Journal of General Internal Medicine

, Volume 29, Issue 12, pp 1615–1623 | Cite as

Financial Exploitation of Older Adults: A Population-Based Prevalence Study

  • Janey C. PetersonEmail author
  • David P.R. Burnes
  • Paul L. Caccamise
  • Art Mason
  • Charles R. HendersonJr.
  • Martin T. Wells
  • Jacquelin Berman
  • Ann Marie Cook
  • Denise Shukoff
  • Patricia Brownell
  • Mebane Powell
  • Aurora Salamone
  • Karl A. Pillemer
  • Mark S. Lachs
Original Research



Financial exploitation is the most common and least studied form of elder abuse. Previous research estimating the prevalence of financial exploitation of older adults (FEOA) is limited by a broader emphasis on traditional forms of elder mistreatment (e.g., physical, sexual, emotional abuse/neglect).


1) estimate the one-year period prevalence and lifetime prevalence of FEOA; 2) describe major FEOA types; and 3) identify factors associated with FEOA.


Prevalence study with a random, stratified probability sample.


Four thousand, one hundred and fifty-six community-dwelling, cognitively intact adults age ≥ 60 years.


New York State.


Comprehensive tool developed for this study measured five FEOA domains: 1) stolen or misappropriated money/property; 2) coercion resulting in surrendering rights/property; 3) impersonation to obtain property/services; 4) inadequate contributions toward household expenses, but respondent still had enough money for necessities and 5) respondent was destitute and did not receive necessary assistance from family/friends.


One-year period FEOA prevalence was 2.7 % (95 % CI, 2.29–3.29) and lifetime prevalence was 4.7 % (95 % CI, 4.05–5.34). Greater relative risk (RR) of one-year period prevalence was associated with African American/black race (RR, 3.80; 95 % CI, 1.11–13.04), poverty (RR, 1.72; 95 % CI, 1.09–2.71), increasing number of non-spousal household members (RR, 1.16; 95 % CI, 1.06–1.27), and ≥ 1 instrumental activity of daily living (IADL) impairments (RR, 1.69; 95 % CI, 1.12–2.53). Greater RR of lifetime prevalence was associated with African American/black race (RR, 2.61; 95 % CI, 1.37–4.98), poverty (RR, 1.47; 95 % CI, 1.04–2.09), increasing number of non-spousal household members (RR, 1.16; 95 % CI, 1.12–1.21), and having ≥1 IADL (RR, 1.45; 95 % CI, 1.11–1.90) or ≥1 ADL (RR, 1.52; 95 % CI, 1.06–2.18) impairment. Living with a spouse/partner was associated with a significantly lower RR of lifetime prevalence (RR, 0.39; 95 % CI, 0.26–0.59)


Financial exploitation of older adults is a common and serious problem. Elders from groups traditionally considered to be economically, medically, and sociodemographically vulnerable are more likely to self-report financial exploitation.


financial exploitation elder financial abuse elder abuse elder mistreatment economic abuse 



We wish to thank the Cornell Survey Research Institute and the many older adults who participated in the study. Drs. Peterson and Lachs had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.


  1. a.

    This work was supported by funding from the New York State William B. Hoyt Memorial Children and Family Trust Fund, administered under the New York State Office of Children and Family Services. The funding agency had no role in the design and conduct of the study, in the collection, analysis, and interpretation of data, or in the preparation, review, or approval of the manuscript.

  2. b.

    Dr. Peterson is the recipient of a Paul B. Beeson Award from the National Institute on Aging, the American Federation for Aging Research, The John A. Hartford Foundation and The Atlantic Philanthropies under award K23AG042869. Dr. Peterson also received research support to complete this analysis from the Department of Medicine, Weill Cornell Medical College, NY, NY.

  3. c.

    Dr. Lachs is the recipient of a Mid-Career Mentoring Award in Patient Oriented Research from the National Institute on Aging K24 AG022399. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.


Prior presentations

  1. a.

    Portions of this work were presented at the Department of Medicine Grand Rounds, Weill Cornell Medical College, 16 September 2013.

