Abstract
The COVID-19 pandemic has had profound societal and economic effects. Concerns were raised that domestic violence might increase because of the enacted infection control measures. Previous findings on this issue have been contradictory. Since existing studies mainly rely on official reports, administrative data, helpline calls, or retrospective measures, their findings are likely to prove unreliable. Few population-based surveys include pre-pandemic data, limiting their ability to test for causality regarding increasing violence. Therefore, the aim of this study was to compare findings from population-representative surveys on the prevalence of intimate partner violence (IPV) and violence against children (VAC) before and during the COVID-19 pandemic. Based on the data of N = 3,639 individuals living with a romantic partner and N = 1,313 parents living with at least one of their children from three German representative population surveys, we estimated average marginal effects for the temporal trends (i.e., pre vs. post infection control measures) of domestic violence separately for males and females. To minimize bias across survey waves, inverse probability weighting was used. Results show no statistically significant increase in either physical or psychological forms of IPV or VAC as a result of the implementation of COVID measures. On the contrary, the 1-year prevalence was decreasing for certain forms of violence. Our findings suggest that the assessment of the consequences of infection control measures needs an empirical basis. Further research should be conducted using high-quality data sources. Therefore, the present study should be considered a stepping stone for ongoing research efforts to examine the consequences of pandemic-related infection control measures on the general population.
Keywords: assessment, child abuse, domestic violence, mental health and violence
Introduction
Since the outbreak of the COVID-19 pandemic, there has been a heightened concern about an increase of domestic violence, including intimate partner violence (IPV) and violence against children (VAC) (Campbell, 2020; Eisner & Nivette, 2020; UN Women, 2020) as a result of the various infection control measures that have been implemented worldwide since March 2020 (e.g., contact and travel restrictions, working from home, school closures). Although mitigating the virus’ spread and preventing a health system overload (Nussbaumer-Streit et al., 2020), these regulations have enhanced potential violence-related risk factors such as social isolation, reduced accessibility to help and support services (Boxall et al., 2020), increased parenting responsibilities, and loss of income leading to financial worries (Ebert & Steinert, 2021). In terms of the serious physical, psychological, and economic effects of IPV (Campbell, 2002) and VAC (Norman et al., 2012), research addressing this topic is needed.
Previous Studies Show Mixed Results
There are multiple studies investigating this matter that rely on various data sources and provide inconsistent evidence. Based on a recent meta-analysis including 18 studies with a total of 37 estimates (pre- and post-percentage differences), domestic violence (including IPV) has increased by an average of 7.86%, with the majority of the included studies showing a significant increase after lockdown orders came into effect (Piquero et al., 2021). Helpline calls about domestic violence have risen by 12.0 to 75.0% in the early months of the pandemic worldwide (Kourti et al., 2023). Conversely, VAC seems to have decreased remarkably (Cappa & Jijon, 2021; Whelan et al., 2021), which might also be explained by the fact that cases are noticed less by outsiders due to temporary closures of schools and leisure facilities. Beyond that, there is evidence suggesting an increased severity of IPV (Gosangi et al., 2021) and VAC (Cappa & Jijon, 2021; Kovler et al., 2021) during the pandemic. It should be noted, however, that all of these studies are based on official reports and administrative data (i.e., crime statistics, hospital records, calls to support hotlines). Since only a small proportion of domestic violence is actually reported (Allroggen et al., 2016; Anderberg et al., 2021; Boxall et al., 2020), this data suffers from serious reliability issues.
Representative population-based surveys, on the other hand, have the advantage of being independent of the initiative to report or an increase in social control by others (e.g., neighbors). There are a few of such surveys using retrospective assessments to estimate the development of domestic violence rates during the pandemic, presenting mixed results. According to an Australian study, 65.4% of women, who experienced physical or sexual violence from a current or former partner, reported that the violence had just begun or increased in severity or frequency since the beginning of the pandemic (Boxall et al., 2020). In contrast, a study based on the data of N = 15,336 participants from 30 different countries suggest a significant decrease of physical and sexual IPV from 9.2% before to 7.0% during the pandemic (Campbell et al., 2021). However, in this study, convenience samples (k = 23) or online samples (k = 6) were predominantly pooled, which is linked to several limitations (e.g., selection and response bias; Bethlehem, 2010). Only two of the included samples were based on representative surveys. Furthermore, in all of the pooled surveys, the prevalence of IPV before the lockdown measures was estimated retrospectively, which is also associated with various limitations (e.g., hindsight, recall, and reporting bias; Coughlin, 1990).
