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Risk Factors for Late-Stage HIV Disease Presentation at Initial HIV Diagnosis in Durban, South Africa

  • Paul K. Drain ,

    pdrain@partners.org

    Affiliations Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, United States of America, Medical Practice Evaluation Center, Massachusetts General Hospital, Boston, Massachusetts, United States of America, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America

  • Elena Losina,

    Affiliations Division of General Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America, Medical Practice Evaluation Center, Massachusetts General Hospital, Boston, Massachusetts, United States of America, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America, Division of Rheumatology, Brigham and Women's Hospital, Boston, Massachusetts, United States of America, Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America

  • Gary Parker,

    Affiliation Department of Medicine, McCord Hospital, Durban, South Africa

  • Janet Giddy,

    Affiliation Department of Medicine, McCord Hospital, Durban, South Africa

  • Douglas Ross,

    Affiliation Department of Medicine, St. Mary's Hospital, Durban, South Africa

  • Jeffrey N. Katz,

    Affiliations Department of Orthopedic Surgery, Brigham and Women's Hospital, Boston, Massachusetts, United States of America, Division of Rheumatology, Brigham and Women's Hospital, Boston, Massachusetts, United States of America, Departments of Epidemiology and Health Policy and Management, Harvard School of Public Health, Boston, Massachusetts, United States of America

  • Sharon M. Coleman,

    Affiliations Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, United States of America, Data Coordinating Center, Boston University School of Public Health, Boston, Massachusetts, United States of America

  • Laura M. Bogart,

    Affiliations Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, United States of America, Boston Children's Hospital, Boston, Massachusetts, United States of America

  • Kenneth A. Freedberg,

    Affiliations Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, United States of America, Division of General Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America, Medical Practice Evaluation Center, Massachusetts General Hospital, Boston, Massachusetts, United States of America, Center for AIDS Research, Harvard Medical School, Boston, Massachusetts, United States of America, Departments of Epidemiology and Health Policy and Management, Harvard School of Public Health, Boston, Massachusetts, United States of America, Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, United States of America

  • Rochelle P. Walensky,

    Affiliations Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, United States of America, Division of General Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America, Medical Practice Evaluation Center, Massachusetts General Hospital, Boston, Massachusetts, United States of America, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America, Center for AIDS Research, Harvard Medical School, Boston, Massachusetts, United States of America

  • Ingrid V. Bassett

    Affiliations Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, United States of America, Division of General Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America, Medical Practice Evaluation Center, Massachusetts General Hospital, Boston, Massachusetts, United States of America, Center for AIDS Research, Harvard Medical School, Boston, Massachusetts, United States of America

Risk Factors for Late-Stage HIV Disease Presentation at Initial HIV Diagnosis in Durban, South Africa

  • Paul K. Drain, 
  • Elena Losina, 
  • Gary Parker, 
  • Janet Giddy, 
  • Douglas Ross, 
  • Jeffrey N. Katz, 
  • Sharon M. Coleman, 
  • Laura M. Bogart, 
  • Kenneth A. Freedberg, 
  • Rochelle P. Walensky
PLOS
x

Abstract

Background

After observing persistently low CD4 counts at initial HIV diagnosis in South Africa, we sought to determine risk factors for late-stage HIV disease presentation among adults.

Methods

We surveyed adults prior to HIV testing at four outpatient clinics in Durban from August 2010 to November 2011. All HIV-infected adults were offered CD4 testing, and late-stage HIV disease was defined as a CD4 count <100 cells/mm3. We used multivariate regression models to determine the effects of sex, emotional health, social support, distance from clinic, employment, perceived barriers to receiving healthcare, and foregoing healthcare to use money for food, clothing, or housing (“competing needs to healthcare”) on presentation with late-stage HIV disease.

Results

Among 3,669 adults screened, 830 were enrolled, newly-diagnosed with HIV and obtained a CD4 result. Among those, 279 (33.6%) presented with late-stage HIV disease. In multivariate analyses, participants who lived ≥5 kilometers from the test site [adjusted odds ratio (AOR) 2.8, 95% CI 1.7–4.7], reported competing needs to healthcare (AOR 1.7, 95% CI 1.2–2.4), were male (AOR 1.7, 95% CI 1.2–2.3), worked outside the home (AOR 1.5, 95% CI 1.1–2.1), perceived health service delivery barriers (AOR 1.5, 95% CI 1.1–2.1), and/or had poor emotional health (AOR 1.4, 95% CI 1.0–1.9) had higher odds of late-stage HIV disease presentation.

