Conditions: Tuberculosis HIV/AIDS Alcohol Abuse
Keywords: Adherence Hepatotoxicity Phosphatidtylethanol
Allocation: NA | Intervention model: SINGLE_GROUP
Enrollment: 302
Healthy volunteers: no

Brief summary:
The Alcohol Drinkers' Exposure to Preventive Therapy for TB (ADEPTT) will examine the safety and tolerability of, and adherence to, 6 months of daily INH (6H) in 300 TB and HIV-infected persons (200 drinkers and 100 non-drinkers) in Uganda. The first aim is to evaluate the safety and tolerability of 6H overall and by level of alcohol use. The second aim is to estimate adherence and compare adherence by level of alcohol use and at 3 and 6 months. Self-reported measures of alcohol use will be augmented by phosphatidylethanol (PEth), an established biomarker of alcohol use. Objective measures of adherence will include electronic pill bottle monitoring and a novel measure of INH exposure, INH concentration in hair. The study will actively monitor for hepatotoxicity using the U.S. standard of care for TB preventive therapy for heavy drinkers and discontinue if any Grade 3/4 toxicities are detected. The investigators will use the safety, tolerability, and adherence results, together with the known efficacy and mortality benefit of TB preventive therapy in HIV-infected persons in SSA, and an established decision analytic model of TB preventive therapy to conduct the third aim: to determine whether the benefits of TB preventive therapy outweigh the toxicity risks for HIV-infected drinkers in resource limited settings. The study will additionally follow the cohort every 6 months after completing INH to monitor drinking and the development of active TB.

Detailed description:
Tuberculosis (TB) is the leading cause of mortality in persons with HIV worldwide, accounting for 20-33% of HIV-related deaths, and is a high-priority area of research in HIV/AIDS by the NIH. TB preventive therapy decreases both all-cause mortality and active TB in persons with HIV by 30-50% above and beyond the benefits of antiretroviral therapy (ART) alone. Based on these findings, the World Health Organization (WHO) recommends isoniazid (INH) preventive therapy (IPT) for all persons with HIV in resource constrained settings. However, the WHO warns against the use of IPT in persons with "regular and heavy alcohol use." This exclusion stems from concern for increased hepatotoxicity in heavy drinkers in settings where liver enzymes are not routinely monitored. Heavy drinking in persons with HIV is very common, approximately 25%, in sub-Saharan Africa (SSA). Heavy drinking increases the risk for active TB at least threefold; thus, HIV-infected alcohol users should be prioritized for TB prevention. However, no studies have systematically assessed the safety of TB preventive therapy in heavy drinkers with or without HIV infection. It is critical to examine the safety and tolerability of TB preventive therapy for HIV-infected drinkers, given the high rates of HIV, TB infection, and alcohol comorbidities worldwide. While the risk of toxicity exists, the risk of TB disease could outweigh the toxicity harms. Thus, it is also crucial to determine whether the mortality benefits outweigh the toxicity risks for this significant portion of the HIV-infected population.

In addition, TB preventive therapy is only effective if taken consistently for the full course. Alcohol use is an established risk factor for decreased ART pill taking and active TB treatment discontinuation. Whether HIV-infected drinkers on ART can be adherent to TB preventive therapy is not known. Therefore it is essential to determine the level of adherence to TB preventive therapy by HIV-infected drinkers on ART, thus this study aims to examine adherence to TB preventative therapy as well.

This is a study to examine 6 months of daily INH (6H) among N=300 persons co-infected with HIV and TB. The aims of the study are:

Aim 1: To examine the safety and tolerability of 6H in HIV/TB co-infected drinkers, measured by hepatotoxicity and treatment discontinuation rates. The main aim is to estimate safety and tolerability overall among drinkers (primary) and by level of drinking (secondary).

Aim 2: To determine the level of TB preventive therapy adherence overall among drinkers and by level of drinking, and at 3 and 6 months. The main goal of this aim is to estimate adherence overall among drinkers (primary). Secondarily the investigators will estimate adherence by level of drinking (heavy, current but not heavy drinkers, and non-drinkers) and compare adherence across drinking levels. The investigators hypothesize that adherence will be highest among the non-drinkers.

Aim 3: To determine whether the benefits of providing TB preventive therapy to HIV-infected drinkers in resource-limited settings outweigh the risks compared to no treatment. The investigators hypothesize that providing TB preventive therapy will result in longer life expectancy and quality-adjusted life expectancy than not providing TB preventive therapy (current standard of care).

Arms:
arm 1: Isoniazid 300 mg oral tablet: 300 mg daily by mouth for 6 months. Pyridoxine 25 mg oral tablet: 25mg daily by mouth for 6 months.

Interventions: intervention 1: Isoniazid 300 Mg ORAL TABLET intervention 2: Pyridoxine 25 Mg Oral Tablet
Intervention descriptions: intervention 1: The study intervention will include a single arm given (1) 6H: 300 mg INH daily by mouth for 6 months. intervention 2: All participants will also receive 25 mg pyridoxine (vitamin B-6) daily by mouth for 6 months for the treatment duration to reduce the risk of INH-induced peripheral neuropathy.

Protocol document:
Prot_SAP_001.pdf: 
NCT ID: NCT03302299
Brief title: Alcohol Drinkers' Exposure to Preventive Therapy for TB (ADEPTT) 
Official title: URBAN ARCH (3/5) Uganda Cohort TB preventive therapy for HIV-infected alcohol users in Uganda: an 
evaluation of safety tolerability and adherence 
Unique protocol ID: U01AA020776-06 
Document Version: May 9. 2017 
Approved by: Judith Hahn, Study PI. Debbie Cheng, Study Statistician. 


SPECIFIC AIMS   
  Tuberculosis (TB) is the leading cause of mortality in persons with HIV worldwide, accounting for 20-33% of 
HIV-related deaths,1,2 and is a high-priority area of research in HIV/AIDS by the NIH.3 TB preventive therapy 
decreases both all-cause mortality and active TB in persons with HIV by 30-50% above and beyond the 
benefits of antiretroviral therapy (ART) alone.4-8 Based on these findings, the World Health Organization 
(WHO) recommends isoniazid (INH) preventive therapy (IPT) for all persons with HIV in resource constrained 
settings.9-11 However, the WHO warns against the use of IPT in persons with “regular and heavy alcohol use.”9 
This exclusion stems from concern for increased hepatotoxicity in heavy drinkers in settings where liver 
enzymes are not routinely monitored. Heavy drinking in persons with HIV is very common, approximately 25%, 
in sub-Saharan Africa (SSA).12-17 Heavy drinking increases the risk for active TB at least threefold;18-20 thus, 
HIV-infected alcohol users should be prioritized for TB prevention. However, no studies have systematically 
assessed the safety of TB preventive therapy in heavy drinkers with or without HIV infection. It is critical to 
examine the safety and tolerability of TB preventive therapy for HIV-infected drinkers, given the high 
rates of HIV, TB infection, and alcohol comorbidities worldwide. While the risk of toxicity exists, the risk of TB 
disease could outweigh the toxicity harms. Thus, it is also crucial to determine whether the mortality 
benefits outweigh the toxicity risks for this significant portion of the HIV-infected population. 
 
TB preventive therapy is only effective if taken consistently for the full course.21 Alcohol use is an established 
risk factor for decreased ART pill taking22 and active TB treatment discontinuation.23 Whether HIV-infected 
drinkers on ART can be adherent to TB preventive therapy is not known. Therefore it is essential to determine 
the level of adherence to TB preventive therapy by HIV-infected drinkers on ART.  
 
We propose the Alcohol Drinkers’ Exposure to Preventive Therapy for TB (ADEPTT) study to examine 6 
months of daily INH (6H) among N=300 persons co-infected with HIV and TB. Our main aim is to evaluate the 
safety and tolerability of the TB preventive therapy regimens overall among drinkers and by level of drinking. 
We will also determine adherence to the regimen at 3 and 6 months overall among drinkers and by level of 
drinking. Alcohol use will be measured by self-report and augmented by phosphatidylethanol (PEth), an 
established biomarker for alcohol consumption.30 We will use objective measures of adherence including 
electronic medication monitoring (EMM) that records pill bottle opening, and INH concentration in hair, a novel 
measure of INH exposure.31,32 We will actively monitor for hepatotoxicity, using the U.S. standard of care for 
laboratory monitoring during TB preventive therapy for heavy drinkers, thus it would not be ethical to include a 
no-treatment control group. We will use the safety, tolerability, and adherence results, the known efficacy and 
mortality benefit of TB preventive therapy in HIV-infected persons in SSA, together with our established 
decision analytic model of TB preventive therapy33 to determine whether the clinical benefits of TB preventive 
therapy outweigh toxicity risks for HIV-infected drinkers in resource limited settings. Our study aims are: 
 
 
Aim 1: To examine the safety and tolerability of 6H in HIV/TB co-infected drinkers, measured by 
hepatotoxicity and treatment discontinuation rates. Our main aim is to estimate safety and tolerability 
overall among drinkers (primary) and by level of drinking (secondary). 
 
