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Abstract

Exposure to violence is a well-established risk factor for physical and psychological health problems, including increased sickness and disability. However, most research has focused on women, and little research has examined whether the impact of violence exposure differs by gender. This study examined whether gender moderated the association between violence exposure, timing of exposure (i.e., childhood only, adulthood only, adulthood, and childhood [revictimization]), and doctor-certified sickness absence. The sample included 2,473 Norwegian adults (M_age = 43.6, 51% men). Survey data on violence exposure were linked to registry data on sickness absence from the Norwegian Labor and Welfare Administration and Statistics Norway. Unadjusted and adjusted hurdle models controlling for age, civil status, education, and income, examined the difference between any sickness absence and length of absence if sick. Women had higher overall odds of sickness absence than men (Adjusted Odds Ratio [aOR] = 2.29, 95% CI [1.90, 2.75]). Violence exposure increased the odds of any sickness absence (aOR = 1.49 [1.22, 1.75]) and risk of longer absence (Adjusted Rate Ratio [aRR] = 1.25 [1.02, 1.52]). Revictimization increased the odds of any sickness absence (aOR = 1.94 [1.53, 2.45]) and risk of longer absence (aRR = 1.31 [1.04, 1.64]). Gender did not moderate these associations. Although sickness absence differs by gender overall, the impact of violence exposure appears comparable for men and women. Public health and occupational sectors should recognize violence as a structural workforce health issue and implement trauma-informed, gender-inclusive prevention and support strategies.

Keywords

gender work participation sickness absence violence victimization

Introduction

Violence victimization is a well-established risk factor for both physical and psychological health problems, including increased risk of sickness absence and work disability (Lassemo & Sandanger, 2018; Steine et al., 2020; Strøm et al., 2013). In Norway, domestic violence alone costs an estimated 8 billion Euros annually, with 27% of that attributed to reduced work participation (Pedersen et al., 2023). Victims of violence, whether in childhood, adulthood, or both, face an elevated risk of sick leave due to a range of health challenges, including somatic illness and mental health difficulties (Clemente-Teixeira et al., 2022; Laskey et al., 2019). These individuals are more likely to utilize healthcare services, report chronic or severe illness, and spend more days bedridden (Campbell, 2002; Ornstein, 2017). Work disruption can worsen these challenges, leading to financial strain, reduced social support, lower quality of life, and elevated risk of mental illness (Mousteri et al., 2018; Perreault et al., 2017).

Despite growing recognition of violence as a public health issue, its work-related consequences have predominantly been studied in women, particularly in the context of intimate partner violence (IPV). IPV is linked to employment instability, decreased work participation, and negative emotional experiences at work, such as fear, shame, and guilt (Alsaker et al., 2016; Showalter, 2016). However, recent research suggests a similar risk for men, suggesting a potential cycle between violence exposure and employment instability (Mellar et al., 2023). This points to a need for a gender-inclusive understanding of how violence affects work functioning.

Theoretical Framework

Two perspectives inform this study. Gender-neutral frameworks such as the biopsychosocial model (Engel, 1977) and the stress-vulnerability model (Zubin & Spring, 1977) suggest that violence can disrupt biological, psychological, and social functioning, leading to sleep disturbances, anxiety, chronic pain, or post-traumatic stress that impair work capacity regardless of gender. Consistent with this view, physical and sexual assault have been associated with difficulty completing work responsibilities in a U.S. sample (Posick et al., 2021).

However, prior research has rarely examined whether these associations differ by gender, particularly using sickness absence as an outcome. Gendered health behavior theories suggest that norms shape how men and women experience, report, and respond to trauma. Hegemonic masculinity theory (Connell, 1995) and gendered response to trauma models (Tolin & Foa, 2006) indicate that men may be less likely to acknowledge distress, seek help, or take sick leave, despite symptom burden. Thus, the effects of violence on men may be underrecognized within both clinical and occupational systems (Courtenay, 2000; Seidler et al., 2016). From this perspective, gender may moderate the link between victimization and sickness absence, not necessarily because violence affects men and women differently at a biological level, but because gendered norms and institutional practices influence how health consequences are translated into work absence.

Together, these frameworks show the importance of empirically testing whether gender moderates the link between severe violence and sickness absence, particularly in a Norwegian welfare context characterized by generous sick leave policies and high gender equality.

Violence Victimization and Sickness Absence

Violence, defined as intentional, unwanted, nonessential, and harmful behavior (Hamby, 2017), can have far-reaching effects beyond immediate physical or psychological harm. Victims are more likely to face employment-related challenges, including increased absenteeism, job turnover, and receipt of disability benefits (Adams et al., 2012; Ahmed & Lång, 2019; Hanson et al., 2010; Loya, 2015; Macmillan, 2000; Tolman & Wang, 2005). In Norway, prior studies have linked violence exposure to long-term work disability (Lassemo & Sandanger, 2018; Steine et al., 2020), but few have examined sickness absence, despite its role as a precursor to unemployment and disability (Strøm et al., 2013; Virtanen et al., 2006).

Timing of Violence Exposure

Childhood adversity has long-term effects on health and employment. Studies from Norway, the United Kingdom, and the United States show that childhood abuse and multiple adverse childhood experiences predict higher risk of unemployment, sickness absence, and disability in adulthood (Björkenstam et al., 2023; Fahy et al., 2017; Hardcastle et al., 2018; Liu et al., 2013; Strøm et al., 2013; Zielinski, 2009). In adulthood, IPV and sexual violence are associated with increased medically certified sick leave, lower income, and long-term work disruption, especially among women (Andersen & Christensen, 2022; Breiding et al., 2017; Hensing & Alexanderson, 2000; Ornstein, 2017; Showalter et al., 2023). Although data on male victims are limited, recent evidence suggests similar occupational disruptions (Garro et al., 2024).