Conclusions

Independent risk factors for late-stage HIV disease presentation were from diverse domains, including geographic, economic, demographic, social, and psychosocial. These findings can inform various interventions, such as mobile testing or financial assistance, to reduce the risk of presentation with late-stage HIV disease.

Introduction

South Africa has more HIV-infected people than any other country, and over 1.4 million South Africans are now receiving antiretroviral therapy (ART) [1], [2]. However, despite this progress, 45–51% of ART-eligible people are still not receiving treatment [1], [3]. Not only do these HIV-infected people constitute nearly one-quarter of all AIDS-related deaths in sub-Saharan Africa, but they also transmit HIV to others [1]. Furthermore, late-stage HIV disease at initial HIV diagnosis has been associated with poor treatment response rates and higher mortality [4][11]. In August 2011, the South African Department of Health increased the ART initiation threshold from CD4<200 to <350 cells/mm3 to help reduce AIDS-related deaths [12]. However, the median CD4 count at the time of ART initiation was 111 cells/mm3 in a recent large South African cohort [4].

Increasing the CD4 count treatment threshold will have little benefit if the majority of people continue to present with dangerously low CD4 counts and late-stage HIV disease. Strategies to reduce AIDS-related mortality and decrease HIV transmission must include earlier diagnosis of HIV [13], which has been shown to improve survival [14]. Intensified efforts to promote early diagnosis of HIV-infected people in resource-limited settings are needed, but little is known about how to best target people in HIV testing campaigns [15], [16]. We conducted a large, prospective study to assess both the real and perceived barriers to presenting for HIV care in South Africa.

Methods

Sites and participants

We studied adults who presented for voluntary HIV counseling and testing at four outpatient clinical sites in Durban from August 2010 to November 2011. McCord Hospital is an urban, state-aided general hospital that serves the greater Durban area. St. Mary's Hospital in Mariannhill is a state-aided general hospital that serves a resource-limited population in a peri-urban area of Durban. Both McCord Hospital and St. Mary's Hospital have high-volume outpatient HIV clinics that have been providing ART since 2001 and 2003, respectively, and receiving President's Emergency Plan for AIDS Relief (PEPFAR) support since 2004. The other two sites, Tshelimnyama and Marianridge, are municipal primary health clinics located within the catchment area of St. Mary's Hospital. Throughout the course of the study, all four outpatient sites offered free HIV counseling and testing during normal working business hours.

We offered enrollment to all adults ≥18 years of age prior to HIV counseling and testing. We excluded those already known to be HIV-infected, pregnant, or unwilling to share their HIV test result with the research team. HIV testing, as well as participation in the study, carried no financial costs to the participant. All participants provided written informed consent either in English or Zulu. The study was approved by the ethics committees of McCord Hospital and St. Mary's Hospital in Durban, and Partners HealthCare (Protocol #: 2006-P-001379) in Boston.

Data collection

We asked participants about personal demographics, proximity to the HIV clinic, and prior healthcare usage and HIV testing. We recorded responses to 12 questions related to perceived personal barriers for seeking HIV testing and medical care during the prior 6 months [17]. We asked 5 questions about emotional health over the previous month with each response rated on a 6-point Likert scale (ranging from 1 being “all of the time” or “always” to 6 being “none of the time” or “never”). These questions, which were adapted from the 5-item Mental Health Inventory (MHI-5) screening test, were used to calculate a mental health composite (MHC) score [18]. We asked 13 questions about availability of personal social support with each response rated on a 5-point Likert scale (ranging from 1 being “none of the time” to 5 being “all of the time”). These questions incorporate four social support scales (emotional/informational, tangible, positive interaction, affectionate), and were used to calculate the Social Support Index (SSI), from the Medical Outcomes Study [19]. Both the MHC and SSI scores were independently averaged and then transformed to scores ranging from 1 to 100, with higher numbers signifying better emotional health and more social support. We asked participants if they had gone without food, clothing, or housing (“basic necessities”) during the prior 6 months because they needed money for healthcare, or if they had foregone healthcare during the prior 6 months because they needed money for food, clothing, or housing [20].

