Browsing by Author "White, Richard G."

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Costs and effects of different ART scale-up options in Uganda
    (MRC/UVRI Research Unit on AIDS., 2016) McCreesh, Nicky; Andrianakis, Loannis; Nsubuga, Rebecca N.; Hayes, Richard; White, Richard G.; London School of Hygiene and Tropical Medicine, MRC/UVRI Research Unit on AIDS.
  • Thumbnail Image
    Item
    The proportion of HIV incidence due to unsafe injections, unsafe blood transfusions and mother to child transmission in rural Masaka, Uganda
    (Proc Natl Acad Sci USA, 2007) White, Richard G.; Kedhar, Anusha; Orroth, Kate K.; Biraro, Sam; Baggaley, Rebecca; Whitworth, Jimmy; Korenromp, Eline L.; Boily, Marie-Claude; Hayes, Richard J.
    To estimate the proportion of all-age HIV incidence attributable to unsafe injections, unsafe blood transfusions and mother-to-child transmission (MTCT) in rural Masaka, Uganda, during the early 1990s.Observed HIV incidence and prevalence, and injection and transfusion rates were calculated using data from a general population cohort study in Masaka (1989-2000). Injection and blood transfusion safety was estimated from observational surveys within Uganda and East Africa. HIV transmission probabilities were estimated from scientific literature review. Model: A model was used to estimate the incidence via unsafe injections (assuming random or age-dependent mixing of injection equipment) and unsafe transfusions. An age-specific model of fertility was used to estimate the incidence via MTCT.Unsafe injections accounted for 5.1% [95% uncertainty bounds (UB) 0.0-10.3] or 12.4% [95%UB 0.0-27.0] of all-age HIV incidence in the random and age-dependent mixing scenarios respectively. Unsafe blood transfusions accounted for 0.4% [95%UB 0.2-0.6], and MTCT accounted for 23.4% [95%UB 15.3-31.5]. 64-71% of all-age HIV incidence was left unexplained by these three routes of transmission. Among 13+ year olds, unsafe injections accounted for 1.4% [95%UB 0.0-2.8] or 12.1% [95%UB 0.0- 26.5] of HIV incidence in the random and age-dependent mixing scenarios respectively. Unsafe blood transfusions accounted for 0.3% [95%UB 0.1-0.4], leaving 87.6-98.3% of HIV incidence left unexplained by these three routes of transmission.This study does not support the hypothesis that unsafe injections or blood transfusions played a major role in HIV transmission in this population during the study period. The safety of both injections and transfusions should be improved to reduce HIV transmission via these routes still further, but particular efforts should be made to reduce the larger proportion of HIV transmission due to MTCT, and among 13+ year olds, the unexplained incidence, presumably primarily due to sexual transmission.
  • Thumbnail Image
    Item
    Quantifying HIV-1 Transmission due to Contaminated Injections
    (Proceedings of the National Academy of Sciences, 2007) White, Richard G.; Cooper, Ben S.; Kedhar, Anusha; Biraro, Sam
    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.