Operationalization and Validation of the Stopping Elderly Accidents, Deaths, and Injuries (STEADI) Fall Risk Algorithm in a Nationally Representative Sample

Details

• Alternative Title:
  J Epidemiol Community Health
• Personal Author:
  Lohman, Matthew C. ; Crow, Rebecca S. ; DiMilia, Peter R. ; Nicklett, Emily J. ; Bruce, Martha L. ; Batsis, John A.
• Description:
  Background
  
  Preventing falls and fall-related injuries among older adults is a public health priority. The Stopping Elderly Accidents, Deaths, and Injuries (STEADI) tool was developed to promote fall risk screening and encourage coordination between clinical and community-based fall prevention resources; however, little is known about the tool’s predictive validity or adaptability to survey data.
  
  Methods
  
  Data from five annual rounds (2011–2015) of the National Health and Aging Trends Study (NHATS), a representative cohort of adults age 65 and older in the US. Analytic sample respondents (n=7,392) were categorized at baseline as having low, moderate, or high fall risk according to the STEADI algorithm adapted for use with NHATS data. Logistic mixed-effects regression was used to estimate the association between baseline fall risk and subsequent falls and mortality. Analyses incorporated complex sampling and weighting elements to permit inferences at a national level.
  
  Results
  
  Participants classified as having moderate and high fall risk had 2.62 (95% CI: 2.29, 2.99) and 4.76 (95% CI: 3.51, 6.47) times greater odds of falling during follow-up compared to those with low risk, respectively, controlling for sociodemographic and health related risk factors for falls. High fall risk was also associated with greater likelihood of falling multiple times annually but not with greater risk of mortality.
  
  Conclusion
  
  The adapted STEADI clinical fall risk screening tool is a valid measure for predicting future fall risk using survey cohort data. Further efforts to standardize screening for fall risk and to coordinate between clinical and community-based fall prevention initiatives are warranted.
  
  
• Keywords:
  Accidental Falls Aged Aged, 80 And Over Algorithms Death Falls Female Geriatric Assessment Geriatrics Humans Injuries Male Measurement Predictive Value Of Tests Reproducibility Of Results Risk Assessment Screening Surveys And Questionnaires Wounds And Injuries

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• Source:
  J Epidemiol Community Health. 71(12):1191-1197
• Pubmed ID:
  28947669
• Pubmed Central ID:
  PMC5729578
• Document Type:
  Journal Article
• Funding:
  R01 MH096441/MH/NIMH NIH HHSUnited States/ ; U01 AG032947/AG/NIA NIH HHSUnited States/ ; T32 MH073553/MH/NIMH NIH HHSUnited States/ ; U48 DP005018/DP/NCCDPHP CDC HHSUnited States/ ; U48DP005018/ACL HHSUnited States/ ; K23 AG051681/AG/NIA NIH HHSUnited States/
• Volume:
  71
• Issue:
  12
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:7f44702b827a5ee6391d4dc091cbd62e602740c2c7ac260610139cd590f41a74
• Download URL:
  https://stacks.cdc.gov/view/cdc/50545/cdc_50545_DS1.pdf
• File Type:
  [PDF - 887.82 KB ]