Aim 2: To determine the level of TB preventive therapy adherence overall among drinkers and by level 
of drinking, and at 3 and 6 months. The main goal of this aim is to estimate adherence overall among 
drinkers (primary).  Secondarily we will estimate adherence by level of drinking (heavy, current but not heavy 
drinkers, and non-drinkers) and compare adherence across drinking levels.  We hypothesize that adherence 
will be highest among the non-drinkers.  
 
Aim 3: To determine whether the benefits of providing TB preventive therapy to HIV-infected drinkers 
in resource-limited settings outweigh the risks compared to no treatment. We hypothesize that providing 
TB preventive therapy will result in longer life expectancy and quality-adjusted life expectancy than not 
providing TB preventive therapy (current standard of care). 
 
In this renewal application, we will leverage the Uganda cohort of the Uganda Russia Boston Alcohol Network 
for Alcohol Research Collaboration on HIV/AIDS (URBAN ARCH), which includes over 400 HIV-infected 
drinkers, to accomplish the study aims. The ADEPTT study will generate unique data on the safety and 
tolerability of TB preventive therapy for HIV/TB co-infected drinkers, and HIV-infected drinkers’ level of 
adherence at 3- and 6-months. Our results will provide critical new insights for guidelines on how to best 
deliver TB preventive therapy to alcohol using, HIV/TB co-infected persons at high risk for TB disease and 
reduce morbidity and mortality. 
 
 
 


B. BACKGROUND and SIGNIFICANCE 
 
There is an urgent global need to decrease the high mortality of tuberculosis (TB) in persons with HIV. 
TB is the leading cause of death in persons infected with HIV, accounting for 390,000 deaths in 2014 (20-33% 
of HIV-associated deaths).1,2 At least one-third of the 37 million people living with HIV worldwide are infected 
with latent TB; these individuals are 26 times more likely to develop active TB disease than those without HIV. 
Although aggressive strategies to find and treat all active TB cases are needed to reduce HIV/TB mortality, the 
only strategy to prevent new active TB cases in exposed individuals is via preventive treatment. 
 
Heavy drinking is common among HIV-infected persons and heavy drinkers are at high risk for active 
TB. In several studies of persons in HIV care in SSA, a median of 25% of persons self-reported heavy 
drinking,12-17 with heavy drinking defined as exceeding the NIAAA drinking limits (Table 1) or via the Alcohol 
Use Disorders Identification Test – Consumption (AUDIT-C).34,35 Heavy alcohol consumption is an established 
risk factor for HIV infection,36,37 and is also a risk factor for becoming infected with TB via increased immune 
system suppression and time spent in settings where TB is prevalent.20 The risk of active TB is increased 3-
fold among heavy drinkers compared to non-drinkers,18-20,38 and heavy drinkers are more likely to have smear 
positive disease, slower TB treatment response, and higher mortality while on TB treatment.20,39 Thus, HIV-
infected drinkers are at high risk of morbidity and mortality due to active TB disease.  
 
Preventive treatment for TB is life-saving among persons with HIV. Randomized trials conducted prior to 
widespread ART use showed high efficacy of IPT to prevent TB in those with HIV.4 Recent studies, including 
the TEMPRANO ANRS trial, showed the additive benefit of IPT and ART in reducing mortality for patients at all 
CD4 counts.4-6,8 IPT-associated decreases in active TB and mortality ranged from 30% to 50%, supporting the 
concomitant use of IPT and ART. Thus the WHO recommends IPT, intensified TB case-finding, and infection 
control for persons with HIV. Major implementation of IPT in HIV-infected persons in South Africa is underway,1 
scale up is occurring in Tanzania40 and Botswana,41 and calls for further roll-out have been made.10,11  
 
The WHO lists “regular and heavy alcohol use” as a contraindication to IPT, but data on the 
hepatotoxicity of IPT in HIV-infected drinkers are limited. INH is metabolized by the liver, and the rate of 
Grade 3/4 (serious) hepatotoxicity due to INH ranges from 0.1-4.0% of all patients.42 Most INH-associated 
hepatotoxicity is reversible by stopping therapy, but liver injury can, rarely, lead to the need for liver 
transplantation or death, with mortality rates of 0.05-0.10%.43 In the U.S., concern about toxicity is mitigated by 
the ability to perform liver enzyme monitoring in patients deemed at highest risk of INH-related hepatitis.44 
    


 
    Alcohol users are considered at high risk for INH-
related hepatotoxicity, based on an early study of IPT 
in  which any and daily alcohol use were associated 
with 2- and 4- fold increases in toxicity rates, 
respectively.45 However, the high rates of toxicity in 
this study have since been questioned.46 Only a 
handful of studies since this 1978 publication have 
reported INH-related toxicity stratified by alcohol 
consumption level, and the findings of these 
subsequent studies have been mixed.26,47-51 Thus, 
data are urgently needed on the safety and tolerability 
of TB preventive therapy in HIV-infected drinkers to 
inform worldwide guidelines.52 
 
Adherence to TB preventive therapy among 
drinkers on ART is unknown.  Among those with 
HIV, alcohol use has been consistently associated 
with reduced ART adherence,22,53,54 and we 
(Jacobson) and others have found that alcohol use is 
associated with discontinuation of active TB 
treatment.23,48,55 Reports of adherence to IPT among 
all comers range widely, from 19-96%.52,56 While 
completion of 9-months of IPT in a study of persons 
with HIV in Uganda was 34%,56 several studies in HIV-infected persons have shown good completion rates 
(82%-87%), with the highest rates among those on ART.41,57-59 The addition of IPT to an established pill taking 
routine may be feasible, but adherence to and completion of TB preventive therapy among HIV-infected 
drinkers on ART is not known.  
Valid measurement of medication adherence is critical to evaluating IPT. A key component of the scale 
up of TB preventive therapy for persons with HIV will ultimately be medication adherence. Self-reported 
adherence measures have limitations,60-63 especially in settings in which socially desirable responses are likely 
to be high. This problem was recently illustrated by the lack of efficacy of pre-exposure prophylaxis for HIV in a 
large trial, due to patients’ misreporting taking the prescribed antiretrovirals.64  
      EMM and pharmacy pill counts are considered objective measures, although limitations, such as when 
patients remove multiple pills at once, have also been noted.60 Other objective adherence measures include 
drug concentrations determined in plasma, saliva, or dried blood spots (DBS).65-67 However, those measures 
reflect recent drug administration and are affected by increased pill taking just prior to a visit.68 Drug 
concentrations in hair, which reflect exposure over weeks to months,69 have been linearly related to ART dose 
frequency70 and strongly predictive of HIV viral suppression.65,66,71-74 We (Gandhi) have pioneered methods to 
extract and analyze ART levels from hair samples,75,76 demonstrated high feasibility and acceptability of hair 
collection in East Africa,77-80 and established the feasibility of measuring INH in hair as well.31   
 
PEth provides an objective measure of alcohol use. Patients in our study will be advised to reduce their 
alcohol consumption or abstain to avoid INH-related liver toxicity, thus under-report of alcohol use is a concern. 
While it will be important to examine the levels of hepatotoxicity by self-report measures available to clinicians 
prior to prescribing TB preventive therapy, accurate measurement of actual heavy alcohol use is also needed. 
We (Hahn) have utilized PEth, a sensitive and specific biomarker of alcohol consumption over the prior 3 
weeks,30 to improve our measurement of heavy alcohol use.81,82   
 
The benefits and risks of TB preventive therapy for HIV-infected drinkers need to be compared. The 
WHO recommendation against IPT for HIV-infected “regular and heavy drinkers” in resource constrained 
settings9 makes the assumption that the risk of toxicity from therapy outweighs the risk of developing active TB 
disease. Given the high prevalence of latent TB infection (LTBI) in endemic settings, the high risk of active TB 
for drinkers, and the serious consequences of TB disease, it is possible that TB preventive therapy, even 
without monitoring liver enzymes, has better long-term outcomes than avoiding therapy in this population.  
Table 1. Frequently used acronyms and definitions 
6H 
6 months daily INH 
ALT/AST 
Alanine and aspartate 
aminotransferases 
ART 
Antiretroviral therapy 
AUDIT-C 
Alcohol Use Disorders Identification 
Test – Consumption 
CRF 
Case report form 
Grade 3/4 
hepatotoxicity 
ALT or ALT >3x ULN and symptoms 
(nausea, vomiting, jaundice, or 
fatigue) OR ALT or AST >5x ULN 
NIAAA  heavy 
drinking  
Men: ≥4 drinks/day or ≥14 
drinks/week;  
Women: ≥3 drinks/day or ≥7 
drinks/week    
INH 
Isoniazid 
IPT 
Isoniazid preventive therapy 
EMM 
Electronic medication monitoring 
PEth 
Phosphatidylethanol 
TST 
Tuberculin skin test  
ULN 
Upper limit of normal 


    Decision analysis modeling provides a quantitative approach to integrating multi-dimensional risk and 
benefits to identify optimal treatment strategies.87 Decision models have examined use of IPT among those 
with HIV in resource limited settings;88-91 however, none have incorporated the hepatotoxicity and adherence 
rates relevant to heavy drinking. We will leverage our (Linas’) validated decision-analytic Markov model of TB 
preventive therapy33 to examine TB preventive therapy benefits and risks in this population.  
 
