Number of new HIV diagnoses of HIV infection per 100,000 population by year of diagnosis, sex, age, race and ethnicity, from 2008-2015

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Headline data

Number of new HIV diagnoses of HIV infection per 100,000 US population by year of diagnosis
Year Value
2008 18.6
2009 17.5
2010 16.7
2011 15.8
2012 15.4
2013 14.8
2014 14.9
2015 14.8
2016 14.5
2017 14.0
2018 13.5
2019 13.2
2020 10.8
2021 12.7
2022 13.3

Source: Division of HIV/AIDS Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention (CDC), U.S. Department of Health and Human Services, Atlanta, Georgia/ National HIV/AIDS Surveillance System/HIV Surveillance Report.

Geographical Area:

Unit of Measurement:

Footnote:

This table provides metadata for the actual indicator available from United States statistics closest to the corresponding global SDG indicator. Please note that even when the global SDG indicator is fully available from American statistics, this table should be consulted for information on national methodology and other American-specific metadata information.

Indicator

Number of new HIV diagnoses of HIV infection per 100,000 population by year of diagnosis, sex, ages 13-54 years and race_ethnicity, 2008-2022
Organisation

Division of HIV/AIDS Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention (CDC), U.S. Department of Health and Human Services, Atlanta, Georgia/ National HIV/AIDS Surveillance System/HIV Surveillance Reports. Accessed via https://gis.cdc.gov/grasp/nchhstpatlas/tables.html.
Unit of measure

Number of new HIV diagnoses per 100,000 population
Data sources

Division of HIV/AIDS Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention (CDC), U.S. Department of Health and Human Services, Atlanta, Georgia/ National HIV/AIDS Surveillance System/HIV Surveillance. Accessed via https://gis.cdc.gov/grasp/nchhstpatlas/tables.html
Data providers

https://gis.cdc.gov/grasp/nchhstpatlas/tables.html
Comment and limitations

Note. Data include persons with a diagnosis of HIV infection regardless of stage of disease at diagnosis. Data are presented for diagnoses of HIV infection reported to CDC through June 2016 and presented without adjustment for reporting delay.
Method of computation

(Number of people (ages 13-54 years) who are newly diagnosed with HIV infection (regardless of stage of disease at diagnosis) in a specific year and are reported to public health authority in state of residence / Total number of perople in the population from which the cases were reported)* 100,000 population.
Actual indicator available - description

Number of new HIV diagnoses of HIV infection per 100,000 population by year of diagnosis, sex, ages 13-54 years, and race_ethnicity, 2008-2022
Date of national source publication

November 2024
Periodicity

Annual
Scheduled update by national source

November 2024
Date metadata updated

2024-10-15
Disaggregation geography

NA
Disaggregation categories

sex (male, female); age-group (all ages, 13+ years, 13-24, 24-34, 35-44, 45-54 years; race/ethnicity (Black, Non-Hispanic, White, Non-Hispanic, Hispanic)
International and national references

Centers for Disease Control and Prevention. HIV Surveillance. Accessed via https://gis.cdc.gov/grasp/nchhstpatlas/tables.html
Time period

2008 - 2022

This table provides information on metadata for SDG indicators as defined by the UN Statistical Commission. Complete global metadata is provided by the UN Statistics Division.

Indicator

Indicator 3.3.1: Number of new HIV infections per 1,000 uninfected population, by sex, age and key populations

Target

Target 3.3: By 2030, end the epidemics of AIDS, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, water-borne diseases and other communicable diseases
Organisation

The Joint United Nations Programme on HIV/AIDS (UNAIDS)
Definition and concepts

Definition:

The number of new HIV infections per 1,000 uninfected population, by sex, age and key populations as defined as the number of new HIV infections per 1,000 persons among the uninfected population.
Unit of measure

Number of newly infected people per 1,000 uninfected population.
Data sources

Spectrum modelling is used for the data presented here which incorporates programme data, surveillance data, survey data and region-specific assumptions about the HIV epidemic. Alternative methods of measures include household or key population surveys with HIV incidence-testing, or routine surveillance among key populations.

The model development is guided by the UNAIDS Reference Group on Estimates, Modelling and Projections provides technical guidance on the development of the HIV component of the Spectrum software (www.epidem.org). The Spectrum software is developed by Avenir Health (www.avenirhealth.org)—which includes a module, the Estimates and Projections Package, which is developed by the East-West Center (www.eastwestcenter.org).
Data providers

The estimates are produced by a team of national experts consisting of ministry of health, national AIDS advisory groups and development partners. The results are signed off on by senior managers at the ministries of health.
Comment and limitations

The methods and limitations for estimating HIV incidence vary based on the data and surveillance systems available in countries.

  • Countries with high HIV prevalence in the general population have relatively strong surveillance systems with household surveys contributing to the information required to estimate incidence. In epidemics concentrated in key populations, the surveillance systems for key hard-to-reach populations are often not comparable over time due to changing survey and sampling methods. The estimated size of key populations, a critical input to the Spectrum model for concentrated epidemics, can also lead to important under or over estimation of HIV incidence in concentrated epidemics.
  • In many countries trends in recent new infections rely on prevalence data from routine antenatal clinic testing. If those data are biased because women with known positive HIV status are not captured when calculating prevalence, or women found to be negative at initial antenatal care visit are retested later in the pregnancy, the derived incidence trends might be biased. While some limitations of the models are reflected in the uncertainty bounds the measurement biases and the uncertainty caused by these biases are not easily quantified and are thus not included.
  • Although HIV prevalence and incidence among children appears to be reasonably robust in generalized epidemics, estimating the pediatric HIV epidemic in concentrated epidemics remains a challenge because no robust measures of fertility exist among key populations living with HIV.
  • Currently UNAIDS only supports the HIV estimates development in countries with populations greater than 250,000. This is primarily due to support capacity at UNAIDS.
Method of computation

Longitudinal data on individuals newly infected with HIV would be the most accurate source of data to measure HIV incidence, however these data are rarely available for representative populations. Special diagnostic tests in surveys or from health facilities can also be used to obtain data on HIV incidence but these require very large samples to accurately estimate HIV incidence and the latter are also rarely representative. HIV incidence is thus modelled using the Spectrum software. The software incorporates data on HIV prevalence, the number of people on treatment, demographics and other relevant indicators to estimate historical HIV incidence, among other indicators. A full description of the model is available in peer-reviewed articles and in the most recent UNAIDS Global AIDS Update Reports. https://onlinelibrary.wiley.com/toc/17582652/2021/24/S5

The path that ends AIDS: UNAIDS Global AIDS Update 2023 | UNAIDS

Metadata update

2024-03-28
International organisations(s) responsible for global monitoring

The Joint United Nations Programme on HIV/AIDS (UNAIDS)
Related indicators

Achieving this target will positively impact multiple SDG goals and by reaching other goals will improve countries ability to reduce new HIV infections. The goals that are linked to HIV include goals 1 through 8, 10, 11, 16 and 17.

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