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Developing Standard Data for Elder Abuse Multidisciplinary Teams: A Critical Objective David Burnes, PhDa, Darin Kirchin, MSWa, Alyssa Elman, LMSWb, Risa Breckman, LMSWc, Mark S. Lachs, MD, MPHc, Tony Rosen, MD, MPHb aUniversity of Toronto, Factor-Inwentash Faculty of Social Work, 246 Bloor Street West, Toronto, Ontario, Canada, M5S1V4 bWeill Cornell Medicine, Department of Emergency Medicine, 525 East 68th Street, New York, NY 10065-487 cWeill Cornell Medicine, Division of Geriatrics and Palliative Medicine, 525 East 68th Street, New York, NY 10065-487 Abstract Multidisciplinary teams (MDTs) represent a prominent and growing form of elder abuse intervention in communities across the U.S. and around the world. Despite the proliferation and promise of MDTs as a model of elder abuse intervention, the field lacks infrastructure, including a standardized data collection strategy, to facilitate a coordinated and informed MDT effort. This commentary presents an exploratory study, which sought to examine existing strategies of case- level electronic data collection implemented by MDTs across the U.S. Using a snowball sampling strategy, we identified 11 MDTs using an electronic data collection strategy. Our analysis found tremendous range in both the extent and nature of data collection across MDTs, yet it identified common domains of data. A standardized MDT data collection strategy would benefit several MDT stakeholders, including coordinators tracking everyday operations, funders requiring reporting, and researchers conducting large-scale comparative research to identify best MDT practices. Keywords Elder Abuse; Multidisciplinary Teams; Standardized Data; Commentary COMMENTARY Elder abuse is a pervasive problem in our society with serious consequences. Approximately 10% of older adults living in the community experience some form of elder abuse each year (Pillemer, Burnes, Riffin, & Lachs, 2016). Elder abuse victimization is associated with shortened survival, hospitalization, nursing home placement, and poor physical and mental Correspondence to: David Burnes. DISCLOSURE STATEMENT The authors have no conflict of interest to report HHS Public Access Author manuscript J Elder Abuse Negl. Author manuscript; available in PMC 2021 August 01. Published in final edited form as: J Elder Abuse Negl. 2020 ; 32(4): 377–384. doi:10.1080/08946566.2020.1782301. Author Manuscript Author Manuscript Author Manuscript Author Manuscript health outcomes (Yunus, Hairi, & Choo, 2017). Without effective prevention strategies in place, the absolute scope of this problem will increase substantially in accordance with population aging demographic trends. Multidisciplinary teams (MDTs) have emerged as a prominent form of community-based elder abuse response intervention throughout the U.S. in both urban and rural contexts (Rosen et al., 2019). While some receive support from foundations, private philanthropists and state and/or local government funding, the proliferation of MDTs is also due to support received from federal funding mechanism. For example, the Office for Victims of Crime, Office of Justice Programs, U.S. Department of Justice has supported the development of the Elder Justice Initiative’s MDT toolkit guide (U.S. Department of Justice, 2016), granted millions of dollars to support the development or enhancement of MDTs across the country (U.S. Department of Justice, 2019), and administers Victims of Crime Act (VOCA) funding that has also fueled MDT expansion in some states. MDTs represent the most evidenced- based form of community-based elder abuse intervention to date (Teresi et al., 2016). Evidence suggests that MDTs improve elder abuse cases outcomes, such as prosecution and conservatorship (Gassoumis, Navarro, & Wilber, 2014; Navarro, Gassoumis, & Wilber, 2013). A multidisciplinary elder abuse intervention approach provides the necessary diversity of professional resources and skills, used at different points along the assessment and intervention process, to address the complex, multi-faceted nature of elder abuse cases (Connolly, Brandl, & Breckman, 2014). An integrated and coordinated multidisciplinary approach is thought to facilitate greater service delivery efficiency than navigating through a silo system of disconnected disciplines and services (Navarro et al., 2016). Although MDT formats vary across jurisdictions, MDTs typically comprise an array of professionals across many disciplines and systems, including geriatric health, social work, mental health, law enforcement, legal-justice, victim services, and/or financial services (Breckman et al., 2014). While some MDTs work directly with victims of elder abuse, many teams work alongside referring agencies, such as Adult Protective Services, in a third-party consulting role. MDTs can also drive collaboration between the elder justice field and other allied fields involved with older adults (Nerenberg, 