Understanding Service Utilization in Cases of Elder Abuse to Inform Best Practices

Abstract

Elder abuse (EA) case resolution is contingent upon victims accepting and pursuing protective service interventions. Refusal/underutilization of services is a major problem. This study explored factors associated with extent of EA victim service utilization (SU). Data were collected from a random sample of EA cases (n = 250) at a protective service program in New York City. In cases involving financial abuse, higher SU was associated with females, poor health, perceived danger, previous help-seeking, and self or family referral. In physical abuse cases, higher SU was associated with family referral and previous help-seeking; lower SU was related to Hispanic race/ethnicity, being married, and child/grandchild perpetrator. In emotional abuse cases, higher SU was associated with self or family referral, victim–perpetrator gender differential, perceived danger, and previous help-seeking; lower SU was related to child/grandchild perpetrator. Findings carry implications for best practices to retain and promote service use among elder victims of abuse.

Keywords

elder abuse elder mistreatment service utilization Adult Protective Services

Introduction

With a growing aging population, elder abuse (EA) is increasingly recognized as a public health crisis among policymakers, researchers, and clinicians (U.S. Government Accountability Office, 2011; White House Office of Public Engagement, 2012). EA refers to an intentional act or omission occurring in a relationship of trust that causes harm or serious risk of harm to an older adult or deprives an older adult of basic needs. EA encompasses physical, sexual, emotional, and financial abuse or neglect toward an older adult (National Research Council [NRC], 2003). One-year EA incidence among community-dwelling, cognitively intact older adults in the United States ranges from 7.6% to 11.4% (Acierno et al., 2010; Lachs & Berman, 2011). Unresolved EA is associated with increased risk of pre-mature death (Lachs, Williams, O’Brien, Pillemer, & Charlson, 1998), hospitalization (Dong, Simon, & Evans, 2012b), nursing home placement (Lachs, Williams, O’Brien, & Pillemer, 2002), financial ruin, psychological distress, and poor health (Fisher, Zink, & Regan, 2011).

Every U.S. state is mandated to develop and maintain programs to protect EA victims. In most states, community-based EA cases are handled by Adult Protective Services (APS), while some jurisdictions enact specific provisions for elder protection. Regardless of administrative structure, protective service programs share the fundamental goal to alleviate and resolve risk contributing to EA cases. In the current service delivery model, EA case resolution is contingent upon the elder victim accepting and pursuing program safety plan interventions. This model differs from intimate partner violence (IPV) programs that place a larger emphasis on perpetrator intervention. Unlike child protective services, elder protection programs are voluntary unless the older adult lacks cognitive capacity. Therefore, cognitively intact EA victims can choose to accept protective interventions completely, partially, or refuse them altogether. Factors accounting for this variation in service utilization (SU) are under-studied and misunderstood.

Refusal or underutilization of protective EA interventions is a major problem. Approximately, 93% to 96% of EA victims living in the community do not use formal support services (Pillemer & Finkelhor, 1988; Lachs & Berman, 2011). Among EA victims who interface with protective service programs, 13% to 58% refuse services completely. Among clients who accept protective service support, only 16% to 28% pursue all of the interventions recommended in their safety plans (Barker & Himchak, 2006; Rizzo, Burnes, & Chalfy, 2013). Lower EA victim SU is associated with lower levels of case resolution (Burnes, Rizzo, & Courtney, 2014; Rizzo et al., 2013), which leaves elder victims susceptible to heightened risks of mortality and morbidity. Identifying SU barriers and facilitators will inform the development of effective service delivery models and best practices in elder protection programs.

Service Utilization Theory

The Behavioral Model of Health Services Use (BMHSU; Andersen, 1995) guided our examination of SU. The BMHSU is the predominant theoretical framework used to understand SU in social gerontology (Alley, Putney, Rice, & Bengtson, 2010). According to the BMHSU, SU is a function of three dimensions: one’s predisposition to use services (predisposing), resources that enable or inhibit use (enabling), and one’s need for services (need). Predisposing factors consist of socio-structural/status factors (e.g., individual EA victim and perpetrator sociodemographics). The enabling dimension represents personal, family, social and community resources, relationship-level variables, and organization contextual factors that enable SU (e.g., EA victim social support, living arrangement, victim–perpetrator relationship variables, and protective service context). The need dimension includes both perceived and evaluated need; it reflects the nature and magnitude of the primary/presenting problem as judged by both the victim (perceived need) and the service provider (evaluated need; for example, EA type, EA severity, victim perceptions of the EA problem, and protective service evaluations of the EA problem). This dimension also includes secondary health issues that might exacerbate need for services (e.g., health status, functional status). The BMHSU proposes that greater predisposing sociocultural advantage, enabling resources, and need predict higher levels of SU (Andersen, 1995).

Literature Review

Few studies have examined factors associated with EA victim protective SU. Using a purposive sample of case records, Barker and Himchak (2006) found the following factors associated with extent of EA victim SU: female perpetrator gender (predisposing factor); victim living arrangement (alone) and victim–perpetrator relationship co-dependence (enabling factors); and poor victim self-reported health, victim activities of daily living (ADL) impairment, and cases involving EA sub-type neglect (need factors). In a convenience sample of female APS case records, Roberto, Teaster, and Duke (2004) found that victims of African American ethnicity and younger age (predisposing factors) were associated with EA victim refusal of APS services. These prior studies did not include need factors representing the primary/presenting EA problem (e.g., EA severity, victim-perceived need, and protective service program evaluated need). Furthermore, these studies examined SU only in relation to all EA cases (undifferentiated), regardless of EA sub-type. In a seminal NRC report, EA experts recognized abuse and neglect sub-types as differentiated phenomena that should be examined separately (NRC, 2003). To this end, it is likely that EA victim SU barriers/facilitators vary across cases involving different forms of abuse and neglect given the substantive, clinical, and circumstantial differences that exist between sub-types.

