About AIDSVu
AIDSVu empowers people to end the HIV epidemic in their communities and is a leading free resource for visualizing the HIV epidemic in the United States. The platform visualizes data, presents insights, and catalyzes research to drive public health action across the U.S. and seeks to engage and provide users with a highly interactive and easy way to access HIV surveillance data at enhanced geographic levels. AIDSVu currently maps HIV prevalence and new diagnoses data at the state, county, and city level, including ZIP Code level for all cities and community area/ward and census tract-level for select cities. At the state and county level, we map data on PrEP use and PrEP-to-Need Ratio (PNR), and at the state and ZIP Code level, we map late HIV diagnoses, linkage to HIV care, receipt of HIV care, and HIV viral suppression data. HIV mortality data and HIV testing data are also mapped at the state level. Maps are available to be viewed by demographic breakdowns and can be overlaid with PrEP providers and HIV testing and treatment service locations to enable examination of these resources relative to HIV prevalence and diagnosis data at different geographic levels.
HIV Awareness Day infographics, as well as city, county, state, regional, and national profile pages, provide supplementary visualizations and information, and complement the data presented in the maps and existing surveillance reports. Other public data, such as Census data on social determinants of health (i.e. poverty, education, health insurance, etc.) can be viewed alongside interactive maps and provide context. Downloadable resources include map images, infographic panels that can be used for presentations or other materials, and downloadable datasets. Below you can learn more about the data methods and sources for the state and county data on AIDSVu. Click here to learn more about data methods and sources for ZIP Code, census tract, and community area/ward data.
Definitions
HIV Prevalence Data: The data reflect people living with HIV infection or persons living with HIV infection ever classified as stage 3 (AIDS) at the end of 2021. The map title “People Living with HIV” has been used to encompass people living with HIV and people living with HIV infection ever classified as AIDS. Cases are based on most recent known address.
New HIV Diagnoses Data: The data reflect people newly diagnosed with HIV infection, defined as a diagnosis of HIV infection regardless of the stage of disease (stage 0, 1, 2, 3 [AIDS], or unknown) and refers to all people with a diagnosis of HIV infection during a given one-year time period (i.e. 2021). Multiple single-year data are available from 2008-2021. The map title “People Newly Diagnosed with HIV” has been used to encompass people newly diagnosed with HIV and/or AIDS.
Mortality Data: The data reflect deaths of people diagnosed with HIV infection or with diagnosed HIV infection classified as stage 3 (AIDS) regardless of the cause of death at the end of 2021. The map title “Deaths of People Diagnosed with HIV” has been used to encompass deaths of persons diagnosed with HIV and/or AIDS.
PrEP Data: The data reflect the number of people prescribed TDF/FTC, TAF/FTC, or cabotegravir for PrEP in a calendar year from 2012 to 2022. These individuals are referred to as “PrEP users”. AIDSVu’s PrEP data reflect a weighted estimate of the number of PrEP users in each state and county in the U.S. by year. Data are described as “Users” for the number of PrEP users and as “Rates” for the number of PrEP users per 100,000 population.
PrEP-to-Need (PNR) Data: The data reflect a ratio of the number of PrEP users from 2012-2022 over the number of newly diagnosed with HIV in each respective year. For 2022 the PNR ratios reflect the number of PrEP users over the number of people newly diagnosed with HIV in 2021 (since 2021 new diagnoses are the latest year available on AIDSVu). The ratio is used to describe the distribution of prescriptions relative to the epidemic need. On the maps, lighter shading indicates fewer PrEP users relative to the number of new diagnoses.
Testing Data: The data reflect the weighted estimates from the Behavioral Risk Factor Surveillance System (BRFSS) questionnaire for the percent of people who reported ever being tested for HIV as of 2021.
Late HIV Diagnoses Data: The data reflect persons aged 13 years and older diagnosed with HIV infection who were diagnosed with stage 3 HIV (AIDS) within 3 months of initial HIV diagnoses in 2021. Cases are based on residence at the time of diagnoses. Data are displayed as cases and percentages. For the percent calculation, the numerator is the number of individuals aged 13 years and older who were diagnosed with HIV and were diagnosed with stage 3 HIV (AIDS) within 3 months of the initial HIV diagnoses. The denominator is the number of individuals aged 13 years and older who were newly diagnosed with HIV in 2021.
Linkage to Care Data: The data reflect persons aged 13 years and older linked to HIV care (defined as a CD4 or HIV viral load count) within 1 month of their diagnosis, including same day linkages, in 2021. Cases are based on residence at time of diagnoses. Data are displayed as cases and percentages. For the percent calculation, the numerator is the number of individuals aged 13 years and older diagnosed with HIV who had a CD4 or HIV viral load count within 1 month of their initial HIV diagnoses in 2021. The denominator is the number of individuals aged 13 years and older who were newly diagnosed with HIV in a given year.
Receipt of HIV Care Data: The data reflect persons aged 13 years and older living with HIV who were engaged in care in a given year (i.e. 2021) (defined as at least one CD4 or HIV viral load count in that given year). Cases are based on most recent known address. Data are displayed as cases and percentages. For the percent calculation, the numerator is the number of individuals aged 13 years and older living with diagnosed HIV in a given year (diagnosed as of year-end the previous year (i.e. 2020) and alive at the end of the year (i.e. 2021)), who had at least one CD4 count or HIV viral load test in that year. The denominator is the number of individuals aged 13 years and older living with diagnosed HIV in a given year (excluding those newly diagnosed in that year).
HIV Viral Suppression Data: The data reflect persons aged 13 years and older living with HIV whose most recently reported HIV viral load was less than 200 copies/mL in a given year (i.e. 2021). Cases are based on most recent known address. Data are displayed as cases and percentages. For the percent calculation, the numerator is the number of individuals aged 13 years and older living with HIV in a given year (diagnosed as of year-end the previous year (i.e. 2020) and alive at the end of the year (i.e. 2021)), whose most recently reported HIV viral load count was <200 copies/mL in that year. The denominator is the number of individuals aged 13 years and older living with diagnosed HIV in a given year (excluding those newly diagnosed in that year).
Data Source
The HIV prevalence, new HIV diagnoses, late HIV diagnoses, linkage to HIV care, receipt of HIV care, HIV viral suppression, and mortality data presented on AIDSVu are collected by state and local health departments, and de-duplicated and processed by the U.S. Centers for Disease Control and Prevention (CDC) to meet data quality standards for comparability and reliability. All 50 states, the District of Columbia (DC), and U.S. territories collect comparable confidential, name-based case reports of people living with HIV infection. For linkage to HIV care, receipt of HIV care, and HIV viral suppression, the CDC only provides data for states with complete laboratory data (at least 95% of laboratory results are reported to the surveillance programs and transmitted to the CDC). All diagnoses are based on an established case definition. Medical providers, laboratories, and other organizations providing HIV testing services are required, by law, to report people diagnosed with HIV to the state or local health department. Health departments report case data without names to CDC for monitoring of the national HIV epidemic.
All state- and county-level HIV surveillance data for AIDSVu were obtained from CDC’s national HIV surveillance database housed in the Division of HIV/AIDS Prevention’s HIV Incidence and Case Surveillance Branch in Atlanta, GA. Data were released to AIDSVu by CDC in accordance with HIV-surveillance-specific data re-release agreements between CDC and each state/U.S. territory health department, and in accordance with other CDC data release guidelines. Data on AIDSVu may slightly differ from data obtained directly from state HIV surveillance programs (e.g., on a state health department website or through a data request to a state surveillance program) because states may use analysis criteria that are different from the criteria used by AIDSVu. Further, data on AIDSVu may differ from data obtained directly from the states because AIDSVu’s data source was the CDC’s National HIV Surveillance System, from which duplicate records are removed and statistical adjustments were made to the data to account for missing transmission category. In addition to the national data described in these data methods, AIDSVu also displays ZIP Code data for over 50 jurisdictions (i.e., cities or Metropolitan Statistical Areas or select counties or ZIP Codes), with a few cities displaying census tract- and/or community area/ward-level data as well. More information about where these data come from and their presentation on AIDSVu can be found here.
