title: Test-and-treat in Los Angeles: a mathematical model of the effects of test-and-treat for the population of men who have sex with men in Los Angeles County.
abstract: BACKGROUND
  There is evidence to suggest that antiretroviral therapy (ART) and testing for human immunodeficiency virus (HIV) reduce the probability of transmission of HIV. This has led health officials across the United States to take steps toward a test-and-treat policy. However, the extent of the benefits generated by test-and-treat is debatable, and there are concerns, such as increased multidrug resistance (MDR), that remain unaddressed.
  
  
  METHODS
  We developed a deterministic epidemiologic model to simulate the HIV/AIDS epidemic for men who have sex with men (MSM) in Los Angeles County (LAC). We calibrated the model to match the HIV surveillance data from LAC across a 10-year period, starting in 2000. We then modified our model to simulate the test-and-treat policy and compared epidemiologic outcomes under the test-and-treat scenario to the status quo scenario over the years 2012-2023. Outcome measures included new infections, deaths, new AIDS cases, and MDR.
  
  
  RESULTS
  Relative to the status quo, the test-and-treat model resulted in a 34% reduction in new infections, 19% reduction in deaths, and 39% reduction in new AIDS cases by 2023. However, these results are counterbalanced by a near doubling of the prevalence of MDR (9.06% compared to 4.79%) in 2023. We also found that the effects of increasing testing and treatment were not complementary.
  
  
  CONCLUSIONS
  Although test-and-treat generates substantial benefits, it will not eliminate the epidemic for MSM in LAC. Moreover, these benefits are counterbalanced by large increases in MDR.
authors.affiliation: Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles, CA, USA.
authors.forename: Neeraj
authors.initials: N
authors.lastname: Sood
authors.forename: Zachary
authors.initials: Z
authors.lastname: Wagner
authors.forename: Amber
authors.initials: A
authors.lastname: Jaycocks
authors.forename: Emmanuel
authors.initials: E
authors.lastname: Drabo
authors.forename: Raffaele
authors.initials: R
authors.lastname: Vardavas
mesh_terms: D044966:Anti-Retroviral Agents / Q000627:therapeutic use; D024882:Drug Resistance, Viral; D015658:HIV Infections / Q000175:diagnosis / Q000188:drug therapy / Q000453:epidemiology* / Q000635:transmission; D018451:Homosexuality, Male / Q000706:statistics & numerical data*; D006801:Humans; D015141:Los Angeles / Q000453:epidemiology; D008297:Male; D008403:Mass Screening; D015233:Models, Statistical; D062486:Public Health Surveillance / Q000379:methods; D016896:Treatment Outcome
chemical_list: D044966:Anti-Retroviral Agents
keywords: HIV/AIDS; antiretroviral therapy; drug resistance; mathematical model; test and treat