Saathoff, Elmar PhD*; Pritsch, Michael MD*; Geldmacher, Christof PhD*; Hoffmann, Oliver MD*; Koehler, Rebecca N PhD†; Maboko, Leonard MD, PhD‡; Maganga, Lucas MD‡; Geis, Steffen MD*‡; McCutchan, Francine E PhD†§; Kijak, Gustavo H PhD†; Kim, Jerome H MD†; Arroyo, Miguel A PhD∥; Gerhardt, Martina PhD*‡; Tovanabutra, Sodsai PhD†; Robb, Merlin L MD†¶; Williamson, Carolyn PhD#; Michael, Nelson L MD, PhD†**; Hoelscher, Michael MD, PhD*
Sub-Saharan Africa is most heavily affected by the HIV epidemic. In some countries, it has reduced overall life expectancy by more than 20 years.1 If untreated, infected individuals show an extreme heterogeneity in the clinical course and outcome of HIV infection. The identification of factors that influence the natural course of infection is of great importance for prognosis and for the timing of antiretroviral treatment.
The viral load is an important predictor of HIV-1 disease progression. Higher viral loads are associated with faster progression to AIDS and death.2 During acute HIV-1 infection, the viral load reaches peak levels that subsequently drop to a lower, more stable level of viremia, known as the viral load setpoint (VLS). This is explained by the balance between the virulence of the infecting virus and the host immune system's potential to control the infection.3 Because there is no standard method for the calculation of the VLS, researchers use different empirical approaches.4 Despite these methodological differences, the association between an elevated VLS and faster disease progression to AIDS is widely accepted.5-7 The VLS can thus be used as a prognostic marker to identify individuals at risk for rapid disease progression. Such prognostic markers may lead to a better understanding of HIV-1 infection, improved clinical monitoring, and a better timing of the initiation of antiretroviral therapy. Virus-related and host-related factors play an important role in determining the VLS. Thus the VLS can differ considerably between individuals and between populations.
The HIV-1 epidemic is characterized by high genetic diversity with multiple subtypes and circulating and unique intersubtype recombinant forms in different parts of the world.8,9 Previous studies suggest that the infecting subtype and multiple HIV infection are important factors that might influence the VLS and HIV disease progression.9-11 Possible host-related factors associated with differences in VLS include gender, age, race, other diseases, and human genetic variation.12-14
The impact of HLA class I alleles on viral load during the chronic phase of HIV has been examined in 2 studies in South Africa, where different alleles were identified as either “protective” or “harmful” according to their effect on viremia at VLS.15,16 The expression of protective HLA class I alleles is thought to correlate with HIV-specific CD8 T-cell responses of potent antiviral efficiency.17,18 However, only very limited data regarding the VLS and its correlates exist for sub-Saharan Africa.19
The main objectives of our study were to determine the VLS in our study population and to identify virus and host factors that might have an impact on the VLS. Below we therefore examine the association of the VLS with HLA class I genetic background, infection with different HIV-1 subtypes, and with sociodemographic and behavioral factors.
Data for this study were collected from HIV seroconverters who were identified in 2 different cohorts from Mbeya Region in southwestern Tanzania. All laboratory and cohort work done in these 2 studies was in accordance with the Helsinki Declaration of 1975 as revised in 2000 and was also approved by the appropriate ethics committees of involved partners. All participants provided written informed consent before enrollment.
Longitudinal HIV Superinfection Study
The seroconverters in this study were part of a larger well-characterized high-risk open cohort of female bar workers enrolled in a prospective study of HIV-1 infection in Mbeya Region.20 A total of 753 female volunteers, aged between 18 and 35 years were recruited from September 2000 onwards from 17 high-transmission areas located along the Pan-African Highway. Each participant provided blood samples at enrollment and every 3 months thereafter, for a period of up to 4 years. During the study, all participants received health care that included treatment of acute infectious diseases, screening and-if necessary-treatment for sexually transmitted diseases and, since 2003, cotrimoxazole prophylaxis against opportunistic infections for women with CD4+ T-cell counts below 200 cells per microliter. Since 2005, antiretroviral treatment has been available for participants with AIDS-defining symptoms or CD4 T-cell counts below 200 cells per microliter. During the collection of data for this study, however, all individuals were antiretroviral naive.