Few Surveys Include Pre-pandemic Data
To date (April 24, 2022), only one representative population-based study providing data on the prevalence of domestic violence before and during the COVID-19 pandemic from a European country could be identified. This study examined the rates of physical IPV as well as physical and psychological VAC, comparing data from two representative survey studies conducted in 2016 and 2021 in Germany (Kliem et al., 2021). According to the authors, no significant changes were found in the 12-month prevalence of either IPV or VAC. Moreover, one study that surveyed a representative sample of Iranian women before and during the pandemic found a relative increase of more than 20.0% for physical, psychological, or sexual IPV as well as an incidence of first-time experience of violence during the pandemic of 25.5% (Fereidooni et al., 2021).
These results highlight the need for further reliable data to assess the impact of the infection control measures on the prevalence of domestic violence. Therefore, the aim of the present study was to compare rates of IPV and VAC before and during the COVID-19 pandemic using data from three representative surveys, conducted among the German general population.
Method
The present study draws on data from three representative population surveys conducted among the general population in the Federal Republic of Germany. The first survey was conducted in the period of January to March 2016 (Survey A: “Pre-COVID-19 Survey”), the second survey (2021a) in the period of February to March 2021 and the third (2021b) in the period of June 2021 to October 2021 (Surveys B and C: “during general infection control measures”). All surveys used the same questionnaires to record the rates of domestic violence (physical violence in a romantic partnership, sexual violence in a romantic partnership, psychological VAC, physical VAC) within the last year (12-month prevalence). Figure 1 illustrates the procedure and presents the observation periods to which the surveys refer, as well as a rough overview of the lockdown phases with the corresponding infection control measures.
Figure 1.
Overview of the study design.
Survey Design
For the surveys during 2016 as well as 2021b, sampling was carried out following a three-step procedure. First, sample points were drawn from nonoverlapping regions representative of the Federal Republic of Germany. Within these areas, the target household was randomly identified via a given starting address. In a third and final step, the target person within a target household was identified via a kish selection grid (“true random sampling”). The 2021a survey is based on two complementing sampling strategies: (1) a multilevel, multistage stratified random sample and (2) a multistage quota sample (nonrandom sampling). Accordingly, it is not possible to obtain a response rate for the 2021a survey. Interviews were conducted in the participant’s home. For all interviews, the interviewer assessed eligibility on location (i.e., age ≥16 years and sufficient ability to understand the written German language). After interviewers provided detailed background information on the study as well as on data privacy protection, informed consent was obtained from each participant. In case a target person was not at home, a maximum of four additional attempts was made to contact the potential participant. The first part of the survey consisted of an interviewer-led structured interview assessing basic sociodemographic variables. After that, the interviewer provided the participant with a questionnaire to be filled out by the participant in the presence of but without interference from the interviewer. The study design was approved by the ethics committee of the medical faculty of Leipzig University (Survey A: 452-15-21122015; Survey C: 298/21-ek) and the ethics committee of Hildesheim University (Survey B: #172). Survey A comprised N = 2,524 interviews (response rate 51.9%, see Supplemental eFigure 1 for details). Survey B collected data from N = 2,029 participants (response rate cannot be computed due to quota sampling), and Survey C collected data from N = 2,515 participants (response rate 42.4%, see Supplemental eFigure 2 for details). Of all participants (A) N = 1,317 (52.2%)/(B) N = 1,005 (49.5%)/(C) N = 1,317 (52.4%) reported living in the same household with their partner and (A) N = 508 (20.1%)/(B) N = 378 (18.6%) (C) N = 427 (17.0%) of participants reported having at least one child living in the same household. Survey B (2021a) and C (2021b) will be combined for all subsequent analyses. Tables 1 and 2 present sample descriptives for the subsample of participants living with a partner and the subsample of participants living with at least one child.
Table 1.
