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. 2025 Feb 3;65(5):gnaf039. doi: 10.1093/geront/gnaf039

Do Higher Levels of Lifetime Victimization Predict an Earlier Onset of First IADL Limitation in Later Life?

Mallory J Bell 1,2,✉, Jinnan Ren 3,4, Madison R Sauerteig-Rolston 5,6,7, Kenneth F Ferraro 8,9
Editor: Joseph E Gaugler
PMCID: PMC11979741  PMID: 39898442

Abstract

Background and Objectives

This study examines the relationship between lifetime victimization and health in later life and asks whether the level of lifetime victimization is associated with an earlier onset of an IADL limitation.

Research Design and Methods

Using data from the Health and Retirement Study (N = 11,143), we construct 3 levels of lifetime victimization (none, monovictimization, and polyvictimization). We use Weibull accelerated failure time models to examine the relationship between the level of lifetime victimization and the onset of an IADL limitation.

Results

Monovictimization and polyvictimization were associated respectively with a 9% and 18% earlier time to onset of an IADL limitation compared with individuals who did not experience victimization. Additionally, polyvictimization respondents had an earlier onset of an IADL limitation compared toQ monovictimization respondents.

Discussion and Implications

Lifetime victimization is a life course antecedent to IADL limitations in later life, especially among individuals who have experienced multiple types of victimization. Future research should examine resources that slow health declines among people who have experienced victimization.

Keywords: Analysis—longitudinal analysis, Function/mobility, Stress and coping

Instrumental Activities of Daily Living (IADLs) are activities that support one’s health and home, including the ability to cook, make a phone call, take medication, manage money, and shop for groceries (Edemekong et al., 2019). Limitations in IADLs are widely recognized as threats to independent living, quality of life, and disability-free life expectancy (Crimmins et al., 2009; Edemekong et al., 2019; Fusco et al., 2012). The prevalence of IADL limitations is high in the United States, with nearly 20% of adults age 50+ reporting at least one IADL limitation (Qi et al., 2023). More research on life course antecedents of IADL limitations is warranted given the prevalence and significance of difficulties with these activities.

Although there is a breadth of research on life course stress and health (Boen, 2020; Sauerteig-Rolston and Ferraro, 2024; Shrira and Litwin, 2014), the victimization-and-health literature tends to measure victimization during only one life course stage and examine its contemporaneous or long-term health consequences. Therefore, more research is warranted that examines the consequences of cumulative lifetime victimization for health and well-being in later life. Perhaps adults who have experienced multiple types of lifetime victimization (i.e., polyvictimization) are at greater risk for adverse health in later life than adults who have experienced a single type of lifetime victimization (i.e., monovictimization) or no victimization. We contribute to current research by using a longitudinal study design to examine whether the level of lifetime victimization is associated with an earlier onset of an IADL limitation.

Theoretical Framework

Lifetime criminal victimization (herein lifetime victimization) refers to traumatic events over the life course that involve a criminal component, such as being the victim of physical abuse at the hands of a parent, having one’s home burglarized, or being the victim of fraud. Stress process models are a common theoretical framework used in research on victimization and health (Semenza et al., 2021; Turanovic, 2018, 2023; Wong and Waite, 2017). The stress process model argues that stressful events, such as experiences of lifetime victimization, diminish positive concepts of self, self-esteem, and mastery, each of which increases the likelihood that victimizations adversely affect health status (Pearlin et al., 1981). Indeed, victimization is associated with adverse mental (Charak et al., 2020) and physical health outcomes (Kong et al., 2024).

Often, victimization represents a turning point that increases the risk for future disadvantage (Turanovic, 2018)—an argument that aligns with well-known life course theories on stress proliferation (Pearlin et al., 2005) and cumulative inequality (Ferraro and Shippee, 2009; Ferraro et al., 2009). Cumulative inequality (CI) theory is particularly useful for research on the relationship between lifetime victimization and health because it contends that disadvantage increases the risk for future disadvantage. In the current study, we conceptualize lifetime victimization as a salient life course disadvantage that may increase the risk for health disadvantages in later life and we focus on a prevalent health disadvantage reported by older adults—IADL limitations.