After completing the survey, participants were offered free HIV counseling and testing, and HIV-infected participants were offered free CD4 count testing. Those who tested positive for HIV were referred for appropriate care and treatment. All HIV testing, care, and treatment was provided in accordance with current South African Department of Health HIV testing and treatment guidelines [3].

Statistical analyses

Late-stage HIV disease presentation was defined as a CD4 count <100 cells/mm3 at the time of initial HIV diagnosis. To allow incorporation of the perceived barriers into multivariate models, while also minimizing possible collinearity, we categorized the 12 barriers into 5 groups (service delivery, financial, personal health perception, logistical, and structural). Service delivery barriers included “have to wait too long to see the nurse/doctor,” “the nurse/doctor does not speak my language,” and “not treated with respect by the nurse/doctor.” Financial barriers included “could not afford medications” and “could not afford the cost of transportation.” Personal health perception barriers included “didn't think it was necessary, because didn't feel sick” and “felt too sick.” Logistical barriers included “could not get time off work” and “had to take care of someone else.” Structural barriers included “could not get to the clinic during the hours it was open,” “could not arrange transport to the clinic,” and “did not know where to find care.” Poor emotional health and poor social support were defined as an MHC and SSI score below the median value, respectively.

We used Chi-squared and Fisher's Exact tests to compare potential risk factors between those presenting with and without late-stage HIV disease. We used an iterative model building approach to construct a series of logistic regression models to identify factors associated with late-stage disease presentation. First, we used bivariate logistic regression models to determine odds ratios (OR) of presenting with late-stage HIV disease. To build a multivariate logistic regression model and generate adjusted odds ratios (AOR), we included age, sex, and any variable with a p-value <0.15 in bivariate analyses into a single model. We then removed one variable at a time for those variables with a p-value >0.15, and after each variable was removed, the model was refit to evaluate the remaining variables. Finally, variables not selected based on the initial unadjusted analyses were included in the multivariate model to assess their significance in the presence of other variables, and all variables were retained if they had p-values <0.15. To minimize the potential for collinearity, we assessed the correlation between all pairs of independent variables and verified that no pair of variables included in the same regression model was highly correlated with a Spearman r >0.60. All reported p-values were two-tailed, and a p-value <0.05 was considered statistically significant. We conducted analyses using SAS software (version 9.2; SAS Institute, Cary, NC).

Results

Cohort characteristics

Among 3,669 people screened for the study, 2,694 met eligibility criteria and enrolled in the study. Among those enrolled, 1,026 (38.1%) tested positive for HIV, of which 830 adults (80.9% of HIV-infected participants) completed both CD4 testing and the study survey. Among those, the median CD4 count was 186 cells/mm3 (interquartile range 70–345 cells/mm3), and 279 (33.6%) participants had late-stage HIV disease at the time of their initial HIV diagnosis.

Within the cohort, 249 (30.0%) were >40 years of age, 415 (50.0%) were male, and 449 (54.1%) had not completed high school (Table 1). Over half (56.5%) worked outside the home, and 688 (82.9%) participants reported living ≥5 kilometers from the clinic. Most participants (79.7%) had never previously been tested for HIV, including 54 of the 60 participants (90.0%) who had spent an overnight in a hospital during the prior year. Reported HIV testing of participants hospitalized during the prior year (6/60 or 10.0%) was significantly lower than the reported HIV testing among participants who had not been hospitalized during the prior year (162/769 or 21.1%) (p = 0.04).

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Table 1. Characteristics of HIV-infected adults with and without late-stage disease (CD4<100 cells/mm3) at the time of initial HIV diagnosis in a study of HIV testing in South Africa (N = 830).

https://doi.org/10.1371/journal.pone.0055305.t001

Competing needs to healthcare

Overall, 227 (27.3%) participants had ever gone without healthcare because they needed money for basic necessities (food, clothing, or housing). In bivariate analysis, this was more common among those presenting with late-stage HIV disease (p = 0.02). Participants who presented with late-stage disease were also more likely to have gone without healthcare to pay for food (23.3% vs. 16.3%, p = 0.02), housing (22.5% vs. 15.6%, p = 0.04), and food and housing (13.3% vs. 7.6%, p = 0.009) (Figure 1, top). Similarly, 196 participants (23.6%) had ever foregone basic necessities because they needed money for healthcare. Participants who presented with late-stage disease were more likely to have foregone housing (18.6% vs. 13.2%, p = 0.04), and food and housing (10.8% vs. 6.4%, p = 0.03) to pay for healthcare (Figure 1, bottom).