  2. b.

    We previously released a report of frequency counts of the quantitative data only, which can be found at:


Conflict of Interest

The authors declare that they do not have a conflict of interest.


  1. 1.
    National Research Council. Elder mistreatment: Abuse, neglect, and exploitation in an aging America. Washington, DC: The National Academy Press; 2003.Google Scholar
  2. 2.
    Demakis GJ. Neuropsychological evaluation of decision-making capacity in older adults. Psychol Inj Law. 2013;6(1):41–50.CrossRefGoogle Scholar
  3. 3.
    Han SD, Boyle PA, Yu L, Fleischman DA, Arfanakis K, Leurgans S, et al. Financial literacy is associated with medial brain region functional connectivity in old age. Arch Gerontol Geriatr. 2014; May 16.Google Scholar
  4. 4.
    Roush RE, Moye JA, Mills WL, Kunik ME, Wilson NL, Taffet GE, et al. Why clinicians need to know about the elder investment fraud and financial exploitation program. Generations. 2012;36(2):94–97.Google Scholar
  5. 5.
    Acierno R, Hernandez MA, Amstadter AB, Resnick HS, Steve K, Muzzy W, 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.PubMedCentralPubMedCrossRefGoogle Scholar
  6. 6.
    Amstadter AB, Zajac K, Strachan M, Hernandez MA, Kilpatrick DG, Acierno R. Prevalence and correlates of elder mistreatment in South Carolina: the South Carolina elder mistreatment study. J Interpers Violence. 2011;26(15):2947–2972.PubMedCentralPubMedCrossRefGoogle Scholar
  7. 7.
    Begle AM, Strachan M, Cisler JM, Amstadter AB, Hernandez M, Acierno R. Elder mistreatment and emotional symptoms among older adults in a largely rural population: the South Carolina elder mistreatment study. J Interpers Violence. 2011;26(11):2321–2332.PubMedCentralPubMedCrossRefGoogle Scholar
  8. 8.
    Laumann EO, Leitsch SA, Waite LJ. Elder mistreatment in the United States: prevalence estimates from a nationally representative study. J Gerontol Ser B Psychol Sci Soc Sci. 2008;63(4):S248–S254.CrossRefGoogle Scholar
  9. 9.
    Pillemer K, Finkelhor D. The prevalence of elder abuse: a random sample survey. The Gerontologist. 1988;28(1):51–57.PubMedCrossRefGoogle Scholar
  10. 10.
    Genesys Marketing Systems Group. Landline Random-Digit Dial (RDD) SamplesCopyright © 1987–2012 2/15/2013. Available from:
  11. 11.
    Swain DG, Nightingale PG. Evaluation of a shortened version of the abbreviated mental test in a series of elderly patients. Clin Rehabil. 1997;11(3):243–248.PubMedCrossRefGoogle Scholar
  12. 12.
    US Census Bureau, The New York State Department of Labor, . New York State Civilian Population Estimates by Demographic Characteristics - Age, Sex, Race, and Hispanic Origin, 2000–2009. [June 3, 2014]; Available from:
  13. 13.
    American Association for Public Opinion Research. Standard Definitions: Final Dispositions of Case Codes and Outcome Rates for Surveys. Lenexa, KS2009 [March 23, 2013]; Version 4:[Available from:
  14. 14.
    American Association for Public Opinion Research. Response Rate Calculator V3.1 Lenexa, KS2011 [March 23, 2013]; Version 3.1:[Available from:
  15. 15.
    Podnieks E. National survey on abuse of the elderly in Canada. J Elder Abuse Negl. 1992;4(1/2):5–58.Google Scholar
  16. 16.
    Manthorpe J, Biggs S, McCreadie C, Tinker A, Hills A, O’Keefe M, et al. The U.K. national study of abuse and neglect among older people. Nurs Older People. 2007;19(8):24–26.PubMedCrossRefGoogle Scholar
  17. 17.
    Corbin J, Strauss A. Basics of qualitative research. 3rd ed. Thousand Oaks, California: Sage Publications; 2008.Google Scholar
  18. 18.
    Center for the Study of Aging and Human Development. Multidimensional functional assessment: the OARS methodology. A manual. 2nd ed. Durham, NC: Center for the Study of Aging and Human Development; 1978.Google Scholar
  19. 19.
    U.S. Census Bureau. State Intercensus Estimates. October 2012 [February 11, 2014]; Available from:
  20. 20.
    Greenland S. Model-based estimation of relative risks and other epidemiologic measures in studies of common outcomes and in case–control studies. Am J Epidemiol. 2004;160(4):301–305.PubMedCrossRefGoogle Scholar
  21. 21.
    McNutt LA, Wu C, Xue X, Hafner JP. Estimating the relative risk in cohort studies and clinical trials of common outcomes. Am J Epidemiol. 2003;157(10):940–943.PubMedCrossRefGoogle Scholar
  22. 22.
    Zou G. A modified poisson regression approach to prospective studies with binary data. Am J Epidemiol. 2004;159(7):702–706.PubMedCrossRefGoogle Scholar
  23. 23.
    Strauss A, Corbin J. Basics of qualitative research: techniques and procedures for developing grounded theory. 2nd ed. Thousand Oaks: Sage Publications; 1998. 312 p.Google Scholar
  24. 24.
    Corbin J, Strauss A. Integrating categories. Basics of qualitative research. 3rd ed. Thousand Oaks, California: Sage Publications; 2008:263.Google Scholar
  25. 25.
    Glaser B. Theoretical sensitivity Middle Valley. CA: Sociology Press; 1978.Google Scholar
  26. 26.
    Census Bureau US. Poverty Thresholds 2008. Washington, DC:; 2009. [April 23, 2014]; Available from: Scholar
  27. 27.
    Beach SR, Schulz R, Castle NG, Rosen J. Financial exploitation and psychological mistreatment among older adults: differences between African Americans and non-African Americans in a population-based survey. The Gerontologist. 2010;50(6):744–757.PubMedCentralPubMedCrossRefGoogle Scholar
  28. 28.
    Blumberg SJ, Luke JV, Nadarajasundaram GN, Davern ME, Boudreaux MH, Soderberg K. Wireless substitution: State-level estimates from the National Health Interview Survey, January 2007–June 2010. Hyattsville, MD: National Center for Health Statistics; 2011. April 3, 2013.Google Scholar
  29. 29.
    Blumberg SJ, Luke JV. Wireless substitution: Early release of estimates from the National Health Interview Survey, July-December 2009. National Center for Health Statistics; May 2010 [April 3, 2013]; Available from:

