (aOR =1.81, 95%
CI: 1.12–2.92) compared to no child also experienced increased
odds of IPV. Women who did not have enough food for the fam-
ily (aOR =1.67, 95% CI 1.04-2.70) compared to the group with
sucient preparation were associated with increased odds of IPV.
In contrast, the experience of IPV for men was not associated with
any of these factors.
Correlates of IPV types by sex
Women who experienced income loss (insult: aOR =2.18, 95%
CI: 1.19–3.98), job stress (insult: aOR =1.73, 95% CI: 1.06–2.96;
scream: aOR =1.95, 95% CI: 1.17–3.25) or had at least one
child at home (insult: one child: aOR =1.74, 95% CI: 1.10–2.74;
≥two or more children: aOR =1.80, 95% CI: 1.05–3.07; scream:
one child: aOR =1.64, 95% CI: 1.03–2.62) compared to their
reference groups experienced increased odds of verbal violence
(i.e., insult or scream). Women who reported not having enough
water for the family experienced increased odds of physical IPV
(aOR =3.33, 95% CI: 1.79–6.25) and threat (aOR =2.08, 95% CI:
1.04–4.00). Similarly, women who did not have enough daily sup-
plies for family had an associated increased odds of physical IPV
(aOR =2.77, 95% CI: 1.18–4.35) and sexual IPV (aOR =2.17, 95%
CI: 1.03–4.55). Men who lost their jobs experienced an increased
odds of physical IPV (aOR =2.27, 95% CI: 1.09–4.69) and threat
(aOR =2.50, 95% CI: 1.25–5.01). Men who had two or more chil-
dren (aOR =2.95, 95% CI: 1.33–7.69) had an increased odds of
physical IPV (see Table 4).
Discussion
e current study utilized survey data from a large sample that
experienced the Shanghai 2022 lockdown and analysed IPV preva-
lence and corresponding risk factors stratied by sex. Results
revealed a gendered pattern whereby signicant associations
between economic/household stress and IPV were largely expe-
rienced by women but with few eects only for physical violence
for men.
Compared to men, women were more likely to experience IPV
during the lockdown (27.1% women vs. 19.8% men). is is near
the prevalence of lifetime IPV in China for women but more than
twice that previously reported for men (Yang et al.,2019). e
prevalence of verbal violence was higher than other IPV types for
both men and women. Although there is limited comparable data
https://doi.org/10.1017/S2045796024000155 Published online by Cambridge University Press
Epidemiology and Psychiatric Sciences 5
Figure 2. Associations between sociodemographic characteristics and intimate partner violence for men (N=1058). Bar charts represent the proportion of IPV victimization
by sociodemographic characteristics. All percentages were weighted. Pvalues were calculated using the two-sided Pearson’s chi-squared test.
in Shanghai exploring the prevalence of IPV, a study conducted
in 2013 used data from 216 male and female adolescents, aged
15–19, who were immigrants to explore the prevalence and health
impact of IPV, and the result showed the past-year IPV prevalence
was 10.2% in Shanghai (Decker et al.,2014). Another study con-
ducted among married rural migrant women of reproductive age
in April and May of 2010 in Shanghai found that 18.7% women
reported any kind of IPV (emotional, physical or sexual abuse)
in the past year. Overall, the prevalence of IPV was higher than
estimates obtained before COVID-19.
Over 60% of men and 70% of women experienced income loss
and over 10% of men and women lost their jobs during the lock-
down. Income loss, unemployment and nancial worry were all
correlates of experiencing increased overall IPV among women but
not men. According to Connell’s (1987) integrative theory of gen-
der and power (TGP), people with weaker division of labour are
more likely to become victims of IPV (Connell, 1987). Although
more women work now, unemployment was a key correlate of their
experienced IPV. Meanwhile, considering the higher percentage
income loss among men, violence may have played a compensatory
role. e traditional gender identity of men – being responsible
for nancial stability – may have led to instability of gender power
structure during lockdown. According to TGP, the increase in IPV
victimization may have been a tool used to compensate for income
loss or unemployment and to assert dominance through violent
means in the current study (Clare et al.,2021). Based on our results,
women should not only full the obligations of the household but
may have also be blamed for their nance loss.
e results showed that men also reported IPV, especially those
with higher education, which was supported by Zhang et al. (2015).