Revictimization, which refers to experiencing violence in both childhood and adulthood, is increasingly recognized as a significant risk factor for poor mental health and adverse behavioral outcomes (Barnes et al., 2009; Kimerling et al., 2007; Widom et al., 2008). Research from Norway shows that childhood victimization increases the risk of severe violence revictimization in adulthood (Aakvaag et al., 2019; Strøm et al., 2025). A recent population study from Sweden found that revictimization was associated with markedly higher odds of mental health issues and risky health behaviors (Pettersson et al., 2025). Despite these findings, few studies have examined the impact of revictimization on sickness absence, and even fewer have explored whether these patterns differ by gender.

Operationalization of Violence Exposure

Most research to date examines domestic violence and sexual violence as distinct forms of contact violence. However, this study combined multiple forms of severe physical violence, including both sexual violence (e.g., rape) and physical assault (e.g., being beating, punched, kicked, or strangled). These acts are not differentiated by the victim–perpetrator relationship (e.g., partner, stranger) but are categorized by the timing of exposure: childhood, adulthood, or both (revictimization). This life-course approach reflects prior population-level studies (Pettersson et al., 2024, 2025) and allows examination of cumulative violence exposure. While this operationalization provides a comprehensive view of physical violence exposure, no research has tested whether gender moderates the link between such severe violence and sickness absence, a key gap that the present study addresses.

Gender Differences and Sickness Absence

Across Nordic countries, women consistently have higher rates of medically certified sick leave than men in most diagnostic categories and occupational sectors (Allebeck & Mastekaasa, 2004; Lima, 2024), and this gap has widened over time (Nossen, 2019). Biological and reproductive health factors, such as pregnancy, contribute to this disparity but explain only a small portion of it, and do not account for the growing gap (Sydsjö et al., 2001). Gendered patterns of sick leave are typically explained by a combination of structural and psychosocial factors, including occupational segregation, caregiving responsibilities, and differential thresholds for help-seeking and emotional expression (Lidwall, 2021). Women are overrepresented in health and social care sectors, which involve high emotional demands, but these occupational factors do not fully explain their elevated sickness absence either (Lima, 2024). A large body of research suggests that women have a higher overall disease burden than men, which may contribute to their greater work absence. For example, women are more frequently diagnosed with conditions such as depression, anxiety, migraines, and autoimmune disorders (Lima, 2024). They also report symptoms of tiredness, burnout, and stress-related reactions more often, which are frequent causes of sick leave. In contrast, men tend to have higher sickness absence related to physical injuries, usually due to hazardous occupations. Importantly, these trends are not entirely attributed to job type or role, suggesting that biological, psychological, and social differences play a role.

Violence exposure may amplify these gender differences. Indeed, violence and gender are deeply intertwined, with gender not only shaping the likelihood of victimization but also influencing how individuals process and respond to trauma (O’Donnell et al., 2024). Some studies suggest that women are more vulnerable to the mental health consequences of violence than men. For instance, a population-based study in Sweden found that women had double the odds of poor psychological health compared to men following violence (Fridh et al., 2014). Similarly, studies from Norway and Denmark found that physical violence was associated with worse psychological outcomes for women, but not for men (Friborg et al., 2015; Sundaram et al., 2004). Moreover, women are more likely to report internalizing symptoms, such as depression and anxiety, while men are more likely to respond with externalizing behaviors, such as alcohol and substance use (Archer, 2000), suggesting that the manifestation may differ by gender. Other findings complicate this narrative. For example, one study found that among men, and not among women, violence exposure was linked to later sickness absence and psychological problems, including anxiety and mental distress (Kivimäki et al., 2002). Other research finds no gender difference in the psychological or occupational consequences of violence, emphasizing severity and prevalence of violence itself as the most important predictor of adverse outcomes (Pimlott-Kubiak & Cortina, 2003; Turner et al., 2006). Taken together, the evidence is mixed. While women generally report higher sickness absence and emotional consequences of violence, men’s responses may be more difficult to detect in clinical and occupational systems, possibly due to prevailing social norms around masculinity and emotional suppression (Connell, 1995; Courtenay, 2000). Thus, it is of empirical importance to investigate the role of gender in the association between severe violence and sickness absence.

The Present Study

This study examined whether gender moderated the link between severe physical violence and sickness absence. Specifically, it tested whether exposure to violence during childhood, adulthood, or across both periods (revictimization) was differentially related to sickness absence in women and men. It was hypothesized that violence exposure would be associated with increased sickness absence after controlling for gender (Hypothesis 1). Drawing on prior research suggesting that women tend to experience greater psychological consequences of violence (Friborg et al., 2015; Kivimäki et al., 2002; Turner et al., 2006), it was hypothesized that the link between violence and sickness absence would be stronger for women than men (Hypothesis 2). Based on recent findings linking revictimization to more severe health outcomes (Pettersson et al., 2025), it was expected that revictimization would confer a greater risk of sickness absence than childhood or adulthood violence alone, and that this risk would be more pronounced among women than men (Hypothesis 3).

Method

Participants

This study combined data from a survey on violence exposure with registry data on sickness absence, welfare benefits, and income obtained from the Norwegian Welfare Services (NAV) and Statistics Norway (SSB). The survey initially included 4,299 participants, of whom 3,650 (85%) consented to the merger with registry data.

Several exclusion criteria were applied. Participants were excluded if they were 70 years or older and therefore ineligible for sickness absence compensation (n = 478); if they fell within the lowest 3% of after-tax income distribution (n = 111); if they lacked registry data on planned workdays and sickness absence during the 15-month observation period (n = 324); if they were registered as permanently disabled (i.e., receiving disability pension) or received Work Assessment Allowance (AAP) during the 15-month period used to estimate income (n = 126), as these individuals were not considered part of the active workforce due to long-term or ongoing work capacity limitations, which could confound associations between employment, health and income; and if they did not have registry data on income (n = 126). Participants were also excluded if they had missing data on violence measures or covariates, specifically civil status (n = 12).