A broader literature review was completed due to the low volume of available EA vicitm SU studies. Research has identified several factors linking elders to general community social SU: gender, age, ethnicity, and marital status (predisposing); living arrangement and social support (enabling); reported health status, previous connection to the social service system, elder perceived need, and organizational formulation of need (need; Alkema, Reyes, & Wilber, 2006; Cantor & Brennan, 1993; Lehning, Kim, & Dunkle, 2013; Mitchell & Krout, 1998). In an extensive review, Rodríguez, Valentine, Son, and Muhammad (2009) identified barriers to SU among adult IPV victims, including cultural impediments (predisposing), victim–perpetrator relationship dynamics, organizational context (enabling), and victim perception of the mistreatment problem (need).

Service Utilization Measurement

In protective service settings, each EA case requires a unique combination of safety plan interventions to achieve the goal of case resolution. The constellation of interventions composing a given safety plan depends on the distinct circumstances surrounding that EA situation (e.g., EA type, EA severity, living arrangement, victim proximity to perpetrator, level of social isolation, health status, etc.). Consequently, each safety plan contains a different number of interventions. Furthermore, safety plan interventions vary according to level of completion difficulty. For example, victim housing relocation represents a more difficult safety plan intervention for elder victims to pursue and complete compared to receiving financial assistance.

Varying safety plan intervention compositions across cases poses a measurement challenge when comparing cases on the extent of SU. Barker and Himchak (2006) measured SU by the total number of interventions a victim completed in their safety plan. However, using this measurement approach, cases with a higher number of initial safety plan interventions are biased toward higher SU scores. Similarly, safety plans consisting of more interventions with a low level of completion difficulty are biased toward higher scores. Roberto et al. (2004) used a binary service acceptance/refusal operationalization. However, service acceptance occurs along a continuum in protective service settings. Importantly, the extent of client acceptance along this continuum determines level of case resolution (Rizzo et al., 2013). Grouping clients who accept one or more interventions into a single acceptance category removes this critical variation. A comprehensive measure of SU should incorporate acceptance variation and control for differences across cases in both the number of safety plan interventions and the completion-difficulty level attached to each intervention.

Research Question and Hypotheses

Given the low volume of elder protective SU research, this literature requires further exploratory research to help identify SU barriers and facilitators. The present study aimed to identify factors that impede or facilitate extent of protective SU among cognitively intact EA victims. The following research question was addressed: What predisposing (victim gender, race/ethnicity, age, marital status, perpetrator gender, race/ethnicity, and age), enabling (organizational context, referral source, victim social engagement, living arrangement, and victim–perpetrator relationship dynamics), and/or need (EA type, EA severity, victim-perceived need, organization evaluated need, and victim health status) factors are associated with SU? To help advance the literature, we investigated this question in relation to all EA cases (undifferentiated) and according to separate EA sub-types (differentiated). Guided by the BMHSU, we hypothesized that greater predisposing sociocultural advantage, enabling resources, and need would be associated with higher levels of SU. We also expected that SU barriers/facilitators would vary according to different EA sub-types.

Method

Data Collection and Procedures

Data for this study were collected from the Jewish Association Serving the Aging (JASA) Legal/Social Work Elder Abuse Program (LEAP). In New York City (NYC), EA cases involving cognitively impaired older adults are handled by state-funded APS, and cases involving cognitively intact older adults are handled by protective service programs funded by the NYC Department for the Aging housed within community-based agencies serving older adults. JASA-LEAP is the largest such protective service program in NYC serving community-dwelling, cognitively intact older adults (>60 years). In accordance with standard NRC (2003) EA definitions, JASA-LEAP defines EA as any form of intentional mistreatment that occurs in a relationship of trust, which results in harm or loss to the older person; neglect is the willful deprivation of resources necessary to ensure the health and well-being of the older adult. JASA-LEAP handles more than 700 EA cases annually across three sites in Manhattan, Brooklyn, and Queens, respectively. JASA-LEAP uses a multidisciplinary model integrating social workers and lawyers to investigate, assess, intervene, and resolve EA cases. For substantiated EA cases, JASA-LEAP collaborates with the EA victim to formulate a protective safety plan. Table 1 outlines the range of interventions that could be included in a JASA-LEAP protective safety plan. A detailed description of JASA-LEAP is available elsewhere (Schecter & Dougherty, 2009).

Table 1.

Level of Completion Difficulty Assigned to JASA-LEAP Safety Plan Interventions.

Safety plan intervention	Level of completion difficulty
Financial assistance	1
Lock replacement	1
Victim alert notification system	1
Legal referral	1
Healthcare referral	1
Mobile crisis	2
Domestic violence office or police intervention	2
Housing referrals	3
Individual counseling	3
Support group	3
Family counseling	3
Order of protection	3
Victim relocation	3
District Attorney’s office	3
Perpetrator eviction	3
Perpetrator hospitalization	3

Note. JASA = Jewish Association Serving the Aging; LEAP = Legal/Social Work Elder Abuse Program.

A multisite, random sample of JASA-LEAP case records (n = 250), closed between 2009 and 2011, was generated using systematic random sampling. Sampling was stratified according to JASA-LEAP site caseload size: Manhattan (15%, n = 37), Brooklyn (40%, n = 100), and Queens (45%, n = 113). Data were manually extracted from routine, categorized assessment and case closure forms that, in consultation with the second author (V.M.R.), were originally constructed for the purpose of uniform data collection across sites. Data from an initial sub-sample of case records (n = 20) were extracted by two independent researchers and verified for consistency. Data from all case records were coded by two independent raters. Seventeen cases were omitted from the study because EA was unsubstantiated.

