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Original Contribution |

Risk Factors for Recent HIV Infection in Uganda FREE

Jonathan Mermin, MD, MPH; Joshua Musinguzi, MBChB, MSc; Alex Opio, MBChB, PhD; Wilford Kirungi, MBChB, MSc; John Paul Ekwaru, MSc; Wolfgang Hladik, MD, MSc; Frank Kaharuza, MBChB, PhD; Robert Downing, PhD; Rebecca Bunnell, ScD, MEd
[+] Author Affiliations

Author Affiliations: Global AIDS Program, National Center for HIV, Viral Hepatitis, STD and TB Prevention, Centers for Disease Control and Prevention (CDC)–Uganda, Entebbe, Uganda (Drs Mermin, Hladik, Kaharuza, Downing, and Bunnell and Mr Ekwaru); Uganda Ministry of Health (Drs Musinguzi, Opio, and Kirungi); and Coordinating Office for Global Health, CDC-Kenya, Nairobi, Kenya (Dr Mermin).


JAMA. 2008;300(5):540-549. doi:10.1001/jama.300.5.540.
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Published online

Context Studies of factors associated with acquiring human immunodeficiency virus (HIV) are often based on prevalence data that might not reflect recent infections.

Objective To determine demographic, biological, and behavioral factors for recent HIV infection in Uganda.

Design and Setting Nationally representative household survey of cross-sectional design conducted in Uganda from August 2004 through January 2005; data were analyzed until November 2007.

Participants There were 11 454 women and 9905 men aged 15 to 59 years who were eligible. Questionnaires were completed for 10 826 women (95%) and 8830 men (89%); of those interviewed, blood specimens were collected for 10 227 women (94%) and 8298 men (94%).

Main Outcome Measure Specimens seropositive for HIV were tested with the BED IgG capture-based enzyme immunosorbent assay to identify recent seroconversions (median, 155 days) using normalized optical density of 0.8 and adjustments.

Results Of the 1023 HIV infections with BED results, 172 (17%) tested as recent. In multivariate analysis, risk factors associated with recent HIV infection included female sex (adjusted odds ratio [aOR], 2.4; 95% confidence interval [CI], 1.1-5.2); current marital status (widowed vs never married, aOR, 6.1; 95% CI, 2.8-13.3; divorced vs never married, aOR, 3.0; 95% CI, 1.5-6.1); geographic region (north central Uganda vs central Uganda/Kampala, aOR, 2.6; 95% CI, 1.7-4.1); number of sex partners in past year (≥2 compared with none; aOR, 2.9; 95% CI, 1.6-5.5); herpes simplex virus type 2 infection (aOR, 3.9; 95% CI, 2.6-5.8); report of a sexually transmitted disease in the past year (aOR, 1.7; 95% CI, 1.2-2.4); and being an uncircumcised man (aOR, 2.5; 95% CI, 1.1-5.3). Among married participants, recent HIV infection was associated with never using condoms with partners outside of marriage (aOR, 3.2; 95% CI, 1.7-6.1) compared with individuals having no outside partners. The risk of incident HIV infection for married individuals who used condoms with at least 1 outside partner was similar to that of those who did not have any partners outside of marriage (aOR, 1.0; 95% CI, 0.3-2.7).

Conclusion A survey of individuals in Uganda who were tested with an HIV assay used to establish recent infection identified risk factors, which offers opportunities for prevention initiatives.

Figures in this Article

Implementing effective human immunodeficiency virus (HIV) prevention programs requires information on risk factors associated with HIV acquisition, and data should reflect country-specific epidemiology. Prevalence data from routine, anonymous antenatal clinic testing1,2 and cross-sectional sampling of specific populations3,4 have been used to approximate incidence—the measure of how many new infections occurred over a period of time2,5—but are vulnerable to selection bias,6,7 might not represent infections among older people and men, often have limited behavioral data, and present difficulties distinguishing the temporal relationship between potential risk factors and HIV infection. Repeat testing in large population cohorts810 overcomes many of these obstacles.11,12 However, cohorts are expensive and may be subject to participation biases, intervention effects, and differential loss to follow-up. Nationally representative cross-sectional surveys that include HIV testing overcome challenges to generalizability but have been limited to estimating HIV prevalence.6

Incidence of HIV has recently been estimated using laboratory-based assays that distinguish between recent and long-term HIV infections.1317 An advantage of this method is that it can be applied to specimens collected during cross-sectional surveillance15 and avoids some of the biases inherent in repeatedly testing participants in cohorts.17 The BED capture enzyme immunosorbent assay (BED represents the HIV subtype epitopes used in the assay; ie, subtypes B, E, and D) (Calypte Biomedical Corp, Portland, Oregon) is based on the increasing proportion of anti-HIV IgG antibody in total IgG following seroconversion; infections are classified as recent if blood samples test positive by a standard HIV enzyme immunosorbent assay and have a normalized absorbance below a given cutoff on the BED assay.14,18 It has been applied to a variety of populations in southern Africa,19,20 east Africa,14 Ethiopia,21 Thailand,13 China,22 and North America23; however, only 1 of these was a national sample, and none developed a model to identify risk factors for recent infection.

In the 1990s, Uganda experienced nationwide declines in HIV prevalence among clinic attendees1 and voluntary counseling and testing clients24 and reduced incidence within population-based cohorts.8,9 These declines have been attributed to increased age of sexual debut,1,25 reductions in sexual partnerships outside of marriage,25 and increased use of condoms.1,26 However, risk factors may have changed.

To better understand demographic, biological, and behavioral risk factors associated with recent HIV infection in Uganda, we tested with the BED incidence assay HIV-reactive serum samples from participants aged 15 to 59 years included in the 2005 Uganda HIV/AIDS Sero-Behavioral Survey (UHSBS).

Survey Design, Sampling, and Participation

We analyzed data from the 2005 UHSBS, which is a nationally representative, population-based survey. Data from these participants on overall HIV prevalence and sexual behavior of all individuals with HIV infection were previously reported.27 The prior report did not include an examination of risk factors for acquisition of incident infection using the BED assay or focus on biological and behavioral risk factors associated with new infections.

The survey used a 2-stage sample design. The first involved selecting 417 clusters from a list of enumeration areas covered in the 2002 Uganda national census. The second stage involved random sampling of households from the list for each cluster. Sample size calculations indicated that 20 854 individuals aged 15 to 59 years from 417 clusters in 9 regions with a 70% response rate and estimated 5% HIV prevalence would provide a relative standard error of 1.5% for regions and less than 1% for national estimates. A total of 10 437 households were selected, 25 households per cluster plus 12 additional households. All individuals aged 15 to 59 years residing in the selected households (either usual residents or visitors present in the household on the night before the survey) were eligible for participation.