Sociodemographic Characteristics of the Subsample Participants Living With Partner.
| Survey 2016 (N = 1,317) | Survey 2021a (N = 1,005) | Survey 2021b (N = 1,317) | |
|---|---|---|---|
| Gender | |||
| Male | 635 (48.2) | 482 (48.0) | 675 (51.3) |
| Female | 682 (51.8) | 523 (52.0) | 642 (48.7) |
| Age in years | |||
| Respondents | 50.09 (15.00); 17–92 | 52.17 (15.68); 18–91 | 50.40 (15.62); 17–101 |
| Partners | 50.09 (14.84); 18–90 | 52.40 (15.79); 16–99 | 50.28 (15.61); 16–99 |
| Age group: Respondents | |||
| 14–29 | 127 (9.6) | 83 (8.3) | 136 (10.3) |
| 30–59 | 822 (62.4) | 585 (58.2) | 787 (59.8) |
| 60–99 | 368 (27.9) | 337 (33.5) | 394 (29.9) |
| Marital status | |||
| Married | 1,049 (79.7) | 820 (81.6) | 1,020 (77.4) |
| Unmarried | 268 (20.3) | 185 (18.4) | 297 (22.6) |
| Relationship duration in years | |||
| 21.75 (15.02); 0–70 | 23.49 (15.83); 1–68 | 21.47 (15.16); 0–65 | |
| Relationship duration in years group | |||
| 0–9 | 338 (25.7) | 220 (21.9) | 361 (27.4) |
| 10–29 | 577 (43.8) | 421 (41.9) | 563 (42.7) |
| 30–49 | 322 (24.4) | 285 (28.4) | 316 (24.0) |
| 50 or more | 80 (6.1) | 79 (7.9) | 77 (5.8) |
| Number of children in household | |||
| None | 891 (67.7) | 700 (69.7) | 986 (74.9) |
| 1 | 208 (15.8) | 145 (14.4) | 176 (13.4) |
| 2 | 183 (13.9) | 131 (13.0) | 131 (9.9) |
| 3 or more | 35 (2.7) | 29 (2.9) | 24 (1.8) |
| Educational background | |||
| No/basic school leaving certificate | 409 (31.1) | 247 (24.6) | 331 (25.1) |
| Intermediate school leaving certificate | 623 (47.3) | 385 (38.3) | 662 (50.3) |
| Advanced school leaving certificate (university entrance level or university degree) | 285 (21.6) | 373 (37.1) | 324 (24.6) |
| Job status | |||
| Studying | 23 (1.7) | 17 (1.7) | 34 (2.6) |
| (Currently) Unemployed | 131 (9.9) | 76 (7.6) | 82 (6.2) |
| Employed (marginally/part-time) | 251 (19.1) | 186 (18.5) | 244 (18.5) |
| Employed (full-time) | 607 (46.1) | 432 (43.0) | 651 (49.4) |
| Retired | 290 (22.0) | 262 (26.1) | 293 (22.2) |
| Other | 15 (1.2) | 32 (3.1) | 13 (1.1) |
| Household Income in € per month | |||
| <500 | 5 (0.4) | 5 (0.5) | 2 (0.2) |
| 500–1,000 | 18 (1.4) | 4 (0.4) | 10 (0.8) |
| 1,000–1,500 | 96 (7.3) | 32 (3.2) | 58 (4.4) |
| 1,500–2,000 | 188 (14.3) | 62 (6.2) | 110 (8.4) |
| 2,000–2,500 | 262 (19.9) | 115 (11.4) | 240 (18.2) |
| 2,500–3,500 | 423 (32.1) | 314 (31.2) | 415 (31.5) |
| 3,500–5,000 | 255 (19.4) | 292 (29.1) | 352 (26.7) |
| >5,000 | 70 (5.3) | 181 (18.0) | 130 (9.9) |
| Nationality | |||
| German citizen | 1,255 (95.3) | 895 (89.1) | 1,273 (96.7) |
| Foreign citizen | 62 (4.7) | 110 (10.9) | 44 (3.3) |
Note. Data are presented as the frequency n (%) or mean (SD); range.
Table 2.
Sociodemographic Characteristics of the Subsample Participants Living With Child.
