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Document Title: Exploring Elder Financial Exploitation 

Victimization: Identifying Unique Risk 

Profiles and Factors to Enhance Detection, 

Prevention and Intervention 

Author(s): Jason Burnett, Ph.D., Rui Xia, Ph.D., Robert 

Suchting, Ph.D., Carmel B. Dyer, Ph.D. 

Document Number: 250756 

Date Received: May 2017 

Award Number: 2013-IJ-CX-0050 

This resource has not been published by the U.S. Department of 

Justice. This resource is being made publically available through the 

Office of Justice Programs’ National Criminal Justice Reference 

Service. 

Opinions or points of view expressed are those of the author(s) and 

do not necessarily reflect the official position or policies of the U.S. 

Department of Justice. 
 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

National Institute of Justice Final Technical Report 

Exploring Elder Financial Exploitation Victimization: Identifying Unique Risk Profiles and 

Factors to Enhance Detection, Prevention and Intervention 

Grant # 2013-IJ-CX-0050 

1

Jason Burnett, PhD, 1Rui Xia, PhD, 2Robert Suchting, PhD and 1Carmel B. Dyer, PhD 

Affiliations: 

1

The University of Texas Health Science Center at Houston, UTHealth, McGovern Medical 

School, Division of Geriatric and Palliative Medicine 

2

The University of Texas Health Science Center at Houston, UTHealth, McGovern Medical 

School, Department of Psychiatry and Behavioral Sciences 
 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

The authors are currently preparing a manuscript, describing the primary findings, which will be 

submitted to the American Journal of Public Health. Other peer-reviewed journals of interest for 

further dissemination of relevant findings such as understanding the association between FE 

types and recidivism and different combinations of FE include the Journal of the American 

Geriatrics Society, the Gerontologist and Psychology of Violence. 
 

 

 

 

 

 

 

 

 

 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

National Institute of Justice Final Technical Report 

Exploring Elder Financial Exploitation Victimization: Identifying Unique Risk Profiles and 

Factors to Enhance Detection, Prevention and Intervention
 

Grant # 2013-IJ-CX-0050 

Abstract 

Statement of Purpose: Explore risk factors across the socioecological framework (i.e. individual, 

perpetrator and community-levels) to identify the most important factors that differentiate elder 

financial exploitation (FE) from other forms of abuse as well as pure FE from hybrid FE. 

Description of Research Subjects: Older adults 65 years and older with a confirmed case of abuse 

(i.e. financial exploitation, caregiver neglect, physical abuse, emotional abuse) by Texas Adult 

Protective Services between the years 2009 – 2014. Methods: Secondary data analysis of a 5­

year statewide aggregated cohort of Texas Adult Protective Services confirmed cases of abuse 

between the years 2009 – 2014. Case investigation data such as demographics, reported and 

confirmed abuse types, victim and perpetrator mental and physical health, substance use, social 

and financial factors along with community-level data (Geographic Information Systems) were 

analyzed. Supervised Learning, which provides a step-by-step statistical decision-making process 

was used to identify the most reliable, interpretive and predictive risk factor models. Training and 

test sampling was included for replication purposes. Results: Financially-based variables are the 

best predictors of FE versus other forms of abuse, but apparent injury appears to be the most 

important indicator of other forms of abuse even in the presence of FE. Hybrid FE may be strongly 

related to poorer outcomes compared to pure FE however, the most predictive model found 

negative effects of others, alcohol and substance use by others as well as foreclosure and 

inadequate medical supplies to be the most important predictors of hybrid FE. Models that 

accounted for less linearity between the variables resulted in greater accuracy in group 
 

 

 

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classification indicating the need to account for complex interactions across the socioecological 

context. Conclusion: Different factors across the socioecological context are needed to reliably 

differentiate between elder FE and other forms of abuse as well as pure versus hybrid FE. These 

factors will also vary depending on the perspective one takes regarding the linearity of the 

interactions between the different factors. The findings provide support for the need to differentiate 

between types of abuse and subtypes of elder FE and the need for frontline workers and social 

service agencies and researchers to account for variables across the socioecological context when 

developing surveillance, intervention and prevention programs. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Executive Summary 

Financial exploitation (FE) in older adults is the “illegal taking, misuse or concealment of 

funds, property or assets of a vulnerable elder” (National Center on Elder Abuse: 

https://ncea.acl.gov/faq/index.html#faq1) and poses a serious public health problem. In two US 

national prevalence studies, FE represented the highest percent (5.2%) of self-reported abuse 

among cognitively intact community-dwelling older adults (Acierno et al., 2010) and occurred in 

21% of all cases reported to Adult Protective Services (APS) (Teaster et al., 2006). The estimated 

financial loss among older Americans in 2012, as a result of FE, was 2.9 billion (MetLife, 2012). 

Other outcomes include financial ruin (Dessin, 2000), loss of independence and security (Choi et 

al., 1999), decline in quality of life (Coker, 1997), decreased resources for health care (Kemp et 

al., 2005), depression and suicide (Nerenberg, 2000; Podneiks, 1992), emergency room visits and 

hospital admissions (Dong et al, 2013; Dong et al., 2013) and increased risk of 5-year all-cause 

mortality (Burnett et al., 2016). 

Individual studies have found that victim characteristics associated with increased risk of 

FE include impaired activities of daily living and dependence on others for care, (Peterson et al, 

2014; Acierno et al., 2010; Amstadter et al., 2011), not having a spouse (Laumann et al., 2008) 

reporting poor self-rated health (Amstadter et al., 2011) and non-use of social services (Acierno et 

al., 2010). Culturally and ethnically relevant victim characteristics such as being AfricanAmerican or Non-White (Peterson et al., 2014; Amstadter et al., 2011; Laumann et al. 2008) have 

also been linked to higher risks. Highly probable perpetrators commonly depend on the older adult 

for finances (Hafemeister, 2003), abuse substances (Anetzberger, 1994) and are chronically 

unemployed (Jackson and Hafemeister, 2012). Likewise, increasing the number of non-spousal 

household members, living below the poverty threshold, and perceiving social support to be low 
 

 

 

 

 

 

 

 

 

 

 

 

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(Peterson et al., 2014; Amstadter et al., 2011) also appear to be associated with increased incidents 

of FE victimization. 

Adding complexity, recent evidence by Jackson and Hafemeister, (2012) suggests that FE 

should be considered a construct with two unique subtypes (i.e. Hybrid FE and Pure FE). Hybrid 

FE consists of FE victimization plus physical abuse and/or neglect and was associated with higher 

percentages of victims reporting fair/poor health and fear of the abuser. The perpetrators were also 

more likely to be a relative, chronically unemployed and financially dependent on the older adult. 

Cohabitation and change in living arrangement from living alone to living with the perpetrator 

were also associated with HFE versus Pure FE (i.e. no other forms of abuse or neglect). Moreover, 

HFE victims suffered abuse longer and experienced a 2-fold higher financial loss over the course 

of the victimization. 

These studies provide evidence that factors associated with incidents of FE cut across multiple 

levels of the socio-ecological context and that unique sets of factors are associated more so with 

specific subtypes of FE. These findings could have important implications for intervention and 

prevention programs. These efforts could be further facilitated by attempting to understand the 

interactions between variables within and across the socioecological context that influence the risk 

for FE or a specific subtype of FE. Because these interactions are not always linear and instead are 

likely highly complex and require a tremendous amount of statistical computation, it is often 

difficult to identify replicable models for identifying and classifying events as multifaceted as elder 

abuse and FE victimization. Modeling the complexity of the interactions within large datasets can 

be cumbersome and poses data analytic challenges based on sample size, variable load and the 

ways in which the variables work together to influence the risk of an outcome. Nevertheless, large 

APS derived datasets provide good sources of case relevant data regarding victim, perpetrator and 
 

 

 

 

 

 

 

 

 

 

 

 

 

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environmental characteristics and good opportunity for using commonly collected socioecological data to determine which factors together can be used to most accurately classify victims 

of elder FE victimization. Utilizing sophisticated analytic approaches designed to handle such 

large datasets and complex variable interactions across multiple levels could move corroborate and 

move the field beyond what has been learned from previous risk factor model building strategies 

for both FE and its subtypes and provide evidence supporting the use of socio-ecological models 

when studying elder FE. 

Building upon the previous elder FE work, the current study utilizes five years of statewide 

APS confirmed abuse cases to identify variables of highest importance across the socio-ecological 

model for accurately distinguishing and classifying elder maltreatment victims as 1): FE versus 

other forms of abuse and 2): Pure FE vs Hybrid FE. Findings from multiple analytic models 

varying from the most interpretive to the most predictive will be reported to address these aims. 

Implications for using a socio-ecological perspective to study elder FE victimization and its 

subtypes as well as the utility of supervised learning algorithms to improve public health FE victim 

surveillance and prevention will be discussed. 

Methods 

Sample 

APS are state agencies charged with investigating reports of abuse, neglect and exploitation 

in adults 18 years of age and older. These agencies perform investigations that include in-depth 

data collection capturing victim, perpetrator and environmental details needed to substantiate or 

fail to substantiate an allegation of abuse, neglect and/or exploitation. The data used to conduct 

this secondary analysis were obtained from the Texas Department of Family and Protective 

Services, Division of Adult Protective Services (APS). The data provided by APS for the current 
 

 

 

 

 

 

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study represent Texas statewide confirmed cases of elder abuse within the years of 2009 – 2014. 

Only records for adults 65 years and older were included. The confirmed abuse types included 

financial exploitation, physical abuse, emotional/verbal abuse (i.e. psychological abuse) and 

caregiver neglect. Caregiver neglect consisted of any of the physical, medical and mental health 

neglects where a perpetrator, other than the victim, was identified and confirmed. 

