Data Methods

Learn more about the data that informs AHEAD.

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

Estimates of new HIV infections (incidence) annually are based on data from the National HIV Surveillance System (NHSS) and includes data for persons aged ≥ 13 years. Incidence estimates are useful in many ways, including but not limited to: 

  1. Policy and Programmatic Planning Efforts 

  1. Making Decisions around Funding Allocations 

  1. 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.  

Formula for the Incidence indicator


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 infection 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). 

The CD4 Model allows for subject-specific estimates of expected time to cross a specific CD4 threshold. The model also is used to estimate the subject-specific probability of having a CD4 count above a pre-specified threshold at specific timepoints. 

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 adults and adolescents. 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. Estimates should be interpreted with caution for EHE areas (including Phase 1 EHE states, EHE jurisdictions, or states that contain EHE jurisdictions) that do not have laws requiring complete reporting of laboratory data or have incomplete reporting. Currently there are two EHE states (Idaho & New Jersey) that do not have laws requiring complete reporting of laboratory data. Additionally though all jurisdictions report some laboratory data to CDC, there are 5 EHE states (Kansas, Kentucky, Pennsylvania, Puerto Rico, and Vermont) with incomplete reporting based on evaluation of their surveillance data as of 2021.  

HIV incidence among adults and adolescents were obtained via the following: 

  1. Among all 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. 

  1. The distribution of delay (from HIV infection to diagnosis) was used to estimate the annual number of HIV incidences, (new infections in a given year) which includes persons with diagnosed infections and persons with undiagnosed infection. 

To reflect model uncertainty, all estimates were rounded to the nearest 100 for estimates of more than 1,000 and to the nearest 10 for estimates of less than 1,000. The relative standard error (RSE) is a measure that shows how large the standard error is, relative to the size of the estimated value. It is calculated by dividing the standard error of an estimated value by the estimated value itself, and then multiplied by 100 and expressed as a percent. Smaller RSEs indicate more reliable results, and larger RSEs indicative of less reliable results. Estimates with a relative standard error (RSE) of ≥30% do not meet the standard of reliability and are represented in the following way: 

  • Estimates with an RSE of 30% - 50% are marked with an asterisk (*), indicating that they should be used with caution. 

  • Estimates with an RSE>50% are not shown, and are replaced with the phrase “Data N/A due to high relative standard error.” 

More information can be found at: HIV Surveillance Report Supplemental Report Volume 26, Number 1. 

For more details, please refer to the following source:

  1. Centers for Disease Control and Prevention. Terms, Definitions, and Calculations Used in CDC HIV Surveillance Publications. Available at: Terms, Definitions, and Calculations | Surveillance Overview | Statistics Center | HIV/AIDS | CDC (Accessed: May 14, 2021).