The final sample included 2,473 participants (51% men; mean age = 43.63, SD = 12.86). Among them, 75% were in a relationship, 69% had university or college education, 58% had experienced severe violence, and 39% had registered sickness absence during the observation period.

Procedure

Survey data were collected by Ipsos MMI using a phone interview survey on violence exposure among the Norwegian population between June 2021 and 2022 (Figure 1). Participants were randomly selected from the Norwegian Population Register, which contains information about everyone who resides in Norway. To increase representation, recruitment was stratified by gender, age, and geographical area. Before the interview, participants were given a written and verbal explanation of the study and provided verbal consent. The debrief provided information about available counseling. The interview structure was based on three national phone interviews from the United States (Kilpatrick et al., 2003; Resnick et al., 1993). Of the people contacted, 25.3% participated in the survey. For more information about the survey, see Dale et al. (2023).

Figure 1.

Timeline of data collection.

Statistics Norway (SSB) granted access to registry data from the NAV and SSB and performed data linkage (based on personal identification numbers) and de-identification. This study received approval from the Regional Committee for Medical and Health Research Ethics (REC), and a risk analysis was conducted in accordance with the European General Data Protection Regulations.

Measures

Independent Variables

Violence Exposure

Violence exposure was a binary variable, representing whether participants had experienced at least one form of physical and/or sexual violence. The variable was formed based on responses to the following assessments: Sexual violence involved experienced forcible rape, incapacitated rape, and/or childhood sexual assault. Forcible rape was assessed using the question: “Has anyone ever forced you into intercourse, oral sex, anal sex, or put fingers or objects in your vagina or anus by use of physical force or by threatening to hurt you or someone close to you?”(Kilpatrick et al., 1992; Thoresen & Hjemdal, 2014). Incapacitated rape was assessed by the question: “Have you ever experienced unwanted sexual contact while you were so intoxicated or asleep that you could not stop what was happening?” with confirmation that this involved penetration. Childhood sexual assault was assessed by the question: “Sometimes children can be tricked, rewarded, or threatened to engage in sexual acts they don’t understand or are unable to stop. Before you were 13 years of age, did anyone at least 5 years older than you have any form of sexual contact with you?” (Kilpatrick et al., 2011).

Physical violence in adulthood involved having experienced any of the following six violent acts after the age of 18: (1) hit with a fist or a hard object, (2) kicked, (3) strangled, (4) threatened with a weapon, (5) beaten, (6) physically attacked in other ways. Physical violence in childhood involved exposure to parental violence and peer violence. Parental violence included exposure to any of the following four violent acts from parents or parental guardians before age 18: (1) hit with a fist or a hard object, (2) kicked, (3) beaten up, (4) physically attacked in other ways. While peer violence was measured using the following item from the California Bullying Victimization Scale (Felix et al., 2011): “Did it ever happen to you that someone on purpose in a mean or hurtful way hit, pushed, or physically injured you (during primary and secondary school)?”.

Time of Violence

This variable was based on the same items as violence exposure but categorized into four groups based on the developmental stage in which violence occurred: (1) no violence, (2) childhood violence, (3) adulthood violence, or (4) revictimized (both in childhood and adulthood). Childhood violence included parental violence, peer violence, childhood sexual abuse, and sexual violence (i.e., forcible rape, incapacitated rape) before aged 18. Adulthood violence included exposure to physical violence, forcible rape, and incapacitated rape after aged 18. Revictimization was defined as exposure to both childhood and adulthood violence. Because the assessments of forcible rape and incapacitated rape pertained to the entire lifespan, follow-up questions on age of the first and last occurrence were utilized to classify whether the instance(s) had occurred in childhood, adulthood, or both.

Dependent Variable

Sickness Absence

Doctor-certified sickness absence was based on employment records from SSB and registry data on sick leave from NAV for the 15-month period spanning July 2022 to October 2023. Weekends and public holidays are not considered potential workdays. The variable reflects the number of scheduled workdays lost due to doctor-certified sick leave, adjusted based on each person’s employment percentage (with a full-time position equivalent to 1.0) and the extent of their certified work disability (ranging from 0% to 100%). The sickness absence and expected workdays for the 5 quarters included were summed separately, and the variables were rounded up to the nearest whole number to meet the whole number criteria for hurdle regression in R (e.g., sickness absence of 1.70 was transformed to 2).

Moderator

Gender was tested as a moderator to examine whether the relation between violence exposure and sickness absence differed by participants’ gender. No participants self-identified as non-binary, therefore, gender was coded as (1) man or (2) woman.

Covariates

Covariates that could potentially influence the link between violence exposure and sickness absence were included to account for potential confounders. The main analysis included the following covariates: Civil status, education, age, and income after tax. Civil status was coded as (1) partner (i.e., married, cohabitant, in a romantic relationship) or (2) single (i.e., never married, previously married or cohabitant, separated, divorced, widow/widower). Education was coded as (1) some university and/or college, or (2) no university and/or college. Age was categorized into five cohorts: (1) 18–29 years, (2) 30–39 years, (3) 40–49 years, (4) 50–59 years, (5) 60–69 years. Income data were sourced from SSB and calculated as after-tax income. This measure accounted for earnings from wages, self-employment income, capital income, and transfers received, with deductions for taxes and negative transfers. Some participants may have substantial wealth despite relatively low after-tax income. In line with prior research using data from the same registry (Kinge et al., 2019), the bottom 3% of the income distribution was excluded. Income was categorized into quartiles for analysis.

