Evidence map›Paper›PMID 42253878›Full record

ArticleHealth affairs scholar2026

Protecting safety-net equity monitoring after federal sexual orientation and gender identity data rollbacks.

Dhruv Khurana, Andrew Short

Abstract read
In one paragraph

Article in Health affairs scholar, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

2 authors.

Dhruv KhuranaDepartment of Health Policy & Management, UCLA Fielding School of Public Health, Los Angeles, CA 90095, United States.ORCID https://orcid.org/0000-0002-3108-9517
Andrew ShortDepartment of Health Policy & Management, UCLA Fielding School of Public Health, Los Angeles, CA 90095, United States.ORCID https://orcid.org/0009-0003-8379-7704

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent federal actions have weakened sexual orientation and gender identity (SOGI) data collection across safety-net systems, threatening the capacity to measure and govern SOGI health disparities. A January 2025 executive order directed agencies to eliminate gender identity data from federal programs and surveys; many agencies applied it to sexual orientation as well: of approximately 360 affected federal data collections, 338 removed gender identity questions and 60 removed sexual orientation questions, reflecting disproportionate targeting of gender identity data. Subsequent rescissions in June 2025 of guidance on SOGI data collection in Medicaid and federal health center reporting have further dismantled equity accountability infrastructure. Drawing on whole-person health screening data from >35 000 safety-net patients, we emphasize 2 policy-relevant patterns: a "generational reversal" (largest disparities concentrated among younger LGB+ adults; eg, depression rates 24 percentage points higher among LGB+ adults ages 18-25, shrinking to 7 points by age 46 and older) and an "education paradox" (higher educational attainment alongside greater material hardship). Illustrative interviews with 7 LGB+ adults stress-tested policy implications and helped incorporate community interpretation. We propose SOGI data accountability mechanisms, workflow-embedded warm handoffs, and contracting and workforce strategies to reduce discrimination exposure and improve access to benefits and preventive care.

Indexed as

discriminationEHRhealth equityHRSAMedicaidquality measurementsafety-netsexual orientationsocial needsSOGIUniform Data Systemwarm handoffWhole PERSON Health Scorewhole-person screeningworkforce policy

Identifiers

PMID42253878
PMCPMC13235683

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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 OpenQuestion graph.