HIV viral load levels and CD4+ cell counts of youth in 14 cities : AIDS

Secondary Logo

Journal Logo

Epidemiology and Social: CONCISE COMMUNICATIONS

HIV viral load levels and CD4+ cell counts of youth in 14 cities

Ellen, Jonathan M.a; Kapogiannis, Billb; Fortenberry, J. Dennisc; Xu, Jiahongd; Willard, Nancye; Duval, Annae; Pace, Jillb; Loeb, Jackied; Monte, Dinad; Bethel, Jamesd the Adolescent Medicine Trials Network for HIVAIDS Interventions

Author Information
AIDS 28(8):p 1213-1219, May 15, 2014. | DOI: 10.1097/QAD.0000000000000183
  • Free



The National HIV/AIDS Strategy emphasizes the importance of linkage to care (LTC) for persons with newly diagnosed HIV infection [1]. Timely diagnosis and initiation of care has been associated with a reduction in patients diagnosed with late-stage clinical disease and improvements of temporal mortality trends [2]. Furthermore, in a large prospective cohort of HIV-infected adults, temporal increases in HAART utilization were associated with increases in viral load suppression rates and higher median CD4+ T-cell counts [3]. Early linkage to and retention in care also may lead to earlier viral load suppression and may lower the cumulative individual viral load burden. Moreover, on an epidemiologic scale, improved viral load suppression is associated with reductions in community viral loads and with reductions in new cases of HIV infection [4–8]. However, this landscape is vastly unexplored and not characterized in adolescents and young adults, who represent a growing concern domestically.

In the United States, HIV disproportionately impacts vulnerable youth and minority MSM populations. Youth ages 13–24 years accounted for 26% of incident HIV infections in 2010 [9]. About 87% of all new youth infections among men occurred in young MSM (YMSM) [9]. About 60% of youth with HIV do not know they are infected and cannot receive treatment [9]. Among black YMSM, new HIV infections increased 48% from 2006 to 2009 [10].

Strategic Multisite Initiative for the Identification, Linkage and Engagement in Care of Youth with Undiagnosed HIV Infection (SMILE in Caring for Youth) is a National Institutes of Health, Centers for Disease Control and Prevention, and Adolescent Medicine Trials Network for HIV/AIDS Interventions (ATN) collaboration designed to improve the identification of youth with undiagnosed HIV infection, link them to care at a clinical site with adolescent HIV medicine expertise, and to ultimately inform best practices for the continuum of care for youth with HIV in the United States. The objective of this article is to describe the HIV viral load and CD4+ cell counts of youth reported to the SMILE program.


Strategic Multisite Initiative for the Identification, Linkage and Engagement in Care of Youth with Undiagnosed HIV Infection program

The SMILE program was established at 15 ATN sites located in 14 cities in the United States and Puerto Rico. An ATN adolescent-expert outreach worker was provided as a clinical resource to testing facilities and health departments to help facilitate the linkage service process between the testing and treatment sites. This was accomplished by creation of memoranda of understanding between local collaborating ATN sites and testing and health department sites, which delineated roles and responsibilities and included aspects on communication and bidirectional data sharing. The study was conducted between March 2010 and November 2011.

An administrative database was created to collect the records of HIV-positive youth, ages 12–24, who were referred as a new case, linked to care within 42 days of referral, and engaged in care within 16 weeks of linkage. Additionally, demographics, case dispositions, and reasons for failure in care linkage/engagement were recorded by the outreach worker and included for both ATN sites and their affiliates.

ATN 093

ATN 093 was a research protocol designed to evaluate the effectiveness of the SMILE program using de-identified data abstracted electronically. Data were electronically abstracted in a central location and in an anonymous manner through a random computer-generated coding system without any ability to link codes to individual cases. This anonymized research database provided HIV viral load and CD4+ cell count data that were compared by demographics, socioeconomics, risk behaviors, and ATN site using Kruskal–Wallis tests. Both univariate and multivariable linear regression analyses were performed to assess the relationship of viral load with potentially associated factors. Because of skewed data, raw viral load was transformed using ranks for the regression analysis. Covariates with an unadjusted P value <0.05 were entered into the initial full multivariable model and retained in the final model if the overall P value was less than 0.05 after the model selection. Data included in this analysis were abstracted for cases reported to SMILE that had both CD4+ cell count and viral load levels from 2010 to 2011. To evaluate the possible effect of excluding these cases, the overall mean viral load was recalculated including the excluded cases and assuming that patients of the same sexual orientation and HIV acquisition mode would have about the same viral load as those who were included. Each ATN site's local Institutional Review Board approved or exempted the protocol prior to implementation.


There were 1409 cases reported to SMILE [median age 21 (12–24 years), 78.1% men, 18.0% women, and 3.5% transgendered; 18.3% Hispanic/Latino ethnicity, 67.1% black, 5.7% white]. Among all 1409 cases, 852 (60%) participants had data on both CD4+ cell counts and viral load for this analysis. The remaining 557 (40%) did not have both data points and were not included in this analysis. Table 1 shows a comparison of demographics and risk characteristics of these two groups. Of the 852 with sufficient data, 671 (78.8%) were linked to care during the study period, 119 (14%) had been linked to care prior to the study, and 62 (7.3%) that could not be located or were not linked to care for other reasons. The mean viral load and CD4+ cell counts were 94 398 copies/ml and 456 cells/μl, respectively.

