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Abstract

Adult maltreatment is a pervasive problem in the United States and has serious individual and societal consequences. Adult protective services (APS) agencies are the social services programs responsible for serving older adults and adults with disabilities who may be experiencing adult maltreatment. The adult maltreatment literature differentiates elder maltreatment from the maltreatment of adults with disabilities, yet APS agencies serve both groups. Understanding the etiology of adult maltreatment as well as the associated risk and protective factors is crucial for APS workers, clinical practitioners, researchers, and policymakers. To advance the evidence in this area, we undertook a scoping review to examine recent evidence on risk and protective factors associated with adult maltreatment. Searches of nine electronic databases were conducted in 2020 to identify studies published in peer-reviewed journals since 2010. A total of 29 studies were included in the final review. The findings identified several categories of risk factors associated with the individual: demographic traits, socioeconomic characteristics, physical and mental health, interpersonal issues, and historical events. Several studies identified caregiver and alleged perpetrator risk factors. However, the current body of research lacks community and contextual risk and protective factors. Therefore, we present several potential data sources that may be leveraged to examine the links between social-contextual characteristics and adult maltreatment. These data may be combined with APS data to advance the field’s understanding of risk and protective factors through advanced analytic techniques.

Keywords

adult maltreatment risk and protective factors adult protective services county-level data sources National Adult Maltreatment Reporting System

Introduction

In the United States (U.S.), over 1.3 million alleged reports of adult maltreatment occurred in 2020 (McGee and Urban, 2020). Adult maltreatment generally refers to the abuse, neglect, or exploitation of older adults or adults with disabilities. Incidents of adult maltreatment can include physical, sexual, emotional, or psychological abuse, abandonment, financial exploitation, caregiver neglect, or self-neglect. Definitions of adult maltreatment within existing literature sometimes vary depending on the population of interest as well as the maltreatment type(s) one may be experiencing (Jackson & Hafemeister, 2011). For example, while self-neglect is the most commonly reported form of maltreatment of older adults, self-neglect is not always included in definitions of adult maltreatment (Mosqueda & Dong, 2011). Additionally, not all populations susceptible to experiencing adult maltreatment have been researched to the same degree.

Elder abuse, or the mistreatment of older adults, is the most commonly researched and recognized type of adult maltreatment. Within the field of elder abuse research, risk factors are commonly organized using socioecological models such as the abuse intervention model (AIM) (Mosqueda et al., 2016; Schiamberg & Gans, 1999, 2000). The AIM framework prioritizes clinical application while conceptualizing adult maltreatment as the result of complex and multidimensional relationships between individuals at risk for maltreatment, trusted others, and contextual factors. The AIM framework examines maltreatment as a manifestation of dysfunction in a broader ecosystem (Belsky, 1980). As such, the AIM framework serves as a model to increase the field’s understanding of risk factors associated with adult maltreatment.

Adult protective services (APS) agencies serve as the primary responders for reports of adult maltreatment. The populations served by APS vary by state but primarily include adults aged 60 and older or adults aged 18 and older who also have a disability. APS agencies are responsible for investigating cases of alleged adult maltreatment. However, recent calls from APS field workers and policymakers emphasize the need for services that aid in the prevention of maltreatment before it is brought to the attention of an APS agency. Prevention of adult maltreatment necessitates an understanding of the etiology of maltreatment. To date, however, there have been no published reviews of adult maltreatment literature to identify risk and protective factors of adult maltreatment that (a) include all populations served by APS within the U.S. and (b) take a socioecological approach using a framework such as the AIM to organize and interpret findings.

The field of adult maltreatment is largely informed by studies based on data that are: (1) focused on subsets of the populations served by APS such as providing an examination of only older adults (e.g., elder mistreatment) or adults with disabilities; (2) missing key variables, including those related to socioeconomic status, family-level traits, and community influences; and (3) focused on a narrow set of indicators (e.g., substantiation, subsequent referral for maltreatment). Little attention has been paid to broader and longer term health and well-being outcomes that may serve as important indicators of system successes and potential failures in maltreatment service efforts. Although these empirical limitations constrain the fields’ current understanding of the risk and protective factors associated with the population served by APS, a more immediate concern is that they also prevent the development of pragmatic programs and policies that address the most salient risk factors while promoting protective advantages. However, this need not be the case. The data we need to examine may not be limited to the APS system.

To address the empirical limitations described above, we set out to explore the research landscape by conducting a scoping review on risk and protective factors for the APS population—including both adults with disabilities and older individuals. The focus was on studies that specified adult maltreatment as the primary dependent variable. The primary goal of the scoping review was to identify and review studies that included risk and protective factors across multiple domains such as the vulnerable adult, trusted other, and context. In addition, efforts were made to identify administrative data that could be used to better understand the risk and protective factors associated with adult maltreatment. Many administrative data sources are collected annually and offer complete coverage of a given population often not subject to the uncertainty of sampling errors. Another added benefit to exploring administrative data sources is that they offer the ability to be linked at various levels of geography such as the state, region, county, zip code, census tract, and so on. In the context of adult maltreatment, linked administrative data have the potential to provide longitudinal, retrospective information concerning interactions between dynamic risk and protective factors that might be applied to developing maltreatment intervention and prevention programs. This study will provide some examples of data that might be leveraged to deepen and expand our understanding of adult maltreatment risk and protection.

Background

Research on adult maltreatment has focused on individual-level or proximate-level risk factors associated with abuse, neglect, and exploitation excluding broader contextual factors that may lie at the root of an individual’s experience of maltreatment. In a systematic review of over 100 articles examining the prevalence, etiology, and related outcomes of elder abuse, the author did not identify any environmental factors with a statistically significant association to elder abuse (Dong, 2015). Identifying risk factors for adult maltreatment at the county and state level could help inform community-based programs and policies about the “person-centered” and “place-centered” characteristics that contribute to risk of adult maltreatment. Theoretical models of adult maltreatment, such as the AIM framework, supports the importance of examining contextual factors such as community level disadvantages that might contribute to the risk of adult maltreatment. To our knowledge, these associations have not been empirically tested.