To calculate the main SU outcome (see below), it was necessary to assign each JASA-LEAP intervention a level of completion difficulty. To determine intervention completion-difficulty levels, a group consensus process was undertaken with nine JASA-LEAP clinical staff from all three sites (one site director, one site assistant director, two clinical supervisors, three front-line social workers, and two front-line social work interns). The clinical group was asked to rate each JASA-LEAP intervention on a scale from 1 to 3 in terms of how difficult the intervention would be for a client to pursue and complete (higher ratings indicated more difficult interventions to complete). The group reached agreement on intervention completion-difficulty levels, although quantitative analysis was not conducted on this issue (e.g., Kappa coefficient). Lower completion-difficulty-level interventions involved receiving information or installing home security measures. Higher completion-difficulty-level interventions included intrusive legal measures (e.g., seeking an order of protection), intrusive living arrangement alterations (e.g., victim relocation or perpetrator eviction), and clinical treatments (e.g., individual, family or group counseling). Table 1 presents the completion-difficulty level assigned to each JASA-LEAP intervention.

Dependent Variable

SU is a variable that measured the extent to which EA victims pursued interventions in their JASA-LEAP safety plans. Each client safety plan was given an initial total score based on both the number and completion-difficulty level of interventions comprising the plan. This initial total score was calculated by summing the completion-difficulty level of each intervention that was recommended in the initial safety plan. Initial total safety plan scores ranged from 3 to 20 (M = 10.03; SD = 3.95). A pursued safety plan score was then calculated by summing the completion-difficulty level of each intervention in the safety plan that was ultimately completed by the client. Pursued safety plan scores ranged from 0 to 18 (M = 4.97; SD = 4.69). Using these two weighted scores, SU was operationalized as the proportion of interventions that a client pursued out of the initial total safety plan.

Independent Variables

We considered predisposing, enabling, and need factors identified as important in previous EA studies and in the broader SU literatures outlined above. In accordance with BMHSU principles, we also considered a strong set of need factors that were directly relevant to the presenting EA problem.

Predisposing factors included victim gender, ethnicity, age, and marital status, and perpetrator gender, ethnicity, and age. To isolate differences between the youngest- and oldest-old age groups, victim age (in years) was operationalized using the following categories: 60-74, 75-84, and >85.

Enabling factors included JASA-LEAP organization site, referral source, victim social engagement, living arrangement, and victim–perpetrator relationship dynamics. In accordance with the BMHSU, referral source was categorized according to the presence of personal (self), family, or community-enabling resources. Adopting an approach used by Dong, Simon, and Evans (2012a), social engagement was evaluated at initial assessment by how often the victim participated in social activities outside of the home (less than once per month, more than once per month but less than once per week, or one or more times per week). The landmark NRC (2003) EA theoretical framework proposes that victim–perpetrator relationship dynamics should be an essential feature of any EA research analysis, particularly the relationship type, living arrangement, and presence of status inequality. Accordingly, we included the victim–perpetrator relationship type (perpetrator is spouse/partner, child/grandchild offspring, or another trusted party), living arrangement (victim lives with/without perpetrator), and indications of victim–perpetrator status inequality, including gender differential (same/different genders), ethnicity differential (same/different ethnicities), and age dynamic (perpetrator is younger/older than victim).

The need dimension included EA sub-type, EA severity, victim-perceived need, JASA-LEAP evaluated need, victim previous help-seeking, and victim health status. As is standard in APS research and practice, EA sub-type was professionally assessed by a certified social worker in conjunction with the JASA-LEAP program supervisor (Hwalek, Neale, Goodrich, & Quinn, 1996). Abuse/neglect substantiation was based on evidence gathered during client home-visit and structured interview assessments. EA severity was measured according to both longevity and frequency. Longevity was determined at initial assessment by asking the client how long the EA had been occurring (<1 year, 1-5 years, or more than 5 years). Frequency was determined by asking the client how often the EA occurred (less than once per month, more than once per month but less than once per week, or one or more times per week). Victim-perceived need was assessed at initial assessment by asking the victims whether they perceived themselves as being in danger as a result of the EA situation (yes/no). In accordance with NYC Department for the Aging guidelines, evaluated need was assessed by the case social worker, who was licensed to classify each case according to one of the following risk-levels: no risk, low risk, moderate risk, high risk, or emergency. The “no risk” and “emergency” categories were combined with the “low risk” and “high risk” categories, respectively, due to low sample representation in the former risk categories. Previous help-seeking was treated as a need factor because it reflected the recurrent nature and magnitude of the EA problem. Previous help-seeking was based on whether or not the victim had previously taken steps in the formal support system to resolve the EA (yes/no).

Victim health status was measured continuously as a count of self-reported health conditions (0-10), with higher scores indicating poorer health (Dong et al., 2012a; Fisher et al., 2011). We used Fisher et al.’s (2011) criteria to define 10 health condition groups as follows: blood pressure/heart problems, lung problems, diabetes/thyroid problems, bone/joint problems, depression/anxiety, digestive problems, stroke/nerve problems, blood problems, chronic pain, and any type of cancer.

Analytic Plan

Generalized linear model binomial logistic regression was used to predict the proportion of services pursued in each safety plan. SU was examined in relation to all cases in the sample (undifferentiated) and for each EA sub-type sample (differentiated). Low EA sub-type sample sizes precluded differentiated analysis for elder sexual abuse (n = 2) and neglect (n = 11). Bivariate, unadjusted binomial logistic regression was first conducted on each independent variable individually to examine exploratory associations. Multivariate, adjusted regression was then conducted on independent variables simultaneously. For the undifferentiated analysis, selection of independent variables for the multivariate model was based on significance in bivariate analysis (p < .10), tolerance/variance inflation factor diagnostics, and to ensure representation across each BMHSU dimension. Due to the smaller EA sub-type sample sizes in differentiated analyses, fewer independent variables could be loaded into multivariate models; selection was based on reaching a more restrictive alpha level in bivariate analysis (p < .05). Models controlled for the presence of co-occurring EA types. A fully conditional specification multiple imputation approach was used for missing data with 10 imputed datasets pooled for analysis.