Definitions of Abuse 

The Texas Human Resource code Section 48.002 [a] defines the different types of elder 

abuse investigated by Texas APS (Texas Department of Family and Protective Services, May 

2010). For purposes of this study, these include: (a) emotional/verbal abuse—“any use of verbal 

communication or other behavior to humiliate, intimidate, vilify, degrade, or threaten harm”; (b) 

physical abuse —“abuse with resulting physical or emotional harm or pain to an elderly person

or adult with a disability by the person’s caretaker, family member, or other individual who has an 

ongoing relationship with the person”; (c) caregiver neglect—“the failure of a caretaker to provide 

the goods and/or services, including medical, physical or mental health to meet the needs of the 

older adult” (d) financial exploitation—“the illegal or improper act or process of a caretaker,

family member, or other individual who has an ongoing relationship with a person age 65 or older 

or an adult with a disability.” 

Given the specific aims of this study and because FE can co-occur with other forms of 

abuse, we chose to define FE in three ways: 1) FE with or without other forms of abuse, 2) Pure 

FE –only confirmed FE and no other confirmed forms of abuse and 3) FE with other confirmed 

forms of abuse. 

Demographic Characteristics 
 

 

 

 

 

 

 

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As part of the APS assessment, standard demographic variables were collected for both the 

alleged victims and perpetrators, when necessary. These variables include age, race/ethnicity, 

gender and living status. Race/ethnicity classification followed the coding provided by the United 

States Department of Health and Human Services. Other descriptive characteristics, including, but 

not limited to cognitive status, mobility, drug abuse and hearing impairments were included. A 

full list of these additional descriptive variables which are collected and recorded during the APS 

investigation in conjunction with the Client Assessment and Risk Evaluation variables are included 

in Appendix A. 

Risk Assessment and Contextual Variables 

All Texas APS referred cases of elder abuse receive an in-home investigation by an APS 

caseworker. These investigations include a comprehensive risk assessment guided by the Client 

Assessment and Risk Evaluation (CARE) tool. The CARE tool is used in conjunction with 

ancillary data assessment questions to help confirm elder abuse incidents. This tool was initially 

developed by the Texas Department of Family and Regulatory Services, Division of Adult 

Protective Services and the Texas Health and Human Services to improve the assessment and 

service delivery process for cases of mistreatment and self-neglect. Each APS caseworker receives 

extensive field and manual based training (i.e. 6 weeks) on how to properly administer and record 

data using the CARE tool. The CARE tool demonstrated efficiency and comprehensiveness when 

field tested on adults 60 years of age and older. 

The CARE tool consists of 57 items assessing the presence and absence of risks for harm 

associated with the different types of elder mistreatment (i.e. verbal abuse, physical abuse, 

psychological abuse, financial exploitation etc.) and self-neglect. These items are clustered in into 

5 broad categories (i.e. living conditions, financial status, physical/medical status, mental status, 
 

 

 

 

 

 

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social interaction) with 15 subcategories. The subcategories contain different risk indicators 

related to the different types of elder mistreatment and self-neglect. Each risk indicator follows an 

ordinal scale of measurement with the available response options of no problem, managed risk, 

problem, severe problem, not applicable and unable to determine. Each level of risk has a 

descriptive phrase to help the assessor decide on its appropriateness for the given client. A 

validated allegation is indicated by the identification of a problem or severe problem in any 

category. 

A recent study conducted by Burnett et al. (2014) investigated the construct validity and 

measurement invariance of the CARE tool. The findings validated the 5-factor structural model, 

but resulted in the removal of 14 items. The new 5-factor model was cross-validated on a randomly 

allocated hold-out sample and also showed adequate factor and item-threshold invariance across 

gender and ethnicity. Because the CARE tool was only validated using data from one region in 

Texas, the full CARE tool was utilized in this study. The CARE tool can be found in Appendix B. 

U.S Census Data and Geographic Information Systems 

The US Census Data for the years 2009 – 2014 were used in conjunction with Geographic 

Information Systems to identify community-level risk and protective factors associated with elder 

FE. Some of the community-level variables to be assessed are located in Appendix A. Geographic 

information systems (GIS) are computer systems designed to collect, manage, manipulate, overlay, 

analyze, and visualize spatial and non-spatial data (Steinberg & Steinberg, 2007). GIS makes it 

possible to link personal attributes or circumstances (e.g. health, demographic information, and 

financial exploitation) with features situated in space, which can then be analyzed for spatial 

patterns or graphed on a map. Public health researchers have used GIS to gain a better 

understanding of how environmental factors contribute to specific diseases and outcomes as well 
 

 

 

 

 

 

 

 

 

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as to develop targeted service plans. However, to date, GIS remains underutilized in the literature 

(Hirshorn & Stewart, 2003) and to the best of our knowledge, few studies have utilized GIS as a 

tool to study the issue of elder abuse and specifically, FE (e.g. Payne & Gainey, 2009) despite its 

evidence-based use in examining patterns of child mistreatment (Ernst, 2000). A list of these 

variables can be found in Appendix A. 

Analytic Strategy 

Standard data cleaning techniques described by Tabachnick and Fidell (2001) were used 

to review the data for missing variables and values as well as out of range values based on each 

variable. Missing data were assessed and in instances where missingness accounted for more than 

10% and a sensitivity analysis was conducted to permit evaluation of the robustness of findings to 

missing data assumptions. These cleaning methods resulted in a final dataset used for subsequent 

analyses. 

Data Mining Overview 

The present study used data mining to examine financial elder abuse. Data mining is a 

broad term for the process of detecting previously unknown patterns from data (Witten, Frank, & 

Hall, 2011). Other related (and sometimes interchangeable) words for data mining include machine 

learning and statistical learning (Hastie, Tibshirani, & Friedman, 2009). Data mining algorithms 

are used for prediction and knowledge discovery (insight into relationships underlying data). There 

are myriad different algorithms for use in data mining, and no one algorithm is best for every data 

set (e.g., James, Witten, Hastie, & Tibshirani, 2013). Further, algorithms vary in their levels of 

interpretability and raw predictive power; that is, given a set of raw input, some algorithms may 

provide excellent predictive performance through “black box,” opaque inner workings. 
 

 

 

 

 

 

 

 

 

 

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Generally speaking, data mining algorithms work by tuning on a “training” set of data and

predicting outcomes on a “test” set of data. Optimally, these data sets are collected independently. 

However, in practical situations, researchers often have one data set that must be split into sections 

for training and testing. Several different methods for data splitting have been proposed and used 

for data mining (for a review, see Kuhn & Johnson, 2013). The present study uses a two-way 

random split for training and testing of 80% and 20%, respectively, stratified by a binary outcome 

to ensure adequate representation of both categories in both splits. Of key importance is that after 

splitting, the test set is completely held out and never used for training. 

There are three major types of data mining algorithms: classification, regression, and 

clustering. Classification and regression algorithms are considered “supervised learning” in that

they attempt to predict observations on an outcome variable of interest; the difference is that 

classification examines categorical or binary outcomes, while regression investigates continuous 

outcomes. Clustering algorithms are considered “unsupervised learning” where instead of 

predicting an outcome, the algorithms seek to find underlying structure within the data. The present 

study will focus on classification, as the outcome in question (elder abuse type) is categorical. 

Many data mining algorithms, including those used in the present study, feature 

hyperparameters: algorithmic constants that work as tuning knobs, whereby several different 

values are tested to optimize performance. For example, in penalized regression, the magnitude of 

shrinkage is governed by a hyperparameter that ranges between 0 and 1 called lambda; the closer 

this value is to zero, the more a model resembles traditional ordinary least squares (OLS) 

regression (e.g, at lambda = 0, there is no shrinkage). 

Model tuning, including hyperparameter optimization, requires resampling procedures 

within the training set. This may be accomplished in several ways (Kuhn & Johnson, 2013); in the 
 

 

 

 

 

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present study, this was realized using a technique called 10-fold cross-validation. This procedure 

involves a ten-way split of the data, whereby 9/10 of the data and 1/10 of the data are selected in 

turns to serve as sub-training and sub-test sets. Using this procedure, we can minimize the variance 

in prediction (i.e., reduce the probability of poorly tuned algorithms that may result from 

lucky/unlucky splits of the data). 

In the present study, hyperparameters were tuned by randomly searching a grid space of 

potential hyperparameter values, testing different values, and choosing the best fitting algorithm 

of each type based on a scoring criteria. Several scoring criteria are typically available for this 

purpose; here, the squared difference between predicted and observed values (mean squared error; 

MSE) is used. 

Data Mining 

The present study used the open-source software H2O (Aiello, Kraljevic, & Maj, 2016) 

scripted in the R statistical computing environment (R Core Team, 2016) and implemented in Java 

to compare the performance of four data mining/machine learning algorithms in classifying 

different types of elder abuse. H2O provided excellent tools for addressing the research problem: 

cutting-edge implementations of some of the most powerful algorithms (generalized linear 

modeling, random forests, gradient boosting machines, and deep learning), parallel processing 

capabilities to optimize computational resources, and the capacity to scale for use in small and 

large datasets alike, including “big” datasets (here, the present data included more than 150,000

observations). 

Elder abuse in the present study comprised four categorizations: pure financial abuse, 

hybrid financial abuse, other abuse, and no confirmed abuse. These categories were re-classified 

into pairs for separate analyses to address specific research questions as follows: 
 

 

 

 

 

 

 

 

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(1) Given that we have evidence of abuse, can we discriminate between other confirmed 

abuse (i.e. physical, caregiver and psychological) and financial exploitation? 

(2) Given that we have evidence of financial exploitation, can we differentiate between 

confirmed pure FE and confirmed hybrid FE? 

This sequential questioning process was developed for two reasons. First, in a practical 

sense, many machine learning algorithms are not well-equipped for classification with more than 

two categories. The present approach affords the flexibility to use a wider range of algorithms 

while simultaneously fine-tuning our exploration of the data. Second, the process mirrors the 

manner in which an investigator may approach a novel instance of potential financial elder abuse 

in the field: they would first assess the probability of any elder abuse, then proceed to discern 

evidence for financial abuse, and finally determine whether that abuse was purely financial or 

multi-faceted. 

Data mining in the present study utilized four algorithms: penalized generalized linear 

modeling, random forest, gradient boosting machines, and deep learning. Details of each algorithm 

follow. Missing data on continuous predictors was handled natively within each algorithm; 

however, categorical missingness was addressed by creating a unique category for each variable 

that encapsulated all of the missing observations. 