All HIV prevalence surveillance data displayed on AIDSVu maps and state profile pages are data for people aged 13 and older living with HIV infection as of December 31, 2021. New HIV diagnoses data include people aged 13 and older who received a diagnosis of HIV infection between January 1, 2008 and December 31, 2021, displayed year-by-year. Late diagnoses data are for people aged 13 and older who received a diagnoses of HIV infection in 2021 and an AIDS (stage 3 HIV) diagnoses within three months of initial HIV diagnoses. Linkage to care data are for people aged 13 and older who received a diagnosis of HIV infection in 2021 and had at least one CD4 or viral load test within 1 month of diagnoses. Receipt of care include people aged 13 and older living with HIV infection as of December 31, 20120 and alive at the end of 2021 who had at least one CD4 or viral load test at the end of 2021. Viral suppression includes people aged 13 and older living with HIV infection as of December 31, 2020 and alive at the end of 2021 who were virally suppressed at their most recent viral load test. Data have been statistically adjusted to account for missing transmission category (See CDC methodology for using multiple imputation to assign a transmission category). Most recent known address was used in all analyses except for new HIV diagnoses, late diagnoses, and linkage to care, which are calculated based on residence at diagnosis. Denominators used to calculate rates for all state and county populations were obtained by CDC from the U.S. Census Bureau’s census for each respective year. Population denominators are restricted to people aged 13 and older. There are no county-level maps for the District of Columbia because there are no counties in DC.
HIV surveillance data are displayed as case counts, rates, and/or proportions (represented as % cases). Rates were calculated per 100,000 population to permit data standardization and comparison. For rate ratios by race/ethnicity displayed on state profile pages, White race is the referent group.
PrEP and PNR Data:
The release of the PrEP use data on AIDSVu was made possible through a data-sharing agreement, in which data were obtained from IQVIA with the support of Gilead Sciences, Inc., and compiled by researchers at the Rollins School of Public Health at Emory University.
IQVIA provides Emory a limited dataset of aggregated and anonymized pre-exposure prophylaxis (PrEP) data, along with age, sex, and race/ethnicity classifications. The IQVIA database contains anonymized individual-level prescription records collected electronically from US retail pharmacies, traditional pharmacies, specialty mail-order pharmacies, long-term care (LTC) facilities, and “other” pharmacies (e.g., in-hospital pharmacies, HMO pharmacies). The IQVIA database uses national estimates of prescription fills to estimate PrEP prescriptions for the small percentage of US prescriptions that are not tracked directly by IQVIA. The prescriptions database includes age and sex and was linked to a claims database to obtain diagnoses codes. Race/ethnicity was provided through a consumer database of self-reported information. Race/ethnicity data were available for about a third of PrEP users, and data summarized by race/ethnicity are based on that subset of users.
An algorithm was used to differentiate the HIV treatment and HIV PrEP indications using prescription and diagnoses data for individuals taking FTC/TDF, FTC/TAF after its approval in 2019, or cabotegravir after its approval in 2021. For AIDSVu, the analyses determined total PrEP usage with FTC/TDF, FTC/TAF, and cabotegravir and not individually for each product. IQVIA excluded prescriptions for TDF/FTC, TAF/FTC, and cabotegravir that were made for other known indications, such as, post-exposure prophylaxis (PEP), chronic hepatitis B management, and treatment for HIV and other opportunistic infections. An individual with diagnosis or treatment codes for HIV, chronic Hepatis B or codes for PEP prior to the exposure era of FTC/TDF, FTC/TAF, or cabotegravir would be considered not to be taking PrEP.
IQVIA shared aggregate datasets at the state- and county-level with Emory researchers. To account for underestimations of PrEP use due to misclassified prescriptions, the county-level PrEP users’ data were upweighted by using state-specific percentages of unclassified prescriptions (prescriptions that have an unknown indication). The method was adopted from Sullivan et al.’s published article in Annals of Epidemiology titled “Methods for county-level estimation of pre-exposure prophylaxis coverage and application to the U.S. ending the HIV epidemic jurisdictions.” The unrounded number of weighted PrEP users per county were then summed to obtain state-, regional-, and national-level estimates. Finally, Emory applied data suppression rules and developed the publicly available maps and data sets for AIDSVu. AIDSVu provides downloadable PrEP maps and datasets at the county-and state-level and PrEP datasets at the regional and national-level for researchers and health departments to utilize in their own analyses.
PrEP data are displayed as number of PrEP users and rates. Rates of PrEP use were calculated per 100,000 population to permit data standardization and comparison. American Community Survey (ACS) one-year population estimates (2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2021) were used for the denominator for yearly state-level data. PrEP rates for 2020 used the 2019 ACS one-year population estimates and PrEP rates for 2021 and 2022 used the 2021 ACS one-year population estimates for the denominator. ACS combined five-year population estimates were used for the denominator for yearly county-level data (ACS 2010-2014 5-year population estimates were used for 2012, 2013, and 2014 PrEP county rates, ACS 2013-2017 5-year population estimates were used for 2015, 2016, and 2017 PrEP county rates, and ACS 2016-2020 5-year population estimates were used for 2018, 2019, 2020, 2021, and 2022 PrEP county rates). Denominator totals were for ages 13+, which were obtained from the ACS data by taking the 10-14 age grouping and multiplying by 2/5ths to estimate 13-14-year-olds and then adding with all the other age groups. The denominator totals for the ≤24 age grouping was developed by combining 13-14 (again obtained by multiplying the 10-14 age grouping by 2/5ths), 15-19 and 18-24, to create one age category of 24 or less.
The PNR data compare the ratio of the number of PrEP users from 2012-2022 to the number of people newly diagnosed with HIV in each corresponding year. The numerator is number of PrEP users, by year, and the denominator is new HIV diagnoses cases, by year. Since 2021 new diagnoses is the latest year available on AIDSVu, it is used to calculate 2022 PNR. As the HIV diagnoses data becomes available, the 2022 PNR will be updated with future data launches.
The statements, findings, conclusions, views, and opinions contained and expressed on the AIDSVu website are based in part on data obtained under license from the following information service(s): LAAD Longitudinal Access and Adjudication Dataset HIV data, January 2012 – December 2022), IQVIA Inc. All Rights Reserved. The statements, findings, conclusions, views, and opinions contained and expressed herein are not necessarily those of IQVIA Inc. or any of its affiliated or subsidiary entities. Any analysis is independently arrived at by Emory University, on the basis of the data and other information.
Testing Data:
The data on AIDSVu are from the Behavioral Risk Factor Surveillance System using the calculated variable for adults who have ever been tested for HIV . The data are based on a “yes” response to the question, “Have you ever been tested for HIV?” Non-response categories are not included. For additional information about the BRFSS survey, weighting, and data methods, please visit the CDC BRFSS website here: https://www.cdc.gov/brfss/.
HIV Unstable Housing or Homelessness and HIV Stigma Data:
The release of HIV Unstable Housing or Homelessness and HIV Stigma Data was made possible through a collaboration with the Medical Monitoring Project (MMP). The unstable housing or homelessness data was collected from persons aged 18 years and older living with HIV. Unstable housing was defined as moving in with others due to financial issues, moving two or more times, or being evicted at any time during the past 12 months. Homelessness was defined as living on the street, in a shelter, in a single-room–occupancy hotel, or in a car during the past 12 months. Persons were considered to have experienced unstable housing or homelessness if they reported any form of unstable housing or homelessness during the past 12 months.
The HIV stigma data reflect self-reported HIV stigma scores of persons aged 18 years and older living with HIV. The stigma score was defined as the weighted median score on a 10-item scale ranging from 0 (no stigma) to 100 (high stigma) that measures 4 dimensions of HIV stigma: personalized stigma during the past 12 months, current disclosure concerns, current negative self-image, and current perceived public attitudes about people living with HIV. The HIV stigma scale used for this measure is discussed in Wright, et al.
