title: Quantifying HIV-1 transmission due to contaminated injections.
abstract: Assessments of the importance of different routes of HIV-1 (HIV) transmission are vital for prioritization of control efforts. Lack of consistent direct data and large uncertainty in the risk of HIV transmission from HIV-contaminated injections has made quantifying the proportion of transmission caused by contaminated injections in sub-Saharan Africa difficult and unavoidably subjective. Depending on the risk assumed, estimates have ranged from 2.5% to 30% or more. We present a method based on an age-structured transmission model that allows the relative contribution of HIV-contaminated injections, and other routes of HIV transmission, to be robustly estimated, both fully quantifying and substantially reducing the associated uncertainty. To do this, we adopt a Bayesian perspective, and show how prior beliefs regarding the safety of injections and the proportion of HIV incidence due to contaminated injections should, in many cases, be substantially modified in light of age-stratified incidence and injection data, resulting in improved (posterior) estimates. Applying the method to data from rural southwest Uganda, we show that the highest estimates of the proportion of incidence due to injections are reduced from 15.5% (95% credible interval) (0.7%, 44.9%) to 5.2% (0.5%, 17.0%) if random mixing is assumed, and from 14.6% (0.7%, 42.5%) to 11.8% (1.2%, 32.5%) under assortative mixing. Lower, and more widely accepted, estimates remain largely unchanged, between 1% and 3% (0.1-6.3%). Although important uncertainty remains, our analysis shows that in rural Uganda, contaminated injections are unlikely to account for a large proportion of HIV incidence. This result is likely to be generalizable to many other populations in sub-Saharan Africa.
authors.affiliation: Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London WC1E 7HT, United Kingdom. richard.white@lshtm.ac.uk
authors.forename: Richard G
authors.initials: RG
authors.lastname: White
authors.forename: S Cooper
authors.initials: SC
authors.lastname: Ben
authors.forename: Anusha
authors.initials: A
authors.lastname: Kedhar
authors.forename: Kate K
authors.initials: KK
authors.lastname: Orroth
authors.forename: Sam
authors.initials: S
authors.lastname: Biraro
authors.forename: Rebecca F
authors.initials: RF
authors.lastname: Baggaley
authors.forename: Jimmy
authors.initials: J
authors.lastname: Whitworth
authors.forename: Eline L
authors.initials: EL
authors.lastname: Korenromp
authors.forename: Azra
authors.initials: A
authors.lastname: Ghani
authors.forename: Marie-Claude
authors.initials: MC
authors.lastname: Boily
authors.forename: Richard J
authors.initials: RJ
authors.lastname: Hayes
mesh_terms: D000367:Age Factors; D001499:Bayes Theorem; D003428:Cross Infection / Q000453:epidemiology* / Q000821:virology; D015658:HIV Infections / Q000453:epidemiology* / Q000517:prevention & control / Q000635:transmission*; D015497:HIV-1; D006801:Humans; D015994:Incidence; D007275:Injections, Intravenous / Q000009:adverse effects*; D008962:Models, Theoretical; D015995:Prevalence; D014454:Uganda / Q000453:epidemiology