Evidence map›Paper›PMID 39739278›Full record

ArticleAIDS and behavior2025

County-Level Factors Associated with Reversal of Insurer-Approved HIV Pre-Exposure Prophylaxis Prescriptions in the United States, 2018.

Rahel Dawit, William C Goedel, Hsien-Yen Chang, Amy S Nunn, Philip A Chan, Jalpa A Doshi, Lorraine T Dean

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in AIDS and behavior, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Rahel DawitDepartment of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA. rdawit1@jhu.edu.ORCID http://orcid.org/0000-0002-3599-3597
William C GoedelDepartment of Epidemiology, Brown University, Providence, RI, USA.
Hsien-Yen ChangJanssen Pharmaceutical Companies, Titusville, NJ, USA.
Amy S NunnDepartment of Behavioral and Social Sciences, Brown University, Providence, RI, USA.
Philip A ChanBrown University AIDS Program, The Miriam Hospital, Providence, RI, 02906, USA.
Jalpa A DoshiPerelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Lorraine T DeanDepartment of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.

Funding

Vaccine Response and Immunotherapeutics SWGP30AI094189 · NIAID · JOHNS HOPKINS UNIVERSITY · PI Richard E. Chaisson · 2012 to 2026
$67.0M
Johns Hopkins HIV Epidemiology and Prevention Sciences Training ProgramT32AI102623 · NIAID · JOHNS HOPKINS UNIVERSITY · PI Shruti H Mehta · 2013 to 2026
$5.3M
Assessing Gaps in HIV Pre-Exposure Prophylaxis (PrEP) Initiation and Retention using Pharmacy Claims DataR21NR018387 · NINR · JOHNS HOPKINS UNIVERSITY · PI DEAN, LORRAINE TIERA, NUNN, AMY STEWART · 2019 to 2020
$481k
Center for AIDS Research, Johns Hopkins University P30AI094189National Institute of Health R21NR018387National Institute of Health T32AI102623NIAID NIH HHS P30 AI094189NIAID NIH HHS T32 AI102623NINR NIH HHS R21 NR018387
6 · The paper itself

Abstract

Identifying county-level factors that influence pre-exposure prophylaxis (PrEP) adherence is critical for ending the HIV epidemic in the United States (US). PrEP primary reversal is a term used to describe patients who do not obtain their prescribed medication from the pharmacy. This study sought to identify factors associated with PrEP reversal at the county level in 2018. Data were collected from Symphony Health Analytics, AIDS Vu, the US Census Bureau, and the Centers for Disease Control and Prevention National Prevention Information Network. Bivariate Choropleth maps were created to identify counties with high and low levels of PrEP reversal and HIV incidence. This was followed by bivariate analysis to determine the association between predictor variables and percent PrEP reversal. Finally multivariable logistic regressions were used to assess the association between percent PrEP reversal and variables that were significant from the bivariate analysis. A total of 308 counties were included in this analysis, where the mean number of PrEP prescriptions for counties was 44, with a median of 14 (Interquartile range 7-34). In the multivariable analysis, counties with higher level of unemployment (aOR: 1.10, 95% CI: 1.05-1.16) and rural counties (1.10: 1.04-1.17) had higher odds of PrEP reversal; while counties with higher household crowding (0.97: 0.95-0.99) had lower odds of PrEP reversal. Findings show the need for expanding and implementing programs as well as policies to improve PrEP services that are tailored to local socioeconomic circumstances.

Indexed as

Anti-HIV AgentsDrug PrescriptionsHIV InfectionsMedication AdherencePre-Exposure ProphylaxisAdultFemaleHumansInsurance, HealthMaleMiddle AgedUnited StatesAnti-HIV AgentsCounty-level socioeconomic factorsHIV/AIDSPrEP reversal

Identifiers

PMID39739278
PMCPMC11985303

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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.