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Using the past to predict the future: latent class analysis of patterns of health service use of older adults in the emergency department.

Publication ,  Journal Article
Hastings, SN; Whitson, HE; Sloane, R; Landerman, LR; Horney, C; Johnson, KS
Published in: J Am Geriatr Soc
April 2014

OBJECTIVES: To classify older adults in the emergency department (ED) according to healthcare use and to examine associations between group membership and future ED visits and hospital admissions. DESIGN: Secondary analysis. SETTING: Medicare Current Beneficiary Survey. PARTICIPANTS: Adults aged 65 and older with at least one treat-and-release ED visit between January 1, 2000, and September 30, 2007 (N = 4,964). MEASUREMENTS: Measures of health service use included primary care visits, treat-and-release ED visits, and hospital days in the 12 months preceding the index ED visit. RESULTS: Five groups of individuals in the ED with distinct patterns of health service use were identified. "Primary Carederly" (39%) had low rates of ED and hospital use and a high mean number of primary care visits. "Wellderly" (34%) had fewer visits of all types than other groups. "Chronically Illderly" (14%) had the highest mean number of primary care visits and hospital days. "Acute Carederly" (9.8%) had lowest mean number of primary care visits but higher ED visits and hospital days than all other groups except the "Sickest Elderly." Sickest Elderly (3.2%) had the highest number of ED visits; mean number of hospital days was more than four times that of any other group. Primary Carederly and Wellderly had a lower risk of hospital admission within 30 days of the index ED visit than the other groups. CONCLUSION: In older adults released from an ED, group membership was associated with future health services use. Classification of individuals using readily available previous visit data may improve targeting of interventions to improve outcomes.

Duke Scholars

Published In

J Am Geriatr Soc

DOI

EISSN

1532-5415

Publication Date

April 2014

Volume

62

Issue

4

Start / End Page

711 / 715

Location

United States

Related Subject Headings

  • United States
  • Time Factors
  • Retrospective Studies
  • Outcome Assessment, Health Care
  • Medicare
  • Male
  • Humans
  • Hospitalization
  • Geriatrics
  • Forecasting
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Hastings, S. N., Whitson, H. E., Sloane, R., Landerman, L. R., Horney, C., & Johnson, K. S. (2014). Using the past to predict the future: latent class analysis of patterns of health service use of older adults in the emergency department. J Am Geriatr Soc, 62(4), 711–715. https://doi.org/10.1111/jgs.12746
Hastings, S Nicole, Heather E. Whitson, Richard Sloane, Lawrence R. Landerman, Carolyn Horney, and Kimberly S. Johnson. “Using the past to predict the future: latent class analysis of patterns of health service use of older adults in the emergency department.J Am Geriatr Soc 62, no. 4 (April 2014): 711–15. https://doi.org/10.1111/jgs.12746.
Hastings SN, Whitson HE, Sloane R, Landerman LR, Horney C, Johnson KS. Using the past to predict the future: latent class analysis of patterns of health service use of older adults in the emergency department. J Am Geriatr Soc. 2014 Apr;62(4):711–5.
Hastings, S. Nicole, et al. “Using the past to predict the future: latent class analysis of patterns of health service use of older adults in the emergency department.J Am Geriatr Soc, vol. 62, no. 4, Apr. 2014, pp. 711–15. Pubmed, doi:10.1111/jgs.12746.
Hastings SN, Whitson HE, Sloane R, Landerman LR, Horney C, Johnson KS. Using the past to predict the future: latent class analysis of patterns of health service use of older adults in the emergency department. J Am Geriatr Soc. 2014 Apr;62(4):711–715.
Journal cover image

Published In

J Am Geriatr Soc

DOI

EISSN

1532-5415

Publication Date

April 2014

Volume

62

Issue

4

Start / End Page

711 / 715

Location

United States

Related Subject Headings

  • United States
  • Time Factors
  • Retrospective Studies
  • Outcome Assessment, Health Care
  • Medicare
  • Male
  • Humans
  • Hospitalization
  • Geriatrics
  • Forecasting