Data Analysis Plan

The study was preregistered on the Open Science Framework (OSF: osf.oi/72hcg). To model the count outcome, a Poisson regression model was first compared to a negative binomial regression model using a log link function, given that Poisson models assume the mean and variance of the outcome are equal. Assessment of the Poisson model revealed evidence of overdispersion, prompting consideration of a negative binomial model (NBM). An NBM was fitted using the “glm.nb()” function in the “MASS” package in R (Venables & Ripley, 2002), which includes an additional parameter to model overdispersion. Model comparison based on the Akaike Information Criterion (AIC; Poisson = 105,663.63, NBM = 11,571.75) and likelihood ratio tests (p < .001) favored the NBM, indicating that it provided a significantly better fit to the data. Given excess zeros in the outcome distribution, a negative binomial hurdle model was conducted using the hurdle() function in the “pscl” package in R (Zeileis et al., 2008). The hurdle models included two components: (1) a logistic model estimating the probability of any sick days taken (vs. none) reported as Odds Ratio, and (2) a truncated NBM estimating the count of sick days among those with at least one sick day, reported as Rate Ratio. First, unadjusted models that included only the two primary predictors of interest, violent victimization and gender, were fitted. Then, adjusted models were estimated, which added four covariates: age, education, civil status, and income after tax. To account for differing employment contracts, an offset term for the log of expected workdays was included in all models. All predictors were included in both the logistic (zero-hurdle) and count models. Model fit was assessed using AIC, Bayesian Information Criterion (BIC), and log-likelihood values.

Missing

Missing responses on violence measured were permitted if participants could be categorized within each variable based on violence items with responses. If at least one item indicated exposure to violence, the participant was categorized as exposed, regardless of missing data on other items. However, if all available responses were negative and some items were unanswered, the participant was coded as missing. This applied to 10 (0.4%) individuals. Additionally, two participants with missing civil status information were excluded. Chi-square distribution tests show that the 12 participants excluded due to missing data did not differ in gender (x² = 0.88, p = .35), age (x² = 0.52, p = .47), civil status (x² = 0.37, p = .83), or education (x² = 2.17, p = .70).

For the time of violence, participants were classified into a time category if they had at least one affirmative response indicating exposure during that developmental period (e.g., childhood, adulthood, or both). The participant was coded as missing if responses were limited to “no” and missing values. An additional 14 participants were classified as missing and excluded from those analyses. Chi-square distribution tests show that the 26 participants excluded did not differ in gender (x² = 0.49, p = .49), age (x² = 0.48, p = .49), civil status (x² = 0.65, p = .72), or education (x² = 3.27, p = .51).

Results

Descriptive Information

Table 1 presents descriptive information for the total sample and for men and women with and without sickness absence. Overall, more women than men had any sickness absence (45.82% vs. 28.4%). Among women with sickness absence, 57.58% had experienced violence, most often in adulthood (19.86%) or revictimization (19.68%). These women were also most often aged 50 to 59 (26.71%), in a relationship (72.74%), with a college or university degree (77.08%), and in the lowest income quartile (31.05%). Among men with sickness absence, 69.92% had experienced violence, and most often revictimization (32.59%). These men were also most often aged 50 to 59 (25.63%), in a relationship (73.82%), with a college or university degree (51.25%), and equally often in the second lowest or highest income quartile (26.74%).

Table 1.

Sample Descriptive Information.

Sample characteristic	Total (n = 2,473)	Men (n = 1,264)			Women (n = 1,209)
	Total (n = 2,473)	No sickness absence (n = 905)	Sickness absence (n = 359)	p-value	No sickness absence (n = 655)	Sickness absence (n = 554)	p-value
	% (n)	% (n)	% (n)	p-value	% (n)	% (n)	p-value
Age				.716			.179
18–29	18.8 (465)	18.45 (167)	18.94 (68)		19.54 (128)	18.41 (102)
30–39	20.1 (498)	20.77 (188)	22.28 (80)		18.32 (120)	19.86 (110)
40–49	23.8 (589)	22.87 (207)	21.73 (78)		24.43 (160)	25.99 (144)
50–59	25.0 (616)	23.87 (216)	25.63 (92)		24.43 (160)	26.71 (148)
60–69	12.3 (305)	14.03 (127)	11.42 (41)		13.28 (87)	9.03 (50)
Civil status				.480			.136
Partner	75.1 (1,857)	75.91 (687)	73.82 (265)		76.64 (502)	72.74 (403)
Single	24.9 (616)	24.09 (218)	26.18 (94)		23.36 (153)	27.26 (151)
Education				<.001			.333
College/university	69.4 (1,716)	64.53 (584)	51.25 (184)		79.54 (521)	77.08 (427)
No college/university	30.6 (757)	35.47 (321)	37.60 (135)		20.46 (134)	21.12 (117)
After tax income quartile				<.001			<.001
80,812–381,471	25.03 (619)	19.34 (175)	23.40 (84)		20.70 (188)	31.05 (172)
381,528–476,410	24.99 (618)	19.78 (179)	26.74 (96)		26.56 (174)	30.51 (169)
476,440–601,491	24.99 (618)	23.09 (209)	23.12 (83)		25.95 (170)	28.16 (156)
601,921–14,115,770	24.99 (618)	37.79 (342)	26.74 (96)		18.78 (123)	10.29 (57)
Violence exposure				.011			<.001
No	41.89 (1,036)	37.90 (343)	30.08 (108)		53.44 (350)	42.42 (235)
Yes	58.11 (1,437)	62.10 (562)	69.92 (251)		46.56 (305)	57.58 (319)
Time of violence				<.001			<.001
None	42.13 (1,036)	37.90 (343)	30.08 (108)		53.44 (350)	42.42 (235)
Childhood only	15.78 (388)	16.80 (152)	13.93 (50)		13.89 (91)	17.15 (95)
Adulthood only	21.88 (538)	23.87 (216)	22.28 (80)		20.15 (132)	19.86 (110)
Revictimized	20.21 (497)	21.10 (191)	32.59 (117)		12.21 (80)	19.68 (109)

Note. Percentages are based on the total of the respective column. p-values are based on chi-square comparison within each gender of those with and without sickness absence.

