Top

Published in:

Open Access 01-12-2022 | Research article

Predictive validity of the Stopping Elderly Accidents, Deaths & Injuries (STEADI) program fall risk screening algorithms among community-dwelling Thai elderly

Authors: Sriprapa Loonlawong, Weerawat Limroongreungrat, Thanapoom Rattananupong, Kamonrat Kittipimpanon, Wanvisa Saisanan Na Ayudhaya, Wiroj Jiamjarasrangsi

Published in: BMC Medicine | Issue 1/2022

Abstract

Background

Fall risk screening using multiple methods was strongly advised as the initial step for preventing fall. Currently, there is only one such tool which was proposed by the U.S. Centers for Disease Control and Prevention (CDC) for use in its Stopping Elderly Accidents, Death & Injuries (STEADI) program. Its predictive validity outside the US context, however, has never been investigated. The purpose of this study was to determine the predictive validity (area under the receiver operating characteristic curve: AUC), sensitivity, and specificity of the two-step sequential fall-risk screening algorithm of the STEADI program for Thai elderly in the community.

Methods

A 1-year prospective cohort study was conducted during October 2018–December 2019. Study population consisted of 480 individuals aged 65 years or older living in Nakhon Ratchasima Province, Thailand. The fall risk screening algorithm composed of two serial steps. Step 1 is a screening by the clinician’s 3 key questions or the Thai Stay Independent brochure (Thai-SIB) 12 questions. Step 2 is a screening by 3 physical fitness testing tools including Time Up and Go test (TUG), 30-s Chair Stand, and 4-stage balance test. Participants were then followed for their fall incidents. Statistical analyses were conducted by using Cox proportional hazard model. The AUC, sensitivity, specificity, and other relevant predictive validity indices were then estimated.

Results

The average age of the participants was 73.3 ± 6.51 years (range 65–95 years), and 52.5% of them were female. The screening based on the clinician’s 3 key questions in Step 1 had a high AUC (0.845), with the sensitivity and specificity of 93.9% (95% CI 88.8, 92.7) and 75.0% (95% CI 70.0, 79.6), respectively. Appropriate risk categorization however differed slightly from the original STEADI program.

Conclusions

With some modification, the fall risk screening algorithm based on the STEADI program was applicable in Thai context.

Available only for authorised users

World Health Organization. Falls. 2018. Available from: https://www.who.int/news-room/fact-sheets/detail/falls. Cited February 4, 2021.

World Health Organization. WHO Global report on Falls prevention in older age. 2007. Available from: https://www.who.int/ageing/publications/Falls_prevention7March.pdf?ua=1. Cited February 4, 2021.

Department of Disease Control, Ministry of Public Health. Annual report 2017. 1st ed. Bangkok: Aksorn Graphic and Design Publishing Limited Partnership; 2016. (Thai language)

Phelan EA, Mahoney JE, Voit JC, Stevens JA. Assessment and management of fall risk in primary care settings. Med Clin North Am. 2015;99(2):281–93. https://doi.org/10.1016/j.mcna.2014.11.004.CrossRefPubMedPubMedCentral

Centers for Disease Control and Prevention (CDC). Resource: algorithm for fall risk screening, assessment, and intervention. 2018. Available from: https://www.cdc.gov/steadi/pdf/STEADI-Algorithm-print.pdf. Cited February 4, 2020.

Romli MH, Tan MP, Mackenzie L, Lovarini M, Suttanon P, Clemson L. Falls amongst older people in Southeast Asia: a scoping review. Public Health. 2017;145:96–112. https://doi.org/10.1016/j.puhe.2016.12.035.CrossRefPubMed

Lockhart TE, Liu J. Differentiating fall-prone and healthy adults using local dynamic stability. Ergonomics. 2008;51(12):1860–72.CrossRefPubMedPubMedCentral

Gillespie LD, Robertson MC, Gillespie WJ, Lamb SE, Gates S, Cumming RG, et al. Interventions of preventing falls in older people living in the community. Cochrane Database Sys Rev. 2012;9:CD007146. https://doi.org/10.1002/14651858.CD007146.pub3.CrossRef

Gates S, Smith LA, Fisher JD, Lamb SE. Systematic review of accuracy of screening instruments for predicting fall risk among independently living older adults. J Rehabil Res Dev. 2008;45:1105–16. https://doi.org/10.1682/JRRD.2008.04.0057.CrossRefPubMed

10.