Table 1-a:
Selected demographic, sexual risk characteristics, and testing locations by viral loads and CD4+ cell count availability status among selected 1409 clients from SMILE program as of 30 November 2011.
Table 1-b:
Selected demographic, sexual risk characteristics, and testing locations by viral loads and CD4+ cell count availability status among selected 1409 clients from SMILE program as of 30 November 2011.

Table 2 shows the mean viral load and its distribution over a range of categories (<400 to >100 000 RNA copies/ml) by demographic and clinical variables. Young men had significantly higher viral loads than young women. Whereas there were variations by site, with median viral load ranging from 4427 to 30 363, there were no significant differences by geographic region. Youth who self-reported their sexual orientation as homosexual or bisexual had higher viral loads compared with those reporting to be heterosexual. More than 30% of homosexual or bisexual youth had viral loads greater than 50 000 copies, whereas 21.5% of heterosexual youth had viral loads greater than 50 000 copies. In addition, youth whose transmission risk was male-to-male sexual contact had higher viral loads compared with youth whose transmission risk was perinatal or heterosexual contact. Greater than 30% of those who report male-to-male sexual contact had viral loads greater than 50 000 copies, whereas less than 20% of heterosexual contact youth had viral loads greater than 50 000 copies. In an adjusted, multivariable model, only CD4+ cell count and transmission risk were significantly associated with viral load. Sensitivity analyses based on sexual orientation and mode of HIV acquisition indicated that the overall mean viral load may be overestimated by about 4.6% due to excluding cases for which viral load and CD4+ cell data were not available.

Table 2-a:
HIV viral load by selected demographic, sexual risk characteristics, and testing locations.
Table 2-b:
HIV viral load by selected demographic, sexual risk characteristics, and testing locations.


These data show that almost 30% of infected youth have HIV viral load levels of 50 000 copies or more and that the majority of youth have CD4+ cell counts below 500. These viral load levels are associated with high rates of transmission, both in person-years and per coital act [11,12]. YMSM are well represented in these data, which is consistent with testing frequency [13], and they have the highest viral loads, which is consistent with the high rates of ongoing transmission in this population in the United States [14,15].

These data provide important benchmarks for evaluations of ‘treatment as prevention’ strategies and indicate areas of focus for additional secondary transmission interventions. Few data allow direct comparison of disease status at entry into care among adolescents and young adults. A study using San Francisco HIV surveillance data of 12 512 HIV-positive men and women from 2005 to 2008 found a population mean viral load of 23 348 and that among MSM, the mean viral load was 36 764 (compared with 94 398 and 115 213, respectively in our study) [6]. A study of 676 HIV-infected persons entering care (mean age 36 years) in Birmingham, and Seattle, Washington HIV clinics found that the mean viral load of these individuals to be approximately 39 000 [3]. Finally, a study of 4684 men and women diagnosed with HIV infection in the District of Columbia in 2008 (mean age 45) found a mean viral load of 33 847 [16].

We recognize that the use of mean viral load as an indicator of transmission and incidence at the community or network level has its limitations. As supported by our higher mean CD4+ cell counts and proportions with CD4+ cell count more than 350 (over 2/3) compared with adults (43%) [4], youth are likely diagnosed closer to their time of infection than adults and therefore, viral dynamics in earlier HIV infection may skew viral load data to higher levels characteristic of this stage of infection. Additionally, as Miller et al. argue [17], among other things, the mean viral load of a study population may not be representative of ongoing transmission because of selection bias (who was tested and linked to care and from whom viral load information was collected). This is clearly a limitation of our study. Further, they contend that infectivity, as measured by viral load, is only one of several determinants of transmission. The prevalence of HIV and the mixing patterns within a community are also important factors not captured by the assessment of the mean viral load of a population. Finally, the necessary exclusion of participants missing viral load data may have resulted in an overestimate of the mean viral load by about 4.6%.

Nonetheless, in the context of existing literature, our findings suggest that the population of HIV-infected youth may be a highly infectious population, suggesting they are being identified at an earlier critical window of opportunity for prevention. This concern is most acute in YMSM. If these youth remain untreated, they might support high rates of ongoing transmission at the community level. With new treatment guidelines recommending treatment for all HIV-positive individuals, emphasis needs to be placed on breaking down barriers to care [18]. Furthermore, it is essential that any public health test and treat strategy put a strong emphasis on youth, particularly YMSM.


The authors would like to acknowledge the entire 093 protocol team, including our partners at Westat, the Centers for Disease Control and Prevention, and the 14 US ATN sites conducting ATN 093.

J.M.E., B.K., J.D.F. provided substantial contribution to concept, design, analysis, and interpretation of data; contributed to drafting, reviewing, and revising the article; and provided final approval for publication.