Violence Exposure (Y/N) and Gender

Table 2 presents the four hurdle models for violence exposure. Model 1A (unadjusted) showed reasonable fit (AIC = 11,292.04, BIC = 11,332.73), with a McFadden’s pseudo-R² of .013, suggesting limited explanatory power, which is expected in models addressing overdispersed count data with excess zeros. Violence exposure (OR = 1.48, 95% CI [1.24, 1.77]) and gender (OR = 2.41 [2.03, 2.87]) were significant predictors in the logistic component of the hurdle model, indicating that individuals exposed to violence (vs. not exposed) and women (vs. men) were more likely to have sickness absence. However, neither variable was significant in the count component, indicating that violence exposure did not relate to the number of sick days once sickness absence occurred.

Table 2.

Two-Part Hurdle Mixed Effects Models: Violence Exposure and Gender on Sickness Absence (Yes/No) and Weighted Days of Sickness Absence With Covariates (n = 2,473).

	Model 1A		Model 1B, adjusted		Model 2A		Model 2B, adjusted
Variables	Logistic model, OR	Count model, RR	Logistic model, aOR	Count model, aRR	Logistic model, OR	Count model, RR	Logistic model, aOR	Count model, aRR
Any violence (ref. No violence)	1.48^{^<.001} [1.24, 1.77]	1.05^.602 [0.87, 1.26]	1.46 ^{^<.001} [1.22, 1.75]	1.07^.434 [0.90, 1.29]	1.36^.018 [1.06, 1.79]	1.17^.315 [0.86, 1.59]	1.40^.016 [1.07, 1.83]	1.24^.160 [0.92, 1.67]
Gender (ref. Man)	2.41 ^{^<.001} [2.03, 2.87]	1.15^.135 [0.96, 1.38]	2.29 ^<.001 [1.90, 2.75]	1.25^.030 [1.02, 1.52]	2.23^<.001 [1.69, 2.93]	1.28^.114 [0.94, 1.75]	2.18^<.001 [1.64, 2.89]	1.44^.023 [1.05, 1.98]
Any Violence × Gender					1.14^.461 [0.80, 1.63]	0.85^.393 [0.58, 1.24]	1.09^.651 [0.76, 1.55]	0.80^.249 [0.55, 1.17]
Age 30–39 (ref. Age 19–29)			1.19^.262 [0.88, 1.60]	1.12^.436 [0.82, 1.52]			1.19^.262 [0.88, 1.61]	1.11^.498 [0.82, 1.51]
Age 40–49 (ref. Age 19–29)			1.23^.182 [0.91, 1.66]	1.28^.118 [0.94, 1.74]			1.23^.180 [0.91, 1.66]	1.28^.116 [0.94, 1.75]
Age 50–59 (ref. Age 19–29)			1.26^.117 [0.94, 1.69]	1.86 ^<.001 [1.40, 2.48]			1.27^.115 [0.94, 1.70]	1.85 ^<.001 [1.39, 2.47]
Age 60–69 (ref. Age 19–29)			1.32^.126 [0.92, 1.89]	2.35 ^<.001 [1.62, 3.41]			1.32^.128 [0.92, 1.89]	2.37 ^<.001 [1.64, 3.44]
Civil status (ref. Partner)			1.12^.262 [0.92, 1.38]	1.09^.412 [0.89, 1.34]			1.12^.259 [0.92, 1.38]	1.09^.398 [0.89, 1.34]
Education (ref. College/university)			1.46 ^{^<.001} [1.19, 1.78]	1.12^.280 0.91, 1.38]			1.46 ^<.001 [1.19, 1.78]	1.13^.256 0.92, 1.39]
Income Q2 (ref. Q1)			0.80^.089 [0.62, 1.03]	1.04^.785 [0.81, 1.33]			0.80^.089 [0.62, 1.03]	1.03^.813 [0.80, 1.33]
Income Q3 (ref. Q1)			0.66 ^.003 [0.51, 0.87]	0.79^.095 [0.61, 1.04]			0.67 ^.003 [0.51, 0.87]	0.80^.103 [0.61, 1.05]
Income Q4 (ref. Q1)			0.41 ^<.001 [0.31, 0.55]	0.72 ^.035 [0.53, 0.98]			0.41 ^<.001 [0.31, 0.56]	0.72 ^.034 [0.53, 0.97]

Note. p-values are in upper superscript. Adjusted models controlled for age, civil status, education, and household income. OR = Odds Ratio [95% CI] for sickness absence; RR = Rate Ratio [95% CI] for sickness absence length; a = adjusted.

Boldfaced values indicate statistical significance at p < .05.

Model 1B (adjusted) showed a slightly improved fit (AIC = 11,217.91, BIC = 11,353.24) and pseudo-R² (.023) compared to the unadjusted model. Violence exposure (Adjusted Odds Ratio [aOR] = 1.46, 95% CI [1.22, 1.75]) and gender (aOR = 2.29 [1.90, 2.75]) remained significant in the adjusted logistic model. In the adjusted count model, only gender (Adjusted Rate Ratio [aRR] = 1.25 [1.02, 1.52]) was significant, indicating that women had more sickness absence than men. Among covariates, income in the highest quartile was associated with a lower likelihood and amount of sickness absence. No college/university education increased the likelihood of sickness absence, but not the amount of absence. In contrast, those aged 60 to 69 were at higher risk for more sickness absence days, but not for having sickness absence in the first place.

Model 2A (unadjusted) and 2B (adjusted) included the interaction term between violence exposure and gender. However, the interaction was nonsignificant. This suggests that gender did not moderate the association between violence exposure and sickness absence.

