Common Crime and Domestic Violence Victimization of Older Chinese in Urban China: The Prevalence and Its Impact on Mental Health and Constrained Behavior

Abstract

This article examines the prevalence of victimization among older Chinese living in urban China and its psychological and behavioral impacts. A representative sample of 453 older adults aged 60 or above was recruited from Kunming, the People’s Republic of China, using multistage sampling method. Participants were individually interviewed on their demographic characteristics, experience of common crime and domestic violence victimization, fear of common crime and domestic violence, mental health, and constrained behavior. Results showed that 254 participants (56.1%) reported one or more types of common crime and 21 (4.6%) reported experiencing domestic violence in the past. Seventeen participants (3.8%) reportedly experienced both common crime and domestic violence victimization. There was no gender difference in the overall incidence of victimization but in some subtypes. Regression analyses indicated that past experience of common crime victimization was significantly associated with greater fear of common crime (β = .136, p = .004), poorer mental health (β = .136, p = .003), and more constrained behavior (β = .108, p = .025). Fear of common crime predicted increased constrained behavior (β = .240, p < .001) independent of gender, age, education, household finances, living arrangement, and physical health. Domestic violence victimization was not significant in predicting poor mental health and constrained behavior but was significant in predicting fear of domestic violence (β = .266, p < .001), which was related to poorer mental health (β = .102, p = .039). The study suggests the importance of taking older people’s risk and experience of victimization into consideration in gerontological research, practice, and policymaking.

Keywords

mental health and violence cultural contexts elder abuse

Introduction

Crime is one of the most pressing social issues in many societies, including China. Older people account for a considerable and increasing proportion of the victims of common crime and domestic violence victimization (Yan, Chan, & Tiwari, 2015). Common crimes include offenses committed by strangers such as personal crimes (robbery, murder, physical attack, rape or sexual assault, etc.) and property crimes (burglary, snatch theft, theft, fraud, etc.). Domestic violence is defined as maltreatment conducted by family members, including physical, psychological, emotional, financial, and sexual abuse, neglect, and abandonment (Yan et al., 2015). Despite the established influence of crime on the well-being of victims (Lorenc et al., 2012), little is known about the suffering of the fast expanding aging population in China. As researchers (Heap, 2008; Powell & Wahidin, 2008) have indicated, the experiences of older victims have largely been marginalized by victimology and gerontology scholars and practitioners.

Prevalence of Common Crime Victimization

Since the development of Hentig’s typology of easy victims in 1948, older people have been included as one category of easy victims because of their high risk of being the targets of property crimes due to their decreased physical strength and mental alertness (Meadows, 2010). To be specific, consumer fraud is believed to victimize older people the most and to have no less severe consequences than violent crime (Schuller, 2006). Drawing on the data of the computerized crime-recording system in the United Kingdom, Alford (2003) found that people aged 60 or above were very likely to be burgled when at home and to experience repeat victimization. As for personal crime, an empirical study sampling older women living in a semirural English county identified their risk of being assaulted by a single male at their dwelling for sexual purposes (Ball & Fowler, 2008). Although the British Crime Survey and the National Crime Victimization Survey in the United States indicated that the proportion of older victims was not the largest among all age groups for personal and property crimes in the United Kingdom (Heap, 2008) and the United States (Klaus & Rennison, 2002), there is a belief that this proportion will increase with worldwide population aging. The situation with regard to criminal victimization among older adults is not explicit in China. Empirical research on their experience of victimization is scant, but the need for such research is imperative.

Prevalence of Domestic Violence Victimization

The World Health Organization (2015) has recognized elder abuse as a severe public health threat across the world. According to its conservative estimation, 10% of older people are victimized by abuse each month. In high- or- middle-income countries, the prevalence of specific types of abuse has been estimated as follows: 0.2% to 4.9% for physical abuse, 0.7% to 6.3% for psychological abuse, 1.0% to 9.2% for financial abuse, 0.04% to 0.82% for sexual abuse, and 0.2% to 5.5% for neglect. A global review of elder abuse among the Chinese population documented a prevalence rate of 0.2% to 64% for elder abuse and its subtypes (Dong, 2015). Empirical evidence shows a high prevalence of elder abuse in mainland China. A survey study sampling 412 older people (aged 60 and above) living in urban communities in Jiangsu province found that 35.2% of the participants had experienced elder abuse and neglect: Specifically, caregiver neglect accounted for 16.9%, financial exploitation 13.6%, emotional abuse 11.4%, physical abuse 5.8%, sexual abuse 1.2%, and abandonment 0.7% (Dong, Simon, & Gorbien, 2007). A cross-sectional study conducted in rural communities of Hubei province found that 36.2% of 2,000 participants reported elder mistreatment: Caregiver neglect accounted for 15.8% of this mistreatment, financial mistreatment 2.0%, psychological mistreatment 27.3%, and physical mistreatment 4.9% (Wu et al., 2012). The prevalence of elder abuse has been reported by both victims and offenders in Hong Kong, with 27.5% of older persons living at their own homes reporting such abuse (verbal abuse 26.8%, violation of personal rights 5.1%, and physical abuse 2.5%; Yan & Tang, 2004) and 42.3% of family caregivers to older persons with dementia admitting to such abuse (verbal abuse 40.3% and physical abuse 15.4%; Yan, 2014).

Impact of Crime Victimization

Crime has diverse detrimental consequences on victims in the short or long run, including economic costs (property loss, medical care expenses, or absence from work), physical injuries, functional impairment, mental trauma, self-segregation, and higher mortality (Daigle, 2012; Lorenc et al., 2012). According to the VicHealth framework in Australia, freedom from violence is a key determinant of mental health (Keleher & Armstrong, 2006). Victimization often leads to mental health issues such as fear, depression, anxiety, posttraumatic stress disorder, and so forth (Daigle, 2012; Russo & Roccato, 2010). Because of their increased vulnerability and the difficulty they experience in recovering from the consequences of victimization (Powell & Wahidin, 2008), crime might have more severe effects on older people, especially their mental health and behavioral well-being (Pain, 1995).