Generalized Linear Modeling (GLM). The most general form of a linear model, GLMs 

can be considered a type of data mining algorithm in their ability to predict an outcome using a set 

of inputs (predictors). The most basic form of a GLM is simple linear regression with one predictor 

and one normally-distributed outcome; however, the model may be extended to situations with 

many predictors and non-Gaussian outcomes, as in logistic regression. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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The GLM as implemented in H2O (Nykodym, Kraljevic, Hussami, Rao, & Wang, 2016) 

is further extended to include regularization in the form of an elastic net, a type of shrinkage and 

variable selection penalty that mixes ridge regression and the lasso (Hastie, Tibshirani, & 

Friedman, 2009). The elastic net requires two hyperparameters: (1) lambda, the magnitude of the 

shrinkage penalty to the coefficients, and (2) alpha, the degree of mixing between ridge regression 

and lasso. The elastic net GLM provides an interpretable equation to describe the relationship 

between outcome and predictor(s) that is often familiar even to non-statisticians. The algorithm 

here handles missing continuous data by mean imputation, and variable importance is ranked by 

the highest magnitude (absolute value) of the regression coefficients. 

Random Forest (RF). The random forest was originally developed by Breiman (2001) by 

creating an ensemble of decision trees across bootstrapped resamples of data with random selection 

among a subset of predictors. A single decision tree is a simple data mining algorithm in its own 

right that iteratively partitions a data set by splitting on the variable that may best discriminate an 

outcome. At each stage, the decision tree selects the best possible variable for splitting without 

regard to how future splits may be influenced. This constitutes a “greedy” approach that is 

susceptible to high variance. 

The random forest improves on the decision tree in two notable ways: (1) variance is 

reduced by averaging across many trees and (2) splitting at any given tree node only uses a random 

subset of predictors, effectively decorrelating the trees in the forest. The algorithm may also 

internally handle missing data by allowing splits for missing values. Random forests are governed 

by three primary hyperparameters (although others may be included): the number of trees in the 

forest, the depth of each tree (the total number of splits a given tree may make), and mtry, the 

number of randomly-chosen variables to consider at each split. Random forest variable importance 
 

 

 

 

 

 

 

 

 

 

 

 

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is calculated by randomly permuting the values of each predictor; to the extent that the 

performance of the algorithm changes, the variable may be considered important. 

Gradient Boosting Machine (GBM). Similar to the random forest, the GBM (Friedman, 

2001, 2002) builds a strong prediction model from an ensemble of weaker models (here, decision 

trees, although other types of models may be used). However, rather than building a collection of 

trees on resampled data sets, boosted models are built sequentially by (1) fitting an initial model 

(a decision tree), then (2) fitting a new decision tree to the residuals of the initial model and adding 

it to the fitted first model (with a shrinkage penalty) to update the residuals. This process is repeated 

over several iterations, constantly updating residuals, to find ways to fit the hardest-to-learn 

observations. This algorithm as implemented in H2O (Click, Malohlava, Candel, Roark, & Parmar, 

2016) handles missing data similarly to the random forest, and is governed by several 

hyperparameters including the shrinkage rate lambda (on average, slower learning algorithms 

perform better, but may be more computationally expensive), the number of trees to fit, and the 

depth of the trees. Variable importance is also provided by the algorithm in terms of absolute and 

relative prediction strength of each input variable. 

Deep Learning (DL). Deep learning, implemented in H2O as a novel version of the neural 

network (Candel, Parmar, LeDell, & Arora, 2016) builds a model of weighted nonlinear 

relationships between nodes called neurons (mimicking the brain structure) by conceiving one or 

more hidden layers between a set of inputs and an outcome. The weights of each linked neuron 

adapt to minimize error in training data. Deep learning algorithms are particularly useful for speech 

and image recognition tasks. Hyperparameters include values for regularization, the number of 

hidden layers, the number of nodes within each hidden layer, and others. Missingness on 
 

 

 

 

 

 

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continuous variables is handled via mean imputation, and variable importance is calculated by 

examining the weights of the first two layers of the network. 

Data Mining Model Evaluation 

After tuning each algorithm using a random grid search to optimize hyperparameter values, 

variable importance metrics were collected, and the best algorithm of each type was evaluated on 

the test set. This evaluation yields a predicted probability for each classification for each algorithm; 

for example, when comparing hybrid versus pure financial elder abuse, each of the best-tuned 

generalized linear models, random forests, gradient boosting machines, and deep learning models 

provided a probability between 0 and 1 of a test set observation being a “pure” or a “hybrid” case

of financial elder abuse. 

These predicted probabilities require further investigation. The naïve investigative 

principal would be to determine classification of outcomes based on a predicted probability of 0.5 

or greater; that is, if an observation is at least 50% likely to be a given classification (e.g., pure), 

then that observation would be labelled thusly. However, this threshold does not always optimize 

classification in a practical sense. First, it does not optimize accuracy itself: one may have a larger 

percentage of correct classification into either group using a different threshold. Second, correct 

classifications and incorrect classifications may not be equally important. One commonly-used 

threshold is the F1 value: the harmonic mean between sensitivity (true positive rate) and precision 

(ratio of observations correctly classified to the number of observations predicted for a given 

category). By default, H2O will provide the optimized threshold to maximize F1 and report a 

contingency table based thereon. However, given that correct and incorrect predictions may have 

different costs, researchers should still examine and discern the cutoff that best optimizes the costs 

inherent to a given research problem.In addition to finding an optimal metric for classifying 
 

 

 

 

 

 

 

 

 

 

 

 

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observations into categories, the area under the receiver operating characteristic curve (AUROC, 

or more commonly AUC) provides a general idea of model discrimination performance. The ROC 

curve plots true positives against false positives and the closer to 1.0, the better. 

A final note about model evaluation bears note: the model with the best predictive 

power/best discrimination of the outcome may also be the most difficult to interpret. The GLM is 

the most readily interpretable algorithm in every case: parameter coefficients describe the 

magnitude and direction of influence for each variable in the model. The random forest, gradient 

boosting machine, and deep learning algorithms provide variable importance metrics, but as noted 

these are generally “black boxes” and determining the direction of influence is nebulous. Stacked 

ensembles are even more difficult to interpret: these provide raw predictive power, and the only 

interpretable output is the individual contribution of the constituent algorithms from the tuning 

process (e.g., each algorithm has a different weight). 

Modeling Decisions 

The approach taken for this analysis was to each individual’s first case of substantiation to 

define their type of abuse. This decision was made for two reasons. First individuals could be in 

the system multiple times and thus, using the first date of substantiation reduced the need for 

randomly selecting which case to use. Second, because these data derive from APS cases, 

recidivistic cases may confer more information than first time cases and thus, might need to be 

weighted differently because an investigator may know more about the case due to prior known 

information and thus, may validate the case based on this information. This could affect the ability 

to identify important risk factors and characteristics associated with the outcome. We also modeled 

the data using a variety of approaches within the supervised learning program. This allows us to 

identify the most robust and accurate model given the data. 
 

 

 

 

 

 

 

 

 

 

 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

Results 

Because recidivism may play a unique role in the level of known information about a case 

(i.e. assessor may have more information on previously investigated cases than on initial 

investigations) and thus adding an unequal prediction weight, we chose to truncate the analysis to 

include only the first episode of confirmed abuse. The total count for the confirmed elder abuse 

cases between the years 2009 and 2014 was N = 8,800. A total of N = 2514 or (29%) of the 

confirmed elder abuse cases over the 5-years include FE. A total of N=1964 (78%) had 

substantiated FE only (i.e. Pure FE) and N = 550 had FE plus some other form(s) of abuse (i.e. 

Hybrid FE), excluding self-neglect. Tables 1-4 describe the victim and perpetrator demographics 

as well as cognitive, functional and substance abuse characteristics that are collected as part of the 

routine APS investigation in addition to the CARE tool variables. 

For brevity, only the main results pertaining to the specific aims are provided in the 

narrative. Two sets of results are presented to balance interpretation and predictive accuracy of the 

models used to address the specific aims. These models include the General Linear Model (GLM) 

for interpretation and the Gradient Boosting Machine (GBM) for predictive and classification 

accuracy. Tables 5-10 provide the details regarding the GLM and GBM findings when trying to 

differentiate between confirmed FE and other forms of confirmed abuse and confirmed pure FE 

versus confirmed Hybrid FE. Receiver operating curves for all algorithms presented below can be 

found in Figures 1-4 located in the Appendix. 

Beginning with the most interpretative model, 4 of the top 10 most important variables for 

differentiating confirmed FE versus other forms of confirmed abuse included financial based 

questions (Table 5). Interestingly, the second most important variable in the model was apparent 

injuries which predicted other forms of abuse. Three of the last 5 most important variables are 
 

 

 

 

 

 

 

 

 

 

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related to caregiver neglect issues such as stress and burnout, knowledge and ability and physical 

neglect of the older adult. Differentiating characteristics of the perpetrator include being a spouse 

and older than 65 years of age; both of which are protective against FE. While area under the curve 

(AUC) for classifying confirmed FE versus other confirmed abuse cases was good (0.97), this 

model had the highest mean square error (MSE) rate (0.059) indicating less classification 

reliability compared to the other models (Table 6). 

[Insert Table 5 & 6] 

These AUC (0.799) and the MSE (0.125) worsen when trying to differentiate confirmed 

pure FE from confirmed hybrid FE (Table 8). Likewise, the variables of importance changed with 

no financial based questions making the top 10 list and the addition of other variables including 

inadequate medical supplies, foreclosure and evictions, restricted autonomy, inadequate food 

supply and alcohol and drug use by others in the home (Table 7). 

[Insert Table 7 & 8] 

The GBM sacrifices interpretative detail to maximize predictive accuracy. Table 9 provides 

the top 10 variables of importance for differentiating confirmed FE from other confirmed types of 

abuse. This model, like the GLM model found 3 financial questions to be within the top 4 most 

important variables for classification. Unauthorized use of the victim’s income/assets by others 

was the most important variable from which other variables were scaled in relation to this variable. 