Between September 2002 and April 2003, 3096 volunteers (1778 females and 1318 males) from the general population were recruited from 1 rural and 2 urban sites in and around Mbeya town. Cohort-development (CODE) participants were followed-up at approximately 6 monthly intervals for 3.5 years.21
Below, we refer to the 3 different groups of seroconverting participants, HIV Superinfection Study (HISIS) female barworkers, CODE general population females, and CODE general population males as “bar workers”, “CODE females” and “CODE males” to simplify the narrative of our observations in this article. The HIV prevalence at enrollment into the 2 cohorts was 67%, 19%, and 14% in these 3 groups, respectively.21,22
CODE and HISIS participants were only included into the below analysis if they were HIV negative at enrollment (247 barworkers, 1440 CODE females, and 1138 CODE males) and got HIV infected during follow-up (49 barworkers, 63 CODE females, and 38 CODE males). Further requirements were that the last HIV-negative visit was less than 9 months before the first HIV-positive visit (important to estimate the time point of infection with the necessary accuracy) and that participants also had attended at least 1 study visit after the first positive visit between 5 and 12 months after the estimated time point of infection (necessary for calculation of the VLS). These criteria were satisfied by 46 bar workers, 41 CODE females, and 21 CODE males, respectively. Of the 42 newly HIV-infected participants who could not be included (3 barworkers, 22 CODE females, and 17 Code males), 10 were lost to follow-up directly after their first HIV-positive visit (1 barworker, 4 CODE females, and 5 CODE males), 19 could not be followed-up because their first HIV-positive visit coincided with the last scheduled follow-up (2 barworkers, 12 CODE females, and 5 CODE males), and 13 participants missed 1 or more appointments after their first positive visit, so that the VLS could not be calculated, but returned at a later stage (6 CODE females and 7 CODE males).
Interviews, Clinical Examinations, and Specimen Collections
Interviews regarding the social, demographic, and behavioral background of participants were conducted in Kiswahili by trained staff members using structured questionnaires. The responses were recorded in English. Clinical examinations and specimen collections took place in a suitable locality.
The HIV serostatus was determined using 2 diagnostic enzyme-linked immunosorbent assay tests (Enzygnost Anti HIV1/2 Plus; Dade Behring, Liederbach, Germany and Determine HIV ½; Abbott, Wiesbaden, Germany). Discordant results were verified by HIV-1 Western Blot (HIVblot 2.2 Genelabs/Abbott, Wiesbaden, Germany). HIV-1 plasma RNA load was determined with the Roche Amplicor HIV-1 Monitor Test version 1.5 (Roche Diagnostics, Basel, Switzerland) with a range of quantitation of ≥400 to ≤750,000 virus copies per milliliter. The first HIV-positive study visit was defined as the visit where the participant was positive by viral load determination and/or serotesting, but the visit before had been negative both by viral load and by serotesting. This, and all subsequent samples over a period of up to 3.5 years were used for viral load determination. The criteria which of these viral load measurements were used for calculation of the VLS are described below.
CD4+ counts were performed using a FACSCalibur MultiSET System with Trucount tubes (Becton; Dickinson and Company, Franklin Lakes, NJ). The infecting HIV-1 subtype and the presence of infection with multiple HIV-1 subtypes were determined at VLS by the Multiregion Hybridization Assay (MHA), using subtype A-, C-, and D-specific fluorescent probes in 5 genomic regions in a real-time polymerase chain reaction format.8,18 Multiple infections were inferred when more than 1 subtype-specific probe hybridized to the same genomic region. HLA class I typing was performed as described by Turner et al23 and Koehler et al (High-Throughput High-Resolution Class I HLA Genotyping in East Africa; unpublished data). HLA class I alleles were classified as either “protective” (A*0205, B*5801, B*8101, B*4201 and B*5703), “harmful” (B*5802, B*4501, B*1801, and B*1503 in subtype C epidemic), or “neutral” (all others) according to previous studies.15,16 Due to logistic reasons, CD4+ count, HLA class I typing, and viral diversity determination were not done for all participants, resulting in lower participant numbers for these assays.