2002). While the proliferation of MDTs across the country represents promise in addressing the issue of elder abuse, the field lacks infrastructure to facilitate a coordinated and informed MDT effort. In particular, a common data collection strategy across MDTs is currently missing, which undermines our capacity to understand what works under which circumstances and for whom. Standardized MDT data collection represents a critical form of infrastructure to strengthen cohesiveness and analytic power and to drive learning and informed change in this model of elder abuse intervention. Standardized MDT data collection would provide several benefits, including capacity to monitor day-to-day operations, a mechanism to report common data to funding agencies, and opportunities for large-scale research to understand best MDT practices and formats. Currently, little is known about how or to what extent MDTs collect case-level data or the level of data collection consistency across MDTs. The purpose of the current exploratory study was to examine Burnes et al. J Elder Abuse Negl. Author manuscript; available in PMC 2021 August 01. Author Manuscript Author Manuscript Author Manuscript Author Manuscript existing strategies of case-level electronic data collection conducted by MDTs across the United States. METHODS Identifying MDTs that Collect Data Electronically To comprehensively identify MDTs currently collecting data electronically, we used several strategies. Initially, we contacted MDTs with whom we already had existing relationships due to our experience in the field. We also reached out to colleagues and national leaders to recommend additional MDTs. To supplement this, we sent an e-mail requesting participation of any MDTs collecting data electronically to the National Center on Elder Abuse listserv. The listserve is subscribed to by over 1,000 professionals working to prevent elder abuse and protect vulnerable adults and serves as a forum for raising questions, discussing issues, and sharing research and best practices. Further, we used snowball sampling, a methodology that has been employed by other elder abuse researchers (Stolee, Hiller, Etkin, & McLeod, 2012), to assist with identifying MDTs. In snowball sampling, each identified participant MDT was asked to identify additional MDTs that collected data electronically until recommendations for further participants were exhausted or repeated (Goodman, 1961). Snowball sampling built on the resources of existing networks in order to identify participants who may have otherwise been difficult to find (Patton, 2002). Given that MDTs often develop informally from a local group of interested professionals that may not be closely connected to national organizations or elder abuse professionals from other communities, a snowball sampling methodology was deemed particularly appropriate for use in this study. Our sampling strategy specifically targeted MDTs with electronic data collection strategies, as opposed to sampling the entire universe of MDTs and proceeding to exclude those without electronic data collection. Among those MDTs identified with data collection strategies, we did not apply exclusion criteria, since we were interested in understanding the full scope/range of data collection strategies. Although our sampling strategy was designed to reach as many MDTs with electronic data collection as possible throughout the country, we cannot say that our final sample of MDT sites is exhaustive. Review and Analysis of Electronic Data Collection Strategies We conducted a detailed review of the data elements (fields) collected by MDTs that reported using electronic data collection. For each data field, we examined the type of field (e.g. radio button, check box(es), text field, drop-down list), as well as the list of options (if a drop-down list) and whether multiple responses were allowed. Members of the research team analyzed, coded, and categorized these data fields into domains and sub-domains using a consensus process over several meetings. This domain categorization process was informed by the Abuse Intervention Model (Mosqueda et al., 2016) and our own recent work developing a model to describe risk of elder abuse re-victimization (Burnes et al., 2020). Burnes et al. J Elder Abuse Negl. Author manuscript; available in PMC 2021 August 01. Author Manuscript Author Manuscript Author Manuscript Author Manuscript RESULTS We identified and reviewed the electronic data collection strategies of 11 MDT databases. Table 1 describes the frequency of MDTs by state. These strategies varied significantly in scope and approach. Database systems ranged from an MS Excel spreadsheet, to a modified MS Access database, to a large database custom-designed and managed by a third-party vendor. The extent of data collection varied substantially across MDTs. The total number of data fields collected across MDTs ranged from 12 to 338. Data collection formatting and variable