PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
J Acquir Immune Defic Syndr. Author manuscript; available in PMC 2014 June 1.
Published in final edited form as:
PMCID: PMC3654002
NIHMSID: NIHMS452179

Failure to Initiate Antiretroviral Therapy, Loss to Follow-up and Mortality among HIV-infected Patients during the Pre-ART period in Uganda

Abstract

Background

Delays and failures in initiation of antiretroviral therapy (ART) among treatment eligible patients may compromise the effectiveness of HIV care in Africa. An accurate understanding, however, of the pace and completeness of ART initiation and mortality during the waiting period is obscured by frequent losses to follow-up.

Methods

We evaluated newly ART-eligible HIV-infected adults from 2007 to 2011 in a prototypical clinic in Mbarara, Uganda. A random sample of patients lost to follow-up was tracked in the community to determine vital status and ART initiation after leaving the original clinic. Outcomes among the tracked patients were incorporated using probability weights, and a competing risks approach was used in analyses.

Results

Among 2,633 ART-eligible patients, 490 were lost to follow-up, of whom a random sample of 132 was tracked and 111 (84.0%) had outcomes ascertained. After incorporating the outcomes among the lost, the cumulative incidence of ART initiation at 30, 90 and 365 days after eligibility was 16.0% (95% CI: 14.2–17.7), 64.5% (95% CI: 60.9–68.1), and 81.7% (95% CI: 77.7–85.6). Death prior to ART was 7.7% at one year. Male sex, higher CD4 count, and no education were associated with delayed ART initiation. Lower CD4 level, malnourishment and travel time to clinic were associated with mortality.

Conclusions

Using a sampling-based approach to account for losses to follow-up revealed that both the speed and completeness of ART initiation were sub-optimal in a prototypical large clinic in Uganda. Improving the kinetics of ART initiation in Africa is needed to make ART optimally effective.

Keywords: Antiretroviral therapy, Africa, loss to follow-up, mortality

INTRODUCTION

For HIV-infected patients with indications for antiretroviral therapy (ART) in Africa, timely ART initiation during this period of high clinical risk is essential to effective and efficient care. Initiating ART, however, even for patients who have already been tested and enrolled into treatment programs, requires overcoming significant psychological, socio-structural, and operational barriers. First, patients must first accept their diagnosis in order to make rational choices about prioritizing care, but stigma and denial may take time to overcome 1. Second, disclosure of HIV status to family and friends can ease the burden of taking a life-long medication, but patients also fear that disclosure may lead to hostility or rejection 2. Third, requirements for multiple adherence counseling sessions before ART initiation and for “treatment supporters“ present at many clinics may improve medication adherence after initiation, but these processes may inadvertently magnify barriers to ART initiation for patients unable meet these criteria 3. Fourth, patients often reside far away from the clinic, and therefore travel time and costs as well as competing priorities from work and childcare may delay completion of pre-ART evaluation 4,5.

The magnitude and consequences of failures or delays in ART initiation are incompletely understood because loss to follow-up between eligibility and ART initiation (i.e., unknown outcomes) is high – from 15% to greater than 50% -- in many African clinics 612. “Loss to follow-up,” however, is not in and of itself a patient outcome, but rather an artifact of what the clinics can conveniently observe, and yields limited actionable public health information. Meaningful outcomes, hidden by loss to follow-up, include deaths prior to ART initiation; “silent” transfers (including possibly ART initiation at new sites); and disengagement from care with failure to start ART. Understanding outcomes in the group classified as “lost to follow-up” among patients already identified as eligible for ART is needed in order to assess the magnitude of delays and failures in ART initiation, the rate of death during the waiting period, and reasons for disengagement from care before ART initiation.

In this paper, we examine delays and failures of ART initiation in a clinic-based cohort of ART-eligible patients over a four-year period in rural Uganda. To address the selection bias potentially introduced by loss to follow-up, we identified a random sample of lost patients, intensively sought their outcomes in the community and used these outcomes to reclassify outcomes among all lost patients. We have previously applied this approach to evaluate outcomes among patients who had already initiated ART 5,13. In the current paper, we apply this approach to recover valid estimates of the magnitude and determinants of ART initiation, deaths, and disengagement from in a population of ART-eligible patients.