Two existing and robust bodies of research can be used to guide our understanding of adult maltreatment. The first is research on Child Protective Services (CPS) agencies. Many APS programs were built with a CPS model framework and as such data collection systems such as the National Adult Maltreatment Reporting System (NAMRS) were indirectly influenced by CPS IT Systems (Shusterman & Urban, 2022). As such, the research on child maltreatment can inform the study of adult maltreatment. In the child maltreatment literature, decades of research on the etiology of child maltreatment have underscored the need to examine the interplay between multiple risk and protective factors at the individual level as well as in the broader context. Belsky’s (1993) developmental-ecological theory on the etiology of child maltreatment proposes that child maltreatment arises due to the transactional processes involving characteristics of parents, children, and the multiple contexts in which they are embedded. Individual-level factors are assumed to contribute to one’s experience of maltreatment in the context of the environment in which they live (Finkelhor, 2008). The broader context comprises community factors (such as social and family supports) as well as cultural factors (such as a society’s attitude toward violence) across various levels of the social ecology including the family system, neighborhood, county, and state. Studies to examine community-level factors have examined the extent to which characteristics of communities predict child maltreatment above and beyond individual-level influences—attempting to distinguish between person centered characteristics (e.g., poverty rate, employment rate, race/ethnicity, and single-parenthood rate) and place-centered characteristics such as population density, housing stability, substance use/abuse, or fresh food sources (Freisthler et al., 2006; Smith et al., 2017). Researchers have suggested that the most reliable and robust community-level correlates of substantiated child maltreatment are socioeconomic indicators (Morris et al., 2019).

Using a risk terrain modeling approach to examine the cumulative effect of environmental factors associated with locations with incidences of child maltreatment, investigators found several significant risk factors of the community including poverty, domestic violence, aggravated assaults, runaways, murder, and drug crimes (Daley et al., 2016). In another study, researchers employed a longitudinal spatial model to examine county-level socioeconomic risk factors on substantiated cases of child maltreatment and found that neighborhood crime and violence were associated with increased risk of confirmed CPS reports of physical and sexual abuse at the zip code level (Morris et al., 2019). Results from these two studies indicate key risk and protective factors identified using place-based approaches, which may help strengthen community-level prevention strategies.

Research in the intimate partner violence domain has further elucidated community contextual factors that increase the risk of maltreatment. Communities play a unique role in relative risk for intimate partner violence. Members of rural communities experience an elevated risk due to factors such as geographic isolation, communal economic hardship, and help-seeking norms (Brossoie & Roberto, 2015). Rural communities also more frequently place a higher emphasis on patriarchal-structured family systems (Brossoie & Roberto, 2015; Finfgeld-Connett, 2014). These family systems have been associated with higher rates of intimate partner violence (Finfgeld-Connett, 2014). Even in communities that are not geographically isolated or without an emphasis on patriarchal structure, a lack of accessible professional support may increase the likelihood that violence will occur and be perpetuated (Nelson & Lund, 2017).

Results from a published scoping review on interpersonal violence found that social determinants of health (e.g., housing, quality health care, and employment status) play a role in facilitating or preventing incidences of interpersonal violence. The authors draw conclusions for public health policy suggesting that responses to violence should move beyond an individual-level approach, to considering how structural and interpersonal-level violence and power relations shape risk of violence (Montesanti & Thurston, 2015).

Furthering the understanding of community-level risk and protective factors could not only improve upon the overall ability to predict and address adult maltreatment but also foster advances in the equity of service provision. Given the vast predominance of published studies that focus on an individual’s circumstances without consideration of the community’s role in maltreatment, one can assume that evidence-based services operate on the premise that adult maltreatment is an individual’s problem, not a communal problem. This may in turn limit the efficacy of risk reduction efforts to individuals’ resources for change. Therefore, the field of adult maltreatment supplements its literary void with the learnings of other fields regarding communal factors.

The current study presents findings from a scoping review of risk factors of adult maltreatment and seeks to determine the most salient risk factors across multiple domains including demographic and socioeconomic characteristics, health, interpersonal factors, historical events, and risk factors associated with the caregiver and alleged perpetrator as well as contextual or environmental risk factors. The specific objectives were to (a) review the current state of the literature on individual and community-level factors associated with adult maltreatment, (b) identify potential data sources that could be examined to further the field’s understanding of community-level risk, and (c) present some potential applications whereby administrative and publicly available data sources can be leveraged to answer questions about adult maltreatment.

Method

Scoping Review of Risk and Protective Factors

The study followed a scoping review methodology (Arksey & O’Malley, 2005; Levac et al., 2010), to synthesize recent evidence on risk and protective factors associated with adult maltreatment. In addition to providing an indication of the size and nature of the literature in the last decade, this study aimed to identify research gaps in the extant literature, specifically community-level risk and protective factors. Following the scoping review methodology enhancements suggested by Levac et al. (2010) the study followed a six-stage methodological framework: (1) identify the research questions; (2) identify relevant studies; (3) study selection; (4) charting the data; (5) collating, summarizing, and reporting the results; and (6) stakeholder consultation. The search for relevant studies was guided by the following research question: What are the risk and protective factors associated with the abuse, neglect, and exploitation of older adults and adults with disabilities?

The following databases were searched for articles: EBSCOhost, Google Scholar, MEDLINE, ProQuest Central, PsychInfo, SAGE Journals, Taylor & Francis, Wiley Online Library, and PubMed Central. To qualify for inclusion, each article was required to be peer-reviewed, and explicitly address the risk and protective factors associated with adult maltreatment. Multiple search terms were used in various combinations and included the following: APS, risk factors, protective factors, elder abuse prevention, individuals with disabilities, elder abuse, abuse, neglect, exploitation, financial exploitation, and sexual abuse. Additionally, articles were sourced using citation chaining, where articles were collected if they were cited in relevant articles. Apart from a few foundational articles, we limited our review to research from the last decade (since 2010) and largely focused on those from the U.S. given the focus on the population served by APS. Interestingly, we did not identify much literature specific to individuals reported and investigated by APS. As such, the literature review focused on the phenomenon of adult maltreatment more broadly and not specifically on those who are reported and investigated by APS.