INNOVATION  
The ADEPT-T study is unique in its focus on HIV-infected drinkers and for examining the safety and tolerability 
of and adherence to TB preventive therapy among a population at very high risk for developing active TB but 
potentially excluded from receiving treatment under the current guidelines. Innovation includes: (1) generating 
unique data on the safety and tolerability of TB preventive therapy in HIV-infected alcohol users, (2) using trial-
based estimates in a decision analytic model to determine whether the benefit of treatment exceeds the harm, 
(3) monitoring adherence to standard INH TB preventive therapy (6H) in drinkers, a group at risk for low 
adherence, using objective adherence measures, (4) using an alcohol biomarker to augment self-report and 
avoid misclassification, because patients are advised to reduce their alcohol use and are thus likely to under-
report, (5) examining a biomarker of INH concentration to represent long-term exposure to INH as an objective 
measurement of adherence, and (6) embedding this study in the existing Uganda ARCH cohort, a unique 
cohort of HIV-infected drinkers in Uganda with regular assessment of alcohol use and specimen collection.  
 
STUDY DESIGN OVERVIEW  
We propose a single arm trial of TB preventive therapy to assess its toxicity, measure adherence, and 
determine whether its benefits outweigh its risks when given to TB/HIV-infected drinkers (n=200). We will 
provide 6 months of INH to HIV/TB infected drinkers (n=200) and non-drinkers (n=100) to describe safety, 
tolerability, and adherence among drinkers and by level of drinking. We will conduct frequent monitoring for 
toxicity, and assess toxicity as well as adherence. We will evaluate the safety, tolerability and adherence by 
level of drinking, measured by self-report and augmented by phosphatidylethanol (PEth), an established 
biomarker for alcohol consumption. We will employ objective measures of adherence, including electronic 
medication monitoring (EMM) of pill bottle opening, pharmacy pill counts, and a novel measure of INH 
exposure, that is, INH concentration in hair. We will actively monitor for toxicity, using the U.S. standard of care 
for laboratory monitoring during TB preventive therapy for heavy drinkers. We will use the safety, tolerability, 
and adherence results, together with the known efficacy and mortality benefit of TB preventive therapy in HIV-
infected persons in SSA, and our established decision analytic model of TB preventive therapy, to determine 
whether the mortality and quality of life benefits of TB preventive therapy outweigh the toxicity risks for HIV-
infected drinkers in resource limited settings. 
  
The target population will be HIV/TB-infected men and women who are either current drinkers (past 3 months, 
n=200) or non-drinkers (no alcohol in the past year, n=100), who meet screening criteria (i.e. >18 years old, on 
ART, TST positive, no history of prior active TB, live within 2 hours travel time of the study site), and are 
patients of the Mbarara Regional Referral Hospital (MRRH) Immune Suppression Syndrome (ISS) Clinic. This 
study will build on the foundation of the Uganda ARCH cohort, including its predecessor cohort as well. We will 
contact Uganda ARCH study participants (which included drinkers and non-drinkers) to determine their current 
eligibility for the proposed study. Those who are eligible and interested will be enrolled in the study of TB 
preventive therapy and receive continued follow-up as part of the Uganda ARCH cohort within the URBAN 
ARCH consortium. We will enroll additional participants recruited from the ISS Clinic, as needed, to meet our 
enrollment targets. A screening phase will include blood testing to test liver enzymes (ALT and AST) to rule out 
patients with elevations (>2x the upper limit of normal [ULN]), and to rule out active TB (symptom screening, 
and if symptomatic: AFB and Xpert MTB/RIF testing and chest X-ray if not pregnant). Once considered eligible 
for the study, INH and pyridoxine (vitamin B6) will be dispensed and the study will include regular assessments 
of hepatotoxicity and other side effects, measures of medication adherence, assessment of drinking, and 
collection of dried blood spots (DBS) for PEth and small hair samples (approximately 100 hair fibers) for PEth 
and INH concentration, respectively. After the course of INH (6 months, daily) is completed, study participants 
will participate in twice yearly study visits as part of the continued cohort. The follow-up cohort study visits will 
include a survey, symptom assessment and follow-up testing as needed to detect active TB, and blood 
collection for DBS for PEth, and storage in the URBAN ARCH repository. Baseline blood testing will also 
include tests for hepatitis B surface antigen, confirmation of HIV status, CD4 cell count, and HIV viral load. 


NIH-REQUIRED PROGRESS REPORT 
This report describes the progress of 
Uganda ARCH from September 2011 - 
November 2015. The aims of the study 
were the following: (1) To determine the 
effect of heavy alcohol consumption on 
HIV disease progression prior to the start 
of ART; and (2) To explore biological and 
behavioral pathways by which heavy 
alcohol consumption may accelerate HIV 
disease progression prior to the initiation 
of ART. Together with the predecessor 
cohort of HIV-infected drinkers entering 
HIV care in Uganda (Changes in  
Alcohol Consumption in HIV Positives in 
Uganda, R01 AA018631, 2010-2015) we 
recruited a total of 751 persons, including 
465 current drinkers reported here (Table 
2). Retention in the cohorts was 90% at 6- and 12-months.  
The Uganda ARCH team was highly productive, meeting and exceeding our study aims. In total, eleven 
manuscripts were published,30,81,82,92-100 four are under review, and seven conference abstracts were published 
(see Progress Report Publications List). Recent published manuscripts are as follows: 
• 
Carrico A. et al. Unhealthy alcohol use is associated with monocyte activation prior to starting antiretroviral 
therapy.82 
• 
Woolf-King S.E. et al. Development and validation of the East Africa Alcohol Expectancy Scale (AFEXS).99  
• 
Hahn J.A. et al. Declining and rebounding unhealthy alcohol consumption during the first year of HIV care 
in rural Uganda, using phosphatidylethanol to augment self-report..81  
• 
Asiimwe S.B. et al. Comparison of traditional and novel self-report measures to an alcohol biomarker for 
quantifying alcohol consumption among HIV-infected adults in sub-Saharan Africa.101  
• 
Bajunirwe F. et al. Comparison of self-reported alcohol consumption to phosphatidylethanol measurement 
among HIV infected patients initiating antiretroviral treatment in south western Uganda.93 
  
Dr. Hahn and several trainees from the University of California San Francisco (UCSF) and the Mbarara, 
Uganda, University of Science and Technology (MUST) leveraged the Uganda ARCH cohort into newly funded 
alcohol/HIV research studies and fellowships. Dr. Hahn received a prestigious K24 Mid-career Investigator 
Award in Patient-Oriented Research (NIA K24AA022586) to build her research and mentoring program in 
HIV/alcohol comorbidity, a UCSF Center for AIDS Research (CFAR) Established Investigator pilot award, and 
an award from the US Department of Defense. Three junior investigators (Dr. Stephen Asiimwe, Dr. Julian 
Adong, and Dr. Sarah Woolf-King) received UCSF CFAR pilot awards, and Dr. Adong received 2 training 
awards, allowing her to spend 4 months learning research methods and writing in San Francisco.  
 
C. APPROACH 
 
C.1 The proposed study builds on existing collaborative relationships. This research will be conducted in 
the context of the URBAN ARCH Consortium, leveraging the infrastructure developed over the past five years 
of the Uganda ARCH cohort as a collaborative effort between UCSF, Boston University (BU), Boston Medical 
Center (BMC), and MUST. The team includes international experts in alcohol and HIV epidemiology and 
biomarkers of alcohol use (Dr. Judy Hahn), clinical research in persons with HIV in Uganda (Dr. Winnie 
Muyindike), TB outcomes in resource limited settings (Dr. Karen Jacobson), decision analytic modeling (Dr. 
Benjamin Linas), clinical trial design and analysis (Dr. Debbie Cheng), and hair drug concentration (Dr. Monica 
Gandhi). Consultants include world experts in clinical trials of TB medications (Dr. C. Robert Horsburgh) and 
HIV/substance use research (Dr. Jeffrey Samet). 
 