METHODS

Patients

We evaluated HIV-infected adults with a CD4 count based indication for ART (i.e., CD4 + T-cell ≤ 250 /µl in contemporaneous Uganda National Guidelines) between Oct. 1, 2007 and Jan. 27, 2011 at the Immune Suppression Syndrome (ISS) Clinic in Mbarara District in southwestern Uganda. Of note, the study population includes both patients who were enrolled in the clinic before October 1, 2007 but who were not ART eligible until after this date. The ISS Clinic is a “prototypical” scale-up ART clinic in that the patient volume is high (up to 150 visits a day is common), no routine HIV RNA testing is available, and it is supported by the Uganda Ministry of Health through implementing partners of the US President’s Emergency Fund for AIDS Relief (PEPFAR). In this analysis, observation began on the date of first CD4 ≤ 250 /µl, and patients were followed until time of death, loss to follow-up (defined as 60 days late for last scheduled appointment), ART initiation or closure of database on January 27, 2011. Among those lost to follow-up, a random sample was selected for tracking in the community to obtain updated data on vital status, ART use, and HIV care either through contact with the patient directly or a close relation of the patient when the patients could not be found in person 5. The size of the sample was determined by operational capacity: we tracked as many patients as we could with available resources.

Measurements

Sociodemographic and clinical information was collected in the course of routine care by providers using standardized paper forms from the Uganda Ministry of Health and supplemented by forms developed at the clinic. Data are then subsequently hand-entered into an Open MRS database. Diagnosis of tuberculosis at time of eligibility was defined as any documentation of clinical or microbiologic tuberculosis in the nine months before or 30 days after ART-eligibility. Malnourishment was defined by standardized WHO criterion of a body mass index < 18.5 kg/m2. For the sample of lost patients, a patient tracker went into the community to ascertain outcomes using a standardized questionnaire. Key information sought included vital status and date of death, if applicable. Patients found in person were asked whether they had seen a doctor or nurse for HIV care in the proceeding 90 days, whether they were on ART (defined as any ART within the last 14 days), and if so, when they started ART.

Analyses

In the clinic population as originally observed (i.e., without incorporating outcomes obtained through tracking a sample of the lost patients), we estimated the incidence of loss to follow-up through one year after ART-eligibility (defined as 60 or more days late for an appointment). New patients not on ART are typically asked to return in 2 to 4 weeks after their first visit and established patients with CD4 levels above treatment thresholds not on ART are customarily asked to return in 1 to 4 months. For this estimate we used the cumulative incidence approach in which ART initiation and deaths were considered competing events 1416. We also analyzed the group of patients who were lost to follow-up and subsequently successfully tracked to estimate the cumulative incidence of mortality from time of last clinic visit using the Kaplan-Meier approach, the hazard of mortality after last clinic visit, and the predictors of mortality using a proportional hazards regression.

We then analyzed the entire clinic population in which we used the outcomes discovered through tracking a random sample of lost patients to represent outcomes in all lost patients. Loss to follow-up that is differential on either the outcome alone or on outcomes and exposures of interest is a well-recognized cause of bias. Conceptually, incorporating outcomes of the sample of successfully tracked patients addresses the effects of differential loss to follow-up by replacing unknown outcomes with known outcomes in a representative sample which allows recovery of unbiased estimates. Statistically, lost patients with outcomes ascertained through tracking were given a weight inverse to the sampling probability, lost patients without updated outcomes were assigned a weight of zero, and all patients who remain under observation at the clinic receive a weight of one 13,17. Operationally, consider a hypothetical clinic population of 10,000 patients in which 1000 patients are lost to follow-up and in which the ascertainer seeks 100 of the lost patients and successfully finds outcomes in 90. In this example, after tracking, 910 patients have outcomes that remain unknown (900 of whom were lost and never sought and 10 of whom were unsuccessfully sought). They are given a weight of zero and effectively removed from the analysis. The 90 patients who were initially lost to follow-up but then successfully sought (and who therefore now have known outcomes) are used to represent the 910 without known outcomes through a weight of 10.1 (910/90). The patients who remain under observation are assigned a weight of one (thus representing only themselves), as their outcomes are by definition completely known.

