Sexual Violence as a Key Predictor of Depressive Symptoms in Women: Korean Longitudinal Survey of Women and Families

Abstract

The purpose of this study was to identify the influence of violence on depressive symptoms in women. We analyzed panel data from the Korean Longitudinal Survey of Women and Families (n = 6,632). Exposure to sexual violence was a significant predictor of the onset of depressive symptoms. After adjusting for all covariates, other predictors included the perception of a poor or very poor health status than normal and participants in their 40s and 50s versus participants younger than 40 years. Assessing exposure to sexual violence might be beneficial for evaluating depressive symptoms in women who are newly diagnosed with depression.

Keywords

sexual violence depressive symptoms longitudinal study

Introduction

Depression is the leading cause of both global disease burden and disability (Friedrich, 2017), and it has caused a massive acceleration in healthcare expenditures (Bilal et al., 2020). According to the World Health Organization (2017), 322 million people worldwide (4.4%) have suffered from depressive symptoms, with women (5.1%) being at greater risk than men (3.6%) (World Health Organization, 2017). This pattern holds true in the Korean population, which has one of the highest prevalences of depression in the world. According to the Ministry of Health and Welfare in South Korea, 7.2% of women have experienced depressive symptoms compared to only 4.0% of men (Ministry of Health and Welfare, 2016). Moreover, for depressive symptoms subjectively reported by individuals, 16.5% of women report suffering from depression compared to 9.5% of men in South Korea (Ministry of Health and Welfare, 2015).

Depressive symptoms in women fluctuate (Song, 2011) and are recurrent (Hardeveld et al., 2010). Scholars have attempted to describe patterns of depressive symptoms. Conclusions are mixed, however, with evidence pointing in different directions. Overall, depressive symptoms are believed to increase or change (Rawana & Morgan, 2014) with time (Stapinski et al., 2013) or to occur in a U-shape pattern in which symptoms decrease during middle age and increase again with older age (Sutin et al., 2013). Evidence of the patterns of depressive symptoms in Koreans is also conflicting. Most research has reported that depressive symptoms decrease over time in Korea (Jung, 2018; Kang & Jeon, 2013; Lee et al., 2017), while other studies have reported the opposite pattern (Heo, 2014). It is important to identify factors that influence the onset of depressive symptoms using a longitudinal study because it is difficult to predict patterns of depressive symptoms.

According to the cognitive behavioral model of depression, the occurrence of negative events may influence depressive symptoms (Kim, 2015). Specifically, when a negative event occurs, negative perceptions of the event accumulate and cause depressive symptoms. Among the many factors that contribute to the onset of depressive symptoms, exposure to sexual violence has been the focus of increasing attention (Honda et al., 2018; Kucharska, 2017; Yoo et al., 2019). For example, in the United States, exposure to sexual violence has been identified as a predictor of depressive symptoms in college (Chang et al., 2015) and among middle-aged women (Thurston et al., 2019). Globally, sexual violence has been reported to aggravate or increase the risk of depressive symptoms in women in Europe (Domenech Del Rio & Sirvent Garcia Del Valle, 2017; Kucharska, 2017), Australia (Tarzia et al., 2018), Peru (Barrios et al., 2015), and Korea (Yoo et al., 2019).

Identification of the predictive role of sexual violence for the manifestation of depressive symptoms is especially important in South Korea, where the prevalence of depression is high among women (Oh et al., 2013) and where sexual violence is seldom openly discussed (Kwon & Lee, 2013; Roh, 2018). The incidence of sexual violence in South Korea is 63.4 per 100,000 people (Supreme Public Prosecutors’ Office, 2018), which is far higher than the incidences in Japan (6.8 per 100,00 people) (UNODC, 2017) and Hong Kong (24.8 per 100,000 people) (UNODC, 2017). Due to the cultural stigmatization of victims of sexual violence in East Asian countries, very few women disclose their exposure to sexual violence (Sawrikar & Katz, 2017). However, without a clear understanding of the role of sexual violence in the onset of depressive symptoms, treatment of depression can be difficult and inefficient. Tailored approaches for assessment and treatment of depression in women who experience adult-onset depression may increase positive health outcomes and reduce healthcare costs.

Other factors that might cause a negative perception and influence the development of depressive symptoms have been identified in the literature. Perceived health-related factors such as perception of poor health status (Park et al., 2016), increased stress levels (Williams et al., 2018), perception of increased body weight (Kim et al., 2018), and health behavior factors including low physical activity (Almeida et al., 2013) and smoking (Bakhshaie et al., 2015) have also been reported to impact depressive symptoms.