Results

Similar to the broader sociodemographic trends of documented EA cases in NYC (Lachs & Berman, 2011), victims in this study sample were mostly female (79.4%), younger than 75 years (57.9%), and of minority racial status (African American, 41.9%; Hispanic, 17.7%; other minority group, 10.6%). Mean victim age was 73.7 years (SD = 9.15). Most victims were unmarried (69.7%). On average, victims reported 1.7 medical conditions (SD = 1.23; range = 0-4). Perpetrators were mostly male (60.9%) and from a minority racial group (African American, 40.6%; Hispanic, 18.1%; other minority group, 10.1%) with mean age of 43.7 years (SD = 16.28). Victims tended to live with the perpetrator (63.8%). Perpetrators were mostly children/grandchildren (62.6%) of the victims, followed by other relatives/friends/roommates/aid (21.3%) and spouses/partners (16.1%). Victim–perpetrator dyads were mostly characterized by younger perpetrators (93.4%), same ethnicity (90.9%), and different genders (57.5%). The majority of cases involved emotional abuse (81.8%), followed by financial abuse (43.7%), physical abuse (42.0%), neglect (4.8%), and sexual abuse (0.9%). Cases were evaluated by JASA-LEAP as high (12%), moderate (54%), or low (34%) risk.

Results of the undifferentiated analysis among all EA cases are shown in Table 2. In multivariate analysis, increased SU was significantly (p < .05) associated with the following: self-referral (odds ratio [OR] = 1.45) or family referral (OR = 1.52), victim–perpetrator dyads composed of different genders (OR = 1.42), presence of financial (OR = 1.28) or emotional (OR = 1.78) abuse, victims who perceived themselves as being in danger (OR = 1.58), victims who had previously pursued formal support (OR = 4.22), and poor victim health (OR = 1.11). Lower SU was significantly associated with victims being married/partnered (OR = 0.56) and having an offspring child/grandchild perpetrator (OR = 0.61).

Table 2.

Generalized Linear Model Binomial Logistic Regression to Predict Service Utilization for All Cases (Undifferentiated).

	Undifferentiated EA (n = 233)
	Bivariate	Multivariate
Variables	OR [95% CI]	OR [95% CI]
Predisposing
V gender female¹	1.08 [0.88, 1.31]	—
V ethnicity African American²	0.85 [0.69, 1.05]	0.93 [0.54, 1.60]
V ethnicity Hispanic²	0.70 [0.54, 0.91]**	0.72 [0.43, 1.22]
V ethnicity Other²	1.45 [1.06, 1.97]*	1.38 [0.83, 2.30]
V age 75 to 85³	0.52 [0.43, 0.63]***	0.75 [0.52, 1.06]
V age > 85³	0.36 [0.28, 0.48]***	0.86 [0.52, 1.44]
V married/partnered⁴	0.82 [0.68, 0.99]*	0.56 [0.40, 0.80]**
P gender male⁵	0.93 [0.79, 1.10]	—
P ethnicity minority-status⁶	0.79 [0.63, 0.98]*	0.84 [0.50, 1.41]
P age	0.99 [0.98, 0.99]***	0.99 [0.97, 1.00]^†
Enabling
Organizational site Queens⁷	1.18 [0.93, 1.50]	—
Organizational site Brooklyn⁷	1.23 [0.96, 1.56]	—
Referral source self⁸	1.41 [1.16, 1.72]**	1.45 [1.12, 1.88]**
Referral source family⁸	0.71 [0.57, 0.89]**	1.52 [1.02, 2.28]*
Social engage <1 per week⁹	0.77 [0.34, 1.77]	—
Social engage >1 per week⁹	0.93 [0.56, 1.54]	—
V lives with P¹⁰	0.97 [0.81, 1.15]	—
V–P gender differential¹¹	1.17 [0.99, 1.38]^†	1.42 [1.11, 1.82]**
V–P ethnicity differential¹²	1.33 [0.93, 1.88]	—
P younger than V¹³	1.44 [0.98, 2.11]^†	1.09 [0.56, 2.13]
P spouse/partner to V¹⁴	0.79 [0.61, 1.03]^†	1.31 [0.78, 2.19]
P child/grandchild to V¹⁴	0.50 [0.41, 0.62]***	0.61 [0.44, 0.86]**
Need
Financial abuse¹⁵	0.95 [0.81, 1.12]	1.28 [1.03, 1.60]*
Emotional abuse¹⁶	1.13 [0.91, 1.40]	1.78 [1.28, 2.47]**
Physical abuse¹⁷	1.18 [1.59, 2.21]***	1.23 [0.93, 1.64]
Neglect¹⁸	0.74 [0.51, 1.06]	1.22 [0.72, 2.06]
EA longevity 1-5 years¹⁹	0.79 [0.60, 1.03]^†	0.89 [0.52, 1.50]
EA longevity > 5 years¹⁹	0.86 [0.66, 1.13]	0.72 [0.41, 1.27]
EA frequency <1 per week²⁰	1.32 [0.87, 2.00]	1.19 [0.65, 2.16]
EA frequency >1 per week²⁰	0.77 [0.58, 1.04]^†	0.87 [0.51, 1.47]
V perceives danger²¹	2.07 [1.73, 2.47]***	1.58 [1.21, 2.07]**
Evaluated moderate risk²²	1.32 [1.09, 1.61]**	0.83 [0.59, 1.18]
Evaluated high risk²²	2.47 [1.86, 3.30]***	1.19 [0.57, 2.19]
V previous EA help-seeking²³	5.35 [4.14, 6.91]***	4.22 [2.73, 6.53]***
V health conditions	1.33 [1.23, 1.44]***	1.11 [1.00, 1.24]*