Communities were informed of the survey in advance through radio, television, and newspaper reports, as well as meetings with community leaders. Using a standard script, potential survey participants were told that the Ministry of Health was conducting a national HIV/AIDS survey that included a questionnaire about information such as age and education, health, and sexual behavior; that participation was voluntary; and that they were free to refuse to answer any questions or stop the interview at any time. Separate oral informed consent was obtained for the questionnaire and blood collection. Participants were told that their blood would be tested for HIV and other infections.

The survey was translated, back-translated for accuracy, and implemented in the language requested by the respondent; questionnaires were available in English, Ateso-Karamojong, Luganda, Lugbara, Luo, Runyankole-Rukiga, and Runyoro-Rutoro. The questionnaires and logistical aspects of the survey were pretested in June 2004 using 5 teams that completed 300 individual interviews in local languages. Collection of data included information on behavioral, social, and demographic indicators, and samples were tested for HIV, syphilis, and herpes simplex virus type 2 (HSV-2); dried blood spots from fingersticks were collected from individuals who refused venous blood draw. Interviews and sample collection were conducted from August 2004 through January 2005, and data were analyzed until November 2007.

Laboratory Testing

Testing for HIV, syphilis, and HSV-2 was conducted using standard testing and quality-control procedures. Two HIV enzyme immunosorbent assays based on different antigens (Murex 1.2.0, Abbott, Dartford, United Kingdom; and Vironostika Uni-form II Plus O, bioMérieux, Marcy l’Etoile, France) were used for HIV testing. Retesting with the same enzyme immunosorbent assays occurred for specimens with equivocal or discordant test results, and if results were still discordant, Western blot testing was used for resolving them. For all HIV-positive specimens and 5% of HIV-negative specimens, retesting occurred at a different laboratory using the same testing algorithm; equivocal results were resolved by repeat testing at a third laboratory. For syphilis testing, rapid plasma reagin testing was conducted in the field, and if requested at the time of interview, results were provided by a nurse to participants the next day. Treatment with benzathine penicillin was provided for patients with test results with titers greater than or equal to 1:8. Syphilis testing was repeated using rapid plasma reagin and Treponema pallidum hemagglutination assay testing in the laboratory, and results from this testing were used in analyses.

Specimens were unlinked from identifying information before testing for HIV, and results could not be provided to participants. The Uganda Ministry of Health was concerned with potential logistics and loss of confidentiality associated with home-based provision of results several weeks after interviews and possible harmful social outcomes and stigma. However, at the time of interview, vouchers were provided to all adult household members for free HIV counseling and testing at a local or mobile testing site. It is indicated in the “Guidelines for measuring national HIV prevalence in population-based surveys” (from the Joint United Nations Programme on HIV/AIDS and World Health Organization) that participants in HIV-related surveys should be given access to counseling and testing, and in protecting individual confidentiality, the survey may not be structured so as to convey test results directly to individuals.28 (However, in that case, referral to free voluntary counseling and testing should be included in the protocol.28) It has been suggested that stripping data of unique identifiers and using data control procedures are helpful in preserving confidentiality.29 Specimens were tested for HSV-2 using indirect enzyme immunosorbent assays (Kalon Biological HSV-2 IgG, Guildford, United Kingdom). Laboratory test results for individuals were anonymously linked to questionnaire information through bar codes.

To determine recent HIV infections, we tested all HIV-1–seropositive specimens with the BED IgG capture-based enzyme immunosorbent assay that identifies infections for which seroconversion occurred during the past 155 days (95% confidence interval [CI], 146-165 days).14,18 Serum samples were diluted 1:100 and incubated on goat-antihuman IgG-coated microwell plates to allow capture of HIV and non-HIV IgG. HIV-specific IgG was detected by a multisubtype-derived branched peptide (BED-biotin) followed by streptavidin peroxidase. For each run, optical density values were normalized using a calibrator specimen. Specimens with an optical density of 1.2 or less during initial testing were retested in triplicate, and the mean value was used as a final result. Samples positive on BED assay testing were categorized as “recent.” Because data from seroconverter panels indicated that use of a cutoff optical density of 0.8 without adjustment may overestimate HIV incidence, we calculated absolute incidence values accounting for established sensitivity and varying specificity in relation to time from initial infection.23,30 We repeated risk factor analyses using a conservative optical density cutoff of 0.4, a value derived empirically from maximizing concordance between the BED assay and an HIV avidity assay (B. S. Parekh, PhD, written communication, November 2007), which, based on seroconverter specimen data, should have a very high positive predictive value.14 Results from BED tests were not provided to participants because the assay has only been validated for population-level testing.31

Data Analysis

Data were double entered using CSPro (ORC Macro, Calverton, Maryland) and analyzed using SAS 9.1 (SAS Institute, Cary, North Carolina). Analyses of overall and group-specific prevalence and incidence were weighted to provide national estimates accounting for the sampling design probability of household selection, household response rate, and individual response rate for blood draw. Weights were normalized to sum to sample size. Annual incidence rates and 95% CIs were calculated using established formulas14 (eAppendix available here). Weighted results are presented unless otherwise specified.

To evaluate associations between potential risk factors and recent infection, we compared people with recent infection with the population uninfected with HIV. Results with a P < .05 were considered significant; all comparisons were 2-sided. Separate multivariate logistic regression models were developed for sexually active, married participants and the entire population. Multivariate results used a backward elimination method with a .05 Wald χ2 significance level, and weighted results were used in analyses and presented unless otherwise specified. Demographic variables of age, sex, and rural or urban residence were retained in all models because they were associated with HIV prevalence. Other variables were retained if they were statistically significant at P < .05. Few data were missing; when data for a variable were not present, we inserted a missing category indicator. Missing BED results were accounted for in estimates (eAppendix available here). Population attributable risk for individual factors was calculated using the equation P(aOR– 1)/aOR × 100 where P is the proportion of the population with a particular risk factor and aOR is the adjusted odds ratio from multivariate logistic regression models.32

The institutional review boards of the Uganda Virus Research Institute and ORC Macro approved the survey protocol, including the interview, blood collection, testing procedures, and dissemination of results; the protocol was also approved by the Uganda National Council of Science and Technology and the Centers for Disease Control and Prevention.