Although evidence has established an association between exposure to sexual violence and the manifestation of depressive symptoms, the current findings are based on cross-sectional studies, which have inherent limitations because of their inability to analyze depressive symptoms prior to exposure to violence. We, therefore, lack concrete knowledge regarding the role of violence in the onset of depressive symptoms among women. Thus, we aimed to investigate the influence of violence exposure on depressive symptoms in women. We analyzed survey data from Korean women who were grouped according to patterns of depressive symptoms over time and tested the model to identify the effect of violence exposure on depressive symptoms.

Materials and Methods

Sample

Data for this study were drawn from the Korean Longitudinal Survey of Women and Families (KLoWF) (Joo et al., 2018). This survey is a nationally representative longitudinal survey conducted by the Korean Women's Development Institute using computer-assisted personal telephone interviews or face-to-face interviews. The purpose of the KLoWF was to investigate the economic activities and family lives of women to analyze the effectiveness of policies intended to improve the well-being of women. Based on three main areas of family, work, and daily life, the survey was launched in 2007, and follow-up was conducted in 2008 (wave 2), 2010 (wave 3), 2012 (wave 4), 2014 (wave 5), 2016 (wave 6), and 2018 (wave 7). Questions about women's health, such as health status, health behavior, depressive symptoms, and stress, were added after 2012 (wave 4). Our analysis included 6,632 women between 19 and 64 years of age obtained from survey data for waves 4 through 6.

Panel sampling methods were based on general survey areas from the 2005 census in Korea. Primary sampling units were used for stratification to extract survey units. Stratification parameters of the panel were specified considering the degree of urbanization, proportion of workers by industry, proportion of households by type of dwelling, distribution of households by the number of people, age of household members, and sex of household members. Among each stratified survey sample, we applied a measure of probability proportional to the sample size. Data for a total of 9,997 women between 19 and 64 years of age were extracted from the first survey. After the first wave, the retention rates for panel data were 85.3%, 80.1%, 75.2%, 72.6%, and 70.1% for the second, third, fourth, fifth, and sixth surveys, respectively (Joo et al., 2018).

Measures

Depressive symptoms

The Center for Epidemiological Studies Depression Scale (CES-D) 10 (Irwin et al., 1999) measures depressive symptoms and was added to the panel starting in wave 4. The Korean version of the CES-D 10 has been validated previously (Lee, 2019). The CES-D 10 consists of 10 items scored on a 4-point Likert scale to measure depressive symptoms. A cutoff score of 10 was used to present depressive symptoms based on studies conducted in the United States (Irwin et al., 1999) and in Korean (Shin, 2011) and East Asian samples (Boey, 1999). This cutoff score has been used with success to explore depressive symptom clusters (Cha & Nam, 2015) and their influence on menopause in Korean women (Kim et al., 2019). We used this score to distinguish between women who were and were not experiencing depressive symptoms. Subsequently, we classified the women in waves 4 through 6 into four groups: a group that had never experienced depressive symptoms (no depressive symptoms in waves 4, 5, and 6); a group that continually experienced depressive symptoms (depressive symptoms in waves 4, 5, and 6); a group showing depressive symptoms in wave 6 (no depressive symptoms in waves 4 and 5 but depressive symptoms in wave 6); and a group that experienced changing depressive symptoms (depressive symptoms on and off during waves 4, 5, and 6). This grouping system primarily accounts for characterizations of critical concepts of duration or change in the dependent variable of our study. Classifying depressive symptoms in the panel for analysis allowed us to overcome limitations regarding whether or not depressive symptoms were present at any one time point in this cross-sectional research.

Exposure to sexual violence

Exposure to sexual violence was assessed with two items: (1) “During the past year, I have been sexually harassed or insulted verbally or physically even though direct physical contact was not involved,” and (2) “During the past year, I was a victim of a sexual crime (sexual assault or sexual violence).” If the answer was yes to at least one of the two questions, respondents were classified as having been exposed to sexual violence.