Further, CI theory emphasizes the salient role that accumulation processes play in shaping health and well-being in later life. We examine the health implications of exposure to varied types of crime including abuse, assault, fraud, and theft, not simply repeated exposure to one type of crime. Exposure to multiple types of crime is consistent with the concept of polyvictimization whereas exposure to a single type of crime is consistent with the concept of monovictimization. Polyvictimization therefore aligns with the argument made by Ferraro and Shippee (2009) regarding how disadvantage often spans multiple domains of one’s life. It is hard to deal with one type of victimization, but when victimization accumulates across some combination of abuse, assault, fraud, and theft, the toll on health may be greater. Therefore, we investigate whether polyvictimization and monovictimization are associated with an earlier onset of an IADL limitation compared to individuals who report no experiences of victimization and whether polyvictimization is associated with an earlier onset of an IADL limitation compared to monovictimization.

Contributions

A breadth of literature examines the relationship between life course stress and health; these studies typically examine adversity in childhood, exposure to financial strain, chronic stressors, and stressful life events (Boen, 2020; Sauerteig-Rolston and Ferraro, 2024; Shrira and Litwin, 2014). We narrow our focus to victimization because traumatic events are a unique stressor that have been identified as particularly onerous (Pearlin et al., 2005) and exert significant health costs (Turanovic, 2022). The victimization-and-health literature tends to measure victimization at one life course stage and examine either contemporaneous or long-term health consequences. That is, previous research examines the relationship between victimization and health in either childhood or adulthood (Britt, 2001; Dong and Simon, 2013; Finkelhor et al., 2007; Schofield et al., 2013; Yunus et al., 2019; for a review, see Turanovic, 2023) or the long-term consequences of early life victimization on later-life health (Evans-Lacko et al., 2017; Laditka and Laditka, 2018; Takizawa et al., 2014).

A few studies have examined lifetime victimization but rely on samples of individuals younger than age fifty, with a particular emphasis on children, adolescents, and young adults (Cyr et al., 2017; Finkelhor et al., 2009; Turanovic and Pratt, 2015; Turner et al., 2006; Turnovic, 2022). Studies on the health implications of cumulative lifetime victimization on health in adulthood and later life examine the relationship between lifetime victimization and mental health (Eslami et al., 2017; Rapsey et al., 2019; Wiklund et al., 2022) or use more general measures of health status (Simmons et al., 2021).

A cross-sectional study by Riedl et al. (2019) reports that child polyvictimization was associated with increased odds of having an array of physical diseases during adulthood, and this risk was higher if they had also experienced domestic violence, underscoring the importance of considering multiple types of victimization across the life course. An exemplary study by Kong et al. (2024) examined the relationship between lifetime victimization and three indicators of health and well-being in later life: psychological health, physical health, and cognitive function. Results revealed that higher lifetime victimization was associated with higher risk for depression and worse physical health, as measured by limitations in physical functioning. We extend this study by focusing on an upstream and distinct indicator of health (i.e., IADL limitations) and by using longitudinal data to examine the relationship between lifetime victimization and age at onset of first IADL limitation. Drawing from stress process models and CI theory we expect that polyvictimization and monovictimization are associated with an earlier onset of an IADL limitation compared to individuals who report no exposure to victimization and that polyvictimization is associated an earlier onset of an IADL limitation compared to monovictimization.

Method

Data

Data for this study are from six waves (2008–2018) of the Health and Retirement Study (HRS)—a nationally representative, longitudinal study of adults 50 years and older in the United States. The HRS is sponsored by the National Institute on Aging (grant number NIA U01AG009740) and is conducted by the University of Michigan. We use data from two surveys within the HRS: the Core survey and the Psychosocial and Lifestyle Questionnaire. The former collects information on socioeconomic status, demographics, and health status whereas the latter collections information on well-being, lifestyle, social relationships, personality, work, and self-related beliefs. Data for our focal dependent variable, as well as covariates, are from the Core survey, whereas data for our focal independent variable are from the Psychosocial and Lifestyle Questionnaire.

