The states with the highest rates of HIV diagnoses in 2022 included Georgia, Louisiana, and Florida. However, the states with the highest number of people with HIV were Texas, California, and Florida. In Texas, there were around 4,896 people diagnosed with HIV. HIV/AIDS diagnoses In 2022, there were an estimated 38,043 new HIV diagnoses in the United States, a slight increase compared to the year before. Men account for the majority of these new diagnoses. There are currently around 1.2 million people living with HIV in the United States. Deaths from HIV The death rate from HIV has decreased significantly over the past few decades. In 2023, there were only 1.3 deaths from HIV per 100,000 population, the lowest rate since the epidemic began. However, the death rate varies greatly depending on race or ethnicity, with the death rate from HIV for African Americans reaching 19.2 per 100,000 population in 2022, compared to just three deaths per 100,000 among the white population.

In 2022, the states with the highest number of HIV diagnoses were Texas, California, and Florida. That year, there were a total of around 37,601 HIV diagnoses in the United States. Of these, 4,896 were diagnosed in Texas. HIV infections have been decreasing globally for many years. In the year 2000, there were 2.8 million new infections worldwide, but this number had decreased to around 1.3 million new infections by 2023. The number of people living with HIV remains fairly steady, but the number of those that have died due to AIDS has reached some of its lowest peaks in a decade. Currently, there is no functional cure for HIV or AIDS, but improvements in therapies and treatments have enabled those living with HIV to have a much improved quality of life.

This dataset contains surveillance data on diagnoses of HIV for the United States in estimates rates and numbers for Human Immunodeficiency Virus (HIV) infection diagnosis and stage 3 infection Acquired Immunodeficiency Syndrome (AIDS) as collected by the Centers for Disease Control and Prevention (CDC).

United States US: Prevalence of HIV: Total: % of Population Aged 15-49 data was reported at 0.500 % in 2014. This stayed constant from the previous number of 0.500 % for 2013. United States US: Prevalence of HIV: Total: % of Population Aged 15-49 data is updated yearly, averaging 0.500 % from Dec 2008 (Median) to 2014, with 7 observations. The data reached an all-time high of 0.500 % in 2014 and a record low of 0.500 % in 2014. United States US: Prevalence of HIV: Total: % of Population Aged 15-49 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Prevalence of HIV refers to the percentage of people ages 15-49 who are infected with HIV.; ; UNAIDS estimates.; Weighted Average;

Prevalence of HIV, total (% of population ages 15-49) in United States was reported at 0.4 % in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. United States - Prevalence of HIV, total (% of population ages 15-49) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

This data set includes tables on persons living with HIV/AIDS, newly diagnosed HIV cases and all cause deaths in HIV/AIDS cases by gender, age, race/ethnicity and transmission category.

In all tables, cases are reported as of December 31 of the given year, as reported by January 9, 2019, to allow a minimum of 12 months reporting delay.

Gender is determined by both current gender and sex at birth variables; transgender values are assigned when current gender is identified as "Transgender" or when a discrepancy is identified between a person's sex at birth and their current gender (e.g., cases where sex at birth is "Male" and current gender is "Female" will become Transgender: Male to Female.) Prior to 2003, Asian and Native Hawaiian/Pacific Islanders were classified as one combined group. In order to present these race/ethnicities separately, living cases recorded under this combined classification were split and redistributed according to their expected proportional population representation estimated from post-2003 data.

United States US: Incidence of HIV: per 1,000 Uninfected Population data was reported at 0.110 Ratio in 2019. This stayed constant from the previous number of 0.110 Ratio for 2018. United States US: Incidence of HIV: per 1,000 Uninfected Population data is updated yearly, averaging 0.120 Ratio from Dec 2010 (Median) to 2019, with 10 observations. The data reached an all-time high of 0.130 Ratio in 2012 and a record low of 0.110 Ratio in 2019. United States US: Incidence of HIV: per 1,000 Uninfected Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Social: Health Statistics. Number of new HIV infections among uninfected populations expressed per 1,000 uninfected population in the year before the period.;UNAIDS estimates.;Weighted average;This is the Sustainable Development Goal indicator 3.3.1 [https://unstats.un.org/sdgs/metadata/].

This shapefile provides HIV statistics by state that can be used in conjunction with the co-morbidities risk profile to provide more nuance on levels of risk by state. Note that values of 0 mean there is no data for that particular state.The source of data for HIV prevalence rates is the Nigeria Institute for Health Metrics and Evaluation (IHME), HIV Prevalence Geospatial Estimates 2000-2017.