Persons with diagnosed HIV were sampled for Medical Monitoring Project (MMP) using data from the National HIV Surveillance System (NHSS). Sampled persons were recruited to participate by mail, by telephone, or in person. To be eligible for MMP, the person had to be living with diagnosed HIV infection, aged ≥18 years, and residing in an MMP project area. National estimates are representative of adults with diagnosed HIV in the United States and Puerto Rico; jurisdiction-level estimates are representative of adults with diagnosed HIV in that jurisdiction.
Estimates with a coefficient of variation ≥0.30 or those based on a denominator sample size <30 may be unreliable and are therefore suppressed. Please see CDC’s AtlasPlus for additional details and to download the datasets.
HIV Criminalization Data:
The release of HIV criminalization data was made possible through a collaboration with The Center for HIV Law and Policy (CHLP). The data reflect state criminal laws, civil laws, case interpretations, and public health control measures related to the utilizing of a person’s HIV status to criminalize legal behavior by applying criminal laws specifically to people living with HIV, prosecuting people living with HIV under general criminal laws, or applying sexually transmitted infections laws for increased sex or solicitation crimes and punishments. CHLP provides on their website legal and policy material such as a map on HIV Criminalization in United States, a Sourcebook on State and Federal HIV Criminal Law and Practice, and a chart of U.S. HIV Laws and Prosecutorial Tools.
HIV Philanthropic Funding Data:
The release of HIV philanthropic funding data was made possible through a collaboration with Funders Concerned About AIDS (FCAA). The data comes from FCAA’s signature resource tracking report, of which the most recent edition — based on calendar year 2020 grant making — captures data on more than 5,000 grants, awarded by 323 foundations in 10 countries, in an effort to identify gaps, trends, and opportunities in HIV-related philanthropy. In addition to aggregate funding totals, FCAA analyzes HIV philanthropy by top funders, geographic locations of impact, populations served, and intended use or strategies of funding. The report, including historical versions dating back to the year 2000, can be found on their website, as well as deep-dive infographics on varying topics and other research reports on the impacts of COVID-19, and community-rooted intermediary funders on the field of HIV philanthropy.
Faith-Based Organization Locator and Survey Data:
The release of the FBO service locator and survey data on AIDSVu was made possible through a collaboration with the COMPASS Initiative Faith Coordinating Center and Dr. Allison Mathews, Executive Director. A convenience sample of 225 people were surveyed as a part of this project and 81 locations were ultimately included in the FBO service locator. The organizations were excluded from the final FBO service locator due to unverified locations and contact information, and others requested to not be listed publicly.
Of those who responded to the demographic questions, survey results show that the majority of respondents were women (n=42, 58.3%), Black or African American (n=56, 78.8%), and non-Hispanic, Latino/a, or Spanish origin (n=60, 84.5%). The majority of respondents’ organizations were Christian (n=59, 68.6%), and 65.1% (n=82) of respondents were leaders of their faith communities. Of the faith leaders from non-Christian faith traditions, eight (9.3%) were Interfaith, three were Buddhist (3.5%), two were Muslim (2.3%), two were other (2.3%) and one practiced traditional African spirituality (1.2%). The majority of respondents were located in North Carolina (n=49, 43.8%), Georgia (n=13, 11.6%), Texas (n=10, 8.9%) and Florida (n=9, 8%) and represented nine Southern states. Respondents were also from the West, Midwest and East Coast. (See Table I for more details). Lastly, there was a diversity of respondents from politically and theologically conservative, centrist, and liberal organizations. Politically speaking, the majority (n=42, 55.4%) of respondents indicated that their organizations were “right in the middle,” with other respondents indicating their organizations were liberal (n=19, 25%) and conservative (n=15, 19.7%). Theologically speaking, the majority (n=37, 48.7%) of respondents indicated that their organizations were “right in the middle,” with other respondents indicating their organizations were liberal (n=24, 31.6%) and conservative (n=15, 19.7%).
The majority of faith leaders who completed the survey had personal connections to people living with or impacted by HIV/AIDS, such that 83.3% (n=135) of respondents personally knew someone who was living with HIV/AIDS, 71.4% (n=115) had made a home visit or a hospital visit to a patient living with HIV/AIDS, and 65.6% (n=105) had attended a funeral for a person who died from complications of AIDS. Twenty-eight (38.9%) respondents’ organizations had an HIV/AIDS ministry, 54 (73.0%) respondents were interested in receiving HIV/AIDS education for their organization, and 52 (74.3%) were interested in receiving HIV/AIDS testing for their organization.
In addition to having personal connections with people living with and/or were affected by HIV, we assessed respondents’ perceptions about how to address HIV stigma in Black faith communities. There were some respondents who expressed concern about potential negative treatment they might receive for discussing topics related to HIV, such as being perceived as gay if they talk about HIV/AIDS in a faith setting. (n=20, 8.9%). Others expressed concern that discussing reproductive health issues, including HIV/AIDS, may conflict with their faith tradition (n=30, 13.4%) and others expressed hesitation about implementing sexual education programs in their faith community (n=30, 20.3%). There were a minority of respondents (n=22, 14.9%) who also indicated that discussing HIV may negatively influence tithing and/or offering (financial or other) in their faith community/setting and may impact their faith community’s economic welfare (n=19, 12.8%).
Despite some expressing concerns about implementing HIV/AIDS programming and discussing issues related to HIV/AIDS, the majority of respondents expressed overwhelmingly positive attitudes about implementing HIV/AIDS programming. The majority of respondents (n=127, 56.7%) agree that faith leaders should get tested for HIV publicly to destigmatize and encourage widespread testing and the majority (n=176, 78.9%) agree that leaders should openly discuss HIV/AIDS with members of their faith community. Faith leaders can influence their faith communities by modeling health promotion behaviors such as HIV testing; being open and honest about HIV/AIDS in faith communities; and providing resources and support to people living with HIV/AIDS in faith communities.
The majority of respondents (n=59, 39.9%) indicated that their faith communities do not have the financial resources to host HIV/AIDS prevention programs as opposed to the 53 respondents (35.9%) who said they do have the necessary financial resources. This suggests that there is a need to address the financial and economic implications of discussing HIV in faith communities and the financial resources available for HIV/AIDS prevention programs in faith communities.
Despite not having financial resources to conduct HIV/AIDS programming, there are ways that faith leaders can promote HIV/AIDS awareness and health promotion behaviors. Faith leaders can play a key role in destigmatizing HIV/AIDS by getting tested for HIV publicly and talking openly about HIV/AIDS with their congregations. Faith communities can also provide resources and support to people living with HIV/AIDS.
If you would like your organization added to the FBO service locator, complete the form here: https://wakeforest.qualtrics.com/jfe/form/SV_aWOGumUBxcvtyOW
Data Suppression and Rate Stability
For HIV data, the following data suppression rules were applied:
To protect the privacy of persons living with HIV infection, AIDSVu does not display rates and case counts for states and/or counties when one or more of the following suppression criteria are met:
- For populations ≥500,000, when the denominator (total number of people in the county in that population group) is less than 100. For populations <500,000, when the numerator (number of persons living with or newly diagnosed with HIV infection) is greater than zero and less than five and/or the denominator (total number of people in the county in that population group) is less than 100. If the overall county case count is suppressed, all data for the county are suppressed.
- For breakdowns by sex at the county-level, if either the male or female rate and/or case count is suppressed because of the numerator/denominator thresholds mentioned in #1, both sex groups are suppressed to prevent indirect identification.
- For breakdowns by age at the county level, if the rate and/or case count for only one age group is suppressed in a county because of the numerator/denominator thresholds mentioned in #1, then the 55+ age group is also suppressed to prevent indirect identification. If the 55+ age group is the only age group suppressed for a county, then the 45-54 age group is also suppressed.