Owing to their fear of criminal victimization, older people often resort to a more conservative way of behaving, such as installing safety alarms, entry phones and cameras, special doors and window grilles, and security guards and even keeping a weapon at home (Aromaa & Heiskanen, 2001; Jackson & Gray, 2010; van Dijk, van Kesteren, & Smit, 2007). To lessen the victimization risk, they are also inclined to become suspicious and solitary (Indermaur, 1995; Jönson, 2003), remain housebound (Hayman, 2011), avoid outside places (Spalek, 2005; Yodanis, 2002), refuse home visits (Moore & Trojanowicz, 1988), and withdraw from social activities (Abbott & Sapsford, 2005; Hayman, 2011). This lifestyle is detrimental for older persons in the long run as it may lead to declining physical health due to decreased mobility (Lorenc et al., 2012); increased mental illness such as depression, distress, and anxiety (Abbott & Sapsford, 2005; Beaulieu et al., 2002); shrinking social support and social networks due to reduced interpersonal interaction (Ditton & Innes, 2005; Thompson & Krause, 1998) ; and ultimately intensified isolation and marginalization as a result of decreased community participation (Spalek, 2005; Warr, 2000).

The severe consequences of domestic violence on the physical and mental health of older people have been revealed. The physical injuries can range from a minor wound to disability, and the psychological consequences, including anxiety, distress, and depression, are often serious and long lasting (WHO, 2015). Increased morbidity and mortality have also been identified as the possible results of elder abuse (Dong et al., 2009). Suicidal ideation was found to be related to psychological and financial abuse. In a cross-sectional study drawing on the data of 2,000 older adults residing in rural areas of mainland China (Wu et al., 2012), psychological distress, including depression, anxiety, social dysfunction, and somatic complaints, was noted to be associated with verbal abuse and physical abuse among older Chinese (N = 355) living in Hong Kong (Yan & Tang, 2001).

This article aims to provide a better understanding of how common crime and domestic violence influence older people living in urban China, including the prevalence of these crimes and their impact on the mental and behavioral well-being of older people. We hypothesized that older people’s experience of victimization would be significantly associated with fear of crime while victimization and the consequent fear of crime would be correlated with poorer mental health and more constrained behavior.

Method

Data Collection

A survey was administered from June to August 2011 in Kunming, Yunnan province, China, with the ethical approval of the Human Research Ethics Committee of the authors’ affiliated university. A total of 800 Chinese older people aged 60 years or over and living in urban areas were selected through multistage sampling (the administrative district, the City-Street Office, the neighborhood committee, and the individual). A total of 456 participants, representing a response rate of 57%, gave written consent to be individually interviewed on the basis of a questionnaire by trained interviewers in their homes for approximately 30 min. Discounting three incomplete questionnaires, the final sample consisted of 453 cases.

Instruments

Sociodemographics

Five demographic variables were included. Gender was coded as male (= 1) and female (= 2). Age was a continuous variable measured in years. Education was an ordinal variable with replies coded as no or minimal education (1), primary education (2), secondary or vocational education (3), and college or above level education (4). Household finances was measured in three discrete categories: well-off (1), average (2), and poorly off (3). Living arrangement was coded as living alone (= 0) and living with others (= 1).

Physical health

The physical component of the 8-item Short-Form (SF-8™) Health Survey 1-week recall was employed to measure the participants’ physical health. This scale consists of eight items, each assessing one of the following health concepts: “general health, physical functioning, role physical, bodily pain, vitality, social functioning, mental health, and emotional roles” (Lefante, Harmon, Ashby, Barnard, & Webber, 2005, p. 666). These eight items can be reduced to two subscales, the Physical Component Summary (PCS) and the Mental Component Summary (MCS; Ware, Kosinski, Dewey, & Gandek, 2001). The Chinese version of SF™-8 has an acceptable Cronbach’s alpha reliability of .75 and a correlation coefficient of .56 between SF-8™ and SF-36™ (Wang et al., 2007). A T score based scoring was used, with lower scores representing worse functional health and well-being. For this study, the SF-8 exhibited excellent internal consistency (Cronbach’s α = .90).

Common crime victimization

A seven-item index was constructed to measure participants’ lifetime experience of crime victimization by asking “have you been victimized by theft/fraud/burglary/snatch theft/robbery/physical attach/rape or sexual assault in the past?” The responses were coded as 0 (no) and 1 (yes). The first four types were grouped into property crime and the remaining three types into personal crime. The total scale score was generated by summing each item, with higher scores representing more experience of victimization. The internal reliability of this scale was .60, which is basically acceptable considering the large divergence among the items. Its mean interitem correlation was .21.

Domestic violence victimization

The Potentially Harmful Behavior (PHB) scale (Beach et al., 2005) was revised to assess the participants’ past experience of domestic violence. The checklist includes five indicators of psychological mistreatment, namely, being screamed and yelled at, being threatened with placement in a nursing home, being threatened with the use of physical force, being threatened with abandonment, and being verbally abused, and five indicators of physical mistreatment, namely, the withholding of food, being hit or slapped, being shaken, being handled roughly in other ways, and fear that a caregiver might hit or try to hurt you. For the present study, the last indictor of physical mistreatment was excluded because of irrelevance. Participants were asked about their past experience of the other nine types of domestic violence (0 = no; 1 = yes). The score for each item was aggregated as the scale score, with higher scores indicating more domestic violence victimization. The reliability was quite satisfactory (α = .96).

