Exploring the population-level impact of antiretroviral treatment: the influence of baseline intervention context.
OBJECTIVE
To compare the potential population-level impact of expanding antiretroviral treatment (ART) in HIV epidemics concentrated among female sex workers (FSWs) and clients, with and without existing condom-based FSW interventions.


METHODS
Mathematical model of heterosexual HIV transmission in south India.


METHODS
We simulated HIV epidemics in three districts to assess the 10-year impact of existing ART programs (ART eligibility at CD4 cell count ≤350) beyond that achieved with high condom use, and the incremental benefit of expanding ART by either increasing ART eligibility, improving access to care, or prioritizing ART expansion to FSWs/clients. Impact was estimated in the total population (including FSWs and clients).


RESULTS
In the presence of existing condom-based interventions, existing ART programs (medium-to-good coverage) were predicted to avert 11-28% of remaining HIV infections between 2014 and 2024. Increasing eligibility to all risk groups prevented an incremental 1-15% over existing ART programs, compared with 29-53% when maximizing access to all risk groups. If there was no condom-based intervention, and only poor ART coverage, then expanding ART prevented a larger absolute number but a smaller relative fraction of HIV infections for every additional person-year of ART. Across districts and baseline interventions, for every additional person-year of treatment, prioritizing access to FSWs was most efficient (and resource saving), followed by prioritizing access to FSWs and clients.


CONCLUSIONS
The relative and absolute benefit of ART expansion depends on baseline condom use, ART coverage, and epidemic size. In south India, maximizing FSWs' access to care, followed by maximizing clients' access are the most efficient ways to expand ART for HIV prevention, across baseline intervention context.
aDepartment of Infectious Disease Epidemiology, Imperial College London bSocial and Mathematical Epidemiology, London School of Hygiene and Tropical Medicine, London, UK cGovernment of Karnataka, Bangalore, India dSchool of Population Health, University of Queensland, Brisbane, Australia eUNAIDS India, New Delhi fKarnataka Health Promotion Trust, Bangalore, India gDepartment of Community Health Sciences, Centre for Global Public Health, University of Manitoba, Winnipeg hCentre de recherche du CHU de Québec, Département de médecine sociale et préventive, Université Laval, Québec City, Québec, Canada. iDivision of Infectious Diseases, St. Michael's Hospital, University of Toronto, Toronto, Canada jSt. John's Medical College Research Institute, Bangalore, India.
Sharmistha
S
Mishra
Elisa
E
Mountain
Michael
M
Pickles
Peter
P
Vickerman
Suresh
S
Shastri
Charles
C
Gilks
Nandini K
NK
Dhingra
Reynold
R
Washington
Marissa L
ML
Becker
James F
JF
Blanchard
Michel
M
Alary
Marie-Claude
MC
Boily
D000293:Adolescent; D000328:Adult; D019380:Anti-HIV Agents / Q000627:therapeutic use*; D017280:Condoms / Q000706:statistics & numerical data; D018562:Disease Transmission, Infectious / Q000517:prevention & control*; D004363:Drug Utilization; D005260:Female; D015658:HIV Infections / Q000188:drug therapy* / Q000453:epidemiology / Q000517:prevention & control* / Q000635:transmission; D006801:Humans; D007194:India / Q000453:epidemiology; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D055815:Young Adult
D019380:Anti-HIV Agents