- If data for only one-county in a state is suppressed, then one additional county is also suppressed, with the additional county selected based on having the smallest total population of the remaining counties. In the event there are multiple counties in a state with the exact same total population, the county with the lowest number of HIV cases is also suppressed. In the event there are multiple counties in a state with the same total population and the same total number of HIV cases, then both or all of these counties are suppressed.
States and counties are noted by the color white when one or more of these conditions are met. As is standard in the display of health statistics, rates generated from a numerator less than 12 are considered unstable and should be interpreted with caution. An “unreliable rate” indicator will display when the numerator is less than 12.
Data are suppressed in accordance with the state- and county-level data suppression requirements approved by each state under a data re-release agreement with CDC. States may choose to suppress cases/rates for counties with a population below a specified threshold at the overall level, or for specific stratification levels. States may also choose to suppress data at the state-level overall, or for specific stratification levels. Rates by race/ethnicity are not released for Puerto Rico. The data for those states and counties are not released to AIDSVu and appear gray on the map.
For year-by-year- new HIV diagnoses data, if the overall county case count breakdown is greater than zero and less than five, all data for the county are suppressed.
For PrEP data, the following data suppression rules were applied:
To protect the privacy of persons using PrEP, AIDSVu will not display PrEP data for counties when one or more of the following suppression criteria are met:
- The number of persons using PrEP is greater than zero and less than five at the county-level and/or the denominator (number of people in the county in that population group) is less than 100. If the overall county has more than zero but less than five PrEP users and/or the overall county population is less than 100, all data for the county are suppressed.
- For breakdowns by sex at the county-level, if either the male or female PrEP data are suppressed because of the numerator/denominator thresholds mentioned in #1, both sex groups are suppressed to prevent indirect identification.
- For breakdowns by age at the county-level, if the PrEP data for only one age group are suppressed in a county because of the numerator/denominator thresholds mentioned in #1, then the ≤24 age group is also suppressed to prevent indirect identification. If the ≤24 age group is the only age group suppressed for a county, then the 55+ age group is also suppressed.
- If data for only one county in a state is suppressed, then one additional county is also suppressed, with the additional county selected based on having the smallest total population of the remaining counties.
PrEP data at the state level are not displayed for a subgroup if the number of cases in that group greater than zero and less than five. States and counties are noted by the color white when one or more of these conditions are met. As is standard in the display of health statistics, rates generated from a numerator less than 12 are considered unstable and should be interpreted with caution. Because rates at the county-level are not displayed when the numerator is less than five, the “unreliable rate” indicator will only display when the numerator is five or greater and less than 12.
For PNR data, the following data suppression rules were applied:
Due to suppression criteria for PrEP users and new diagnoses cases, AIDSVu will not display PNR data for counties when one or more of the following suppression criteria are met:
- If PrEP users or newly diagnoses cases is suppressed for a county based on the criteria mentioned above, then the PNR for that county is also suppressed.
- If the number of newly diagnosed cases for a county is 0, then the PNR for that county cannot be calculated.
PNR data at the state level are also not displayed for a subgroup when the PrEP cases have been suppressed for that group or the number of newly diagnosed cases is 0 for that subgroup.
Data Stratification
HIV Surveillance Data:
AIDSVu allows viewers to look at HIV surveillance data at the overall geographic level, and for some indicators by race/ethnicity, sex, and age groups. Additionally, AIDSVu displays transmission categories with two-way stratification possibilities at the state level for both prevalence and new HIV diagnoses and displays one-way stratification possibilities at the state level for mortality. The county-level HIV prevalence and new diagnoses data includes one-way transmission categories in addition to the other demographic data.
All race groups are non-Hispanic, and the Hispanic/Latinx ethnicity is inclusive of all races.
Sex is defined as “sex at birth.” HIV surveillance personnel collect data on gender identity, when available, from sources such as case report forms submitted by health care or HIV testing providers and medical records, or by matching with other health department databases (e.g., Ryan White program data). However, these data are limited and currently not available on AIDSVu. AIDSVu recognizes the importance of increasing the accessibility of information on transgender persons nevertheless, so for city-level profiles, information specific to transgender persons, male-to-female and female-to-male, when available, are included. The use of more inclusive data collection methods can help increase the likelihood that transgender people are correctly identified in HIV surveillance programs.
Prevalence, receipt of care, viral suppression, and mortality cases were assigned to age groups based on “age at the end of 2021.” For new diagnoses, late diagnoses, and linkage to care cases were assigned to age group based on “age at diagnoses.”
“Transmission category” is an HIV surveillance term that refers to the single risk factor that most likely resulted in HIV transmission. Men who had sexual contact with other men and who also injected drugs makes up a separate transmission category if both risk factors are equally possible.
The transmission categories are:
- Male-to-male sexual contact (MSM): men who have had sexual contact with men (i.e., homosexual contact) and men who have had sexual contact with both men and women (i.e., bisexual contact)
- Injection drug use (IDU): injected non-prescription drugs
- Male-to-male sexual contact and Injection drug use (MSM&IDU): men who have had sexual contact with other men and injected non-prescription drugs
- Heterosexual contact: persons who have ever had heterosexual contact with a person known to have, or to be at high risk for, HIV infection
- Other: all other transmission categories (e.g., blood transfusion, hemophilia, perinatal exposure, risk factor not reported or not identified)
PrEP and PNR Data:
The PrEP and PNR data can be stratified by age, race, and sex. Age is defined by “year at birth” and displayed as 24 and under (13-24), 25 to 34, 35 to 44, 45 to 54, 55+. Sex is defined as “sex at birth”. Race is self-reported and displayed as Black, Hispanic, and White.
BRFSS Testing Data:
The testing data can be stratified by age, sex, and race/ethnicity. Age used the calculated variable for 6-level imputed age category (AGE_G). Sex used the variable (SEX1), and race/ethnicity used the calculated variable for 8 level race (_IMPRACE).
Data Caveats
Data for 2020 and 2021 should be interpreted with caution due to the COVID-19 pandemic.
HIV Surveillance Data:
Race/ethnicity data for Puerto Rico is displayed only by case counts and not by rate.
Transmission category stratification displays cases and percent cases (or proportions) for all transmission categories. In addition, rates of HIV infection among men who have sex with men (MSM) can also be viewed on the State-level for HIV prevalence, new diagnoses, and mortality and on the county-level for prevalence. These rates were developed utilizing the first population estimates of MSM in every state and county in the U.S. The estimates were used as the denominator, and the rates are per 100 MSM. Due to a lack of population estimates for IDU, IDU/MSM, and heterosexual male and female populations, rates for the transmission categories aside from MSM are unable to be calculated or displayed.
PrEP and PNR Data:
There is currently no single entity or data source that collects data on all users of PrEP across the U.S. Because IQVIA does not directly collect data on every prescription in the US, they have to estimate for those they do not collect; however, they use national estimates of prescription fills to estimate PrEP prescriptions and only have to do this for the small percentage of US prescriptions they do not track. AIDSVu’ s PrEP dataset excludes prescriptions for TDF/FTC, TAF/FTC, and cabotegravir that were made for other known indications, such as, post-exposure prophylaxis, chronic hepatitis B management, and treatment for HIV and other opportunistic infections. To account for underestimations of PrEP use due to misclassified prescriptions, the county-level PrEP users data were upweighted by using state-specific percentages of unclassified prescriptions (prescriptions with an unknown indication) to create the AIDSVu county PrEP data. The unrounded and unsuppressed number of weighted PrEP users per county were then summed by state to obtain state-, regional-, and national-level estimates. Thus, the county downloadable dataset may not sum to the state downloadable dataset due to suppression at the county level.