| Survey 2016 (N = 508) | Survey 2021a (N = 378) | Survey 2021b (N = 427) | |
|---|---|---|---|
| Gender: Caregiver | |||
| Male | 187 (36.8) | 146 (38.6) | 162 (37.9) |
| Female | 321 (63.2) | 232 (61.4) | 265 (62.1) |
| Gender: Child | |||
| Male | 235 (46.3) | 190 (50.3) | 204 (47.8) |
| Female | 273 (53.7) | 188 (49.7) | 223 (52.2) |
| Age in years | |||
| Caregiver | 39.67 (8.78); 21–89 | 39.47 (10.11); 18–79 | 39.40 (7.92); 19–61 |
| Child | 8.30 (4.82); 0–17 | 7.80 (5.42); 0–17 | 8.41 (5.12); 0–17 |
| Age group: Caregivers | |||
| 14–29 | 53 (10.4) | 60 (15.9) | 51 (11.9) |
| 30–59 | 442 (87.0) | 309 (81.7) | 375 (87.8) |
| 60–99 | 13 (2.6) | 9 (2.4) | 1 (0.2) |
| Marital status | |||
| Married | 350 (68.9) | 262 (69.3) | 309 (72.4) |
| Unmarried | 158 (31.1) | 116 (30.7) | 118 (27.6) |
| Single parent | |||
| Yes | 110 (21.7) | 73 (19.3) | 77 (18.0) |
| No | 398 (78.3) | 305 (80.7) | 350 (82.0) |
| Number of children in household | |||
| None | 1 (0.2) | 0 (0.0) | 55 (12.9) |
| 1 | 255 (50.2) | 196 (51.9) | 204 (47.8) |
| 2 | 206 (40.6) | 149 (39.4) | 143 (33.5) |
| 3 or more | 46 (9.1) | 33 (8.8) | 25 (5.8) |
| Educational background | |||
| No/basic school leaving certificate | 109 (21.5) | 59 (15.6) | 74 (17.3) |
| Intermediate school leaving certificate | 272 (53.5) | 159 (42.1) | 234 (54.8) |
| Advanced school leaving certificate (university entrance level or university degree) | 127 (25.0) | 160 (42.3) | 119 (27.9) |
| Job status | |||
| Studying | 5 (1.0) | 16 (4.2) | 4 (0.9) |
| (Currently) Unemployed | 87 (17.1) | 41 (10.8) | 43 (10.1) |
| Employed (marginally/part-time) | 146 (28.7) | 105 (27.8) | 150 (35.1) |
| Employed (full-time) | 249 (49.0) | 183 (48.4) | 215 (50.4) |
| Retired | 7 (1.4) | 8 (2.1) | 3 (0.7) |
| Other | 14 (2.8) | 25 (6.7) | 12 (2.8) |
| Household income in € per month | |||
| <500 | 6 (1.2) | 4 (1.1) | 2 (0.5) |
| 500–1,000 | 19 (3.7) | 8 (2.1) | 4 (0.9) |
| 1,000–1,500 | 56 (11.0) | 13 (3.4) | 22 (5.2) |
| 1,500–2,000 | 56 (11.0) | 22 (5.8) | 32 (7.5) |
| 2,000–2,500 | 90 (17.7) | 35 (9.3) | 55 (12.9) |
| 2,500–3,500 | 167 (32.9) | 115 (30.4) | 155 (36.3) |
| 3,500–5,000 | 87 (17.1) | 110 (29.1) | 124 (29.0) |
| >5,000 | 27 (5.3) | 71 (18.8) | 33 (7.7) |
| Nationality | |||
| German citizen | 475 (93.5) | 321 (84.9) | 396 (92.7) |
| Foreign citizen | 33 (6.5) | 57 (15.1) | 31 (7.3) |
Note. Data are presented as the frequency n (%) or mean (SD); range.
Assessment of Domestic Violence
The different facets of domestic violence were assessed with three modules from the Family Maltreatment Measure (FM; Heyman et al., 2020). The FM assesses victimization experiences of clinically relevant sexual (only within an existing relationship), physical, and psychological violence against a partner or the youngest child. Items are based on relevant Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association, 2013) and International Statistical Classification of Diseases and Related Health Problems (ICD-11; Heyman et al., 2018) criteria. The FM was developed by the U.S. Air Force during several assessment waves in 2006, 2008, 2011, and 2014 based on data from >150,000 participants (Foran et al., 2011).
Physical violence in the context of an intimate partnership
Different aspects of physical violence (pushed or shoved; grabbed; hit with an open hand; item thrown at with the intention to hurt; bitten) were assessed with 6 items. The items were rated on a seven-point scale (ranging from 0 = “never” to 6 = “more than 20 times in the past 12 months”) and subsequently dichotomized (0 = none of the described behavior has occurred; 1 = at least one of the described behaviors has occurred at least once).
Sexualized violence in the context of an intimate partnership
Two items assessed sexualized violence from the victim’s perspective (being violently forced to engage in sexual activities; engaged in sexual activities out of fear of the partner’s reaction). As for physical violence, the items were subsequently dichotomized (0 = none of the described behavior has occurred; 1 = at least one of the described behaviors has occurred at least once).
