Dataset Information

Using Latent Class Analysis to Identify Profiles of Elder Abuse Perpetrators.

ABSTRACT:

Objectives

Research suggests that abuser risk factors differ across elder mistreatment types, but abuse interventions are not individualized. To move away from assumptions of perpetrator homogeneity and to inform intervention approaches, this study classifies abusers into subtypes according to their behavior profiles.

Method

Data are from the Older Adult Mistreatment Assessment administered to victims by Adult Protective Service (APS) in Illinois. Latent class analysis was used to categorize abusers (N = 336) using victim and caseworker reports on abusers' harmful and supportive behaviors and characteristics. Multinomial logistic regression was then used to determine which abuser profiles are associated with 4 types of mistreatment-neglect, physical, emotional, and financial-and other sociodemographic characteristics.

Results

Abusers fall into 4 profiles descriptively labeled "Caregiver," "Temperamental," "Dependent Caregiver," and "Dangerous." Dangerous abusers have the highest levels of aggression, financial dependency, substance abuse, and irresponsibility. Caregivers are lowest in harmful characteristics and highest in providing emotional and instrumental support to victims. The 4 profiles significantly differ in the average age and gender of the abuser, the relationship to victims, and types of mistreatment committed.

Discussion

This is the first quantitative study to identify and characterize abuser subtypes. Tailored interventions are needed to reduce problem behaviors and enhance strengths specific to each abuser profile.

SUBMITTER: DeLiema M

PROVIDER: S-EPMC6283306 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Publications

Using Latent Class Analysis to Identify Profiles of Elder Abuse Perpetrators.

DeLiema Marguerite M Yonashiro-Cho Jeanine J Gassoumis Zach D ZD Yon Yongjie Y Conrad Ken J KJ

The journals of gerontology. Series B, Psychological sciences and social sciences 20180601 5

<h4>Objectives</h4>Research suggests that abuser risk factors differ across elder mistreatment types, but abuse interventions are not individualized. To move away from assumptions of perpetrator homogeneity and to inform intervention approaches, this study classifies abusers into subtypes according to their behavior profiles.<h4>Method</h4>Data are from the Older Adult Mistreatment Assessment administered to victims by Adult Protective Service (APS) in Illinois. Latent class analysis was used to ...[more]

PMID: 28329841

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Project description:BackgroundResearch on elder abuse has defined it as a multidimensional construct that encompasses a set of different abusive behaviours, victims, perpetrators and settings. The array of possible elder abuse configurations is difficult to capture. This study sought to identify victimization patterns that represent distinct elder abuse configurations based on specific abusive behaviours and on the relationship with the perpetrator; it also sought to determine the association between these latent classes with victims' characteristics.MethodData comes from two elder abuse surveys: a representative sample of community-dwelling adults and a convenience sample of older adults reporting elder abuse to four state and NGOs institutions. Latent Class Analysis (LCA) was used to categorize victimization in the population-based (N?=?245) and in the victims' sample (N?=?510) using 7 items measuring physical, psychological and financial abuse, and appointed perpetrators. Association tests were conducted to determine differences and similarities of victims' characteristics between the different obtained classes.ResultsThe LCA procedure identified six different latent classes of victimization experiences in each of the samples, which were statistically and plausibly distinct. In the population-based survey: verbal abuse by others (29%); psychological abuse from children/grandchildren (18%); overlooked by others (18%); stolen by others (15%); verbal Intimate Partner Violence (IPV) (14%) and physical and psychological IPV (6%). In the victims' survey: physical abuse by children/grandchildren (29%); physical IPV (26%); psychological abuse by children/grandchildren (18%); polyvictimization by others (16%); physical abuse by others (6%) and physical and psychological IPV (4%). In the victims survey the 6 groups significantly differ in age, gender, civil status, living arrangements, perceived social support and functional status.ConclusionsThe results support the possibility of the multidimensionality of elder abuse not being accounted by the "classical" abuse typologies. Elder abuse victims seeking help may represent a distinct group from that included in population-based prevalence studies. The appointed perpetrators may be the most meaningful and relevant aspect in distinguishing victimization experiences. Further research is needed to develop tailored interventions to specific elder abuse cases and enhance successful outcomes.

Project description:ImportanceMedically complex patients are a heterogeneous group that contribute to a substantial proportion of health care costs. Coordinated efforts to improve care and reduce costs for this patient population have had limited success to date.ObjectiveTo define distinct patient clinical profiles among the most medically complex patients through clinical interpretation of analytically derived patient clusters.Design, setting, and participantsThis cohort study analyzed the most medically complex patients within Kaiser Permanente Northern California, a large integrated health care delivery system, based on comorbidity score, prior emergency department admissions, and predicted likelihood of hospitalization, from July 18, 2018, to July 15, 2019. From a starting point of over 5000 clinical variables, we used both clinical judgment and analytic methods to reduce to the 97 most informative covariates. Patients were then grouped using 2 methods (latent class analysis, generalized low-rank models, with k-means clustering). Results were interpreted by a panel of clinical stakeholders to define clinically meaningful patient profiles.Main outcomes and measuresComplex patient profiles, 1-year health care utilization, and mortality outcomes by profile.ResultsThe analysis included 104 869 individuals representing 3.3% of the adult population (mean [SD] age, 70.7 [14.5] years; 52.4% women; 39% non-White race/ethnicity). Latent class analysis resulted in a 7-class solution. Stakeholders defined the following complex patient profiles (prevalence): high acuity (9.4%), older patients with cardiovascular complications (15.9%), frail elderly (12.5%), pain management (12.3%), psychiatric illness (12.0%), cancer treatment (7.6%), and less engaged (27%). Patients in these groups had significantly different 1-year mortality rates (ranging from 3.0% for psychiatric illness profile to 23.4% for frail elderly profile; risk ratio, 7.9 [95% CI, 7.1-8.8], P < .001). Repeating the analysis using k-means clustering resulted in qualitatively similar groupings. Each clinical profile suggested a distinct collaborative care strategy to optimize management.Conclusions and relevanceThe findings suggest that highly medically complex patient populations may be categorized into distinct patient profiles that are amenable to varying strategies for resource allocation and coordinated care interventions.

Dataset Information

Using Latent Class Analysis to Identify Profiles of Elder Abuse Perpetrators.

Objectives

Method

Results

Discussion

Publications

Using Latent Class Analysis to Identify Profiles of Elder Abuse Perpetrators.

Similar Datasets

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