Studies were selected based on the above criteria through a process that included both title and abstract review. A total of 29 studies were included in the review. The studies that were ultimately selected for review were mostly survey-based with a few employing cross-sectional designs and only a few using longitudinal approaches. The articles also varied in terms of sample size with some (n = 9) reflecting nationally representative data while most of the studies were conducted in smaller geographic areas—primarily one or a few cities and/or counties.

After study selection, two researchers independently reviewed the abstracts to determine study selection. Once this was completed, the articles were systematically evaluated for relevant findings. Atlas.ti was used to conduct thematic analyses of the literature. The findings were summarized in a Microsoft Excel document to synthesize the types of maltreatment referred to in each article and the associated risk and protective factors. Following this process, a table (like Tables 1 and 2) was developed to summarize the findings from the literature review. In this table, the risk and protective factors associated with the maltreatment categories were aggregated to reflect how many articles identified a given risk factor to be associated with each maltreatment category. Members of a Technical Expert Panel reviewed this table to confirm that the findings are in line with the state of the literature and their experience as well as identify any gaps that might need to be incorporated. The feedback received in the consultation phase was favorable with a consensus that community/contextual information on risk and protective factors has not been well established in the literature. The summary table was further refined following expert consultation to only reflect studies that included statistical tests of significance (e.g., odd’s ratios, relative risk scores) of the specific risk factors. Statistical significance is reflected in Cohen’s d. Findings were converted to Cohen’s d given the variations of odds ratio, Relative Risk scores, and other analytical outcomes presented in the studies. Computing a summary effect across the studies allows for greater interpretability. In some studies, the authors did not provide enough detail to compute effect sizes. In such cases, positive or negative is noted in the table to reflect the directionality of the association. Cohen’s d classified effect sizes as small (d = .2), moderate (d = .5), and large (d > .8).¹ Effect sizes are presented for abuse in general, which refers to a global definition of abuse (including physical, psychological, and emotional), and more specific forms of abuse including emotional, financial, physical, neglect, and sexual abuse. Self-neglect was also included in the review as a form of adult maltreatment since it was identified in two studies; however, it is important to note that the review focused on a definition of abuse that is perpetrated by another individual. Tables 1 and 2 reflect the significant effects identified in the studies, excluding nonsignificant effects.

Table 1.

Risk and Protective Factors Table.