While other variables are included in the top 10, their scaled importance is quite limited dropping 

to less than 10% contribution to the group prediction beginning with perpetrator relationship 

identified as other. The model AUC and MSE were 0.972 and 0.053, respectively (Table 6). 

[Insert Table 9] 
 

 

 

 

 

 

 

 

 

 

 

 

 

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necessarily reflect the official position or policies of the U.S. Department of Justice.

Differentiating confirmed pure FE versus confirmed Hybrid FE provided a set of top 10 

variables which individually contributed more in comparison to the model found in Table 10. Table 

10 identified a different set of variables with APS region where the allegation was substantiated 

being the top variable of importance for differentiating the group. In this model, it is also 

determined that the variables related to negative effects of others, alcohol and drug use by others 

in the home, facing foreclosure and inadequate medical supplies were ranked the highest in 

importance for predicting pure FE. Interestingly, the only financial-based question included in the 

top 10 was evidence of substantial unusual activity with the client’s financials or assets by other(s). 

The AUC and MSE for this model 0.831 and 0.123, respectively (Table 8). 

[Insert Table 10] 

Discussion 

This study utilized a large statewide dataset of confirmed elder abuse cases to form datadriven risk factor models that differentiate elder FE from other types of abuse and pure FE from 

hybrid FE. These models were developed using machine learning algorithms capable of handling 

very large aggregated datasets in which it is suspected that victim level, perpetrator level and 

community-level data interact in myriad unspecified ways to increase or decrease the risk of the 

outcome. Seeking to balance interpretative model building with predictive accuracy model 

building we found parallels with earlier research (Jackson & Hafemesiter, 2012), but also 

identified new factors, across multiple levels of the socioecological context, to be considered when 

trying to differentiate other forms of abuse from FE and when trying to differentiate pure vs hybrid 

FE. 

To create the context in which our findings should be considered, we first present a few 

study limitations. Although this was a large dataset of confirmed elder abuse cases over a five­
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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year timeframe, the data represented case findings from a single APS organization. It is understood 

that APS agencies across the country may have different statutes, definitions and investigation 

techniques thus, reducing the generalizability of the findings and the use of the data algorithms 

available for predicting the outcomes. Also, the APS organization from which these data were 

derived only investigates FE that occurs within a trusted relationship between the victim and 

perpetrator. Thus, financial scams and most cases of fraud were not included. Moreover, only elder 

abuse cases were included in the analysis, therefore excluding self-neglect which could also be 

important for understanding the risks of being financially exploited (Dong et al., 2013). Crime data 

at the community-level were not included due to little comparable and available data across the 

counties. This information could be highly useful for identifying locations where public service 

announcements may have the highest impact for prevention and intervention. Finally, due to 

truncating the data analysis to the first confirmed case of abuse, recidivism was not included as a 

risk factor despite its potential importance in differentiating types of abuse (Jackson and 

Hafemeister, 2012). 

Differentiating FE from other forms of abuse resulted in a few variables of importance that 

were not altogether unsuspected. It was no surprise that three of the top four most important 

variables in both the GLM and GBM models were financially focused given that evidence of these 

would indicate some sort of financial exploitation attempt. Nor was it of great surprise that 

caregiver stress and burnout, knowledge and willingness to care for the client were more predictive 

of other forms of abuse since these conditions have been linked both theoretically and empirically 

to poor provision of medical and physical care (Reis & Namiash, 2008). It was also found in this 

model that spousal perpetrators and those 65 years of age and older were more likely to be 

associated with other forms of abuse. This finding fits with previous research and suggests that 
 

 

 

 

 

 

 

 

 

 

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when FE is involved perpetrators outside of these characteristics (i.e. adult children, grandchildren, 

neighbors) should be considered as more probable (Jackson & Hafemeister, 2012; Hafemeister, 

2003). 

Interestingly, apparent injury to the client was the second most important variable in the 

GLM model, outperforming caregiver’s management of victim’s finances and evidence of 

substantial unusual activity with the victim’s financials by others. In the pure FE versus hybrid 

FE model, apparent injuries became the variable with the greatest importance for predicting FE 

status. This indicates that apparent injury is a salient indicator that should be looked for even when 

FE is the only reported allegation. The presence of an apparent injury during an FE investigation 

may very well be an indicator of some concurrent form of abuse (i.e. Hybrid FE). Alternatively, 

when an apparent injury is present it may also indicate the need to concurrently rule out FE as a 

motive for the injury. Jackson and Hafemeister (2012) found that Hybrid FE victims experienced 

various forms of abuse which could have manifested as apparent injuries. Another interesting 

variable that predicted other forms of abuse was thoughts of suicide and self-injury. While previous 

studies have linked FE to outcomes such as depression and suicide (Nerenberg, 2000; Podnieks, 

1992), it appears that in these data, FE victimization was not as strongly associated with these 

feelings compared to being victimized in other ways. It could also be that the earlier study did not 

account for the presence of other forms of abuse when assessing the association between FE and 

depression and suicide therefore, making these distinctions is of underlying importance. 

The GLM model differentiating pure FE from hybrid FE should be given more specific 

consideration. Previous research has found that FE victimization is the least likely form of abuse 

in older adults to be prosecuted and to receive follow-up from APS caseworkers and other agencies 

(Jackson and Hafemeister, 2011). A plausible reason for this finding is the lack of attention paid 
 

 

 

 

 

 

 

 

 

 

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to the need for differentiating the types of abuse and making the association with outcomes that 

impact quality of life. Review of the GLM model, shows that when FE is associated with other 

forms of abuse a range of quality of life issues are negatively impacted. The older adult may not 

be receiving their adequate medical supplies due to a lack of funds. They may be facing 

foreclosure, living with no utilities and inadequate food supplies and in conditions that may be 

condemnable. Moreover, they may be deprived of one of the most essential conditions of being an 

adult which is autonomy to make one’s own decisions and have one’s own purpose. This could

account for the previous finding that these older adults are having more thoughts of suicide and 

self-injury. It is highly plausible that these conditions are the result of longer-term abuse (Jackson 

and Hafemeister, 2012) and thus, speak to the need for identifying FE and differentiating pure FE 

from hybrid FE when a case is first investigated. 

The GBM model for both differentiating FE from other forms of abuse and pure FE from 

hybrid FE provided the best classification accuracy. As mentioned above, financially-based 

questions were of the greatest importance in the differentiating the former. However, it was found 

that APS region was the fifth most important predictor of FE in this model, but was the first most 

important in the model predicting pure versus hybrid FE. This may point to regions where public 

service announcements about financial crimes may benefit older adults living in the areas or point 

to other community-level variables that were not included such as crime data which are often hard 

to standardize and obtain in a way that allows comparison across counties. Other highly important 

variables that predicted pure FE was negative effects of others on the older adult as well as alcohol 

and drug use by others in the house. While GBM models are less interpretive it is plausible that 

when victims are more affected by others in the home and when alcohol and drug use by others 

are present, then pure FE is less likely. Negative effects may be related to any of the other forms 
 

 

 

 

 

 

 

 

 

 

 

 

 

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of abuse including psychological abuse and alcohol and drug abuse by others in the home was 

predictive of hybrid FE in the earlier GLM model which also fits with literature about perpetrator 

characteristics. Other variables such as having an ongoing relationship conflict with others and the 

perpetrator being more likely to be a child also suggest that these variables are predictive of hybrid 

FE. 

This study expanded on the earlier study by Jackson and Hafemeister (2012) which 

identified victim and perpetrator factors associated with pure FE versus hybrid FE. While different 

sets of variables were considered based on the available data and the definitions of FE, there was 

overlap between variables and several of the findings were comparable. These studies provide 

complementary data for understanding FE and its subtypes. Both studies identified troubling 

patterns of abuse with the current study point out the importance of apparent injury in 

differentiating pure versus hybrid FE. Both studies also identified a lack of appropriate medical 

supervision and inadequate food supplies associated with hybrid FE. While Jackson and 

Hafemeister reported that a change in living status and longer-term abuse was associated with 

hybrid FE the current study found that the hybrid FE victims were more likely to be facing was 

facing foreclosure and eviction which plausibly points to both a future change in living status and 

longer-term abuse. Moreover, neither study found perpetrator mental health or criminal history to 

be important predictors, but the current study did find that alcohol and drug use by others in the 

home was more predictive of hybrid FE. 

The current study also expanded on previous research by identifying factors of importance 

that could help frontline workers differentiate FE from other forms of abuse. Similarly with pure 

FE versus hybrid FE, these factors emerge across multiple levels of the socioecological context 

suggesting the need for comprehensive assessments when trying to determine whether FE has 
 

 

 

 

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occurred. Because FE and other forms often occur in highly complex conditions where the change 

in a single factor, among many, could affect the probability of an outcome such as pure FE versus 

hybrid FE it is important to be able to identify reliable predictors of the outcome to decrease the 

likelihood of misclassifying a case which could be detrimental especially if a hybrid FE case is 

falsely classified as a pure FE case. 

To this end, this study utilized a new and unparalleled approach in the field of elder abuse 

and was able to analyze the many different possible interactions within a large and robust APS 

dataset to derive the most important variables across the different socioecological levels for 

predicting FE from other forms of abuse and pure FE from hybrid FE. Such modeling can be used 

to create replicable data algorithms that can be transformed into web-based applications for 

immediate broad-based dissemination and use for public health surveillance and program 

development by social service and criminal justice agencies as well as researchers. 
 

 

 

 

 

 

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References 

Acierno R, Hernandez MA, Amstadter AB, et al. Prevalence and correlates of emotional, physical,
 

sexual, and financial abuse and potential neglect in the united states: The national elder
 

mistreatment study. Am J Public Health. 2010;100(2):292-297.
 

Amstadter A. B., Zajac K., Strachan M., Hernandez M. A., Kilpatrick D. G., Acierno R. (2011). 

Prevalence and correlates of elder mistreatment in South Carolina: The South Carolina elder
 

mistreatment study. Journal of Interpersonal Violence, 26, 2947-2972.
 