In this sample-weighted population, we used the cumulative incidence method to estimate the incidence of ART initiation (at the original or any other clinic), mortality prior to ART initiation, and disengagement from care (defined as 90 days without a visit to any facility) over the first year after ART eligibility for the entire clinic population. For the specific outcome of disengagement from care (and its complement “retention in care”), we assumed that care utilization among those interviewed in person represented all lost patients who were alive. Therefore, patients who were lost and interviewed in person were additionally weighted in inverse proportion to the probability that a living patient was interviewed in person. Confidence intervals for weighted descriptive estimates were obtained through bootstrapping.

We conducted two proportional hazards regressions of the reweighted data: one to identify factors associated with ART initiation and the second to identify factors associated with mortality – both in the entire clinic population. Predictor selection was guided by the desire to evaluate the influence of as many predictors as possible but avoid colliders and overfitting. Missing predictor data was addressed with multiple imputation 18,19 under the “missing at random” assumption. Of note, multiple imputation was not used to address missing outcome data, a problem that we addressed with the sampling-based approach. All analyses were conducted in Stata version 11.1 (College Station, TX). Ethical approval was granted by the University of California, San Francisco and the Mbarara University of Science and Technology.

RESULTS

Patient characteristics

Between October 1, 2007 and January 27, 2011, 2,633 patients first became eligible for ART as defined by immunological criteria (i.e., a CD4 count < or = 250 cells/µl). The median age was 32 years (IQR: 27–39) and 1,070 were men (40.6%). The median CD4 count at eligibility was 131 cells/µl (IQR: 53 to 198), 645 (24.5%) were WHO Stage 4 and 115 (4.4%) had a diagnosis of tuberculosis at eligibility. In 2,247 (85.3%) patients, the first CD4 count taken at the ISS Clinic was < 250 cells/µl, and we considered these patients eligible at the time of clinic enrollment (Table 1).

Table 1
Patient characteristics among all patients eligible for antiretroviral therapy by CD4 criteria at the ISS Clinic in southwestern Uganda from October 1, 2007 to January 27, 2011 and among those lost to follow-up, a random sample of the lost and those who ...

Tracking a sample of the patients lost to follow-up

The cumulative incidence of loss to follow-up at one year was 21.3% (95% CI: 19.6–23.0). Of the 490 patients lost to follow-up, we sought a random sample of 132 (27%) for tracking (Figure 1). In 111/132 (84.0%), updated information about vital status or care status was obtained. Among the 111 successfully tracked patients, 42 had died and the cumulative incidence of mortality one year after last clinic visit was 36.8% (95% CI: 28.2–47.0). The hazard, or instantaneous rate, of death was highest in the 60 days after last visit at 38/100 person-years, falling to 11/100 person-years from 60 to 180 days and to 8/100 person-years from 180 to 365 days after last visit (Figure 2). Of the 69 successfully tracked patients who were alive, in 22 cases (32%) the patient him or herself was interviewed and in 47 (68%), a sibling, spouse, close friend or other close acquaintance acted as the informant. Of 22 patients who were alive and found in person, 14 (64%) reported having seen a doctor or nurse for HIV care at a different facility within the last 90 days. The remaining 8 of 22 (36%) reported no recent contact with health care. In a proportional hazards regression of predictors of survival among those lost to follow-up, nearer district of residence, lower enrollment CD4 level, lower BMI and earlier calendar date of last visit were associated with higher mortality (Supplemental Digital Content 1).

