The 2016 HIV diagnosis and care cascade in New South Wales, Australia: meeting the UNAIDS 90‐90‐90 targets

Abstract

IntroductionThe HIV Strategy in New South Wales (NSW) Australia aims to virtually eliminate HIV transmission by 2020. We estimated the 2016 HIV diagnosis and care cascade for the state of NSW, with a focus on introducing population‐based data to improve data quality and assess progress towards the UNAIDS 90‐90‐90 targets.MethodsTo estimate the number of people living with diagnosed HIV (PLDHIV) we used NSW data from the Australian National HIV Registry, enhanced by surveillance among people recently diagnosed with HIV to improve migration estimates. The number of undiagnosed PLHIV was estimated using back‐projection modelling by CD4 count at diagnosis. De‐duplicated prescription claims data were obtained from the Australian Pharmaceutical Benefits Scheme (PBS), and were combined with an estimate for those ineligible, to determine the number of PLDHIV on antiretroviral therapy (ART). Data from a clinic network with 87% coverage of PLDHIV in NSW enabled the estimation of the number on ART who had HIV suppression.Results and discussionWe estimated that 10,110 PLHIV resided in NSW in 2016 (range 8400 to 11,720), among whom 9230 (91.3%) were diagnosed, and 8490 (92.0% of those diagnosed) were receiving ART. Among PLDHIV receiving ART, 8020 (94.5%) had suppressed viral load (<200 HIV‐1 RNA copies/mL). Overall, 79.3% of all PLHIV had HIV virological suppression.Conclusion NSW has met each of the UNAIDS 90‐90‐90 targets. The enhanced surveillance methods and data collection systems improved data quality. Measuring and meeting the 90‐90‐90 targets is feasible and could be achieved in comparable parts of the world.

Gaps in the HIV diagnosis and care cascade for migrants in Australia, 2013–2017: A cross-sectional study

“…To estimate the number of people living with diagnosed HIV (PLDHIV) we primarily used New South Wales (NSW) and Victorian (VIC) data from the Australian National HIV Registry. Additional data from the Australian Bureau of Statistics (ABS) and follow-up studies of people recently diagnosed with HIV were used to inform population movement estimates [14]. The number of undiagnosed PLHIV was estimated using back-projection modelling from CD4+ count at diagnosis [15].…”

“…ACCESS data have been used previously to report on HIV treatment outcomes mostly in NSW and VIC [14,19]. The ACCESS database has a high coverage of treatment and viral load data for PLHIV in NSW and VIC.…”

“…A total of 7,998 and 9,727 PLHIV were captured in the ACCESS network in 2016 and 2017, respectively, across seven general practice clinics, two hospitals, and 33 sexual health clinics and linked. ACCESS captures 53%-57% of the estimated PLHIV (diagnosed and nondiagnosed) and 59%-63% of the estimated diagnosed PLHIV in NSW and VIC [14,20].…”

Applying National Estimates of Adults With Indications for Pre-Exposure Prophylaxis to Populations of Men Who Have Sex With Men and People Who Inject Drugs in Colorado: Modeling Study (Preprint)

“…Additional methodologies using surveys and more precise population-level statistics as have been employed in other jurisdictions as alternate approaches to estimating PrEP demand or PrEP targets. [16][17][18] However, for states with relatively smaller epidemics and for whom resources for population-level HIV prevention analyses are more limited, we offer this approach as a feasible option that provides immediate, actionable estimates that can be quickly revised as new estimates for key populations become available.…”

Optima attempts to objectively and pragmatically assist countries meet their targets most efficiently and effectively

“…Indeed, we have shown long-term support of these laudable targets: not only was Optima HIV used in the initial modelling efforts around the release of the first UNAIDS launch of the 90-90-90 strategy [4], but we also have since continued to promote this strategy in other academic publications relevant to our local setting and internationally (e.g. [5][6][7][8][9]). Whenever country teams work with the Optima HIV model, our recommendation has been to explicitly model 90-90-90 targets and many countries chose to do so in the original studies using the model.…”