Serological examinations for syphilis were conducted using the Serodia Treponema pallidum particle agglutination assay (TPPA) (Fujirebio Inc, Tokyo, Japan) and the rapid plasma reagin test (VD25; Murex Diagnostics, United Kingdom).22,24 On enrollment, all participants were tested for Hepatitis B by enzyme-linked immunosorbent assay (MONOLISA HBsAg ULTRA, Bio-Rad, Hercules, CA), and positive results were confirmed by a neutralization method of HBsAg (MONOLISA HBsAg Confirmation test, Bio-Rad).
Data were double entered into Microsoft Access (Microsoft Corp, Redmond, WA), compared and corrected for entry errors and analyzed using Stata 10 statistics software (Stata Corp, College Station, TX). Because both VLS and CD4+ cell counts were not normally distributed, differences of these parameters between groups were tested for significance using the nonparametric Wilcoxon rank sum test. The VLS was defined according to Mei et al4 as the median of the participants' viral load between 5 to 12 months after the estimated time point of infection. Because HISIS participants were seen at 3 monthly intervals, in most instances, their VLS was calculated from more than 1 VL assessment (2 participants with only 1 assessment, 40 with 2 assessments and 4 participants with 3 assessments), whereas none of the CODE participant had more than 1 VL assessment during this time span.
Probability values for group differences in binary characteristics (eg, presence/absence of multiple infections) was calculated using the Pearson χ2 test.25 Associations between different risk factors and a binary outcome that indicated whether the VLS was below or above the overall median were analyzed using univariate and multivariate Poisson regression models with robust variance estimates.26,27
To estimate the time point of infection, the average time span from infection to HIV seropositivity was assumed to be 33 days and that to virus positivity was assumed to be 11 days.28,29 The date of infection was estimated as the midpoint of the resulting time window. To give an example, for participants whose first HIV-positive study visit was both virus positive and seropositive, the infection time point was estimated as the midpoint between 11 days before the last negative and 33 days before the first positive visit. For participants, who were virus positive, but had not seroconverted at their first HIV positive follow-up, the time point of infection was estimated as 22 days before this follow-up.
Social and Demographic Characteristics
Selected characteristics of the 3 groups of seroconverters at enrollment into their respective cohorts are shown in Table 1. The median age was between 22 and 25 years. The prevalence of Hepatitis B and Syphilis Treponema pallidum particle agglutination assay positivity were higher among bar workers (17.4% and 41.3%, respectively) than among CODE participants (7.1% and 12.5% for females and 0% and 4.8% for males).
Comparison of the characteristics shown in Table 1 and of initial viral loads and CD4 counts of the included participants with those of the 42 newly HIV-infected subjects who could not be included into this study revealed some differences between these 2 groups. However, none of these differences was statistically significant at the 95% confidence level.
HIV-1 Viremia and CD4 Counts at VLS
The mean time between the estimated time point of infection and the mean date of VLS measurements was 8.4 months (range: 5.4-10.1) for bar workers, 9.1 months (5.7-11.9) for CODE females, and 8.7 months (6.1-9.7) for CODE males. The overall median viremia at VLS for all 3 participant groups was 69,850 copies per milliliter, which was identical to the median VLS for bar workers (Table 2). Viremia at VLS was lower for female CODE participants (28,600 copies/mL) and higher for male CODE participants (158,000 copies/mL), and this difference between CODE females and males was significant (P = 0.011). Concordantly, CODE males had a significantly lower median CD4+ T-cell count at VLS than the 2 female groups.
The 3 groups also differed by their proportion of participants with a VLS below 2000 copies per milliliter (referred to as “VLS controllers” below). Although 26.8% of CODE females were VLS controllers, only 8.7% of the bar workers (P = 0.025) and 9.5% of CODE men (P = 0.113) had viral loads below 2000 copies per milliliter at VLS (Table 2). This is also demonstrated in Figure 1, where, however, only participants with complete data were included.