Fear of common crime

The Fear of Crime scale (Ferraro & LaGrange, 1992) was cross-culturally adapted in this study and employed to assess participants’ fear of common crime. The 10 crimes included fraud, theft, burglary while away, burglary while at home, vandalism, snatch thefting, robbery, physical attach, rape or sexual assault, and murder. The response categories ranged from 1 (not afraid at all) to 10 (very afraid). The responses on the 10 items were averaged, with higher scores representing a stronger fear of crime. The internal consistency of this scale was strong (α = .94).

Fear of domestic violence

The adapted PHB scale (Beach et al., 2005) was used for participants to assess their fear of nine types of domestic violence on a 10-point scale which ranged from 1 (not afraid at all) to 10 (very afraid). The scale score was generated by averaging each item response value, with higher scores revealing a stronger fear of domestic violence. A good internal reliability coefficient of .98 was reported for this measure.

Constrained behavior

Participants’ constrained behavior was measured using the culturally adapted constrained behavior index originally developed by Ferraro (1995). The index consists of two domains, defensive behavior and avoidance behavior, each measured by nine items. Responses were coded (0 = no, 1 = yes). The total score on this scale was generated by summing all item scores, with higher total scores indicating more constrained behavior. Participants’ scores related to constrained behavior ranged from 0 to 17, with a mean of 9.68 (SD = 3.59). The internal reliability of this scale was satisfactory, with a Cronbach’s value of .81.

Poor mental health

Mental health was assessed using the 12-item General Health Questionnaire (GHQ). The GHQ was developed by Goldberg (1972) to measure the severity of nonpsychotic disturbances. The validity of the GHQ-12 has been verified in Chinese populations (Pan & Goldberg, 1990). This scale demonstrated good internal consistency, with an alpha of .75 (Boey & Chiu, 1998; Yan, 2008). The total score of this scale ranged from 0 to 12, with higher scores representing worse mental health status. A cutoff score (≥ 3) identified 234 cases (51.7%) presenting potential mental disorder. The Cronbach’s alpha value was .67.

Data Analysis

Data were analyzed using SPSS 20.0. Descriptive analyses were conducted to establish the participants’ profiles, the frequency of prominent types of victimization, fear of crime, and constrained behavior. The bivariate correlation analysis technique was utilized to explore the relationships between the major variables. Hierarchical regression analyses were conducted to examine the contributions of victimization to the variance in fear of crime, poor mental health, and constrained behavior.

Results

Descriptive Statistics

The demographic characteristics of participants are presented in Table 1. The final sample comprised 198 males (43.7%) and 255 females (56.3%). The participants were aged from 60 to 100, with a mean age of 72.29 (SD = 8.27). The distribution across age groups was as follows: 263 (58.1%) in the 60 to 74 group, 156 (34.4%) in the 75 to 84 group, and 34 (7.5%) in the 85 and above group. About three in every 10 (n = 134, 29.6%) participants had received no or minimal education, 30.5% (n = 138) had received primary education, 30.5% (n = 138) had a secondary or vocational education, and 9.4% (n = 43) had a college level education or higher. With regard to living arrangement, 90.9% (n = 412) lived with their spouse or other people, while 9.1% (n = 41) lived alone. The majority of the participants (n = 338, 74.6%) rated their financial status at an average level, compared with 12.6% (n = 57) who considered themselves well-off and 12.8% (n = 58) who rated themselves as poorly off.

Table 1.

Demographic Characteristics of the Sample (N = 453).

Variable	Male (n = 198)	Female (n = 255)	Total (N = 453)	Gender difference
Variable	n (%)	n (%)	n (%)	χ²	p
Education				46.96	<.001
None, or minimal	29 (14.6)	105 (41.2)	134 (29.6)
Primary	69 (34.8)	69 (27.1)	138 (30.5)
Secondary/vocational	68 (34.3)	70 (27.5)	138 (30.5)
College or above	32 (16.2)	11 (4.3)	43 (9.4)
Living arrangement				0.64	.424
Living alone	15 (7.6)	26 (10.2)	41 (9.1)
Living with others	183 (92.4)	229 (89.8)	412 (90.9)
Household finances				0.49	.782
Well-off	26 (13.1)	31 (12.2)	57 (12.6)
Average	149 (75.3)	189 (74.1)	338 (74.6)
Poorly off	23 (11.6)	35 (13.7)	58 (12.8)

Concerning gender differences, males were significantly more likely than females to have a higher level of educational attainment, χ²(3, N = 453) = 46.96, p < .001, Cramer’s V = .32; however, the difference in terms of age (p = .107), living arrangement (p = .424), and household finances (p = .782) was nonsignificant.

Prevalence of Victimization

Table 2 depicts the prevalence rate of victimization. There were 254 participants (56.1%) who had experienced one or more types of common crime victimization. The prevalence of specific types ranged from 0.2% to 43.7%, with property crimes (n = 253, 55.8%) such as theft (n = 198, 43.7%), fraud (n = 140, 30.9%), and burglary (n = 119, 26.3%) ranked the top three most prevalent common crimes and personal crimes (n = 18, 4.0%) such as physical attach (n = 5, 1.1%) and rape or sexual assault (n = 1, 0.2%) ranked the least prevalent. The only significant gender difference was in relation to snatch theft, χ²(1, N = 453) = 6.99, p = .008, Cramer’s V = .13, with females being at higher risk.

Table 2.

Frequency of Direct Victimization by Crime Type and Gender (N = 453).

