Ruiguang Song

Estimation of HIV Incidence in the United States

CONTEXT Incidence of human immunodeficiency virus (HIV) in the United States has not been directly measured. New assays that differentiate recent vs long-standing HIV infections allow improved estimation of HIV incidence. OBJECTIVE To estimate HIV incidence in the United States. DESIGN, SETTING, AND PATIENTS Remnant diagnostic serum specimens from patients 13 years or older and newly diagnosed with HIV during 2006 in 22 states were tested with the BED HIV-1 capture enzyme immunoassay to classify infections as recent or long-standing. Information on HIV cases was reported to the Centers for Disease Control and Prevention through June 2007. Incidence of HIV in the 22 states during 2006 was estimated using a statistical approach with adjustment for testing frequency and extrapolated to the United States. Results were corroborated with back-calculation of HIV incidence for 1977-2006 based on HIV diagnoses from 40 states and AIDS incidence from 50 states and the District of Columbia. MAIN OUTCOME MEASURE Estimated HIV incidence. RESULTS An estimated 39,400 persons were diagnosed with HIV in 2006 in the 22 states. Of 6864 diagnostic specimens tested using the BED assay, 2133 (31%) were classified as recent infections. Based on extrapolations from these data, the estimated number of new infections for the United States in 2006 was 56,300 (95% confidence interval [CI], 48,200-64,500); the estimated incidence rate was 22.8 per 100,000 population (95% CI, 19.5-26.1). Forty-five percent of infections were among black individuals and 53% among men who have sex with men. The back-calculation (n = 1.230 million HIV/AIDS cases reported by the end of 2006) yielded an estimate of 55,400 (95% CI, 50,000-60,800) new infections per year for 2003-2006 and indicated that HIV incidence increased in the mid-1990s, then slightly declined after 1999 and has been stable thereafter. CONCLUSIONS This study provides the first direct estimates of HIV incidence in the United States using laboratory technologies previously implemented only in clinic-based settings. New HIV infections in the United States remain concentrated among men who have sex with men and among black individuals.

Estimated HIV Incidence in the United States, 2006–2009

Background The estimated number of new HIV infections in the United States reflects the leading edge of the epidemic. Previously, CDC estimated HIV incidence in the United States in 2006 as 56,300 (95% CI: 48,200–64,500). We updated the 2006 estimate and calculated incidence for 2007–2009 using improved methodology. Methodology We estimated incidence using incidence surveillance data from 16 states and 2 cities and a modification of our previously described stratified extrapolation method based on a sample survey approach with multiple imputation, stratification, and extrapolation to account for missing data and heterogeneity of HIV testing behavior among population groups. Principal Findings Estimated HIV incidence among persons aged 13 years and older was 48,600 (95% CI: 42,400–54,700) in 2006, 56,000 (95% CI: 49,100–62,900) in 2007, 47,800 (95% CI: 41,800–53,800) in 2008 and 48,100 (95% CI: 42,200–54,000) in 2009. From 2006 to 2009 incidence did not change significantly overall or among specific race/ethnicity or risk groups. However, there was a 21% (95% CI:1.9%–39.8%; p = 0.017) increase in incidence for people aged 13–29 years, driven by a 34% (95% CI: 8.4%–60.4%) increase in young men who have sex with men (MSM). There was a 48% increase among young black/African American MSM (12.3%–83.0%; p<0.001). Among people aged 13–29, only MSM experienced significant increases in incidence, and among 13–29 year-old MSM, incidence increased significantly among young, black/African American MSM. In 2009, MSM accounted for 61% of new infections, heterosexual contact 27%, injection drug use (IDU) 9%, and MSM/IDU 3%. Conclusions/Significance Overall, HIV incidence in the United States was relatively stable 2006–2009; however, among young MSM, particularly black/African American MSM, incidence increased. HIV continues to be a major public health burden, disproportionately affecting several populations in the United States, especially MSM and racial and ethnic minorities. Expanded, improved, and targeted prevention is necessary to reduce HIV incidence.

Life expectancy after HIV diagnosis based on national HIV surveillance data from 25 states, United States.

Introduction:We estimate life expectancy and average years of life lost (AYLL) after an HIV diagnosis using population-based surveillance data from 25 states that have had name-based HIV surveillance since 1996. Methods:We used US national HIV surveillance data (cases ≥13 years old) to model life expectancy after an HIV diagnosis using the life table approach. We then compared life expectancy at HIV diagnosis with that in the general population of the same age, sex, and race/ethnicity in the same calendar year using vital statistics data to estimate the AYLL due to an HIV diagnosis. Results:Average life expectancy after HIV diagnosis increased from 10.5 to 22.5 years from 1996 to 2005. Life expectancy (years) was better for females than for males but improved less for females (females: 12.6-23.6 and males: 9.9-22.0). In 2005, life expectancy for black males was shortest, followed by Hispanic males and then white males. AYLL for cases diagnosed in 2005 was 21.1 years (males: 19.1 and females: 22.7) compared with 32.9 years in 1996. Conclusions:Disparity in life expectancy for females and both black and Hispanic males, compared with males and white males, respectively, persists and should be addressed.