When excluding the VLS controllers, the median VLS in CODE women is 2.8 times higher (81,100 instead of 28,600 copies/mL) than if VLS controllers are included, but only 1.7 times higher in bar workers (116,825 instead of 69,850 copies/mL). The previously observed differences in VLS are thus paralleled by different frequencies of VLS controllers in the 3 groups.
We also analyzed the development of viremia over time for the 3 groups individually, excluding VLS controllers and for all VLS controllers separately (Figure 2). All seroconverters for whom we had a valid VLS estimate were included for all time ranges for which they had VL data. Despite small participant numbers for time intervals beyond 2 years after infection, the viral load levels remain relatively stable in bar workers and CODE males, they even decline over time in CODE females. However, this pattern could be influenced by a higher drop out rate of participants with elevated viral loads.
HLA Class I Alleles, HIV-1 Subtype and Multiple Infection
A subsequent analysis of the distribution of HLA class I alleles identified marked but nonsignificant differences between the 3 groups (Table 2). CODE males had the lowest proportion of protective (A*0205, B*5801, B*8101, B*4201, and B*5703) and the highest proportion of harmful alleles (B*5802, B*4501, B*1801, and B*1503 in subtype C epidemic).
There was no major difference in subtype distribution between male and female CODE participants with more than 50% of new infections caused by subtype C and only few multiple HIV infections (6.3% and 11.1%, respectively). In contrast, participants who were infected with multiple HIV subtypes at VLS represented the largest group (36.4%) in the bar workers, where subtype C caused only 24% of new infections. The large number of multiple HIV infections in female bar workers at VLS is especially striking when considering that this was determined only 5-12 months after HIV infection.
Risk Factors for Elevated VLS
To identify potential risk factors for elevated viremia at VLS, we initially performed univariate Poisson regression analyses on a binary outcome variable that indicated whether the individual viremia at VLS was above or below the median for all participants (69,850 copies/mL). If the univariate P value for at least 1 stratum of these risk factor variables was ≤0.1, they were included into a multivariate model to examine their association with the VLS for mutual independence. The univariate and multivariate results for these variables are shown in Table 3.
Male gender, the presence of harmful HLA class I alleles, and infection with multiple HIV-1 subtypes were strongly and significantly associated with the VLS both in univariate and multivariate analysis. CODE males had a 76% higher risk of having an elevated VLS than CODE females. The presence of harmful HLA class I alleles was associated with a 70% increase in elevated VLS and infection with multiple HIV-1 subtypes with a 65% increase when comparing with the respective control groups.
Bar workers were considerably (35%) more likely to have an elevated viral load than general population females and participants with protective HLA class I alleles had a 40% lower prevalence of elevated viral loads than those with only neutral HLA class I alleles. However, both these differences were not significant. The similarity of point estimates and significance of the above associations in univariate and multivariate analysis indicates that their respective influence is largely independent of the other factors.
Potential risk factors that did not qualify for initial inclusion into the above model were re-examined by introducing them into this model one by one to see whether their association to the viremia at setpoint would change when adjusted for other important variables. However, all other biological factors such as the infecting subtype (A, C, D, or recombinant strains), and syphilis and hepatitis B infection at enrollment were unassociated with the viremia at setpoint (data not shown). Similarly neither age nor the other sociodemographic and behavioral factors that we examined (religious denomination, education level, household size, marital status, number of children, use of contraceptives, and previous HIV testing) showed an association with the VLS nor did they exhibit any strong influence on other variables when added to the multivariate model.