Risk Factors	Abuse (Nonspecific)	Emotional Abuse	Financial Abuse	Physical Abuse	Neglect	Self-Neglect	Sexual Abuse
Demographics
Age: Older vs. younger		−0.95 (Beach et al., 2010)−0.66 (Burnes et al., 2015)		−0.51 (Burnes et al., 2015)	−0.66 (Burnes et al., 2015)
Age: Continuous	−0.06 (DeLiema et al., 2012)		−0.002 (Wood et al., 2016)
Age: Younger vs. older		0.63 (Acierno et al., 2010)		0.78 (Acierno et al., 2010)
Female with disabilities	Positive (Mitra et al., 2014)*			Positive (Mitra et al., 2014)*			Positive (Mitra et al., 2014)* 0.51 (Platt et al., 2017)
Male	−0.40 (Dong et al., 2014)0.94 (Strasser et al., 2013)0.38 (Powers et al., 2008)*		−0.54 (Wood et al., 2016)0.46 (Powers et al., 2008)*
African American/Black	0.85 (Dong et al., 2014)	0.46 (Beach et al., 2010)	1.18 (Beach et al., 2010)0.53 (Peterson et al., 2014)			1.01 (M), 0.83 (F) (Dong et al., 2012b)
Hispanic	−0.40 (Schafer & Koltai, 2015)1.36 (Strasser et al., 2013)				−0.89 (Burnes et al., 2015)
Non-Caucasian					0.35 (Acierno et al., 2010)0.69 (Amstadter et al., 2011)
Socioeconomic status
Health-care insecurity	0.83 (Rosay & Mulford, 2017)
Higher education	0.02 (Schafer & Koltai, 2015)		0.05 (Lichtenberg et al., 2013)0.08 (Wood et al., 2016)
Higher numeracy			−0.06 (Wood et al., 2016)
Less than high school education (cf. college or beyond)	0.95 (DeLiema et al., 2012)	−0.51 (Burnes et al., 2015)		−0.79 (Burnes et al., 2015)		0.55 (M) 0.28 (F) (Dong et al., 2012b)
High school or college		−0.51 (Burnes et al., 2015)		−0.66 (Burnes et al., 2015)
College				−0.66 (Burnes et al., 2015)
Low income (less than US$35,000)	−0.44 (Wiglesworth et al., 2010)	0.29 (Burnes et al., 2015)	0.26 (Peterson et al., 2014)	0.43 (Burnes et al., 2015)	0.38 (Acierno et al., 2010)0.43 (Burnes et al., 2015)	0.89 (M, less than US$15K), 0.90 (F, less than US$15K), 0.52 (M, US$15–30K), 0.50 (F, US$15–30K) (Dong et al., 2012b)
Self-reported financial satisfaction			−0.15 (Lichtenberg et al., 2013)
Unemployment		0.21 (Acierno et al., 2010)
Physical health
Greater functional capacity		−0.12 (Burnes et al., 2015)		−0.08 (Burnes et al., 2015)
Behavioral problems—males with disabilities (smoking and sleep)	0.29 (smoking), 0.74 (poor sleep) (Mitra et al., 2014)
Need assistance with activities of daily living		0.33 (Acierno et al., 2010)0.45 (Amstadter et al., 2011)	0.38 (Acierno et al., 2010)0.56 (Amstadter et al., 2011)0.37 (Beach et al., 2010) Positive (Peterson et al., 2014)
Need assistance with instrumental activities of daily living			Positive (Peterson et al., 2014)
Physical disability status (including functional/motor and hearing impairment)	0.14 (DeLiema et al., 2012)0.88 (Dong et al., 2012a)0.55 (Dong et al., 2012c)0.23 (Rosay & Mulford, 2017)0.26 (Schafer & Koltai, 2015)0.40 (VandeWeerd et al., 2013)					0.03 (Dong et al., 2010)
Poor self-rated health		0.26 (Hispanic), 0.28 (non-White) (Alexandra Hernandez-Tejada et al., 2013)		0.36 (Hispanic) (Alexandra Hernandez-Tejada et al., 2013)	0.43 (Acierno et al., 2010)0.73 (Amstadter et al., 2011)0.41 (Burnes et al., 2015)
Somatic complaints	0.16 (Roepke-Buehler et al., 2015)					0.09 (Roepke-Buehler et al., 2015)
Subjective physical health			−0.03 (Wood et al., 2016)
Substance abuse					Positive (Conrad et al., 2019)
Mental health
Alzheimer’s/dementia symptoms	0.87 (VandeWeerd et al., 2013)
Cognitive impairment	0.79 (Dong et al., 2012b)0.21 (Dong et al., 2014)	0.20 (Dong et al., 2011)Negative (Liu et al., 2019)	0.26 (Dong et al., 2011)	0.35 (Dong et al., 2011)	0.36 (Dong et al., 2011)
Depression	0.38 (Dong, 2014)0.31 (Roepke-Buehler et al., 2015)0.45 (Schafer & Koltai, 2015)0.99 (Strasser et al., 2013)	1.00 (Beach et al., 2010)	0.80 (Beach et al., 2010)0.05 (Lichtenberg et al., 2013)			0.15 (Roepke-Buehler et al., 2015)
Poor mental health among males with disabilities	0.29 (Mitra et al., 2014)
Subjective mental health			−0.03 (Wood et al., 2016)
Interpersonal
Care receiver/victim aggression	0.79 (VandeWeerd et al., 2013)0.36 (physical agg), 0.90 (psych agg) (Wiglesworth et al., 2010)
Caregiver self-esteem	−0.23 (VandeWeerd et al., 2013)
Interpersonal problems	0.32 (Roepke-Buehler et al., 2015)
Older adult/adult child relationship quality	−0.83 (Von Heydrich et al., 2012)
Living with a spouse or partner			−0.54 (Peterson et al., 2014)
Living with family member other than spouse or children			0.43 (Beach et al., 2010)
Number of non-spousal household members			Positive (Peterson et al., 2014)
Marital status: Single, divorced, or widowed males	0.32 (Powers et al., 2008)*		0.31 (Powers et al., 2008)*		0.53 (Powers et al., 2008)*		0.32 (Powers et al., 2008)*
Marital status: Divorced or widowed		0.55 (Burnes et al., 2015)		0.38 (Burnes et al., 2015)	0.46 (Burnes et al., 2015)
Marital status: Never married		−1.17 (Beach et al., 2010)
Partnered/romantically involved	0.31 (Rosay & Mulford, 2017)
Self-reported social-status need fulfillment			−0.04 (Lichtenberg et al., 2013)
Social isolation/low social support	0.59 (Vandecar-Burdin & Payne, 2010)Positive (Von Heydrich et al., 2012)	0.64 (Acierno et al., 2010)0.45 (Hispanic), 0.42(non-White) (Alexandra Hernandez-Tejada et al., 2013)0.69 (Amstadter et al., 2011)		0.60 (Acierno et al., 2010)0.44 (Hispanic), 0.37(non-White) (Alexandra Hernandez-Tejada et al., 2013)	0.78 (Acierno et al., 2010)1.05 (Amstadter et al., 2011)		0.96 (Acierno et al., 2010)0.81 (Hispanic), 0.82 (non-White) (Alexandra Hernandez-Tejada et al., 2013)
Social support	−0.31 (Schafer & Koltai, 2015)
Use of social service (no)			−0.16 (Acierno et al., 2010)
History
Prior abuse	1.42 (DeLiema et al., 2012)		0.59 (DeLiema et al., 2012)
Prior trauma		0.45 (Acierno et al., 2010)0.49 (Amstadter et al., 2011)		0.90 (Amstadter et al., 2011)
Years lived in U.S.					0.03 (DeLiema et al., 2012)

Note. F = female; M = male.

Findings associated with individuals with disabilities.

Table 2.

Caregiver and Alleged Perpetrator Risk Factors.

Risk Factors	Abuse (Nonspecific)	Emotional Abuse	Financial Abuse	Physical Abuse
Caregiver and alleged perpetrator
Caregiver education (lower mean education level)	−0.47 (Wiglesworth et al., 2010)
Caregiver depression	0.42 (Wiglesworth et al., 2010)
Caregiver perceived burden	0.49 (Wiglesworth et al., 2010)
Caregiver role limitations due to emotional problems	−0.47 (Wiglesworth et al., 2010)
Caregiver state anxiety	0.50 (Wiglesworth et al., 2010)
Caregiver’s subjective emotional status	−0.47 (Wiglesworth et al., 2010)
Perpetrator substance abuse	0.64 (VandeWeerd et al., 2013)	Positive (Conrad et al., 2019)	Positive (Conrad et al., 2019)	Positive (Conrad et al., 2019)
Alleged abuser behavioral problem		Positive (Liu et al., 2019)

Environmental Scan of County-Level Data Sources

To assess the availability of data sources that provide information about community/contextual risk and protective factors associated with adult maltreatment, the team employed an environmental scan and methods of data asset cataloging and monitoring. This allowed the researchers to identify, describe, and prioritize data for project use. Building on a list of datasets presented at the USC Judith D. Tamkin Symposium on Elder Abuse (Gassoumis, 2018) and expanded by New Editions Consulting, Inc. under contract by the Administration for Community Living (ACL), the researchers began developing a repository of datasets in Microsoft Excel with metadata for each data source considered for inclusion. The researchers consulted with subject matter experts and federal stakeholders at the Centers for Disease Control and Prevention and the U.S. Department of Justice, among other agencies. In addition, the researchers conducted Google searches on established or theorized risk and protective factors to identify potentially relevant data sources.