One possible explanation is that at least some portion of men’s
reporting of experienced IPV may be the violence women engage
in for self-defence (Kimmel, 2002; Zhang et al.,2015). Women
were likely to use violence to protect themselves or retaliate against
prior violence (Allen, 2011). Besides, the focus of IPV measure-
ment in this study was the violence itself, which fails to reect
to reasons of violence and behaviours leading to violence. e
measurements without the context of violence may exaggerate the
frequency of men self-reporting victimization (Zhang et al.,2015).
Future research should incorporate additional information to clar-
ify the context of violence against men. In fact, the results indicated
that increased violence was reported by men who lost their job,
had two more children at home and did not have sucient water
for their family. Women may have perpetrated verbal and physical
IPV when their partners were unable to full their social role as a
breadwinner (Oinas, 2018). Although IPV reported by women was
more severe, victimization of men should not be ignored.
Migration status was a key risk factor for both men and women,
compared to Shanghai locals, migrants experienced a higher bur-
den of IPV, which is supported by previous studies conducted
in China (Chen et al.,2016; Li and Wang, 2023; Tu and Lou,
2017). However, empirical work surrounding migrants and IPV
has mostly focused on women (Chen et al.,2016). In fact, both men
and women migrants may face employment, income and social
welfare insecurity (Wong et al.,2008). Income and job instability
among migrants may increase the possibility of IPV.
https://doi.org/10.1017/S2045796024000155 Published online by Cambridge University Press
6 Yang et al.
Figure 3. Associations between sociodemographic characteristics and intimate partner violence for women (N=968). Bar charts represent the proportion of IPV
victimization by sociodemographic characteristics. All percentages were weighted. Pvalues were calculated using the two-sided Pearson’s chi-squared test.
Figure 4. Associations between lockdown-related stressors and intimate partner violence for men (N=1058). Bar charts represent the proportion of IPV victimization by
lockdown-related stressors. All percentages were weighted. Pvalues were calculated using the two-sided Pearson’s chi-squared test.
https://doi.org/10.1017/S2045796024000155 Published online by Cambridge University Press
Epidemiology and Psychiatric Sciences 7
Figure 5. Associations between lockdown-related stressors and intimate partner violence for women (N=968). Bar charts represent the proportion of IPV victimization by
lockdown-related stressors. All percentages were weighted. Pvalues were calculated using the two-sided Pearson’s chi-squared test.
Table 2. Prevalence of IPV for men (n=1058) and women (n=968)
Notes: Overall means the prevalence of IPV experienced by men and women calculated by the screening threshold score of E-HITS. IPV types means the prevalence of IPV types experienced
by men and women calculated by the screening threshold score of each type.
Limitations
ere are several limitations that should be noted in this study.
First, limited by the lockdown, our data were collected by online
self-report questionnaires. Self-report data may be inuenced by
social desirability and recall biases, which might lead to misesti-
mation of the prevalence of IPV. However, given the anonymity
and condentiality protections in the study, the participants
might not have been motivated to intentionally give socially
desirable responses. Second, the research design of this study
was cross-sectional, and conclusions cannot be drawn regarding
causality. ird, the participants were in romantic relationships but
may not have been paired. e TGP emphasizes gender actions
and relations in civil institutions. Data from paired couples liv-
ing together may be more helpful to understand the relationship
between labour, power and IPV. Finally, the data collection and
assumptions of romantic partnerships adhere to a heteronorma-
tive narrative, and the lived experiences of LGBTQ+participants
are not reected in these data. Future studies should examine
IPV from an intersectional lens, exploring gender, sexuality and
violence within a Chinese context. Finally, it should be noted
https://doi.org/10.1017/S2045796024000155 Published online by Cambridge University Press