In HIV infection, the VLS is an important and generally accepted predictor for the progression to AIDS. It is also an important marker for the evaluation of vaccines and microbicides that do not protect from infection but may ameliorate viral load. A comparison of our VLS results with those from other parts of sub-Saharan Africa shows that the median VLS of CODE females is at the lower end of the spectrum of 4.45 to 4.61 log10 RNA copies per milliliter found in 3 other studies from East Africa concerning females from the general population.3,9,30 In contrast, the bar workers in our study had a median VLS that was well above the range found in 4 other studies from sub-Saharan Africa concerning high-risk females (3.71 to 4.33 log10 copies/mL),10,19,31,32 and the VLS of CODE males also was considerably higher than those from 2 other studies concerning general population males in other parts of East Africa (4.74 and 4.76 log10 copies/mL).3,9 Our finding that general population males had a higher VLS than females is in accordance with 2 studies from East Africa that included both sexes and also with findings from other parts of the world, although this difference does not seem to imply a difference in HIV progression between sexes.3,9,33 A final biological or behavioral explanation for this phenomenon is still lacking.
Participants with multiple HIV-1 infections had a higher risk of elevated viremia compared with those with a single infection. This important finding is in agreement with the results of a previous smaller study10 and is also consistent with the finding that multiple HIV infections can accelerate disease progression.34
Although one would expect more multiple infections with different HIV subtypes in high-risk populations, the difference in prevalence of multiple HIV infections between female bar workers (36.4%) and the other 2 groups (6.3% and 11.1%) so early after seroconversion is striking. It would be interesting to determine whether these multiple infections are mostly acquired during 1 infection event (ie, transmission from an individual that is already multiply infected), or whether the multiple infection is caused by an additional (super-) infection. If the latter was true, this would hint to a higher susceptibility to HIV infection shortly after initial seroconversion. In any case, our data indicate that specifically within such subpopulations there is a high risk for coinfection with multiple HIV strains and that this is also a risk factor for elevated viral loads. Because high viral loads are associated with increased transmission risk, an existing multiple infection is thus likely to increase the risk of HIV transmission.
Unfortunately, the proportion of multiple infections is not fully comparable between the 2 cohorts. Cohort-specific differences in visit intervals (3 monthly in HISIS and 6 monthly in CODE), and thus the number of MHA tests performed may result in a lower sensitivity for detection of multiple HIV infections in CODE participants. Nevertheless, even if only 1 of the MHA assessments for bar workers had been used, as in CODE participants, where only 1 assessment was available, the proportion of multiple infections in the bar workers would still have been 3 times as high as in male and female CODE participants combined (24% vs. 8%, P = 0.105).
The influence of host genetic polymorphisms within the HLA class I allele gene locus on HIV viral load and on disease progression during chronic infection is well-documented.15-17,35-38 Particularly, the expression of HLA class I allele B57 correlates with the absence of a symptomatic HIV-1 seroconversion illness.37 This is important because patients with more severe symptoms during acute HIV-1 infection and longer duration of the acute infection syndrome tend to progress more rapidly to AIDS.37 In addition, there is an association between HLA-B57 and favorable clinical, virological, and immunological events during acute HIV-1 infection.17 Our analysis demonstrates that the expression of harmful HLA class I alleles that are associated with poor viral control during the chronic phase of HIV is significantly associated with poor control even early after HIV infection. This indicates that viral control is not lost gradually over time but rather that it has never been efficient in these individuals in the first place.
Female bar workers had a 35% higher risk of having an elevated VLS than CODE females after adjustment for HLA type and multiple infections (P = 0.341). Although nonsignificant, this might hint to a real difference between general population females and high-risk females, which our model was unable to account for. One possible reason for this difference could be a higher prevalence of genital tract coinfections in this group, that, apart from syphilis, we did not assess during this study.
Figure 1 illustrates that VLS differences between the 3 groups were small when not considering the VLS controllers. However, this should not be interpreted as an explanation of the VLS differences between bar workers, CODE females, and CODE males because being able to control viremia at VLS is not an independent explanatory factor but just another way to categorize our outcome, the viremia at VLS.
In conclusion, our data show that gender, expression of different HLA class I alleles, and multiple infection with different HIV-1 subtypes are strongly associated with the viremia at VLS in our study population. The first 2 of these findings confirm results of previous studies from other parts of the world. The latter finding substantiates the results of a smaller study from South Africa10 but has-to our knowledge-not been reported from other studies yet. The infecting HIV-1 subtype, hepatitis B, and syphilis infection before seroconversion and the various sociodemographic and behavioral factors that we examined did not seem to influence the viremia at VLS in our study population. Another important result that merits further investigation in other settings is the high prevalence of infection with different HIV-1 subtypes early after seroconversion in the bar workers cohort.
