This study adheres to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines. The study protocol was followed PRISMA-Protocol (PRISMA-P; Multimedia Appendix 1). The protocol of this study was registered in PROSPERO (CRD42022351634)

The review question was framed based on the PICO (Population, Intervention, Comparison, and Outcomes) statement as follows:

As secondary outcomes, all factors and determinant associated with DV will be extracted. The determinants can be but are not limited to age, gender, educational status, decision-making power, social support, wealth index, and unemployment. In cases of missing data or articles with no available full text, we will contact the corresponding author 3 times. Articles will be excluded if no response is received.

Observational studies will be considered eligible if they have a population-based design, report the number of cases or prevalence of DV during the COVID-19 pandemic, and report potential determinants. Studies in languages other than English, those with unclear data, case reports, conference proceedings, reviews, and letters will be excluded.

We will conduct a comprehensive literature review using databases such as PubMed (including Medline), Embase, PsycINFO, Google Scholar, Cochrane COVID-19 register, and Applied Social Sciences Index and Abstracts, up to July 2024. In addition, we will manually search the reference lists of selected studies and other relevant reviews to maximize the identification of eligible studies and consider results from gray literature.

To identify relevant studies for our systematic review and meta-analysis, we will develop and execute a comprehensive search strategy across multiple electronic databases. Our search will incorporate Medical Subject Headings (MeSH), keywords, and specific search terms as outlined in Table 1. The search strategy will be tailored to each database to ensure optimal retrieval of relevant articles. The search strategy will be adapted to align with the database’s indexing and search capabilities. Specifically, in PubMed we will use MeSH terms in combination with keywords. The search string will incorporate MeSH terms and keywords to capture the breadth of literature related to DV during the COVID-19 pandemic (“Domestic Violence”[MeSH] OR “Spouse Abuse”[MeSH] OR “Elder Abuse”[MeSH] OR “Child Abuse, Sexual”[MeSH] OR “Intimate Partner Violence”[MeSH] OR “Physical Abuse”[MeSH]) AND (“child*” OR “Infant” OR “adolescen*” OR “young people” OR “adults” OR “Women” OR “Frail Elderly” OR “Aged”) AND (“Coronavirus” OR “COVID-19” OR “SARS-CoV-2” OR “2019 novel coronavirus”) AND (“prevalence” OR “incidence” OR “rate” OR “diagnosis” OR “disease burden” OR “epidemiology” OR “frequency” OR “determinants” OR “predictors” OR “correlates” OR “risk factors” OR “protective factors”). For other databases, keywords and subject headings relevant to each database’s search functionality will be used.

The titles, abstracts, and full texts of selected studies will be screened independently by 2 authors based on the eligibility criteria. The following data will be extracted from eligible studies: the first author’s name, publication year, study design, sample size, characteristics of population, determinants, and outcome measurements. The accuracy of data before the meta-analysis will be assessed by double-checking the data extraction process to ensure no bias in the data extraction and data entry. Any discrepancies will be resolved through discussion with the senior review author.

We will use the Joanna Briggs Institute (JBI) Critical Appraisal Checklist [30] for Prevalence Studies, which includes criteria for evaluating aspects such as study design, sample selection, measurement methods, and statistical analysis. Two independent reviewers will conduct the methodological quality assessment of each included study. Each reviewer will assess the studies separately to ensure a thorough evaluation. Any disagreements between the reviewers regarding the appraisal of study quality will be resolved through discussion. If consensus cannot be reached, a third reviewer, who is not involved in the initial assessment, will be consulted to adjudicate and provide a final decision. The results of the quality assessments will be documented and used to inform the interpretation of the review’s findings. Studies will be classified based on their methodological quality to help in the synthesis and analysis of data.

The STATA software package (version 14; STATA Inc) and JASP 0.19.1 (GNU Affero General Public License [GNU AGPL]) software will be used to conduct statistical analysis. Heterogeneity will be evaluated using the chi-square test and I² index. Publication bias will be assessed using funnel plots and statistical tests. In case of significant results, the robust Bayesian approach will be applied. The random effect model will be used for the estimation of the pooled prevalence via the meta-prop method with a pooled estimate after Freeman-Tukey double arcsine transformation to stabilize the variances. Forest plots for each outcome and by any extracted subgroups such as gender, regions, type of DV, educational level, etc, will be illustrated as well. Meta-regression analysis will be run to assess the effect of extracted determinants as the potential sources of heterogeneity. Sensitivity analysis will be applied to find influential studies in case.

The protocol of this study was registered in PROSPERO (CRD42022351634). This is a systematic review of published papers, which does not require informed consent. In addition, this study was considered by the ethics committee of the University of Social Welfare and Rehabilitation Sciences, Tehran, Iran (ethics code: IR.USWR.REC.1401.140).