C.2 Preliminary data for this application 
     Little evidence of liver damage due to alcohol use. We (Hahn) conducted liver enzyme testing in 169 HIV-
infected participants in the Uganda ARCH cohort at the baseline visit. A small proportion, 7.6%, had Grade 1/2 
Table 2. Baseline characteristics of the Uganda ARCH 
cohorts (U01 AA020776 and  R01 AA018631), n=751. 
Male 
41% 
Age, median (Interquartile range [IQR]) 
31 (26-39) 
CD4 cell count, median (IQR) cells/mm3 
462 (330-638) 
Log HIV viral load 
3.79 (3.00-4.39) 
Self-reported alcohol use, prior 3 months 
62% 
Among current drinkers (prior 3 months, n=465) 
    Self-reported heavy drinking: AUDIT-C+ 
52% 
    PEth >=50 ng/ml 
59% 
    Heavy drinking (AUDIT-C+ or PEth ≥50 
ng/ml) 
73% 
    PEth level, ng/ml, median (IQR) 
75.8 (16.0-
274.0) 


(mild/moderate) ALT/AST elevations (2-5x the upper limit of normal [ULN]), and none had Grade 3 elevations 
(≥5 times the ULN); there was no difference by drinking status (p=0.90). ALT/AST elevations were also low in 
our study of alcohol biomarkers in Ugandan HIV-infected drinkers (n=77).30 These very low levels of underlying 
liver injury among HIV-infected drinkers in Uganda, suggest that they are at low risk for hepatotoxicity. 
     Under-report of alcohol consumption. We (Hahn) have repeatedly found evidence of under-reporting of 
alcohol consumption in several studies conducted among persons with HIV in Uganda.81,93,96 For example, we 
found a doubling of self-reported alcohol use when specimen collection for alcohol biomarkers was introduced 
into an ongoing cohort study.96 In another study, we conducted PEth testing on stored specimens, and found of 
those with quantifiable PEth, over half had denied prior month alcohol use.93 Objective measurement of alcohol 
consumption is needed; we will use PEth levels as a biological marker of heavy alcohol consumption. 
     Reductions and rebounds in alcohol use over one year. We (Hahn) studied heavy alcohol consumption, 
defined as AUDIT-C positive or PEth ≥50 ng/ml, during the first year of HIV care and found a significant 
interaction with ART use, such that there was a decline in heavy drinking prior to ART start (per-month 
adjusted odds ratio [AOR] 0.91; 95% confidence interval [CI]: 0.83-0.99), but increases after ART start (per-
month AOR 1.11; 95% CI: 1.01-1.22).81 Thus, drinkers may reduce alcohol use when advised to, but heavy 
drinking often rebounds. Repeated objective measurement of alcohol use will be needed to determine whether 
drinkers can reduce their alcohol use for the duration of a 3- or 6- month course of TB preventive therapy.  
     Alcohol use leads to poorer TB treatment adherence In a retrospective cohort of 225 patients who received 
treatment for multi-drug resistant (MDR) TB in South Africa, we (Jacobson) found that self-reported recent 
alcohol use led to increased risk of treatment discontinuation (adjusted HR=2.1, CI 1.1-4.0, p=0.02).23 This 
work supports that alcohol is a particularly important variable in predicting poor TB treatment completion. 
     Feasibility of measuring INH in hair. We (Gandhi) developed extraction and laboratory methods to measure 
INH concentration in small hair samples (20-30 strands, 2 mg). The method can quantify a wide range of INH 
levels in hair (0.05-50 ng INH/mg hair), with low error and high precision.31 In a study of INH concentration in 
the hair from adults receiving 300 mg of INH daily for treatment of active TB (n=10), via directly observed 
therapy, the median concentration was 1.60 ng/mg, (interquartile range [IQR]: 0.64-1.15), while the median 
concentration was 8.8 ng/mg (IQR: 4.98 - 15.20) in a study of 38 children undergoing treatment for active TB. 
31 These data illustrate that INH taken in varied settings is detectable in hair.       
     Prior model of TB preventive therapy widely used. We (Linas) have conducted comparative- and cost-
effectiveness analyses of various strategies for identifying latent TB infection.33 This work is part of the basis 
for current TB control policy in the U.S.102 and WHO guidance for TB management in low burden countries.103 
We will adapt this highly cited decision analytic model to the Ugandan context, integrating data collected in 
Aims 1 and 2, to develop clinically relevant guidelines for TB preventive therapy in HIV-infected heavy drinkers. 
 
C.3 Study methods for Aims 1 and 2.  
 
C.3.1 Overview The ADEPTT study will include a single-arm trial of TB preventive therapy to assess its 
toxicity, measure adherence, and determine whether its benefits outweigh its risks when given to TB/HIV-
infected drinkers (n=200 current drinkers, 100 past or never drinkers). After the course of TB preventive 
therapy is completed, study participants will have twice yearly study visits as part of the Uganda ARCH cohort.  
 
C.3.2 Study design considerations. We made several key decisions in designing this study:  
     (1) The primary outcome of this study is safety only, rather than the standard drug trial outcomes of safety 
and efficacy. We made this decision because, while a study examining the efficacy of TB preventive therapy 
for HIV-infected drinkers would be valuable, such a study would require thousands of participants and would 
be premature if safety cannot be established. Because the WHO guidelines exclude heavy drinkers due to 
safety concerns, we felt that careful examination of safety and tolerability would be a key contribution to the 
development of an evidence-based global recommendation on how to give TB preventive therapy to HIV-
infected alcohol users in resource constrained settings.  
   (2) We decided not to include an untreated control group, because we will be meeting the U.S. standard of 
care of providing TB preventive therapy to drinkers with ALT/AST monitoring.44 Given the known reductions in 
morbidity and mortality of TB preventive therapy for persons with HIV, and the safety of this treatment when 
monitoring is conducted, an untreated control group would represent a lower level of care provision with 
questionable ethics.44 In addition, it will not be necessary to have an untreated control group to determine the 
level of ALT/AST elevations without therapy, because we will have an estimate of the background level of 
elevations in this cohort of drinkers via baseline testing.  


Aim 1: To examine the safety and tolerability of 6H for HIV-infected drinkers, 
measured by hepatotoxicity. Our main aim is to estimate safety overall among drinkers 
(primary) and by level of drinking (secondary). We will additionally compare safety and 
tolerability by level of drinking in secondary analyses. 
Aim 2: To estimate the level of adherence to TB preventive therapy, overall among 
drinkers (primary), by month on therapy.  Secondarily, we will estimate adherence 
by drinking level and compare adherence levels across drinking levels. We 
hypothesize that adherence will be highest among non-drinkers. 
  
      (3) We will include only participants with positive tuberculin skin test (TST) results. TB preventive therapy is 
more effective for persons with HIV who are TST positive,7 although the WHO recommends providing IPT to all 
persons with HIV in resource limited settings because skin testing is often not available9 and may have lower 
sensitivity.104 However, in the research setting, TST testing is possible and we will limit our study population to 
TST positive participants who represent those most at risk for developing TB in this population. 
     (5) The eligibility criterion for the proposed study is current (prior 3 month) drinking OR non drinking in the 
prior year, while the WHO recommendations refer to “regular and heavy alcohol consumption”. We will use 
“current drinking” because we have found that while half of current drinkers report heavy drinking by the 
AUDIT-C criteria (52%), a large proportion of current drinkers (21%) have high PEth levels (≥50 ng/ml) but do 
not report heavy drinking (Table 2, above). Thus, an eligibility criterion of current drinking will allow us to 
examine toxicities among drinkers who do not fully report their level of drinking so that future guidelines can 
account for under-reporting of drinking. 
 
C.3.3 Study setting and eligibility. This study will be conducted in the Immune Suppression Syndrome (ISS) 
HIV Clinic of the Mbarara Regional Referral Hospital (MRRH) at MUST, the site of the Uganda ARCH. There 
are over 12,000 active patients at the ISS Clinic. The ISS Clinic and pharmacy use electronic medical records 
(EMR) via the Open Medical Record System (OpenMRS). Eligibility criteria will be: ISS clinic patient age ≥18, 
living within 2 travel hours of the clinic, fluent in Runyankole or English, on ART, and self-reported alcohol 
consumption in the prior 3 months or abstainer. Exclusion criteria will be as follows: plans to move in the 
coming 6 months, on nevirapine (NVP, an ART drug that is declining in usage due to high risk for 
hepatotoxicity), history of active TB, current active TB, prior TB preventive therapy, and ALT or ALT elevation 
(>2x ULN).  
 
C.3.4 Enrollment – Eligibility screening and baseline study activities. Participants will initially be recruited 
from the Uganda ARCH cohorts, in which participants have given permission for re-contact for further study. Of 
the 465 current drinkers recruited in these cohorts, we estimate that we will be able to contact 75% (N=349), 
and that 60% of those will meet eligibility criteria (n=209). The remaining participants will be recruited from 
persons reporting any alcohol use on the AUDIT, approximately 25% of clinic patients,109 which is routinely 
administered at initial ISS Clinic visit. We will also recruit patients who report no prior year alcohol use for the 
abstainer group. We will review the EMR for potential participants and those that meet preliminary study 
eligibility (i.e. age, on ART for 6 months) will be invited to meet with a research assistant to determine further 
eligibility. If further eligibility is established, the research assistant, a licensed clinical officer, will seek informed 
consent for further screening to rule out active TB, existing liver disease, and to assess TST status (Figure 1). 
A blood draw for ALT/AST testing will be performed, a TST will be placed, and if the participant reports 
symptoms of possible active TB (cough >24 hours, fever, weight loss, night sweats), further tests (chest X-ray 
and sputum collection for acid fast bacilli [AFB] stain and Xpert MTB/RIF assay) will be conducted. Those with 
abnormal chest X-ray results or a positive AFB or Xpert test will be brought to the MRRH TB clinic per hospital 
protocol. Patients will be asked to return in 2 days for TST reading, informed consent will be requested if active 
TB has been ruled out and TST is positive. After informed consent is obtained, the baseline visit will include 
study questionnaire completion, blood draw, and medication dispensing. 
 
