Evidence map›Paper›PMID 34020849›Full record

ArticleAmerican journal of preventive medicine2021

Adapted Stopping Elderly Accidents, Deaths, and Injuries Questions for Falls Risk Screening: Predictive Ability in Older Drivers.

Sneha Kannoth, Thelma J Mielenz, David W Eby, Lisa J Molnar, Haomiao Jia, Guohua Li, David Strogatz

Open access · greenAbstract read
In one paragraph

Article in American journal of preventive medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.6field-weighted citation impact, top 17% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 5 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 1 country.

Sneha KannothCenter for Injury Science and Prevention, Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York.
Thelma J MielenzCenter for Injury Science and Prevention, Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York. Electronic address: tjm2141@cumc.columbia.edu.
David W EbyBehavioral Sciences Group, University of Michigan Transportation Research Institute, Ann Arbor, Michigan.
Lisa J MolnarBehavioral Sciences Group, University of Michigan Transportation Research Institute, Ann Arbor, Michigan.
Haomiao JiaOffice of Scholarship & Research Development, Columbia University Irving Medical Center, Columbia University School of Nursing New York, New York; Department of Biostatistics, Columbia University Mailman School of Public Health, New York, New York.
Guohua LiDepartment of Epidemiology, Columbia University Mailman School of Public Health, New York, New York; Department of Anesthesiology, Columbia University Vagelos College of Physicians and Surgeons, New York, New York.
David StrogatzCenter for Rural Community Health, The Bassett Research Institute, Bassett Healthcare Network, Cooperstown, New York.
Columbia University · USBassett Healthcare Network · USMichigan Department of Transportation · USUniversity of Michigan–Ann Arbor · US

Funding

Supplemental Training in Making Data FAIR and AI/ML ReadyT32ES007322 · NIEHS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Pam R Factor-Litvak, Allison Kupsco · 2000 to 2026
$12.1M
Columbia Injury Control Research CenterR49CE002096 · CE · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI LI, GUOHUA · 2012 to 2018
$5.5M
ACL HHS R49CE002096NCIPC CDC HHS R49 CE002096NCIPC CDC HHS R49 CE003094NIEHS NIH HHS T32 ES007322
6 · The paper itself

Abstract

introductionFall fatality rates among U.S. older adults increased 30% from 2007 to 2016. In response, the Centers for Disease Control and Prevention developed the Stopping Elderly Accidents, Deaths, and Injuries algorithm for fall risk screening, assessment, and intervention. The current Stopping Elderly Accidents, Deaths, and Injuries algorithm with 2 levels (at risk and not at risk) was adapted to an existing cohort of older adult drivers.

methodsA U.S. multisite prospective cohort (N=2,990) of drivers (aged 65-79 years), from 2015 to 2017, was used for these analyses completed in January 2020-October 2020. To measure the adapted Stopping Elderly Accidents, Deaths, and Injuries key questions for fall risk screening performance in predicting future falls, adjusted logistic regression determined the area of the receiver operating characteristic curve. An adjusted mixed logistic regression modeled the association between the adapted Stopping Elderly Accidents, Deaths, and Injuries key questions and future falls.

resultsThe adapted Stopping Elderly Accidents, Deaths, and Injuries key questions yielded an area under the curve of 0.65 in determining any fall over 2 years. The adjusted mixed logistic regression model suggests that those at risk for falls at baseline were associated with 2.37 times higher odds of any fall (95% CI=2.00, 2.80) and 3.60 times higher odds of multiple falls (95% CI=2.88, 4.51) over 2 years.

conclusionsThe adapted Stopping Elderly Accidents, Deaths, and Injuries key questions for fall risk screening yielded fair predictive ability for falls over 2 years and were strongly associated with future falls for older adult drivers. The adapted Stopping Elderly Accidents, Deaths, and Injuries key questions can be applied to existing data in nonclinical settings to strengthen fall screening and prevention at a population level.

Indexed as

Accidental FallsMass ScreeningAgedAlgorithmsHumansLogistic ModelsProspective StudiesRisk AssessmentRisk Factors

Identifiers

PMID34020849
PMCPMC9945655
OpenAlexW3160041021

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.