Figure 1
Flow-chart depicting HIV-infected patients who became newly eligible for antiretroviral therapy according to CD4+ T-cell based criteria at the ISS Mbarara Clinic.
Figure 2
Cumulative incidence and hazard (instantaneous rate) of mortality among treatment-eligible, HIV-infected patients who were lost to follow-up after date of last clinic visit, N=111

Sample-weighted outcomes following ART eligibility

After incorporating the outcomes among the lost with probability weights, for the entire clinic population the cumulative incidence of ART initiation at 30, 90 and 365 days after eligibility was 16.0% (95% CI: 14.2 to 17.7), 64.5% (95% CI: 60.9 to 68.1), and 81.7% (95% CI: 77.7 to 85.6). The cumulative incidence of death prior to ART initiation at 30, 90 and 365 days following ART eligibility was 1.6% (95% CI: 0.05 to 2.5), 5.5% (95% CI: 2.8 to 8.1), and 7.7% (95% CI: 5.6 to 10.1). One year after eligibility, 10.0% of eligible patients were alive but had never started ART (Figure 3). The large majority of these patients not on ART after 1 year – 69.5% – were lost to follow-up from the ISS Clinic.

Figure 3
Proportions of patients who initiate ART, died before ART initiation and awaiting ART following the date of first treatment-eligible CD4+ T-cell level at the ISS Mbarara Clinic, N=2,633

In multivariable analyses, higher age at enrollment and, notably, being unemployed were associated with faster ART initiation after adjustment for other socio-demographic and clinical factors. Male sex, higher CD4 count levels at presentation, having no formal education and being new to care at the time of eligibility were all associated with a decreased rate of ART initiation. In multivariate analysis to identify factors associated with mortality, malnutrition, lower CD4 level at clinic enrollment, greater travel time from residence to clinic and district of residence was associated with higher risk of mortality (Table 2).

Table 2
Factors associated with time to ART initiation and mortality.

DISCUSSION

Using a sampling-based approach to understand the experience of ART-eligible patients under routine program conditions in Uganda where loss to follow-up is high, we found that about 1 in 5 patients eligible for ART by CD4 count did not initiate ART one year after eligibility, and that 1 in 12 died during awaiting ART initiation. We identified a number of patient (CD4 level, male sex), system (whether or not you were new to care at the time of ART eligibility) and structural factors (travel time) to be associated with the rate of ART initiation as well as in a separate analysis, mortality, which shed light on the multi-dimensional determinants of barriers to effective care. These observations were made through application of a sampling-based approach in a clinic-based cohort to address biases presented by loss to follow-up. This approach strengthens both internal validity and external validity since the data come from a “real world” setting.

Although there is no absolute criterion about how soon eligible patients should start ART, our estimate of time to ART initiation demonstrates two concerning features. First, ART initiation is not rapid – at 60 days only 50% of eligible patients had initiated. In particular, a large fraction of eligible patients were classified as WHO Stage 3 or 4 and therefore likely had active opportunistic infections. These patients would likely benefit from ART initiation within 14 days as suggested by ACTG protocol 5164 20. Second, uptake of ART remained incomplete one year after eligibility. The ISS Clinic, like most clinics in Africa, typically required 2–3 counseling sessions before ART initiation and also a treatment supporter. In resource-limited settings, these requirements may magnify barriers to ART initiation even though they may strengthen adherence. Failure to achieve this last step of ART initiation is tantamount to a failed investment in testing and linkage.

