Evidence map›Paper›PMID 38756919›Full record

ArticleHealth affairs scholar2024

American clusters: using machine learning to understand health and health care disparities in the United States.

Diana M Bowser, Kaili Maurico, Brielle A Ruscitti, William H Crown

Erratum issuedAbstract read
In one paragraph

Article in Health affairs scholar, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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

  1. Pooled it
  2. Article
  3. Review
  4. Social Media Influences on Dietary Awareness in Children.Healthcare (Basel, Switzerland) · 2024
    Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Diana M BowserConnell School of Nursing, Boston College, Chestnut Hill, MA 02467, United States.ORCID https://orcid.org/0000-0001-9582-7761
Kaili MauricoConnell School of Nursing, Boston College, Chestnut Hill, MA 02467, United States.ORCID https://orcid.org/0009-0005-1271-3512
Brielle A RuscittiHeller School for Social Policy and Management, Brandeis University, Waltham, MA 02454, United States.
William H CrownHeller School for Social Policy and Management, Brandeis University, Waltham, MA 02454, United States.ORCID https://orcid.org/0000-0002-8822-2737

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Health and health care access in the United States are plagued by high inequality. While machine learning (ML) is increasingly used in clinical settings to inform health care delivery decisions and predict health care utilization, using ML as a research tool to understand health care disparities in the United States and how these are connected to health outcomes, access to health care, and health system organization is less common. We utilized over 650 variables from 24 different databases aggregated by the Agency for Healthcare Research and Quality in their Social Determinants of Health (SDOH) database. We used

Indexed as

American clustershealth disparitiesmachine learningsocial determinants of health

Identifiers

PMID38756919
PMCPMC10986293

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.