Evidence map›Paper›PMID 32842872›Full record

ArticleMedical care research and review : MCRR2021

Development of a Predictive Algorithm to Identify Adults With Mobility Limitations Using VA Health Care Administrative Data.

Yochai Eisenberg, Lisa M Powell, Shannon N Zenk, Elizabeth Tarlov

Open access · greenAbstract read
In one paragraph

Article in Medical care research and review : MCRR, 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
0.1field-weighted citation impact, top 43% 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, 2 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

4 authors at 2 institutions in 1 country.

Yochai EisenbergDepartment of Disability and Human Development, University of Illinois at Chicago, Chicago, IL, USA.ORCID 0000-0001-5598-771X
Lisa M PowellDepartment of Health Policy and Administration, University of Illinois at Chicago, Chicago, IL, USA.
Shannon N ZenkDepartment of Health System Sciences, University of Illinois at Chicago, Chicago, IL, USA.
Elizabeth TarlovCollege of Nursing, University of Illinois at Chicago, Chicago, IL, USA.
University of Illinois Chicago · USEdward Hines, Jr. VA Hospital · US

Funding

Environmental Attributes and Weight Control: Study of Over 1.3 Million VeteransR01CA172726 · NCI · UNIVERSITY OF ILLINOIS AT CHICAGO · PI TARLOV, ELIZABETH, ZENK, SHANNON N · 2013 to 2016
$2.3M
Neighborhoods and Health LabZIAMD000021 · NIMHD · NATIONAL INSTITUTE ON MINORITY HEALTH AND HEALTH DISPARITIES · PI ZENK, SHANNON · 2021 to 2025
$1.9M
HSRD VA I01 HX001273NCI NIH HHS R01 CA172726
6 · The paper itself

Abstract

An estimated 31.5 million Americans have a mobility limitation. Health care administrative data could be a valuable resource for research on this population but methods for cohort identification are lacking. We developed and tested an algorithm to reliably identify adults with mobility limitation in U.S. Department of Veterans Affairs health care data. We linked diagnosis, encounter, durable medical equipment, and demographic data for 964 veterans to their self-reported mobility limitation from the Medicare Current Beneficiary Survey. We evaluated performance of logistic regression models in classifying mobility limitation. The binary approach (yes/no limitation) had good sensitivity (70%) and specificity (79%), whereas the multilevel approach did not perform well. The algorithms for predicting a binary mobility limitation outcome performed well at discriminating between veterans who did and did not have mobility limitation. Future work should focus on multilevel approaches to predicting mobility limitation and samples with greater proportions of women and younger adults.

Indexed as

Mobility LimitationVeteransAdultAgedAlgorithmsDelivery of Health CareFemaleHumansMedicareUnited StatesUnited States Department of Veterans Affairsadministrative dataalgorithmmobility limitationveterans

Identifiers

PMID32842872
PMCPMC8525631
OpenAlexW3080797648

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.