Although the Core survey is administered biennially, the Psychosocial and Lifestyle Questionnaire is collected on half samples in a leave-behind survey during the Enhanced Face-to-Face (EFTF) interview. Thus, we arrayed the data for Wave 1 (W1) with the half sample of HRS respondents who completed the questionnaire in 2008 and the other half who completed it in 2010. By combining the 2008 and 2010 half-samples, we treat those two halves as W1 (baseline). We then matched covariates to respondents’ EFTF assignment such that respondents who completed the Psychosocial and Lifestyle Questionnaire in 2008 have information on covariates and victimization from 2008, whereas respondents who completed the Psychosocial and Lifestyle Questionnaire in 2010 have information on covariates and victimization from 2010.

The analytic sample is comprised of respondents who met the following criteria: (a) completed the 2008 or 2010 Psychosocial and Lifestyle Questionnaire (N = 15,387), (b) reported no IADL limitations prior to 2008 or 2010, depending on one’s EFTF assignment (N = 12,446), (c) were not cognitively impaired at baseline (N = 12,209), and (d) were nonmissing on covariates and had a self-reported race of Non-Hispanic White, Non-Hispanic Black, or Hispanic (herein White, Black, and Hispanic, respectively), yielding a final analytic sample of 11,143.

Instrumental Activities of Daily Living (IADL)

The focal dependent variable in this study is first onset of an IADL limitation during the study period. HRS respondents were asked whether they have any difficulty (a) preparing a hot meal, (b) making phone calls, (c) taking medication, (d) managing money, or (c) shopping for groceries, “because of a health or memory problem.” The number of activities that respondents had difficulty with were summed, 0–5, where higher scores indicated more IADL limitations. Because we are interested in the onset of first IADL limitation, we recoded the count measure into a binary measure where 1 indicates at least one IADL limitation. We excluded respondents who reported IADL limitations prior to the baseline wave to ensure correct temporal ordering between independent and dependent variables.

Lifetime Victimization

Lifetime victimization is our focal independent variable, and we limit our conceptualization of victimization to potentially traumatic experiences that have a criminal component. Victimization items span multiple life course stages and multiple types of victimization. Specifically, respondents were asked whether they were: (a) physically abused by a parent before age 18; (b) ever been the victim of a serious physical attack or assault; (c) robbed or had their home burglarized in the past five years; and (d) the victim of fraud in the past five years. Respondents could answer either “yes” or “no” to each item. Affirmative answers were summed to create a count measure of lifetime victimization, 0–4, where higher scores indicated more types of victimization. We then created three categories to capture level of lifetime victimization: zero (reported no victimization), monovictimization (reported one type of victimization), and polyvictimization (reported two or more types of victimization). Using three categories of lifetime victimization is methodologically advantageous over treating the variable as a count variable because only five respondents experienced all four types of lifetime victimization.

Covariates

We adjust for several variables given their known association with IADL limitations (McGuire et al., 2006; Portela et al., 2020). Socioeconomic and demographic covariates included in the analysis are gender, race, ethnicity, age, education, wealth, and marital status. Women are coded as 1 and men are the reference group. Race and ethnicity are self-reported as Non-Hispanic White (reference group), Non-Hispanic Black, or Hispanic (hereafter, White, Black, and Hispanic, respectively). Education is a continuous variable that captures years of education and ranges 0–17. Wealth is a continuous variable calculated by estimating participants’ net worth and subtracting debt. Because of the wide distribution of wealth values, the variable was reported in 10,000s and transformed by taking the cube root. We use a binary variable to differentiate married people from nonmarried people and nonmarried people serve as the reference group.