	Male (n = 198)		Female (n = 255)		Total (N = 453)		Gender Difference
	n	%	n	%	n	%	χ²	p
Direct common crime victimization
Theft	90	45.5	108	42.4	198	43.7	0.32	.572
Fraud	66	33.3	74	29.0	140	30.9	0.78	.377
Burglary	50	25.3	69	27.1	119	26.3	0.11	.745
Snatch theft	6	3.0	25	9.8	31	6.8	6.99	.008
Robbery	7	3.5	10	3.9	17	3.8	0.00	1.000
Attack	2	1.0	3	1.2	5	1.1	—	1.000
Rape or sexual assault	1	0.5	0	0.0	1	0.2	—	.437
Property crime	110	55.6	143	56.1	253	55.8	0.01	.911
Personal crime	8	4.0	10	3.9	18	4.0	0.00	.949
Subtotal (one or more subtypes)	111	56.1	143	56.1	254	56.1	0.00	1.000
Direct domestic violence victimization
Abused verbally	4	2.0	13	5.1	17	3.8	2.13	.144
Screamed or yelled at	4	2.0	12	4.7	16	3.5	1.64	.201
Handled roughly in other ways	3	1.5	10	3.9	13	2.8	1.53	.216
Threatened with abandonment	1	0.5	9	3.5	10	2.2	—	.048
Shaken	2	1.0	8	3.1	10	2.2	—	.197
Threatened with use of physical force	1	0.5	8	3.1	9	2.0	—	.084
Hit or slapped	2	1.0	7	2.8	9	1.9	—	.133
Food withheld	0	0.0	7	2.8	7	1.6	3.84	.049
Threatened with placement in nursing home	0	0.0	7	2.8	7	1.6	—	.020
Psychological mistreatment	6	3.0	14	5.5	20	4.4	1.60	.206
Personal mistreatment	3	1.5	11	4.3	14	3.1	2.92	.088
Subtotal (one or more subtypes)	7	3.5	14	5.5	21	4.6	0.57	.449
Total	114	57.6	146	57.3	260	57.4	0.00	1.000

Note. A dash was used when chi-square statistic was inapplicable because its assumption that at least 80% of cells had expected frequencies of 5 or more was violated (Pallant, 2005) The Fisher’s exact test (FET) significance level (two-sided) was reported.

Only 4.6% (n = 21) of the older adults surveyed in this study had experienced domestic violence. The type-specific prevalence was between 1.6% and 3.8%. In particular, 17 (3.8%) had been verbally abused, 16 (3.5%) had been screamed or yelled at, and 13 (2.8%) had been handled roughly in other ways by a family member. Being threatened with placement in a nursing home and having food withheld had the same lowest incidence (n = 7, 1.6%). On average, psychological mistreatment (n = 20, 4.4%) was more prevalent than physical mistreatment (n = 14, 3.1%). A chi-square test demonstrated that females were significantly more likely to experience being threatened with placement in a nursing home (p = .020, FET), being threatened with abandonment (p = .048, FET), and having food withheld, χ²(1, N = 453) = 3.84, p = .049, Cramer’s V = .11, by family members.

Overall, 260 participants (57.4%) had been victimized by crime in the past. No significant gender difference was observed for the total score.

Prevalence of Fear of Crime

The prevalence rate of fear of common crime was the percentage of the participants who reported fear of common crime. As depicted in Table 3, more than half of the participants (n = 258, 57.0%) reported fear of common crime: that means they felt fearful of one or more types of common crime. Concerning the specific types of common crime, theft (n = 234, 51.7%), fraud (n = 221, 48.8%), and burglary when away from home (n = 215, 47.5%) were the most prevalent common crimes invoking fear, while physical attach (n = 126, 27.8%), murder (n = 62, 13.7%), and rape or sexual assault (n = 57, 12.6%) were the least prevalent.

Table 3.

Frequency of Fear of Crime by Crime Type and Gender (N = 453).

	Male (n = 198)		Female (n = 255)		Total (N = 453)		Gender Difference
	n	%	n	%	n	%	χ²	p
Fear of common crime
Theft	89	45.0	145	56.9	234	51.7	5.87	.015
Fraud	82	41.4	139	54.5	221	48.8	7.14	.008
Burglary while away	83	41.9	132	51.8	215	47.5	3.95	.047
Burglary while at home	60	30.3	115	45.1	175	38.6	9.68	.002
Vandalism	57	28.8	99	38.8	156	34.4	4.54	.033
Snatch theft	56	28.3	103	40.4	159	35.1	6.65	.010
Robbery	53	26.8	93	36.5	146	32.2	4.37	.037
Attack	46	23.2	80	31.4	126	27.8	3.28	.070
Murder	23	11.6	39	15.3	62	13.7	0.98	.321
Rape or sexual assault	17	8.6	40	15.7	57	12.6	4.48	.034
Subtotal (one or more subtypes)	101	51.0	157	61.6	258	57.0	4.65	.031
Fear of domestic violence
Screamed or yelled at	14	7.1	31	12.2	45	9.9	2.68	.102
Threatened with placement in nursing home	11	5.6	31	12.2	42	9.3	5.02	.025
Threatened with abandonment	14	7.1	27	10.6	41	9.1	1.28	.259
Threatened with use of physical force	13	6.6	27	10.6	40	8.8	1.77	.184
Abused verbally	15	7.6	25	9.8	40	8.8	0.44	.508
Handled roughly in other ways	11	5.6	20	7.8	31	6.8	0.59	.442
Food withheld	10	5.1	20	7.8	30	6.6	0.99	.320
Shaken	7	3.5	21	8.2	28	6.2	3.47	.062
Hit or slapped	7	3.5	20	7.8	27	6.0	2.96	.085
Subtotal (one or more subtypes)	18	9.1	36	14.1	54	11.9	2.23	.136
Total	103	52.0	159	62.4	262	57.8	4.47	.035

Compared with the males, the females were significantly more likely to report fear of theft, χ²(1, N = 453) = 5.87, p = .015, Cramer’s V = .12; fraud, χ²(1, N = 453) = 7.14, p = .008, Cramer’s V = .13; burglary when away from home, χ²(1, N = 453) = 3.95, p = .047, Cramer’s V = .15; burglary when at home, χ²(1, N = 453) = 9.68, p = .002, Cramer’s V = .12; vandalism, χ²(1, N = 453) = 4.54, p = .033, Cramer’s V = .11; snatch theft, χ²(1, N = 453) = 6.65, p = .010, Cramer’s V = .13; robbery, χ²(1, N = 453) = 4.37, p = .037, Cramer’s V = .10; and rape or sexual assault, χ²(1, N = 453) = 4.48, p = .034, Cramer’s V = .11; and crime in general, χ²(1, N = 453) = 4.65, p = .031, Cramer’s V = .11.