J.X., N.W., J.P., J.L. and J.B. provided substantial contribution to analysis and interpretation of data; contributed to drafting, reviewing, and revising the article; and provided final approval for publication.

A.Du. and D.M. provided substantial contributions to data acquisition, reviewing and revising article, and provided final approval for publication.

ATN 093 was supported by The Adolescent Medicine Trials Network for HIV/AIDS Interventions (ATN) from the National Institutes of Health (U01 HD 040533 and U01 HD 040474) through the National Institute of Child Health and Human Development (B. Kapogiannis, C. Worrell), with supplemental funding from the National Institutes on Drug Abuse (K. Davenny) and Mental Health (P. Brouwers, S. Allison).

Conflicts of interest

There are no conflicts of interest to report.


1. Millett GA, Crowley JS, Koh H, Valdiserri RO, Frieden T, Dieffenbach CW, et al. A way forward: the National HIV/AIDS Strategy and reducing HIV incidence in the United States. J Acquir Immune Defic Syndr 2010; 55 (Suppl 2):S144–S147.
2. Seal PS, Jackson DA, Chamot E, Willig JH, Nevin CR, Allison JJ, et al. Temporal trends in presentation for outpatient HIV medical care 2000-2010: implications for short-term mortality. J Gen Intern Med 2011; 26:745–750.
3. Mugavero MJ, Amico KR, Westfall AO, Crane HM, Zinski A, Willig JH, et al. Early retention in HIV care and viral load suppression: implications for a test and treat approach to HIV prevention. J Acquir Immune Defic Syndr 2012; 59:86–93.
4. Tanser F, Barnighausen T, Grapsa E, Zaidi J, Newell ML. High coverage of ART associated with decline in risk of HIV acquisition in rural KwaZulu-Natal, South Africa. Science 2013; 339:966–971.
5. Montaner JS, Lima VD, Barrios R, Yip B, Wood E, Kerr T, et al. Association of highly active antiretroviral therapy coverage, population viral load, and yearly new HIV diagnoses in British Columbia, Canada: a population-based study. Lancet 2010; 376:532–539.
6. Das M, Chu PL, Santos GM, Scheer S, Vittinghoff E, McFarland W, et al. Decreases in community viral load are accompanied by reductions in new HIV infections in San Francisco. PLoS One 2010; 5:e11068.
7. Gill VS, Lima VD, Zhang W, Wynhoven B, Yip B, Hogg RS, et al. Improved virological outcomes in British Columbia concomitant with decreasing incidence of HIV type 1 drug resistance detection. Clin Infect Dis 2010; 50:98–105.
8. Cohen MS, Chen YQ, McCauley M, Gamble T, Hosseinipour MC, Kumarasamy N, et al. Prevention of HIV-1 infection with early antiretroviral therapy. N Engl J Med 2011; 365:493–505.
9. Centers for Disease Control, PreventionVital signs: HIV among youth in the U.S. MMWR Morb Mortal Wkly Rep 2012; 47:61.
10. Prejean J, Song R, Hernandez A, Ziebell R, Green T, Walker F, et al. Estimated HIV incidence in the United States, 2006–2009. PLoS One 2011; 6:e17502.
11. Quinn TC, Wawer MJ, Sewankambo N, Serwadda D, Li C, Wabwire-Mangen F, et al. Viral load and heterosexual transmission of human immunodeficiency virus type 1. Rakai Project Study Group. N Engl J Med 2000; 342:921–929.
12. Wawer MJ, Gray RH, Sewankambo NK, Serwadda D, Li X, Laeyendecker O, et al. Rates of HIV-1 transmission per coital act, by stage of HIV-1 infection, in Rakai, Uganda. J Infect Dis 2005; 191:1403–1409.
13. Chandra A, Billioux VG, Copen CE, Balaji A, DiNenno E. HIV testing in the U.S. household population aged 15–44: data from the National Survey of Family Growth. National Health Statistics Report 2012; 54:1–27.
14. Millett GA, Peterson JL, Wolitski RJ, Stall R. Greater risk for HIV infection of black men who have sex with men: a critical literature review. Am J Public Health 2006; 96:1007–1019.
15. Flores SA, Bakeman R, Millett GA, Peterson JL. HIV risk among bisexually and homosexually active racially diverse young men. Sex Transm Dis 2009; 36:325–329.
16. Castel AD, Befus M, Willis S, Griffin A, West T, Hader S, et al. Use of the community viral load as a population-based biomarker of HIV burden. AIDS 2012; 26:345–353.
17. Miller WC, Powers KA, Smith MK, Cohen MS. Community viral load as a measure for assessment of HIV treatment as prevention. Lancet Infect Dis 2013; 13:459–464.
18. Panel on Antiretroviral Guidelines for Adults and Adolescents. Guidelines for the use of antiretroviral agents in HIV-1-infected adults and adolescents. Department of Health and Human Services. 2013. Retrieved from

CD4+ cell count; HIV; linkage to care; viral load; youth

© 2014 Lippincott Williams & Wilkins, Inc.