 


 
C.3.5 Intervention. The study intervention will include a single arm given (1) 6H: 300 mg INH daily for 6 
months. All participants will also receive 25 mg pyridoxine (vitamin B-6) daily for the treatment duration to 
reduce the risk of INH-induced peripheral neuropathy.111 Patients will be encouraged to continue taking all 
other concurrently prescribed drugs (i.e., ART and cotrimoxazole). Patients will receive adherence advice, 
advice to avoid hepatotoxic substances (e.g. acetaminophen, alcohol, herbal medicines), instructions on 
monitoring side effects, phone numbers for contacting the medical staff in case of serious side effects, and 
instructions on using the EMM bottle like a regular medicine bottle. The pills will be dispensed at the pharmacy 
where the participants receive their ART.  
 
C.3.6 Follow-up study visits. Assessments for toxicity will occur week 2 and months 1 through 7 (Table 3). A 
clinical officer will screen for symptoms of hepatotoxicity and other side effects using a Case Report Form 
(CRF) based on the TEMPRANO ANRS adverse event grading6 and perform ALT/AST testing. Treatment will 
be stopped if a patient develops a Grade 3/4 toxicity, as determined by the study physician. In addition, the 
monthly study visits while on medication will include pharmacy pill counts, EMM data downloads, and brief 
surveys on alcohol use and adherence. Hair collection will occur at 3 and 6 months. Blood collection will occur 
at baseline (for PEth, CD4 cell count, HIV viral load, and repository storage), 3 and 6 months, and every 6 
months thereafter. Participants will also be given a card with phone numbers to contact the clinical staff if side 
effects occur between study visits; if side effects occur, participants will be asked to come in for additional 
ALT/AST testing and follow-up. These calls and visits will also be recorded in a CRF. All CRFs will be reviewed 
weekly by the study physician. Screening for symptoms for active TB, and sputum testing and chest X-ray if 
symptoms are reported, will occur at every follow-up visit, including those after the completion of the trial. High 
retention will be maintained as in our prior studies using follow up phone calls, alerts when patients come to 
the ISS clinic, and home visits when needed. 
 
C.3.7 Data management. Data management and quality assurance will be jointly led by the UCSF and the BU 
Data Coordinating Center (DCC). As in the previous studies, tracking information will be entered into a 
database which will generate reminders for follow-up visits. The tracking database will also be used to 
automatically randomize patients to study arms. The questionnaire data, brief assessments, laboratory results, 
and the CRF data will be entered directly into a laptop computer in Uganda while offline, and later uploaded to 
a secure server at UCSF. The EMM adherence data will be uploaded weekly to a secure website, cleaned, and 
merged with the other study data. All data will undergo weekly checks for completeness and range criteria.  
 
C.3.8 Measurements. 
    Safety and tolerability. Safety will be assessed by the occurrence of a Grade 3/4 hepatotoxicity at any time 
during the assigned treatment period. Lack of tolerability will be defined as any treatment discontinuation prior 
to completion of the prescribed course due to side effects or ALT/AST elevations. 
    Adherence. We will measure medication adherence using EMM, pharmacy pill count, self-report, and INH 
concentration in hair. While there is no gold standard for determining medication adherence, EMM adherence 
has been highly predictive of undetectable HIV viral load112-114 is considered the reference standard,62,112,115,116 
and therefore will be our primary measure of adherence. EMM adherence will be defined as the number of pill 
bottle openings (no more than 1 per day counted)113 divided by the number of prescribed doses over the study 
period. Adherence to vitamin B-6, which will be provided in a separate bottle, will not be part of the adherence 
measurement.  
    Self-reported adherence measures will include the visual analog scale (VAS) which asks the patient to 
identify their past month percent adherence on a visual scale that ranges from 0 to 100%, and the Self Rating 
Single Item (SRSI) adherence scale,117,118 which asks participants to rate their ability to take their medications 
as prescribed over the past month.119 Both of these measures are brief118 and are strong predictors of viral 
suppression.118 Lastly, we will collect hair samples at 3 months and 6 months, and will measure the INH 
concentration (ng/mg) in the hair.  
     Alcohol consumption.  Heavy drinking will be defined as either PEth ≥50 ng/mL or AUDIT-C positive (≥3 for 
women, ≥4 for men). The AUDIT-C will be modified to cover the prior 3 months. By combining two highly 
specific measures, we will increase the sensitivity for detecting heavy alcohol use over using each measure 
alone, as in our previous analyses of heavy alcohol use by persons with HIV in Uganda.81,82  
    We will collect self-reported measures of alcohol use at baseline using the AUDIT-C and the CAGE,120 which 
are readily available for use in clinical practice. For analyses that examine level of drinking, we will categorize 


the scores as low risk drinking, (women: AUDIT-C 0-2; men: AUDIT-C 0-3), heavy drinking (women: AUDIT-C 
3-5; men: AUDIT-C 4-5), and very high risk drinking (AUDIT-C ≥8).121 The corresponding cutoffs for the CAGE 
will be scores of 0, 1, and 2-4.122 We will additionally measure alcohol consumption using tools that are not 
dependent on standardized drink volume or concentration, e.g. money spent drinking, level of intoxication, and 
PEth level, as in our previous studies.101  
Covariates. Covariates measured at baseline will include gender, age, body mass index (BMI), CD4 cell 
count, HIV viral load, ALT/AST, active hepatitis B viral infection (HBsAg), symptoms of depression,123 smoking, 
nicotine dependence,124 and duration on ART – also recently added on baseline platelet count, FIB-4 score, 
and eGFR. Covariates measured at baseline and follow-up will include current ART regimen, and the VAS and 
SRSI for self-reported ART adherence.  
 
C.3.9 Statistical analyses for Aim 1. 
Primary analysis: Safety. The focus of this aim is the evaluation of safety among drinkers overall (primary) and 
by drinking levels (secondary). Thus the primary analysis set will be all participants who receive at least one 
dose of TB preventive therapy who are current drinkers. We will report the incidence of Grade 3/4 
hepatotoxicity at any time during prescribed treatment along with 95% exact binomial confidence intervals 
(CIs).  
Secondary analyses: Safety and tolerability will be estimated by level of alcohol use at baseline and during 
treatment. To examine safety and tolerability by alcohol consumption prior to treatment, we will report 
hepatotoxicity and treatment discontinuation stratified by level of drinking (heavy alcohol consumption, current 
but not heavy drinking, no drinking) at baseline. Fisher’s exact test and exact logistic regression models 
adjusting for gender, hepatitis B co-infection, Cd4 cell count and/or viral suppression will be used to compare 
safety outcomes across drinking levels. To inform clinical practice, we will also report the outcomes by self-
reported alcohol consumption levels using the AUDIT-C and the CAGE. We will also examine toxicity by level 
of PEth, using recently suggested PEth cutoffs.  We will use generalized estimating equation logistic 
regression models to examine whether drinking level during the treatment period impacts safety and tolerability 
outcomes. 
 
Secondary analysis: Comparison of hepatotoxicity rates to previous studies. We will also compare estimates 
and 95% CIs to those reported in published studies of TB preventive therapy in those with HIV, which found 
hepatotoxicity rates of 0.3-5.5%. 
 
Sample size justification. We anticipate a total of 200 drinkers will be enrolled into the current study. Based on 
the Uganda ARCH cohort retention, we expect 90% 6-month study visit retention resulting in N=180 evaluable 
participants. We expect that the proportion of participants with any Grade 3/4 hepatotoxicity within the 
treatment period will be approximately at the midpoint of the range of toxicity rates published previously, i.e. 
2.6%.  Given this, we would expect approximately 5 safety events, and the length of a 95% exact binomial CI 
around the estimate would be 5.5%, (i.e. 0.9% to 6.4%). 
Table 3. Schedule of study visits and activities. 
 
 
Eligibility 
Baseline 
Week 2 
Month 1 
Month 2 
Month 3 
Month 4 
Month 5 
Month 6 
Month 7 
Follow-up 
Initial eligibility determined, ALT/AST and TST testing. Chest X-
ray and sputum collected if symptoms of active TB are reported. 
X 
 
 
 
 
 
 
 
 
 
 
Enrollment/ baseline: Study interview, blood draw, medication 
dispensing  
 
X 
 
 
 
 
 
 
 
 
 
Toxicity assessment (side effects and ALT/AST testing) 
 
 
X X X X 
X 
X 
X 
X 
 
Medication refill, pharmacy pill count, and EMM data download, 
and brief alcohol and adherence assessments 
 
 
X X X X 
X 
X 
X 
 
 
Hair collection for INH concentration  
 
 
 
 
 
X 
 
 
X 
 
 
Follow-up:  Study interview, blood draw, and screening for active 
TB 
 
 
 
 
 
X 
 
 
X 
 
X 


 
To illustrate power for secondary analyses, we also calculate the minimum detectable difference to compare 
safety between alcohol groups.  I.e., to compare toxicity between any group of drinkers (either heavy drinkers 
or moderate drinkers, each expected to be approximately 1/3 of the sample) to non-drinkers (1/3 of the 
sample), we will have 80% power to detect a difference in toxicity rate of 0.095 or greater if the toxicity rate in 
the lighter drinkers/abstainers is 1% and alpha is set to 0.05 based on a Fisher’s Exact test. 
 