8 Yang et al.
Table 3. Bivariable relationships between lockdown-related stressors and intimate partner violence by type of experienced IPV
Intimate partner violence Physically hurt you Insulted you Threatened to harm you Screamed or cursed at you Forced you to have sexual activities
OR (95%CI)
Men
OR (95%CI)
Women
OR (95%CI)
Men
OR (95%CI)
Women
OR (95%CI)
Men
OR (95%CI)
Women
OR (95%CI)
Men
OR (95%CI)
Women
OR (95%CI)
Men
OR (95%CI)
Women
OR (95%CI)
Men
OR (95%CI)
Women
Income
loss due to
lockdown
No 1 1 1 1 1 1 1 1 1 1 1 1
Yes 2.48
(1.29−4.75)
2.35
(1.26−4.37)
3.18
(1.00−10.10)
2.46
(0.83−7.32)
2.90
(1.53−5.53)
2.30
(1.25−4.20)
3.17
(1.10−9.12)
3.94
(1.75−8.89)
2.33
(1.29−4.23)
1.68
(0.89−3.16)
1.98
(0.75−5.28)
2.35
(0.81−6.81)
Job loss
due to
lockdown
No 1 1 1 1 1 1 1 1 1 1 1 1
Yes 1.71
(1.11−2.64)
1.86
(1.22−2.86)
2.05
(1.02−4.15)
1.56
(0.79−3.11)
1.57
(1.02−2.41)
1.91
(1.26−2.90)
2.09
(1.12−3.91)
1.10
(0.54−2.26)
1.53
(1.01−2.32)
1.85
(1.21−2.81)
1.71
(0.93−3.15)
1.89
(0.91−3.88)
Finance
worry
Not at all 1 1 1 1 1 1 1 1 1 1 1 1
Not more
than usual
2.11
(0.88−5.08)
0.81
(0.39−1.70)
1.19
(0.27−5.31)
0.39
(0.11−1.37)
1.54
(0.67−3.52)
0.63
(0.31−1.30)
1.57
(0.35−7.00)
0.55
(0.18−1.62)
2.45
(1.10−5.49)
0.60
(0.29−1.26)
2.48
(0.91−6.74)
0.44
(0.11−1.67)
More than
usual
2.30
(1.04−5.36)
1.20
(0.66−2.19)
1.40
(0.35−5.69)
0.52
(0.18−1.55)
2.60
(1.19−5.66)
1.30
(0.74−2.31)
1.71
(0.41−7.06)
0.88
(0.33−2.33)
2.33
(1.11−4.93)
1.02
(0.56−1.85)
2.55
(1.10−5.94)
1.09
(0.34−3.48)
Much
more than
usual
2.94
(1.33−6.49)
1.99
(1.11−3.57)
1.40
(0.36−5.47)
1.25
(0.46−3.40)
2.44
(1.16−5.15)
2.02
(1.15−3.54)
1.83
(0.46−7.36)
1.13
(0.45−2.87)
2.59
(1.26−5.34)
1.62
(0.90−2.90)
3.42
(1.56−7.54)
0.99
(0.29−3.36)
Job worry
No 1 1 1 1 1 1 1 1 1 1 1 1
Yes 1.12
(0.67−1.87)
1.16
(0.77−1.75)
1.64
(0.77−3.53)
0.90
(0.43−1.89)
1.13
(0.67−1.88)
1.29
(0.86−1.91)
1.17
(0.57−2.42)
1.56
(0.80−3.06)
0.89
(0.54−1.48)
1.17
(0.78−1.75)
2.15
(1.11−4.17)
0.85
(0.37−1.94)
Children at
home
None 1 1 1 1 1 1 1 1 1 1 1 1
One 1.19
(0.67−2.11)
1.58
(0.97−2.57)
1.62
(0.58−4.56)
0.78
(0.36−1.68)
1.31
(0.76−2.27)
1.56
(0.96−2.51)
1.24
(0.50−3.04)
0.88
(0.41−1.86)
1.21
(0.70−2.11)
1.56
(0.96−2.54)
0.87
(0.40−1.90)
1.20
(0.54−2.66)
Two or
more
1.32
(0.74−2.39)
1.52
(0.86−2.68)
2.21
(0.87−5.59)
0.92
(0.36−2.37)
1.67
(0.91−3.04)
1.77
(1.01−3.08)
1.47
(0.65−3.30)
1.33
(0.52−3.40)
1.02
(0.57−1.82)
1.31
(0.75−2.31)
1.20
(0.49−2.93)
1.14
(0.39−3.33)
(Continued)
https://doi.org/10.1017/S2045796024000155 Published online by Cambridge University Press
Epidemiology and Psychiatric Sciences 9
Table 3. (Continued.)