operationalization also differed. For example, basic elder abuse information tracked by most MDTs, such as “type of mistreatment,” was collected and operationalized inconsistently. Some MDTs specified types of mistreatment that could be selected from a list, while others designed the question to be open-ended. This stylistic difference was present throughout the databases, some of which consisted entirely of open-ended questions, while others had checkboxes, drop-down menus, among other formats. In regard to “type of mistreatment,” some data systems included self-neglect and abandonment as unique types of mistreatment, while others did not accept these issues. The “type of victim-perpetrator relationship” was another common elder abuse data field across sites that was captured inconsistently. Data collection on this field ranged from an open-ended response option to highly detailed drop-down lists of possible relationships (e.g., husband/romantic partner, wife/romantic partner, boyfriend, girlfriend, etc.). Despite significant variation, we were able to identify key domains and sub-domains of information collected across the MDT databases, which mirrored the MDT workflow/ process as follows: intake/initial baseline information, case tracking/follow-up, and outcomes/case closure (see Table 2). Within the broad data domain of intake/baseline information, data fields were assigned to various ecological levels influencing cases of elder abuse, including the individual victims, individual perpetrator, victim-perpetrator relationship, home environment, and social environment. The case tracking/follow-up domain included sub-domains reflecting the MDT intervention process itself, including number of meetings spent discussing a case, the type of professionals attending meetings, task assignments, and recommended case interventions. The outcomes/case closure domain included data fields that could be used to understand the status of a case at the point of case closure, such as interventions completed, assessment of re-victimization risk in relation to key factors (e.g., victim social support, victim physical functioning or health status, perpetrator mental health, dependence in the victim-perpetrator relationship), and other pieces of information attached to the victim and perpetrator that may have changed over the course of MDT intervention. DISCUSSION Our research shows that few MDTs currently track case-level data electronically. Among those that do, we found that some only collected minimal data. Strategies for data collection varied widely, with very little standardization. Even when MDTs collected the same variable (e.g., mistreatment type), the variable was operationalized and captured differently, making comparisons or data pooling across MDTs challenging. Burnes et al. J Elder Abuse Negl. Author manuscript; available in PMC 2021 August 01. Author Manuscript Author Manuscript Author Manuscript Author Manuscript We believe that standardizing data collection among MDTs has large potential benefits for stakeholders including MDT coordinators, MDT members, older adults served, government agencies, community service providers, funders, researchers, and the community. Collecting standard data allows for better case tracking, management, and oversight of MDT processes, including identifying potential opportunities to improve quality and impact. Additionally, it allows for benchmarking over time and for comparisons between MDTs to identify best practices. Using standardized data allows for accurate reporting to funders on the impact of their investment and to government agencies who may be tasked with regulating MDTs. It also offers the opportunity for researchers to conduct large-scale comparative studies to describe the types of cases discussed in MDTs, as well as MDT recommendations and their impact. This research would improve our understanding of the MDT phenomenon, filling an important gap in the existing literature related to the effectiveness of elder abuse interventions (Pillemer et al., 2016). The nursing home Minimum Data Set (MDS) (Kaiser Family Foundation, 2007; Saliba et al., 2012), introduced in 1991, illustrates the power of standardized data collection. MDS data are collected on each resident by U.S. nursing homes and reported regularly to the Centers for Medicare and Medicaid Services (CMS) as part of a federally-mandated process. These data have been used to assist nursing homes to identify issues and improve care. MDS data have also been used to develop quality indicators (Zimmerman et al., 1995), allowing for comparison between facilities. CMS has used MDS data to regulate nursing homes and to determine reimbursement for care (Saliba et al., 2012). The MDS has been an indispensable data source for researchers to improve understanding of aspects of nursing home care and has been used in hundreds of studies. That many MDTs are not collecting data electronically at all or doing so only minimally suggests