Psychological VAC
Four items assessed different aspects of physical VAC (i.e., hurtful name-calling and verbal abuse [slut, worthless]; destroying or damaging objects which are of special importance to the child [pet, favorite doll]; threatening to leave the child or physically hurt him or her; degrading the child in front of others, ridiculing or spitting at him or her). In order to minimize effects of social desirability, the questionnaire offered various response options (“I have done this to teach a lesson”; “I have done this because I lost composure”; “I have done this to discipline my child”; “I have never done this”). Items were dichotomized (0 = none of the described behavior has occurred; 1 = at least one of the described behaviors has occurred at least once).
Physical VAC
Four items assessed physical VAC (slapped on the bottom, slapped on the hand, grabbed the child, hit the child with an object). As for psychological violence, the questionnaire offered various response options to minimize socially desired answers. Items were dichotomized (0 = none of the described behavior has occurred; 1 = at least one of the described behaviors has occurred at least once).
Statistical Analyses
Even though the surveys can be regarded as representative, it cannot be ruled out that minor deviations in relevant characteristics might bias the comparison between groups. Therefore, the present study employs an inverse probability of treatment weighting (IPTW) approach. The basic idea of the IPTW approach is to weigh individual observations from the different surveys (2016 vs. 2021 [B and C]) according to their propensity scores (PS). PS’s were estimated from the data taking into account potentially confounding variables (i.e., factors that were unevenly distributed across survey times). A binary logistic regression model was estimated with membership of one of the two assessment points as the dependent variable. PS’s for each participant were computed from the parameter estimates of this model. In Supplemental eTable 1 (living with a partner) and Supplemental eTable 2 (living with at least one child), the relevant sociodemographic characteristics are tabulated and compared regarding statistically significant differences (via binary logistic regression) across surveys (2016 vs. 2021 [B and C]). Differences regarding these factors across survey points were accounted for in subsequent analyses via a weight (IPTW; Kuss et al., 2016). In the next step, random effect binominal logistic models were estimated for males and females including time points (2016 vs. 2021 [B and C]) as covariates. The multilevel structure of the data consisted of individual observations (Level 1) being nested in (federal) states (i.e., Bundesländer, Level 2). We estimated average marginal effects (AMEs; Norton et al., 2019) to facilitate interpretation. The AMEs (multiplied by 100) indicate the average change in the prevalence of the respective outcome in percentage points (PP) for the temporal trends separately for male and female gender. Since the underlying models are nonlinear models (bivariate outcome), this effect varies from person to person. When calculating the AME, the marginal effect is determined for each individual and then the average is calculated.
Occasionally, questionnaires were not (completely) filled out. The rate of such missing values on the item level was below 2% across all questionnaires. Missing data was estimated using chained equation modeling and imputed via predictive mean matching (White et al., 2011).
Results
Physical and Sexualized Violence in Intimate Partner Relationships
Table 3 presents the results of the (weighted) multilevel analysis regarding the prevalence development of physical violence in intimate partner relationships. In 2016, the estimated odds ratios correspond to a 12-month prevalence of victimization of 10.1% for male participants and of 8.1% for female participants. In both groups, the 12-month prevalence decreased in the 2021 survey. For male participants, AMEs indicate a decrease of 3.1 PP (p < .001) down to a prevalence of 7.0% and for female participants a decrease of 1.7 PP (p = .049) down to a prevalence of 6.4%. The 12-month prevalence rate of sexualized violence for 2016 was estimated at 0.6% for male participants and 2.4% for female participants. Based on the estimated AMEs (males: −0.5 PP, p = .152; females: +0.8 PP, p = .181), the prevalence rates for 2021 correspond to a prevalence rate of 0.1% for male participants and of 3.2% for female participants. Neither of these changes were found to be statistically significant on the 0.05 level.
Table 3.