We would like to thank the participants of this study for their patience and the MMRP staff involved in this study for their dedicated support.
1. UNAIDS. Report on the Global HIV/AIDS Epidemic 2008. Geneva, Switzerland: Joint United Nations Programme on HIV/AIDS (UNAIDS); 2008.
2. Mellors JW, Munoz A, Giorgi JV, et al. Plasma viral load and CD4+ lymphocytes as prognostic markers of HIV-1 infection. Ann Intern Med. 1997;126:946-954.
3. Richardson BA, Mbori-Ngacha D, Lavreys L, et al. Comparison of human immunodeficiency virus type 1 viral loads in Kenyan women, men, and infants during primary and early infection. J Virol. 2003;77:7120-7123.
4. Mei Y, Wang L, Holte SE. A comparison of methods for determining HIV viral set point. Stat Med. 2008;27:121-139.
5. Sterling TR, Vlahov D, Astemborski J, et al. Initial plasma HIV-1 RNA levels and progression to AIDS in women and men. N Engl J Med. 2001;344:720-725.
6. Hansmann A, Schim van der Loeff MF, Kaye S, et al. Baseline plasma viral load and CD4 cell percentage predict survival in HIV-1- and HIV-2-infected women in a community-based cohort in The Gambia. J Acquir Immune Defic Syndr. 2005;38:335-341.
7. Gottlieb GS, Sow PS, Hawes SE, et al. Equal plasma viral loads predict a similar rate of CD4+ T cell decline in human immunodeficiency virus (HIV) type 1- and HIV-2-infected individuals from Senegal, West Africa. J Infect Dis. 2002;185:905-914.
8. Hoelscher M, Dowling WE, Sanders-Buell E, et al. Detection of HIV-1 subtypes, recombinants, and dual infections in east Africa by a multi-region hybridization assay. AIDS. 2002;16:2055-2064.
9. Kiwanuka N, Laeyendecker O, Robb M, et al. Effect of human immunodeficiency virus Type 1 (HIV-1) subtype on disease progression in persons from Rakai, Uganda, with incident HIV-1 infection. J Infect Dis. 2008;197:707-713.
10. Grobler J, Gray CM, Rademeyer C, et al. Incidence of HIV-1 dual infection and its association with increased viral load set point in a cohort of HIV-1 subtype C-infected female sex workers. J Infect Dis. 2004;190:1355-1359.
11. Kanki PJ, Hamel DJ, Sankale JL, et al. Human immunodeficiency virus type 1 subtypes differ in disease progression. J Infect Dis. 1999;179:68-73.
12. Donnelly CA, Bartley LM, Ghani AC, et al. Gender difference in HIV-1 RNA viral loads. HIV Med. 2005;6:170-178.
13. Fellay J, Shianna KV, Ge D, et al. A whole-genome association study of major determinants for host control of HIV-1. Science. 2007;317:944-947.
14. Phillips A. Short-term risk of AIDS according to current CD4 cell count and viral load in antiretroviral drug-naive individuals and those treated in the monotherapy era. AIDS. 2004;18:51-58.
15. Kiepiela P, Leslie AJ, Honeyborne I, et al. Dominant influence of HLA-B in mediating the potential co-evolution of HIV and HLA. Nature. 2004;432:769-775.
16. Frahm N, Kiepiela P, Adams S, et al. Control of human immunodeficiency virus replication by cytotoxic T lymphocytes targeting subdominant epitopes. Nat Immunol. 2006;7:173-178.
17. Altfeld M, Kalife ET, Qi Y, et al. HLA Alleles associated with delayed progression to AIDS contribute strongly to the initial CD8(+) T cell response against HIV-1. PLoS Med. 2006;3:e403.
18. Geldmacher C, Currier JR, Gerhardt M, et al. In a mixed subtype epidemic, the HIV-1 Gag-specific T-cell response is biased towards the infecting subtype. AIDS. 2007;21:135-143.