Risk Factor Redistribution of the National HIV/AIDS Surveillance Data: An Alternative Approach

Objective. The purpose of this study was to assess an alternative statistical approach—multiple imputation—to risk factor redistribution in the national human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) surveillance system as a way to adjust for missing risk factor information. Methods. We used an approximate model incorporating random variation to impute values for missing risk factors for HIV and AIDS cases diagnosed from 2000 to 2004. The process was repeated M times to generate M datasets. We combined results from the datasets to compute an overall multiple imputation estimate and standard error (SE), and then compared results from multiple imputation and from risk factor redistribution. Variables in the imputation models were age at diagnosis, race/ethnicity, type of facility where diagnosis was made, region of residence, national origin, CD-4 T-lymphocyte cell count within six months of diagnosis, and reporting year. Results. In HIV data, male-to-male sexual contact accounted for 67.3% of cases by risk factor redistribution and 70.4% (SE=0.45) by multiple imputation. Also among males, injection drug use (IDU) accounted for 11.6% and 10.8% (SE=0.34), and high-risk heterosexual contact for 15.1% and 13.0% (SE=0.34) by risk factor redistribution and multiple imputation, respectively. Among females, IDU accounted for 18.2% and 17.9% (SE=0.61), and high-risk heterosexual contact for 80.8% and 80.9% (SE= 0.63) by risk factor redistribution and multiple imputation, respectively. Conclusions. Because multiple imputation produces less biased subgroup estimates and offers objectivity and a semiautomated approach, we suggest consideration of its use in adjusting for missing risk factor information.

County-Level Socioeconomic Status and Survival After HIV Diagnosis, United States

PURPOSE To estimate relative survival (RS) after human immunodeficiency virus (HIV) diagnosis, by race/ethnicity and county-level socioeconomic status (SES). METHODS We estimated 5-year RS by age, race/ethnicity, transmission category, sex, diagnosis year, CD4 count, and by county-level SES variables from the U.S. Census. Data, from the national HIV/AIDS Reporting System, were for HIV-infected persons ages > or =13 years (diagnosis during 1996-2003 and follow-up through 2005). We calculated RS proportions by using a maximum likelihood algorithm and modeled the relative risk of excess death (RR) using generalized linear models, with poverty as a random effect. RESULTS For men, RS was worse in counties with larger proportions of people living below the 2000 U.S. poverty level (87.7% for poverty of > or =20% vs. 90.1% for poverty of <5.0%) and where unemployment was greater (87.8% where unemployment > 7.1% vs. 90.5% where unemployment < 4.0%). The effects of county-level SES on RS of women were similar. In multilevel multivariate models, RR for men and women within 5 years after an HIV diagnosis was significantly worse in counties where 10.0-19.9% (compared with <5.0%) lived below the poverty level (RR = 1.3 [95% CI 1.2-1.5] and RR = 1.8 [95% CI 1.4-2.2], respectively). CONCLUSIONS RS was worse in lower SES areas. To help address the impact of county-level SES, resources for HIV testing, care, and proven economic interventions should be directed to areas with concentrations of economically disadvantaged people.

Identifying the Impact of Social Determinants of Health on Disease Rates Using Correlation Analysis of Area-Based Summary Information

Objectives. We developed a statistical tool that brings together standard, accessible, and well-understood analytic approaches and uses area-based information and other publicly available data to identify social determinants of health (SDH) that significantly affect the morbidity of a specific disease. Methods. We specified AIDS as the disease of interest and used data from the American Community Survey and the National HIV Surveillance System. Morbidity and socioeconomic variables in the two data systems were linked through geographic areas that can be identified in both systems. Correlation and partial correlation coefficients were used to measure the impact of socioeconomic factors on AIDS diagnosis rates in certain geographic areas. Results. We developed an easily explained approach that can be used by a data analyst with access to publicly available datasets and standard statistical software to identify the impact of SDH. We found that the AIDS diagnosis rate was highly correlated with the distribution of race/ethnicity, population density, and marital status in an area. The impact of poverty, education level, and unemployment depended on other SDH variables. Conclusions. Area-based measures of socioeconomic variables can be used to identify risk factors associated with a disease of interest. When correlation analysis is used to identify risk factors, potential confounding from other variables must be taken into account.

Using CD4 Data to Estimate HIV Incidence, Prevalence, and Percent of Undiagnosed Infections in the United States.