From August 2004 to January 2005, we collected information from 9529 households (Figure 1). A total of 11 454 women and 9905 men aged 15 to 59 years were eligible for individual interviews and blood sample collection. Of eligible participants, individual questionnaires were completed for 10 826 women (95%) and 8830 men (89%); of those interviewed, blood specimens were collected for 10 227 women (94%) and 8298 men (94%), rates similar to those from other home-based HIV testing activities in Uganda.33

Place holder to copy figure label and caption
Figure 1. Study Profile
Graphic Jump Location

BED is an enzyme immunosorbent assay that detects recent human immunodeficiency virus (HIV) infections (Calypte Biomedical Corp, Portland, Oregon).

Among 18 525 participants screened for HIV, it was determined that 1092 (6%) were infected with HIV27; of these, 69 (6%) were missing BED results. Of the remaining 1023, 172 infections (17%) were categorized as recent. Weighted HIV prevalence was 6.35% (95% CI, 6.0%-6.7%); weighted incidence was 1.8 infections per 100 person-years (95% CI, 1.5-2.1) (Table 1). Incidence of HIV for women was 2.1 infections per 100 person-years (95% CI, 1.7-2.5), and for men, 1.5 (95% CI, 1.1-1.9); for individuals aged 25 years or older, incidence was 2.3 infections per 100 person-years (95% CI, 1.8-2.7), and for those younger than 25 years, 1.1 infections per 100 person-years (95% CI, 0.7-1.5). For currently married individuals, incidence was 2.0 infections per 100 person-years (95% CI, 1.6-2.4), and for never married individuals, 0.7 per 100 person-years (95% CI, 0.4-1.1); this varied by household wealth and geographic region. Both incidence and prevalence of HIV increased with age in both men and women; however, the proportion of prevalent infections that were recent was greatest at ages 15 to 24 years (Figure 2 and Figure 3).

Table Graphic Jump LocationTable 1. HIV Incidence in Different Demographic Groups in Uganda in 2005
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Figure 2. Weighted HIV Prevalence Among Men and Women in Uganda
Graphic Jump Location

HIV indicates human immunodeficiency virus. Error bars indicate 95% confidence intervals.

Place holder to copy figure label and caption
Figure 3. Weighted HIV Incidence Among Men and Women in Uganda
Graphic Jump Location

HIV indicates human immunodeficiency virus. Error bars indicate 95% confidence intervals.

In the multivariate analysis, there was no significant interaction between sex, recent infection, and assessed risk factors, so we analyzed data for men and women together. Adjusting for demographic factors, risk factors associated with recent HIV infection included female sex (adjusted odds ratio [aOR], 2.4; 95% CI, 1.1-5.2), current marital status (widowed vs never married, aOR, 6.1; 95% CI, 2.8-13.3; divorced vs never married, aOR, 3.0; 95% CI, 1.5-6.1); geographic region (north central Uganda vs central Uganda/Kampala, aOR, 2.6; 95% CI, 1.7-4.1; west Nile vs central Uganda/Kampala, aOR, 0.3; 95% CI, 0.1-1.0), number of sex partners in past year (1 vs none, aOR, 1.7; 95% CI, 1.0-2.8; ≥2 compared with none, aOR, 2.9; 95% CI, 1.6-5.5), HSV-2 infection (aOR, 3.9; 95% CI, 2.6-5.8), report of a sexually transmitted disease in the past year (aOR, 1.7; 95% CI, 1.2-2.4), and being an uncircumcised man (aOR, 2.5; 95% CI, 1.1-5.3) (Table 2). Potential factors not statistically associated with recent infection in multivariate analysis included age, education, urban residence, wealth, age at first sex, lifetime number of sexual partners, syphilis infection, having received a blood transfusion in the previous year, number of medical injections by a health professional in previous year, current pregnancy, religion, having drank alcohol during last sex, and current employment.

Table Graphic Jump LocationTable 2. Risk Factors Associated With Recent HIV Infection Among Individuals Aged 15-59 Years in Uganda in 2005

Among sexually active, married participants, recent HIV infection was associated with never using condoms with partners outside of marriage (aOR, 3.2; 95% CI, 1.7-6.1) compared with individuals having no outside partners (Table 3). The risk of incident HIV infection for married individuals who used condoms with at least 1 outside partner was similar to that of those who did not have any partners outside of marriage (aOR, 1.0; 95% CI, 0.3-2.7). Recent HIV infection in sexually active, married participants was associated with HSV-2 infection (aOR, 4.1; 95% CI, 2.4-6.8), reported sexually transmitted disease in the past year (aOR, 1.6; 95% CI, 1.0-2.4), and being an uncircumcised man (aOR, 4.1; 95% CI, 1.2-13.8).

Table Graphic Jump LocationTable 3. Risk Factors Associated With Recent HIV Infection Among Sexually Active, Married Individuals Aged 15-59 Years in Uganda in 2005 (n=10189)

We repeated the risk factor analysis using a 0.4 optical density cutoff for the BED assay and found results similar to those with use of a 0.8 cutoff. For example, among all participants, the ORs were similar for living in the north central region compared with central Uganda/Kampala (aOR, 3.0; 95% CI, 1.7-5.1, vs aOR, 2.6; 95% CI, 1.7-4.1), having 2 or more sexual partners in the last 12 months compared with none (aOR, 4.2; 95% CI, 1.9-9.0, vs aOR, 2.9; 95% CI, 1.6-5.5), HSV-2 infection (aOR, 4.0; 95% CI, 2.4-6.6, vs aOR, 3.9; 95% CI, 2.6-5.8), and being an uncircumcised man (aOR, 2.6; 95% CI, 1.0-6.4, vs aOR, 2.5; 95% CI, 1.1-5.3), respectively. For sexually active, married participants, risks were also similar for not using a condom with outside partners (aOR, 4.5; 95% CI, 2.2-9.1, vs aOR, 3.2; 95% CI, 1.7-6.1) and HSV-2 infection (aOR, 5.2; 95% CI, 2.7-10.2, vs aOR, 4.1; 95% CI, 2.4-6.8).

In a descriptive analysis of 74 married participants with recent infection for whom HIV testing was also conducted for their spouses, 28 of 74 incident infections (38%) occurred among people whose spouses had long-standing HIV infection, 10 of 74 (14%) had spouses with recent HIV infection, and 36 of 74 (49%) had spouses who were not infected with HIV.

The population attributable risk for incident infection was substantial for several potentially modifiable factors. For example, among all participants, the population attributable risk for having HSV-2 infection was 31.9% (95% CI, 26.3%-35.6%) and for being an uncircumcised man was 20.1% (95% CI, 4.0%-27.5%) (Table 2). For sexually active, married participants, the population attributable risk for having HSV-2 infection was 37.8% (95% CI, 29.6%-42.7%) and for being an uncircumcised man was 24.1% (95% CI, 5.9%-29.6%) (Table 3).