We identified patient, structural and operational factors associated with the rate of ART initiation that point to actionable gaps in public health ART delivery. Previous cross-sectional analyses have found that male sex is a consistent predictor of late stage presentation among HIV infected persons in Africa 21, and in this analysis men appear to start ART almost 30% more slowly than women after adjusting for presentation CD4 level. Further research on the psychological aspects of male health care behavior in Africa is needed. Patients with no education, most of whom are likely to be illiterate, started ART at a slower rate. Patients without education should be flagged as a vulnerable group who may require additional assistance to successfully navigate the health care system. Unemployed patients initiated ART faster. Work responsibilities have been identified as a barrier to retention in previous work and therefore likely explain diminished ART initiation rates 5. Exploration of alternative initiation strategies that are part community-based, using peer educators or “community adherence groups” are needed to enable access health care without jeopardizing the livelihoods of patients 22,23. Finally, we found an interesting programmatic effect: patients who are new to care at eligibility are initiated on ART faster than patients who become eligible after already being in care. This is likely because patients already in care receive longer return visits since they appear to be “stable.” Yet, this introduces a longer interval to their next visit when they do become eligible by CD4 count criteria. Randomized trails of both community-based peer advocates and SMS technology have been shown to reduce HIV RNA rebound in Africa for patients already on ART. These evidence-based strategies should be explored as methods to facilitate ART initiation newly eligible patients as well.

This analysis identified four factors associated with mortality that can be used to guide clinical attention to high-risk patients. The CD4 level at ART eligibility is a consistent predictor of mortality and likely reflects opportunistic infections already in progress at the time of ART initiation. Rolling out enhanced diagnostics for smear-negative tuberculosis, C. neoformans and other common infections is critical in preventing these deaths 24,25. A body mass index < 18.5 kg/m2 conferred a threefold rise in the rate of death even after adjusting for CD4 level, TB diagnosis, and other sociodemographic factors. These patients likely represent a mix of individuals with undiagnosed infections as well as persons with macro-nutritional deficiency. Food supplementation in this subset of patients may improve survival and wider dissemination of nutritional interventions may improve outcomes 2628. Travel time from residence to clinic was associated in a dose response relationship to survival. This finding suggests that addressing geographic and physical accessibility through decentralization may merit greater prioritization as a strategy to make treatment delivery more effective 29,30.

This study has certain limitations. First, although our sample was formally a random subset of the lost to follow-up, not all outcomes were ascertained, and therefore the reweighted estimates may be biased. We believe given more than 80% of outcomes were ascertained, the opportunity for bias is not large. Second, although we believe the ISS Clinic is representative of large ART programs in Africa, processes are likely better than the “average clinic” because of its affiliation with Mbarara University and membership in the IeDEA consortium. If true, this would imply that the shortcomings with uptake of ART are even more concerning than those we observed here.

In summary, we used a sampling-based approach – which allows us to maximize validity with a highly generalizable patient population – to understand uptake of ART among eligible patients at a scale-up clinic in Uganda. This approach is broadly feasible. On-site start up costs for three months to track 120 patients was is approximately 5,000 dollars. This includes 1,200 dollars for a motorbike, 600 dollars per month as salary for the ascertainer, 500 dollars for fuel, 500 dollars per month for other consumables and administrative costs. We observed a strong relationship between death and loss to follow-up, which suggests that analyses of mortality in the pre-ART population based on passively ascertained vital status information likely biased and possibly severely so 31,32. We found the rate and total uptake of ART was neither optimally fast nor complete. Factors associated with uptake identify groups of patients who may benefit from targeted interventions, namely, men and patients with little or no education. Enhanced program features such as point-of-care, distributed care sites, and text messages to notify patients already in care when they become eligible for ART may improve ART uptake and delivery and potentially survival. ART is highly efficacious; improving the utilization of ART under program conditions through implementation strategies can make this intervention more effective in routine care.

Supplementary Material

ACKNOWLEDGEMENTS

We are grateful to Hassan Baryahikwa, Megan Lazzar and Michael Kanyesigye

Funding Sources: National Institutes of Health (K23 AI084544, U01 AI069911, and P30 AI027763) and the United States President’s Emergency Plan for AIDS Relief (PEPFAR).