Anetzberger GJ, Korbin JE & Austin C. Alcoholism and Elder Abuse. J Interpers. Violence, 1994,
 

9(2):184-193.
 

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.
 

Burnett J, Dyer CB, Green CE, Flores DV, Booker JG & Diamond PM. Community-Based Risk
 

Assessment of Elder Mistreatment and Self-Neglect: Evidence of Construct Validity and
 

Measurement Invariance Across Gender and Ethnicity. The Journal of the Society for Social Work
 

and Research, 2014.
 

Burnett J, Jackson SL, Sinha A, Aschenbrenner AR, Xia R, Murphy KP & Diamond PM.
 

Differential Mortality across Five Types of Substantiated Elder Abuse. Journal of Elder Abuse and
 

Neglect, 2016; 28:2, 59-75.
 

Candel, A., Parmar, V., LeDell, E., & Arora, A. (2016). Deep learning with H2O (5th ed.). 

Mountain View, CA: H2O.ai, Inc.
 

Choi, NG, Kulick DB., & Mayer, J. (1999). Financial exploitation of elders: Analysis of risk 

factors based on county adult protective services data. Journal of Elder Abuse and Neglect, 10, 39­

62. 
 

 

 

 

 

 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

Click, C., Malohlava, M., Candel, A., Roark, H., & Parmar, V. (2016). Gradient boosted models 

with H2O (6th ed.). Mountain View, CA: H2O.ai, Inc.
 

Coker J, & Little B. (1997). Investing in the future: Protecting the elderly from financial abuse.
 

FBI Law Enforcement Bulletin, 1-5. 

Department of Human Services and Department of Protective and Regulatory Services.
 

Investigations and Protective Services for Elderly and Disabled Persons, Human Resources Code,
 

Chapter 48. Subchapter A. General Provisions. 2009; 

http://www.statutes.legis.state.tx.us/Docs/HR/htm/HR.48.htm. Accessed February 27, 2017
 

Dessin, CL. (2000). Financial abuse of the elderly. Idaho Law Review, 36, 203-226.
 

Dong X, Simon MA. Association between elder abuse and use of ED: findings from the Chicago
 

Health and Aging Project. American Journal of Emergency Medicine 2013;31:693-698.
 

Dong X & Simon MA. Elder abuse as a risk factor for hospitalization in older persons. JAMA 

Intern Med. 2013, 173(10):911-917.
 

Dong X, Simon M, Evans D. Elder self-neglect is associated with increased risk for elder abuse in 

a community-dwelling population: Findings from the Chicago health and aging project. J Aging
 

Health. 2013;25(1):80-96.
 

Ernst JS. (2000). Community-level factors and child maltreatment in a suburban county. Soc Work
 

Res. 2001. 25(3):133-142.
 

Friedman, J.H. (2001). Greedy function approximation: A gradient boosting machine. Annals of 

Statistics, 29(5), 1189-1232.
 

Friedman, J. H. (2002). Stochastic gradient boosting. Computational Statistics and Data Analysis,
 

38(4), 367-378.
 
 

 

 

 

 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

Hafemeister, TL. Elder Mistreatment: Abuse, Neglect and Exploitation in an Aging America. In 

the National Research Council (US) Panel to Review Risk and Prevalence of Elder Abuse and 

Neglect. J. Bonnie & R. Wallace (eds.) National Academies Press (US). Washington DC. 2003 

Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The elements of statistical learning: Data 

mining, inference, and prediction (2nd ed.). New York, NY: Springer. 

Hirshorn, B., & Stewart, J. (2003). Geographic information systems in community-based 

gerontological research and practice, Journal of Applied Gerontology, 22(1), 134-151. 

Jackson, S. L., & Hafemeister, T. L. (2011). Risk factors associated with elder abuse: The 

importance of differentiating by type of elder maltreatment. Violence and Victims, 26(6), 738-757. 

Jackson SL &TL Hafemeister. Pure financial exploitation versus hybrid financial exploitation cooccurring with physical and/or neglect of elderly persons. Psychol of Violence, 2012; 2(3):285­

296.
 

Kemp BJ & Mosqueda LA. Elder financial abuse: an evaluation framework and supporting
 

evidence. J Am Geriatr. Soc., 2005, Jul. 53(7):1123-7.
 

Kuhn, M. & Johnson, K. (2013). Applied predictive modeling. New York, NY: Springer.
 

Aiello, S., Kraljevic, T., & Maj, P. (2016). h2o: R Interface for H2O. R package version 3.8.3.3.
 

http://www.h2o.ai/.
 

Laumann E. O., Leitsch S. A., Waite L. J. (2008). Elder mistreatment in the United States: 

Prevalence estimates from a nationally representative study. Journal of Gerontology, 63, 48-54.
 

LeDell, E. (2016). h2oEnsemble: H2O ensemble learning. R package version 0.1.8.
 

https://github.com/h2oai/h2o-3/tree/master/h2o-r/ensemble/h2oEnsemble-package.
 

Metlife (Metlife Mature Market Institute, National Committee for the Prevention of
 

Elder Abuse, & the Center for Gerontology at Virginia Polytechnic Institute and
 
 

 

 

 

 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

State University) (2009). Broken trust: Elders, family, and finances. Retrieved 

from: http://www.metlife.com/assets/cao/mmi/publications/studies/mmistudybroken-trust-elders-family-finances.pdf 

National Center on Elder Abuse. What is elder abuse? https://ncea.acl.gov/faq/index.html#faq1 

Nerenberg L. (2000). Forgotten victims of financial crime and abuse: Facing the challenge. Journal 

of Elder Abuse and Neglect, 12, 49-72. 

Nykodym, T., Kraljevic, T., Hussami, N., Rao, A., & Wang, A. (2016). Generalized linear 

modeling with H2O (5th ed.). Mountain View, CA: H2O.ai, Inc. 

Peterson JC, Burnes DPR, Caccamise PL, Mason A, Henderson Jr. CR, Wells MT, Berman J, 

Cook AM, Shukoff D, Brownell P, Powell M, Salamone A, Pillemer KA & Lachs M. Financial 

exploitation of older adults: A problem-based prevalence study. J Gen Intern Med. 2014, Dec. 

29(12):1615-1623. 

Payne BK & Gainey RR. Mapping elder mistreatment cases: interactions between mistreatment, 

dementia, service utilization, access to services and disadvantage. Journal of Human Behavior and 

Social Environment, 2009; 19(8):1025-1041. 

Pillemer K. (2005). Elder abuse is caused by deviance and dependence of abusive caregivers. In 

D.R. Loseke, R.J. Gelles, & M.M. Cavanaugh (Eds.), Current controversies on family violence 

(2nd ed. Pp. 207-220). Thousand Oaks, CA: Sage.
 

Podnieks E. National survey on abuse of the elderly in Canada. Journal of Elder Abuse and 

Neglect, 1992, 4, 5-58.
 

R Core Team (2016). R: A language environment for statistical computing. R Foundation for 

Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.
 
 

 

 

 

 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

Reis M, Nahmiash D. Validation of the indicators of abuse (IOA) screen. Gerontologist. 

1998;38:471–480.
 

Steinberg SJ, & Steinberg SL. GIS: Geographic Information Systems for Social Sciences: 

Investigating Space and Place. 1st Edition. 2006. SAGE Publications, Thousand Oaks, CA 91320.
 

Teaster PB, Otto JM, Dugar TA, Mendiondo MS, Abner EL, Cecil KA. The 2004 survey of state 

adult protective services: Abuse of adults 60 yrs and older.
 

http://www.ncea.aoa.gov/NCEAroot/Main_Site/pdf/2-14-06%20FINAL%2060+REPORT.pdf. 

Updated 2010. Accessed April/4, 2011.
 

Tabachnick BG, Fidell LS. Using Multivariate Statistics. 4th ed. Needham Heights, MA: Pearson
 

Education Company; 2001.
 

Witten, I. H., Frank, E., & Hall, M. A. (2011). Data mining: Practical machine learning tools and
 

techniques (3rd ed.). Burlington, MA: Morgan Kaufmann.
 

Witten, JD, Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: With
 

applications in R. New York, NY: Springer. 
 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

Table 1: Victim Demographic Characteristics for N = 113561 Adult Protective Services Substantiated 

Cases of Elder Abuse in Texas Between the Years of 2009 - 2014 

All Confirmed 

FE 

Confirmed 

Pure FE 

Confirmed 

Hybrid FE 

Confirmed Other 

Abuse-No FE 

N=2514 N=1964 N=550 N=6286 

Age 

65-69 331(13.2) 238(12.1) 93(16.9) 1456(23.2) 

70-74 397(15.8) 282(14.4) 115(20.9) 1334(21.2) 

75-79 462(18.4) 358(18.2) 104(18.9) 1224(19.5) 

80-84 565(22.5) 454(23.1) 111(20.2) 1176(18.7) 

85-89 475(18.9) 390(19.9) 85(15.5) 731(11.6) 

90+ 284(11.1) 242(12.3) 42(7.6) 365( 5.8) 

Gender 

Female 1692(67.3) 1311(66.8) 381(69.3) 4365(69.4) 

Male 814(32.4) 648(33.0) 166(30.2) 1908(30.4) 

Missing 8(0.30) 5( 0.3) 3( 0.5) 13(0.2) 

Ethnicity 

White 1561(62.1) 1221(62.2) 340(61.8) 3519(56.0) 

Black 331(13.2) 247(12.6) 84(15.3) 789(12.6) 

Hispanic 423(16.8) 312(15.9) 111(20.2) 1735(27.6) 

Other 199(7.9) 184(9.4) 15( 2.7) 226( 3.6) 

Marital 

Married 499(19.8) 380(19.3) 119(21.6) 2532(40.3) 

Divorced 159(6.3) 109( 5.5) 50( 9.1) 405(6.4) 

Widowed 851(33.9) 648(33.0) 203(36.9) 1436(22.8) 

Separated 16(0.7) 13( 0.7) 3( 0.5) 58( 0.9) 

Single 66(2.6) 43( 2.2) 23( 4.2) 154( 2.4) 
 

 