Introduction: The incidence and prevalence of HIV infection are important measures of HIV trends; however, they are difficult to estimate because of the long incubation period between infection and symptom development and the relative infrequency of HIV screening. A new method is introduced to estimate HIV incidence, prevalence, and the number of undiagnosed infections in the United States using data from the HIV case surveillance system and CD4 test results. Methods: Persons with HIV diagnosed during 2006–2013 and their CD4 test results were used to estimate the distribution of diagnosis delay from HIV infection to diagnosis based on a well-characterized CD4 depletion model. This distribution was then used to estimate HIV incidence, prevalence, and the number of undiagnosed infections. Results: Applying this method, we estimated that the annual number of new HIV infections decreased after 2007, from 48,300 (95% confidence interval [CI]: 47,300 to 49,400) to 39,000 (95% CI: 36,600 to 41,400) in 2013. Prevalence increased from 923,200 (95% CI: 914,500 to 931,800) in 2006 to 1,104,600 (95% CI: 1,084,300 to 1,124,900) in 2013, whereas the proportion of undiagnosed infections decreased from 21.0% in 2006 (95% CI: 20.2% to 21.7%) to 16.4% (95% CI: 15.7% to 17.2%) in 2013. Conclusions: HIV incidence, prevalence, and undiagnosed infections can be estimated using HIV case surveillance data and information on first CD4 test result after diagnosis. Similar to earlier findings, the decreases in incidence and undiagnosed infections are encouraging but intensified efforts for HIV testing and treatment are needed to meet the goals of the National HIV/AIDS Strategy.

Population-Based Estimates of Life Expectancy After HIV Diagnosis: United States 2008-2011.

Introduction: Using National HIV surveillance system data, we estimated life expectancy and average years of life lost (AYLL) among persons diagnosed with HIV infection during 2008–2011. Methods: Population-based surveillance data, restricted to persons with diagnosed HIV infection aged 13 years or older, from all 50 states and Washington, D.C. were used to estimate life expectancy after HIV diagnosis using the life table method. Generated estimates were compared with life expectancy in the general population in the same calendar year to calculate AYLL. Life expectancy and AYLL were also estimated for subgroups by age, sex, and race/ethnicity. Results: The overall life expectancy after HIV diagnosis in the United States increased by 3.43 years from 25.43 (95% CI: 25.37 to 25.49) in 2008 to 28.86 (95% CI: 28.80 to 28.92) in 2011. Improvements were observed irrespective of sex, race/ethnicity, transmission category, and stage of disease at diagnosis, though the extent of improvement varied by different characteristics. Based on the life expectancy in the general population, in 2010, the AYLL were 12.8 years for males and 16.5 years for females. By race/ethnicity, on average, blacks (13.3 years) and whites (13.4 years) had fewer AYLL than Hispanics/Latinos (14.7). Conclusions: Despite improvements in life expectancy among people diagnosed with an HIV infection during 2008–2011, disparities by sex and by race/ethnicity persist. Targeted efforts should continue to further reduce disparities and improve life expectancy after HIV diagnosis.

Brief report: Time from infection with the human immunodeficiency virus to diagnosis, United States.

HIV testing efforts increased in recent years to reduce the percentage of persons with HIV unaware of their infection and to detect HIV early. An analysis of CD4 data from national HIV surveillance indicates that diagnosis delays decreased during 2003-2011; on average, persons diagnosed in 2011 had been infected 5.6 years before their diagnosis compared with 7.0 years among those diagnosed in 2003. Diagnosis delays were longer among females, blacks, Hispanics/Latinos, and older persons, but shorter among men who have sex with men, compared with their counterparts. Continued efforts to implement routine testing can help reduce diagnosis delays.

Mean Recency Period for Estimation of HIV-1 Incidence with the BED-Capture EIA and Bio-Rad Avidity in Persons Diagnosed in the United States with Subtype B

HIV incidence estimates are used to monitor HIV-1 infection in the United States. Use of laboratory biomarkers that distinguish recent from longstanding infection to quantify HIV incidence rely on having accurate knowledge of the average time that individuals spend in a transient state of recent infection between seroconversion and reaching a specified biomarker cutoff value. This paper describes five estimation procedures from two general statistical approaches, a survival time approach and an approach that fits binomial models of the probability of being classified as recently infected, as a function of time since seroconversion. We compare these procedures for estimating the mean duration of recent infection (MDRI) for two biomarkers used by the U.S. National HIV Surveillance System for determination of HIV incidence, the Aware BED EIA HIV-1 incidence test (BED) and the avidity-based, modified Bio-Rad HIV-1/HIV-2 plus O ELISA (BRAI) assay. Collectively, 953 specimens from 220 HIV-1 subtype B seroconverters, taken from 5 cohorts, were tested with a biomarker assay. Estimates of MDRI using the non-parametric survival approach were 198.4 days (SD 13.0) for BED and 239.6 days (SD 13.9) for BRAI using cutoff values of 0.8 normalized optical density and 30%, respectively. The probability of remaining in the recent state as a function of time since seroconversion, based upon this revised statistical approach, can be applied in the calculation of annual incidence in the United States.