Intimate partner violence Physically hurt you Insulted you Threatened to harm you Screamed or cursed at you Forced you to have sexual activities
OR (95%CI)
Men
OR (95%CI)
Women
OR (95%CI)
Men
OR (95%CI)
Women
OR (95%CI)
Men
OR (95%CI)
Women
OR (95%CI)
Men
OR (95%CI)
Women
OR (95%CI)
Men
OR (95%CI)
Women
OR (95%CI)
Men
OR (95%CI)
Women
Food
Yes 1 1 1 1 1 1 1 1 1 1 1 1
No 1.29
(0.76−2.22)
1.61
(1.03−1.61)
1.49
(0.69−3.22)
0.96
(0.98−3.85)
1.45
(0.87−2.44)
1.54
(0.99−2.38)
1.35
(0.67−2.70)
1.23
(0.60−2.56)
0.93
(0.55−1.56)
1.39
(0.89−2.17)
1.10
(0.57−2.13)
1.72
(0.80−3.70)
Water
Yes 1 1 1 1 1 1 1 1 1 1 1 1
No 0.59
(0.33−1.06)
1.30
(0.77−2.17)
0.51
(0.24−1.08)
3.57
(1.79−7.14)
0.73
(0.40−1.32)
1.12
(0.68−1.89)
0.56
(0.27−1.14)
2.04
(1.01−4.17)
0.71
(0.40−1.27)
1.15
(0.68−1.92)
0.76
(0.38−0.54)
1.45
(0.65−3.23)
Daily
supplies
Yes 1 1 1 1 1 1 11 1 1 1
No 1.00
(0.56−1.79)
1.15
(0.75−1.75)
0.62
(0.29−1.32)
2.94
(1.49−5.88)
1.02
(0.56−1.89)
1.43
(0.94−2.17)
0.57
(0.28−1.18)
2.00
(0.98−4.00)
1.11
(0.63−2.00)
1.12
(0.74−1.72)
0.64
(0.31−1.32)
2.56
(1.27−5.26)
Notes: OR =odds ratio which is the unadjusted association between the exposure and outcome. Bold estimates are p<.05.
https://doi.org/10.1017/S2045796024000155 Published online by Cambridge University Press
10 Yang et al.
Table 4. Multivariable relationships between lockdown-related stressors and intimate partner violence by type of experienced IPV
Intimate partner violence Physically hurt you Insulted you Threatened to harm you Screamed or cursed at you Forced you to have sexual activities
aOR
(95%CI)
Men
aOR
(95%CI)
Women
aOR
(95%CI)
Men
aOR
(95%CI)
Women
aOR
(95%CI)
Men
aOR
(95%CI)
Women
aOR
(95%CI)
Men
aOR
(95%CI)
Women
aOR
(95%CI)
Men
aOR
(95%CI)
Women
aOR
(95%CI)
Men
aOR
(95%CI)
Women
Income
loss due to
lockdown
No 111111111111
Yes 1.76
(0.89−3.51)
2.42
(1.28−4.56)
2.86
(0.81−10.05)
1.91
(0.71−5.15)
2.03
(0.99−4.12)
2.18
(1.19−3.98)
2.75
(0.83−9.04)
2.75
(1.14−6.65)
1.63
(0.87−3.05)
1.53
(0.85−2.75)
1.21
(0.56−2.59)
1.62
(0.54−4.92)
Job loss
due to
lockdown
No 111111111111
Yes 1.58
(0.99−2.54)
1.73
(1.02−2.92)
2.27
(1.09−4.69)
1.12
(0.49−2.58)
1.20
(0.68−2.12)
1.73
(1.06−2.96)
2.50
(1.25−5.01)
0.75
(0.35−1.60)
1.45
(0.92−2.26)
1.95
(1.17−3.25)
1.41
(0.70−2.85)
2.66
(1.16−6.12)
Finance
worry
Not at all 1 1 1 1 1 1 1 1 1 1 1 1
Not more
than usual
1.65
(0.72−3.81)
0.83
(0.41−1.68)
0.97
(0.28−3.34)
0.40
(0.11−1.46)
1.16
(0.53−2.53)
0.65
(0.33−1.29)
1.21
(0.36−4.09)
0.59
(0.20−1.74)
1.96
(0.94−4.11)
0.61
(0.30−1.21)
2.05
(0.70−6.00)
0.51
(0.14−1.82)
More than
usual
1.63
(0.77−3.45)
1.16
(0.63−2.16)
1.03
(0.34−3.16)
0.48
(0.15−1.30)
1.70
(0.84−3.48)
1.25
(0.69−2.25)
1.24
(0.40−3.83)
0.61
(0.23−1.67)
1.58
(0.82−3.05)
0.96
(0.53−1.75)
1.86
(0.73−4.75)
0.86
(0.28−2.68)
Much more
than usual
1.86
(0.85−4.07)
1.89
(1.00−3.57)
0.89
(0.30−2.67)
0.87
(0.29−2.60)
1.41
