Evidence map›Paper›PMID 27057747›Full record

ArticleMedical care2016

Claims-based Identification Methods and the Cost of Fall-related Injuries Among US Older Adults.

Geoffrey J Hoffman, Ron D Hays, Martin F Shapiro, Steven P Wallace, Susan L Ettner

Abstract read
In one paragraph

Article in Medical care, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

25 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  12. Identification of Fall-Related Injuries in Nursing Home Residents Using Administrative Claims Data.The journals of gerontology. Series A, Biological sciences and medical sciences · 2022
    Article
  13. Claims-Based Frailty and Outcomes: Applying an Aging Measure to Older Adults with Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2021
    Observational
  14. Article
  15. Article
  16. Article
  17. Observational
  18. Imputation and characterization of uncoded self-harm in major mental illness using machine learning.Journal of the American Medical Informatics Association : JAMIA · 2020
    Article
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  20. Observational
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

5 authors.

Geoffrey J Hoffman*Department of Systems, Populations and Leadership, University of Michigan School of Nursing, Ann Arbor, MI †UCLA Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine, UCLA ‡Department of Health Policy and Management, UCLA Fielding School of Public Health §Department of Community Health Sciences, UCLA Fielding School of Public Health.
Ron D Hays
Martin F Shapiro
Steven P Wallace
Susan L Ettner

Funding

The Center for Health Improvement of Minority ElderlyP30AG021684 · NIA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Homero Erwin del Pino · 2002 to 2026
$19.7M
TRAINING COREP20MD000182 · NIMHD · CHARLES R. DREW UNIVERSITY OF MED &SCI · PI MILBURN, NORWEETA GERMAINE, SHAPIRO, MARTIN F. · 2002 to 2016
$16.5M
The National Person-Centered Assessment Resource (PCAR)U2CCA186878 · NCI · NORTHWESTERN UNIVERSITY AT CHICAGO · PI COOK, KARON F · 2014 to 2018
$11.9M
UCLA/RAND HEALTH SERVICES RESEARCH TRAINING PROGRAMT32HS000046 · AHRQ · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI EISENBERG, DANIEL · 1992 to 2024
$8.7M
UCLA Clinical and Translational Science InstituteTL1TR000121 · NCATS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI DUBINETT, STEVEN M. · 2012 to 2015
$2.7M
AHRQ HHS T32 HS000046NCATS NIH HHS TL1 TR000121NCI NIH HHS U2C CA186878NIA NIH HHS P30 AG021684NIMHD NIH HHS P20 MD000182
6 · The paper itself

Abstract

objectivesCompare expenditures of fall-related injuries (FRIs) using several methods to identify FRIs in administrative claims data. RESEARCH

designUsing 2007-2009 Medicare claims and 2008 Health and Retirement Survey data, FRIs were identified using external-cause-of-injury (e-codes 880/881/882/884/885/888) only, e-codes plus a broad set of primary diagnosis codes, and a newer approach using e-codes and diagnostic and procedural codes. Linear regression models adjusted for sociodemographic, health, and geographic characteristics were used to estimate per-FRI, service component, patient cost share, expenditures by type of initial FRI treatment (inpatient, emergency department only, outpatient), and total annual FRI-related Medicare expenditures. SUBJECTS: The analysis included 5497 community-dwelling adults ≥65 (228 FRI, 5269 non-FRI individuals) with continuous Medicare coverage and alive during the 24-month study.

resultsThe 3 FRI identification methods produced differing distributions of index FRI type and varying estimated expenditures: $12,171 [95% confidence interval (CI), $4662-$19,680], $5648 (95% CI, $3819-$7476), and $9388 (95% CI, $5969-$12,808). In all models, most spending occurred in hospital, outpatient, and skilled nursing facility (SNF) settings, but greater proportions of SNF and outpatient spending were observed with commonly used FRI identification methods. Patient cost-sharing was estimated at $691-$1900 across the 3 methods. Inpatient-treated index FRIs were more expensive than emergency department and outpatient-treated FRIs across all methods, but were substantially higher when identifying FRI using only e-codes. Estimated total FRI-related Medicare expenditures were highly variable across methods.

conclusionsFRIs are costly, with implications for Medicare and its beneficiaries. However, expenditure estimates vary considerably based on the method used to identify FRIs.

Indexed as

Accidental FallsInsurance Claim ReviewMedicareAgedAged, 80 and overCross-Over StudiesFemaleHumansMaleUnited StatesWounds and Injuries

Identifiers

PMID27057747
PMCPMC4907826

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.