Copyright information

© Society of General Internal Medicine 2014

Authors and Affiliations

  • Janey C. Peterson
    • 1
    Email author
  • David P.R. Burnes
    • 4
  • Paul L. Caccamise
    • 3
  • Art Mason
    • 3
  • Charles R. HendersonJr.
    • 2
  • Martin T. Wells
    • 8
  • Jacquelin Berman
    • 5
  • Ann Marie Cook
    • 3
  • Denise Shukoff
    • 3
  • Patricia Brownell
    • 6
  • Mebane Powell
    • 5
  • Aurora Salamone
    • 5
  • Karl A. Pillemer
    • 2
  • Mark S. Lachs
    • 7
  1. 1.Division of Clinical Epidemiology and Evaluative Sciences Research, Center for Integrative Medicine, Department of Cardiothoracic SurgeryWeill Cornell Medical CollegeNew YorkUSA
  2. 2.Department of Human DevelopmentCornell UniversityIthacaUSA
  3. 3.Lifespan of Greater RochesterRochesterUSA
  4. 4.Factor-Inwentash Faculty of Social WorkUniversity of TorontoTorontoCanada
  5. 5.New York City Department for the AgingNew YorkUSA
  6. 6.Fordham UniversityBronxUSA
  7. 7.Division of Geriatrics and Palliative MedicineWeill Cornell Medical CollegeNew YorkUSA
  8. 8.Departments of Statistical Science and Social StatisticsCornell UniversityIthacaUSA

Personalised recommendations