DC has a goal: Fewer than 130 new HIV infections a year by 2030.
DC’s first plan to end the HIV epidemic, known as the 90/90/90/50 Plan, had four goals: 90% of people with HIV knowing their status; 90% of people diagnosed with HIV being on treatment; 90% of people with HIV on treatment achieving viral suppression; and a 50% reduction in new diagnoses by the year 2020.
For this updated plan, DC Health has made the 90/90/90 a floor rather than the ceiling. By 2030, we are aiming for a new minimum of 95%/95%/95% of people knowing their HIV status, people diagnosed being on treatment, and people on treatment reaching viral suppression.
Instead of a percentage decrease in new diagnoses, we want to give DC a solid number — an ambitious but achievable number that represents ending the epidemic. A number that means we have maximized all the tools we have to end the epidemic. It is not zero, because we do not yet have a cure or vaccine. However, the number means we are making new HIV diagnoses as rare as possible in DC.
That number is 130 new diagnoses in 2030.
To precisely assess progress toward our goal of fewer than 130 new HIV diagnoses a year by 2030, DC Health developed a predictive model for estimating the planned impact of scale-up of plan strategies. DC Health needed to determine starting points, or baseline values, for several parameters: people living with HIV; of those living with HIV, the number who are diagnosed; of those diagnosed, the number who are “in treatment” — defined here to mean on ART; and of those in treatment, the number who have reached viral suppression. Those parameters are known as the HIV care continuum.
DC Health attributes most of the goal of 130 diagnoses by the year 2030 to increasing the numbers of the care continuum. However, to reach the goal will also require expansion of PrEP. Given gaps in certain data, establishing baseline model inputs required the estimation of several parameters.
To measure the progress of the DC Ends HIV plan and to achieve its goal by 2030, DC Health calculated its baseline for several parameters. DC Health used a data-driven model<sup>1</sup> that uses local surveillance data to project care continuum targets for each year. The model calibrated predicted incidence estimates from HAHSTA’s Annual Epidemiology & Surveillance Report 2 for the years 2015–2019 by scaling predicted values using the following formula:
where n is the number of years and x is the year in question. To bring the latest surveillance estimates to the year 2020, the model assumed that care continuum parameters of percent diagnosed, on treatment, and virally suppressed continued to improve at a linear rate since last observed; after 2020, these parameters formed the basis of intervention scenarios. The denominator remained n-1 for the years after 2020.
DC Health changed the model and assumed the mortality rate continued at the 0.97% annual decline observed during 2015-2019. After 2019, the model projected the end epidemic goal by achieving 95% of people diagnosed, 95% on treatment, and 95% virally suppressed by 2030 with 20% of new diagnoses averted by PrEP. The 20% of diagnoses averted by PrEP scenario approximately corresponds to the AIDSVu estimate of PrEP use for DC3.
As of November 2020, the number of people diagnosed and residing in DC was 12,408 (89%), and the number retained in care, which is based on laboratory visits reported to DC Health, was 9,745 (78.5%) in 2019. The number of people virally suppressed retained in HIV care at the end of 2019 was 8,495 (87.2%). The model estimated 12,202 (95%) diagnosed, 11,592 (95%) retained, and 11,012 (95%) virally suppressed (Figures 1 and 2) in 2030. DC Health used the model to estimate the number of new diagnoses of 130 by 2030. The number of death estimates includes all deaths not restricted to HIV-related only.
As additional data and information are available, the model will be updated to reflect the evolving knowledge of the characteristics of the HIV epidemic within the District and the efficacy of intervention strategies within the local population.
1. Bradley H, Rosenberg ES, Holtgrave DR. Data-Driven Goals for Curbing the U.S. HIV Epidemic by 2030. AIDS and Behavior. 2019;23(0123456789):557-563. doi:10.1007/s10461-019-02442-7.
2. HAHSTA. Annual Epidemiology & Surveillance Report; 2019.
3. AIDSVu. All States PrEP Data Sets. Retrieved from https://aidsvu.org/resources/#/.