(0.68−2.92)
1.94
(1.05−3.59)
1.25
(0.41−3.80)
0.68
(0.25−1.87)
1.67
(0.83−3.35)
1.63
(0.89−2.98)
2.40
(0.92−6.26)
0.77
(0.22−2.68)
Job worry
No 111111111111
Yes 0.79
(0.49−1.27)
1.21
(0.81−1.80)
1.33
(0.60−2.94)
0.84
(0.38−1.84)
0.80
(0.50−1.29)
1.31
(0.89−1.92)
0.87
(0.40−1.56)
1.28
(0.62−2.61)
0.61
(0.39−0.93)
1.16
(0.79−1.72)
1.83
(0.94−3.59)
0.63
(0.26−1.51)
Children at
home
None 1 1 1 1 1 1 1 1 1 1 1 1
One 1.30
(0.71−2.38)
1.81
(1.12−2.92)
2.01
(0.66−6.12)
0.91
(0.44−1.90)
1.45
(0.83−2.54)
1.74
(1.10−2.74)
1.45
(0.53−3.97)
1.02
(0.48−2.17)
1.22
(0.71−2.11)
1.64
(1.03−2.62)
0.75
(0.34−1.62)
1.38
(0.56−3.37)
Two or
more
1.38
(0.79−2.40)
1.58
(0.91−2.75)
2.95
(1.33−7.69)
0.98
(0.38−2.55)
1.65
(0.94−2.91)
1.80
(1.05−3.07)
1.76
(0.75−4.13)
1.44
(0.59−3.50)
0.96
(0.57−1.63)
1.28
(0.75−2.17)
1.07
(0.47−2.46)
1.13
(0.37−3.50)
(Continued)
https://doi.org/10.1017/S2045796024000155 Published online by Cambridge University Press
Epidemiology and Psychiatric Sciences 11
Table 4. (Continued.)
Intimate partner violence Physically hurt you Insulted you Threatened to harm you Screamed or cursed at you Forced you to have sexual activities
aOR
(95%CI)
Men
aOR
(95%CI)
Women
aOR
(95%CI)
Men
aOR
(95%CI)
Women
aOR
(95%CI)
Men
aOR
(95%CI)
Women
aOR
(95%CI)
Men
aOR
(95%CI)
Women
aOR
(95%CI)
Men
aOR
(95%CI)
Women
aOR
(95%CI)
Men
aOR
(95%CI)
Women
Food
Yes 111111111111
No 1.01
(0.60−1.72)
1.67
(1.04−2.70)
1.19
(0.57−2.50)
1.92
(0.92−4.00)
1.09
(0.65−1.82)
1.59
(1.02−2.44)
1.06
(0.53−2.13)
1.20
(0.54−2.63)
0.71
(0.54−1.19)
1.45
(0.93−2.27)
0.89
(0.47−1.69)
1.59
(0.72−3.45)
Water
Yes 111111111111
No 0.56
(0.31−1.03)
1.28
(0.81−2.04)
0.47
(0.22−0.98)
3.33
(1.79−6.25)
0.68
(0.38−1.23)
1.12
(0.72−1.75)
0.49
(0.24−1.01)
2.08
(1.04−4.00)
0.72
(0.40−1.28)
1.18
(0.75−1.85)
0.70
(0.34−1.47)
1.69
(0.79−3057)
Daily
supplies
Yes 1 1 1 1 1 1 11111
No 0.99
(0.58−1.69)
0.98
(0.65−1.49)
0.54
(0.26−1.12)
2.27
(1.18−4.35)
0.90
(0.52−1.54)
1.22
(0.81−1.82)
0.50
(0.24−1.01)
1.35
(0.68−2.70)
1.19
(0.70−2.04)
1.01
(0.67−1.52)
0.56
(0.26−1.20)
2.17
(1.03−4.55)
Notes: aOR =adjusted odds ratio. Models were adjusted for demographic characteristics (age, income, education, employment, migrant status, past psychological distress). Bold estimates are p<.05.
https://doi.org/10.1017/S2045796024000155 Published online by Cambridge University Press
12 Yang et al.
that in this study all participants came from Shanghai, arguably
the most developed city in China. Compared to other provinces,
Shanghai has a close contact with Western culture, more developed
community-based organization sectors and convenient welfare to
residents. Our results should be interpreted with caution when
generalized to other populations in China. Future work is also
needed to understand the nature of IPV in Shanghai and among
men, outside the COVID-19 context as current data are limited.