In terms of health behaviors and health status, we adjust for depressive symptoms, physical activity, body-mass index (BMI), and cognitive function. Depressive symptoms are measured by an 8-item version of the Center for Epidemiologic Studies—Depression (CESD) scale. The measure queries about the presence of eight depressive symptoms in the past week (felt depressed, everything was an effort, sleep was restless, was happy, felt lonely, felt sad, could not get going, and enjoyed life). Positively worded items were reverse coded. Responses on the measure were summed, 0-8, where higher scores indicate the presence of more depressive symptoms. A self-reported physical activity scale gathered information on both frequency and intensity of physical activity. Participants were asked how often they participated in mild, moderate, and vigorous physical activity. The physical activity measure weighted the type of activity by the intensity based on metabolic equivalent recommendations and was a continuous measure that ranged 0–17.6. We use a three category BMI variable to distinguish between respondents who are normal or underweight (reference group), overweight, or obese. Cognitive function is a continuous variable that ranges 7–27 and is from the modified version of the Telephone Interview for Cognitive Status (TICS) survey. Respondents who scored lower than a 7 at baseline were excluded from the sample due to concerns regarding accurate recall of lifetime victimization. The cognitive function measure includes (a) a memory test of immediate and delayed recall, scored 0–20, (b) a serial 7s subtraction test to gauge working memory, scored 0–5, and (c) a counting backwards test to measure attention and processing speed, scored 0–2.

Analytic Strategy

We use Weibull accelerated failure time (WAFT) models to examine the relationship between lifetime victimization and onset of first IADL limitation. We use age as the time metric, which is common in longitudinal studies of older adults (Ferraro et al., 2023; Sauerteig-Rolston and Ferraro, 2024) and has been recommended over time-in-study metrics when analyzing epidemiologic cohort data (Thiébaut and Bénichou, 2004). Two variables are necessary for event history analysis: an incidence variable and a duration variable. The incidence variable is the first report of an IADL limitation during the study period, and age at onset of first IADL limitation is the duration variable. We use age at last report for respondents who did not report an IADL but left the sample during the study period (i.e., censored).

We use coefficients (β) and time ratios (eβ) to describe the relationship between lifetime victimization and first IADL limitation. A negative coefficient indicates earlier onset of an IADL limitation, whereas a positive coefficient indicates a later onset. A time ratio of less than one indicates an earlier onset of IADL limitation, whereas a time ratio greater than one indicates a later onset. Time ratios can be used to quantify how much earlier or later, in percent, an IADL limitation occurs using the following formula: [(1 − eβ) × 100]. For example, for a categorical variable, a time ratio of 0.90 would indicate a 10% earlier onset of an IADL limitation compared with the reference group.

We use nested WAFT models to investigate the influence of additional variables. First, we examine the relationship between lifetime victimization and IADL limitation onset in an unadjusted model. Next, we examine lifetime victimization and IADL limitation onset after adjusting for socioeconomic and demographic characteristics, including gender, race and ethnicity, education, wealth, and marital status. The third and fully adjusted model includes variables for lifetime victimization, socioeconomic and demographic information, as well as health characteristics, including depressive symptoms, physical activity, BMI, and cognitive function.

Results

Descriptive statistics are displayed in Table 1. Nearly a quarter of respondents (22.47%) experienced an incident IADL limitation during the study period, and this occurred, on average, around age 77. The majority (82.02%) of respondents reported zero experiences of lifetime victimization, whereas 14.97% and 3.01% experienced monovictimization and polyvictimization, respectively.

Table 1.

Descriptive Statistics for the Analytic Sample, Health and Retirement Study (HRS), N = 11,143

Variable Range Percent Mean (SD)
IADL limitation 0,1 22.47 —
Age of IADL limitation 50–101 — 76.89(9.61)
Lifetime victimization
 Zeroa 0,1 82.02 —
 Monovictimization 0,1 14.97 —
 Polyvictimization 0,1 3.01 —
Covariates
 Mena 0,1 41.69 —
 Women 0,1 58.31 —
 Non-Hispanic Whitea 0,1 78.47 —
 Non-Hispanic Black 0,1 13.37
 Hispanic 0,1 8.16
 Education 0-17 — 13.10(2.79)
 Wealth −4.39 to 15.47 — 2.89(1.80)
 Not Marrieda 0,1 32.34 —
 Married 0,1 67.66 —
 Depressive symptoms 0-8 — 1.05(1.64)
 Physical activity 0-17.6 — 8.27(4.00)
 Normal weighta 0,1 29.08 —
 Overweight 0,1 38.18 —
 Obese 0,1 32.74 —
 Cognitive function 7-27 — 15.92(3.73)
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Notes: SD = standard deviation. Lifetime victimization and covariates are measured at baseline in 2008/2010. a Reference group.