Notes. EA = elder abuse; OR = odds ratio; CI = confidence interval; V = victim; P = perpetrator; V–P = victim–perpetrator. Referent groups: ¹male; ²Caucasian; ³age < 75; ⁴unmarried (widowed, divorce, or single); ⁵female; ⁶Caucasian; ⁷Manhattan; ⁸community referral source; ⁹less than once per month; ¹⁰lives without perpetrator; ¹¹same gender; ¹²same ethnicity; ¹³perpetrator older; ¹⁴other relative, friend, roommate or aid; ¹⁵no financial abuse; ¹⁶no emotional abuse; ¹⁷no physical abuse; ¹⁸no neglect; ¹⁹<1 year; ²⁰less than once per month; ²¹does not perceive danger; ²²evaluated low risk; ²³no previous help-seeking.

Multivariate model satisfied the Omnibus Test of Model Coefficients (p < .001, χ² = 415.82).

†

p < .10. *p ≤ .05. **p < .01. ***p < .001.

Results of the differentiated, multivariate analyses of sub-sample cases involving emotional, financial, and physical abuse, respectively, are shown in Table 3. Among cases involving emotional abuse, increased SU was significantly associated with the following: self (OR = 1.79) or family (OR = 1.60) case referral, victim–perpetrator dyads composed of different genders (OR = 1.42), co-occurring physical abuse (OR = 1.75), victim perception of danger (OR = 1.59), and previous victim help-seeking (OR = 4.03). Lower SU was significantly associated with victims in the middle–old age group (OR = 0.71) and having an offspring child/grandchild perpetrator (OR = 0.68).

Table 3.

Generalized Linear Model Binomial Logistic Regression to Predict Service Utilization for Elder Abuse Sub-Types (Differentiated).

	Emotional abuse (n = 189)		Financial abuse (n = 101)		Physical abuse (n = 99)
	Bivariate	Multivariate	Bivariate	Multivariate	Bivariate	Multivariate
Variables	OR [95% CI]	OR [95% CI]	OR [95% CI]	OR [95% CI]	OR [95% CI]	OR [95% CI]
Predisposing
V gender female¹	1.23 [0.99, 1.54]^†	—	1.44 [1.08, 1.92]*	1.45 [1.00, 2.11]*	1.25 [0.91, 1.72]	—
V African American²	0.89 [0.63, 1.24]	—	0.98 [0.63, 1.51]	—	0.84 [0.55, 1.27]	0.73 [0.45, 1.20]
V Hispanic²	0.67 [0.43, 1.04]^†	—	1.25 [0.72, 2.17]	—	0.54 [0.33, 0.89]*	0.44 [0.24, 0.80]**
V Other ethnicity²	1.29 [0.75, 2.16]	—	1.44 [0.77, 2.70]	—	1.27 [0.75, 2.13]	1.28 [0.74, 2.21]
V age 75 to 85³	0.41 [0.33, 0.51]***	0.71 [0.52, 0.97]*	0.80 [0.61, 1.05]	1.03 [0.61, 1.75]	0.47 [0.33, 0.67]***	0.51 [0.34, 0.80]**
V age > 85³	0.34 [0.25, 0.47]***	0.83 [0.53, 1.32]	0.54 [0.37, 0.78]**	0.89 [0.62, 1.27]	0.47 [0.29, 0.77]**	1.09 [0.47, 2.52]
V married/partnered⁴	0.94 [0.73, 1.21]	—	0.46 [0.32, 0.65]	—	0.62 [0.46, 0.83]**	0.35 [0.22, 0.54]***
P gender male⁵	0.83 [0.69, 1.00]*	0.90 [0.69, 1.18]	0.80 [0.62, 1.02]^†	—	1.14 [0.88, 1.47]	—
P ethnic minority-status⁶	0.84 [0.58, 1.20]	—	1.01 [0.79, 1.29]	—	0.62 [0.41, 0.96]*	—^b
P age	0.99 [0.98, 1.00]*	0.99 [0.97, 1.00]^†	0.98 [0.97, 0.99]**	0.98 [0.96, 0.99]**	0.99 [0.98, 1.00]^†	—
Enabling
Site Queens⁷	1.53 [1.16, 2.02]**	1.42 [0.97, 2.08]^†	1.11 [0.79, 1.57]	2.03 [1.27, 3.43]**	1.25 [0.86, 1.83]	—
Site Brooklyn⁷	1.31 [1.00, 1.72]*	1.40 [0.94, 2.08]	1.43 [1.01, 2.04]*	2.13 [1.45, 3.22]**	0.85 [0.59, 1.24]	—
Referral source self⁸	1.55 [1.25, 1.93]***	1.79 [1.37, 2.35]***	1.54 [1.11, 2.14]*	2.58 [1.60, 4.15]***	1.17 [0.88, 1.56]	1.24 [0.83, 1.84]
Referral source family⁸	0.63 [0.50, 0.81]***	1.60 [1.07, 2.40]*	1.17 [0.87, 1.57]	1.72 [1.14, 2.58]*	2.02 [1.32, 3.10]**	4.43 [2.49, 7.88]***
Social engage <1 per week⁹	0.76 [0.33, 1.72]	—	0.96 [0.40, 2.34]	—	0.95 [0.27, 3.38]	—
Social engage >1 per week⁹	0.94 [0.56, 1.56]	—	1.25 [0.72, 2.17]	—	0.73 [0.33, 1.64]	—
V lives with P¹⁰	1.02 [0.83, 1.26]	—	1.10 [0.84, 1.44]	—	0.93 [0.71, 1.21]	—
V–P gender differential¹¹	1.27 [1.06, 1.53]**	1.42 [1.05, 1.92]*	1.11 [0.87, 1.42]	—	1.07 [0.83, 1.38]	—
V–P ethnic differential¹²	0.92 [0.51, 1.67]	—	0.75 [0.37, 1.51]	—	1.05 [0.40, 2.76]	—
P younger than V¹³	1.13 [0.66, 1.94]	—	2.04 [1.08, 3.85]	—	1.46 [0.81, 2.62]	—
P spouse/partner to V¹⁴	0.90 [0.67, 1.20]	0.91 [0.54, 1.53]	0.81 [0.51, 1.37]	—	0.50 [0.35, 0.71]***	1.02 [0.60, 1.74]
P child/grandchild to V¹⁴	0.53 [0.42, 0.67]***	0.68 [0.49, 0.94]*	0.99 [0.71, 1.37]	—	0.47 [0.34, 0.65]***	0.56 [0.37, 0.85]**
Need
Financial abuse¹⁵	0.78 [0.65, 0.94]**	0.97 [0.78, 1.22]	NA	NA	0.88 [0.68, 1.15]	0.72 [0.51, 1.02]^†
Emotional abuse¹⁶	NA	NA	0.63 [0.46, 0.59]**	0.53 [0.33, 0.87]**	2.38 [1.77, 3.20]***	3.68 [2.53, 5.37]***
Physical abuse¹⁷	2.46 [2.04, 2.96]***	1.75 [1.32, 2.31]***	1.52 [1.17, 1.97]**	1.25 [0.87, 1.73]	NA	NA
Neglect¹⁸	0.63 [0.42, 0.94]*	1.28 [0.65, 2.52]	0.34 [0.16, 0.73]**	0.50 [0.17, 1.38]	—^a	—^a
EA longevity 1-5 years¹⁹	0.83 [0.50, 1.37]	—	0.80 [0.45, 1.43]	—	1.33 [0.70, 2.49]	—
EA longevity > 5 years¹⁹	0.71 [0.45, 1.11]	—	0.80 [0.44, 1.47]	—	1.28 [0.80, 2.04]	—
EA frequency > 1 per month²⁰	0.80 [0.32, 1.98]	—	1.15 [0.42, 3.13]	—	1.08 [0.49, 2.38]	—
EA frequency > 1 per week²⁰	0.76 [0.38, 1.52]	—	1.24 [0.63, 2.45]	—	1.10 [0.68, 1.78]	—
V perceives danger²¹	2.30 [1.87, 2.82]***	1.59 [1.16, 2.17]**	2.53 [1.91, 3.35]***	2.25 [1.49, 3.21]***	1.62 [1.23, 2.13]**	1.10 [0.78, 1.55]
Evaluated mod risk²²	1.43 [1.11, 1.85]**	0.79 [0.50, 1.26]	1.03 [0.79, 1.36]	—	1.11 [0.71, 1.72]	—
Evaluated high risk²²	2.34 [1.51, 3.64]***	0.85 [0.42, 1.73]	1.63 [0.94, 2.82]^†	—	1.76 [0.88, 3.54]	—
V previous help-seek²³	4.84 [3.25, 7.19]***	4.03 [2.47, 6.59]***	3.79 [2.47, 5.80]***	2.72 [1.55, 4.53]***	2.67 [1.40, 5.07]**	6.66 [2.13, 20.78]**
V health conditions	1.28 [1.14, 1.43]***	1.06 [0.90, 1.25]	1.32 [1.17, 1.50]***	1.19 [1.02, 1.41]*	1.20 [1.05, 1.37]**	1.15 [0.95, 1.39]