 
C.3.10 Statistical analyses for Aim 2. 
Primary analysis: The goal of this aim is to estimate the level of adherence overall among drinkers and by 
drinking level (including abstainers). The analysis will consist of all participants enrolled. For those who are 
advised to discontinue therapy by the study physicians, adherence up until discontinuation will be used; 
however, additional sensitivity analyses will be conducted using alternative imputation strategies (e.g. using the 
midpoint between best and worst weeks, and assuming the worst case scenario of zero for the discontinued 
time period). We will report the percent adherence using EMM data over the treatment duration and 95% CIs 
overall among drinkers and by drinking level (heavy, non-heavy, abstainer), and fit multiple linear regression 
models, controlling for gender, to compare adherence across drinking categories. If the data are skewed, 
transformations will be performed (e.g., log transformation) or median regression if no appropriate 
transformation is identified. 
Secondary analyses: Alternate measures of adherence and adherence over time and by level of alcohol use. 
Secondary measures of adherence will be analyzed using the same approach as described above. We will 
additionally conduct all adherence analyses excluding the first 40 days to remove a possible Hawthorne effect 
that may occur when patients are aware they are being monitored, but that often abates after one month. We 
will also conduct repeated measures analyses of monthly adherence data using linear mixed effects regression 
models, with month on treatment included as a predictor, to test the hypothesis that adherence changes over 
time. We will additionally use these analyses to assess whether drinking level at baseline and during treatment 
(using time-updated covariates) impact adherence. 
Secondary analyses: INH concentration in hair by drinking level. We will examine the level of INH drug 
concentration at 3 months and at the end of treatment overall among drinkers and by drinking level (3 
groups).  The analysis of this continuous measure will be as described for adherence, i.e. mean INH drug 
concentration will be estimated along with 95% CIs and the groups will be compared using multiple regression 
models.   
 
C.3.11  Exploratory Aim 1 and Aim 2 analyses: predictors of toxicity and adherence. We will also perform 
analyses to identify potential predictors of hepatotoxicity and/or treatment discontinuation (grouped together to 
increase statistical power) and adherence measures. Several potential predictors will be considered including 
demographics, body mass index, time updated heavy alcohol use, ART adherence, baseline HbSag, measures 
of cirrhosis (Fib-4 score and/or platelet count), and kidney function (eGFR). Given the limited number of events 
compared to predictors of interest, we will build a regression model using the lasso (least absolute shrinkage 
and selection operator) method, a variable selection approach that fits a regression model including all 
independent variables and covariates of interest but constrains some coefficient estimates by shrinking them to 
zero to reduce variance.130,131   
 
C.3.13 Missing data. We will evaluate differences in baseline characteristics between those lost to follow-up 
versus those who are retained to examine missing data mechanisms, e.g., missing completely at random 
(MCAR), missing at random (MAR), or missing not at random (MNAR).132 If it is reasonable to assume that 
data are MAR, multiple model based imputation methods  will be applied to account for the missing data.132,133  
 
C.3.12 Aims 1 and 2 potential pitfalls. It is possible that our hepatotoxicity and treatment discontinuation 
estimates will be lower than in settings where baseline ALT/AST screening is not performed because we will 
exclude those with baseline elevations (>2x ULN). Based on our prior work (see C.2), we expect only a small 
proportion will be screened out. However, using the number screened out, we will be able to calculate “worst 
case scenario” hepatotoxicity rates assuming that these HIV-infected drinkers would have developed Grade 
3/4 toxicities while on treatment. It is also possible that adherence rates in the study will be artificially high, due 
to the frequent adherence and toxicity monitoring. In that case, our estimates of adherence will represent the 
upper bound, while the comparison of adherence between the two randomization arms will remain important. If 


we establish safety without frequent monitoring, then future studies could measure adherence under more real-
world conditions. 
 
C.4  Study methods for Aim 3. 
 
C.4.1 Overview. We will utilize our 
previously developed Markov 
model that simulates TB preventive 
therapy and the lifetime clinical 
progression of a cohort of 
hypothetical individuals.33 We will 
modify this model to fit the 
Ugandan context by including a 
detailed simulation of TB 
preventive therapy safety 
monitoring. We will then leverage 
the unique toxicity, tolerability, and 
adherence data collected in Aims 1 
and 2, as well as the medical 
literature, to project long-term 
outcomes, stratified by level of 
alcohol consumption, assuming a 
variety of monitoring approaches. If 
our hypothesis is correct, this study 
will provide evidence needed to 
change the paradigm of TB 
preventive therapy and extend life-
saving treatment to a population at 
very high risk of death from HIV/TB 
co-infection. Below we describe the 
proposed approach.  
 
C.4.2 Model components. The 
following model components are 
part of our previously developed 
model and will be modified for the 
context of HIV-infected drinkers in 
Uganda as described.       
     TB epidemiology. The 
simulation model generates a 
cohort using the distribution in the 
target population of age, sex, 
prevalence of positive TSTs (>5 
mm induration), and prevalence of sub-clinical (asymptomatic, so not identified) active TB. We will stratify all 
demographic and epidemiologic parameters by level of self-reported alcohol use at baseline, as above (any 
current drinking, heavy drinking, and very heavy drinking – see C.3.9), using the AUDIT-C that is available to 
treating clinicians in the real-world.  
     TB preventive therapy. The simulation of TB preventive therapy includes treatment discontinuation due to 
hepatotoxicity, as well as poor adherence or default. The model is structured to simulate any TB preventive 
treatment regimen. All parameters related to TB preventive therapy are regimen-specific and we will stratify 
them by level of alcohol use. We will model the benefits of TB preventive therapy as a relative reduction in the 
Table 4: Select model parameters for decision model proposed in 
Aim 3. 
 
Parameter 
estimates 
Data 
source 
TB epidemiology 
 
 
Age/sex demographics 
 
ADEPTT 
Prevalence reactive TST 
 
ADEPTT 
Prevalence subclinical TB at baseline 
0.085-
0.094 
135,136 
Level of alcohol use prior to therapy 
 
ADEPTT 
TB preventive therapy 
 
 
Transitions between AST/ALT categories 
 
ADEPTT 
Probability of symptoms by AST/ALT 
0.35 
137 
Toxicity-related mortality by AST/ALT 
0.0-0.05 
137-139 
Adherence 
 
ADEPTT 
Health state utility for those with 
hepatotoxicity 
0.65-1.0 
140 
Health state utility for those w/o 
hepatotoxicity 
0.82-1.0 
140 
Progression to TB and TB mortality 
 
 
Incidence TB (per 100 person-years) 
 
141-143 
      TST positive 
3.41 
 
      TST negative 
3.06 
 
Hazard ratio for active TB  
 
144-147 
     100% adherence vs. no therapy 
0.11-0.41 
 
      Partial (50%) therapy vs. no therapy 
0.25-0.91 
 
TB-related case-fatality proportion 
0.23 
148 
Health state utility with active TB 
0.66-0.85 
140 
HIV and other non-TB mortality 
 
 
Life expectancy (starting age 31) CD4 >500 
66 years 
149-152 
Standardized mortality ratio by CD4 count  
 
151-154 
      CD4 350-499 cells/mm3 
3.5 
 
      CD4 200-349 cells/mm3 
5.6 
 
      CD4 <200 cells/mm3 
30.3 
 
Health state utility for HIV-infected persons 
0.62-1.0 
155-157 
Aim 3: To determine whether the benefits of providing TB preventive therapy to HIV-infected 
drinkers in resource limited settings outweigh the risks compared to no treatment. We hypothesize 
that providing TB preventive therapy will result in longer life expectancy and quality-adjusted life expectancy 
than not providing TB preventive therapy (current standard of care). 