The results of this systematic review and meta-analysis will estimate the prevalence of DV and its determinant in the COVID-19 pandemic and is poised to address critical gaps in the existing literature. It is anticipated that the study will reveal a significant increase in DV rates both globally and regionally, driven by pandemic-related conditions such as lockdowns, economic stress, and restricted access to support services. The analysis is expected to identify critical determinants contributing to this rise, including socioeconomic status, household size, and preexisting conditions of abuse. By illuminating these factors, the study seeks to inform effective interventions and policy responses to mitigate DV in the context of ongoing and future public health crises.

The occurrence of a natural disaster typically leads to an increased risk of social health problems [31]. Disease outbreaks such as COVID-19 pandemic necessitate emergency prevention and control measures in the community; however, they also result in psychosocial side effects [29,32-34].

Generally, there are several potential factors associated with DV, including individual traits such as age; ethnicity; previous history of exposure or experience of violence; psychological disorders such as depression, anxiety, substance abuse, and suicidality; as well as sociocultural factors that foster a culture of violence against women and social norms supporting male authority and control over women [24,35,36].

Evidence showed that COVID-19 preventive interventions such as physical distancing, lockdown, and social isolation were also associated with psychiatric problems, including anxiety, depression, suicidal thoughts, and DV [25,26,37-39]. A systematic review study that included 18 studies showed an increase in reported incidents of DV due to COVID-19–related restrictions, such as stay-at-home and lockdown orders [12]. Another integrative review that included 38 studies also indicated some factors increasing women’s vulnerabilities to violence, such as social distancing and lockdown [40]. It seems that COVID-19 preventive and controlling policies as a mediator factor can exacerbate potential related factors, leading to an escalation of violence and abusive behaviors. Therefore, results of studying the dynamics of this violent behavior, its associated factors, and COVID-19 measures can be extrapolated to global disaster situations [41].

In addition, Kifle et al [42], in a systematic review of 14 observational studies, estimated the prevalence of intimate partner violence on women during the COVID-19 pandemic. They reported that nearly 1 in 3 women experienced intimate partner violence during the COVID-19 pandemic. Subgroup analysis based on region showed that the highest prevalence of intimate partner violence was in resource-limited regions (33%). All forms of intimate partner violence (physical, sexual, emotional, and economic) were prevalent.

However, some limitation in those studies, such as narrow inclusion criteria, often focusing exclusively on women, thereby neglecting other vulnerable groups such as older individuals. Furthermore, important databases such as Embase were not included in some available studies, potentially limiting the scope and robustness of their findings. In addition, most of them did not report the determinants and risk factors for DV.

Crisis management organizations, health care providers, and policy makers should be aware of the potential increase in rate of DV during state of emergency. They should provide suitable health care facilities such as local referral pathways of services, support and counseling services, protection services, hotlines, and shelters. Humanitarian and nongovernment organizations could also step up to provide necessary services to support DV survivors during crises.

The strength of our study lies, first, in the breadth of evidence it produces. Bayesian meta-analysis will be applied to address potential publication bias, a critical mythological concern in prevalence meta-analysis [43,44]. Second, we will identify significant determinants through a meta-regression approach. Additionally, we will ensure high methodological quality by adhering to the AMSTAR (Assessing the Methodological Quality of Systematic Reviews) guideline. However, our study has limitations. DV is widely underreported worldwide, and obstacles to data collection during COVID-19 intensify this issue [45,46]. One limitation regarding the control of violence could be the lack of accurate data and evidence. Therefore, response organizations and authorities need to include support services to facilitate the safe collection of data on reported cases of DV in their crisis plans. Studies included in the meta-analysis may use different definitions and methodologies to assess DV, which could contribute to heterogeneity and affect the reliability of pooled prevalence estimates. DV prevalence and its determinants may vary significantly across different global regions and cultural contexts. Addressing this heterogeneity adequately in the meta-analysis could be challenging but crucial for accurate regional estimates. Access to the full text of the articles may be restricted, which will be resolved by corresponding with authors. In addition, strong heterogeneity among studies may exist, which will be addressed using random effect models and appropriate subgroup analyses.

This systematic review and meta-analysis will address significant gaps in our understanding of the pandemic’s impact on DV. Although there are some limitations, prior research has highlighted the increase in DV incidents linked to COVID-19 restrictions. This study will provide a comprehensive assessment and analysis, encompassing a broader range of vulnerable groups and incorporating diverse data sources. By doing so, it seeks to offer a more detailed and accurate picture of the prevalence and factors contributing to DV during the pandemic. The findings will be crucial in informing targeted interventions, shaping policy responses, and ultimately mitigating the impact of future global crises on DV rates.