Perceived health status

Perceived health status was evaluated by the following variables: subjective health status (excellent, good, normal, poor, or very poor), perceived body image (very thin, thin, normal, overweight, or obese), and stress level. These variables were chosen because they were previously reported to be strong indicators of health. For example, self-rated health has been found to have predictive value for perceived health status variables, such as mortality rate and hospital admission rate (Halford et al., 2012; Isaac et al., 2015; Vie et al., 2019). In addition, previous studies have found that perceived body image is more closely related to depression than actual body mass index (Al Mamun et al., 2007; Byeon, 2013), suggesting that perception of body image has a greater influence on depressive symptoms than actual weight. Assessment of stress level involved scoring 8 items on a 4-point Likert scale. Examples of responses included “I am stressed at my work and home, and I am stressed by relationships with people.” Possible scores ranged from 8 to 32. Higher scores indicated higher stress levels, with a cutoff score of 20.

Health behavior

Variables related to health behavior included exercise and smoking. Exercise was measured by one dichotomous item: Have you engaged in more than 10 min of intense physical activity during the past week? Physical activity means an activity that is very hard on your body or that makes it harder than usual to breathe. This question was extracted from the Korean version of the International Physical Activity Questionnaire Short Form. The original scale was developed for American adults aged 15–69 years (IPAQ Research Committee, 2019) and was later translated and validated in Korean (Oh et al., 2007). Smoking was measured by one dichotomous item: Are you currently a smoker or have you smoked in the past?

Demographics

Demographic characteristics were selected based on a literature review (Aravena et al., 2020; Jung & Lee, 2017; Park & Lee, 2011; Parreira et al., 2017). Age, education level (middle school or lower, high school, vocational school or higher), marital status (married; unmarried; divorced, separated or widowed), number of children, and occupation (administrator, expert, or office worker; service worker or sales worker; agricultural, forestry, or fishery worker; functional and device mechanic or assembly worker; unemployed).

Analysis

Prior to analysis, this study was reviewed by the institutional review board at the institution of the principal investigator. Using SPSS 23.0 (IBM, NY, USA), we performed a descriptive analysis to explore patterns in depressive symptoms over time using panel data from waves 4, 5, and 6. To evaluate differences among groups of participants with varying depressive symptom experiences, independent sample t-tests were used.

We conducted a logistic regression analysis to identify predictors of depressive symptoms in wave 6 because the main explanatory variable, namely, exposure to sexual violence, was only present in wave 6. Using panel data, we analyzed changes in depressive symptoms over time for each respondent. Thus, we detected a unique and important group for whom depressive symptoms occurred in wave 6. The logistic regression analysis allowed us to investigate the effects of exposure to sexual violence along with other explanatory variables for depressive symptoms in wave 6.

Results

Descriptive Statistics for Depressive Symptoms

We explored patterns of depressive symptoms over time in panel data for our sample. Women who had never experienced depressive symptoms in waves 4, 5, and 6 comprised 85.1% (n = 5,640) of the total participants (n = 6,632) analyzed in wave 6 (Table 1). Participants who reported continual depressive symptoms during waves 4, 5, and 6 comprised 5.2% (n = 346) of the total sample. Participants who reported depressive symptoms in wave 6 comprised 2.7% (n = 182) of the sample. Those reporting inconsistent depressive symptoms comprised 7.0% (n = 464) of the sample.

Table 1.

The Patterns of Depressive Symptom Presentations from Wave 4 Through Wave 6 (n = 6,632).

Patterns of depressive symptoms	n (%)
No depressive symptoms consecutively in waves 4, 5, and 6	5640 (85.1)
Depressive symptoms consecutively in waves 4, 5, and 6	346 (5.2)
No depressive symptoms in waves 4 and 5, but depressive symptoms in wave 6	182 (2.7)
Depressive symptoms on and off during waves 4, 5, and 6	464 (7.0)

Respondents who reported depressive symptoms in wave 6 were compared to the other groups using independent sample t-tests (Figure 1). The severity of presentation of depressive symptoms in the group with depressive symptoms in wave 6 was significantly higher than that in the group who never experienced depressive symptoms (t = −51.13, p < .001); however, it was significantly lower than that in the group who continually experienced depressive symptoms during waves 4, 5, and 6 (t = 2.75, p < .01).

Figure 1.

Independent Sample t-Test for Center for Epidemiological Studies Depression (CES-D) 10 Scores by Group of Depressive Symptom Presentation Patterns at Wave 6 (n = 6,632).

