# Detection of Elder abuse Through Emergency Care Technicians (DETECT)

> **NIH NIH R01** · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · 2021 · $339,854

## Abstract

Project Summary
Elder mistreatment (EM) is commonly defined as an intentional act, or failure to act, by a caregiver or another person in a
relationship involving an expectation of trust, that causes harm or creates a risk of harm to an older adult. EM includes
financial abuse/exploitation, neglect, emotional/psychological abuse, physical abuse, and sexual abuse. Exposure is often
chronic, and commonly involves multiple forms of abuse. This is concerning as there is an estimated 11% annual
prevalence among cognitively intact adults, and higher amongst those with dementia. Further, EM is linked with increased
risk of physical injury, hospitalization, emergency room visits, psychological distress, morbidity, and early mortality.
Nevertheless, EM is difficult to detect and often goes unrecognized. Effective and efficient EM screening tools are
urgently needed to improve early detection efforts. We will gather the data required to determine the sensitivity and
specificity of the DETECT tool, a screening tool designed specifically to help medics identify potential EM occurring in
the community. Our proposed methodology includes in-person follow-up assessments with a random sample of older
adults screened using DETECT (N = 2,500 total participants). We will conduct a brief capacity assessment and follow-up
assessment 1 month after the original DETECT screening. This assessment will include validated measures of emotional,
physical, and sexual abuse, neglect, financial exploitation, and poly-victimization. It will also include validated measures
of other potentially modifiable risk and protective factors.23 The specific aims of the proposed project are:
AIM 1: Validation of an innovative EM screening tool (DETECT). We will match DETECT screening results with an
expert LEAD panel determination “gold standard” to calculate diagnostic performance for each screening item, and all
screening items collectively. This comprehensive approach to validating DETECT will provide the needed empirical
foundation for its broader implementation.
AIM 2: Optimizing the DETECT Tool Via Systematic Item Reduction. We will use confirmatory factor analysis to
determine the relative predictive value of each DETECT item. Results will inform systematic item reduction efforts,
streamlining the tool for optimally efficient administration.
AIM 3: Identify risk and protective factors for EM. Follow-up in-person interviews will provide rich contextual data that
highlight modifiable personal and contextual factors (e.g., previous EM and/or polyvictimization, social support, unmet
health and daily living needs, financial problems, and mental factors). The large sample size of the proposed study
(N=2,500) will permit an unprecedented opportunity to examine predictors of EM.

## Key facts

- **NIH application ID:** 10210352
- **Project number:** 5R01AG059993-04
- **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
- **Principal Investigator:** Michael Bradley Cannell
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $339,854
- **Award type:** 5
- **Project period:** 2018-09-15 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10210352

## Citation

> US National Institutes of Health, RePORTER application 10210352, Detection of Elder abuse Through Emergency Care Technicians (DETECT) (5R01AG059993-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10210352. Licensed CC0.

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