Following the identification and cataloging of promising data sources, information on each data source was entered into the data asset catalog. Relevant information included the name and description of the dataset, smallest geographic or individual unit of analysis, federal agency or organization owning the data, population or sample, type of data collected, variables of interest, data access restrictions, and year(s) of data collection. After cataloging, each of the data sets was prioritized based on important attributes, including type of data source (surveillance prioritized over survey), design (longitudinal prioritized over repeated cross-sectional, repeated cross-sectional prioritized over cross-sectional), coverage (national prioritized over state or local), unit of geography available (county prioritized over state or region), availability of data (public prioritized over restricted), inclusion of relevant risk and protective factor data, and data collected recently or in an ongoing fashion.

The identification and assessment of potentially relevant data sources yielded a large inventory of more than 60 data sources. Once the data sources were identified, the variables were cross-referenced with the findings from the scoping review. Data sources that contained relevant risk and protective factor information, were accessible (e.g., publicly available), and had nationally representative information at the county level were included. We excluded data sources that were limited to specific geographies (e.g., regional level) and that did not collect data on a regular, ongoing basis. This approach would ensure that the final list of data sources would include risk and protective factors and county-level demographics that researchers could analyze now and in future studies.

Results

Scoping Review of Risk and Protective Factors

Risk and protective factors for victims of adult maltreatment are highlighted in Table 1. Risk and protective factors associated with the caregiver or alleged perpetrator are identified in Table 2. All but one of the identified studies reported results of individual-level risk factors, associated with the victim of maltreatment, the caregiver, or perpetrator. One study evaluated the association between elder abuse and regional levels of substance abuse and found that higher investigation rates were significantly associated with illicit drug use, lower median household income, lower proportion of the population who graduated from high school, and a higher population of Hispanics (Jogerst et al. 2012). To our knowledge, this is the only study to examine community-level risk factors associated with an adult, specifically elder maltreatment. In interpreting the results of the current scoping review, we characterized the individual-level risk factors by domains such as demographics and socioeconomic status, physical health, mental health, interpersonal characteristics, historical events, and features related to caregivers and alleged perpetrators.

Demographics

The effect sizes for the relationship between demographic factors and adult maltreatment ranged from −0.002 to 1.36. Moderate to large effect sizes were found between age and emotional and physical abuse as well as neglect. In four studies of older adults, negative effect sizes are observed between older individuals compared to the younger groups indicating that the older groups were less likely to experience abuse compared to the younger age groups (Beach et al., 2010; Burnes et al., 2015; DeLiema et al., 2012; Wood et al., 2016). Acierno et al. (2010) observed a similar trend in the opposite direction with moderate positive effect sizes found between younger, older adults compared to the older age groups. Two studies identified females as being at a greater risk of adult maltreatment (Mitra et al., 2014; Platt et al., 2017). Associations between males and maltreatment were inconsistent (Dong et al., 2014; Powers et al., 2008; Strasser et al., 2013; Wood et al., 2016). Moderate to large effect sizes were observed for increased risk of maltreatment among African Americans, and Non-Caucasians (Acierno et al., 2010; Amstadter et al., 2011; Beach et al., 2010; Dong et al., 2012b; Dong et al., 2014; Peterson et al., 2014). For Hispanics, the direction of the effect was inconsistent with two studies citing a reduced risk for maltreatment among Hispanics and one study citing a strong positive effect for increased risk (Burnes et al., 2015; Schafer & Koltai, 2015; Strasser et al., 2013).

Socioeconomic status

A large effect size was observed between health-care insecurity and abuse, indicating that individuals with greater health-care insecurity were at a greater risk of abuse (Rosay & Mulford, 2017). Small effects were observed between higher education and higher numeracy skills and abuse (Lichtenberg et al., 2013; Schafer & Koltai, 2015; Wood et al., 2016). Two studies found moderate to high positive effects associated with less than high school education and abuse (DeLiema et al., 2012; Dong et al., 2012b), while one study found moderate negative effects with less than high school education, high school education, or college education (Burnes et al., 2015). Low income was consistently associated with abuse indicating that adults with low incomes are at greater risk of adult maltreatment (Acierno et al., 2010; Burnes et al., 2015; Dong et al., 2012b; Peterson et al., 2014; Wiglesworth et al., 2010). Similarly, unemployment was identified as having an association, albeit weak, with emotional abuse (Acierno et al., 2010).

Physical health

Poor physical health and functional impairment, including needing assistance with activities of daily living (ADL) and instrumental activities of daily living, are consistently associated with various experiences of abuse including maltreatment in general, neglect, financial and emotional abuse as well as self-neglect (Acierno et al., 2010; Amstadter et al., 2011; Beach et al., 2010; DeLiema et al., 2012; Dong et al., 2010, 2012a, 2012c; Peterson et al., 2014; Rosay & Mulford, 2017; Schafer & Koltai, 2015; VandeWeerd et al., 2013). One study on men with disabilities found moderate to strong associations between behavioral problems such as smoking and poor sleep, and maltreatment (Mitra et al., 2014).

Mental health

Cognitive impairment and Alzheimer’s/dementia were found to have strong associations with maltreatment in general (Dong et al., 2012b; VandeWeerd et al., 2013). Small effect sizes were also found between cognitive impairment and specific abuse types (Dong et al., 2011, 2014; Liu et al., 2019). Depression was found to have moderate to strong associations with maltreatment in general, emotional abuse, and financial abuse and a weaker association with self-neglect (Beach et al., 2010; Lichtenberg et al., 2013; Roepke-Buehler et al., 2015; Schafer & Koltai, 2015; Strasser et al., 2013).