Park SH. Tools for assessing fall risk in the elderly: a systematic review and meta-analysis. Aging Clin Exp Res. 2017;30(1):1–16.CrossRefPubMed

11.

Obrist S, Rogan S, Hilfiker R. Development and evaluation of an online fall-risk questionnaire for nonfrail community-dwelling elderly persons: a pilot study. Curr Gerontol Geriatr Res. 2016;2016:1–16. https://doi.org/10.1155/2016/1520932.CrossRef

12.

Lusardi MM, Fritz S, Middleton A, Allison L, Wingood M, Phillips E, et al. Determining risk of falls in community dwelling older adults: a systematic review and meta-analysis using posttest probability. J Ggeriatr Phys Ther. 2017;40(1):1–36. https://doi.org/10.1519/JPT.0000000000000099.CrossRef

13.

American Geriatrics Society British Geriatrics Society. Summary of the updated American Geriatrics Society/British Geriatrics Society clinical practice guideline for prevention of falls in older persons. J Am Geriatr Soc. 2011;59(1):148–57. https://doi.org/10.1111/jgs.2011.59.issue-1.CrossRef

14.

Muir SW, Berg K, Chesworth B, Klar N, Speechley M. Application of a fall screening algorithm stratified fall risk but missed preventive opportunities in community-dwelling older adults: a prospective study. J Geriatr Phys Ther. 2010;33:165–72. https://doi.org/10.1097/JPT.0b013e3181ff23cc.CrossRefPubMed

15.

American Geriatrics Society, British Geriatrics Society, and American Academy of Othropaedic Surgeons Panel on falls Prevention. Guideline for the prevention of falls in older persons. J Am Geriatr Soc. 2001;49:664–72.CrossRef

16.

Lee R. The CDC’s STEADI initiative: promoting older adult health and independence through fall preventive. Am Fam Physician. 2017;96(4):220–1.PubMedPubMedCentral

17.

Rubenstein LZ, Vivrette R, Harker JO, Stevens JA, Kramer BJ. Validating an evidence-based, self-rated fall risk questionnaire (FRQ) for older adults. J Safety Res. 2011;42(6):493–9. https://doi.org/10.1016/j.jsr.2011.08.006.CrossRefPubMed

18.

Mielenz TJ, Kannoth S, Jia H, Pullyblank K, Sorensen J, Estabrooks P, et al. Evaluating a two-level vs. three-level fall risk screening algorithm for predicting falls among older adults. Front Public Health. 2020;8:1–11. https://doi.org/10.3389/fpubh.2020.00373.CrossRef

19.

Lohman MC, Crow RS, DiMilia PR, Nicklett EJ, Bruce ML, Batsis JA. Operationalisation and validation of the Stopping Elderly Accidents, Deaths, and Injuries (STEADI) fall risk algorithm in a nationally representative sample. J Epidemiol Community Health. 2017;71(12):1191–7. https://doi.org/10.1136/jech-2017-209769.CrossRefPubMed

20.

Nithman RW, Vincenzo JL. How steady is the STEADI? Inferential analysis of the CDC fall risk toolkit. Arch Gerontol Ggeriatr. 2019;83:185–94. https://doi.org/10.1016/j.archger.2019.02.018.CrossRef

21.

Department of Medical Services, Ministry of Public Health. Elderly health screening manual. 2nd ed. Bangkok: The War Veterans Organization of Thailand Publishing; 2015. (Thai language)

22.

Jitapunkul S. Disability: a problem of the elderly. Chula Med J. 1994;38(2):67–75.

23.

Prasat Neurological Institute, Department of Medical Services. Clinical practice guidelines: dementia. 1st ed. Bangkok: Tanapress company limited; 2014. (Thai language)

24.