rate of progression to active TB. In the model, patients with sub-clinical TB do not benefit from the preventive 
therapy. 
      Hepatotoxicity. We will substantially enhance our previous model to include detailed simulation of 
preventive therapy as follows: First, in every treatment month, simulated individuals on therapy will have a 
probability of developing an ALT or AST elevation and a probability of developing symptoms of hepatotoxicity.  
Simulated patients will fall into one of 6 toxicity-related health states (3 levels of ALT/AST levels; normal, 3-5x 
the ULN, >5x ULN) and 2 levels of symptoms (yes/no). The rates of movement between each of the 6 
hepatotoxicity states will be a function of the treatment regimen, and an individual’s level of alcohol 
consumption. Each toxicity-related state will be associated with toxicity-related quality of life, and mortality.  
Importantly, the model will distinguish between the underlying true ALT/AST value and the clinical 
awareness of that value. For example, in the simulation model of no monitoring, patients without symptoms 
whose true ALT/AST is >5x ULN will not be identified, nor their treatment stopped, and they will have an 
increased risk of developing symptoms and death.  
 Adherence and treatment default. In each month while taking TB preventive therapy, simulated individuals 
have a probability of non-adherence and/or treatment default, i.e. treatment discontinuation that is unrelated to 
hepatotoxicity. Simulated patients who stop medications do not accrue the full benefit of prevention, though 
incomplete courses of therapy confer partial preventive benefits.21 One challenge to incorporating treatment 
adherence data in a decision analytic model is that the effectiveness of various levels of adherence to TB 
preventive therapy is not known. We will therefore model discontinuation/default and low adherence in two 
ways: First, we will use WHO definitions of treatment loss to follow-up for active tuberculosis treatment to 
model TB preventive therapy adherence as a binary outcome (yes/no).9  We will define “not adherent” as any 
patient who misses two consecutive monthly refill visits, or who, for 2 consecutive months, self-reports taking 
<80% of his/her intended doses of therapy, or whose EMM cap data indicates <80% of doses taken. In the 
simulation model, individuals will not accrue any benefits (or risks) of therapy after the month that the patient is 
classified as “non-adherent.” Second, we will use EMM adherence data to model non-adherence as a 
continuous outcome. We will assume that taking fewer than the total number of TB preventive therapy doses 
throughout the entire course of therapy has the same effect as taking 100% of doses for fewer months than 
recommended. For example, we will assume that taking 50% of doses for six months of therapy provides 
similar treatment efficacy as taking 100% of all doses for only 3 months. We will compare the results of these 
two strategies and thus gain information on the relative importance of how adherence is modeled.  
      Progression to active TB and death. Individuals in the simulation model will have a monthly probability of 
developing active TB that is stratified by drinking status.141,158 Those who develop active TB experience 
increased mortality and decreased quality of life. In addition, a portion of simulated patients with active TB will 
develop drug resistant disease, characterized by longer treatment courses, higher mortality, and lower quality 
of life than drug sensitive disease. We will model the effectiveness of TB preventive therapy as a relative 
reduction in the rate of developing active TB. A complete course of 6H provides a 30-50% reduction in 
reactivation,4-8 with a larger absolute reduction in TB incidence among those who have reactive TST than in 
those who do not. Studies among HIV-infected persons in settings of TB incidence, similar to Uganda, 
demonstrate that the benefits of TB preventive therapy are durable for five years.145,159 We will therefore model 
a 5-year reduction in the incidence of active TB, followed by a rapid return to the baseline TB incidence. 
     Competing risks of death Individuals in the existing model have an age- and sex-stratified risk of death from 
causes other than TB. In this proposed analysis of TB preventive therapy in HIV-infected persons in Uganda, 
the competing risks of death will reflect mortality related to HIV infection and all other non-TB mortality. 
  
C.4.3 Data sources for the model. Whenever possible, we will use the novel data from Aims 1 and 2 of this 
study (Table 4, denoted as ADEPTT) for model parameters. Other model parameters will be obtained from the 
medical literature.  
     TB epidemiology. The ADEPTT cohort will inform the age and sex distributions, and the prevalence of 
reactive TST (>5 mm) at baseline and level of self-reported alcohol use. We will use published reports of the 
prevalence of subclinical TB from established African cohorts.135,136  
     TB preventive therapy. The ADEPTT study includes frequent assessment of ALT/AST and symptoms. 
These data, stratified by level of self-reported alcohol consumption, will inform all of the parameters governing 
movement between toxicity-related health states. Because the study will stop treatment at the identification of 
any Grade 3/4 hepatotoxicity, we anticipate no medication-related deaths in ADEPTT. Therefore, we will use 
the medical literature to inform mortality from each toxicity-related health state in the model, as well as the 
health state utility of patients with toxicity-related symptoms.137-140      


     Progression to active TB and death. Because the number of observable cases of active TB in the cohort 
after therapy is expected to be low, we will use the medical literature to inform the incidence of active TB, 
stratified by TST status, without preventive therapy,141-143 and will account for the reported efficacy of TB 
preventive therapy.144-147  We will use reports of health state utility with active TB collected in SSA.140 
     Competing risks of death. We will estimate mortality from all causes other than TB using long-term follow-up 
of cohorts of HIV-infected persons in SSA. We will use the literature to estimate non-TB mortality among a 
cohort of HIV-infected persons on ART in SSA with baseline CD4>500149-152 and published reports of the HR 
for mortality by lower CD4 counts.151-154 We will also use the medical literature to estimate health state utilities 
for HIV-infected individuals with and without TB. 
 
C.4.4 Aim 3 analyses. We will use the model to simulate the long-term outcomes of a cohort of HIV-infected 
drinkers in Uganda with demographic and clinical characteristic similar to those of the ADEPT-TB cohort 
assuming that TB preventive therapy is provided to all HIV-infected patients who drink alcohol using each of 
the following TB preventive therapy and monitoring strategies:  
(1) No therapy (standard of care) 
(2) TB preventive therapy without hepatotoxicity monitoring unless patient is symptomatic (no monitoring)  
(3) TB preventive therapy with hepatotoxicity monitoring at week 2 of therapy only (week 2 monitoring)  
(4) TB preventive therapy with hepatotoxicity monitoring monthly throughout (monthly monitoring)  
The simulated model outcomes will include: hepatotoxicity events and deaths, counts of treatment completion 
and non-adherence, cases of active TB, TB deaths, life expectancy, and quality-adjusted life expectancy. We 
will first compare life expectancy and quality-adjusted life expectancy for “no monitoring” to “no therapy” to 
determine whether TB preventive therapy provides net benefit to HIV-infected drinkers even when monitoring 
is simply not possible. Next, we will compare outcomes from “no monitoring,” “week 2 monitoring” and “monthly 
monitoring”, to identify the optimal monitoring approach in settings that have some ability to monitor liver 
functions tests. We will define the “optimal” approach as that which maximizes quality-adjusted life expectancy. 
Once we have modeled outcomes for the simulated cohort, we will investigate whether the approach to TB 
preventive therapy should differ based on level of drinking alcohol. We may find, for example, that no 
monitoring is needed for non-heavy drinkers, while it is necessary for heavy and very heavy drinkers. 
 
C.4.5 Sensitivity analyses. We will use one- and two-way deterministic sensitivity analyses to test the role of 
uncertainty in driving conclusions and to identify model parameters that have the largest impact on outcomes. 
We will display the results of such deterministic sensitivity analyses in standard format, such as tornado 
diagrams and two-way sensitivity graphs.160-162 Next, we will use probabilistic sensitivity analysis (PSA) to 
quantify the impact of simultaneous uncertainty in all model parameters on the qualitative conclusions of the 
analysis.160,163 PSA defines every model parameter as a probability density function centered around the best 
estimate of that parameter’s true value. PSA then performs thousands (typically 10,000) of iterations of the 
simulation, each time drawing a value for each model parameter from its defined probability density function. 
The end result is N (10,000) realizations of the simulation, each with its own life expectancy and quality-
adjusted life expectancy. By comparing the percentage of the simulations whose conclusions concurred with 
the base case scenario, it is possible to quantify the degree of certainty in the conclusions of the analysis. 
Model parameters that will be inherently uncertain and candidates for PSA include the efficacy of TB 
preventive therapy, the rate of reactivation of TB, and the impact of less than perfect adherence. 
  
C.4.6 Aim 3 potential pitfalls. We will conduct decision analytic modeling of monitoring TB preventive therapy 
in HIV-infected heavy drinkers in Uganda, but will not include costs or cost-effectiveness. We chose not to 
include cost-effectiveness analysis at this stage, because the constraint on monitoring TB preventive therapy in 
Uganda is likely feasibility, (i.e. due to stock outs of the test reagents), rather than cost. Therefore, we propose 
to investigate and compare clinical outcomes in a classical decision modeling framework without cost, for the 
time being. We include a secondary analysis of the optimal monitoring strategy in ideal circumstances. Another 
potential challenge is the approach to modeling HIV-related mortality. Study inclusion criteria ensure that all 
patients will be on highly active ART at baseline, but if we observe non-adherence to ART in the cohort over 
the long term, we will modify the model to include two strata: “on ART” and “off ART,” with appropriate mortality 
estimates for each. Finally, estimates of health state utilities are inherently uncertain. We will use reports 
collected using standard instruments in the African context, and plan broad sensitivity analyses. Further, we 
consider both life expectancy and quality-adjusted life expectancy as primary outcomes. 
 


C.5 Elements unique to this site. This study is situated in Uganda, a low income country with high 
prevalence of HIV (7.3%)164 and heavy alcohol use.165 Uganda is considered a TB endemic country with 
incidence of 161/100,000 population.166  
 
C.6 Integration and synergy with URBAN ARCH Consortium components The URBAN ARCH Consortium 
will bring together multidisciplinary researchers to examine the consequences of alcohol use on comorbidities 
among people living with HIV so as to increase availability of treatments and improve outcomes. The Uganda 
ARCH team will work with the Administrative (Admin) and Biostatistics and Data Management (BDM) Cores to 
implement their projects (e.g., data and repository management, Data and Safety Monitoring Board, statistical 
analyses). The structure of URBAN ARCH will enable administrative and scientific integration, making the 
whole greater than the sum of its parts. The URBAN ARCH Steering Committee, comprised of all Principal 
Investigators, will meet monthly via web conference and annually in Boston. It is responsible for decisions 
regarding Core resource allocation and scientific priorities and is a venue to discuss analyses, challenges, and 
funding opportunities. The Committee is also a conduit for introducing trainees to URBAN ARCH investigators, 
which has previously resulted in several cross-cohort analyses and broadening of mentoring teams.  
     The cohorts have similarities and differences that allow for comparisons and contrasts, for example, high 
prevalence of injection drug use and hepatitis C virus (Boston/Russia); young age, homogeneous race, sizable 
percentage of women (all). Cross-cohort analyses are possible with the planned harmonized data collection, 
integrated data management systems, and stored biological samples. A cross-cohort analysis our group is 
well-positioned to pursue is to characterize PEth response by self-reported drinking level among HIV-infected 
drinkers across all sites. In this analysis we will examine predictors of heterogeneity in PEth response, i.e. 
whether PEth response varies by patterns of drinking and additional variables, such as gender, age, body 
mass index, hematocrit and hemoglobin. The URBAN ARCH studies will add evidence to improve the health 
and quality of life for HIV-infected drinkers. 
 
