Aaron J Siegler1, C Christina Mehta2, Farah Mouhanna3, Robertino Mera Giler4, Amanda Castel5, Elizabeth Pembleton6, Chandni Jaggi6, Jeb Jones6, Michael R Kramer6, Pema McGuinness4, Scott McCallister4, Patrick S Sullivan6. 1. Department of Behavioral Sciences and Health Education, Rollins School of Public Health, Emory University, Atlanta, GA. Electronic address: asiegle@emory.edu. 2. Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA. 3. Department of Behavioral Sciences and Health Education, Rollins School of Public Health, Emory University, Atlanta, GA; Department of Epidemiology, Milken Institute School of Public Health, The George Washington University, Washington, DC. 4. Department of Epidemiology, Gilead Sciences, Foster City, CA, 94404. 5. Department of Epidemiology, Milken Institute School of Public Health, The George Washington University, Washington, DC. 6. Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA.
Abstract
PURPOSE: HIV pre-exposure prophylaxis (PrEP) is highly efficacious, and yet most individuals indicated for it are not currently using it. To provide guidance for health policymakers, researchers, and community advocates, we developed county-level PrEP use estimates and assessed locality and policy associations. METHODS: Using data from a national aggregator, we applied a validated crosswalk procedure to generate county-level estimates of PrEP users in 2018. A multilevel Poisson regression explored associations between PrEP use and (1) state policy variables of Medicaid expansion and state Drug Assistance Programs (PrEP-DAPs) and (2) county-level characteristics from the U.S. Census Bureau. Outcomes were PrEP per population (prevalence) and PrEP-to-need ratio (PnR), defined as the ratio of PrEP users per new HIV diagnosis. Higher levels of PrEP prevalence or PnR indicate more PrEP users relative to the total population or estimated need, respectively. RESULTS: Our 2018 county-level data set included a total of 188,546 PrEP users in the United States. Nationally, PrEP prevalence was 70.3/100,000 population and PnR was 4.9. In an adjusted model, counties with a 5% higher proportion of black residents had 5% lower PnR (rate ratio (RR): 0.95, 95% confidence interval (CI): 0.93, 0.96). Similarly, counties with higher concentration of residents uninsured or living in poverty had lower PnR. Relative to states without Medicaid expansion or PrEP-DAPs, states with only one of those programs had 25% higher PrEP prevalence (RR: 1.25, 95% CI: 1.09, 1.45), and states with both programs had 99% higher PrEP prevalence (RR: 1.99, 95% CI: 1.60, 2.48). There was a significant linear trend across the three policy groups, and similar findings for the relation between PnR and the policy groups. CONCLUSIONS: In a data set comprising approximately 80% of PrEP users in the United States, we found that Medicaid expansion and PrEP-DAPs were associated with higher PrEP use in states that adopted those policies, after controlling for potential confounders. Future research should identify which components of PrEP support programs have the most success and how to best promote PrEP among groups most impacted by the epidemic. States should support the admirable health decisions of their residents to get on PrEP by implementing policies that facilitate access.
PURPOSE: HIV pre-exposure prophylaxis (PrEP) is highly efficacious, and yet most individuals indicated for it are not currently using it. To provide guidance for health policymakers, researchers, and community advocates, we developed county-level PrEP use estimates and assessed locality and policy associations. METHODS: Using data from a national aggregator, we applied a validated crosswalk procedure to generate county-level estimates of PrEP users in 2018. A multilevel Poisson regression explored associations between PrEP use and (1) state policy variables of Medicaid expansion and state Drug Assistance Programs (PrEP-DAPs) and (2) county-level characteristics from the U.S. Census Bureau. Outcomes were PrEP per population (prevalence) and PrEP-to-need ratio (PnR), defined as the ratio of PrEP users per new HIV diagnosis. Higher levels of PrEP prevalence or PnR indicate more PrEP users relative to the total population or estimated need, respectively. RESULTS: Our 2018 county-level data set included a total of 188,546 PrEP users in the United States. Nationally, PrEP prevalence was 70.3/100,000 population and PnR was 4.9. In an adjusted model, counties with a 5% higher proportion of black residents had 5% lower PnR (rate ratio (RR): 0.95, 95% confidence interval (CI): 0.93, 0.96). Similarly, counties with higher concentration of residents uninsured or living in poverty had lower PnR. Relative to states without Medicaid expansion or PrEP-DAPs, states with only one of those programs had 25% higher PrEP prevalence (RR: 1.25, 95% CI: 1.09, 1.45), and states with both programs had 99% higher PrEP prevalence (RR: 1.99, 95% CI: 1.60, 2.48). There was a significant linear trend across the three policy groups, and similar findings for the relation between PnR and the policy groups. CONCLUSIONS: In a data set comprising approximately 80% of PrEP users in the United States, we found that Medicaid expansion and PrEP-DAPs were associated with higher PrEP use in states that adopted those policies, after controlling for potential confounders. Future research should identify which components of PrEP support programs have the most success and how to best promote PrEP among groups most impacted by the epidemic. States should support the admirable health decisions of their residents to get on PrEP by implementing policies that facilitate access.