Notes. OR = odds ratio; CI = confidence interval; V = victim; P = perpetrator; V–P = victim–perpetrator; NA = not applicable; EA = elder abuse. Referent groups: ¹male; ²Caucasian; ³Age < 75; ⁴unmarried (widowed, divorce or single); ⁵female; ⁶Caucasian; ⁷Manhattan; ⁸community referral source; ⁹less than once per month; ¹⁰lives without perpetrator; ¹¹same gender; ¹²same ethnicity; ¹³perpetrator older; ¹⁴other relative, friend, roommate or aid; ¹⁵no financial abuse; ¹⁶no emotional abuse; ¹⁷no physical abuse; ¹⁸no neglect; ¹⁹<1 year; ²⁰less than once per month; ²¹does not perceive danger; ²²evaluated low risk; ²³no previous help-seeking. Multivariate models satisfied the Omnibus Test of Model Coefficients (p < .001; $χ_{emotional}^{2}$ = 350.36; $χ_{physical}^{2}$ = 187.19; and $χ_{physical}^{2}$ = 191.63).

The sub-sample of cases involving physical abuse did not contain any cases with co-occurring neglect.

Perpetrator minority ethnicity status was omitted from multivariate analysis due to high collinearity with victim ethnicity.

†

p < .10. *p ≤ .05. **p < .01. ***p < .001.

Among cases involving financial abuse, increased SU was significantly associated with the following: victim female gender (OR = 1.45), JASA-LEAP Brooklyn (OR = 2.13) or Queens (OR = 2.03) sites, self (OR = 2.58) or family (OR = 1.72) case referral sources; poorer victim health (OR = 1.19), victim perception of danger (OR = 2.25), and previous victim help-seeking (OR = 2.75). Lower SU was associated with higher perpetrator age (OR = 0.98) and co-occurring emotional abuse (OR = 0.53).

Among cases involving physical abuse, increased SU was significantly associated with the following: a family referral source (OR = 4.43), co-occurring emotional abuse (OR = 3.68), and previous victim help-seeking (OR = 6.68). Lower SU was associated with victims of Hispanic race/ethnicity (OR = 0.44), victims in the middle–old age group (OR = 0.51), being married/partnered (OR = 0.35), and having an offspring child/grandchild perpetrator (OR = 0.56).