Results regarding the relationship between level of lifetime victimization and onset of an IADL limitation are presented in Table 2. In Model 1, monovictimization is associated with a 10% (time ratio [TR] = 0.90, p < .001) earlier onset of an IADL limitation compared with respondents who report zero experiences of lifetime victimization. In the same model, polyvictimization is associated with a 21% (TR = 0.79, p < .001) earlier onset of an IADL limitation compared with respondents who reported zero experiences of lifetime victimization.

Table 2.

The Relationship between Lifetime Victimization and Onset of First IADL Limitation from Weibull Accelerated Failure Time Models (N = 11,143)

Independent variables Model 1 Model 2 Model 3
β (SE) Time
ratio
(eβ)		 β (SE) Time
ratio
(eβ)		 β (SE) Time
ratio
(eβ)
Lifetime victimization
 Monovictimization −0.11(0.02)*** 0.90*** −0.10(0.02)*** 0.91*** −0.09(0.02)*** 0.91***
 Polyvictimization −0.24(0.03)*** 0.79*** −0.20(0.03)*** 0.82*** −0.19(0.03)*** 0.82***
Covariates
 Women −0.02(0.01) 0.98 −0.01(0.01) 0.99
 Non-Hispanic Black −0.10(0.02)*** 0.90*** −0.10(0.02)*** 0.91***
 Hispanic −0.08(0.02)*** 0.93*** −0.06(0.02)*** 0.94***
 Education 0.01(0.00)* 1.01* 0.001(0.00) 1.002
 Wealth 0.06(0.00)*** 1.06*** 0.05(0.00)*** 1.05***
 Married −0.09(0.01)*** 0.91*** −0.10(0.01)** 0.90**
 Depressive symptoms −0.04(0.00)*** 0.96***
 Physical activity 0.003(0.01)* 1.003*
 Overweight −0.001(0.01) 1.00
 Obese −0.08(0.01)*** 0.93***
 Cognitive function 0.003(0.00)* 1.01*
Constant 3.81 (0.01)*** 3.66(0.02)*** 3.62(0.03)***
Likelihood ratio χ2 78.73 505.15 579.64
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Notes: SE = standard error.

*p < .05;

**p < .01;

***p < .001.

Model 2 adjusts for demographic and socioeconomic characteristics and reveals that monovictimization and polyvictimization are independently associated with earlier times to onset of an IADL limitation compared with respondents who report zero victimization (respectively, TR = 0.91, p < .001, monovictimization; and TR = 0.82, p < .001, polyvictimization). Model 2 also reveals that Black, Hispanic, and married respondents have a 10%, 7%, and 9% earlier onset of an IADL limitation compared with White and nonmarried respondents, respectively (TR = 0.90, p < .001, Black; TR = 0.93, p < .001, Hispanic; TR = 0.91, p < .001, married). Higher education levels and greater wealth are both associated with a later onset of first IADL limitation (TR = 1.01, p < .05, education; TR = 1.06, p < .001, wealth). Women did not have a significantly different time to onset of an IADL limitation compared with men (p > .05).

Level of lifetime victimization remains associated with onset of an IADL limitation even in the fully adjusted model (i.e., Model 3). Specifically, monovictimization is associated with a 9% earlier time to onset of an IADL limitation (TR = 0.91, p < .001) and polyvictimization is associated with an 18% earlier time to onset of an IADL limitation (TR = 0.82, p < .001).

In Model 3, Black, Hispanic, and married respondents have an earlier time to onset of an IADL limitation, whereas higher levels of wealth are associated with a later onset (TR = 0.91, p < .001, Black; TR = 0.94, p < .001, Hispanic; 0.90, p < .001, married; TR = 1.05, p < .001, wealth). Higher levels of education are not associated with time to onset of an IADL limitation and women do not have a significantly different time to onset of an IADL limitation compared to men (both p > .05).

In terms of health characteristics, more physical activity and higher cognitive function are associated with a later onset of first IADL limitation (TR = 1.003, p < .05, physical activity; TR = 1.01, p < .05, cognitive function). Overweight respondents do not have a significantly different time to onset of an IADL limitation than respondents who had a normal BMI or were underweight (p > .05). Obese respondents have a significantly earlier time to onset of an IADL limitation compared to respondents with a normal BMI (TR = 0.93; p < .001). More depressive symptoms are associated with an earlier onset of an IADL limitation (TR = 0.96; p < .001).

