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About the Six EHE Indicators

There are six EHE indicators: HIV incidence, knowledge of HIV status, diagnoses, linkage to HIV medical care, HIV viral suppression, and PrEP coverage.

Each indicator was chosen with specific public health goals in mind and in line with the four key strategies of the initiative: diagnose, prevent, treat and respond. Incidence measures our overarching goal of reducing new infections by 90% by 2030. Diagnoses, and Knowledge of Status are all key to identifying which individuals need to be linked to care, and represent important steps on the HIV Care Continuum. Data have shown that upon diagnosis, immediate linkage to care and treatment results in improved HIV outcomes, so it is important to track how these indicators change over time. Viral Suppression and PrEP use will have the greatest impact on reducing new transmissions if they are scaled up.

Learn more about the 6 EHE indicators by viewing data either by demographic or geographic location.

What is HIV Incidence?

Uses of these data: Incidence estimates are useful for planning and for the allocation of funds, as well as evaluating the impact of prevention programs.

HIV incidence measures the estimated number of people, newly infected with HIV, whether diagnosed or undiagnosed, over a specific period, in this case one calendar year. It is important to note that there is a difference between HIV “incidence” and “ diagnoses” of HIV infection. HIV incidence refers to persons newly infected with HIV, whereas individuals receiving a diagnosis of HIV during a calendar year may have been infected years before actually receiving a diagnosis. For instance, a person may have been HIV+ for a period (prior to getting tested and receiving a diagnosis) but may have been unaware that they were HIV+ until they tested and received a diagnosis much later. So, the year they receive a diagnosis may not be the same as the year they actually became HIV+.

Estimates derived by using HIV surveillance data and CD4 data for persons aged ≥13 years at diagnosis. Incidence estimates are useful in many ways, including but not limited to:

  1. Policy and Programmatic Planning Efforts
  2. Making Decisions around Funding Allocations
  3. Evaluating the Impact of Prevention Programs

For example, these estimates can be used to assess changes in characteristics of persons most at risk for acquiring HIV infection.

What is CD4 Depletion Model?

CD4+ T-lymphocyte (CD4) cells are a type of white blood cell that help to fight infection. HIV harms CD4 cells, and without treatment, HIV reduces the total number of CD4 cells in the body over time. Because these cells are key to fighting infection and keeping us healthy, CD4 counts are used to determine the health of an HIV+ person’s immune system, and by extension, stage of the disease. The three stages of HIV disease are (1) acute HIV infection, (2) chronic HIV infection, and (3) acquired immunodeficiency syndrome (AIDS)

HIV incidence was calculated using the result of the first CD4+ T-lymphocyte (CD4) test after initial HIV diagnosis and the CD4 Depletion Model (CD4 model). The first CD4 test results after HIV diagnosis are routinely collected by all jurisdictions as part of the National HIV Surveillance System (NHSS).

Before initial treatment, the CD4 cell count can be used to estimate the time from infection to the date of the CD4 test. The CD4 model was applied to NHSS data to estimate (1) the distribution of delay from infection to diagnosis and then to (2) produce incidence and prevalence estimates of HIV among persons aged 13 years and older. Of note, reporting of the first CD4 test result after diagnosis of HIV infection is a required data element on the HIV case report form; however, completeness of reporting varies among states and local jurisdictions.

HIV incidence among persons aged 13 years and older were obtained via the following:

  1. Among HIV diagnoses reported by states to the CDC, the date of HIV infection was estimated for each person with a CD4 test by using the CD4 depletion model.
  2. The distribution of delay (from HIV infection to diagnosis) was used to estimate the annual number of new infections in a given year, which includes persons with diagnosed infections and persons with undiagnosed infection.

About HIV Incidence Data

Estimates are rounded to the nearest 100 for estimates > 1,000 and to the nearest 10 for estimates ≤ 1,000 to reflect model uncertainty. Relative standard errors (RSEs) were calculated for estimates of incidence, prevalence, and percentages of persons living with diagnosed HIV infection and were used to determine the reliability of estimates. Estimates with a RSE of < 30% meet the standard of reliability and is displayed. Estimates with a RSE of 30%–50% meet a lower standard of reliability and is displayed but should be interpreted with caution. Estimates with a RSE of > 50% are statistically unreliable and not displayed.

Estimates should be interpreted with caution for jurisdictions that do not have laws requiring complete reporting of laboratory data or have incomplete reporting. Area without laws: Idaho. Areas with incomplete reporting: New Jersey and Puerto Rico. Areas with a lapse in reporting in 2022: Mississippi and West Virginia.

Estimates for years 2020, 2021, and 2022 should be interpreted with caution due to adjustments made to the monthly distribution of reported diagnoses during those years to account for the impact of COVID-19 on HIV testing and diagnosis in the United States.

Data Sources