Evidence map›Paper›PMID 42085118›Full record

ArticleNoise & health

The End of NIOSH's Hearing Loss Prevention Program: Setback or Opportunity?

James Craner

Abstract read
In one paragraph

Article in Noise & health. 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
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.

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

1 author.

James CranerVerdi Technology, Inc., Reno, Nevada, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The recent staff layoffs and defunding of the United States' National Institute of Occupational Safety and Health's Hearing Loss Prevention Program are perceived by many occupational hearing loss professionals and stakeholders as an unsettling setback. This Commentary explains how and why ending this federal governmental program's research agenda-even if it is temporary-should not stop progress by the private sector toward secondary prevention of noise-induced hearing loss by leveraging the overabundance of under-utilized raw audiometric data already collected for compliance purposes. The resources needed to accomplish this objective include metrics and statistical methods that transform raw audiometric data into risk-based population analytics, optimization of program delivery, and selective prioritization of cost-effective technology and basic research. Integrating population-based audiometric database analysis into routine business practice will give employers the valuable business intelligence they need to continuously measure the actual effectiveness of and return on investment from their hearing conservation programs. Private sector stakeholders who stand the most to benefit-employers, insurers, and suppliers-can pragmatically implement this solution framework to more efficiently manage noise-related worker health risk for relatively little additional cost compared to what employers already pay to conduct audiometric testing and basic compliance reporting. Government agencies' otherwise traditionally reactive enforcement role can concomitantly evolve into a proactive, risk-reducing resource that enhances their role in regulation and technical support.

Indexed as

Hearing Loss, Noise-InducedNational Institute for Occupational Safety and Health, U.S.Noise, OccupationalOccupational DiseasesAudiometryHumansOccupational HealthUnited StatesaudiometryNational Institute for Occupational Safety and HealthNoise-induced hearing losssecondary prevention

Identifiers

PMID42085118
PMCPMC13387454

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.