Hajian-Tilaki K. Sample size estimation in diagnostic test studies of biomedical informatics. J Biomed Inform. 2014;48:193–204. https://doi.org/10.1016/j.jbi.2014.02.013.CrossRefPubMed

25.

Health Systems Research Institude. 5^th Thai National Health Examination Survey: NHES V. 1st ed. Bangkok: Aksorn Graphic and Design Publishing Limited Partnership; 2016. (Thai language)

26.

Beaton DE, Bombardier C, Guillemin F, Ferraz MB. Guideline for the process of cross-cultural adaptatin of self-report measures. Spine. 2000;25:3186–91. https://doi.org/10.1097/00007632-200012150-00014.CrossRefPubMed

27.

Loonlawong S, Limroongreungrat W, Jiamjarasrangsi W. The Stay independent brochure as a screening evaluation for fall risk in an elderly Thai population. Clin Interv Aging. 2019;14:2155–62. https://doi.org/10.2147/CIA.S233414.CrossRefPubMedPubMedCentral

28.

Podsiadlo D, Richardson S. The time “Up & Go”: a test of basic functional mobility for frail elderly persons. J Am Geriatr Soc. 1991;39:142–8.CrossRefPubMed

29.

Lin MR, Hwang HF, Hu MH, Wu HDI, Wang YW, Huang FC. Psychometric comparisons of the Timed Up and Go, One-Leg Stand, Functional Reach, and Tinetti balance measures in community-dwelling older people. J Am Geriatr Soc. 2004;52(8):1343–8.CrossRefPubMed

30.

Centers for Disease Control and Prevention (CDC). Assessment: 30-second chair stand. 2021. Available from: https://www.cdc.gov/steadi/pdf/STEADI-Assessment-30Sec-508.pdf. Cited February 4, 2021.

31.

Centers for Disease Control and Prevention (CDC). Assessment: the 4-stage balance test. 2021. Available from: https://www.cdc.gov/steadi/pdf/STEADI-Assessment-4Stage-508.pdf. Cited February 4, 2021.

32.

Loonlawong S. Predictive validity of three fall risk screening algorithms among community-dwelling Thai elderly. Bangkok: Chulalongkorn University; 2019. (Thai language)

33.

Hosmer DW, Lemeshow S. Applied logistic regression. 2nd ed. New York: Wiley; 2000.CrossRef

34.

Helse BC, Kemper KA, Williams JE, Truong K, Van Puymbroeck M. Multidimensional risk score to stratify community-dwelling older adults by future fall risk using the Stopping Elderly Accidents, Deaths and Injuries (STEADI) framework. Inj Prev. 2020:injuryprev-2020-044014. https://doi.org/10.1136/injuryprev-2020-044014.

Title: Predictive validity of the Stopping Elderly Accidents, Deaths & Injuries (STEADI) program fall risk screening algorithms among community-dwelling Thai elderly
Authors: Sriprapa Loonlawong
Weerawat Limroongreungrat
Thanapoom Rattananupong
Kamonrat Kittipimpanon
Wanvisa Saisanan Na Ayudhaya
Wiroj Jiamjarasrangsi
Publication date: 01-12-2022
Publisher: BioMed Central
Published in: BMC Medicine / Issue 1/2022
Electronic ISSN: 1741-7015
DOI: https://doi.org/10.1186/s12916-022-02280-w

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Predictive validity of the Stopping Elderly Accidents, Deaths & Injuries (STEADI) program fall risk screening algorithms among community-dwelling Thai elderly

Abstract

Background

Methods

Results

Conclusions

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 1/2022

Associations of genetically predicted IL-6 signaling with cardiovascular disease risk across population subgroups

Sequential gene expression analysis of cervical malignant transformation identifies RFC4 as a novel diagnostic and prognostic biomarker

Post-mortem examination of high mortality in patients with heart failure and atrial fibrillation

Metabolomic profile of prostate cancer-specific survival among 1812 Finnish men

Time spent in outdoor light is associated with the risk of dementia: a prospective cohort study of 362094 participants

The lipidomic profile of the tumoral periprostatic adipose tissue reveals alterations in tumor cell’s metabolic crosstalk