RT Journal Article
SR Electronic
T1 Multidimensional risk score to stratify community-dwelling older adults by future fall risk using the Stopping Elderly Accidents, Deaths and Injuries (STEADI) framework
JF Injury Prevention
JO Inj Prev
FD BMJ Publishing Group Ltd
SP 461
OP 466
DO 10.1136/injuryprev-2020-044014
VO 27
IS 5
A1 Brian C Helsel
A1 Karen A Kemper
A1 Joel E Williams
A1 Khoa Truong
A1 Marieke Van Puymbroeck
YR 2021
UL http://injuryprevention.bmj.com/content/27/5/461.abstract
AB Background The Stopping Elderly Accidents, Deaths and Injuries (STEADI) screening algorithm aligns with current fall prevention guidelines and is easy to administer within clinical practice. However, the stratification into low, moderate and high risk categories limits the meaningful interpretation of the fall-related risk factors.Methods Baseline measures from a modified STEADI were used to predict self-reported falls over 4 years in 3170 respondents who participated in the 2011–2015 National Health and Aging Trends Study. A point method was then applied to find coefficient-based integers and 4-year fall risk estimates from the predictive model. Sensitivity and specificity estimates from the point method and the combined moderate and high fall risk STEADI categories were compared.Results There were 886 (27.95%) and 387 (12.21%) respondents who were classified as moderate and high risk, respectively, when applying the stratification method. Falls in the past year (OR: 2.16; 95% CI: 1.61 to 2.89), multiple falls (OR: 2.94; 95% CI: 1.89 to 4.55) and a fear of falling (OR: 1.77; 95% CI: 1.45 to 2.16) were among the significant predictors of 4-year falls in older adults. The point method revealed integers that ranged from 0 (risk: 27.21%) to 44 (risk: 99.71%) and a score of 10 points had comparable discriminatory capacity to the combined moderate and high STEADI categories.Conclusion Coefficient-based integers and their risk estimates can provide an alternative interpretation of a predictive model that may be useful in determining fall risk within a clinical setting, tracking changes longitudinally and defining the effectiveness of an intervention.Data are available in a public, open access repository at https://www.nhats.org/.