Interpersonal

Social isolation/low social support was consistently found to have moderate to strong positive associations across multiple types of abuse suggesting that individuals experiencing low social support or isolation were at increased risk of maltreatment (Acierno et al., 2010; Alexandra Hernandez-Tejada et al., 2013; Amstadter et al., 2011; Vandecar-Burdin & Payne, 2010; Von Heydrich et al., 2012). To further support this, one study identified a negative association between having social support and maltreatment suggesting that having social support may be protective (Schafer & Koltai, 2015). Interestingly, one study found a large negative effect for individuals who were never married and emotional abuse (Beach et al., 2010). Care receiver/victim aggression was found to increase the risk of maltreatment (VandeWeerd et al., 2013; Wiglesworth et al., 2010) while older adult and adult child relationship quality was found to decrease an individual’s risk of maltreatment (Von Heydrich et al., 2012). Moderate effect sizes were also found between one’s living arrangement and financial abuse such that living with a spouse or partner served as a protective factor (Peterson et al., 2014) while living with a family member other than a spouse or children served as a risk factor (Beach et al., 2010; Peterson et al., 2014).

History

Moderate to large effect sizes were found between prior abuse and prior trauma for abuse (nonspecific) and emotional, financial, and physical abuse (Acierno et al., 2010; Amstadter et al., 2011; DeLiema et al., 2012). All three studies found moderate to strong positive associations suggesting that prior abuse and trauma increase an individual’s risk of maltreatment—specifically emotional, financial, and physical abuse. One study found a weak positive association between the number of years one lived in the U.S. with the likelihood of experiencing neglect (DeLiema et al., 2012).

Caregiver and alleged perpetrator

Table 2 highlights the risk and protective factors associated with caregiver and alleged perpetrator characteristics and adult maltreatment. Moderate positive effect sizes were found between caregiver depression, perceived burden, and “state anxiety,” or the state of being anxious in response to transient adverse situations (Wiglesworth et al., 2010). Negative moderate effect sizes were observed between caregiver education, subjective emotional status, and role limitations due to emotional problems (Wiglesworth et al., 2010). Two studies found positive associations between perpetrator substance abuse and maltreatment in general as well as emotional, financial, and physical abuse (Conrad et al., 2019; VandeWeerd et al., 2013).

Environmental Scan of County-Level Data Sources

The findings of the environmental scan of available data sources containing information on county-level risk and protective factors yielded a final list of datasets that researchers can analyze now and in future studies. Table 3 summarizes the data sources containing county population characteristics. We identified 11 data sources of which 10 are currently publicly available. All data sources may be linked at the county level and contain frequently gathered information relevant to risk factors associated with adult maltreatment as well as other types of maltreatment (e.g., child maltreatment and intimate partner violence). Table 3 shows 17 risk and protective factors characterized by demographic, socioeconomic, and health-related characteristics in alphabetical order.

Table 3.

County-Level Datasets.

Data Source	ACS	AHRF	CCD	CHR	IRS 990	LTC Focus	NAMRS	Neighborhood Atlas	UCR Program	SVI	USDA Food Environment Atlas
Adult maltreatment			X				X		X
Demographics
Age	X	X	X	X		X	X		X	X	X
Marital status	X	X					X
Race/ethnicity	X	X		X		X	X		X	X	X
Sex	X	X		X		X	X		X
Socioeconomic status
Area deprivation index								X
Crime									X
Income	X	X		X			X			X	X
Education	X	X		X			X			X
Employment	X	X		X			X			X
Food access	X	X		X							X
Housing	X	X		X						X	X
Health
Alcohol use/ abuse				X
Disability	X	X					X			X
Mental/behavioral health		X		X		X	X
Nonprofit services					X
Physical functioning	X			X		X	X
Physical health		X		X		X					X

Note. ACS = American Community Survey; AHRF = Area Health Resources Files; CCD = Consumer Complaint Database; CHR = County Health Rankings; LTC = Long Term Care Focus; NAMRS = National Adult Maltreatment Reporting System; UCR = Uniform Crime Reporting; SVI = Social Vulnerability Index.

Discussion

Adult maltreatment is a major public health concern in the U.S. (Hall et al., 2016) and its estimated prevalence is likely to be underestimated by official maltreatment reports (Lachs & Pillemer, 2015). The extant literature on adult maltreatment generally focuses on individual-level risk and protective factors. However, little is known about how those factors interact with one another to increase or decrease an individual’s risk of maltreatment. Moreover, even less is known about social-contextual risk factors and how those might interact with individual-level risk factors. Empirical studies on child maltreatment support the notion that multiple interacting risk factors contribute to cumulative risk, which is found to be most predictive of child maltreatment (Patwardhan et al., 2017; Yang & Maguire-Jack, 2018). To our knowledge, only one study has examined community-level risk factors associated with maltreatment in general (Jogerst et al., 2012). The current scoping review identifies this critical gap in the literature and extends the exploration of using community (e.g., county level) data sources to further examine “person”- and “place-centered” risk and protective factors associated with adult maltreatment.

Among the risk factors identified in the review, low income, low social support, depression, and physical disability are most consistently associated with adult maltreatment and display moderate to strong associations with adult maltreatment across multiple studies. Cognitive impairment and Alzheimer’s disease, education attainment, needing assistance with ADL, and self-reported poor health were also identified in several studies as having moderate to strong associations with adult maltreatment. It is important to note that these findings are reflective of the different research methodologies used in the studies. However, the results are consistent with other systematic reviews of elder abuse risk factors citing cognitive and physical impairment as well as psychosocial distress as consistently associated with elder abuse (Dong, 2015). Like other reviews, the current review highlights risk factors at the individual level (i.e., alleged victim) with only four studies examining risk and protective factors associated with the caregiver or alleged perpetrator.

While limited research was available on the subject, caregiver and perpetrator characteristics associated with adult maltreatment were identified. Caregiver depression, perceived burden, and state anxiety were associated with a higher risk of abuse (Wiglesworth et al., 2010). Interestingly, caregiver low mean educational attainment, role limitations due to emotional problems, and subjective emotional status were associated with decreased risk of abuse. Because these results were observed in a single study, additional research is warranted to understand the association between caregiver characteristics and adult maltreatment. Regarding the alleged perpetrator, the current study aligns with previous work suggesting that substance abuse and behavioral problems increase the risk of maltreatment (Conrad et al., 2019; Jackson & Hafemeister, 2011; Johannesen & LoGiudice, 2013; Liu et al., 2019).