19. Lavreys L, Baeten JM, Chohan V, et al. Higher set point plasma viral load and more-severe acute HIV type 1 (HIV-1) illness predict mortality among high-risk HIV-1-infected African women. Clin Infect Dis. 2006;42:1333-1339.
20. Hoffmann O, Zaba B, Wolff B, et al. Methodological lessons from a cohort study of high risk women in Tanzania. Sex Transm Infect. 2004;80(Suppl 2):ii69-ii73.
21. Arroyo MA, Hoelscher M, Sateren W, et al. HIV-1 diversity and prevalence differ between urban and rural areas in the Mbeya region of Tanzania. AIDS. 2005;19:1517-1524.
22. Riedner G, Hoffmann O, Rusizoka M, et al. Decline in sexually transmitted infection prevalence and HIV incidence in female barworkers attending prevention and care services in Mbeya Region, Tanzania. AIDS. 2006;20:609-615.
23. Turner S, Ellexson ME, Hickman HD, et al. Sequence-based typing provides a new look at HLA-C diversity. J Immunol. 1998;161:1406-1413.
24. Riedner G, Rusizoka M, Hoffmann O, et al. Baseline survey of sexually transmitted infections in a cohort of female bar workers in Mbeya Region, Tanzania. Sex Transm Infect. 2003;79:382-387.
25. Koopman PAR. Confidence Intervals for the Ratio of Two Binomial Proportions. Biometrics. 1984;40:513-517.
26. Spiegelman D, Hertzmark E. Easy SAS calculations for risk or prevalence ratios and differences. Am J Epidemiol. 2005;162:199-200.
27. Barros AJ, Hirakata VN. Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio. BMC Med Res Methodol. 2003;3:21.
28. Kahn JO, Walker BD. Acute human immunodeficiency virus type 1 infection. N Engl J Med. 1998;339:33-39.
29. Busch MP, Lee LL, Satten GA, et al. Time course of detection of viral and serologic markers preceding human immunodeficiency virus type 1 seroconversion: implications for screening of blood and tissue donors. Transfusion. 1995;35:91-97.
30. Kumwenda JJ, Makanani B, Taulo F, et al. Natural history and risk factors associated with early and established HIV type 1 infection among reproductive-age women in Malawi. Clin Infect Dis. 2008;46:1913-1920.
31. Gray CM, Mlotshwa M, Riou C, et al. Human immunodeficiency virus-specific gamma interferon enzyme-linked immunospot assay responses targeting specific regions of the proteome during primary subtype C infection are poor predictors of the course of viremia and set point. J Virol. 2009;83:470-478.
32. Sarr AD, Eisen G, Gueye-Ndiaye A, et al. Viral dynamics of primary HIV-1 infection in Senegal, West Africa. J Infect Dis. 2005;191:1460-1467.
33. Napravnik S, Poole C, Thomas JC, et al. Gender difference in HIV RNA levels: a meta-analysis of published studies. J Acquir Immune Defic Syndr. 2002;31:11-19.
34. van der Kuyl AC, Cornelissen M. Identifying HIV-1 dual infections. Retrovirology. 2007;4:67.
35. Carrington M, Nelson GW, Martin MP, et al. HLA and HIV-1: heterozygote advantage and B*35-Cw*04 disadvantage. Science. 1999;283:1748-1752.
36. Tang J, Tang S, Lobashevsky E, et al. Favorable and unfavorable HLA class I alleles and haplotypes in Zambians predominantly infected with clade C human immunodeficiency virus type 1. J Virol. 2002;76:8276-8284.
37. Altfeld M, Addo MM, Rosenberg ES, et al. Influence of HLA-B57 on clinical presentation and viral control during acute HIV-1 infection. AIDS. 2003;17:2581-2591.
38. Geldmacher C, Currier JR, Herrmann E, et al. CD8 T-cell recognition of multiple epitopes within specific Gag regions is associated with maintenance of a low steady-state viremia in human immunodeficiency virus type 1-seropositive patients. J Virol. 2007;81:2440-2448.
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