In this nationally representative survey of individuals in Uganda who were tested with an HIV assay used to establish recent infection, a number of risk factors were identified, which offers opportunities for prevention initiatives. For example, many new HIV infections in Uganda could potentially be prevented if the risks associated with having multiple sexual partners, not using condoms with partners outside of marriage, and lack of circumcision were eliminated. Programs promoting faithfulness are increasingly a part of prevention efforts in Africa; however, they are likely to be most fruitful among couples where both partners have been tested and are not infected with HIV. Consistent condom use has been associated with an 80% reduction in HIV transmission34 and is particularly effective with partners outside of marriage and within HIV-discordant couples.

In a descriptive subanalysis among 74 couples for whom HIV status was known for both partners and at least 1 partner had recent HIV infection, 51% of recent infections occurred among individuals whose spouse had HIV. Thus, for about one-half of all recent infections among these married individuals, infection may have come from spouses; however, we did not perform viral subtyping, and therefore, this cannot be verified. For some, partners may have been infected with HIV before marriage. These data support the importance of HIV testing for married couples who may assume that they are not at risk of acquiring HIV from their spouses, especially if the relationship is monogamous.

Three randomized controlled trials in Africa found that adult male circumcision was associated with a 60% reduction in the risk of HIV acquisition,35 and efforts should be made to operationalize programs in areas of high incidence. Prevalent HSV-2 infection has been associated with increased risk of HIV acquisition and transmission36 and has been cited as a potential driving force of the epidemic in Uganda37,38 and elsewhere in Africa.39 Most behavioral interventions focused on preventing HIV infection should theoretically also prevent HSV-2. However, a large proportion of Ugandans were already infected with HSV-2, raising the question as to whether acyclovir suppressive therapy might be useful in preventing HIV transmission from individuals with HSV-2 and HIV coinfection living with partners who are not infected with HIV, a hypothesis currently being examined in a randomized trial.

Young women in Uganda experienced a higher risk of incident HIV infection than young men, and throughout their lives, men were more likely to have sexual partners outside of marriage. These differences highlight the potential benefit of focusing specific interventions on changing social norms, such as power differentials between men and women regarding sex and reproductive choice, social expectations regarding sex, access to financial resources and education, and sexual violence. The high rate of recent HIV infection among divorced individuals may reflect some of these imbalances. The increased odds of recent infection among widows may result from some of the same factors as well as a high risk for HIV transmission during end-stage HIV disease when an individual's viral load is high.

The response rate to this nationally representative survey was high, supporting the generalizability of the findings. However, there were study limitations. We had a limited sample of recent infections, causing relatively wide CIs around estimates and potentially resulting in identification of only major risk factors for infection. There were associations between geographic region and recent HIV infection that persisted even when adjusting for other variables, which may suggest population-level effects of such factors as HSV-2, circumcision, and sexual networks that could not be assessed in this study. There may have been some misclassification of recent HIV infections. The BED assay has previously overestimated incidence when using a 0.8 value for optical density, although the majority of those misclassified as recent occurred in the past year.4042 To account for this, we adjusted absolute incidence measurements using standard procedures.19 The BED assay is likely to have an increased frequency of false-positive results during long-standing infection, which might have increased estimated incidence in older age groups, and antiretroviral therapy might affect the assay's specificity.19 However, few individuals in Uganda were using antiretroviral therapy at the time of the study. In addition, when we used a more specific cutoff optical density value of 0.4, which would theoretically reduce false-positive results, we found similar associations between risk factors and recent infection.

In support of the use of the BED assay, the HIV incidence estimate of 1.7 per 100 person-years for the central Kampala region (Rakai, Masaka, Ssembabule, Mpigi, Mubende, Wakiso, Kiboga, Luwero, Kampala, and Nakasongola districts) is comparable with the rate of 1.7 per 100 person-years found in a large population-based cohort in Rakai district in 2002 through 2003.43 However, an estimated absolute incidence of 1.8 infections per 100 person-years is high for a national prevalence of 6.35% and is higher than estimates based on mathematical models of prevalence data.44 These data may indicate increasing incidence rates, particularly in certain regions of the country, or an overestimation based on the assay, which has not been as well validated with HIV subtypes A and D, those that are most prevalent in Uganda. As noted previously,5 stable HIV prevalence can mask changes in HIV incidence. This may be especially relevant as antiretroviral therapy becomes increasingly available and HIV prevalence begins to rise because of declines in HIV-associated mortality. Lastly, our CIs are relatively large and assessment of trends in incidence will require a repeat national survey.

There is internal consistency to the estimates: a higher proportion of HIV infections were reactive on the assay among youth (aged 15-24 y) compared with older individuals (15% vs 9% [data not shown]), and the number of sexual partners in the previous year was significantly associated with recent infection whereas number of lifetime sexual partners was not. In addition, the highest incidence was noted in the north central region of the country—twice as high as the central Kampala region—even though HIV prevalence was comparable. The north central region of Uganda was experiencing a civil war, and it is plausible that HIV incidence may be higher there than in other areas and potentially increasing. Lastly, associations between recent HIV infection and HSV-2, circumcision, and number of sexual partners in the past year are reasonable from an epidemiologic perspective supporting the supposition that we are mostly identifying incident rather than prevalent infections. However, potential overestimation of incidence by the BED assay supports currently focusing on distribution, risk factors, and trends in recent infection rather than absolute incidence.45

The HIV epidemic in Uganda may portend the future for other countries in Africa. Prevalence of HIV peaked in Uganda in the early 1990s and in other countries in east Africa several years later.46 In Uganda, HIV prevalence appears to have stabilized for the past few years, and many countries in west and central Africa have mature HIV epidemics with unchanging prevalence.46 Improved prevention programs are necessary to reduce HIV incidence, especially during a time when care and antiretroviral treatment programs are increasing survival. Prevention efforts should focus on evidence-based behaviors that are known to prevent infection, such as reduced number of sexual partners,25 condom use,34 and delayed age of sexual debut,25 as well as explore new approaches, such as circumcising men,35 reducing transmission from individuals with known HIV infection (positive prevention),47 and increasing routine HIV counseling and testing in clinical and community settings.48 Other interventions, such as acyclovir prophylaxis, may have a substantial impact if found effective in clinical trials and implemented on a wide scale, particularly in countries like Uganda where the attributable risk associated with HSV-2 infection is large. Cross-sectional, nationally representative serologic surveys are useful for providing information for monitoring the HIV epidemic and program planning.