Multivariate Analysis

We examined whether exposure to violence was a key predictor of depressive symptoms (Table 2). The sample included 6,625 cases in wave 6 due to missing cases by the variables used in the logistic model. The dependent group in the model was the group of women who first experienced depressive symptoms in wave 6 but had not experienced depressive symptoms in waves 4 and 5. The reference group comprised the groups with no depressive symptoms, alternating depressive symptoms, and continuous depressive symptoms. There were 182 cases in the dependent group and 6,443 cases in the reference group. After controlling for all covariates, women who were exposed to sexual violence during the previous year were 4.13 times (95% CI = 1.81–9.42) more likely to experience depressive symptoms than their counterparts. In terms of demographic variables, respondents in their 40s were 2.06 times (95% confidence interval [CI] = 1.02–4.14) more likely to experience depressive symptoms in wave 6 than respondents younger than 40 years, and respondents in their 50s were 2.32 times (95% CI = 1.08–4.96) more likely to experience depressive symptoms in wave 6 than respondents younger than 40 years. With regard to perceived health status, respondents who perceived themselves to have a poor or very poor health status were 2.08 times (95% CI = 1.43–3.03) more likely to experience depressive symptoms than respondents who perceived themselves to have a normal health status. On the other hand, respondents who perceived themselves to have a good or excellent health status were 0.51 times (95% CI = 0.35–0.74) less likely to experience depressive symptoms than those who perceived themselves to have a normal health status.

Table 2.

Logistic Regression of Explainable Variables on Onset of Depressive Symptoms, Wave 4–6.

Variables		n (%)	No depressive symptoms in waves 4 and 5, but depressive symptoms in wave 6
Variables		n (%)	Adjusted OR (95% CI)
Demographic	Age
	≤39 (REF)	862 (13.0)
	40–49	2049 (30.9)	2.06 (1.02–4.14)*
	50–59	1647 (24.9)	2.32 (1.08–4.96)*
	≥60	2067 (31.2)	1.36 (0.59–3.12)
	Education
	Vocational school/Higher (REF)	2055 (31.0)
	Middle school/Lower	2220 (33.5)	1.08 (0.62–1.89)
	High school	2350 (35.5)	0.93 (0.60–1.43)
	Marital status
	Married (REF)	5363 (81.0)
	Unmarried	287 (4.3)	1.17 (0.47–2.93)
	Divorced/Separated/Widowed	975 (14.7)	0.84 (0.55–1.30)
	Number of children
	0 (REF)	4275 (64.5)
	1	830 (12.5)	0.86 (0.50–1.51)
	2	1166 (17.6)	0.90 (0.51–1.61)
	≥3	354 (5.4)	1.85 (0.96–3.57)
	Job type
	Administrators/Experts/Office workers (REF)	1143 (17.2)
	Service workers/Sales workers	1252 (18.9)	1.19 (0.67–2.12)
	Agricultural/Forestry/Fishery workers	719 (10.9)	1.08 (0.54–2.15)
	Functional/Device mechanics/Assembly workers	761 (11.5)	0.72 (0.35–1.49)
	Unemployed	2750 (41.5)	0.98 (0.58–1.65)
Perceived	Subjective health
health status	Normal (REF)	2349 (35.4)
	Good/excellent	3237 (48.9)	0.51 (0.35–0.74)***
	Poor/very poor	1039 (15.7)	2.08 (1.43–3.03)***
	Stress level
	8–19 (REF)	5598 (84.5)
	≥20	1027 (15.5)	1.19 (0.81–1.73)
	Perceived body image
	Normal (REF)	3939 (59.5)
	Skinny/Very skinny	734 (11.1)	1.46 (0.94–2.28)
	Overweight/Obesity	1952 (29.4)	1.20 (0.86–1.67)
Health behavior	Exercise
	No (REF)	5017 (75.7)
	Yes	1608 (24.3)	1.11 (0.78–1.59)
	Smoking
	Never (REF)	6539 (98.7)
	Yes	86 (1.3)	0.34 (0.05–2.50)
Exposure to sexual violence	No (REF)	6563 (99.1)
	Yes	62 (0.9)	4.13 (1.81–9.42)**

OR = Odds ratio; CI = Confidence interval; REF = Reference group.

p < .05.

p < .01.

***

p < .001.

Discussion

In this study, patterns in the prevalence of depressive symptoms over time were explained using longitudinal data. The percentage of respondents not experiencing depressive symptoms for three consecutive waves was 85.1%. Thus, the percentage of respondents who reported experiencing depressive symptoms at some point was 14.9%, which is slightly higher than previous findings for women in Canada (11.3%) (Patten et al., 2016) and the United States (10.4%) (Brody et al., 2018). These findings support previous national and global data on the prevalence of depressive symptoms. Through the use of a longitudinal dataset, the findings of this study provide a deeper understanding of the prevalence of depressive symptoms by describing changes in the same sample over time. Although the prevalence of depressive symptoms is similar across cross-sectional studies, the results of this study elucidated the proportions of women who are likely to remain depressed over time and those who alternate between being depressed and not being depressed.