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Missing 923(36.7) 771(39.3) 152(27.6) 1701(27.1) 

Living 

Own home/apt 1706(67.9) 1337(68.1) 369(67.1) 5048(80.3) 

Friends/relatives 233(9.3) 173( 8.8) 60(10.9) 718(11.4) 

Nursing home/assisted 

living

513(20.4) 404(20.6) 109(19.8) 357( 5.7) 

Other 61(2.4) 49( 2.5) 12( 2.2) 152( 2.4) 

Missing 1(0.1) 1( 0.1) 0( 0.0) 11( 0.2) 

FE = Financial Exploitation; Pure FE = Financial Exploitation Only Excluding Hybrid Cases; Hybrid 

FE = Financial Exploitation Plus Other Types of Abuse Excluding Pure FE; Other Abuse = Caregiver 

Neglect, Psychological Abuse, Physical Abuse; 

Table 2: Victim Impairment and Substance Abuse Characteristics for N = 113561 Adult Protective 

Services Substantiated Cases of Elder Abuse in Texas Between the Years of 2009 - 2014 

All Confirmed 

FE 

Confirmed 

Pure FE 

Confirmed 

Hybrid FE 

Confirmed Other 

Abuse-No FE 

N=2514 N=1964 N=550 N=6286

Cognitive Impairment 

N 2178(86.6) 1713(87.2) 465(84.5) 5847(93.0) 

Y 336(13.4) 251(12.8) 85(15.5) 439(7.0) 

Alcohol abuse 

N 2500(99.4) 1950(99.3) 550(100.0) 6237(99.2) 

Y 14(0.6) 14( 0.7) 0( 0.0) 49( 0.8) 

Drug abuse 

N 2512(99.9) 1962(99.9) 550(100.0) 6273(99.8) 

Y 2(0.1) 2( 0.1) 0( 0.0) 13( 0.2) 

Physically disabled 

N 1868(74.3) 1472(74.9) 396(72.0) 4993(79.4) 

Y 646(25.7) 492(25.1) 154(28.0) 1293(20.6) 
This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

Mobility impairment 

N 1954(77.7) 1529(77.9) 425(77.3) 5370(85.4) 

Y 560(22.3) 435(22.1) 125(22.7) 916(14.6) 

Visual Impairment 

N 2423(96.4) 1894(96.4) 529(96.2) 6108(97.2) 

Y 91(3.6) 70( 3.6) 21( 3.8) 178( 2.8) 

Hearing impairment 

N 2396(95.3) 1879(95.7) 517(94.0) 6102(97.1) 

Y 118(4.7) 85(4.3) 33(6.0) 184( 2.9) 

Limited English 

N 2392(95.1) 1870(95.2) 522(94.9) 5702(90.7) 

Y 122(4.9) 94( 4.8) 28( 5.1) 584( 9.3) 

Developmental 

Disability 

N 2510(99.8) 1961(99.8) 549(99.8) 6273(99.8) 

Y 4(0.2) 3( 0.2) 1( 0.2) 13( 0.2) 

FE = Financial Exploitation; Pure FE = Financial Exploitation Only Excluding Hybrid Cases; Hybrid 

FE = Financial Exploitation Plus Other Types of Abuse Excluding Pure FE; Other Abuse = Caregiver 

Neglect, Psychological Abuse, Physical Abuse; 
 

 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

Table 3: Characteristics for N = 15705 Texas Adult Protective Services Substantiated Perpetrators of 

Elder Abuse in Between the Years of 2009 - 2014 

All Confirmed 

FE 

Confirmed 

Pure FE 

Confirmed 

Hybrid FE 

Confirmed Other 

Abuse-No FE 

N=4203 N=3099 N=1104 N=11502

Age 

0-24 404(10) 317(10.2) 87( 7.9) 1327(11.5)

25-29 321(8) 264( 8.5) 57( 5.2) 473( 4.1)

30-34 390(9) 298( 9.6) 92( 8.3) 539( 4.7)

35-39 449(11) 336(10.8) 113(10.2) 722( 6.3)

40-44 566(13) 409(13.2) 157(14.2) 1042( 9.1)

45-49 670(16) 466(15.0) 204(18.5) 1352(11.8)

50-54 589(14) 407(13.1) 182(16.5) 1435(12.5)

55-59 421(10) 300( 9.7) 121(11.0) 1044( 9.1)

60+ 393(9) 302( 9.7) 91( 8.2) 3568(31.0)

Gender 

Female 2542(60) 2001(64.6) 541(49.0) 5337(46.4)

Male 1674(39) 1075(34.7) 559(50.6) 6127(53.3)

Missing 27(1) 23( 0.7) 4( 0.4) 38( 0.3)

Ethnicity 

White 2070(49) 1448(46.7) 622(56.3) 5934(51.6)

Black 792(19) 604(19.5) 188(17.0) 1591(13.8)

Hispanic 899(21) 665(21.5) 234(21.2) 3287(28.6)

Other 442(11) 382(12.3) 60( 5.4) 690( 6.0)

Missing 0(0) 0( 0.0) 0( 0.0) 0( 0.0)

Marital 

Married 880(21) 658(21.2) 222(20.1) 3592(31.2)

Divorced 323(8) 215( 6.9) 108( 9.8) 808( 7.0)

Widowed 53(1) 39( 1.3) 14( 1.3) 184( 1.6)

Separated 77(2) 57( 1.8) 20( 1.8) 234( 2.0)

Single 452(11) 301( 9.7) 151(13.7) 1674(14.6)

Missing 2418(58) 1829(59.0) 589(53.4) 5010(43.6)

Living 

Own home/apt 2094(50) 1622(52.3) 472(42.8) 5487(47.7)

Friends/relatives 946(23) 593(19.1) 353(32.0) 3312(28.8)

Nursing 

home/assisted 

living

4(0.009) 2( 0.1) 2( 0.2) 157( 1.4)

Other 330(8) 226( 7.3) 104( 9.4) 793( 6.9)

Missing 829(20) 656(21.2) 173(15.7) 1753(15.2)

FE = Financial Exploitation; Pure FE = Financial Exploitation Only Excluding Hybrid Cases; Hybrid FE 

= Financial Exploitation Plus Other Types of Abuse Excluding Pure FE; Other Abuse = Caregiver Neglect,

Psychological Abuse, Physical Abuse; 
 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

Table 4: Relationship, Co-habitation and Substance Abuse Characteristics for N = 15705 Texas Adult 

Protective Services Substantiated Perpetrators of Elder Abuse in Texas Between the Years of 2009 - 2014 

All 

Confirmed 

FE 

Confirmed 

Pure FE 

Confirmed 

Hybrid FE 

Confirmed Other 

Abuse-No FE 

N=4203 N=3099 N=1104 N=11502

Relation to 

victim 

Spouse 81(0.02) 49( 1.6) 32( 2.9) 2720(23.6)

Daughter 884(21) 595(19.2) 289(26.2) 2466(21.4)

Son 896(21) 537(17.3) 359(32.5) 2962(25.8)

Grandchild 606(14) 445(14.4) 161(14.6) 1558(13.5)

Other Family member 518(12) 387(12.5) 131(11.9) 1137( 9.9)

service provider 763(18) 690(22.3) 73( 6.6) 350( 3.0)

Other 394(9) 346(11.2) 48( 4.3) 289( 2.5)

Missing 61(1) 50( 1.6) 11( 1.0) 20( 0.2)

Co-reside with victim 

Yes 730(17) 453(14.6) 277(25.1) 5205(45.3)

No 1807(43) 1449(46.8) 358(32.4) 2106(18.3)

Missing 1666(40) 1197(38.6) 469(42.5) 4191(36.4)

Alcohol abuse 

N 4066(97) 3019(97.4) 1047(94.8) 10869(94.5)

Y 137(3) 80( 2.6) 57( 5.2) 633( 5.5)

Drug abuse 

N 3448(92) 2892(93.3) 956(86.6) 10793(93.8)

Y 355(8) 207( 6.7) 148(13.4) 709( 6.2)

FE = Financial Exploitation; Pure FE = Financial Exploitation Only Excluding Hybrid Cases; Hybrid 

FE = Financial Exploitation Plus Other Types of Abuse Excluding Pure FE; Other Abuse = Caregiver 

Neglect, Psychological Abuse, Physical Abuse; 
 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

Table 5: Top 10 Variables by Importance for Predicting Financial Exploitation versus Other Forms of 

Abuse Using General Linear Model Algorithms 

Variable Importance Interpretation Predicted Group 

1: CARE Item 24 Unauthorized Use of the victims 

income/assets by others 

Financial Exploitation 

2: CARE Item 26 Apparent Injuries to Client Other Forms of Abuse 

3: CARE Item 23 Caregivers Management of 

Victims Finances are 

Problematic 

Financial Exploitation 

4: CARE Item 25 Evidence of Substantial Unusual 

Activity with the Client’s 

Financials or Assets by Other(s) 

Financial Exploitation 

5: CARE Item 38 Client has Thoughts of Suicide, 

Homicide or Self-Injury 

Other Forms of Abuse 

6: CARE Item 55 Caregiver Stress Burnout Other Forms of Abuse 

7: CARE Item 56 Ability, Knowledge and 

Willingness to Care for the 

Client 

Other Forms of Abuse 

8: Perpetrator is the Spouse The perpetrator is the spouse Other Forms of Abuse 

9: CARE Item 29 Grooming, Hygiene and 

Cleanliness 

Other Forms of Abuse 

10: Number of Perpetrators 

Older than 64 years of age 

The perpetrator is 65 years of 

age or older 

Other Forms of Abuse 

Table 6: Model Fit for Four Supervised Learning Test-Set Algorithms to Predict Financial Exploitation 

Versus Other Forms of Abuse using Victim, Perpetrator and Community-Level data 

Mean Squared Error AUROC 

GLM 0.0592 0.9680 

RF 0.0574 0.9711 

GBM 0.0530 0.9723 

RDL 0.0577 0.9681 

GLM= General Linear Model; RF = Random Forest; GBM = Gradient Boosting Machine; RDL = 