Despite current prevention efforts, 2.5 million new HIV infections occurred worldwide in 2007.49 New initiatives to prevent HIV infection should be at a scale comparable with current programs improving access to antiretroviral therapy.50 Without this, countries in Africa are likely to experience continued, if not worsening, HIV epidemics.

Corresponding Author: Jonathan Mermin, MD, MPH, CDC-Kenya, Kenya Medical Research Institute, Mbagathi Road off Mbagathi Way, Nairobi, Kenya (jhm7@cdc.gov).

Author Contributions: Dr Mermin had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Mermin, Musinguzi, Opio, Kirungi, Kaharuza, Bunnell.

Acquisition of data: Mermin, Musinguzi, Opio, Kirungi, Downing, Bunnell.

Analysis and interpretation of data: Kirungi, Ekwaru, Hladik, Kaharuza, Bunnell.

Drafting of the manuscript: Mermin, Opio, Ekwaru.

Critical revision of the manuscript for important intellectual content: Mermin, Musinguzi, Kirungi, Hladik, Kaharuza, Downing, Bunnell.

Statistical analysis: Mermin, Kirungi, Ekwaru, Hladik, Kaharuza.

Obtained funding: Mermin, Musinguzi, Bunnell.

Administrative, technical, or material support: Mermin, Musinguzi, Opio, Downing, Bunnell.

Study supervision: Mermin, Musinguzi, Bunnell.

Financial Disclosures: None reported.

Funding/Support: This study was funded by the US Agency for International Development (USAID) and Centers for Disease Control and Prevention (CDC) through the US President's Emergency Plan for AIDS Relief and by the Uganda Ministry of Health (MOH) and the Japanese International Cooperation Agency.

Role of the Sponsor: Staff of the CDC and Uganda MOH were involved in the design and conduct of the study; in the collection, management, analysis, and interpretation of the data, and in the preparation, review, and approval of the manuscript.

Disclaimer: The findings and conclusions in this article are those of the authors and do not necessarily represent the views of the Department of Health and Human Services, the CDC, or the Uganda MOH.

Previous Presentation: Presented in part at the Conference on Retroviruses and Opportunistic Infections; February 6, 2008; Boston, Massachusetts.

Additional Contributions: We are grateful for the support and energetic efforts of the survey teams, laboratory staff, data managers, and analysts; the Uganda Bureau of Statistics; the Joint United Nations Programme on HIV/AIDS; ORC Macro; and the World Health Organization.