Results of the Multilevel Analysis Modeling Temporal Development of Domestic Violence (12-Month Prevalence Rate).
| Intimate Partner Violence |
Violence Against Youngest Child |
|||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Physical | Sexual | Physical | Psychological | |||||||||||||
| Fixed effects | ||||||||||||||||
| OR [95% CI] |
p | AME (±PP) [95% CI] |
p | OR [95% CI] |
p | AME (±PP) [95% CI] |
p | OR [95% CI] |
p | AME (±PP) [95% CI] |
p | OR [95% CI] |
p | AME (±PP) [95% CI] |
p | |
| Male participants 2016 | 0.112 [0.080, 0.158] |
<.001 | – | 0.006 [0.002, 0.017] |
<.001 | – | 0.323 [0.223, 0.467] |
<.001 | – | 0.084 [0.049, 0.145] |
<.001 | – | ||||
| Male participants 2021 | 0.626 [0.487, 0.806] |
<.001 | −0.031 [−0.049, 0.013] |
<.001 | 0.484 [0.222, 1.053] |
.067 | −0.005 [−0.012, 0.002] |
.152 | 0.507 [0.363, 0.708] |
<.001 | −0.096 [−0.144, −0.049] |
<.001 | 0.757 [0.473, 1.214] |
.248 | −0.019 [−0.052, 0.013] |
.247 |
| Random effects | ||||||||||||||||
| τ00 | 0.34 | 1.15 | 0.32 | 0.61 | ||||||||||||
| ICC | 0.09 | 0.26 | 0.09 | 0.16 | ||||||||||||
| Observations | 1792 | 1792 | 495 | 495 | ||||||||||||
| Fixed effects | ||||||||||||||||
| OR [95% CI] |
p | AME (±PP) [95% CI] |
p | OR [95% CI] |
p | AME (±PP) [95% CI] |
p | OR [95% CI] |
p | AME (±PP) [95% CI] |
p | OR [95% CI] |
p | AME (±PP) [95% CI] |
p | |
| Female participants 2016 | 0.088 [0.064, −0.120] |
<.001 | – | 0.025 [0.016, 0.039] |
<.001 | – | 0.252 [0.185, 0.344] |
<.001 | – | 0.069 [0.044, 0.107] |
<.001 | – | ||||
| Female participants 2021 | 0.783 [0.614, 0.999] |
.049 | −0.017 [−0.034, −0.000] |
.049 | 1.338 [0.896, 1.998] |
.155 | 0.008 [−0.004, 0.019] |
.181 | 0.624 [0.479, 0.813] |
<.001 | −0.065 [−0.101, 0.028] |
<.001 | 0.822 [0.561, 1.205] |
.315 | −0.012 [−0.037, 0.012] |
.315 |
| Random effects | ||||||||||||||||
| τ00 | 0.24 | 0.42 | 0.21 | 0.33 | ||||||||||||
| ICC | 0.07 | 0.11 | 0.06 | 0.09 | ||||||||||||
| Observations | 1847 | 1847 | 818 | 818 | ||||||||||||
Note. %, 12-month prevalence rates of male and female participants in 2016 /2021. OR = odds ratio; AME = average marginal effects; PP, percentage points; ±PP = change in PP compared to reference year; 95% CI = 95% confidence interval; τ00 = between state variance; ICC = intra class correlation.
Domestic Violence Against Children
Table 3 presents the results of the (weighted) multilevel analysis regarding the prevalence rates of physical and psychological violence against the reference child. The prevalence rates of physical VAC for 2016 were estimated at 24.4% for male caregivers and at 20.1% for female caregivers. For both genders, the change in prevalence rate based on estimated AMEs was found to be statistically significant (male caregivers: decrease of 9.6 PP to 14.8%, p < .001; female caregivers: decrease of 6.5 PP to 13.6%, p < .001).
For psychological violence, the 12-month prevalence rates were estimated to be 7.8% for male caregivers and 6.5% for female caregivers in 2016. Changes in the prevalence rates based on estimated AMEs were not found to be statistically significant for either gender (female caregiver: decrease of 1.2 PP to 6.6%, p = .315; male caregiver: decrease of 1.9 PP to 5.9%, p = .247).
Discussion
The present study examined potential effects of pandemic-related infection control measures on the prevalence of different forms of domestic violence. It compared pre-pandemic survey data from 2016 to survey data from 2021 obtained during the COVID-19 pandemic. Results show no statistically significant increase neither for physical nor for sexual forms of violence in intimate partner relationships or VAC across the different assessment points. On the contrary, the 12-month prevalence was even going down in some instances. The present study hence shows that the measures implemented in Germany did not have such immediate negative consequences as suggested in media and political discussions. Parallel findings from other countries (some of which implemented far more restrictive measures) are still pending. Our findings suggest that evaluating the consequences of pandemic-related infection control measures needs an empirical basis and that further research based on high quality data sources should be conducted. The present study should hence be viewed as a stepping stone in the ongoing research endeavor of investigating the consequences of pandemic-related infection control measures on the general population. Given the detrimental health consequences that have been frequently documented in the literature (Garcia-Moreno et al., 2006; Gilbert et al., 2009) focusing further research resources on domestic violence seems warranted.