In 2024, the number of diagnosed HIV cases in Mexico amounted to approximately 19,000. That year, the State of Mexico, Veracruz, and Mexico City were the federative entities with the highest number of people diagnosed with the human immunodeficiency virus (HIV), with more than 1,000 patients each. Moreover, most registered HIV cases in the Latin American country between 1984 and 2023 corresponded to men. People living with HIV in Latin America In the last few years, the number of people living with HIV in Latin America has been increasing. According to recent estimates, the number of individuals living with this condition rose from around 1.6 million in 2013 to almost 2.2 million by 2022. From a country perspective, Brazil and Mexico were the Latin American nations where most people were living with the disease, reaching approximately 990,000 and 370,000 patients, respectively. ART is more costly in Latin America HIV is commonly treated through antiretroviral therapy (ART), a drug-based treatment aimed at reducing the viral load in the blood to help control the development of the disease while improving the health of those infected. Although the share of deaths among people living with HIV due to causes unrelated to AIDS increased globally since 2010, there are still inequalities in the access to ART therapy. As of 2022, Latin America and the Caribbean recorded the highest average price per person for HIV antiretroviral therapy compared to other regions worldwide.

In 2018, São Paulo was the Brazilian state with the highest number of HIV-positive patients in the country, with ***** cases. It was followed by Rio de Janeiro, with around *** thousand cases and Rio Grande do Sul, with nearly *** thousand patients.

Austin, IN represents a widely disproportionate percentage of New HIV / AIDS Cases in Indiana

Source: State of Indiana at IN.gov http://www.in.gov/isdh/files/Surveillance_Trends(2).pdf http://www.in.gov/isdh/files/Persons_Living_with_AIDS.pdf

United States US: Incidence of HIV: % of Uninfected Population Aged 15-49 data was reported at 0.020 % in 2014. This stayed constant from the previous number of 0.020 % for 2013. United States US: Incidence of HIV: % of Uninfected Population Aged 15-49 data is updated yearly, averaging 0.030 % from Dec 2008 (Median) to 2014, with 7 observations. The data reached an all-time high of 0.030 % in 2012 and a record low of 0.020 % in 2014. United States US: Incidence of HIV: % of Uninfected Population Aged 15-49 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Number of new HIV infections among uninfected populations ages 15-49 expressed per 100 uninfected population in the year before the period.; ; UNAIDS estimates.; Weighted Average;

BackgroundTools using local HIV data to help jurisdictions estimate future demand for medical and support services are needed. We present an interactive prevalence projection model using data obtainable from jurisdictional HIV surveillance and publically available data.MethodsUsing viral load data from Georgia’s enhanced HIV/AIDS Reporting System, state level death rates for people living with HIV and the general population, and published estimates for HIV transmission rates, we developed a model for projecting future HIV prevalence. Keeping death rates and HIV transmission rates for undiagnosed, in care/viral load >200, in care/viral load

This dataset contains death counts, crude rates and adjusted rates for selected causes of death by county and region. For more information, check out: http://www.health.ny.gov/statistics/vital_statistics/, or go to the "About" tab.

Results of state-specific impact model in two example states (high and low prevalence).

Model parameters, underlying data elements, and data sources.

All figures are given as % (95% CI) unless noted. There were 61 counties in the top decile and 551 in the remaining deciles.

The percentage of infection averted, the number of infections averted per 100K person years at risk, the number needed to treat to avert a single infection, prevalence, incidence, person-years HIV positive, and age at infection over 10 years of a PrEP intervention for adult men who have sex with men (MSM) and adolescent sexual minority males (ASMM).

Abstract

The 2018 Nigeria AIDS Indicator and Impact Survey (NAIIS) is a cross-sectional survey that will assess the prevalence of key human immunodeficiency virus (HIV)-related health indicators. This survey is a two-stage cluster survey of 88,775 randomly-selected households in Nigeria, sampled from among 3,551 nationally-representative sample clusters. The survey is expected to include approximately 168,029 participants, ages 15-64 years and children, ages 0-14 years, from the selected household. The 2018 NAIIS will characterize HIV incidence, prevalence, viral load suppression, CD4 T-cell distribution, and risk behaviors in a household-based, nationally-representative sample of the population of Nigeria, and will describe uptake of key HIV prevention, care, and treatment services. The 2018 NAIIS will also estimate the prevalence of hepatitis B virus (HBV), hepatitis C virus (HCV) infections, and HBV/HIV and HCV/HIV co-infections.

Geographic coverage

National coverage, the survey covered the Federal Republic and was undertaken in each state and the Federal Capital.

Analysis unit

Household Health Survey

Universe

1. Women and men aged 15-64 years living in residential households and visitors who slept in the household the night before the survey
2. Children aged 0-14 years living in residential households and child visitors who slept in the household the night before the survey

Kind of data

Sample survey data [ssd]

Sampling procedure

This cross-sectional, household-based survey uses a two-stage cluster sampling design (enumeration area followed by households). The target population is people 15-64 and children ages 0-14 years. The overall size and distribution of the sample is determined by analysis of existing estimates of national HIV incidence, sub-national HIV prevalence, and the number of HIV-positive cases needed to obtain estimates of VLS among adults 15-64 years for each of the 36 states and the FCT while not unnecessarily inflating the sample size needed.