The prevalence of fear of domestic violence was calculated in the same way as fear of common crime. Comparatively, fear of domestic violence was less prevalent than fear of common crime, being reported by only 54 respondents (11.9%). In particular, being screamed or yelled at (n = 45, 9.9%), being threatened with placement in a nursing home (n = 42, 9.3%), and being threatened with abandonment (n = 41, 9.1%) were the top three types the participants were most fearful of, while having food withheld (n = 30, 6.6%), being shaken (n = 28, 6.2%), and being hit or slapped (n = 27, 6.0%) were the least feared types. There was no significant gender difference in the prevalence of fear of domestic violence and all its subtypes except for being threatened with nursing home placement, χ²(1, N = 453) = 5.02, p = .025, Cramer’s V = .11, which was more common in females than in males.

Overall, 262 respondents (57.8%) feared one or more types of crime committed by strangers or family members; females made up a significantly large proportion, χ²(1, N = 453) = 4.47, p = .035, Cramer’s V = .10, of these respondents.

Prevalence of Constrained Behavior

As Table 4 shows, the majority of the participants (n = 446, 98.5%) constrained their daily behavior through defensive or avoidance measures.

Table 4.

Frequency of Constrained Behaviors by Specific Type and Gender (N = 453).

	Male (n = 198)		Female (n = 255)		Total (N = 453)		Gender Difference
	n	%	n	%	n	%	χ²	p
Defensive behaviors
Take as little money as possible	174	87.9	231	90.6	405	89.4	0.60	.438
Keep an eye on belongings in crowded places	166	83.8	225	88.2	391	86.3	1.47	.225
Install extra locks on windows or doors	166	83.8	216	84.7	382	84.3	0.02	.903
Hide valuables	114	57.6	178	69.8	292	64.5	6.75	.009
Keep a weapon in home for protection	54	27.3	46	18.0	100	22.1	5.00	.025
Buy a watchdog	35	17.7	38	14.9	73	16.1	0.45	.504
Learn more about self-defense	35	17.7	38	14.9	73	16.1	0.45	.504
Install safety alarm	12	6.1	19	7.5	31	6.8	0.16	.694
Start carrying something to defend yourself	7	3.5	5	2.0	12	2.7	0.55	.459
Subtotal (one or more subtypes)	193	97.5	250	98.0	443	97.8	0.01	.934
Avoidance behaviors
Avoid opening door to strangers	143	72.2	210	82.4	353	77.9	6.07	.014
Avoid talking to strangers	139	70.2	210	82.4	349	77.0	8.63	.003
Avoid offering help to strangers	138	69.7	203	79.6	341	75.3	5.36	.021
Avoid answering phone calls from strangers	128	64.7	179	70.2	307	67.8	1.33	.249
Avoid unsafe areas during the night	129	65.2	177	69.4	306	67.6	0.74	.390
Avoid unsafe areas during the day	113	57.1	165	64.7	278	61.4	2.43	.119
Avoid going out at night	113	57.1	160	62.8	273	60.3	1.27	.260
Avoid going out in day time	91	46.0	135	52.9	226	49.9	1.90	.168
Avoid participating in social activities	76	38.4	115	45.1	191	42.2	1.79	.180
Subtotal (one or more subtypes)	168	84.9	227	89.0	395	87.2	1.38	.240
Total	194	98.0	252	98.8	446	98.5	0.11	.735

The total proportion of participants who conducted defensive behaviors was 97.8% (n = 443). In particular, taking as little money as possible (n = 405, 89.4%), keeping an eye on belongings in crowded places (n = 391, 86.3%), and installing extra locks on windows or doors (n = 382, 84.3%) were the most common behaviors. Comparatively, installing a safety alarm (n = 31, 6.8%) and carrying something to defend oneself (n = 12, 2.7%) were the least common behaviors among the participants. Gender comparison revealed that the females were significantly more likely to hide valuables, χ²(1, N = 453) = 6.75, p = .009, Cramer’s V = .13, while the males were more likely to keep a weapon in their home for protection, χ²(1, N = 453) = 5.00, p = .025, Cramer’s V = .11.

Meanwhile, 395 (87.2%) participants employed avoidance behaviors to ensure their safety. The largest percentage of the sample avoided opening door to strangers (n = 353, 77.9%). It was also common for them to avoid talking to strangers (n = 349, 77.0%) or offering help to strangers (n = 341, 75.3%). Concerning gender differences, avoiding opening door to strangers, χ²(1, N = 453) = 6.07, p = .014, Cramer’s V = .12; avoiding talking to strangers, χ²(1, N = 453) = 8.63, p = .003, Cramer’s V = .14; and avoiding offering help to strangers, χ²(1, N = 453) = 5.36, p = .021, Cramer’s V = .11, were significantly more common among females than among males.

Bivariate Correlation Between Major Variables

As Table 5 shows, common crime victimization was a salient correlate of domestic violence victimization (r = .14, p = .002), fear of common crime (r = .17, p < .001), fear of domestic violence (r = .14, p = .001), constrained behavior (r = .14, p = .002), and poor mental health (r = .15, p < .001). Meanwhile, fear of common crime was significantly related to fear of domestic violence (r = .29, p < .001), constrained behavior (r = .30, p = .002), and poor mental health (r = .11, p = .006).

Table 5.

Bivariate Correlation Among Major Variables (n = 451).

