Evidence map›Paper›PMID 41487545›Full record

ArticleProceedings (Baylor University. Medical Center)

Trends and racial disparities in mortality related to acute respiratory distress syndrome.

Nikhil Furtado, Sydney Ball, Taylor Billion, Mohsin Mirza

Abstract read
In one paragraph

Article in Proceedings (Baylor University. Medical Center). 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

4 authors.

Nikhil FurtadoSchool of Medicine, Creighton University, Omaha, Nebraska, USA.ORCID https://orcid.org/0009-0008-1392-783X
Sydney BallSchool of Medicine, Creighton University, Omaha, Nebraska, USA.
Taylor BillionSchool of Medicine, Creighton University, Omaha, Nebraska, USA.
Mohsin MirzaDepartment of Internal Medicine, Creighton University, Omaha, Nebraska, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Acute respiratory distress syndrome (ARDS) is a significant cause of morbidity and mortality, particularly among critically ill patients with limited treatment options. This study analyzed long-term trends in ARDS mortality in the US, focusing on racial disparities. By examining mortality rates across racial, regional, and gender groups, the study identified demographic and geographic inequalities to inform targeted interventions. Methods: ARDS mortality data from 1999 to 2022 was obtained from the CDC WONDER database. Age-adjusted mortality rates (AAMRs) were stratified by sex, race, and US census region. Joinpoint regression was used to calculate annual percentage changes (APCs) and average annual percent change (AAPC). Results: The overall AAMR increased from 7.40 (1999) to a peak of 21.72 (2021) before declining to 8.49 (2022, AAPC: 2.92). American Indians had the highest AAMR, increasing from 13.8 (1999) to 18.89 (2022, AAPC: 4.37). Asians had the lowest AAMR, declining from 7.4 (1999) to 6.17 (2022). AAMRs declined from 1999 to 2017, surged from 2017 to 2020, and dropped after 2022. Regional disparities were notable among the Northeast and Midwest, with Black populations having higher mortality. Conclusion: ARDS mortality trends worsened during the COVID-19 pandemic, disproportionately affecting American Indian and Black populations. Addressing these disparities requires equitable healthcare policies and further research into social determinants of health.

Indexed as

Acute respiratory distress syndromeARDSdisparitiesepidemiologymortalityracial inequitytrends

Identifiers

PMID41487545
PMCPMC12758318

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.