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Stoneburner R, Carballo M, Bernstein R, Saidel T. Simulation of HIV incidence dynamics in the Rakai population-based cohort, Uganda.  AIDS. 1998;12(2):226-228
PubMed
Mbulaiteye SM, Mahe C, Whitworth JAG, Nakiyingi JS, Ojwiya A, Kamali A. Declining HIV-1 incidence and associated prevalence over 10 years in a rural population in south-west Uganda: a cohort study.  Lancet. 2002;360(9326):41-46
PubMed   |  Link to Article
Grosskurth H, Mosha F, Todd J,  et al.  Impact of improved treatment of sexually transmitted diseases on HIV infection in rural Tanzania: randomised controlled trial.  Lancet. 1995;346(8974):530-536
PubMed   |  Link to Article
Todd J, Grosskurth H, Changalucha J,  et al.  Risk factors influencing HIV infection incidence in a rural African population: a nested case-control study.  J Infect Dis. 2006;193(3):458-466
PubMed   |  Link to Article
Quinn TC, Wawer MJ, Sewankambo N,  et al.  Viral load and heterosexual transmission of human immunodeficiency virus type 1.  N Engl J Med. 2000;342(13):921-929
PubMed   |  Link to Article
Hu DJ, Vanichseni S, Mock PA,  et al.  HIV type 1 incidence estimates by detection of recent infection from a cross-sectional sampling of injection drug users in Bangkok: use of the IgG capture BED enzyme immunoassay.  AIDS Res Hum Retroviruses. 2003;19(9):727-730
PubMed   |  Link to Article
Parekh BS, Kennedy MS, Dobbs T,  et al.  Quantitative detection of increasing HIV type 1 antibodies after seroconversion: a simple assay for detecting recent HIV infection and estimating incidence.  AIDS Res Hum Retroviruses. 2002;18(4):295-307
PubMed   |  Link to Article
McDougal JS, Pilcher CD, Parekh BS,  et al.  Surveillance for HIV-1 incidence using tests for recent infection in resource-constrained countries.  AIDS. 2005;19:(suppl 2)  S25-S30
PubMed   |  Link to Article
Janssen RS, Satten GA, Stramer SL,  et al.  New testing strategy to detect early HIV-1 infection for use in incidence estimates and for clinical and prevention purposes.  JAMA. 1998;280(1):42-48
PubMed   |  Link to Article
Parekh BS, McDougal JS. Application of laboratory methods for estimation of HIV-1 incidence.  Indian J Med Res. 2005;121(4):510-518
PubMed
Dobbs T, Kennedy S, Pau CP, McDougal JS, Parekh BS. Performance characteristics of the immunoglobulin G-capture BED-enzyme immunoassay, an assay to detect recent human immunodeficiency virus type 1 seroconversion.  J Clin Microbiol. 2004;42(6):2623-2628
PubMed   |  Link to Article
Hargrove JW, Humphrey JH, Mutasa K,  et al.  Improved HIV-1 incidence estimates using the BED capture enzyme immunoassay.  AIDS. 2008;22(4):511-518
PubMed   |  Link to Article
Rehle T, Shisana O, Pillay V, Zuma K, Puren A, Parker W. National HIV incidence measures: new insights into the South African epidemic.  S Afr Med J. 2007;97(3):194-199
PubMed
Wolday D, Meles H, Hailu E,  et al.  Temporal trends in the incidence of HIV infection in antenatal clinic attendees in Addis Ababa, Ethiopia, 1995-2003.  J Intern Med. 2007;261(2):132-137
PubMed   |  Link to Article
Jiang Y, Wang M, Ni M,  et al.  HIV-1 incidence estimates using IgG-capture BED-enzyme immunoassay from surveillance sites of injection drug users in three cities of China.  AIDS. 2007;21:(suppl 8)  S47-S51
PubMed   |  Link to Article
McDougal JS, Parekh BS, Peterson ML,  et al.  Comparison of HIV type 1 incidence observed during longitudinal follow-up with incidence estimated by cross-sectional analysis using the BED capture enzyme immunoassay.  AIDS Res Hum Retroviruses. 2006;22(10):945-952
PubMed   |  Link to Article
Baryarama F, Bunnell RE, Ransom RL,  et al.  Using HIV voluntary counseling and testing data for monitoring the Uganda HIV epidemic, 1992-2000.  J Acquir Immune Defic Syndr. 2004;37(1):1180-1186
PubMed   |  Link to Article
Stoneburner RL, Low-Beer D. Population-level HIV declines and behavioral risk avoidance in Uganda.  Science. 2004;304(5671):714-718
PubMed   |  Link to Article
Ahmed S, Lutalo T, Wawer M,  et al.  HIV incidence and sexually transmitted disease prevalence associated with condom use: a population study in Rakai, Uganda.  AIDS. 2001;15(16):2171-2179
PubMed   |  Link to Article
Bunnell R, Opio A, Musinguzi J,  et al.  HIV transmission risk behavior among HIV-infected adults in Uganda: results of a nationally representative survey.  AIDS. 2008;22(5):617-624
PubMed   |  Link to Article
 Guidelines for measuring national HIV prevalence in population-based surveys. UNAIDS/WHO Working Group on Global HIV/AIDS and STI Surveillance. http://data.unaids.org/pub/manual/2005/20050101_gs_guidemeasuringpopulation_en.pdf. Accessed May 29, 2008
Pappas G, Hyder AA. Exploring ethical considerations for the use of biological and physiological markers in population-based surveys in less developed countries.  Global HealthLink to Article
PubMed
 Interim recommendations for the use of the BED capture enzyme immunoassay for incidence estimation and surveillance (November 21, 2006). Surveillance and Survey and Laboratory Working Groups, Office of the US Global AIDS Coordinator. http://www.cdc.gov/nchstp/od/gap/pa_surveillance.htm. Accessed June 30, 2008
 Calypte HIV-1 BED incidence EIA (IgG-Capture HIV-EIA): enzyme immunoassay for population estimates of HIV-1 incidence. Portland, Oregon: Calypte Biomedical Corporation; 2005
Rockhill B, Newman B, Weinberg C. Use and misuse of population attributable fractions.  Am J Public Health. 1998;88(1):15-19
PubMed   |  Link to Article
Were W, Mermin J, Bunnell R, Ekwaru JP, Kaharuza F. Home-based model for HIV voluntary counselling and testing.  Lancet. 2003;361(9368):1569
PubMed   |  Link to Article
Weller S, Davis K. Condom effectiveness in reducing heterosexual HIV transmission.  Cochrane Database Syst Rev. 2001;(3):CD003255
PubMed
Newell ML, Barnighausen T. Male circumcision to cut HIV risk in the general population.  Lancet. 2007;369(9562):
PubMed
Freeman EE, Weiss HA, Glynn JR, Cross PL, Whitworth JA, Hayes RJ. Herpes simplex virus 2 infection increases HIV acquisition in men and women: systematic review and meta-analysis of longitudinal studies.  AIDS. 2006;20(1):73-83
PubMed   |  Link to Article
Grosskurth H, Gray R, Hayes R, Mabey D, Wawer M. Control of sexually transmitted diseases for HIV-1 prevention: understanding the implications of the Mwanza and Rakai trials.  Lancet. 2000;355(9219):1981-1987
PubMed   |  Link to Article
Brown JM, Wald A, Hubbard H,  et al.  Incident and prevalent herpes simplex virus type 2 infection increases risk of HIV acquisition among women in Uganda and Zimbabwe.  AIDS. 2007;21(12):1515-1523
PubMed   |  Link to Article
Weiss HA, Buve A, Robinson NJ,  et al.  The epidemiology of HSV-2 infection and its association with HIV infection in four urban African populations.  AIDS. 2001;15:(suppl 4)  S97-S108
PubMed   |  Link to Article
 UNAIDS Reference Group on estimates, modelling and projections: statement on the use of the BED assay for the estimation of HIV-1 incidence for surveillance or epidemic monitoring.  Wkly Epidemiol Rec. 2006;81(4):40-41
PubMed
Sakarovitch C, Rouet F, Murphy G,  et al.  Do tests devised to detect recent HIV-1 infection provide reliable estimates of incidence in Africa?  J Acquir Immune Defic Syndr. 2007;45(1):115-122
PubMed   |  Link to Article
Karita E, Price M, Hunter E,  et al.  Investigating the utility of the HIV-1 BED capture enzyme immunoassay using cross-sectional and longitudinal seroconverter specimens from Africa.  AIDS. 2007;21(4):403-408
PubMed   |  Link to Article
Roehr B. Abstinence programmes do not reduce HIV prevalence in Uganda.  BMJ. 2005;330(7490):496
PubMed   |  Link to Article
Hladik W, Musinguzi J, Kirungi W,  et al.  The estimated burden of HIV/AIDS in Uganda, 2005-2010.  AIDS. 2008;22(4):503-510
PubMed   |  Link to Article
Karim SS. HIV incidence estimates are key to understanding the changing HIV epidemic in South Africa.  S Afr Med J. 2007;97(3):190
PubMed
Asamoah-Odei E, Garcia Calleja JM, Boerma JT. HIV prevalence and trends in sub-Saharan Africa: no decline and large subregional differences.  Lancet. 2004;364(9428):35-40
PubMed   |  Link to Article
 Positive prevention: prevention strategies for people with HIV: a guide for NGOs and service providers. International HIV/AIDS Alliance. http://www.aidsalliance.org/custom_asp/publications/view.asp?publication_id=90&language=en. Accessed May 29, 2008
De Cock KM, Bunnell R, Mermin J. Unfinished business: expanding HIV testing in developing countries.  N Engl J Med. 2006;354(5):440-442
PubMed   |  Link to Article
 2007 AIDS epidemic update. Joint United Nations Programme on HIV/AIDS and World Health Organization. http://data.unaids.org/pub/EPISlides/2007/2007_epiupdate_en.pdf. Accessed May 29, 2008
 The 3 by 5 initiative (treat three million people living with HIV/AIDS by 2005). Joint United Nations Programme on HIV/AIDS and World Health Organization. http://www.who.int/3by5/en/. Accessed May 29, 2008

Figures

Place holder to copy figure label and caption
Figure 1. Study Profile
Graphic Jump Location

BED is an enzyme immunosorbent assay that detects recent human immunodeficiency virus (HIV) infections (Calypte Biomedical Corp, Portland, Oregon).