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Figure 1. Competing needs to receiving healthcare among those with and without late-stage disease (CD4<100 cells/mm3) at the time of initial HIV diagnosis (N = 830).

Error bars represent 95% confidence intervals.

https://doi.org/10.1371/journal.pone.0055305.g001

Perceived barriers to medical care

The most commonly reported perceived barriers to medical care were “have to wait too long to see the nurse or doctor” (31.4%), “could not afford medications” (26.0%), and “didn't think it was necessary, because didn't feel sick” (24.7%) (Table 2). Participants who reported a perceived barrier of “felt too sick” had a 2.97-fold higher odds (95% CI 2.00–4.41) of late-stage HIV disease presentation. Perceived barriers of “could not afford the cost of transportation” (OR 1.80, 95% CI 1.29–2.29), “could not afford medications” (OR 1.79, 95% CI 1.30–2.46), “could not arrange transport to the clinic” (OR 1.71, 95% CI 1.20–2.43), “have to wait too long to see the nurse/doctor” (OR 1.49, 95% CI 1.10–2.02), and “could not get to the clinic during the hours it was open” (OR 1.46, 95% CI 1.04–2.05) were also more commonly reported among those presenting with late-stage HIV disease.

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Table 2. Perceived barriers to medical care among those with and without late-stage disease (CD4<100 cells/mm3) at the time of initial HIV diagnosis (N = 830).

https://doi.org/10.1371/journal.pone.0055305.t002

Factors associated with late-stage HIV disease presentation

In the multivariate logistic regression model (Table 3), factors associated with presentation to care with late-stage HIV disease were living ≥5 kilometers from the clinic, having gone without healthcare because money was needed for basic necessities, being male, working outside the home, having a perception of health service delivery barriers, and poor emotional health. Living ≥5 kilometers from the clinic conferred a 2.80-fold (95% CI 1.68–4.67) higher odds of presenting with late-stage HIV disease. Having gone without healthcare because money was needed for basic necessities (AOR 1.67, 95% CI 1.17–2.37), being male (AOR 1.66, 95% CI 1.22–2.26), and working outside the home (AOR 1.48, 95% CI 1.07–2.05) had higher odds of late-stage HIV disease presentation. Among the categories of perceived barriers to medical care, only a perception of barriers related to health service delivery (AOR 1.48, 95% CI 1.07–2.05), which included “have to wait too long to see the nurse/doctor”, was significantly associated with late-stage HIV disease presentation in multivariate analyses. Finally, while both poor emotional health and poor social support were significant in bivariate analyses, only poor emotional health (AOR 1.41, 95% CI 1.03–1.94) was significant in the multivariate model.

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Table 3. Bivariate and multivariate logistic regression models for risk of late-stage HIV disease (CD4<100 cells/mm3) at the time of initial HIV diagnosis (N = 830).

https://doi.org/10.1371/journal.pone.0055305.t003

Discussion

As both guidelines and data increasingly support earlier HIV treatment, it is imperative to understand why patients continue to present with advanced HIV. In a large cohort of outpatient clinic attendees newly diagnosed with HIV in South Africa, the main risk factors for presenting with late-stage HIV disease were living further from the clinic, being male, and having gone without healthcare to pay for basic living necessities. Other variables associated with late-stage disease presentation were working outside the home, having a perception of barriers to health service delivery, such as long wait times, and having poor emotional health. These findings provide focused targets for improving HIV testing programs in order to diagnose people earlier and reduce the number of adults presenting to care with late-stage HIV disease.

Several studies have examined risk factors for late-stage disease presentation in sub-Saharan Africa. In Uganda, studies by Kigozi et al. and Wanyenze et al. found significant risk factors were being male, older, and having no secondary education, similar to our findings [21], [22]. They additionally found that people receiving healthcare in a non-medical setting (pharmacy, home, or by traditional healer) or having many sexual partners, both of which were variables we did not obtain, were more likely to present with late-stage disease. In Ethiopia, which has a very different cultural population than South Africa, non-pregnant women, frequent alcohol users, those in a long-term relationship, and people who perceived ART to have many side effects or HIV as a stigmatizing disease were more likely to present with late-stage disease [23]. In our cohort, relationships longer than 6 months had no impact on late-stage presentation, and we did not assess alcohol use or perception of HIV as a stigmatizing disease. The current study is unique from these previous studies by assessing distance to the clinic, perceived barriers to healthcare, competing needs to healthcare, and emotional health and social support structures, before participants were aware of their HIV-infected status.