Limitations, Strengths, and Future Studies
This study suffers from several limitations. It has to be noted that due to the study design at hand (a survey among the general population), it cannot be ruled out that within specific risk groups (e.g., socially disadvantaged families, single parents, families with special needs children) domestic violence rates substantially increased as a consequence of pandemic-related measures. Furthermore, it could not be assessed whether families already affected by domestic violence might have been subjected to increasing levels of violence. Moreover, the survey only assessed a limited number of violent behaviors. Certain forms of violence such as coercion, threatening to destroy objects or hurting pets could not be assessed due to the limited scope of the surveys at hand. Prevalence estimates of IPV and their change across time, however, are highly dependent on operationalization. Further studies that take into account finer nuances of the concept with regards to both content facets as well as intensity are thus warranted. In this context, it should also be mentioned that the increase in the rates of sexual violence against women, although statistically not significant, should not be neglected. It seems conceivable that this increase also did not become statistically significant due to the lack of statistical power. Future analyses might examine this increase using larger sample sizes.
There are also some limitations regarding the generalizability of our findings. We surveyed representative samples of the German general population, so the results might not apply to all persons of other race, ethnicity, nationality, culture, or religion since sociocultural factors might influence the definition, perception, and experience of interpersonal violence (Bent-Goodley, 2021; Tajima, 2021). However, the questionnaire we used asked about explicit acts of IPV (e.g., “hit with an open hand” or “being violently forced to engage in sexual activities”) or VAC (e.g., “hurtful name-calling and verbal abuse” or “slapped on the bottom”) rather than more abstract constructs (e.g., “emotional abuse” or “violent behavior”), providing little room for interpretation regardless of culturally different definitions of violence. These limitations especially apply to studies based on official reports or administrative data, as the initiative to report is highly dependent on the culturally shaped definition and acceptance of violence. In addition, due to our sample size, minority groups are most likely underrepresented. For example, our results cannot be generalized to persons of all sexual orientations or gender identities. This is particularly relevant because, according to various studies, individuals with gay, lesbian, or bisexual orientation are more likely to experience domestic violence (e.g., Ayhan Balik & Bilgin, 2021; Stephenson & Finneran, 2017). Beyond that, the inclusion criterion of a sufficient ability to understand the written German language might have led to an underrepresentation of people with a migration background, who were excluded from the study if their language skills were insufficient. Furthermore, we surveyed residents of Germany, a high-income country. Our results are therefore not generalizable to middle- or low-income countries where violence-related risk factors such as job loss and resulting financial worries, which were enhanced as a result of the pandemic, might have had an even greater impact on the population (Fereidooni et al., 2021). Since our study is part of a larger survey, variables that would have allowed consideration of sociocultural influences (e.g., sexual orientation, gender identity, culture, religion, or ethnicity) were not explicitly recorded. In future studies, corresponding factors should be taken into account.
Strengths of the study include the fact that our sample covers a wide age range. Furthermore, we included both women and men as perpetrators and victims of IPV, as well as female and male caregivers as perpetrators of VAC. Often, studies on IPV focus on female victims and male perpetrators although various studies show no gender differences regarding perpetration or victimization of IPV (e.g., Archer, 2002; Stoltenborgh et al., 2013).
Conclusions
To our knowledge, the present study is the largest conducted to date combining representative population-based data on IPV and VAC before and during the COVID-19 pandemic from a European country. Results show no statistically significant increase in either physical or psychological forms of IPV or VAC, suggesting that the implementation of infection control measures has not had the feared impact on IPV and VAC. On the contrary, the 1-year prevalence was decreasing for certain forms of violence. This finding contrasts several other studies based on official reports, administrative data, or retrospective measures and underlines the need for further research based on reliable data sources.