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Conflicts of Interest and Source of Funding: None declared

REFERENCES

1. Nam SL, Fielding K, Avalos A, Dickinson D, Gaolathe T, Geissler PW. The relationship of acceptance or denial of HIV-status to antiretroviral adherence among adult HIV patients in urban Botswana. Social science & medicine (1982) 2008 Jul;67(2):301–310. [PubMed]
2. Medley A, Garcia-Moreno C, McGill S, Maman S. Rates, barriers and outcomes of HIV serostatus disclosure among women in developing countries: implications for prevention of mother-to-child transmission programmes. Bull World Health Organ. 2004 Apr;82(4):299–307. [PubMed]
3. Siedner MJ, Lankowski A, Haberer JE, et al. Rethinking the"Pre" in Pre-Therapy Counseling: No Benefit of Additional Visits Prior to Therapy on Adherence or Viremia in Ugandans Initiating ARVs. PLoS One. 2012;7(6):e39894. [PMC free article] [PubMed]
4. Tuller DM, Bangsberg DR, Senkungu J, Ware NC, Emenyonu N, Weiser SD. Transportation Costs Impede Sustained Adherence and Access to HAART in a Clinic Population in Southwestern Uganda: A Qualitative Study. AIDS Behav. 2009 Mar 13; [PMC free article] [PubMed]
5. Geng EH, Bangsberg DR, Musinguzi N, et al. Understanding reasons for and outcomes of patients lost to follow-up in antiretroviral therapy programs in Africa through a sampling-based approach. J Acquir Immune Defic Syndr. 2010 Mar;53(3):405–411. [PMC free article] [PubMed]
6. Rosen S, Fox MP. Retention in HIV care between testing and treatment in sub-Saharan Africa: a systematic review. PLoS medicine. 2011 Jul;8(7):e1001056. [PMC free article] [PubMed]
7. Karcher H, Omondi A, Odera J, Kunz A, Harms G. Risk factors for treatment denial and loss to follow-up in an antiretroviral treatment cohort in Kenya. Trop Med Int Health. 2007 May;12(5):687–694. [PubMed]
8. Tayler-Smith K, Zachariah R, Massaquoi M, et al. Unacceptable attrition among WHO stages 1 and 2 patients in a hospital-based setting in rural Malawi: can we retain such patients within the general health system? Trans R Soc Trop Med Hyg. 2010 May;104(5):313–319. [PubMed]
9. Zachariah R, Harries AD, Manzi M, et al. 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. 2006;1:e121. [PMC free article] [PubMed]
10. Amuron B, Namara G, Birungi J, et al. Mortality and loss-to-follow-up during the pre-treatment period in an antiretroviral therapy programme under normal health service conditions in Uganda. BMC Public Health. 2009;9:290. [PMC free article] [PubMed]
11. Bassett IV, Wang B, Chetty S, et al. Loss to care and death before antiretroviral therapy in Durban, South Africa. J Acquir Immune Defic Syndr. 2009 Jun 1;51(2):135–139. [PMC free article] [PubMed]
12. Micek MA, Gimbel-Sherr K, Baptista AJ, et al. Loss to follow-up of adults in public HIV care systems in central Mozambique: identifying obstacles to treatment. J Acquir Immune Defic Syndr. 2009 Nov 1;52(3):397–405. [PMC free article] [PubMed]
13. Geng EH, Emenyonu N, Bwana MB, Glidden DV, Martin JN. Sampling-based approach to determining outcomes of patients lost to follow-up in antiretroviral therapy scale-up programs in Africa. JAMA. 2008 Aug 6;300(5):506–507. [PMC free article] [PubMed]
14. Prentice RL, Kalbfleisch JD, Peterson AV, Jr, Flournoy N, Farewell VT, Breslow NE. The analysis of failure times in the presence of competing risks. Biometrics. 1978 Dec;34(4):541–554. [PubMed]
15. Satagopan JM, Ben-Porat L, Berwick M, Robson M, Kutler D, Auerbach AD. A note on competing risks in survival data analysis. Br J Cancer. 2004 Oct 4;91(7):1229–1235. [PMC free article] [PubMed]