In our cohort, several structural barriers, such as longer distance to the clinic, a perception of poor service delivery, and working outside the home, were among the strongest risk factors for late-stage HIV disease. Other studies in Africa have shown similar structural barriers, such as longer distances and higher transportation costs, to be related to loss-to-care before ART initiation [24][28]. One potential approach to ensuring better service delivery and earlier HIV diagnosis could be the use of active, mobile HIV testing strategies [29][31].

Despite the frequent occurrence of late-stage HIV disease presentation in sub-Saharan Africa, there has been an incomplete and inconsistent understanding of the perceived personal barriers to HIV testing [32]. In Botswana, perceived barriers to HIV testing included fear of learning one's status, lack of perceived HIV risk, and fear of having to change sexual practices if positive [33]. In Ethiopia, a perception of HIV as a highly stigmatizing disease was common among people who presented with late-stage disease [23]. Although we did not assess HIV-related stigma, our findings indicate that a perception of service delivery, structural, or financial barriers are obstacles that prevented people from learning their HIV-infected status.

Over one-quarter of those in our cohort reported having gone without healthcare because they needed money for basic necessities, and they were more likely to present with late-stage disease. In several sub-Saharan African studies, food insecurity has been associated with poor ART adherence, more opportunistic infections, missed clinic visits, and increased hospitalizations [34][36]. Food insecurity is more common among older, unmarried, HIV-infected adults [34], and thus food insecurity should be addressed as part of comprehensive HIV treatment programs in resource-limited settings [37]. While our findings support the observed negative effects of food insecurity, our results suggest that housing insecurity is also a common problem and associated with late-stage HIV disease presentation.

We found that poor emotional health and social support, both of which were assessed before HIV testing, were associated with a higher likelihood of presentation with late-stage HIV. Depression is common among HIV-infected adults [38], [39], and mental health problems are vastly undertreated in resource-poor settings [40], [41]. Rates of depression are also higher in symptomatic HIV-infected people [42]. While little data exists on outcomes among HIV-infected adults with mental health issues in developing countries, one U.S.-based study found higher rates of mortality among HIV-infected adults with depression [43]. Studies from Uganda and Ethiopia have reported conflicting results about whether poor mental health influences ART adherence [44], [45]. While evidence has been lacking on the association between mental health disorders and uptake of diagnostic and treatment services for HIV [40], our findings suggest that mental health issues and poor social support structures may be important risk factors for delayed HIV testing.

In our study, which excluded known HIV-infected adults, the 60 participants who had been hospitalized during the prior year represented important missed opportunities for HIV testing. Surprisingly, self-reported prior HIV testing was less common among those who had been hospitalized, since South African guidelines recommend “offering HIV testing to all clients attending health-care facilities as a standard component of medical care, unless the client actively refuses” [46]. Our finding suggests that either providers were not adherent to national guidelines or hospitalized patients refused HIV testing at very high rates (90%). Regardless, those missed opportunities represented 7.2% of our cohort, suggesting that HIV testing of hospitalized patients remains suboptimal.

Our findings were primarily limited by the accuracy of self-reported survey data. In this cross-sectional survey, we were not able to determine causality with associations and the analysis was not designed to fully examine the social stigma of HIV as a barrier to testing. We used a definition of late-stage HIV disease of CD4<100 cells/mm3, which portends a major risk for cryptococcal meningitis, a leading cause of AIDS-related deaths in sub-Saharan Africa [47]. This definition allowed us to identify risk factors and perceived barriers among the people with the greatest risk for severe opportunistic infections and mortality. While we did not assess some variables shown to be associated with late-stage presentation in other African studies (alcohol consumption, number of sexual partners, or ownership of material goods), we examined many important measures not studied in other studies of late-stage HIV disease presentation in sub-Saharan Africa. Finally, a small number of HIV-infected participants either did not fully complete our survey or agree to CD4 count testing, so we cannot assess their influence on our findings.