Revictimization and Gender

Table 3 displays the results from the four hurdle models examining associations between the time of violence exposure, gender, and sickness absence. Model 3B (adjusted) demonstrated an improved fit over Model 3A (unadjusted), with a lower AIC (11,093.64 vs. 11,177.98) and a higher pseudo-R² (0.025 vs. 0.015), despite a slightly higher BIC. In Model 3B, revictimization (exposure in both childhood and adulthood) was associated with greater odds of any sickness absence (aOR = 1.94, 95% CI [1.53, 2.45]) and more sickness absence among those absent (aRR = 1.31 [1.04, 1.64]). Childhood-only exposure was also linked to higher odds of any sickness absence (aOR = 1.34 [1.04, 1.73]), although not with the amount of absence (aRR = 1.00 [0.77, 1.29]). Adulthood-only exposure was not significantly related to either outcome.

Table 3.

Two-Part Hurdle Mixed Effects Models: Time of Violence Exposure on Sickness Absence (Yes/No) and Weighted Days of Sickness Absence With Covariates (n = 2,459).

	Model 3A		Model 3B, adjusted		Model 4A		Model 4B, adjusted
Variables	Logistic model, OR	Count model, RR	Logistic model, aOR	Count model, aRR	Logistic model, OR	Count model, RR	Logistic model, aOR	Count model, aRR
Childhood only (ref. No violence)	1.32^.032 [1.02, 1.70]	0.99^.928 [0.76, 1.29]	1.34^.025 [1.04, 1.73]	1.00^.981 [0.77, 1.29]	0.99^.979 [0.67, 1.45]	1.02^.936 [0.65, 1.61]	1.05^.747 [0.72, 1.58]	1.05^.687 [0.70, 1.70]
Adulthood only (ref. No violence)	1.18^.160 [0.94, 1.48]	0.93^.570 [0.73, 1.19]	1.18^.169 [0.93, 1.48]	0.90^.364 [0.71, 1.13]	1.12^.507 [0.80, 1.57]	1.07^.728 [0.72, 1.59]	1.15^.398 [0.82, 1.64]	1.03^.816 [0.72, 1.53]
Revictimized (ref. No violence)	2.04^<.001 [1.62, 2.58]	1.21^.108 [0.96, 1.53]	1.94^<.001 [1.53, 2.45]	1.31^.020 [1.04, 1.64]	1.94^<.001 [1.41, 2.67]	1.28^.168 [0.90, 1.83]	1.87^<.001 [1.35, 2.58]	1.42^.0499 [1.00, 2.00]
Gender (ref. Man)	2.49^<.001 [2.09, 2.97]	1.18^.074 [0.98, 1.42]	2.36^<.001 [1.96, 2.85]	1.31^.009 [1.07, 1.60]	2.23^<.001 [1.69, 2.93]	1.28^.113 [0.94, 1.75]	2.23^<.001 [1.64, 2.90]	1.49^.020 [1.06, 1.99]
Childhood × Gender					1.66^.054 [0.99, 2.79]	0.96^.882 [0.55, 1.68]	1.51^.126 [0.89, 2.53]	0.94^.621 [0.51, 1.50]
Adulthood × Gender					1.07^.769 [0.68, 1.69]	0.79^.366 [0.48, 1.31]	1.06^.979 [0.63, 1.60]	0.79^.316 [0.48, 1.27]
Revictimized × Gender					1.06^.796 [0.67, 1.70]	0.92^.714 [0.57, 1.47]	1.04^.846 [0.65, 1.68]	0.86^.627 [0.56, 1.42]
Age 30–39 (ref. Age 19–29)			1.17^.310 [0.86, 1.58]	1.11^.496 [0.82, 1.51]			1.18^.293 [0.86, 1.59]	1.12^.485 [0.82, 1.52]
Age 40–49 (ref. Age 19–29)			1.20^.240 [0.89, 1.62]	1.25^.151 [0.92, 1.71]			1.20^.229 [0.89, 1.63]	1.26^.147 [0.92, 1.71]
Age 50–59 (ref. Age 19–29)			1.25^.143 [0.93, 1.67]	1.92 ^<.001 [1.44, 2.55]			1.25^.142 [0.93, 1.68]	1.92 ^<.001 [1.44, 2.56]
Age 60–69 (ref. Age 19–29)			1.30^.159 [0.90, 1.86]	2.41 ^<.001 [1.66, 3.50]			1.29^.161 [0.90, 1.86]	2.43 ^<.001 [1.67, 3.53]
Civil status (ref. Partner)			1.10^.351 [0.90, 1.35]	1.05^.645 [0.85, 1.29]			1.11^.329 [0.90, 1.36]	1.06^.604 [0.86, 1.30]
Education (ref. University)			1.46 ^<.001 [1.19, 1.78]	1.14^.222 0.92, 1.41]			1.45 ^<.001 [1.18, 1.77]	1.14^.238 0.92, 1.40]
Income Q2 (ref. Q1)			0.83^.147 [0.64, 1.07]	1.09^.524 [0.84, 1.39]			0.83^.148 [0.64, 1.07]	1.08^.559 [0.84, 1.39]
Income Q3 (ref. Q1)			0.66 ^.003 [0.51, 0.87]	0.83^.174 [0.63, 1.09]			0.66 ^.003 [0.51, 0.87]	0.83^.181 [0.63, 1.09]
Income Q4 (ref. Q1)			0.43 ^<.001 [0.31, 0.55]	0.75^.064 [0.55, 1.02]			0.43 ^<.001 [0.32, 0.58]	0.74^.054 [0.55, 1.00]

Note. p-values are in upper superscript. Adjusted models controlled for age, civil status, education, and household income. Offset = Man-days; Revictimized = childhood and adulthood violence; Q1–Q4 = Quartile; OR = Odds Ratio [95% CI] for sick leave; RR = Rate Ratio [95% CI] for sick leave length; a = adjusted.