We were also interested in determining whether time to onset of first IADL limitation is significantly earlier for polyvictimization respondents compared with monovictimization respondents. We performed additional analyses and re-estimated Model 3 by switching the reference group from “none” to “monovictimization” (see Table 3). Results revealed that polyvictimization respondents had an approximately 10% earlier time to onset of first IADL limitation compared to monovictimization respondents (TR = 0.90, p < .01). Therefore, our results indicate that level of lifetime victimization had an incremental association with time to onset of first IADL: monovictimization respondents had an earlier onset of an IADL limitation compared to respondents who had not experienced victimization and polyvictimization respondents had an earlier onset of an IADL limitation compared with both monovictimization respondents and respondents who had not experienced victimization.

Table 3.

The Relationship Between Lifetime Victimization and Onset of First IADL Limitation Using Monovictimization as the Reference Group From Weibull Accelerated Failure Time Models (N = 11,143)

Independent variables Model 1
β (SE) Time ratio
(eβ)
Lifetime victimization
 Polyvictimization −0.11(0.03)** 0.90(0.03)**
Constant 3.51
Likelihood ratio χ2 579.94
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Notes: Model 1 adjusted for gender, race, ethnicity, education, wealth, marital status, depressive symptoms, physical activity, BMI, and cognitive function. SE = standard error.

*p < .05;

**p < .01;

***p < .001.

Discussion

The present study investigated the relationship between level of lifetime victimization and onset of an IADL limitation. Our findings indicate that higher levels of lifetime victimization are associated with an earlier onset of first IADL limitation, particularly for individuals who have experienced polyvictimization. More specifically, we found that monovictimization respondents had a 10% earlier onset of an IADL limitation compared to respondents who had not experienced victimization and that polyvictimization respondents had an 18% and 10% earlier time to onset of an IADL limitation compared to respondents who reported zero types of victimization and monovictimization respondents, respectively.

The findings from the current study align with CI theory in two ways. First, CI theory specifies that disadvantage can span multiple domains of one’s life and polyvictimization reflects how disadvantage diffuses across different domains because it measures, for example, harm to one’s body (abuse or assault) and property (burglary). Second, CI theory argues that disadvantage can beget future disadvantage and results from our study indicate that that one form of disadvantage—exposure to victimization—is associated with health disadvantages in later life, particularly for respondents who report polyvictimization. Experiencing one type of victimization imposes nontrivial consequences for health in later life, but our results indicate that when the experiences of victimization accumulate across some combination of abuse, assault, fraud, and property crime, the toll on health is greater.

Results regarding level of lifetime victimization support past research that reports that the deleterious health consequences of victimization are particularly pronounced for individuals who have experienced polyvictimization (Charak et al., 2020; Kong et al., 2024; Rapsey et al., 2019; Riedl et al., 2019; Simmons et al., 2021). For example, using latent class analysis, Charak et al. (2020) found that adults in the polyvictimization class were at greater risk for adverse mental health outcomes compared to the limited victimization class. Victimization is a form of disadvantage that may adversely affect sense of self, impair social, cognitive, and physical development, and influence use of deleterious coping strategies, all of which increase the risk for future health disadvantages (Macmillan, 2001; Pearlin et al., 1981, 2005; Turanovic, 2018).

Finkelhor et al. (2007) examined the relationship between victimization and trauma symptoms among children and was one of the first study to demonstrate the importance of taking polyvictimization into account when studying health outcomes. We extend this study by focusing on how lifetime victimization is associated with health in later life. This approach is distinct from much of prior research, which primarily measured victimization during one life course stage (i.e., childhood or adulthood). Further, although prior research reports that victimization is associated with incidence of an array of adverse health outcomes, we use longitudinal data to examine the relationship between lifetime victimization and age at onset of first IADL limitation.