In conclusion, we found independent risk factors for presentation with late-stage HIV disease from diverse domains, including geographic, economic, demographic, social, and psychosocial. These can directly inform efforts to improve HIV testing. Such efforts should focus on various interventions, such as the use of active mobile testing strategies, financial assistance, or providing food supplementation, to reduce late-stage disease presentation in resource-poor settings (Table 4). Simply expanding HIV testing will not ensure ART-eligible people are enrolled in care or initiated on ART; innovative approaches are also needed to improve subsequent linkage to treatment [48], [49]. Providing increased and targeted HIV testing to those at greatest risk for late-stage HIV disease, and subsequently linking them to HIV care should reduce AIDS-related morbidity and mortality.

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Table 4. Identified barriers and possible solutions to reduce the risk of late-stage HIV disease presentation.

https://doi.org/10.1371/journal.pone.0055305.t004

Acknowledgments

We would like to acknowledge the excellent work and valuable contributions of our research staff and nurses. We thank each of the clinical sites for sharing their enthusiasm and space. We thank The U.S. President's Plan for AIDS Relief (PEPFAR), which provided funding for HIV care and services, including ART, for the participants in our study. Finally, we graciously thank all of the men and women who participated in this study.

Author Contributions

Conceived and designed the experiments: PKD EL JG DR JNK KAF RPW IVB. Performed the experiments: PKD GP JG DR IVB. Analyzed the data: PKD GP EL JG DR JNK SMC LMB KAF RPW IVB. Contributed reagents/materials/analysis tools: EL SMC. Wrote the paper: PKD.