16. Marubini E, Valsecchi MG. Analysing Survival Data from Clinical Trials and Observational Studies. Chichester: John Wiley & Sons, Ltd; 1995.
17. Frangakis CE, Rubin DB. Addressing an idiosyncrasy in estimating survival curves using double sampling in the presence of self-selected right censoring. Biometrics. 2001 Jun;57(2):333–342. [PubMed]
18. Schafer JL. Multiple imputation: a primer. Stat Methods Med Res. 1999 Mar;8(1):3–15. [PubMed]
19. Schafer JL, Graham JW. Missing data: our view of the state of the art. Psychol Methods. 2002 Jun;7(2):147–177. [PubMed]
20. Zolopa A, Andersen J, Komarow L, et al. Early antiretroviral therapy reduces AIDS progression/death in individuals with acute opportunistic infections: a multicenter randomized strategy trial. PLoS One. 2009;4(5):e5575. [PMC free article] [PubMed]
21. Geng EH, Kreiswirth BN, Burzynski J, Schluger NW. Transmission trends for human immunodeficiency virus associated tuberculosis in New York City. Int J Tuberc Lung Dis. 2005 Jun;9(6):661–666. [PubMed]
22. Decroo T, Telfer B, Biot M, et al. Distribution of antiretroviral treatment through self-forming groups of patients in Tete province, Mozambique. J Acquir Immune Defic Syndr. 2011 Nov 13; [PubMed]
23. Decroo T, Van Damme W, Kegels G, Remartinez D, Rasschaert F. Are Expert Patients an Untapped Resource for ART Provision in Sub-Saharan Africa? AIDS research and treatment. 2012;(2012):749718. [PMC free article] [PubMed]
24. Boehme CC, Nabeta P, Hillemann D, et al. Rapid molecular detection of tuberculosis and rifampin resistance. The New England journal of medicine. 2010 Sep 9;363(11):1005–1015. [PMC free article] [PubMed]
25. Van Rie A, Page-Shipp L, Scott L, Sanne I, Stevens W. Xpert((R)) MTB/RIF for point-of-care diagnosis of TB in high-HIV burden, resource-limited countries: hype or hope? Expert Rev Mol Diagn. 2010 Oct;10(7):937–946. [PubMed]
26. Tsai AC, Bangsberg DR, Emenyonu N, Senkungu JK, Martin JN, Weiser SD. The social context of food insecurity among persons living with HIV/AIDS in rural Uganda. Social science & medicine (1982) 2011 Dec;73(12):1717–1724. [PMC free article] [PubMed]
27. Miller CL, Bangsberg DR, Tuller DM, et al. Food insecurity and sexual risk in an HIV endemic community in Uganda. AIDS Behav. 2011 Oct;15(7):1512–1519. [PMC free article] [PubMed]
28. Weiser SD, Young SL, Cohen CR, et al. Conceptual framework for understanding the bidirectional links between food insecurity and HIV/AIDS. Am J Clin Nutr. 2011 Dec;94(6):1729S–1739S. [PubMed]
29. Tanser F, Barnighausen T, Cooke GS, Newell ML. Localized spatial clustering of HIV infections in a widely disseminated rural South African epidemic. Int J Epidemiol. 2009 Aug;38(4):1008–1016. [PMC free article] [PubMed]
30. Tanser F, Lesueur D, Solarsh G, Wilkinson D. HIV heterogeneity and proximity of homestead to roads in rural South Africa: an exploration using a geographical information system. Trop Med Int Health. 2000 Jan;5(1):40–46. [PubMed]
31. Geng EH, Glidden DV, Bangsberg DR, et al. A Causal Framework for Understanding the Effect of Losses to Follow-up on Epidemiologic Analyses in Clinic-based Cohorts: The Case of HIV-infected Patients on Antiretroviral Therapy in Africa. Am J Epidemiol. 2012 Feb 3;175(10):1080–1087. [PMC free article] [PubMed]
32. Yiannoutsos CT, An MW, Frangakis CE, et al. Sampling-based approaches to improve estimation of mortality among patient dropouts: experience from a large PEPFAR-funded program in Western Kenya. PLoS ONE. 2008;3(12):e3843. [PMC free article] [PubMed]