Boldfaced values indicate statistical significance at p < .05.

Gender was a significant predictor, with women showing higher odds of any sickness absence (aOR = 2.36, 95% CI [1.96, 2.85]) and more sick days (aRR = 1.31 [1.07, 1.60]). Among covariates, those with income in the two highest quartiles had reduced odds of sickness absence, while those with no higher education showed increased odds of sickness absence. In contrast, those aged 50 to 59 and 60 to 69 had increased odds of more sick days if absent but not increased odds of sickness absence.

Model 4A (unadjusted) and 4B (adjusted) included the interactions between revictimization and gender, childhood violence and gender, and adulthood violence and gender. However, none of the interaction terms was significant. For instance, the interaction between revictimization and gender was nonsignificant in both the logistic (aOR = 1.04, p = .846) and count model (aRR = 0.86, p = .627), indicating that the link between time of violence exposure and sickness absence did not differ by gender.

Discussion

This study examined whether gender moderated the association between severe violence victimization and doctor-certified sick leave. Preliminary results showed that there is a high prevalence of violence in this sample, with 51.61% of women and 64.32% of men reporting exposure. Specifically, 15.38% of women and 15.98% of men reported exposure during childhood, 20.02% of women and 23.42% of men during adulthood, and 15.63% of women and 24.37 % of men experienced revictimization. Additionally, 45.82% of women and 28.40% of men had doctor-certified sick leave. This may reflect the substantial public health burden of violence.

As hypothesized, violence exposure was significantly linked with increased odds of sickness absence after controlling for gender. Revictimization (childhood and adulthood victimization) was the strongest predictive factor for both the likelihood and amount of sickness absence, even after controlling for gender and key socioeconomic covariates. However, contrary to expectations, the association between violence and sickness absence was not moderated by gender. That is, while women had higher overall rates of sickness absence than men, the relative impact of violence exposure on sick leave was comparable across genders. These findings offer important contributions to the literature on trauma, health, and labor market participation.

Consistent with prior studies (Lassemo & Sandanger, 2018; Skauge et al., 2025; Steine et al., 2020; Strøm et al., 2013), this study found that individuals exposed to severe violence were more likely to have sickness absence. Thus, supporting prior research linking violence victimization to labor-marked disadvantages (Adams et al., 2012; Hanson et al., 2010). This was especially true for individuals who had experienced revictimization, partially supporting Hypothesis 3 and aligning with prior research emphasizing the compounding effect of repeated trauma on health and functioning (Aakvaag et al., 2019; Pettersson et al., 2025). That childhood violence was associated with increased risk of sick leave, even when experienced in isolation, adds to the growing body of research demonstrating the long-term occupational effects of early adversity (Björkenstam et al., 2023; Hardcastle et al., 2018; Strøm et al., 2013). In contrast, the absence of a significant link between adulthood-only violence and sickness absence may reflect differences in interpretation and response to traumatic events in adulthood. It could also be that adulthood trauma, unaccompanied by other experiences, is processed as situational or time-limited, rather than as something that disrupts long-term health or work functioning. Further research should examine the role of resilience factors, social support, interpersonal violence, and the length of violence experiences to better understand this difference.

One of the most important findings was that gender did not moderate the relationship between violence exposure and sickness absence, providing no support for Hypothesis 2. This contradicts prior research suggesting stronger psychological or occupational effects of violence for women (Friborg et al., 2015; Fridh et al., 2014; Sundaram et al., 2004) and challenges assumptions within gendered trauma models that emphasize greater internalizing responses among women and externalizing or avoidant responses among men (Tolin & Foa, 2006). While women in this sample did have more sick leave overall, consistent with national statistics on gender and absenteeism (Lima, 2024; Nossen, 2019), the relative effect of violence on doctor-certified sick leave did not differ by gender. This null interaction finding suggests that when violence is severe and sustained, such as in cases of revictimization, its impact on work functioning may transcend traditional gender response patterns. In this way, the finding aligns with gender-neutral frameworks, including the biopsychosocial model (Engel, 1977) and the stress-vulnerability model (Zubin & Spring, 1977), which proposes that severe adversity disrupts functioning regardless of sex or gender. The results also support prior research which find no gender difference in the psychological or occupational effects of violence once severity and context were accounted for (Pimlott-Kubiak & Cortina, 2003; Turner et al., 2006). Importantly, these findings suggest that clinical and occupational systems should not assume that men are less affected by violence simply because they are less likely to disclose it or seek help (Courtenay, 2000; Seidler et al., 2016). Rather, men may experience equally severe consequences, such as sickness absence, without it being immediately visible in traditional support systems. The finding that men had similar vulnerability to work disruption following violence as women, suggests a need for more inclusive workplace support structures.

Implications

The findings call for a reevaluation of gendered assumptions in sick leave protocols, occupational health assessments, and support services. Systems that focus primarily on women’s experiences of trauma may inadvertently overlook men who are equally affected but less likely to express distress in socially expected ways. For example, men may delay seeking help or avoid mental health services but still experience functional impairments that manifest as work disruptions. Encouraging early intervention and normalizing workplace conversations about trauma, regardless of gender, could help reduce long-term work disability. Moreover, given the strong link between revictimization and sickness absence, occupational health providers should consider the possibility of cumulative trauma experiences when assessing individuals with repeated or prolonged sick leave. Tools that screen for both early-life and recent violence may better identify at-risk individuals, enabling targeted interventions before disability becomes permanent.

More broadly, these findings support comprehensive and transdisciplinary health models that conceptualize violence exposure as a multidimensional public health issue (Fanslow, 2025; Krug et al., 2002). Rather than viewing sickness absence solely as an individual response, these models emphasize coordination across healthcare, occupational health services, and social welfare systems. Integrating medical, psychological, and workplace-based interventions may reduce long-term work disability following victimization.