References

  1. 1. Joint United Nations Programme on HIV/AIDS (2011) UNAIDS Global Report 2010. Geneva: UNAIDS. 229 p.
  2. 2. Joint United Nations Programme on HIV/AIDS (2011) UNAIDS Data Tables 2011. Geneva: UNAIDS. 12 p.
  3. 3. South Africa Department of Health (2011) Comprehensive Care, Management, and Treatment of HIV and AIDS. Pretoria: National Data.
  4. 4. May M, Boulle A, Phiri S, Messou E, Myer L, et al. (2010) Prognosis of patients with HIV-1 infection starting antiretroviral therapy in sub-Saharan Africa: A collaborative analysis of scale-up programmes. Lancet 376: 449–457.
  5. 5. Castilla J, Sobrino PB, de la Fuente LAB, Noguer I, Guerra L, et al. (2002) Late diagnosis of HIV infection in the era of highly active antiretroviral therapy: Consequences for AIDS incidence. AIDS 16: 1945–1951.
  6. 6. Egger M, May M, Chene G, Phillips AN, Ledergerber B, et al. (2002) Prognosis of HIV-1-infected patients starting highly active antiretroviral therapy: A collaborative analysis of prospective studies. Lancet 360: 119–129.
  7. 7. Krentz HB, Auld MC, Gill MJ (2004) The high cost of medical care for patients who present late (CD4<200 cells/uL) with HIV infection. HIV Medicine 5: 93–98.
  8. 8. Sabin CA, Smith CJ, Gumley H, Murphy G, Lampe FC, et al. (2004) Late presenters in the era of highly active antiretroviral therapy: Uptake of and responses to antiretroviral therapy. AIDS 18: 2145–2151.
  9. 9. Badri M, Lawn SD, Wood R (2006) Short-term risk of AIDS or death in people infected with HIV-1 before antiretroviral therapy in South Africa: A longitudinal study. Lancet 368: 1254–1259.
  10. 10. Toure S, Kouadio B, Seyler C, Traore M, Dakoury-Dogbo N, et al. (2008) Rapid scaling-up of antiretroviral therapy in 10,000 adults in Cote d'Ivoire: 2-year outcomes and determinants. AIDS 22: 873–882.
  11. 11. Mills EJ, Bakanda C, Birungi J, Chan K, Ford N, et al. (2011) Life expectancy of persons receiving combination antiretroviral therapy in low-income countries: a cohort analysis from Uganda. Ann Intern Med 155: 209–216.
  12. 12. Mascolini M (2011) South Africa raises threshold for starting ART to 350 CD4 cells. International AIDS Society, August 17, 2011. Available: http://www.iasociety.org/Default.aspx?pageId=5&elementId=13825. Accessed 13 May 2012.
  13. 13. Lawn SD, Harries AD, Anglaret X, Myer L, Wood R (2008) Early mortality among adults accessing antiretroviral treatment programmes in sub-Saharan Africa. AIDS 22: 1897–1908.
  14. 14. Ford N, Kranzer K, Hilderbrand K, Jouquet G, Goemaere E, et al. (2010) Early initiation of antiretroviral therapy and associated reduction in mortality, morbidity and defaulting in a nurse-managed, community cohort in Lesotho. AIDS 24: 2645–2650.
  15. 15. Chesney MA, Smith AW (1999) Critical delays in HIV testing and care: the potential role of stigma. Am Behav Scientist 42: 1162–1174.
  16. 16. Samet JH, Freedberg KA, Stein MD, Lewis R, Savetsky J, et al. (1998) Trillion virion delay: Time from testing positive for HIV to presentation for primary care. Arch Intern Med 58: 734–740.
  17. 17. Craw JA, Gardner LI, Marks G, Rapp RC, Bosshart J, et al. (2008) Brief strengths-based case management promotes entry into HIV medical care: results of the antiretroviral treatment access study-II. J Acquir Immune Defic Syndr 47: 597–606.
  18. 18. Hays RD, Sherbourne CD, Mazel RM (1993) The RAND 36-Item Health Survey 1.0. Health Econ. 2: 217–227.
  19. 19. Sherbourne CD, Stewart AL (1991) The MOS social support survey. Soc Sci Med 32: 705–714.
  20. 20. Cunningham WE, Andersen RM, Katz MH, Stein MD, Turner BJ, et al. (1999) The impact of competing subsistence needs and barriers on access to medical care for persons with Human Immunodeficiency Virus receiving care in the United States. Med Care 37: 1270–1281.
  21. 21. Kigozi IM, Dobkin LM, Martin JN, Geng EH, Muyindike W, et al. (2009) Late disease stage at presentation to an HIV clinic in the era of free antiretroviral therapy in sub-Saharan Africa. J Acquir Immune Defic Syndr 52: 280–289.
  22. 22. Wanyenze RK, Kamya MR, Fatch R, Mayanja-Kizza H, Baveewo S, et al. (2011) Missed opportunities for HIV testing and late-stage diagnosis among HIV-infected patients in Uganda. PLoS One 6: e21794.
  23. 23. Abaynew Y, Deribew A, Deribe K (2011) Factors associated with late presentation to HIV/AIDS care in South Wollo Zone Ethiopia: a case-control study. AIDS Res Ther 8: 8.
  24. 24. Zachariah R, Harries AD, Manzi M, Gomani P, Teck R, et al. (2006) Acceptance of anti-retroviral therapy among patients infected with HIV and Tuberculosis in rural Malawi is low and associated with cost of transport. PLoS One 1: e121.
  25. 25. Posse M, Baltussen R (2009) Barriers to access to antiretroviral treatment in Mozambique, as perceived by patients and health workers in urban and rural settings. AIDS Patient Care and STDs 23: 867–875.