Variable	1	2	3	4	5	6	7	8	9	10	11	12
1. Gender	—
2. Age	−.08	—
3. Education	−.29***	−.10*	—
4. Household finances	.03	−.08	−.09	—
5. Living arrangement	−.05	−.08	.02	−.03	—
6. Physical health	−.10*	−.25***	.06	−.07	.07	—
7. Direct common crime victimization	.01	−.02	.02	.01	−.12**	−.04	—
8. Direct domestic violence victimization	.10*	.01	−.02	.08	.05	−.04	.14**	—
9. Fear of common crime	.17***	−.19***	.02	.09*	−.04	−.10*	.17***	.12*	—
10. Fear of domestic violence	.07	.02	−.06	−.00	.00	−.07	.14**	.39***	.29***	—
11. Constrained behaviors	.10*	−.14**	−.02	−.05	−.02	−.08	.14**	.07	.30***	.14**	—
12. Poor mental health	.11*	.07	−.20***	.13**	−.02	−.34***	.15**	.09	.11*	.16***	−.01	—
M		72.29				45.37	1.13	0.22	2.74	1.28	9.68	2.98
SD		8.27				9.26	1.26	1.19	2.12	1.06	3.59	2.44
Minimum		60				19.65	0	0	0	1	0	0
Maximum		100				64.22	6	9	9.10	9.89	17	11

p < 05. **p < .01. ***p < .001.

Domestic violence victimization was associated with fear of common crime (r = .12, p = .005) and fear of domestic violence (r = .39, p < .001), and fear of domestic violence was a significant correlate of constrained behavior (r = .14, p = .001) and poor mental health (r = .16, p < .001).

Regression Analysis Predicting Fear of Crime

Hierarchical regression analyses were conducted to evaluate the relative contributions of experience of victimization in predicting fear of crime. For subsequent analyses, fear of common crime or fear of domestic violence was entered as the outcome variable for separate models. Sociodemographic variables were entered as Block 1. Common crime victimization or domestic violence victimization was entered as Block 2.

As shown in Table 6, the results of regression analyses indicated that common crime victimization was a significant predictor of fear of common crime (β = .136, p = .004) and domestic violence victimization significantly predicted fear of domestic violence (β = .266, p < .001), independent of sociodemographics, and physical health.

Table 6.

Hierarchical Regression Analyses With Crime Victimization Predicting Fear of Crime Among Older Chinese.

	Standardized Coefficients Beta
	Fear of Common Crime		Fear of Domestic Violence
	Block 1Sociodemographics	Block 2Victimization	Block 1Sociodemographics	Block 2Victimization
Gender	.153**	.144**	.053	.013
Age	−.204***	−.203***	−.005	−.017
Education	.056	.055	−.045	−.048
Household finances	.067	.057	−.014	−.052
Living arrangement	−.039	−.028	.008	−.009
Physical health	−.131**	−.125**	−.064	−.055
CCV		.136**		.067
DVV		.032		.266***
CCV × DVV		.066		.154*
R ²	.086	.119	.011	.180
F	7.005***	6.596***	0.831	10.742***
ΔR²		0.032		.169
ΔF		5.366**		30.237***

Note. Listwise n = 451. CCV = common crime victimization; DDV = domestic violence victimization.

p < .05. **p < .01. ***p < .001.

Regression Analysis Predicting Mental Health

Hierarchical regression analyses were performed separately to assess the impacts of fear of common crime and fear of domestic violence on constrained behavior and poor mental health when holding demographic variables, which were set into the first block, constant. Victimization variables were entered as the second block. Fear of common crime and fear of domestic violence were entered as the third block.

As shown in Table 7, a total of 19.0% of the variance in poor mental health was explained by the model. When controlling for sociodemographics and physical health, common crime victimization was consistently a significant predictor of poor mental health (β = .126, p = .006) even when fear of crime variables were entered into the model. Meanwhile, fear of domestic violence predicted poorer mental health (β = .102, p = .039), independent of sociodemographics, physical health, and victimization variables. Poor mental health was also explained by lower education level (β = −.171, p < .001), worse household finances (β = .088, p = .046), and poorer physical health (β = −.311, p < .001).

Table 7.

Hierarchical Regression Analyses With Crime Victimization and Fear of Crime Predicting Poor Mental Health Among Older Chinese.

	Standardized Coefficients Beta
	Block 1Sociodemographics	Block 2Victimization	Block 3Fear of Crime
Gender	.025	.021	.016
Age	−.022	−.018	−.011
Education	−.172***	−.174***	−.171***
Household finances	.088*	.084	.088*
Living arrangement	.008	.022	.023
Physical health	−.325***	−.319***	−.311***
CCV		.136**	.126**
DVV		.037	.010
CCV × DVV		.008	−.009
Fear of common crime			.024
Fear of domestic violence			.102*
R ²	.157	.180	.190
F	13.830***	10.726***	9.338***
ΔR²		.022	.010
ΔF		3.965**	2.715

Note. Listwise n = 451. CCV = common crime victimization; DDV = domestic violence victimization.

p < .05. **p < .01. ***p < .001.

Regression Analysis Predicting Constrained Behavior

As shown in Table 8, the whole model accounted for 12.2% of the variance in constrained behavior. Higher levels of fear of common crime (β = .240, p < .001) was a salient predictor of constrained behavior when sociodemographic variables, physical health, and victimization variables were in the equation. Other significant predictors of more constrained behavior included older age (β = −.126, p = .010) and wealthier household finances (β = −.092, p = .045).

Table 8.

Hierarchical Regression Analyses With Crime Victimization and Fear of Crime Predicting Constrained Behavior Among Older Chinese.