Notwithstanding these limitations, this study provided key
insights and important practical implications. First, income loss,
unemployment and household burden were all risk factors for
overall IPV experienced by women, but the same pattern was
not observed for men, suggesting a key gendered response to
threats against masculine roles still impacted the risk of IPV against
women in Shanghai (Clare, 2021). e economic function of being
a breadwinner was limited by a public health emergency, which
may lead to IPV as a compensatory mechanism for men to bal-
ance their predominance in the gender and power structure. e
Women’s Federation or other social welfare institutions should be
strengthened to maintain access to provide help for women suer-
ing IPV during public health emergencies. Second, men are also at
risk of IPV, especially those with higher levels of education, expe-
riencing job loss due to lockdown, having two or more children
at home or not having sucient water for family. Future research
should examine the risk factors and motivation of IPV against
men to gain further understanding of gender (a)symmetry of IPV.
Finally, verbal IPV appears to be of particularly high prevalence
during the Shanghai 2022 lockdown. Interventions to promote
healthy conict resolution skills within intimate partnerships may
be benecial to reduce this from of IPV in the future.
Conclusion
During the Shanghai 2022 lockdown, 27.1% women and 19.8%
men met screening thresholds for total IPV. is is greater than the
prevalence of lifetime IPV experienced by women (25%) and men
(8%) in mainland China (Hu et al.,2021). e occurrence of verbal
violence was higher than other kinds of IPV. e gender pattern of
IPV experienced by men and women is dierent. Economic stress
and household burden were both risk factors for overall IPV and
all IPV types against women. While for men, economic hardship
may be the root reason to experience verbal or physical IPV. ese
ndings highlighted the importance of improving gender equality
awareness and interventions in China and evidence for the surplus
of IPV experienced during the lockdown period.
References
Allen M (2011) Is there gender symmetry in intimate partner violence? Child
and Family Social Work 16(3), 245–254.
Ansara DL and Hindin MJ (2010) Exploring gender dierences in the patterns
of intimate partner violence in Canada: A latent class approach. Journal of
Epidemiology & Community Health 64, 849–854.
Archer J (2002) Sex dierences in physically aggressive acts between heterosex-
ual partners: A meta-analytic review. Aggression and Violent Behavior 7(4),
313–351.
Archer J (2006) Cross-cultural dierences in physical aggression b etween part-
ners: A social-role analysis. Personality and Social Psychology Review 10(2),
133–153.
Barrett E (2020) e mystery of China’s unemployment rate. Fortune.
https://fortune.com/2020/05/24/china-unemployment-rate-2/ (accessed
26 January 2024).
Bowles J (2020) Social innovation: How societies nd the power to change by
Geo Mulgan. Voluntary Sector Review 11(2), 261–262.
Campbell L, Tucker J, Gabster A, Shamu S, Mark K, Olumide A, Eleuteri S,
Tan R, Saltis H, Kemigisha E, Van de Velde S, Esho T,Schr
oder J, Francis J,
Plá
silová L, Adebayo A, Calvo González S, Hurtado-Murillo F, Perrotta G,
Hendriks J, Uhlich M, Cleeve A and Kosana P (2021) O18.5C omparison
of intimate partner violence prior to and during the COVID-19 pandemic:
Findings from the I-SHARE cross-sectional multi-country study. Sexually
Transmitted Infections 97(Suppl 1), A59–A59.
Ceroni TL, Ennis CR and Franklin CL (2023) e COVID-19 pandemic:
Implications for intimate partner violence. Couple and Family Psychology-
Research and Practice 12(2), 66–72.
Chan CC, Chan YC, Au A and Cheung GOC (2010) Reliability and valid-
ity of the “Extended - Hurt, Insult, reaten, Scream” (E-HITS) screening
tool in detecting intimate partner violence in hospital emergency depart-
ments in Hong Kong. Hong Kong Journal of Emergency Medicine 17(2),
109–117.
Chen L, Yu Z, Luo X and Huang Z (2016) Intimate partner violence against
married rural-to-urban migrant workers in eastern China: Prevalence, pat-
terns, and associated factors. BMC Public Health 16(1), 1232.