Research specifically focused on protective factors was lacking. Similar to the study done by Pillemer and colleagues (Pillemer et al., 2016), the current scoping review found little empirical evidence of factors that can protect elders from maltreatment or promote resilience after mistreatment. However, Pillemer and colleagues reported strong evidence that social embeddedness/support and shared living arrangements lower the risk of elder maltreatment. These findings are consistent with the current review showing negative associations between social support (Schafer & Koltai, 2015) and maltreatment as well as living with a spouse (Peterson et al., 2014) and financial abuse. Future studies should seek to test these associations and confirm as well as identify other individual, familial, and community-level factors that may shield vulnerable adult populations from risk of adult maltreatment.

Future empirical research to examine risk and protective factors associated should investigate the unique and interactive contribution of these factors across multiple levels. The field must move beyond individual-level risk factors existing in isolation and focus on the development of an ecological-contextual framework that underscores the importance of examining contextual factors that precipitate, are interrelated, and influence adult maltreatment (Schiamberg & Gans, 2000). Risk and protective factors detection at the individual, relational, community, and societal level is essential to inform the prevention of adult maltreatment. Hierarchical linear modeling approaches may serve to disentangle the relative contribution of risk factors across the multiple levels of analyses. Additional research is also needed to help researchers and APS workers understand cumulative risk better. To our knowledge, no study to date has examined the relations between risk factors, which may result in an increased risk of adult maltreatment. As a starting point, mediation or moderation analyses could be performed to test the interrelatedness of these factors and regression analysis could be used to test cumulative risk.

The development of advanced statistical techniques offers another opportunity to study risk and protective factors associated with adult maltreatment. Predictive analytics refers to a branch of advanced statistics used to make predictions about unknown future events or activities. The use of cutting-edge tools such as machine learning algorithms may yield accurate prediction of the likelihood that an individual may experience maltreatment which may in turn inform prevention efforts. Place-based modeling may also identify “hot spots” which may enable interventions or prevention efforts to optimize the allocation of resources to the highest need locations. County-level data may be useful in the identification of previously un-tested risk factors associated with adult maltreatment or in the identification of geographic “hot spots” where adult maltreatment rates are high. For example, researchers might test if county-level crime, access to food and social support services as well as indices of area deprivation might increase a vulnerable adult’s risk of maltreatment as these features have been identified in locations that are at the highest risk for child maltreatment cases (Daley et al., 2016). In order to utilize county-level data, however, researchers must first understand where and how to access that data.

The county-level datasets identified in the environmental scan provide information on multiple county-level demographic, socioeconomic, and health-related characteristics identified in the literature to be associated with adult maltreatment. Linking national and county-level data sources will help inform whether and to what extent adult maltreatment reporting rates vary among states and counties. One possible contributor to county-level variability in abuse rates is the resources devoted to APS investigations and services. Finding county-level data on APS resources was difficult, however, we believe these datasets can potentially provide valuable insights into risk and protective factors associated with maltreatment. Investigating county-level characteristics associated with areas with high incidences/reporting of abuse, neglect, and exploitation may help aid in the prioritization of risk-mitigating efforts. The county-level data sources, almost all publicly available, offer great potential to enhance the identification of individuals at high risk as well as high-risk counties which could, in turn, benefit from targeted maltreatment prevention efforts. Table 4 provides a summary of the critical findings.

Table 4.

Summary of Critical Findings.

• To date, low income, low social support, depression, and physical disability are the most common risk factors of adult maltreatment identified in the literature (in the last decade). Multiple studies support moderate to strong associations with adult maltreatment.• Cognitive impairment and Alzheimer’s disease, education attainment, needing assistance with ADL, and self-reported poor health were also identified in several studies as having moderate to strong associations with adult maltreatment.• The current scoping review found little empirical evidence of factors that can protect elders from maltreatment or promote resilience after mistreatment.• The current scoping review found little empirical evidence of contextual (environmental or community level) risk and protective factors.• Administrative data sources may be leveraged to enhance the field’s understanding of contextual risk factors.• Administrative data sources may be used to create current/up-to-date maps of hotspot communities.

Note. ADL = activities of daily living.

Future Research Directions

The adult maltreatment literature summarized in the current review primarily focused on the correlates associated with adult maltreatment. The fields of child maltreatment and intimate partner violence have produced far more studies designed to empirically test risk and protective factors across multiple levels. The empirical evidence base for the field of adult maltreatment would be strengthened by more studies specifically designed to examine risk factors at the individual, relationship, and community levels. Given the complexity of adult maltreatment, more rigorous studies are needed to understand the phenomena. In particular, additional research is needed to examine the interaction between different risk and protective factors as well as assess cumulative risk. The current evidence base would also benefit from longitudinal studies to examine how risk and protective factors may change over time. That is, after considering the risk for occurrence of adult maltreatment explained by the presence of a particular risk factor, whether we can examine how factors that have been previously used as predictors of that risk factor of adult maltreatment alter the risk of maltreatment.

Future research should examine risk and protective factors in other vulnerable populations such as adults with disabilities. Little empirical research on the risk of adult maltreatment for adults with disabilities exists. Fisher et al. (2016) conducted an environmental literature scan where the researchers reviewed 23 international articles to summarize the vulnerability of adults with intellectual disabilities. The authors reported that the presence of an intellectual disability, as well as behavioral problems, poor personal competence, passive, or avoidance decision-making strategies, and lack of friends increased the risk of vulnerability. Furthermore, the type of intellectual disability is associated with an increased risk of vulnerability. Other environmental risk factors include individuals living in congregate settings, and abuse was often perpetrated by other adults with disabilities (Fisher et al., 2016). More evidence-based studies should be conducted to examine the individual and contextual risk factors associated with adults with differing disabilities.