	Standardized Coefficients Beta
	Block 1Sociodemographics	Block 2Victimization	Block 3Fear of Crime
Gender	.077	.071	.036
Age	−.176***	−.175***	−.126*
Education	−.011	−.012	−.023
Household finances	−.073	−.081	−.092*
Living arrangement	−.023	−.014	−.006
Physical health	−.116*	−.111*	−.079
CCV		.108*	.072
DVV		.005	−.014
CCV × DVV		.061	.039
Fear of common crime			.240***
Fear of domestic violence			.044
R ²	.046	.065	.122
F	3.586**	3.420***	5.568***
ΔR²		.019	.057
ΔF		2.992*	14.306***

Note. Listwise n = 451. CCV = common crime victimization; DDV = domestic violence victimization.

p < .05. **p < .01. ***p < .001.

Discussion

Rapid social transition in China along with emerging urbanization and individualism has led to a noticeable rise in the incidence of crime. Nevertheless, relevant statistical data and empirical research are very limited. The current study provides a valuable reference for estimating the prevalence of the common types of criminal victimization experienced by community-dwelling older Chinese. Consistent with domestic media reports and Western research findings, older people seem to have been targeted as easy victims, with more than half of the participants in this study having experienced victimization. Consistent with research conducted in the United Kingdom (Alford, 2003; Heap, 2008), older adults were found to have a greater risk of being the victims of property crimes such as theft, fraud, and burglary. Different from Schuller’s (2006) observation that consumer fraud was most prevalent, we found that theft was the main type of victimization experienced by older people. Meanwhile, we believe there is a high possibility that the prevalence rates of common crime victimization reported in this study were underreported. As Powell and Wahidin (2008) noted, it is not typical for older people to report their own experiences of victimization to the police.

The proportion of participants reporting domestic violence victimization was within the documented prevalence range among Chinese older populations (Dong, 2015) but relatively low in comparison with existing research findings in mainland China (Dong et al., 2007; Wu et al., 2012) and Hong Kong (Yan, 2014; Yan & Tang, 2004). According to the sample inclusion criteria (home-dwelling, cognitively capable, and urban residents), the participants in the current study might be less dependent in terms of physical, cognitive, and financial capacity, which could explain the few incidences of domestic violence reported. It is hoped that the low prevalence of domestic violence in the present study reflects the real picture given that most participants expressed confidence in the conscience and conduct of their family members. However, there are still reasons why it could be argued that the prevalence reported in this study might be an underestimation. Most Chinese still believe they should not wash their dirty laundry in public. To preserve family honor, protect abusing family members from external sanction, or prevent themselves becoming the victims of revenge (Yan & Tang, 2001), some participants might have understated or even totally denied the domestic violence they have experienced. The underreporting could also be due to the participants feeling too ashamed to disclose their experience of being abused by their family or believing it vain or hopeless to tell the interviewers about their unpleasant encounters. Moreover, the lower reportedly prevalence among male participants could be ascribed to dignity based on paternity and masculinity, which are highly valued in China. The prevalence of fear about common crime and domestic violence has been discussed in another article published by the authors (Qin & Yan, 2014).

This study indicates that common crime victimization is associated with increased fear of common crime: This was reported by more than half of the participants. As past research has asserted (Oh & Kim, 2009; Powell & Wahidin, 2008), crime influences older people to a large extent not only through victimization but also through arousing their fear. When looking at the prevalence of the specific types of crime, fear of common crime and common crime victimization were ranked similarly high in the ranking pattern, with theft at the top and rape or sexual assault at the bottom. This provides evidence that fear of common crime among older people is a rational and natural response to the experience of victimization. In other words, the fear experienced by senior citizens does not come out of nowhere but rather is underpinned by the objective risk of victimization (Miethe & Lee, 1984). It was also clear that fear of common crime was a gendered phenomenon, with females being significantly more likely to report fear of almost all types of common crime. The vulnerability of females and the stereotypical image of males may explain the discrepancy.

Domestic violence victimization was found to increase the likelihood of participants feeling fearful of domestic violence. In terms of the prevalence rate, fear of domestic violence influenced older people more than direct experience of domestic violence. This may indicate that a larger proportion of them were exposed to the risk of being abused. Concerning the fear of specific types of domestic violence, indicators of psychological abuse such as being screamed/yelled at, being threatened with placement in a nursing home, and being threatened with abandonment topped the prevalence list, while indicators of physical abuse, such as having food withheld and being shaken or hit/slapped, were at the bottom. Comparison with the prevalence of domestic violence victimization uncovered some intriguing discrepancies in the ranking of the particular types. For instance, a common means that could be used to victimize the participants directly and arouse their perception of risk was verbal abuse, but this form of abuse did not seem very fearsome to many of them. On the contrary, the participants were seldom threatened with placement in a nursing home in reality, but a high percentage reported being fearful of it. One possible explanation may be that placement in a nursing home is a more severe form of maltreatment. In contemporary China, few older people will accept placement in an institution as a good arrangement for their late life. For one thing, the traditional cultural value of raising children for old age has cultivated a preference for living at home with children and grandchildren around. Sending parents to old people’s homes seems immoral and undermines family honor. For another, nursing homes have a poor image among the public, who believe that these institutions are characterized by an awful environment, ill-mannered and abusive staff, poor food, unacquainted residents, and poor management.

This study identified that more than half of the participants had a mental impairment. Experiences of common crime victimization and fear of domestic violence were found to be attributable risk factors for impaired mental health. Victimization committed by strangers might traumatize older victims by intensifying their sense of vulnerability, powerlessness in terms of self-defense (Wittebrood, 2002), and loss of control over their lives (Spalek, 2005), heightening their fear about future victimization and thus arousing more psychological distress, depression, anxiety (Beaulieu, Leclerc, & Dubé, 2003; Beaulieu et al., 2002), pessimism, self-blaming, anger, hatred, and other emotional discomfort (Tallis, Davey, & Capuzzo, 1994). Inconsistent with previous literature (WHO, 2015; Yan & Tang, 2001), this study did not reveal the psychological impacts of domestic violence victimization but rather the resultant fearful emotions of older people. The relatively low incidence of domestic violence victimization may explain its nonsignificant impact on mental health. Family members are believed to be very caring, reliable, and trustworthy. When older people feel fearful of being mistreated, they may experience great distress, disillusionment, despair, and other contradictory emotions, all of which are harmful to their mental health.