Random Deep Learning 
 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

Table 7: Top 10 Variables by Importance for Predicting Pure Financial Exploitation versus Hybrid 

Financial Exploitation Using General Linear Model Algorithms 

Top 10 Variables of 

Importance 

Interpretation Predicted Group 

1: CARE Item 26 Apparent Injuries to Client Hybrid 

2: CARE Item 33 Client has Inadequate Medical 

Supplies, Medications 

Hybrid 

3: CARE Item 02 Client Facing Foreclosure, 

Eviction, Condemnation 

Hybrid 

4: CARE Item 55 Caregiver Stress Burnout Hybrid 

5: CARE Item 17 Utilities not working Hybrid 

6: CARE Item 49 Restricted Autonomy Hybrid 

7: CARE Item 19 Inadequate Food Supply Hybrid 

8: Ethnicity Native American Pure 

9: CARE Item 54 Alcohol, Drug Use by Others in 

the Household 

Hybrid 

10: CARE Item 03 Conditions attract and Harbor 

Pests 

Hybrid 

Table 8: Model Fit for Four Supervised Learning Test-Set Algorithms to Predict Pure Financial 

Exploitation Versus Hybrid Financial Exploitation using Victim, Perpetrator and Community-Level data 

Mean Squared Error AUROC 

GLM 0.1251 0.7986 

RF 0.1255 0.8288 

GBM 0.1230 0.8306 

RDL 0.1318 0.7626 

GLM= General Linear Model; RF = Random Forest; GBM = Gradient Boosting Machine; RDL = 

Random Deep Learning 
This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

Table 9: Top 10 Variables by Importance for Predicting Financial Exploitation versus Other Forms of 

Abuse Using on Gradient Boosting Machine Algorithms 

Variable Interpretation Scaled Importance 

1: CARE Item 24 Unauthorized Use of the victims 

income/assets by others 

1.000 

2: CARE Item 25 Unusual Financial Activity 0.249 

3: CARE Item 23 Caregivers Management of 

Victims Finances are 

Problematic 

0.104 

4: Perpetrator Relationship to 

Client 

Perpetrator relationship is Other 0.058 

5: APS-Region APS Region where Financial 

Victimization Occurred 

0.043 

6: Client and Perpetrator Cohabitation 

Perpetrator not Cohabitating 

with Victim 

0.036 

7: Fiscal Year Year of Substantiation 0.031 

8: CARE Item 50 Client Ongoing Conflict 

Relationships with Others 

0.026 

9: Client and Perpetrator Cohabitation 

Perpetrator Cohabitating with 

the Client 

0.017 

10: Perpetrator Relationship 

to Client 

Perpetrator is or is not the 

Spouse 

0.015 

APS = Adult Protective Services; CARE tool = Client Assessment and Risk Evaluation tool 

Table 10: Top 10 Variables by Importance for Predicting Pure Financial Exploitation versus Hybrid 

Financial Exploitation Using on Gradient Boosting Machine Algorithms 

Variable Interpretation Scaled Importance 

1: APS-Region APS Region where Financial 

Victimization Occurred 

1.000 

2: CARE Item 51 Negative Effects of Others 

Actions on the Client 

0.787 

3: CARE Item 54 Alcohol, Drug Use by Others in 

the Household 

0.570 

4: CARE Item 02 Client Facing Foreclosure, 

Eviction, Condemnation 

0.503 

5: CARE Item 33 Client has Inadequate Medical 

Supplies, Medications 

0.458 

6: CARE Item 50 Client Ongoing Conflict 

Relationships with Others 

0.399 

7: Perpetrator Relationship to 

Client 

Perpetrator Relationship with 

Victim is Other 

0.337 

8: Fiscal Year Year of Substantiation 0.307 

9: Perpetrator Relationship to 

Client 

Perpetrator Relationship to the 

Client is Child 

0.268 

10: CARE Item 25 Evidence of Substantial Unusual 

Activity with the Client’s 

Financials or Assets of Other(s) 

0.266 

APS = Adult Protective Services; CARE tool = Client Assessment and Risk Evaluation tool 
This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

Figure 1. Area Under the Curve for the General Linear Model When Differentiating Confirmed Financial 

Exploitation from Other Confirmed Types of Abuse 

Figure 1: The percent correctly classified by chance is 71.46%. This prediction improved to 

96.80% using the GLM algorithm. 
This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

Figure 2. Area Under the Curve for the General Linear Model When Differentiating Confirmed Pure 

Financial Exploitation from Confirmed Hybrid Financial Exploitation 

Figure 2: The percent correctly classified by chance is 78.09%. This prediction improved to 

79.90% using the GLM algorithm. 
This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

Figure 3. Area Under the Curve for the Gradient Boosting Machine When Differentiating Confirmed 

Financial Exploitation from Other Confirmed Types of Abuse 

Figure 3: The percent correctly classified by chance is 71.46%. This prediction improved to 

97.23% using the GBM algorithm. 
This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

Figure 4. Area Under the Curve for the Gradient Boosting Machine When Differentiating Confirmed 

Pure Financial Exploitation from Confirmed Hybrid Financial Exploitation 

Figure 4: The percent correctly classified by chance is 78.09%. This prediction improved to 

83.06% using the GBM algorithm. 
 

 

 

 

 

 

 

 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

SELF-NEGLECT ABUSE TYPES
 

In original APS report, there are 8 defined types of abuse: 

 EMOTIONAL ABUSE 

 EXPLOITATION 

 MEDICAL NEGLECT 

 MENTAL HEALTH NEGLECT 

 PHYSICAL ABUSE 

 PHYSICAL NEGLECT 

 SUICIDAL THREAT 

 SEXUAL ABUSE 

Table 1 The following variables are for victims: victim demographics, victim characteristics, CARE tool variables. There are two 

race/ethnicity variables: “eth_6” is a 6-category variable while “eth_alt” is 4-category. 

Variable Coding Type(length) 

stage Unique stage ID Num(8) 

id Unique person ID for victims Num(8) 

gend G͊΢͆͊θ (Ά1͞=F͊Ρ̮Λ͊ ͞2͞ = Ͱ̮Λ͊ ͞9͞=Δ΢Θ΢Ωϭ΢) Char(1) 

age Age in years (group into 5 year range) 

0-19 ̮μ Ά0͞΁ 20-25 ̮μ Ά20͞΁  ̈́΁ 90+ ̮μ 90 

Num(3) 

eth_6 Ethnicity (6 category) in APS DRIT 72101 

Ά1͞=ΠΆΉφ͊ 

Ά2͞=Λ̮̼Θ 

Ά3͞=HΉμε̮΢Ή̼ 

Ά4͞=ͱ̮φΉϬ͊ !Ρ͊θΉ̼̮΢ 

Ά5͞=!μΉ̮΢ 

Ά6͞=ͷφΆ͊θ 

Ά9͞=Δ΢Θ΢Ωϭ΢ 

Char(1) 

eth_alt Alternative HHSC grouping (derived from 6 race/ethnicity 

indicators in APS DRIT 70717) 

Ά1͞=ΠΆΉφ͊ 

Ά2͞=Λ̮̼Θ 

Ά3͞=HΉμε̮΢Ή̼ 

Ά4͞=ͷφΆ͊θ 

Ά9͞=Δ΢Θ΢Ωϭ΢ 

Char(1) 

living Categorical variable for living arrangement: 

Ά1͞=ͷϭ΢ HΩΡ͊/!εφ 

Ά2͞=FθΉ͊΢͆μ/Ά͊Λ̮φΉϬ͊μ 

Ά3͞=ͱϡθμΉ΢ͼ HΩΡ͊/!μμΉμφ͊͆ ͪΉϬΉ΢ͼ F̮̼ΉΛΉφϳ 

Ά4͞=ͷφΆ͊θ 

Ά9͞=Δ΢Θ΢Ωϭ΢ 

Char(1) 

marital Categorical variable for Marital status: 

Ά1͞=Ͱ̮θθΉ͊͆ 

Ά2͞=DΉϬΩθ̼͊͆ 

Ά3͞=ΠΉ͆Ωϭ͊͆ 

Char(1) 
 

 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

Ά4͞=Ί͊parated 

Ά5͞=ΊΉ΢ͼΛ͊΁ ͱ͊Ϭ͊θ Ρ̮θθΉ͊͆ 

Ά9͞=Δ΢Θ΢Ωϭ΢ 

*clnt1 - 23 Client Characteristics for victims (ΆΦ͞ Ωθ Άͱ͞) Char(1) 

*care1 - 57 !ΆE ΐͷͷͪ Ήφ͊Ρμ (ΆΦ͞ Ωθ Άͱ͞) Char(1) 

Unavail_street ΟΉ̼φΉΡ͞μ μφθ͊͊φ ̮͆͆θ͊μμ Ήμ ϡ΢̮Ϭ̮ΉΛ̮̻Λ͊ (ΆΦ͞ Ωθ Άͱ͞) Char(1) 

*zcomm1-44 Geocoded community level variables (zip resolution) 

̮μ͊͆ Ω΢ ϬΉ̼φΉΡμ͞ ̮͆͆θ͊μμ 

Num(8) 

*tcomm1-44 Geocoded community level (census tract resolution) 

̮μ͊͆ Ω΢ ϬΉ̼φΉΡμ͞ ̮͆͆θ͊μμ 

Num(8) 

*See attached list for numbered variables. 