Although adult maltreatment studies are common, different research methodologies, measures, and analyses present challenges in interpreting the findings. Often the sample population varied from study to study as well as the instruments used to measure adult maltreatment, making it difficult to compare and understand the risk factors between studies. To address these inconsistencies, operational definitions should be applied to measure maltreatment and considered when working with specific populations (e.g., older adults versus individuals with disabilities). It is important to underscore that the risk factors of adult maltreatment primarily focused on victim characteristics and not necessarily perpetrator characteristics (e.g., substance use, mental illness) or environmental factors (e.g., area deprivation), which may also affect the occurrence of maltreatment. Moreover, a majority of the studies identify the risk factors as being associated with maltreatment rather than employing research designs to inform the causal relationship between vulnerability risk factors and adult maltreatment. As a result, the current study used effect size indices to interpret sample-based estimates of the size of the relationship between the risk factor and abuse; however, the effect size may look different for dichotomized compared to continuous independent variables. Implications for practice, policy and research are summarized in Table 5.

Table 5.

Implications for Practice, Policy, and Research.

Practice• Risk factors may be different for the different maltreatment phenomenon and for subgroups of the APS population. Practitioners and researchers collaborate to identify tools and/or training protocols for assessing risk.• Professionals should be mindful of diversity in adult maltreatment and employ services and supports in culturally and linguistically competent approaches.• Develop more precise, targeted prevention and intervention strategies.• Implement real-time monitoring and evaluation by longitudinally tracking efforts in specific communities, as well as view longitudinal changes in maltreatment rates.Policy• Develop targeted policies that focus on the communities that need it most.• Make cost-effective funding decisions.

Research• Conduct community-level studies of adult maltreatment.• Conduct more longitudinal studies, adding both additional historical and real-time datasets.• Conduct rapid studies with the use of artificial intelligence, machine learning, and predictive analytic tools.

Next Steps

Projects to explore predictive analytics for adult maltreatment using NAMRS data and publicly available county-level data are already underway. Artificial intelligence and machine learning have the potential to elucidate the unique sets of individual and community-level factors that affect the risk of maltreatment. The Administration for Community Living, in collaboration with the Centers for Medicare & Medicaid Services, has launched a project to explore the use of predictive analytics as one component of a broader strategy to predict and prevent adult and elder maltreatment. The project will leverage artificial intelligence, machine learning, and other “big data” tools to investigate patterns of risk and protective factors across multiple data sources to determine if there is an association with the reported incidence of adult maltreatment. The goal of the project is to create and improve interventions to prevent, and effectively intervene in, adult and elder maltreatment, and as an outcome, improve disabled and older adults’ quality of life and health quality outcomes, and reduce health care expenses. NAMRS data were associated with the publicly available data sources identified in the current study to develop algorithms that identified community-level risk factors associated with increased risk of APS system involvement. The preliminary results of this work demonstrated promise for this methodology, as well as elucidated some similarities and differences among the risk factors associated with different maltreatment experiences (e.g., any experience of maltreatment, self-neglect, maltreatment of individuals with adults with disabilities, and maltreatment of older individuals). The addition of other related data sources such as Medicare and Medicaid claims data will build on this work, improving risk factor identification. Using artificial intelligence and machine learning algorithms with big data has the potential to transform approaches to adult maltreatment, improving the ability to identify vulnerable individuals and positively intervene or prevent maltreatment from occurring.

Footnotes

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by the Administration of Community Living, U.S. Department of Health & Human Services, under Contract Number HHSP233201500042I. We are grateful for the guidance of the Administration for Community Living staff, as well as the support of additional members of the Adult Protective Services Technical Assistance Resource Center Team, during our development of this manuscript.

ORCID iD

Nicole Fettig

Notes

Author Biographies

Nicole Fettig, PhD, is a Developmental Psychologist and Director of Research at WRMA, Inc. Her research focuses on factors influencing risk and resilience, particularly in the context of child and adult maltreatment. She has served as an adjunct faculty at two Universities in the D.C. Metropolitan area and published in multiple peer-reviewed journals.

Hilary Mitchell, BSc, is a Senior Research Associate for WRMA, Inc. Her work primarily focuses on data and technical assistance for health services programs such as the Ryan White HIV/AIDS Program. She has supported the National Adult Protective Services Technical Assistance Resource Centers (APS-TARC) through state APS policy analyses and research on risk and protective factors of APS populations.

Zach Gassoumis, PhD, is an Assistant Professor of Family Medicine and Gerontology at the Keck School of Medicine of USC. He conducts research into various aspects of quality of life for older adults, with a primary focus on elder abuse, adult protective services, caregiving, and health and social services. His work centers on improving the well-being of vulnerable populations and reducing disparities between population groups.

Zainab Nizam, PhD, is a behavioral scientist and Senior Research Associate for WRMA, Inc. They are a mixed methodologist with subject matter expertise in sexual violence prevention, particularly among 2LGBTQIA+ communities. Their work prioritizes the use of queer, feminist, and other critical theory frameworks to better understand social and structural determinants of health.

Stephanie Whittier Eliason, MSW, serves as the Elder Rights Team Lead in the U.S. Administration for Community Living (ACL). She has held lead responsibility for ACL’s Adult Protective Services portfolio, including the National Adult Maltreatment Reporting System, and elder abuse prevention research and programming. She has also been an advocate for residents’ rights in residential care facilities for the elderly.

Scott Cory, AB, is the Chief Information Officer at the Administration for Community Living, where he leads efforts with partners in the aging and disability network to use technology to better support services provided to people, regardless of age or disability, allowing them to live where they choose, with the people they choose, and fully participate in their communities.

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Adult Maltreatment Risk Factors: Adding Community-Level Factors to an Individual-Level Field

Abstract

Keywords

Introduction

Background

Method

Scoping Review of Risk and Protective Factors

Environmental Scan of County-Level Data Sources

Results

Scoping Review of Risk and Protective Factors

Demographics

Socioeconomic status

Physical health

Mental health

Interpersonal

History

Caregiver and alleged perpetrator

Environmental Scan of County-Level Data Sources

Discussion

Future Research Directions

Next Steps

Footnotes

Declaration of Conflicting Interests

Funding

ORCID iD

Notes

Author Biographies

References