The PrEP by race/ethnicity data should be interpreted with caution. Race/ethnicity data are available for about one third of individuals with PrEP prescriptions. The race/ethnicity categories include Black, Hispanic, and White. To estimate total PrEP users by race, we assumed that the racial distribution was the same in PrEP users with missing race data as in those with reported race data. Additionally, <1% of PrEP users categorized as Black or White may actually be one or more other races besides Black or White because of Experian Consumer Database decision rules implemented before we received the data.
The overall total population may be fewer than the sum of age group total population for a given year because people may be counted twice if they switch age groups within a certain year (i.e. if a person turns 35 in 2021 then the person is counted in both the 25-34 and 35-44 age groups in 2021).
Additionally, the number of PrEP users by sex, race and age may not perfectly sum up to overall estimates due to differences in sex-, race-and age-specific weights applied to the raw data.
At the time the 2022 PNR data were calculated, 2021 HIV New Diagnoses were the most current datasets available. Therefore, 2021 New Diagnoses data were used as the denominator when calculating 2022 PNR.
Ranges/Legend Values
Range intervals were initially developed using deciles/quintiles/quartiles in SAS analytic software (SAS Institute, Cary, NC). Set cut points were derived using rounded deciles/quintiles/quartiles from previous data and are re-evaluated periodically with current data to consider revising cut points. Rounded current year deciles/quintiles/quartiles were used for data new to this release. In order to illustrate the variation in the data, ranges were developed specific to each geographic level (i.e., states or counties) and for each one-way grouping (i.e., overall, separately by race, by sex, by age group and by transmission category). For two-way groupings (i.e., by race and sex simultaneously), ranges were developed by combining all two-way combinations that included transmission category and by separately combining all two-combinations that did not include transmission category.
Thus, for state prevalence and new HIV diagnoses maps, a total of 15 sets of map scales exist for each (rate/count by overall/race/sex/age, proportion/case by transmission category, MSM rate, proportion/case for two-way combinations with transmission category, and rate/count for two-way combinations without transmission category). For county-level prevalence and new HIV diagnoses maps, a total of 13 sets of map scales exist for each (rate/count by overall/race/sex/age, proportion/case for overall transmission category/transmission category by sex, and MSM rate). For state mortality, there are 13 sets of scales (rate/count by overall/race/sex/age, proportion/case by transmission category, MSM rate, and transmission category total case counts and proportions by sex). For state testing, there are four sets of scales (percent by overall/sex/age/race). For state linkage to care, receipt of care, and viral suppression there are 8 sets of scales each (proportion/case by overall/race/sex/age). For state late diagnoses there are four sets of scales (proportion/case by overall/race). For county linkage to care, receipt of care, and viral suppression there are 8 sets of scales each (proportion/case by overall/race/sex/age). A total of six map scales exist for county-level PrEP data (users/rates by overall/sex/age) and eight map scales exist for state-level PrEP data (users/rates by overall/sex/age/race). For county-level PNR maps, a total of three map scales exist (ratio by overall/sex/age) and for state-level PNR maps, a total of four map scales exist (ratio by overall/sex/age/race).
For each scale calculation, the original deciles/quintiles/quartiles used to create the rounded set cut points were determined by combining the individual rates or counts for all areas with data included on the maps for that geographic level and demographic grouping. For maps with multiple years of data available, the most recent 5 years of data are used to determine the deciles/quintiles/quartiles.
Caution should be exercised when viewing and interpreting different maps because the scales change across the different demographic breakdowns and geographic levels.
Data Comparisons
Data comparison maps including social determinants of health, other infectious diseases, and policy implications are displayed on a secondary map and their associated scales were developed using the same method as above (except for Medicaid expansion status and HIV criminalization). Therefore, there are 12 additional ranges at the state-level. The eight social determinants displayed are: poverty (percent of population living in poverty), high school education (percent of population age 25 and older with a high school degree or equivalent), median household income, income inequality (measured by the Gini Coefficient, a measure of income inequality where 0 reflects complete equality and 1 reflects complete inequality), people < age 65 years without health insurance (percent of population lacking health insurance), unemployment (percent of people unemployed age 16 and older), food insecurity (percent of people living with food insecurity), and severe housing burden cost (percent of people who spend over 50% of their income on housing). Hepatitis C prevalence and syphilis are also available as data comparisons at the state-level. Medicaid expansion is available at the state-level and shows if states have not adopted Medicaid expansion, have adopted but not implemented Medicaid expansion, or have adopted and implemented Medicaid expansion. Lastly, HIV criminalization is available at the state-level with two maps showing states that have laws that criminalize HIV or enhance sentences for people living with HIV and states that prosecute people living with HIV under general criminal laws where their HIV status as an impact on their offense. At the county-level, there are 8 additional map ranges for data comparisons, including poverty, high school education, median household income, income inequality, people without health insurance, severe housing cost burden, and syphilis.
Service Locator
Data on the “FIND SERVICES” tab for Testing, PrEP, and Care are obtained from the National Prevention Information Network (NPIN) through an application programming interface (API), so these data are as real-time as possible. The map overlay data are also from NPIN, however these are downloaded and added several times throughout the year. If you have questions about adding/removing/changing the information for the service locations, please contact NPIN or visit the site here.
COMPASS Data
The enhanced service locator data on the “FIND SERVICES” tab for Stigma Reduction, Overdose Prevention/Reversal, Harm Reduction, and Trauma-Informed Care for the Deep South are provided by Gilead’s COMPASS (COMmitment to Partnership in Addressing HIV/AIDS in Southern States) Initiative® Geospatial Core in partnership with Emory University. To develop this dataset, COMPASS conducted a survey of organizations that provide services to people living with HIV (PLWH) or LGBT populations in the Deep South. Organizations received up to three calls to participate in the survey, and all data are based on organization self-report. In total, over 2,100 phone surveys were conducted. The list of organizations offered survey participation were those that indicated they serve PLWH or LGBT populations in two publicly available sources: Substance Abuse & Mental Health Services Administration (SAMHSA) and National Prevention Information Network (NPIN). Some additional community-based organizations were also offered survey participation. For purposes of the survey, the Deep South is defined as Alabama, Florida, Georgia, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, and Texas.
Limitations: Organizations included in the directory do not comprise all organizations providing HIV, mental health, and substance abuse, trauma-informed care, & HIV stigma mitigation services in the Deep South. Instead, organizations are those that were publicly listed, identified as organizations serving people living with HIV (PLWH) and LGBT populations, and responded to the COMPASS survey.
Please email compass.survey@emory.edu to add your Deep South location to the directory.
Corrections Warning
The HIV prevalence and new diagnoses data displayed on AIDSVu include state and federal correctional populations. Their inclusion may artificially inflate the HIV prevalence rate and case count of counties that house institutions. A correctional warning is displayed in the map hover-over balloon of certain counties when artificial inflation may be present based on predefined criteria.
To determine in which counties the correctional warning should appear, the following process and predefined thresholds were used to categorize counties.
- All U.S. counties were categorized by level of urbanization using the National Center for Health Statistics (NCHS) Urban-Rural Classification Scheme.
- Federal and state correction populations were obtained for each county from the 2010 U.S. Census.
- Within these strata of urbanization, counties are assigned the corrections warning if they had:
- Above average HIV prevalence rate (as compared to counties with no correctional population) AND
- Above average correctional population or above average percent of the population housed in state/federal correctional institutions
Downloadable Datasets
United States data for the overall national-level, along with those at the regional, state, and county levels are available for download. The latest national, regional, state, and county data are available for 2008-2021 HIV New Diagnoses, 2021 HIV Prevalence, Late Diagnoses, Linkage to Care, Receipt of Care, Viral Suppression, and Mortality, and 2012-2022 PrEP utilization and PNR. These datasets are organized by year in the downloadable datasets section of the website under tools and resources, and then are broken out by geography and indicator. The data comparisons are in their own dataset, and data on urbanicity at the county-level can be found in the prevalence and new diagnoses county datasets. There are additional datasets available to download for PrEP at the county-level. Contact info@aidsvu.org if additional information is needed.