1	 CODING OF VARIABLES RELATED TO ABUSE TYPES (APPLIES TO 

“VICTIM_ROWS”,“VIC_PERP_PAIR”,“VIC_FIRST_STAGE”)

Table 2 Summary of abuse information for a victim 

Variable Coding Type(length) 

any_cfm !΢ϳ ̼Ω΢͔ΉθΡ͊͆ ̮̻ϡμ͊ Ή΢ φΆΉμ ͊εΉμΩ͆͊(ΆΦ͞ Ωθ Άͱ͞) Char(1) 

any_uncfm Any un-̼Ω΢͔ΉθΡ͊͆ ̮̻ϡμ͊ Ή΢ φΆΉμ ͊εΉμΩ͆͊(ΆΦ͞ Ωθ Άͱ͞) Char(1) 

any_cfm_sn Any confirmed self-neglect Ή΢ φΆΉμ ͊εΉμΩ͆͊(ΆΦ͞ Ωθ Άͱ͞) Char(1) 

any_uncfm_sn Any un-confirmed self-neglect Ή΢ φΆΉμ ͊εΉμΩ͆͊(ΆΦ͞ Ωθ Άͱ͞) Char(1) 

*Ind_cfm1-11 Indicator for confirmed abuse types per victim (ΆΦ͞ Ωθ Άͱ͞) Char(1) 

*Ind_uncfm1-11 Indicator for unconfirmed abuse types per victim (ΆΦ͞ Ωθ Άͱ͞) Char(1) 

fe Per Case FE status: (P)ure FE, (H)ybrid FE, (O)ther or (N)one Char(1) 

*See attached list for numbered variables. 

2	 CODING OF VARIABLES RELATED TO PERPETRATORS (APPLIES TO 

“PERP_ROWS”,“VIC_PERP_PAIR”)

Table 3 The following variables are for perpetrators: self-indicator, role, relationship to corresponding victim, demographics, 

characteristics, abuse types (confirmed), abuse types (unconfirmed). 

Variable Coding Type(Length) 

self Is perpetrator self to victim(ΆΦ͞ Ωθ Άͱ͞) Char(1) 

p_role Role of the perpetrator: 

ΆD΃͞ = ΆD͊μΉͼ΢̮φ͊͆ ΃͊θε͊φθ̮φΩθ͞ 

Ά!΃͞ = Ά!ΛΛ͊ͼ͊͆ ΃͊θε͊φθ̮φΩθ͞ 

ΆΟ΃͞ = ΆD͊μΉͼ΢̮φ͊͆ ΟΉ̼φΉΡ/΃͊θε͊φθ̮φΩθ͞ 

ΆͱΆ͞ = ΆͱΩ ΆΩΛ͊͞ 

ΆΔͱ͞ = ΆΔ΢Θ΢Ωϭ΢͞ 

Άͪ͞ = ΆΛΉ͊΢φ͞ 

char(2) 

p_rel The ε͊θε͊φθ̮φΩθ͞μ θ͊Λ̮φΉΩ΢μΆΉε φΩ the victim: char(1) 
 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

Ά0͞=Ί͊Λ͔ 

Ά1͞=ΊεΩϡμ͊ 

Ά2͞=D̮ϡͼΆφ͊θ 

Ά3͞=ΊΩ΢ 

Ά4͞=Gθ̮΢̼͆ΆΉΛ͆ 

Ά5͞=ͷφΆ͊θ F̮ΡΉΛϳ Ρ͊Ρ̻͊θ 

Ά6͞=Ί͊θϬΉ̼͊ ΃θΩϬΉ͆͊θ΂ ͊Ϭ͊θϳφΆΉ΢ͼ ͊Λμ͊ ΩφΆ͊θ than 

family member. 

Ά7͞=ͷφΆ͊θ 

Ά9͞=Δ΢Θ΢Ωϭ΢ 

p_orig_rel ͷθΉͼΉ΢̮Λ ͡θ͊Λ̮φΉΩ΢μΆΉε͢ ̮μ Ή΢ !΃Ί θ͊εΩθφ Char(30) 

p_id Unique person ID for perpetrators Num(8) 

p_gend Perpetrator gender(Ά1͞=F͊Ρ̮Λ͊ ͞2͞ = Ͱ̮Λ͊ ͞9͞=Δ΢Θ΢Ωϭ΢) Char(1) 

p_age Perpetrator age in years Num(3) 

p_eth_6 Perpetrator ethnicity (same format as victims) Char(1) 

p_eth_alt Perpetrator alternative ethnicity (same format as victims) Char(1) 

p_living Perpetrator living arrangement(same format as victims) Char(1) 

p_marital Perpetrator marital status (same format as victims) Char(1) 

*p_clnt1 – 22 Perpetrator characteristics (same format as victims) Char(1) 

*type_cfm1– 8 ͛΢͆Ή̼̮φΩθμ ͔Ωθ ̼Ω΢͔ΉθΡ͊͆ ̮̻ϡμ͊ φϳε͊μ (ΆΦ͞ Ωθ Άͱ͞); 

Ά͛͞ Ρ̮͊΢μ ̼Ά̮΢ͼ͊͆ ͔θΩΡ ΆΦ͞ ͆ϡ͊ φΩ Ή΢̼ΩΡε̮φΉ̻Λ͊ type 

Char(1) 

*type_uncfm1 –8 ͛΢͆Ή̼̮φΩθμ ͔Ωθ ϡ΢̼Ω΢͔ΉθΡ͊͆ ̮̻ϡμ͊ φϳε͊μ (ΆΦ͞ Ωθ Άͱ͞); 

Ά͛͞ Ρ̮͊΢μ ̼Ά̮΢ͼ͊͆ ͔θΩΡ ΆΦ͞ ͆ϡ͊ φΩ Ή΢̼ΩΡε̮φΉ̻Λ͊ type 

Char(1) 

P_fe Per perpetrator FE status: (P)ure FE, (H)ybrid FE, (O)ther or 

(N)one 

Char(1) 

*See attached list for numbered variables. 

3 OTHER VARIABLES 

Table 4 Other variables 

Variable Coding Type(length) 

episode Stage sequence # for a victim; counting from his/her first 

stage in APS report 

Num(3) 

FY APS fiscal year Num(3) 

4 LIST OF NUMBERED VARIABLES
 

4.1 CLIENT CHARACTERISTICS (CLNT1-23) 

1. AGED: age >=65 (should be ‘Y’ for all victims)) 

2. ALCOHOL ABUSE 

3. AUTISM 

4. CI: Cognitively Impaired 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

5. DDD: Developmental Disability-Diagnosed 

6. DRUG ABUSE 

7. HEARING IMPAIRED 

8. HIV AIDS 

9. IDD: Intellectual and Developmental Disability 

10. LIMITED ENGLISH 

11. MDCP CLASS: Receiving MDCP/CLASS 

12. MW HCS: Medicaid Waiver: Receiving HCS Services 

13. MW WL: Medicaid Waiver: Waiting List 

14. MENTAL ILLNESS 

15. MILITARY DEPENDENT 

16. MOBILITY IMPAIRED 

17. PHYSICALLY DISABLED 

18. SPEECH DISABLED 

19. VISUALLY IMPAIRED 

20. UNDETERMINED IMMIGRATION STATUS 

21. UNQUALIFIED IMMIGRANT 

22. PERMANENT RESIDENT 

23. US CITIZENSHIP 

4.2	 CONFIRMED ABUSE TYPE (TYPE_CFM1 –8), UNCONFIRMED ABUSE TYPE 

(TYPE_UNCFM1 –8) 

1. EMOTIONAL ABUSE 

2. EXPLOITATION 

3. MEDICAL NEGLECT 

4. MENTAL HEALTH NEGLECT 

5. PHYSICAL ABUSE 

6. PHYSICAL NEGLECT 

7. SUICIDAL THREAT 

8. SEXUAL ABUSE 

4.3	 SUMMARY INDICATOR OF CONFIRMED ABUSE TYPE (IND_CFM1-11), SUMMARY 

INDICATOR OF UNCONFIRMED ABUSE TYPE (IND_UNCFM1-11) 

1. EMOTIONAL ABUSE 

2. EXPLOITATION 

3. MEDICAL NEGLECT (by caregiver) 

4. MENTAL HEALTH NEGLECT (by caregiver) 

5. PHYSICAL ABUSE 

6. PHYSICAL NEGLECT (by caregiver) 

7. SUICIDAL THREAT 

8. SEXUAL ABUSE 

9. MEDICAL NEGLECT (by self) 
 

 

 

 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

10. MENTAL HEALTH NEGLECT (by self) 

11. PHYSICAL NEGLECT (by self) 

4.4 CARE VARIABLES (CARE1-57) 

See variable list in attached CARE document (link). 

4.5 COMMUNITY LEVEL VARIABLES (TCOMM1-44, ZCOMM1-44) 

Variables with name starting with ͡φ͢ ̮θ͊ ͼ͊Ω̼Ω͆͊͆ ͔Ωθ U.S. census tract; variables with name starting 

ϭΉφΆ ͡ϸ͢ ̮θ͊ ͔Ωθ ϸΉε ́ 

1. Total population 

2. Total male population 

3. Total female population 

4. Percent 60 years and older 

5. Percent 60 years and older (male) 

6. Percent 60 years and older (female) 

7. Percent 65 years and older 

8. Percent 65 years and older (male) 

9. Percent 65 years and older (female) 

10. Percent 75 years and older 

11. Percent 75 years and older (male) 

12. Percent 75 years and older (female) 

13. Median age 

14. Median age (male) 

15. Median age (female) 

16. Total Non-Hispanic White 

17. Total Non-Hispanic Black 

18. Total Non-Hispanic American Indian & Alaskan Native 

19. Total Non-Hispanic Asian 

20. Total Non-Hispanic Native Hawaiian & Other Pacific Islanders 

21. Total Non-Hispanic Other Race 

22. Total Non-Hispanic Multiple Races (2 or more) 

23. Total Hispanic 

24. Percent in poverty 

25. Percent in poverty (65 years and older) 

26. Households 

27. Households receiving Food Stamps 

28. Households receiving Social Security 

29. Households receiving Supplemental Security Income 
This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

30. Total Foreign-born 

31. Total Foreign-born / Naturalized Citizens 

32. Total disabled 

33. Total 65 and older 

34. Total 65 and older with disability 

35. Total 65 and older with hearing difficulties 

36. Total 65 and older with vision difficulties 

37. Total 65 and older with cognitive difficulties 

38. Total 65 and older with ambulatory difficulties 

39. Total 65 and older with self-care difficulties 

40. Total 65 and older with independent living difficulties 

41. Total unemployed (16 and over population) 

42. Total population 25 years and older 

43. Percent with high school degree or equivalent (25 years and older) 

44. Percent with ̮̼Ά͊ΛΩθ͞μ degree (25 years and older)