that an ideal window of opportunity currently exists to develop data standards that may be easily integrated into existing processes. This window is likely to close, however, as MDTs will continue to grow and develop in the coming years, with many creating their own electronic data collection strategies in silos. Therefore, we believe that the optimal time to develop data standards for MDTs is now. Recommendations for MDT data collection should likely include an ideal case-level data collection strategy (for large programs with resources) and minimum case-level data collection (for all MDTs). Data fields that should be included are those that are necessary for case tracking, mandatory reporting, and/or contribution to national/international comparative research. This standard data should have details on optimal collection method for each field (e.g., field type, list of options, whether multiple responses allowed). Recommendations should also include best practices in handling personally identifiable information and how to share data for the purpose of reporting or research without compromising confidentiality. We recommend that a multidisciplinary task force/working group comprised of experts in elder abuse, collaborative MDT work, and information technology, as well as other relevant stakeholders, should be convened to design data standards that will be acceptable and adopted by the field. Burnes et al. J Elder Abuse Negl. Author manuscript; available in PMC 2021 August 01. Author Manuscript Author Manuscript Author Manuscript Author Manuscript Acknowledgments FUNDING This work was supported by the Social Sciences and Humanities Research Council under Grant number 435-2017-0578; National Institute on Aging, Paul B. Beeson Emerging Leaders Career Development Award under Grant number K76 AG054866 REFERENCES Burnes D, Elman A, Feir BM, Rizzo V, Chalfy A…Rosen T (in press). Exploring risk of elder abuse re-victimization: Development of a model to inform community response interventions. Under Review. Journal of Applied Gerontology. Connolly MT, Brandl B, & Breckman R (2014). The elder justice roadmap. Retrieved from http:// www.ncea.acl.gov/library/gov_report/docs/ejrp_roadmap.pdf Gassoumis Z, Navarro A, & Wilber K (2014). Protecting victims of elder financial exploitation: The role of an elder abuse forensic center in referring victims for conservatorship. Aging and Mental Health. 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J Elder Abuse Negl. Author manuscript; available in PMC 2021 August 01. Author Manuscript Author Manuscript Author Manuscript Author Manuscript U.S. Department of Justice. (2019). Transforming America’s response to elder abuse: Enhanced multidisciplinary teams (E-MDTs) for older victims of abuse and financial exploitation. Retrieved from https://www.ovc.gov/grants/pdftxt/FY19_CompetitiveSolicitation-EMDT.pdf U.S. Department of Justice. (2016). Developing an elder abuse case review multidisciplinary team in your community. Retrieved from https://www.justice.gov/elderjustice/file/938921/download Yunus RM, Hairi NN, & Choo WY (2017). Consequences of elder abuse and neglect: A systematic review of observational studies. Trauma, Violence, & Abuse, Advance online publication. doi:10.1177/1524838017692798 Zimmerman DR, Karon SL, Arling G, Clark BR, Collins T, Ross R, & Sainfort F (1995). Development and testing of nursing home quality indicators. Health Care Financing Review, 16(4), 107–127. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4193525/ [PubMed: 10151883] Burnes et al. J Elder Abuse Negl. Author manuscript; available in PMC 2021 August 01. Author Manuscript Author Manuscript Author Manuscript Author Manuscript Author Manuscript Author Manuscript Author Manuscript Author Manuscript Burnes et al. Table 1: Frequency of MDTs by State State N (%) New York 4 (36.4%) California 4 (36.4%) Colorado 1 (9.1%) Tennessee 1 (9.1%) Texas 1 (9.1%) J Elder Abuse Negl. Author manuscript; available in PMC 2021 August 01. Author Manuscript Author Manuscript Author Manuscript Author Manuscript Burnes et al. Table 2: Key Domains and Sub-Domains of Information Collected from MDT Databases Domain Sub-Domain Intake / Initial Baseline Information Vulnerable Older Adult Protected (personally identifying) Information Eligibility Pre-MDT Characteristics Victim – Basic Demographics Victim – Detailed Characteristics Abuser / Trusted Other Perpetrator(s) – Basic Demographics Perpetrator(s) – Detailed Characteristics Context Victim / Perpetrator Relationship Abuse / Neglect Characteristics Home Living Environment Victim Social Network Case Tracking / Follow-Up MDT Involvement / Meetings / Key Personnel Tasks / Interventions (Recommended / Pursued) Risk Assessment (in multiple areas) Outcomes / Case Closure Interventions Completed Risk Assessment (in multiple areas) Updated Victim Information Updated Perpetrator Information J Elder Abuse Negl. Author manuscript; available in PMC 2021 August 01.