Limitations

A major strength of this study is the linkage of population-based survey data with objective registry records, which limits common-method bias and recall error often seen in self-reported absence studies. Additionally, the sample size (N = 2,473) provides sufficient power for interaction tests. However, several limitations should be considered. First, the survey response rate (25%) may underrepresent groups with unstable housing or phone access. Compared with the general Norwegian population, respondents had higher levels of education, and fewer immigrants were represented (Dale et al., 2025). Thus, findings may not generalize to socioeconomically disadvantaged or immigrant groups, who may differ in exposure to violence, reporting, and help-seeking. In addition, Norway’s welfare context may limit generalizability to countries with different levels of inequality or diversity. Second, combining multiple violence types maximized statistical power but precluded analysis of context (e.g., partner vs. stranger). Although dichotomizing violence exposure may have reduced specificity, this decision reflected limited power for low-prevalence subtypes and the substantial co-occurrence of multiple forms of victimization. Further work could disaggregate types to test their impact. Third, we did not have measures of clinical diagnosis, workplace-level factors (e.g., job type), and personality traits that may impact both violence risk and sickness absence. Fourth, although violence preceded the 15-month absence window, the possibility of reverse causation (e.g., pre-existing illness increasing vulnerability to assault) cannot be fully ruled out. Fifth, this study excludes individuals receiving disability pension or AAP during the 15-month study period, due to limited information on the extent of the disability. Regardless, it should be noted that many of them had experienced violence. Thus, further research should examine whether there is a difference in violence exposure between individuals receiving disability pension and those with sickness absence. Lastly, this study did not include the frequency or duration of the violence experienced, preventing us from distinguishing between isolated and chronic victimization. Future research should examine whether outcomes differ depending on how often violence occurred and how long it persisted.

Conclusion

Although women had overall higher odds and rates of sickness absence, gender did not moderate the association between violence exposure and sickness absence. This finding suggests that while gender shapes baseline patterns of absenteeism, the functional consequences of severe victimization on work participation may be largely gender-neutral. These results challenge the assumptions embedded in some gendered trauma models and stress the importance of avoiding gender-based expectations about vulnerability or resilience. Trauma-informed public health and workplace policies should recognize that violence carries comparable occupational risk for men and women. Addressing the labor market consequences of victimization is essential not only for individuals in recovery but also for reducing the broader societal cost of trauma.

Footnotes

ORCID iD

Sophie L. Kjærvik

Ethical Considerations

This was approved by the Regional Committee for Medical and Health Research Ethics (REC). A risk analysis was conducted in accordance with the European General Data Protection Regulations (GDPR).

Consent to Participate

Each participant received detailed postal information about the study and had to give verbal consent before the interview. All participants gave their informed consent to use data for research purposes during the interviews, and participants retained the right to withdraw from the study at any time.

Author Contributions

SK: Conceptualization, Formal analysis, Data curation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review and editing. AS: Conceptualization, Data curation, Writing – review and editing. AN: Methodology, Writing – review and editing. MD: Data curation, Conceptualization, Writing – review and editing.

Funding

The authors disclosed receipt of the following financial support for the research and/or authorship of this article: This research was funded by the Norwegian Dam Foundation (Stiftelsen Dam), through the applicant organization, the Norwegian Council of Mental Health (Rådet for psykisk helse): Project number: FORS523821.

Declaration of Conflicting Interests

The authors declared a potential conflict of interest (e.g., a financial relationship with the commercial organizations or products discussed in this article) as follows: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors are solely responsible for the content.

Data Availability Statement

Data in this study may be made available upon request from the corresponding author. Data are not publicly available due to participants’ privacy concerns and the sensitive nature of the topic.*

Author Biographies

Sophie L. Kjærvik, PhD, is a postdoctoral fellow at the Norwegian Center for Violence and Traumatic Stress Studies. Dr. Kjærvik’s research focuses on the causes, consequences, and cures for aggression and violence in society. The aim of her research is to inform the development of violence intervention and prevention.

Anja D. Skauge, MPhil, is a PhD fellow at the Norwegian Centre for Violence and Traumatic Stress Studies in the section for violence and abuse. Ms. Skauge’s research focuses on healthcare utilization and work participation after experiencing violence and abuse.

Alexander Nissen, PhD, MD, is a senior researcher at the Norwegian Center for Violence and Traumatic Stress Studies in the section for forced migration and refugee health. Dr. Nissen’s research focuses on mental health, health and integration in refugees, psychotraumatology, PTSD, and epidemiological methods.

Maria T. G. Dale, PhD, is a senior researcher at the Norwegian Center for Violence and Traumatic Stress Studies. Dr. Dale’s research focuses on violence, abuse, and trauma in a life-course perspective. She specializes in linking large population datasets and conducts prevalence studies in Norway to identify risk factors and vulnerable groups, informing prevention and intervention strategies.

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Sickness Absence After Experiencing Violence: Does Gender Make a Difference?

Abstract

Keywords

Introduction

Theoretical Framework

Violence Victimization and Sickness Absence

Timing of Violence Exposure

Operationalization of Violence Exposure

Gender Differences and Sickness Absence

The Present Study

Method

Participants

Procedure

Measures

Independent Variables

Violence Exposure

Time of Violence

Dependent Variable

Sickness Absence

Moderator

Covariates

Data Analysis Plan

Missing

Results

Descriptive Information

Violence Exposure (Y/N) and Gender

Revictimization and Gender

Discussion

Implications

Limitations

Conclusion

Footnotes

ORCID iD

Ethical Considerations

Consent to Participate

Author Contributions

Funding

Declaration of Conflicting Interests

Data Availability Statement

Author Biographies

References