Dataset Variables
The downloadable datasets may have suppressed/missing values for several reasons. Suppressed and/or missing data are represented as -1, -2, -4, -8, or -9 in the dataset and indicate the following:
-1: Data are not shown to protect privacy because a small number of cases and/or a small population size for reasons listed in the Data Suppression and Rate Stability section.
-2: Data were not released to AIDSVu because the state health department, per its HIV data re-release agreement with CDC, requested not to release data to AIDSVu below a certain population threshold. The data re-release agreement was updated last year, which is why the data for this year may look different than last year.
-4: Data are not available at the county-level.
-8: Data are undefined, for example when rate or proportion cannot be calculated because the denominator is 0.
-9: Data are unavailable.
The downloadable datasets include a rate stability variable for each indicator. As is standard in the display of health statistics, rates generated from a numerator less than 12 are considered unreliable and should be interpreted with caution.
Y: Reliable rates (i.e. those generated with a numerator of 12 or greater)
N: Unreliable rates (i.e. those generated with a numerator less than 12)
Rate stability for suppressed rates is listed as -9 for HIV data and PrEP data.
Please see the “Data Suppression and Rate Stability” section of the Data Methods above for specific information regarding the suppression rules and rate stability criteria.
Citation
Data, maps, and information from AIDSVu may be used, provided credit is given to AIDSVu and the Rollins School of Public Health at Emory University. Recommended citations:
AIDSVu: Sullivan PS, Woodyatt C, Koski C, Pembleton E, McGuinness P, Taussig J, Ricca A, Luisi N, Mokotoff E, Benbow N, Castel AD, Do AN, Valdiserri RO, Bradley H, Jaggi C, O’Farrell D, Filipowicz R, Siegler AJ, Curran J, Sanchez TH. A Data Visualization and Dissemination Resource to Support HIV Prevention and Care at the Local Level: Analysis and Uses of the AIDSVu Public Data Resource. J Med Internet Res 2020;22(10):e23173
State PrEP Manuscript Source: https://www.sciencedirect.com/science/article/pii/S1047279718301066
County-Level PrEP Manuscripts
Sullivan PS, et al “Methods for county-level estimation of pre-exposure prophylaxis coverage and application to the U.S. Ending the HIV Epidemic jurisdictions”, Annals of Epidemiology
Source: https://www.sciencedirect.com/science/article/pii/S1047279719308166
Siegler AJ, et al “Policy and County-Level Associations with HIV Preexposure Prophylaxis Use, United States, 2018”, Annals of Epidemiology
Source: https://www.sciencedirect.com/science/article/pii/S1047279720301459
Data Sources
Surveillance Data
| Data Element | Location on AIDSVu | Data Source | Anticipated Update Frequency on AIDSVu Website | |
| HIV Prevalence Rates and Case Counts, 2021, State & County Data | Maps; Profile Pages; Downloadable Datasets | Centers for Disease Control and Prevention, Division of HIV/AIDS Prevention, HIV Incidence and Case Surveillance Branch; Data Request, February 2023. | Annually | |
| HIV Prevalence Rates and Case Counts, 2021, Regional & National Data | Profile Pages; Downloadable Datasets | Centers for Disease Control and Prevention, Division of HIV/AIDS Prevention, HIV Incidence and Case Surveillance Branch; Data Request, February 2023. | Annually | |
| HIV New Diagnoses Rates and Case, year by year 2008-2021, State & County Data | Maps; Profile Pages; Downloadable Datasets | Centers for Disease Control and Prevention, Division of HIV/AIDS Prevention, HIV Incidence and Case Surveillance Branch; Data Request, February 2023. | Annually | |
| HIV New Diagnoses Rates and Case, year by year 2008-2021, Regional & National Data | Profile Pages; Downloadable Datasets | Centers for Disease Control and Prevention, Division of HIV/AIDS Prevention, HIV Incidence and Case Surveillance Branch; Data Request, February 2023. | Annually | |
| HIV Mortality Rates and Case Counts, 2021, State Data | Maps; Profile Pages; Downloadable Datasets | Centers for Disease Control and Prevention, Division of HIV/AIDS Prevention, HIV Incidence and Case Surveillance Branch; Data Request, February 2023. | Annually | |
| HIV Mortality Rates and Case Counts, 2021, Regional & National Data | Profile Pages; Downloadable Datasets | Centers for Disease Control and Prevention, Division of HIV/AIDS Prevention, HIV Incidence and Case Surveillance Branch; Data Request, February 2023. | Annually | |
| HIV Late Diagnoses, 2021, State Data | Maps; State Profile Pages; Downloadable Datasets | Centers for Disease Control and Prevention, Division of HIV/AIDS Prevention, HIV Incidence and Case Surveillance Branch; Data Request, February 2023. | Annually | |
| HIV Late Diagnoses, 2021, Regional and National Data | Regional and National Profile Pages; Downloadable Datasets | Centers for Disease Control and Prevention, Division of HIV/AIDS Prevention, HIV Incidence and Case Surveillance Branch; Data Request, February 2023. | Annually | |
| HIV Linkage to Care, 2021, State Data | Maps; State Profile Pages; Downloadable Datasets | Centers for Disease Control and Prevention, Division of HIV/AIDS Prevention, HIV Incidence and Case Surveillance Branch; Data Request, February 2023. | Annually | |
| HIV Linkage to Care, 2021, National Data | National Profile Page; Downloadable Datasets | Centers for Disease Control and Prevention, Division of HIV/AIDS Prevention, HIV Incidence and Case Surveillance Branch; Data Request, February 2023. | Annually | |
| HIV Receipt of Care, 2021, State Data | Maps; State Profile Pages; Downloadable Datasets | Centers for Disease Control and Prevention, Division of HIV/AIDS Prevention, HIV Incidence and Case Surveillance Branch; Data Request February 2023. | Annually | |
| HIV Receipt of Care, 2021, National Data | National Profile Page; Downloadable Datasets | Centers for Disease Control and Prevention, Division of HIV/AIDS Prevention, HIV Incidence and Case Surveillance Branch; Data Request, February 2023. | Annually | |
| HIV Viral Suppression, 2021, State Data | Maps; State Profile Pages; Downloadable Datasets | Centers for Disease Control and Prevention, Division of HIV/AIDS Prevention, HIV Incidence and Case Surveillance Branch; Data Request, February 2023. | Annually | |
| HIV Viral Suppression, 2021, National Data | National Profile Page; Downloadable Datasets | Centers for Disease Control and Prevention, Division of HIV/AIDS Prevention, HIV Incidence and Case Surveillance Branch; Data Request, February 2023. | Annually | |
| HIV Prevalence Rate Ratios by Race/Ethnicity | State, Regional, and National Profile Pages | Centers for Disease Control and Prevention, Division of HIV/AIDS Prevention, HIV Incidence and Case Surveillance Branch; Data Request, February 2023. | Annually | |
| HIV Prevalence Case Counts and Percentages by Transmission Category, State & County Level | Maps; Profile Pages; Downloadable Datasets | Centers for Disease Control and Prevention, Division of HIV/AIDS Prevention, HIV Incidence and Case Surveillance Branch; Data Request, February 2023. | Annually | |
| HIV Prevalence Case Counts and Percentages by Transmission Category, Regional & National Level | Profile Pages; Downloadable Datasets | Centers for Disease Control and Prevention, Division of HIV/AIDS Prevention, HIV Incidence and Case Surveillance Branch; Data Request, February 2023. | Annually | |
| STD Case Counts and Rates, 2021 | Profile Pages | Centers for Disease Control and Prevention. Sexually Transmitted Disease ATLAS website. | Annually | |
| HIV Mortality Rates by Year, 2019-2021, State, Regional, & National Data | Profile Pages | Centers for Disease Control and Prevention. Sexually Transmitted Disease ATLAS website. | Annually | |
| Continuum Indicators, 2021, National Data | Profile Page | Centers for Disease Control and Prevention, Division of HIV/AIDS Prevention, HIV Incidence and Case Surveillance Branch; Data Request, February 2023. | Annually | |
| PrEP Users and Rates, 2012-2022, State & County Data | Maps; Profile Pages; Downloadable Datasets | IQVIA and Gilead Sciences, Inc.; Data Request, April 2023. | Annually | |