From a sampling perspective, the three primary objectives of this proposal are based on competing demands, one focused on national incidence and the other on state-level estimates in a large number of states (37). Since the denominator used for estimating VLS is HIV-positive individuals, the required minimum number of blood draws in a stratum is inversely proportional to the expected HIV prevalence rate in that stratum. This objective requires a disproportionate amount of sample to be allocated to states with the lowest prevalence. A review of state-level prevalence estimates for sources in the last 3 to 5 years shows that state-level estimates are often divergent from one source to the next, making it difficult to ascertain the sample size needed to obtain the roughly 100 PLHIV needed to achieve a 95% confidence interval (CI) of +/- 10 for VLS estimates.

An equal-size approach is proposed with a sample size of 3,700 blood specimens in each state. Three-thousand seven hundred specimens will be sufficiently large to obtain robust estimates of HIV prevalence and VLS among HIV-infected individuals in most states. In states with a HIV prevalence above 2.5%, we can anticipate 95% CI of less than +/-10% and relative standard errors (RSEs) of less than 11% for estimates of VLS. In these states, with HIV prevalence above 2.5%, the anticipated 95% CI around prevalence is +/- 0.7% to a high of 1.1-1.3% in states with prevalence above 6%. In states with prevalence between 1.2 and 2.5% HIV prevalence estimates would remain robust with 95% CI of +/- 0.5-0.6% and RSE of less than 20% while 95% CI around VLS would range between 10-15% (and RSE below 15%). With this proposal only a few states, with HIV prevalence below 1.0%, would have less than robust estimates for VLS and HIV prevalence.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used for the 2018 NAIIS: Household Questionnaire, Adult Questionnaire, and Early Adolescent Questionnaire (10-14 Years).

Cleaning operations

During the household data collection, questionnaire and laboratory data were transmitted between tablets via Bluetooth connection. This facilitated synchronization of household rosters and ensured data collection for each participant followed the correct pathway. All field data collected in CSPro and the Laboratory Data Management System (LDMS) were transmitted to a central server using File Transfer Protocol Secure (FTPS) over a 4G or 3G telecommunication provider at least once a day. Questionnaire data cleaning was conducted using CSPro and SAS 9.4 (SAS Institute Inc., Cary, North Carolina, United States). Laboratory data were cleaned and merged with the final questionnaire database using unique specimen barcodes and study identification numbers.

Response rate

A total of 101,267 households were selected, 89,345 were occupied and 83,909 completed the household interview . • For adults aged 15-64 years, interview response rate was 91.6% for women and 88.2% for men; blood draw response rate was 92.9% for women and 93.6% for men. • For adolescents aged 10-14 years, interview response rate was 86.8% for women and 86.2% for men; blood draw response rate was 91.2% for women and 92.3% for men. • For children aged 0-9 years, blood draw response rate was 68.5% for women and men.

Sampling error estimates

Estimates from sample surveys are affected by two types of errors: non-sampling errors and sampling errors. Non-sampling errors result from mistakes made during data collection, e.g., misinterpretation of an HIV test result and data management errors such as transcription errors during data entry. While NAIIS implemented numerous quality assurance and control measures to minimize non-sampling errors, these were impossible to avoid and difficult to evaluate statistically. In contrast, sampling errors can be evaluated statistically. Sampling errors are a measure of the variability between all possible samples.

The sample of respondents selected for NAIIS was only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples could yield results that differed somewhat from the results of the actual sample selected. Although the degree of variability cannot be known exactly, it can be estimated from the survey results. The standard error, which is the square root of the variance, is the usual measurement of sampling error for a statistic (e.g., proportion, mean, rate, count). In turn, the standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of approximately plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

NAIIS utilized a multi-stage stratified sample design, which required complex calculations to obtain sampling errors. The Taylor linearization method of variance estimation was used for survey estimates that are proportions, e.g., HIV prevalence. The Jackknife repeated replication method was used for variance estimation of more complex statistics such as rates, e.g., annual HIV incidence and counts such as the number of people living with HIV.

The Taylor linearization method treats any percentage or average as a ratio estimate, , where y represents the total sample value for variable y and x represents the total number of cases in the group or subgroup under consideration. The variance of r is computed using the formula given below, with the standard error being the square root of the variance: in which Where represents the stratum, which varies from 1 to H, is the total number of clusters selected in the hth stratum, is the sum of the weighted values of variable y in the ith cluster in the hth stratum, is the sum of the weighted number of cases in the ith cluster in the hth stratum and, f is the overall sampling fraction, which is so small that it is ignored.

In addition to the standard error, the design effect for each estimate is also calculated. The design effect is defined as the ratio of the standard error using the given sample design to the standard error that would result if a simple random sample had been used. A design effect of 1.0 indicates that the sample design is as efficient as a simple random sample, while a value greater than 1.0 indicates the increase in the sampling error due to the use of a more complex and less statistically efficient design. Confidence limits for the estimates, which are calculated as where t(0.975, K) is the 97.5th percentile of a t-distribution with K degrees of freedom, are also computed.

Data appraisal

Remote data quality check was carried out using data editor

0.4 (%) in 2021. Prevalence of HIV refers to the percentage of people ages 15-49 who are infected with HIV.