Authors: Samuel M Jenness; Steven M Goodreau; Eli Rosenberg; Emily N Beylerian; Karen W Hoover; Dawn K Smith; Patrick Sullivan Journal: J Infect Dis Date: 2016-07-14 Impact factor: 5.226
Authors: Julia L Marcus; Jonathan E Volk; Jess Pinder; Albert Y Liu; Oliver Bacon; C Bradley Hare; Stephanie E Cohen Journal: Curr HIV/AIDS Rep Date: 2016-04 Impact factor: 5.071
Authors: Julia Thornton Snider; Dana P Goldman; Lisa Rosenblatt; Daniel Seekins; Timothy Juday; Yuri Sanchez; Yanyu Wu; Desi Peneva; John A Romley Journal: Med Care Res Rev Date: 2015-11-03 Impact factor: 3.929
Authors: Philip A Chan; Leandro Mena; Rupa Patel; Catherine E Oldenburg; Laura Beauchamps; Amaya G Perez-Brumer; Sharon Parker; Kenneth H Mayer; Matthew J Mimiaga; Amy Nunn Journal: J Int AIDS Soc Date: 2016-06-13 Impact factor: 5.396
Authors: Rupa R Patel; Leandro Mena; Amy Nunn; Timothy McBride; Laura C Harrison; Catherine E Oldenburg; Jingxia Liu; Kenneth H Mayer; Philip A Chan Journal: PLoS One Date: 2017-05-30 Impact factor: 3.240
Authors: Patrick S Sullivan; Justin Knox; Jeb Jones; Jennifer Taussig; Mariah Valentine Graves; Greg Millett; Nicole Luisi; Eric Hall; Travis H Sanchez; Carlos Del Rio; Colleen Kelley; Eli S Rosenberg; Jodie L Guest Journal: J Int AIDS Soc Date: 2021-04 Impact factor: 5.396
Authors: Stephen Bonett; Nadia Dowshen; José Bauermeister; Steven Meanley; Andrea L Wirtz; David D Celentano; Noya Galai; Renata Arrington-Sanders Journal: AIDS Behav Date: 2021-09-21
Authors: Rebecca Lillis; Jeremy Beckford; Joshua Fegley; Julia Siren; Bruce Hinton; Samuel Gomez; Stephanie N Taylor; Isolde Butler; Jason Halperin; Meredith Edwards Clement Journal: AIDS Patient Care STDS Date: 2021-08-26 Impact factor: 5.944
Authors: Krishna Kiran Kota; Gordon Mansergh; Rob Stephenson; Sabina Hirshfield; Patrick Sullivan Journal: AIDS Patient Care STDS Date: 2021-04-23 Impact factor: 5.078
Authors: Jessica P Ridgway; Eleanor E Friedman; Alvie Bender; Jessica Schmitt; Michael Cronin; Rayna N Brown; Amy K Johnson; Lisa R Hirschhorn Journal: AIDS Patient Care STDS Date: 2021-01 Impact factor: 5.078
Authors: Wenting Huang; Annie Lockard; Colleen F Kelley; David P Serota; Charlotte-Paige M Rolle; Patrick S Sullivan; Eli S Rosenberg; Aaron J Siegler Journal: AIDS Care Date: 2021-08-09