| PrEP Users and Rates, 2012-2022, Regional & National Data | Profile Pages; Downloadable Datasets | IQVIA and Gilead Sciences, Inc.; Data Request, April 2023. | Annually | |
| PrEP to Need Ratio, 2012-2022, State & County Data | Maps; Profile Pages; Downloadable Datasets | Calculated from IQVIA and Gilead Sciences, Inc.; Data Request, April 2023 & Centers for Disease Control and Prevention, Division of HIV/AIDS Prevention, HIV Incidence and Case Surveillance Branch; Data Request, February 2023. | Annually | |
| PrEP to Need Ratio, 2012-2022, Regional & National Data | Profile Pages; Downloadable Datasets | Calculated from IQVIA and Gilead Sciences, Inc. Data Request, April 2023 & Centers for Disease Control and Prevention, Division of HIV/AIDS Prevention, HIV Incidence and Case Surveillance Branch; Data Request, February 2023. | Annually | |
| BRFSS Percent Ever Tested for HIV, 2021 | Maps; Profile Pages; Downloadable Datasets | Centers for Disease Control and Prevention; 2021 BRFSS Data. | Annually | |
| Unstable Housing or Homelessness, 2020-2021 | National and (Applicable) State Profile Pages | Centers for Disease Control and Prevention. Sexually Transmitted Disease ATLAS website. | Annually | |
| HIV Stigma, 2020-2021 | National and (Applicable) State Profile Pages | Centers for Disease Control and Prevention. Sexually Transmitted Disease ATLAS website. | Annually | |
Service Locators and Federal Funding for HIV/AIDS
| Data Element | Location on AIDSVu | Data Source | Anticipated Update Frequency on AIDSVu Website |
| Testing | Find Services | Centers for Disease Control and Prevention, National Prevention Information Network; Ongoing Data Access Request via API. Data include: HIV Testing: Conventional HIV Testing, Mobile Testing Services, Rapid HIV Testing | Constantly |
| PrEP | Find Services | Centers for Disease Control and Prevention, National Prevention Information Network; Ongoing Data Access Request via API. Data include: Pre-Exposure Prophylaxis (PrEP) | Constantly |
| Care | Find Services | Centers for Disease Control and Prevention, National Prevention Information Network; Ongoing Data Access Request via API. Data include: HIV/AIDS Medical Services; HIV/AIDS Medical Treatment | Constantly |
| Stigma Reduction | Find Services | Gilead’s COMPASS Initiative® and Emory University’s Rollins School of Public Health Geospatial Core; COMPASS | As Available |
| Overdose Prevention/Reversal | Find Services | Gilead’s COMPASS Initiative® and Emory University’s Rollins School of Public Health Geospatial Core; COMPASS | As Available |
| Harm Reduction | Find Services | Gilead’s COMPASS Initiative® and Emory University’s Rollins School of Public Health Geospatial Core; COMPASS | As Available |
| Trauma-Informed Care | Find Services | Gilead’s COMPASS Initiative® and Emory University’s Rollins School of Public Health Geospatial Core; COMPASS | As Available |
| HIV Testing Sites | Maps | Centers for Disease Control and Prevention, National Prevention Information Network; Customizable Data Feed; downloaded by AIDSVu. | Periodically |
| Pre-Exposure Prophylaxis (PrEP) Services | Maps | Centers for Disease Control and Prevention, National Prevention Information Network; Customizable Data Feed; downloaded by AIDSVu. | Periodically |
| Ryan White HIV/AIDS Medical Care Providers | Maps | Health Resources and Services Administration, HIV/AIDS Bureau; Data Warehouse; downloaded by AIDSVu. . | Periodically |
| Housing Opportunities for People with AIDS (HOPWA) | Maps | Centers for Disease Control and Prevention, National Prevention Information Network; Customizable Data Feed; downloaded by AIDSVu. | Periodically |
| National Federal Grant Funding for HIV/AIDS, FY 2023 | Profile Pages | Kaiser Family Foundation, Domestic HIV Funding in White House FY 2024 Budget Request, March 2023 | As Available |
Data Comparisons
| Data Element | Location on AIDSVu | Data Source | Anticipated Update Frequency on AIDSVu Website |
| Poverty (Percent of population, all ages, living in poverty) – County & State Level | Maps; Downloadable Datasets; Profile Pages | U.S. Census Bureau, American Community Survey 5- Year Estimates, 2021, Table S1701: Poverty Status in the Past 12 Months
|
Annually |
| Poverty (Percent of population, all ages, living in poverty) – Regional & National Level | Profile Pages | U.S. Census Bureau, American Community Survey 5-Year Estimates, 2021, Table S1701: Poverty Status in the Past 12 Months | Annually |
| Population and Racial/Ethnic Make-Up— County Data | Profile Pages | U.S. Census Bureau. Decennial Census 2020. https://data.census.gov/cedsci/ | As Available |
| Population and Racial/Ethnic Make-Up— State, Regional, & National Data | Profile Pages | U.S. Census Bureau. Population Estimates, Vintage 2021 [entire data set]. | Annually |
| Educational Attainment (Percent of people age 25 and over with at least a high school diploma or equivalent) – County & State Level | Maps; Downloadable Datasets | U.S. Census Bureau, American Community Survey 5- Year Estimates, 2021, Table S1501: Educational Attainment
|
Annually |
| Educational Attainment (Percent of people age 25 and over with at least a high school diploma or equivalent) – Regional & National Level | Profile Pages | U.S. Census Bureau, American Community Survey 5- Year Estimates, 2021, Table S1501: Educational Attainment
|
Annually |
| Median Household Income – County & State Level | Maps; Profile Pages; Downloadable Datasets | U.S. Census Bureau, American Community Survey 5-Year Estimates, 2021, Table S1903: Median Income in the Past 12 Months. | Annually |
| Median Household Income – Regional & National Level | Profile Pages | U.S. Census Bureau, American Community Survey 5-Year Estimates, 2021, Table S1903: Median Income in the Past 12 Months. | Annually |
| Percent of population under age 65 lacking health insurance – State &County Level | Maps; Profile Pages; Downloadable Datasets | U.S. Census Bureau, American Community Survey 5-Year Estimates, 2021, Table 2701: Selected Characteristics of Health Insurance Coverage in the United States | Annually |
| Percent of population under age 65 lacking health insurance –Regional & National Level | Profile Pages | U.S. Census Bureau, American Community Survey 5-Year Estimates, 2021, Table 2701: Selected Characteristics of Health Insurance Coverage in the United States | Annually |
| Gini Coefficient of income inequality – State & County Level | Maps; Profile Pages; Downloadable Datasets | U.S. Census Bureau, American Community Survey 5-Year Estimates, 2020, Table B19083: Gini Index of Income Inequality. | Annually |
| Gini Coefficient of income inequality – Regional & National Level | Profile Pages | U.S. Census Bureau, American Community Survey 5-Year Estimates, 2020, Table B19083: Gini Index of Income Inequality. | Annually |
| Unemployment – State and County Level | Maps; Profile Pages; Downloadable Datasets | U.S. Bureau of Labor Statistics Annual Unemployment Rates, 2021 | Annually |
| Unemployment – Regional and National Level | Profile Pages | U.S. Bureau of Labor Statistics Annual Unemployment Rates, 2021 | Annually |
| Food Insecurity – State Level | Maps; Profile Pages; Downloadable Datasets | United States Department of Agriculture Food Security in the United States, 2019-2021 | Annually |
| Food Insecurity – Regional and National Level | Profile Pages; | United States Department of Agriculture Food Security in the United States, 2021 | Annually |
| Severe Housing Cost Burden – State & County Level | Maps; Profile Pages; Downloadable Datasets | U.S. Census Bureau, American Community Survey 5-Year Estimates, 2021, Table B25074: Household Income by Gross Rent as a Percentage of Household Income in the Past 12 Months
|
Annually |
| Severe Housing Cost Burden – Regional and National Level | Profile Pages | U.S. Census Bureau, American Community Survey 1-Year Estimates, 2021, Table B25074: Household Income by Gross Rent as a Percentage of Household Income in the Past 12 Months | Annually |
| Medicaid Expansion – State Level | Maps; Profile Pages; Downloadable Datasets | Kaiser Family Foundation, Status of State Action on the Medicaid Expansion Decision, May 2023 | Annually |