Discussion

This study explored factors associated with the extent of protective SU among cognitively intact EA victims. We used the BMHSU to organize potential barriers/facilitators into predisposing, enabling and need dimensions, and to guide expectations. SU was examined in relation to all EA cases (undifferentiated) and for separate EA sub-types (differentiated).

Dissimilarity emerged between the undifferentiated and differentiated analyses. Only two variables (family referral source and previous victim help-seeking) were commonly significant to both the undifferentiated analysis and across all three differentiated analyses. Similarly, within the differentiated analysis alone, only these two variables were commonly significant across all three EA sub-types. Thus, consistent with NRC (2003) recommendations to study EA sub-types separately, this study found that the undifferentiated, composite EA construct is not representative of individual EA sub-types with regard to understanding SU. Accordingly, the following discussion focuses on findings from the differentiated EA sub-type analysis.

Predisposing

The BMHSU proposes that clients with greater sociocultural/structural advantage will have higher SU. In this study, gender, ethnicity, and marital status emerged as important predisposing variables with varying consistency to expectations.

Females demonstrated higher SU than males in cases involving financial abuse. This finding countered BMHSU expectations because females hold lower sociostructural advantage than males. From a life-course perspective, older men may feel more shame in pursuing interventions for financial loss due to historically socialized gender expectations to provide and manage household finances. Elder male victims of financial exploitation may require tailored support to remain involved with protective services.

Ethnicity emerged as significant only in cases involving physical abuse. Consistent with expectations, the socially disadvantaged, minority Hispanic group showed lower SU. Similarly, Barker and Himchak (2006) found that Hispanic EA victims were less likely to pursue emergency shelter or adult home protective services. Lower SU among Hispanic EA clients may have been a result of cultural stigma attached to being a victim of mistreatment (Rodríguez et al., 2009) and/or distrust in using social services (Parra-Cardona, Meyer, Schiamberg, & Post, 2007). Understanding and overcoming culturally related barriers to SU is a priority for future EA research.

Being married was associated with lower SU in cases involving physical abuse. This finding is inconsistent with BMHSU expectations because the structure of marriage typically carries social status advantage. In the context of EA, however, having a spouse may impede SU if the spouse is also the perpetrator. Rodríguez et al. (2009) identified abusive partner intrusion and control tactics as barriers to social service use among IPV victims. The structure of marriage may also reinforce a desire to protect family relationships (and, in turn, family perpetrators), which has been found to be a barrier to help-seeking among older female domestic violence victims (Beaulaurier, Seff, Newman, & Dunlop, 2005).

Enabling

The BMHSU proposes that clients with greater enabling resources will demonstrate higher SU. Organizational context, case referral source, and victim–perpetrator relationship type and gender dynamics were important enabling factors in this study.

In cases involving financial abuse, the Queens and Brooklyn JASA-LEAP sites had higher client SU compared with the Manhattan site. Cases of financial exploitation commonly require legal/justice interventions (e.g., prosecution, conservatorship) to reach resolution. Relative to the Manhattan office, the Queens and Brooklyn sites have more deeply entrenched legal/justice mechanisms, such as on-site specialized EA lawyers and/or a closer working relationship with the local District Attorney’s office. An integrated multidisciplinary EA intervention approach with legal capabilities enhances protective service outcomes for elder financial abuse cases (Navarro, Gassoumis, & Wilber, 2013).

Referral source was important to all three EA sub-types. In particular, self or family referrals contributed to higher SU. Relative to community agency referrals, self or family referrals reflect higher level enabling resources because they originate from internally motivated or proximal sources. Community referrals are less likely to be self-directed or carry implicit family support. They are also more likely to be unwelcomed by the client. Learning how to engage elder clients who are unknowingly referred by a community agency represents an important direction of future research.

In accordance with NRC (2003) recommendations, this study investigated victim–perpetrator relationship-level factors including relationship type and indicators of relationship status inequality. Victim–perpetrator relationship type was important in cases of emotional and physical abuse. EA victims were less likely to pursue safety plan interventions when the perpetrator was a child or grandchild. In these cases, victims were mostly mothers or grandmothers of the perpetrator. Elder female victims are perhaps more likely to protect their offspring from involvement with social service/legal-justice systems and reluctant to accept interventions that threaten core family relationships. Negotiating the complex family relationships surrounding most EA cases is a major challenge.

In cases involving emotional abuse, victims were more likely to pursue interventions when the victim–perpetrator relationship was composed of different genders. Emotional abuse represents an act of power and control. As the majority of victims were female in this study, gender differentials mostly reflected dyads with a male perpetrator. Using the BMHSU, higher SU in these circumstances may have reflected a heightened sense of need from female victims on the receiving end of an unbalanced gender power differential. It is surprising that an unbalanced gender differential did not emerge as significant in cases of physical abuse given the elevated threat of danger and power/control dynamics attached to this form of abuse. Further research is required to both confirm these findings and understand how victim–perpetrator gender dynamics affect SU.

Need

The BMHSU suggests that higher levels of perceived and evaluated need are associated with greater SU. In this study, EA victim-perceived need, previous help-seeking, and self-reported health were need variables associated with SU.

Victim-perceived need was relevant to SU in cases of emotional and financial abuse. Victims who perceived themselves in danger due to the mistreatment situation pursued a higher level of SU. Subjective perceptions of a problem inform cognitive scripts and planned behaviors relative to the problem (Ansello, 1996). Change theory suggests that perceptions of higher problem seriousness underlie greater intentions to cease problematic patterns of behavior, create new behaviors, or modify existing behaviors (DiClemente, 2005). Helping EA victims view their mistreatment situation as problematic may be an important intervention component to consider in future research, especially among clients interfacing with programs for the first time. Of note, organizational evaluated need did not emerge as significant to EA victim SU. Thus, in the context of elder protection, it would appear that perceived need is more important than evaluated need. The importance of perceived need may be due to the voluntary nature of services among cognitively intact older adults.