Clare CA, Velasquez G, Martorell GMM, Fernandez D, Dinh J and
Montague A (2021) Risk factors for male perpetration of intimate partner
violence: A review. Aggression and Violent Behavior 56, 101532.
Cole SR and Stuart EA (2010) Generalizing evidence from randomized clin-
ical trials to target populations: e ACTG 320 trial. American Journal of
Epidemiology 172(1), 107–115.
Connell RW (1987) Gender and Power. Stanford: Stanford University Pres.
Decker MR, Peitzmeier S, Olumide A, Acharya R, Ojengbede O,
Covarrubias L, Gao ES, Cheng Y,Delany-Maoretlwe S and Brahmbhatt H
(2014) Prevalence and health impact of intimate partner violence and non-
partner sexual violence among female adolescents aged 15–19 years
in vulnerable urban environments: A multi-country study. Journal of
Adolescent Health 55(6, Supplement), S58–S67.
Evans ML, Lindauer M and Farrell ME (2020) A pandemic within a pan-
demic – Intimate partner violence during Covid-19. New England Journal
of Medicine 383(24), 2302–2304.
Farrington K (1986) e application of stress theory to the study of family vio-
lence: Principles, problems, and prospects. Journal of Family Violence 1(2),
131–147.
Freeman K (2020) Monitoring changes in domestic violence in the wake of
COVID-19 social isolation measures. Crime and Justice Statistics Bureau
Brief 145(4), 1–4.
Gass JD, Stein DJ, Williams DR and Seedat S (2021) Gender dierences in
risk for intimate partner violence among South African adults. Journal of
Interpersonal Violence 26(14), 2764–2789.
Goldstein LA, Jakubowski KP, Huang AJ, Seal KH, Maguen S, Inslicht SS,
Byers AL and Gibson CJ (2023) Lifetime history of interpersonal part-
ner violence is associated with insomnia among midlife women veterans.
Menopause 30(4), 370–375.
Graham-Harrison E (2020) Lockdowns around the worldbring rise in domes-
tic violence. e Guardian.https://www.theguardian.com/society/2020/
mar/28/lockdowns-world-risedomesticviolence (accessed 26 Januar y 2024).
Guterres A (2020) UN chief calls for domestic violence “ceasere” amid
“horrifying global surge”. https://news.un.org/en/story/2020/04/1061052
(accessed 26 January 2024).
Hall BJ, Li G, Chen W, Shelley D and Tang W (2023) Prevalence of depression,
anxiety, and suicidal ideation during the Shanghai 2022 Lockdown: A cross-
sectional study. Journal of Aective Disorders 330, 283–290.
Hamberger LK and Larsen SE (2015) Men’s and women’s experience of inti-
mate partner violence: A review of ten years ofcomparative studies in clinical
samples; Part I. Journal of Family Violence 30, 699–717.
Heath NM, Hall BJ, Canetti D and Hobfoll SE (2013) Exposure to political
violence, psychological distress, resource loss, and benet nding as predic-
tors of domestic violence among Palestinians. Psychological Trauma: eory,
Research, Practice, and Policy 5, 366–376.
Hou J, Yu L, Ting SMR, Sze YT and Fang X (2010) e status and char-
acteristics of couple violence in China. Journal of Family Violence 26,
81–92.
https://doi.org/10.1017/S2045796024000155 Published online by Cambridge University Press
Epidemiology and Psychiatric Sciences 13
Hu R, Xue J, Lin K, Sun IY and Wang XY (2021) Bidirectional intimate part-
ner violence among Chinese women: Patterns and risk factors. Journal of
Interpersonal Violence 36(21–22), NP12252–NP12278.
Kimmel MS (2002) “Gender symmetry” in domestic violence: A substan-
tive and methodological research review. Violence Against Women 8(11),
1332–1363.
Lee MJ (2020) Visits to New York City’s domestic violence website surged amid
coronavirus pandemic. CNN.https://edition.cnn.com/2020/04/07/us/nyc-
domestic-violence-website-surging/index.html (accessed 26 January 2024).
Li T, Barwick PJ, Deng YH, Huang XF and Li SJ (2023) e COVID-19 pan-
demic and unemployment: Evidence from mobile phone data from China.
Journal of Urban Economics 135, 103543.
Li XM and Wang J (2023) Hukou-based rural-urban migration status and
domestic violence against wives in China: Implications for policy and prac-
tice. Alia-Feminist Inquiry in Social Work 38(3), 367–381.