| Medicaid Expansion – Regional Level | Profile Pages | Kaiser Family Foundation, Status of State Action on the Medicaid Expansion Decision, May 2023 | Annually |
| Hepatitis C – State Level | Maps; Downloadable Datasets | Bradley H, Hall EW, Rosenthal EM, et al. Hepatitis C Virus Prevalence in 50 U.S. States and D.C. by Sex, Birth Cohort, and Race: 2013-2016. Hepatology Communications. 2020. doi:10.1002/hep4.1457 | As Available |
| Syphilis (Primary and Secondary) – County and State Level | Maps; Profile Pages; Downloadable Datasets | Centers for Disease Control and Prevention. Sexually Transmitted Disease ATLAS website. | Annually |
| HIV Criminalization- State Level | Maps; Profile Pages; Downloadable Datasets | The Center for HIV Law and Policy; State and federal criminal laws used to prosecute and punish PLHIV. | As available |
Additional Data
| Data Element | Location on AIDSVu | Data Source | Anticipated Update Frequency on AIDSVu Website |
| Urban/Rural | County Profile Pages | National Center for Health Statistics (NCHS) 2013 Urban-Rural Classification Scheme for Counties. | As Available |
| State Health Department Websites | State Profile Pages | State health departments and internet. | Annually |
| FCAA Funding Data | EHE Profile Pages (State and County) | Funders Concerned About AIDS (FCAA) | As Available |
Previous PrEP Data Methods from Symphony Health
In 2022, AIDSVu began using a new data source (IQVIA) for our PrEP and PNR interactive maps and profile pages. Below are the data methods for previously displayed PrEP and PNR data received from Symphony Health (SHA) through a data use agreement with Gilead Sciences Inc. The prior SHA data has been archived on AIDSVu website in the Tools and Resources section.
PrEP and PNR Data:
The release of the PrEP data on AIDSVu was made possible through a unique data-sharing agreement that allowed this proprietary data to be shared publicly for the first time. The data were obtained from Symphony Health with the support of Gilead Sciences, Inc., and compiled by researchers at the Rollins School of Public Health at Emory University.
Symphony Health provided Gilead with national, electronic, patient-level prescription data from an overall sample that represents more than 54,000 pharmacies, 1,500 hospitals, 800 outpatient facilities, and 80,000 physician practices across the U.S. This is an open sample of commercially available data, which excludes entities that do not make their data available to Symphony Health, such as closed healthcare systems like Kaiser Permanente. The dataset contains prescription, medical, and hospital claims data for all payment types, including commercial plans, Medicare Part D, cash, assistance programs, and Medicaid. The dataset also includes data from some clinics in academic settings.
All patient-level prescription data were de-identified and linked to confirmatory data from a de-identified medical insurance claims database. Gilead utilized a validated algorithm[1] to exclude prescriptions for TDF/FTC that were made for other known indications, such as HIV treatment, post-exposure prophylaxis, and chronic hepatitis B management. For each user of TDF/FTC for PrEP, the duration of the PrEP course was determined by examining subsequent renewals of the prescription. A minimum duration of 30 days was required for an individual to be considered a PrEP user, and to be considered a user in a given year, at least one day of that 30-day minimum period was required to fall within that calendar year. Gilead then shared aggregate datasets at the state- and county-level with Emory. To account for underestimations of PrEP use due to misclassified prescriptions, the county-level PrEP users’ data were upweighted by using state-specific percentages of unclassified prescriptions (prescriptions that have an unknown indication). The unrounded number of weighted PrEP users per county were then summed to obtain state-, regional-, and national-level estimates. Finally, Emory applied data suppression rules and developed the publicly available maps and data sets for AIDSVu. AIDSVu provides downloadable PrEP maps and datasets at the county-and state-level and PrEP datasets at the regional and national-level for researchers and health departments to utilize in their own analyses.
PrEP data are displayed as number of PrEP users and rates. Rates of PrEP use were calculated per 100,000 population to permit data standardization and comparison. American Community Survey (ACS) one-year population estimates (2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019) were used for the denominator for yearly state-level data. ACS combined five-year population estimates were used for the denominator for yearly county-level data (ACS 2012-2016 5-year population estimate was used for 2012, 2013, and 2014 PrEP county rates, ACS 2013-2017 5-year population estimate was used for 2015, 2016, and 2017 PrEP county rates, and ACS 2015-2019 5-year population estimate was used for 2018 and 2019 PrEP county rates). Denominator totals were for ages 13+, which were obtained from the ACS data by taking the 10-14 age grouping and multiplying by 2/5ths to estimate 13-14-year-olds and then adding with all the other age groups. The denominator totals for the ≤24 age grouping was developed by combining 13-14 (again obtained by multiplying the 10-14 age grouping by 2/5ths), 15-19 and 18-24, to create one age category of 24 or less.
The PNR data compare the ratio of the number of PrEP users from 2012-2019 to the number of people newly diagnosed with HIV in each corresponding year. The numerator is number of PrEP users, by year, and the denominator is new HIV diagnoses cases, by year.
[1] MacCannell T, Verma S, Shvachko V, Rawlings K, Mera R. Validation of a Truvada for PrEP Algorithm using an Electronic Medical Record. 8th IAS Conference on HIV Pathogenesis, Treatment & Prevention. Vancouver Canada July 2015.
Symphony Health PrEP Data: AIDSVu has three different SHA PrEP use datasets available on the site as archived, downloadable datasets. Each dataset is derived using the different calculation methods described below.
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- Raw PrEP Data: These data are proprietary data collected by Symphony Health and then compiled by researchers at the Rollins School of Public Health at Emory University. These data represent a consistent, conservative, and minimum number of PrEP users by county in the U.S. by year. The raw PrEP data are known to substantially underestimate the actual number of PrEP users.
- Adjusted PrEP Data (Previously Mapped on AIDSVu): These data are calculated using the raw PrEP data collected by Symphony Health and compiled by Emory University researchers, and then adjusted with state-specific weights of unclassified TDF/FTC prescriptions to estimate the number of PrEP users in each county and state in the U.S. by year. Although the exact number of PrEP users is unknown, this data method accounts for TDF/FTC prescriptions in the Symphony Health dataset that could not be linked to medical records to confirm that the prescription was for PrEP and not for any other use. In other words, this calculation method accounts for missing clinical data and provides a reliable and consistent metric for PrEP users at the county- and state-level. The method was adopted from Sullivan et al.’s published article in Annals of Epidemiology titled “Methods for county-level estimation of pre-exposure prophylaxis coverage and application to the U.S. ending the HIV epidemic jurisdictions.”
- Best Estimate PrEP Data: These data are derived by upweighting AIDSVu’s adjusted PrEP data to account for the national estimate that 20% of all PrEP medication prescriptions are filled at pharmacies that are not captured in the Symphony Health database. In other words, this calculation method accounts for missing pharmacy data and provides a best estimate of total PrEP users in each county in the U.S. by year. The state-specific estimates of missing pharmacy data are not available. Therefore, the national weight used to derive the best estimates does not represent the full variation in missing TDF/FTC prescriptions at the state-level.