In this study, we determined the predictive role of exposure to sexual violence for depressive symptoms in women using a regression model. The regression model was developed based on the cognitive behavioral model of depression (Kim, 2015). We developed our regression model by identifying salient variables from the literature that contributed to the presentation of depressive symptoms in women. Although our key independent variable of sexual violence might have an impact on perceived health status and health behaviors, with the logistic regression model used in the study we could elucidate whether each variable controlling each other could affect depression, which was our dependent variable.

In our regression model, sexual violence was the strongest predictor for the onset of depressive symptoms. The association of sexual violence with poor mental health has been reported globally in cross-sectional studies. For example, a cross-sectional study conducted among middle-aged women in Japan reported that among types of abuse including sexual, physical, and psychological domestic violence, sexual violence was an independent predictor of poor mental health (Honda et al., 2018). A cross-sectional study in Australia also revealed an association between sexual violence and poor mental health (Tarzia et al., 2018). In addition, in young adult Latina women in the United States who were exposed to sexual violence, childhood sexual abuse was associated with an increased risk of experiencing depressive symptoms later in life (Ulibarri et al., 2015). However, these studies focused on the association of sexual violence with existing depressive symptoms rather than the onset of depressive symptoms. To our knowledge, this is one of the first studies to identify the role of sexual violence in predicting the onset of depressive symptoms using longitudinal data.

In addition to elucidating the role of sexual violence in predicting the onset of depressive symptoms in Korean women, we found that perceiving one's health to be poor or very poor doubled the probability of developing depressive symptoms. On the other hand, perceiving one's health to be good or excellent decreased the probability of developing depressive symptoms by half. This finding is in line with previous studies that found an association between the presentation of depressive symptoms in women and perceived decreased health status (Tsai et al., 2017). However, it is also well known that sexual violence has detrimental impacts on perceived health status in women, and sexual violence was the strongest predictor of depressive symptoms (Domenech Del Rio & Sirvent Garcia Del Valle, 2017). Although perceived health status and presentation of depressive symptoms are closely related (Park et al., 2016), the interactions among sexual violence, perception of health, and depressive symptoms should not be overlooked.

This study had a few limitations related to our analysis of secondary data. First, women who were younger than 40 years comprised only 13.0% of the sample. Although the sample was generated in 2007 with probability proportional sampling, the participants included in the panel have aged since then. Sexual violence occurs most often in those younger than 40 years, but it has been reported in all age groups (Supreme Public Prosecutors’ Office, 2018). Considering that younger women are at higher risk of experiencing sexual violence, the women in this sample might have been less likely to experience sexual violence. Second, depressive symptoms were measured with the CES-D 10, which is a shortened version of the CES-D 20 (Radloff, 1977). Although the reliability and validity of the CES-D 10 have been well established (Baron et al., 2017; González et al., 2017; Mohebbi et al., 2018), full and accurate reporting of depressive symptoms by women might not have been possible with this instrument. Finally, we must disclose that our data were limited insofar as survey items regarding experiencing sexual violence during the previous year at the time of the survey were added only to the later versions of the panel. We did not have access to any information about whether the women included in the sample had lifetime histories of sexual violence. In addition, the questions used in this study regarding exposure to sexual violence were novel, and their use has not been validated by prior research. A more complete picture of the relationships among the study variables such as depressive symptoms, perceived health status, and exposure to sexual violence may emerge when more longitudinal data are available for these variables.

Conclusion

The findings of this study demonstrate that the strongest predictor of the manifestation of depressive symptoms in women in South Korea is exposure to sexual violence. Due to the association between sexual violence and depressive symptoms, clinicians are encouraged to ask women who developed depressive symptoms about their possible experiences with sexual violence.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A2C2008166).

ORCID iD

Chiyoung Cha

Author Biographies

Youngtak Kim is a research fellow at the Korean Women's Development Institute. His research interests are gender-specific women's health policy, women's health, suicide, obesity, and longitudinal studies.

Chiyoung Cha is an associate professor at Ewha Women's University. Her research interests include minority health, women's health, mobile health, informational technology, longitudinal studies, and qualitative research.

Miran Lee is an assistant professor at Kwangju Women's University. Her research interests are the human rights and health of nurses.

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