Lu JM (2015) Promoting career advancement for highly educated women: An
analysis based on data from three phases of the Shanghai Women’s Social
Status Survey. Chinese Women’s Movement 04, 26–29.
Oinas T (2018) e division of labour within households: Men’s increased
participation? In Tammelin M (ed), Family, Work and Well-Being. Cham:
Springer, 21–33.
Oram S, Fisher HL, Minnis H, Seedat S, Walby S, Hegarty K, Rouf K,
Angénieux C, Callard F, Chandra PS, Fazel S, Garcia-Moreno C,
Henderson M, Howarth E, MacMillan HL, Murray LK, Othman S,
Robotham D, Rondon MB, Sweeney A, Taggart D and Howard LM (2022)
e Lancet Psychiatry Commission on intimate partner violence and men-
tal health: Advancing mental health services, research, and policy. e Lancet
Psychiatry 9(6), 487–524.
Peitzmeier SM, Fedina L, Ashwell L, Herrenkohl TI and Tolman R
(2021) Increases in intimate partner violence during COVID-19:
Prevalence and correlates. Journal of Interpersonal Violence 37(21–22),
NP20482–NP20512.
Relyea MR, Portnoy GA, Combellick JL, Brandt CA and Haskell SG
(2020) Military sexual trauma and intimate partner violence: Subtypes,
associations, and gender dierences. Journal of Family Violence 35(4),
349–360.
Smyth C, Cullen P, Breckenridge J, Cortis N and Valentine K (2021)
COVID-19 lockdowns, intimate partner violence and coercive control.
Australian Journal of Social Issues 56(3), 359–373.
Straus MA (2011) Gender symmetry and mutuality in perpetration of clinical-
level partner violence: Empirical evidence and implications for prevention
and treatment. Aggression and Violent Behavior 16(4), 279–288.
Su CW, Dai K, Ullah S and Andlib Z (2022) COVID-19 pandemic and unem-
ployment dynamics in Europ ean economies. Economic Research-Ekonomska
Istrazivanja 35(1), 1752–1764.
Tu X and Lou C (2017) Risk factors associated with current intimate partner
violence at individual and relationship levels: A cross-sectional study among
married rural migrant women in Shanghai, China. BMJ Open 7(4), e012264.
U.S. Bureau of Labor Statistics (2020) Unemployment rate. https://fred.
stlouisfed.org/series/UNRATE (accessed 26 January 2024).
Usta J,Murr H and El-Jarrah R (2021) COVID-19 lockdown and the increased
violence against women: Understanding domestic violence during a pan-
demic. Violence and Gender 8(3), 133–139.
Wong DFK, He X, Leung G, Lau Y and Chang Y (2008) Mental health of
migrant workers in China: Prevalence and correlates. Social Psychiatry &
Psychiatric Epidemiology 43(6), 483–489.
World Health Organization (2010) Preventing intimate partner and sex-
ual violence against women: Taking action and generating evidence.
https://www.who.int/publications/i/item/9789241564007 (accessed
26 January 2024).
World Health Organization (2022) Preventing intimate partner violence
improves mental health. https://www.who.int/news/item/06-10-2022-
preventing-intimate-partner-violence-improves-mental-health (accessed 6
March 2024).
Yang T, Poon AWC and Breckenridge J (2019) Estimating the prevalence
of intimate partner violence in Mainland China – Insights and challenges.
Journal of Family Violence 34(2), 93–105.
Yasmin S (2016) e Ebola rape epidemic no one’s talking about. https://
foreignpolicy.com/2016/02/02/the-ebola-rape-epidemic-west-africa-
teenage-pregnancy/ (accessed 26 January 2024).
Zhang W (2020) Domestic violence cases surge during COVID-19 epidemic
sixth tone. http://www.sixthtone.com/news/1005253/domestic-violence-
cases-surge-during-covid-19-epidemic (accessed 26 January 2024).
Zhang W, Hong W, Cui Y and Su Y (2015) Gender dierences in intimate
partner violence. e Chinese Journal of Human Sexuality 23, 96–99.
Zhang H, Wong WCW, Ip P, Fan S and Yip PSF (2014a) Intimate partner
violence among Hong Kong young adults: Prevalence, risk factors, and asso-
ciated health problems. Journal of Interpersonal Violence 30(13), 2258–2277.
https://doi.org/10.1017/S2045796024000155 Published online by Cambridge University Press