The results presented in the current study underscore the idea that “accumulation processes” are “central to the aging experience” (Ferraro and Morton, 2018, p. 269). There is considerable diversity in health trajectories in later life (Lowsky et al., 2014) and the results presented in the current study may explain why some adults experience earlier health decline than others. Other research also underscores the adverse relationship between the accumulation of stress and health trajectories in later life. For example, Sauerteig-Rolston and Ferraro (2024) reported that higher cumulative stress burden—as well as exposure to an array of individual stress domains—was associated with an earlier onset of disability.

Our findings have important practical and policy implications. It may be useful for health care institutions, public programs (i.e., victim assistance programs), social workers, and caregivers to screen for polyvictimization. Given that adults who reported polyvictimization were at particular risk for an earlier onset of at least one IADL limitation, they may require greater—or even alternative—types of interventions compared to people who have experienced fewer types of victimization or none at all. In other words, asking for a history of victimization may be useful to address needs associated with distinct types of victimization. Future studies should examine social, material, and psychological resources that buffer, or slow, the negative consequence of lifetime victimization on health outcomes in later life and whether the treatment effects are different for people who have experienced monovictimization vs. polyvictimization (Hawkley et al., 2005).

Limitations

Results from our study should be interpreted in light of two limitations. First, CI theory specifies that studies of older adults should be aware of potential selection effects. That is, “nonrandom selection may give the appearance of decreasing inequality in later life” (Ferraro and Shippee, 2009, p. 337). Our study may underestimate the adverse relationship between lifetime victimization and health if individuals exposed to victimization were at greater risk for premature mortality and died before they could participate in the HRS. The respondents in our study who reported monovictimization and polyvictimization may be a particularly robust group of adults.

Second, and as noted in other victimization research, underreporting may be a factor influencing our results (Kilpatrick et al., 1987; Lynch, 1993). Respondents may be hesitant to report experiences of victimization or feel that their experience was outside the scope of the victimization items asked by the HRS. Criminal victimization items available in the HRS query about physical abuse from parents, physical assault, robbery, and fraud, which are fine for assessing polyvictimization, but those four items are a subset of the types of crime. Future studies assessing a wider range of victimizations would be most helpful. Indeed, respondents could have experienced other types of victimization that are not asked in HRS surveys, such as stalking, sexual harassment, terrorism, and aspects of intimate partner violence that do not constitute a physical assault, including psychological abuse, controlling behaviors (i.e., isolating a person from their friends or family, restricting access to financial resources), and threat of harm.

Conclusion

Drawing from CI theory, we examined whether lifetime victimization puts adults at risk for earlier health decline. We report that exposure to monovictimization and polyvictimization is associated with an earlier onset of first IADL limitation. Lifetime victimization may be a particularly onerous type of stressor—the effects of which may accumulate over the life course and pose substantial health risks for older adults.

Contributor Information

Mallory J Bell, Department of Sociology, Purdue University, West Lafayette, Indiana, USA; Center on Aging and the Life Course, Purdue University, West Lafayette, Indiana, USA.

Jinnan Ren, Department of Sociology, Purdue University, West Lafayette, Indiana, USA; Center on Aging and the Life Course, Purdue University, West Lafayette, Indiana, USA.

Madison R Sauerteig-Rolston, Department of General Internal Medicine and Geriatrics, Indiana University School of Medicine, Indianapolis, Indiana, USA; Indiana University Center for Aging Research, Indianapolis, Indiana; USA; Regenstrief Institute, Inc., Indianapolis, Indiana, USA.

Kenneth F Ferraro, Department of Sociology, Purdue University, West Lafayette, Indiana, USA; Center on Aging and the Life Course, Purdue University, West Lafayette, Indiana, USA.

Funding

This work was supported by grants from the National Institute on Aging awarded to Kenneth F. Ferraro (AG043544, AG068388). The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Conflict of interest

None.

Data Availability

All data files for the Health and Retirement Study used in this analysis were obtained from the HRS website: https://hrs.isr.umich.edu/data-products. The study reported in the manuscript was not preregistered.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

All data files for the Health and Retirement Study used in this analysis were obtained from the HRS website: https://hrs.isr.umich.edu/data-products. The study reported in the manuscript was not preregistered.

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