Evidence map›Paper›PMID 32275654›Full record

ArticlePLoS medicine2020

Pre-exposure prophylaxis for preventing acquisition of HIV: A cross-sectional study of patients, prescribers, uptake, and spending in the United States, 2015-2016.

Stephanie S Chan, Andre R Chappel, Karen E Joynt Maddox, Karen W Hoover, Ya-Lin A Huang, Weiming Zhu, Stacy M Cohen, Pamela W Klein, Nancy De Lew

Abstract read
In one paragraph

Article in PLoS medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Economic Burden of HIV in a Commercially Insured Population in the United States.Journal of health economics and outcomes research · 2023
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. Observational
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

9 authors.

Stephanie S ChanUS Department of Health and Human Services (HHS), Office of the Assistant Secretary for Planning and Evaluation, Office of Health Policy, Washington, DC, United States of America.ORCID 0000-0002-9256-4756
Andre R ChappelUS Department of Health and Human Services (HHS), Office of the Assistant Secretary for Planning and Evaluation, Office of Health Policy, Washington, DC, United States of America.ORCID 0000-0001-7618-9450
Karen E Joynt MaddoxWashington University School of Medicine in St. Louis, St. Louis, Missouri, United States of America.ORCID 0000-0003-3779-8653
Karen W HooverHHS, Centers for Disease Control and Prevention, Division of HV/AIDS Prevention, Epidemiology Branch, Atlanta, Georgia, United States of America.
Ya-Lin A HuangHHS, Centers for Disease Control and Prevention, Division of HV/AIDS Prevention, Epidemiology Branch, Atlanta, Georgia, United States of America.
Weiming ZhuHHS, Centers for Disease Control and Prevention, Division of HV/AIDS Prevention, Epidemiology Branch, Atlanta, Georgia, United States of America.
Stacy M CohenHHS, Health Resources and Services Administration, HIV/AIDS Bureau, Rockville, Maryland, United States of America.ORCID 0000-0002-0475-1122
Pamela W KleinHHS, Health Resources and Services Administration, HIV/AIDS Bureau, Rockville, Maryland, United States of America.ORCID 0000-0003-4613-3725
Nancy De LewUS Department of Health and Human Services (HHS), Office of the Assistant Secretary for Planning and Evaluation, Office of Health Policy, Washington, DC, United States of America.

Funding

The Impact of Bundled Payments for Cardiopulmonary Disease on High-Risk PopulationsR01HL143421 · NHLBI · WASHINGTON UNIVERSITY · PI JOYNT MADDOX, KAREN ELLEN · 2019 to 2022
$3.1M
Improving the Quality of Care Provided Through Bundled Payments for Patients with Alzheimer's Disease or Other Forms of Dementia or FrailtyR01AG060935 · NIA · HARVARD SCHOOL OF PUBLIC HEALTH · PI EPSTEIN, ARNOLD, JOYNT MADDOX, KAREN ELLEN · 2019 to 2021
$2.7M
NHLBI NIH HHS R01 HL143421NIA NIH HHS R01 AG060935
6 · The paper itself

Abstract

backgroundIn 2015, there were approximately 40,000 new HIV diagnoses in the United States. Pre-exposure prophylaxis (PrEP) is an effective strategy that reduces the risk of HIV acquisition; however, uptake among those who can benefit from it has lagged. In this study, we 1) compared the characteristics of patients who were prescribed PrEP with individuals newly diagnosed with HIV infection, 2) identified the specialties of practitioners prescribing PrEP, 3) identified metropolitan statistical areas (MSAs) within the US where there is relatively low uptake of PrEP, and 4) reported median amounts paid by patients and third-party payors for PrEP. METHODS AND

findingsWe analyzed prescription drug claims for individuals prescribed PrEP in the Integrated Dataverse (IDV) from Symphony Health for the period of September 2015 to August 2016 to describe PrEP patients, prescribers, relative uptake, and payment methods in the US. Data were available for 75,839 individuals prescribed PrEP, and findings were extrapolated to approximately 101,000 individuals, which is less than 10% of the 1.1 million adults for whom PrEP was indicated. Compared to individuals with newly diagnosed HIV infection, PrEP patients were more likely to be non-Hispanic white (45% versus 26.2%), older (25% versus 19% at ages 35-44), male (94% versus 81%), and not reside in the South (30% versus 52% reside in the South).Using a ratio of the number of PrEP patients within an MSA to the number of newly diagnosed individuals with HIV infection, we found MSAs with relatively low uptake of PrEP were concentrated in the South. Of the approximately 24,000 providers who prescribed PrEP, two-thirds reported primary care as their specialty. Compared to the types of payment methods that people living with diagnosed HIV (PLWH) used to pay for their antiretroviral treatment in 2015 to 2016 reported in the Centers for Disease Control and Prevention (CDC) HIV Surveillance Special Report, PrEP patients were more likely to have used commercial health insurance (80% versus 35%) and less likely to have used public healthcare coverage or a publicly sponsored assistance program to pay for PrEP (12% versus 45% for Medicaid). Third-party payors covered 95% of the costs of PrEP. Overall, we estimated the median annual per patient out-of-pocket spending on PrEP was approximately US$72. Limitations of this study include missing information on prescription claims of patients not included in the database, and for those included, some patients were missing information on patient diagnosis, race/ethnicity, educational attainment, and income (34%-36%).

conclusionsOur findings indicate that in 2015-2016, many individuals in the US who could benefit from being on PrEP were not receiving this HIV prevention medication, and those prescribed PrEP had a significantly different distribution of characteristics from the broader population that is at risk for acquiring HIV. PrEP patients were more likely to pay for PrEP using commercial or private insurance, whereas PLWH were more likely to pay for their antiretroviral treatment using publicly sponsored programs. Addressing the affordability of PrEP and otherwise promoting its use among those with indications for PrEP represents an important opportunity to help end the HIV epidemic.

Indexed as

Drug PrescriptionsAdolescentAdultAgedAnti-HIV AgentsCross-Sectional StudiesFemaleHIV InfectionsHumansInsurance Claim ReviewInsurance, HealthMaleMiddle AgedPre-Exposure ProphylaxisUnited StatesYoung AdultAnti-HIV Agents

Identifiers

PMID32275654
PMCPMC7147726

What OpenQuestion holds

Textmetadata
LicenceCC0
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.