Evidence map›Paper›PMID 35172791›Full record

ArticleBMC public health2022

The predictive power of geographic health care utilization for unintentional fatal fall rates.

Matthew Gordon Crowson, Jason A Beyea, Justin Cottrell, Faisal Karmali, Giovanni Lampasona, James E Saunders, Richard F Lewis

Open access · goldAbstract read
In one paragraph

Article in BMC public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 98% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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 5 institutions in 2 countries.

Matthew Gordon CrowsonDepartment of Otolaryngology-Head & Neck Surgery, Massachusetts Eye & Ear, 243 Charles Street, Boston, MA, 02114, USA. matthew_crowson@meei.harvard.edu.ORCID 0000-0001-9950-0985
Jason A BeyeaDepartment of Surgery, Division of Otolaryngology-Head and Neck Surgery, Kingston Health Sciences Centre, Queen's University, Kingston, Ontario, Canada.
Justin CottrellDepartment of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada.
Faisal KarmaliDepartment of Otolaryngology-Head & Neck Surgery, Massachusetts Eye & Ear, 243 Charles Street, Boston, MA, 02114, USA.
Giovanni LampasonaFaculté de Médecine et des Sciences de la Santé, l'Université de Sherbrooke, Sherbrooke, Quebec, Canada.
James E SaundersDepartment of Surgery, Division of Otolaryngology-Head and Neck Surgery, Dartmouth-Hitchcock Medical Center, Hanover, NH, USA.
Richard F LewisDepartment of Otolaryngology-Head & Neck Surgery, Massachusetts Eye & Ear, 243 Charles Street, Boston, MA, 02114, USA.
Massachusetts Eye and Ear Infirmary · USDartmouth–Hitchcock Medical Center · USQueen's University · CAUniversité de Sherbrooke · CAUniversity of Toronto · CA

Funding

RESEARCH TRAINING-MEDICAL INFORMATICS 90T15LM007092 · NLM · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI Nils Gehlenborg · 1992 to 2026
$32.8M
NLM NIH HHS T15 LM007092U.S. National Library of Medicine T15LM007092-30
6 · The paper itself

Abstract

backgroundFalls are the leading cause of fatal and nonfatal injuries among adults over 65 years old. The increase in fall mortality rates is likely multifactorial. With a lack of key drivers identified to explain rising rates of death from falls, accurate predictive modelling can be challenging, hindering evidence-based health resource and policy efforts. The objective of this work is to examine the predictive power of geographic utilization and longitudinal trends in mortality from unintentional falls amongst different demographic and geographic strata.

methodsThis is a nationwide, retrospective cohort study using the United States Centers for Disease Control (CDC) Web-based Injury Statistics Query and Reporting System (WISQARS) database. The exposure was death from an unintentional fall as determined by the CDC. Outcomes included aggregate and trend crude and age-adjusted death rates. Health care utilization, reimbursement, and cost metrics were also compared.

resultsOver 2001 to 2018, 465,486 total deaths due to unintentional falls were recorded with crude and age-adjusted rates of 8.42 and 7.76 per 100,000 population respectively. Comparing age-adjusted rates, males had a significantly higher age-adjusted death rate (9.89 vs. 6.17; p <  0.00001), but both male and female annual age-adjusted mortality rates are expected to rise (Male: + 0.25 rate/year, R

conclusionsMachine learning models have high accuracy in predicting geographic age-adjusted mortality rates from health care utilization data. In the United States from 2001 through 2018, adults aged 85+ years carried the highest death rate from unintentional falls and this rate is forecasted to accelerate.

Indexed as

Patient Acceptance of Health CareWounds and InjuriesAdultAgedAged, 80 and overCenters for Disease Control and Prevention, U.S.FemaleHumansMaleRetrospective StudiesSeasonsUnited States

Identifiers

PMID35172791
PMCPMC8848674
OpenAlexW4213261923

What OpenQuestion holds

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LicenceCC BY
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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.