Constrained behavior seems to be very pervasive among older people in urban China. In this study, fear of common crime was proved to play a role in breeding defensive and avoidant behaviors. A possible explanation could be that fear reduces older people’s confidence in their ability to protect themselves and increases their perception of the possibility and severity of being victimized and therefore leads to their tendency to engage in constrained behaviors. The emotional fear related to crime is often expensive because it prompts an individual or household to invest large sums of money in security measures such as safety alarms, special doors and window grilles, security guards, and even keeping a weapon at home (Jackson & Gray, 2010; van Dijk et al., 2007). The expense can be burdensome for financially vulnerable people, including, and especially, many older people. When fear of common crime triggers avoidance behaviors, the consequent individual withdrawal, decline in mobility, and reduced interpersonal interaction will in turn impair the physical, mental, and social functioning of older people (Ditton & Innes, 2005; Lorenc et al., 2012) as well as community cohesion (Jackson & Gray, 2010).

This study has several limitations which might reduce its generalizability. First, it is a cross-sectional study, which precludes the possibility of drawing any conclusion about causality. Second, the response rate might reflect the possible underestimation of the prevalence rate of victimization and fear of crime as victims are more inclined to reject being interviewed due to their fear of crime. Third, the limited types of victimization included in this study resulted in potential specification errors. Fourth, the retrospective nature of the participants’ reports increased the recall bias of this study.

It would be worth conducting further research to collect multiwave data using a longitudinal perspective to further clarify the reciprocal effects between victimization and poor mental health by taking possible feedback into consideration. More efforts, such as community advocacy and propaganda, should be made to increase the participation of the sampled subjects and the accountability of the prevalence estimates. Cultural and contextual factors should be considered in developing measures of older people’s victimization, no matter whether inside or outside the home. To reduce recall bias, multiple information sources (e.g., important others’ reports) should be sought in future studies to increase the reliability of the data. It would be valuable to replicate this study in other cities with a different socioeconomic environment from Kunming, for example, Wuhan (in the middle of China) and Shenyang (in the northeast of China), so as to cross-validate the research findings and establish the test–retest reliability of the culturally adapted scales. Furthermore, rural China is also experiencing a tremendous social transition. Whether and how crime, including domestic violence, influences older people living there has the potential to be a highly promising topic for future research efforts. Last, aging population is admittedly heterogonous and their diversity in risk of and resilience from victimization in terms of gender, age, socioeconomic status and personality, and so on should be respected and addressed in all relevant research.

This study has significant implications for improving the mental health and social well-being of older Chinese by addressing crime. Both prevention and intervention measures should be taken by policymakers, the police authority, and practitioners. To prevent older people experiencing common crime victimization, laws, regulations, and judicial responses should be drawn up and improved to crack down on crime. Moreover, the efficiency and effectiveness of the police in investigating and uncovering criminal cases and their sensitivity to older citizens’ risks and needs should be improved. Crime reduction campaigns against crimes particularly targeting older people, for example, commercial fraud in the name of health care and investment, should be implemented, and more effective precautionary strategies should be disseminated among older people and other residents, especially anticrime knowledge and defensive approaches against theft, fraud, and burglary. These measures may help reduce older victims’ avoidance behaviors that impair their social well-being by increasing their individual withdrawal and reducing their mobility and interpersonal interaction. Considering the significant impacts of fear of domestic violence on mental health, public education and social security should be improved to empower seniors and relieve their fear of being threatened with placement in a nursing home or being abandoned. In terms of protecting older people from psychological abuse, the traditional Chinese cultural values and norms of showing respect and filial piety to older people should be reemphasized in China. Meanwhile, social services for the carers of older adults, such as the provision of resources, skills training, and respite care, may help to reduce burnout among caregivers and the risk of care recipients being abused.

For the purpose of minimizing the loss and suffering of older victims, the compensation and support system should be enforced through legislation. Community services, such as problem solving, counseling, and peer support, are particularly needed to alleviate the mental trauma experienced by older victims. The long-existing Chinese cultural value that there should be no interference in others’ family affairs, even domestic violence, should be replaced by the collective efficacy of people, including physicians, nurses, neighbors, and kin, keeping in touch with older people to detect and report suspicious cases of elder abuse. Police and local community officers should intervene and stop mistreatment by abusers. Family counseling and conciliation might be effective measures social workers can provide when older people are at fault in their conflicts with family members.

Conclusion

This study was a pioneer exploration of the prevalence of victimization and its negative impacts on the well-being of urban older Chinese people. Property crime victimization in terms of theft, fraud, and burglary was found to be common, while personal crime victimization such as physical attack and rape or sexual assault was much less common. Experiences of common crime had a negative impact on the mental health and emotional fear of older victims and indirect consequences on their social well-being by invoking more constrained behavior. Domestic violence victimization, particularly physical abuse, was not prevalent among older people. Nevertheless, fear of domestic violence, which could impair their mental health, was more prevalent. This article suggests the necessity for more gerontological studies to examine the consequences of crime on older people and the importance of evidence-based practical efforts and policy decisions on criminal victimization for their safety, mental health, social participation, and quality of life. The special features and needs of older people should be placed firmly on the future agenda of research, social services, and policy on crime prevention and intervention in China.

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) received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Nan Qin is a post-doctoral fellow at the Department of Applied Social Sciences, the Hong Kong Polytechnic University. Her research interests include criminal victimization and fear of crime of older adults, active and healthy aging and family caregiving, etc.

Elsie Yan is an associate professor at the Department of Applied Social Sciences, the Hong Kong Polytechnic University. Her research interests include family violence with a focus on elder abuse, intergenerational relationships, dementia care and elder sexuality.

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