Previous help-seeking to address EA was associated with increased SU across all three EA sub-types. In other words, a history of help-seeking was predictive of future willingness to pursue support. This factor was treated as a need variable because it reflected the recurrent nature and magnitude of the EA problem. Similar to other domains of interpersonal violence, EA victims may endure multiple cycles of abuse and help-seeking before they feel comfortable taking concrete steps to end the mistreatment. Findings in this study suggest that elder victims do not revolve needlessly through formal support systems, but rather successive involvement promotes greater future service use.

Poorer victim health was associated with higher SU in cases involving financial abuse. This finding is somewhat counter-intuitive because increased medical problems could impede one’s ability to pursue services. However, it is consistent with BMHSU expectations in that poorer health reflects greater need, especially if EA is contributing to those health problems. Victims of financial exploitation may be especially likely to utilize services if they depend on these financial resources to manage health conditions.

Limitations

This study contained limitations, and opportunities exist to build further on the EA SU literature. A case review design carries risks of standard error and researcher bias. We attempted to minimize these risks by collecting data from routine, categorized forms; drawing data from a large, multisite, random sample; and using multiple, independent raters. To date, virtually no prospective EA studies with elder victims have been conducted in APS settings due to ethical and logistical challenges (Ernst et al., 2013). Future studies should seek to overcome these research barriers to help advance knowledge in the EA arena. We were unable to collect quality data on potentially important barriers/facilitators or confounders, such as victim mental health and functional capacity, perpetrator substance abuse, and victim–perpetrator interdependence. This study did not use a standardized social engagement measure and standardized measures of EA severity do not exist; a lack of findings related to these variables may have been, in part, due to measurement limitation. Future research on SU should seek to include EA victims in the process of establishing intervention completion-difficulty levels and incorporate quantitative methods (e.g., Kappa coefficient) to ascertain level of interrater agreement. EA researchers and practitioners widely recognize that the elder protection system is flawed by the lack of focus on perpetrator rehabilitation. The present service delivery model places burden upon potentially frail older adult victims to effect change while the perpetrator is often able to evade responsibility. Future research should focus on program development and SU targeting the perpetrator.

Despite the limitations, we believe this study represents the most comprehensive and rigorous examination of EA victim SU conducted to date. We developed a more rigorous approach to measure elder SU that accounts for differences in both the number and difficulty-level of interventions across cases. In accordance with seminal EA expert recommendations, we examined SU in relation to separate forms of EA. Dissimilar SU findings emerged across undifferentiated and differentiated analyses and between separate forms of EA. The undifferentiated, composite EA construct was not representative of separate EA sub-types with regard to SU and future research should continue taking a differentiated analytic approach. Varying SU barriers/facilitator across EA sub-types also reinforces the need to develop EA sub-type specific practices. Finally, we explored candidate SU barriers/facilitators from several ecological levels, including the victim, perpetrator, victim–perpetrator relationship, and surrounding sociocultural context (NRC, 2003).

Protective service programs across the United States respond to hundreds of thousands of EA cases each year with limited research guidance to inform best practices (Dong, 2014). EA caseloads are growing nationwide, cases are increasingly complex, and agencies lack the necessary information to respond appropriately (U.S. Government Accountability Office, 2011). Findings from this study carry implications to help develop service delivery models and best practices to retain and promote service use among EA victims.

Footnotes

Acknowledgements

We would like to thank staff at the JASA-LEAP Manhattan, Brooklyn, and Queens sites for support with data collection. We would also like to acknowledge the Hartford Partnership Program in Aging Education fellows, Erin Courtney, Christine Ngo, and Dahye Kim, for assisting with data collection and data entry.

Authors’ Note

The study described in the article was approved by the Columbia University, Morningside Campus IRB, protocol no. IRB-AAAI1439.

Declaration of Conflicting Interests

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

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Jewish Association Serving the Aging Board of Trustees through a contract with Columbia University (7-70119 to V.M.R.), and the Columbia University School of Social Work, Office of the Associate Dean for Research (Mayer Endowment).

Author Biographies

David Burnes, PhD, is an assistant professor at the University of Toronto, Factor-Inwentash Faculty of Social Work and an affiliate scientist at the Rotman Research Institute, Baycrest. His program of research focuses on the growing problem of elder abuse in both community and long-term care settings, including development of basic knowledge, prevention/response interventions, and outcome measurement.

Victoria M. Rizzo, LCSW-R, PhD, is the department chair and an associate professor, Department of Social Work, College of Community & Public Affairs, Binghamton University. Her research examines the impact of interventions that include social workers on older adults coping with chronic illnesses, elder abuse, and the demands of informal caregiving. She also examines the implications of health care policy and financing on the provision of social work services to older adults.

Prakash Gorroochurn, PhD, is an associate professor at Columbia University, Department of Biostatistics. His research focuses on the statistical genetics of human disease, and the history of probability and statistics. He is the author of the book Classic Problems of Probability, winner of the 2012 PROSE Award for Mathematics.

Martha H. Pollack, LCSW-R, is the district director of the Jewish Association Serving the Aging (JASA), Queens. She has a long-standing involvement with elder protection practice in the community, including 3 years as the director of Elder Abuse Services at JASA and 15 years as the director of Social Services for Older Adults at the United Jewish Council of the East Side, New York.

Mark S. Lachs, MPH, MD, is the Psaty distinguished professor of medicine and co-chief of geriatrics at the Weill Medical College of Cornell University and the director of geriatrics, New York Presbyterian Health System. He has conducted extensive elder abuse research that focuses on risk factors and outcomes of community elder abuse, and more recently resident-to-resident abuse in long-term care settings.

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