Place holder to copy figure label and caption
Figure 2. Weighted HIV Prevalence Among Men and Women in Uganda
Graphic Jump Location

HIV indicates human immunodeficiency virus. Error bars indicate 95% confidence intervals.

Place holder to copy figure label and caption
Figure 3. Weighted HIV Incidence Among Men and Women in Uganda
Graphic Jump Location

HIV indicates human immunodeficiency virus. Error bars indicate 95% confidence intervals.

Tables

Table Graphic Jump LocationTable 1. HIV Incidence in Different Demographic Groups in Uganda in 2005
Table Graphic Jump LocationTable 2. Risk Factors Associated With Recent HIV Infection Among Individuals Aged 15-59 Years in Uganda in 2005
Table Graphic Jump LocationTable 3. Risk Factors Associated With Recent HIV Infection Among Sexually Active, Married Individuals Aged 15-59 Years in Uganda in 2005 (n=10189)

References

Asiimwe-Okiror G, Opio AA, Musinguzi J, Madraa E, Tembo G, Carael M. Change in sexual behaviour and decline in HIV infection among young pregnant women in urban Uganda.  AIDS. 1997;11(14):1757-1763
PubMed   |  Link to Article
Batter V, Matela B, Nsuami M,  et al.  High HIV-1 incidence in young women masked by stable overall seroprevalence among childbearing women in Kinshasa, Zaïre: estimating incidence from serial seroprevalence data.  AIDS. 1994;8(6):811-817
PubMed   |  Link to Article
Gregson S, Machekano R, Donnelly CA, Mbizvo MT, Anderson RM, Katzenstein DA. Estimating HIV incidence from age-specific prevalence data: comparison with concurrent cohort estimates in a study of male factory workers, Harare, Zimbabwe.  AIDS. 1998;12(15):2049-2058
PubMed   |  Link to Article
Saidel T, Sokal D, Rice J, Buzingo T, Hassig S. Validation of a method to estimate age-specific immunodeficiency virus (HIV) incidence rates in developing countries using population-based seroprevalence data.  Am J Epidemiol. 1996;144(3):214-223
PubMed   |  Link to Article
Wawer MJ, Serwadda D, Gray RH,  et al.  Trends in HIV-1 prevalence may not reflect trends in incidence in mature epidemics: data from the Rakai population-based cohort, Uganda.  AIDS. 1997;11(8):1023-1030
PubMed   |  Link to Article
Boerma JT, Ghys PD, Walker N. Estimates of HIV-1 prevalence from national population-based surveys as a new gold standard.  Lancet. 2003;362(9399):1929-1931
PubMed   |  Link to Article
Changalucha J, Grosskurth H, Mwita W,  et al.  Comparison of HIV prevalences in community-based and antenatal clinic surveys in rural Mwanza, Tanzania.  AIDS. 2002;16(4):661-665
PubMed   |  Link to Article
Stoneburner R, Carballo M, Bernstein R, Saidel T. Simulation of HIV incidence dynamics in the Rakai population-based cohort, Uganda.  AIDS. 1998;12(2):226-228
PubMed
Mbulaiteye SM, Mahe C, Whitworth JAG, Nakiyingi JS, Ojwiya A, Kamali A. Declining HIV-1 incidence and associated prevalence over 10 years in a rural population in south-west Uganda: a cohort study.  Lancet. 2002;360(9326):41-46
PubMed   |  Link to Article
Grosskurth H, Mosha F, Todd J,  et al.  Impact of improved treatment of sexually transmitted diseases on HIV infection in rural Tanzania: randomised controlled trial.  Lancet. 1995;346(8974):530-536
PubMed   |  Link to Article
Todd J, Grosskurth H, Changalucha J,  et al.  Risk factors influencing HIV infection incidence in a rural African population: a nested case-control study.  J Infect Dis. 2006;193(3):458-466
PubMed   |  Link to Article
Quinn TC, Wawer MJ, Sewankambo N,  et al.  Viral load and heterosexual transmission of human immunodeficiency virus type 1.  N Engl J Med. 2000;342(13):921-929
PubMed   |  Link to Article
Hu DJ, Vanichseni S, Mock PA,  et al.  HIV type 1 incidence estimates by detection of recent infection from a cross-sectional sampling of injection drug users in Bangkok: use of the IgG capture BED enzyme immunoassay.  AIDS Res Hum Retroviruses. 2003;19(9):727-730
PubMed   |  Link to Article
Parekh BS, Kennedy MS, Dobbs T,  et al.  Quantitative detection of increasing HIV type 1 antibodies after seroconversion: a simple assay for detecting recent HIV infection and estimating incidence.  AIDS Res Hum Retroviruses. 2002;18(4):295-307
PubMed   |  Link to Article
McDougal JS, Pilcher CD, Parekh BS,  et al.  Surveillance for HIV-1 incidence using tests for recent infection in resource-constrained countries.  AIDS. 2005;19:(suppl 2)  S25-S30
PubMed   |  Link to Article
Janssen RS, Satten GA, Stramer SL,  et al.  New testing strategy to detect early HIV-1 infection for use in incidence estimates and for clinical and prevention purposes.  JAMA. 1998;280(1):42-48
PubMed   |  Link to Article
Parekh BS, McDougal JS. Application of laboratory methods for estimation of HIV-1 incidence.  Indian J Med Res. 2005;121(4):510-518
PubMed
Dobbs T, Kennedy S, Pau CP, McDougal JS, Parekh BS. Performance characteristics of the immunoglobulin G-capture BED-enzyme immunoassay, an assay to detect recent human immunodeficiency virus type 1 seroconversion.  J Clin Microbiol. 2004;42(6):2623-2628
PubMed   |  Link to Article
Hargrove JW, Humphrey JH, Mutasa K,  et al.  Improved HIV-1 incidence estimates using the BED capture enzyme immunoassay.  AIDS. 2008;22(4):511-518
PubMed   |  Link to Article
Rehle T, Shisana O, Pillay V, Zuma K, Puren A, Parker W. National HIV incidence measures: new insights into the South African epidemic.  S Afr Med J. 2007;97(3):194-199
PubMed
Wolday D, Meles H, Hailu E,  et al.  Temporal trends in the incidence of HIV infection in antenatal clinic attendees in Addis Ababa, Ethiopia, 1995-2003.  J Intern Med. 2007;261(2):132-137
PubMed   |  Link to Article
Jiang Y, Wang M, Ni M,  et al.  HIV-1 incidence estimates using IgG-capture BED-enzyme immunoassay from surveillance sites of injection drug users in three cities of China.  AIDS. 2007;21:(suppl 8)  S47-S51