  26. 26. Losina E, Bassett IV, Giddy J, Chetty S, Regan S, et al. (2010) The “ART' of linkage: pre-treatment loss to care after HIV diagnosis at two PEPFAR sites in Durban, South Africa. PLoS One. 5: e9538.
  27. 27. Fox MP, Mazimba A, Seidenberg P, Crooks D, Sikateyo B, et al. (2010) Barriers to initiation of antiretroviral treatment in rural and urban areas of Zambia: a cross-sectional stud of cost, stigma, and perceptions about ART. J Int AIDS Soc 13: 1–11.
  28. 28. Miller CM, Ketlhapile M, Rybassack-Smith H, Rosen S (2010) Why are antiretroviral treatment patients lost to follow-up? A qualitative study from South Africa. Trop Med Int Health 15: 48–54.
  29. 29. Mills EJ, Ford N (2012) Home-based HIV counseling and testing as a gateway to earlier initiation of antiretroviral therapy. Clin Infect Dis 54: 282–284.
  30. 30. Wachira J, Kimaiyo S, Ndege S, Mamlin J, Braitstein P (2012) What is the impact of home-based HIV counseling and testing (HBCT) on the clinical status of newly enrolled adults in a large HIV care program in western Kenya? Clin Infect Dis 54: 275–281.
  31. 31. Tumwesigye E, Wana G, Kasasa S, Muganzi E, Nuwaha F (2010) High uptake of home-based, district-wide, HIV counseling and testing in Uganda. AIDS Patient Care STDs 24: 735–741.
  32. 32. Muula AS, Ngulube TJ, Siziya S, Makupe CM, Umar E, et al. (2007) Gender distribution of adult patients on highly active antiretroviral therapy (HAART) in southern Africa: a systematic review. BMC Public Health 7: 63.
  33. 33. Weiser SD, Heisler M, Leiter K, Percy-de Korte F, Tlou S, et al. (2006) Routine HIV testing in Botswana: a population-based study on attitudes, practices, and human rights concerns. PLoS Med 3: e261.
  34. 34. Nagata JM, Magerenge RO, Young SL, Oguta JO, Weiser SD, et al. (2012) Social determinants, lived experiences, and consequences of household food insecurity among persons living with HIV/AIDS on the shore of Lake Victoria, Kenya. AIDS Care 24: 728–736.
  35. 35. Weiser SD, Tuller D, Frongillo E, Senkungu J, Mukiibi N, et al. (2010) Food insecurity as a barrier to sustained antiretroviral therapy adherence in Uganda. PLoS One 5: e10340.
  36. 36. Weiser SD, Tsai AC, Gupta R, Frongillo EA, Kawuma A, et al. (2012) Food insecurity is associated with morbidity and patterns of healthcare utilization among HIV-infected individuals in a resource-poor setting. AIDS 26: 67–75.
  37. 37. Ivers L, Cullen K, Freedberg K, Block S, Coates J, et al. (2009) HIV/AIDS, undernutrition, and food insecurity. Clin Infect Dis 49: 1092–1102.
  38. 38. Ciesla JA, Roberts JE (2001) Meta-analysis of the relationship between HIV infection and risk for depressive disorders. Am J Psychiatry 158: 725–730.
  39. 39. Justice AC, McGinnis KA, Atkinson JH, Heaton RK, Young C, et al.. (2004) Psychiatric and neurocognitive disorders among HIV-positive and negative veterans in care: veterans aging cohort five-site study. AIDS (Suppl 1): S49–59.
  40. 40. Prince M, Patel V, Saxena S, Maj M, Maselko J, et al. (2007) No health without mental health. Lancet 370: 859–877.
  41. 41. Ramirez-Avila L, Regan S, Giddy J, Chetty S, Ross D, Katz JN, et al.. (2012) Depressive symptoms and their impact on health-seeking behaviors in newly-diagnosed HIV-infected patients in Durban, South Africa. AIDS Behav. Epub ahead of print.
  42. 42. Maj M, Janssen R, Starace F, Zaudig M, Satz P, et al. (1994) WHO neuropsychiatric AIDS study, cross-sectional phase I. Study design and psychiatric findings. Arch Gen Psychiatry 51: 39–49.
  43. 43. Ickovics JR, Hamburger ME, Vlahov D, Schoenbaum EE, Schuman P, et al. (2001) Mortality, CD4 cell count decline, and depressive symptoms among HIV-seropositive women: longitudinal analysis from the HIV Epidemiology Research Study. JAMA 285: 1466–1474.
  44. 44. Byakika-Tusiime J, Oyugi JH, Tumwikirize WA, Katabira ET, Mugyenyi PN, et al. (2005) Adherence to HIV antiretroviral therapy in HIV+ Ugandan patients purchasing therapy. Int J STD AIDS 16: 38–41.
  45. 45. Tadios Y, Davey G (2006) Antiretroviral treatment adherence and its correlates in Addis Ababa, Ethiopia. Ethiop Med J 44: 237–244.
  46. 46. National Department of Health, Republic of South Africa (2010) National HIV Counseling and Testing (HCT) Policy Guidelines. Pretoria: National Department of Health.
  47. 47. Park BJ, Wannemuehler KA, Marston BJ, Govender N, Pappas PG, et al. (2009) Estimation of the current global burden of cryptococcal meningitis among persons living with HIV/AIDS. AIDS 23: 525–530.
  48. 48. Bassett IV, Regan S, Chetty S, Giddy J, Uhler LM, et al.. (2010) Who starts antiretroviral therapy in Durban, South Africa?... not everyone who should. AIDS (Suppl 1): S37–44.
  49. 49. Rosen S, Fox MP (2011) Retention in HIV care between testing and treatment in sub-Saharan Africa: a systematic review. PLoS Med 8: e1001056.