Supplemental Material
Supplemental material, sj-docx-1-jiv-10.1177_08862605221143194 for The Effect of the Covid-19 Pandemic on Domestic Violence in Germany: A Comparison of Three Representative Population Surveys by Sören Kliem, Alexandra von Thadden, Anna Lohmann, Christoph Kröger and Dirk Baier in Journal of Interpersonal Violence
Supplemental material, sj-docx-2-jiv-10.1177_08862605221143194 for The Effect of the Covid-19 Pandemic on Domestic Violence in Germany: A Comparison of Three Representative Population Surveys by Sören Kliem, Alexandra von Thadden, Anna Lohmann, Christoph Kröger and Dirk Baier in Journal of Interpersonal Violence
Supplemental material, sj-docx-3-jiv-10.1177_08862605221143194 for The Effect of the Covid-19 Pandemic on Domestic Violence in Germany: A Comparison of Three Representative Population Surveys by Sören Kliem, Alexandra von Thadden, Anna Lohmann, Christoph Kröger and Dirk Baier in Journal of Interpersonal Violence
Supplemental material, sj-docx-4-jiv-10.1177_08862605221143194 for The Effect of the Covid-19 Pandemic on Domestic Violence in Germany: A Comparison of Three Representative Population Surveys by Sören Kliem, Alexandra von Thadden, Anna Lohmann, Christoph Kröger and Dirk Baier in Journal of Interpersonal Violence
Author Biographies
Sören Kliem, PhD, is a professor with the social welfare department at the EAH Jena University of Applied Sciences. His work focuses on psychological assessment with a special interest in epidemiological surveys as well as evaluation research. He specializes in psychopathology, affective disorders, parenting as well as (intimate partner and parental) violence.
Alexandra von Thadden, MSc, is a PhD candidate at the Department of Clinical Psychology and Psychotherapy at the University of Hildesheim. Her research focuses on domestic violence, including intimate partner violence and violence against children.
Anna Lohmann, MSc, is a researcher with the Social Welfare Department at the EAH Jena University of Applied Sciences. With a background in Statistics and Psychology, her work focuses on quantitative methodology with a special interest in psychometrics and computational reproducibility.
Christoph Kröger, PhD, is a full professor for Clinical Psychology and Psychotherapy at University Hildesheim. He is a state-licensed psychotherapist and clinical supervisor with focus on cognitive behavior therapy. His research interests are psychological assessment and psychosocial treatments for mental illnesses, in particular posttraumatic stress disorders and borderline personality disorder, as well as counselling for couples and parents.
Dirk Baier, PhD, is a professor in the School of Social Work at Zurich University of Applied Sciences, Switzerland. His research focuses on violent crime, youth crime, and extremism. Using comprehensive survey studies, he examines the prevalence and influencing factors of various deviant behaviors, with a particular focus in recent years on intimate partner violence.
Footnotes
The author(s) declared no potential conflicts of interests with respect to the authorship and/or publication of this article.
Funding: The author(s) received no financial support for the research and/or authorship of this article.
ORCID iDs: Sören Kliem 
https://orcid.org/0000-0003-1788-9574
Alexandra von Thadden https://orcid.org/0000-0001-6219-8495
Supplemental Material: Supplemental material for this article is available online.
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Supplementary Materials
Supplemental material, sj-docx-1-jiv-10.1177_08862605221143194 for The Effect of the Covid-19 Pandemic on Domestic Violence in Germany: A Comparison of Three Representative Population Surveys by Sören Kliem, Alexandra von Thadden, Anna Lohmann, Christoph Kröger and Dirk Baier in Journal of Interpersonal Violence
Supplemental material, sj-docx-2-jiv-10.1177_08862605221143194 for The Effect of the Covid-19 Pandemic on Domestic Violence in Germany: A Comparison of Three Representative Population Surveys by Sören Kliem, Alexandra von Thadden, Anna Lohmann, Christoph Kröger and Dirk Baier in Journal of Interpersonal Violence
Supplemental material, sj-docx-3-jiv-10.1177_08862605221143194 for The Effect of the Covid-19 Pandemic on Domestic Violence in Germany: A Comparison of Three Representative Population Surveys by Sören Kliem, Alexandra von Thadden, Anna Lohmann, Christoph Kröger and Dirk Baier in Journal of Interpersonal Violence
Supplemental material, sj-docx-4-jiv-10.1177_08862605221143194 for The Effect of the Covid-19 Pandemic on Domestic Violence in Germany: A Comparison of Three Representative Population Surveys by Sören Kliem, Alexandra von Thadden, Anna Lohmann, Christoph Kröger and Dirk Baier in Journal of Interpersonal Violence