PubMed   |  Link to Article
McDougal JS, Parekh BS, Peterson ML,  et al.  Comparison of HIV type 1 incidence observed during longitudinal follow-up with incidence estimated by cross-sectional analysis using the BED capture enzyme immunoassay.  AIDS Res Hum Retroviruses. 2006;22(10):945-952
PubMed   |  Link to Article
Baryarama F, Bunnell RE, Ransom RL,  et al.  Using HIV voluntary counseling and testing data for monitoring the Uganda HIV epidemic, 1992-2000.  J Acquir Immune Defic Syndr. 2004;37(1):1180-1186
PubMed   |  Link to Article
Stoneburner RL, Low-Beer D. Population-level HIV declines and behavioral risk avoidance in Uganda.  Science. 2004;304(5671):714-718
PubMed   |  Link to Article
Ahmed S, Lutalo T, Wawer M,  et al.  HIV incidence and sexually transmitted disease prevalence associated with condom use: a population study in Rakai, Uganda.  AIDS. 2001;15(16):2171-2179
PubMed   |  Link to Article
Bunnell R, Opio A, Musinguzi J,  et al.  HIV transmission risk behavior among HIV-infected adults in Uganda: results of a nationally representative survey.  AIDS. 2008;22(5):617-624
PubMed   |  Link to Article
 Guidelines for measuring national HIV prevalence in population-based surveys. UNAIDS/WHO Working Group on Global HIV/AIDS and STI Surveillance. http://data.unaids.org/pub/manual/2005/20050101_gs_guidemeasuringpopulation_en.pdf. Accessed May 29, 2008
Pappas G, Hyder AA. Exploring ethical considerations for the use of biological and physiological markers in population-based surveys in less developed countries.  Global HealthLink to Article
PubMed
 Interim recommendations for the use of the BED capture enzyme immunoassay for incidence estimation and surveillance (November 21, 2006). Surveillance and Survey and Laboratory Working Groups, Office of the US Global AIDS Coordinator. http://www.cdc.gov/nchstp/od/gap/pa_surveillance.htm. Accessed June 30, 2008
 Calypte HIV-1 BED incidence EIA (IgG-Capture HIV-EIA): enzyme immunoassay for population estimates of HIV-1 incidence. Portland, Oregon: Calypte Biomedical Corporation; 2005
Rockhill B, Newman B, Weinberg C. Use and misuse of population attributable fractions.  Am J Public Health. 1998;88(1):15-19
PubMed   |  Link to Article
Were W, Mermin J, Bunnell R, Ekwaru JP, Kaharuza F. Home-based model for HIV voluntary counselling and testing.  Lancet. 2003;361(9368):1569
PubMed   |  Link to Article
Weller S, Davis K. Condom effectiveness in reducing heterosexual HIV transmission.  Cochrane Database Syst Rev. 2001;(3):CD003255
PubMed
Newell ML, Barnighausen T. Male circumcision to cut HIV risk in the general population.  Lancet. 2007;369(9562):
PubMed
Freeman EE, Weiss HA, Glynn JR, Cross PL, Whitworth JA, Hayes RJ. Herpes simplex virus 2 infection increases HIV acquisition in men and women: systematic review and meta-analysis of longitudinal studies.  AIDS. 2006;20(1):73-83
PubMed   |  Link to Article
Grosskurth H, Gray R, Hayes R, Mabey D, Wawer M. Control of sexually transmitted diseases for HIV-1 prevention: understanding the implications of the Mwanza and Rakai trials.  Lancet. 2000;355(9219):1981-1987
PubMed   |  Link to Article
Brown JM, Wald A, Hubbard H,  et al.  Incident and prevalent herpes simplex virus type 2 infection increases risk of HIV acquisition among women in Uganda and Zimbabwe.  AIDS. 2007;21(12):1515-1523
PubMed   |  Link to Article
Weiss HA, Buve A, Robinson NJ,  et al.  The epidemiology of HSV-2 infection and its association with HIV infection in four urban African populations.  AIDS. 2001;15:(suppl 4)  S97-S108
PubMed   |  Link to Article
 UNAIDS Reference Group on estimates, modelling and projections: statement on the use of the BED assay for the estimation of HIV-1 incidence for surveillance or epidemic monitoring.  Wkly Epidemiol Rec. 2006;81(4):40-41
PubMed
Sakarovitch C, Rouet F, Murphy G,  et al.  Do tests devised to detect recent HIV-1 infection provide reliable estimates of incidence in Africa?  J Acquir Immune Defic Syndr. 2007;45(1):115-122
PubMed   |  Link to Article
Karita E, Price M, Hunter E,  et al.  Investigating the utility of the HIV-1 BED capture enzyme immunoassay using cross-sectional and longitudinal seroconverter specimens from Africa.  AIDS. 2007;21(4):403-408
PubMed   |  Link to Article
Roehr B. Abstinence programmes do not reduce HIV prevalence in Uganda.  BMJ. 2005;330(7490):496
PubMed   |  Link to Article
Hladik W, Musinguzi J, Kirungi W,  et al.  The estimated burden of HIV/AIDS in Uganda, 2005-2010.  AIDS. 2008;22(4):503-510
PubMed   |  Link to Article
Karim SS. HIV incidence estimates are key to understanding the changing HIV epidemic in South Africa.  S Afr Med J. 2007;97(3):190
PubMed
Asamoah-Odei E, Garcia Calleja JM, Boerma JT. HIV prevalence and trends in sub-Saharan Africa: no decline and large subregional differences.  Lancet. 2004;364(9428):35-40
PubMed   |  Link to Article
 Positive prevention: prevention strategies for people with HIV: a guide for NGOs and service providers. International HIV/AIDS Alliance. http://www.aidsalliance.org/custom_asp/publications/view.asp?publication_id=90&language=en. Accessed May 29, 2008
De Cock KM, Bunnell R, Mermin J. Unfinished business: expanding HIV testing in developing countries.  N Engl J Med. 2006;354(5):440-442
PubMed   |  Link to Article
 2007 AIDS epidemic update. Joint United Nations Programme on HIV/AIDS and World Health Organization. http://data.unaids.org/pub/EPISlides/2007/2007_epiupdate_en.pdf. Accessed May 29, 2008
 The 3 by 5 initiative (treat three million people living with HIV/AIDS by 2005). Joint United Nations Programme on HIV/AIDS and World Health Organization. http://www.who.int/3by5/en/. Accessed May 29, 2008
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