C.7 Timeline and deliverables. The study timeline is below (Table 5).  
Table 5. Study timeline. 
Year 1 
Year 2 
Year 3 
Year 4 
Year 5 
Finalize operating procedures, data systems, hire and train 
staff 
X 
 
 
 
 
 
 
 
 
 
Enrollment, 6 months of TB preventive therapy 
 
X 
X 
X 
X 
X 
X 
X 
 
 
Long-term follow-up 
 
 
X 
X 
X 
X 
X 
X 
X 
 
DBS, hair, and plasma shipping 
 
 
X 
 
X 
 
X 
 
X 
 
Manuscript preparation (see below) 
 
 
 
 
X 
X 
X 
X 
X 
X 
We plan to write the following manuscripts in years 3-5: 
(1) Liver injury among HIV-infected drinkers in Uganda (baseline data) 
(2) Latent TB infection and newly diagnosed active TB among HIV-infected drinkers in Uganda (baseline data) 
(3) Safety and tolerability of TB preventive therapy for drinkers with HIV/TB co-infection in Uganda (Aim 1) 
(4) Predictors of hepatotoxicity of TB preventive therapy among drinkers with HIV/TB co-infection on ART in 
Uganda (Aim 1) 
(5) Adherence to standard TB preventive therapy in HIV-infected drinkers (Aim 2) 
(6) INH concentration in hair by TB preventive therapy compared to other measures of adherence (Aim 2) 
(7) Benefits/risks of TB preventive therapy for Ugandan HIV-infected drinkers - decision analytic model (Aim 3) 
(8) Patterns of alcohol use among HIV-infected drinkers on TB preventive therapy (longitudinal data) 
 
C.8 Future directions. This study will lay important groundwork for revising guidelines on how to best deliver 
TB preventive therapy to alcohol using, HIV-infected persons at high risk for TB disease. Future investigations 
may include developing interventions to improve TB preventive therapy adherence, along with interventions to 
reduce drinking for a beneficial impact on both toxicity and adherence. If the decision analytic models show 
that ALT/AST monitoring is needed, investigations of implementation of monitoring strategies will be needed.  
 
C.9 Summary. In summary, this study will leverage an established cohort of HIV-infected drinkers with TB co-
infection, to collect unique data that will guide recommendations about providing potentially life-saving TB 
preventive therapy. It will be the first to assess safety and tolerability of, and adherence to, TB preventive 
therapy in HIV-infected drinkers, a population that is at high risk for active TB. We will use these novel data to 


conduct decision analytic modeling to examine whether the benefits exceed the risks for providing therapy to 
this population, and determine optimal monitoring strategies as needed. The results of this study will provide 
critical new evidence on how to deliver TB preventive therapy to reduce morbidity and mortality in HIV-infected 
drinkers. 
  
 
References cited 
 
1. 
Global Tuberculosis Report. Geneva: World Health Organization;2014. 
2. 
How AIDS Changed Everything: 2014 Global Statistics. UNAIDS;2015. 
3. 
NIH HIV/AIDS Research Priorities and Guidelines for Determining AIDS Funding. 2015; 
https://grants.nih.gov/grants/guide/notice-files/NOT-OD-15-137.html. Accessed Jan 3, 2016, 2016. 
4. 
Akolo C, Adetifa I, Shepperd S, Volmink J. Treatment of latent tuberculosis infection in HIV infected 
persons. The Cochrane database of systematic reviews. 2010(1):CD000171. 
5. 
Edessa D, Likisa J. A Description of Mortality Associated with IPT plus ART Compared to ART Alone 
among HIV-Infected Individuals in Addis Ababa, Ethiopia: A Cohort Study. PLoS One. 
2015;10(9):e0137492. 
6. 
Group TAS, Danel C, Moh R, et al. A Trial of Early Antiretrovirals and Isoniazid Preventive Therapy in 
Africa. The New England journal of medicine. 2015;373(9):808-822. 
7. 
Ayele HT, Mourik MS, Debray TP, Bonten MJ. Isoniazid Prophylactic Therapy for the Prevention of 
Tuberculosis in HIV Infected Adults: A Systematic Review and Meta-Analysis of Randomized Trials. PLoS 
One. 2015;10(11):e0142290. 
8. 
Golub JE, Pronyk P, Mohapi L, et al. Isoniazid preventive therapy, HAART and tuberculosis risk in HIV-
infected adults in South Africa: a prospective cohort. AIDS (London, England). 2009;23(5):631-636. 
9. 
Guidelines for intensified tuberculosis case-finding and isoniazid preventive therapy for people living 
with HIV in resource-constrained settings.: World Health Organization;2011. 
10. 
Akolo C, Bada F, Okpokoro E, et al. Debunking the myths perpetuating low implementation of isoniazid 
preventive therapy amongst human immunodeficiency virus-infected persons. World journal of 
virology. 2015;4(2):105-112. 
11. 
Rangaka MX, Cavalcante SC, Marais BJ, et al. Controlling the seedbeds of tuberculosis: diagnosis and 
treatment of tuberculosis infection. Lancet. 2015. 
12. 
Kader R, Govender R, Seedat S, Koch JR, Parry C. Understanding the Impact of Hazardous and Harmful 
Use of Alcohol and/or Other Drugs on ARV Adherence and Disease Progression. PLoS One. 
2015;10(5):e0125088. 
13. 
Wandera B, Tumwesigye NM, Nankabirwa JI, et al. Alcohol Consumption among HIV-Infected Persons 
in a Large Urban HIV Clinic in Kampala Uganda: A Constellation of Harmful Behaviors. PLoS One. 
2015;10(5):e0126236. 
14. 
Kader R, Seedat S, Govender R, Koch JR, Parry CD. Hazardous and harmful use of alcohol and/or other 
drugs and health status among South African patients attending HIV clinics. AIDS Behav. 
2014;18(3):525-534. 
15. 
Kekwaletswe CT, Morojele NK. Alcohol use, antiretroviral therapy adherence, and preferences 
regarding an alcohol-focused adherence intervention in patients with human immunodeficiency virus. 
Patient Prefer Adherence. 2014;8:401-413. 
16. 
Medley A, Seth P, Pathak S, et al. Alcohol use and its association with HIV risk behaviors among a 
cohort of patients attending HIV clinical care in Tanzania, Kenya, and Namibia. AIDS Care. 
2014;26(10):1288-1297. 
17. 
Soboka M, Tesfaye M, Feyissa GT, Hanlon C. Alcohol use disorders and associated factors among 
people living with HIV who are attending services in south west Ethiopia. BMC Res Notes. 2014;7:828. 


18. 
Narasimhan P, Wood J, Macintyre CR, Mathai D. Risk factors for tuberculosis. Pulm Med. 
2013;2013:828939. 
19. 
Lonnroth K, Williams BG, Stadlin S, Jaramillo E, Dye C. Alcohol use as a risk factor for tuberculosis - a 
systematic review. BMC Public Health. 2008;8:289. 
20. 
Rehm J, Samokhvalov AV, Neuman MG, et al. The association between alcohol use, alcohol use 
disorders and tuberculosis (TB). A systematic review. BMC Public Health. 2009;9:450. 
21. 
Menzies D, Al Jahdali H, Al Otaibi B. Recent developments in treatment of latent tuberculosis infection. 
Indian J Med Res. 2011;133:257-266. 
22. 
Hendershot CS, Stoner SA, Pantalone DW, Simoni JM. Alcohol use and antiretroviral adherence: review 
and meta-analysis. J Acquir Immune Defic Syndr. 2009;52(2):180-202. 
23. 
Kendall EA, Theron D, Franke MF, et al. Alcohol, hospital discharge, and socioeconomic risk factors for 
default from multidrug resistant tuberculosis treatment in rural South Africa: a retrospective cohort 
study. PLoS One. 2013;8(12):e83480. 
24. 
Sharma SK, Sharma A, Kadhiravan T, Tharyan P. Rifamycins (rifampicin, rifabutin and rifapentine) 
compared to isoniazid for preventing tuberculosis in HIV-negative people at risk of active TB. Evidence-
based child health : a Cochrane review journal. 2014;9(1):169-294. 
25. 
Martinson NA, Barnes GL, Moulton LH, et al. New regimens to prevent tuberculosis in adults with HIV 
infection. The New England journal of medicine. 2011;365(1):11-20. 
26. 
Bliven-Sizemore EE, Sterling TR, Shang N, et al. Three months of weekly rifapentine plus isoniazid is less 
hepatotoxic than nine months of daily isoniazid for LTBI. The international journal of tuberculosis and 
lung disease : the official journal of the International Union against Tuberculosis and Lung Disease. 
2015;19(9):1039-1044, i-v. 
27. 
Person AK, Sterling TR. Treatment of latent tuberculosis infection in HIV: shorter or longer? Curr 
HIV/AIDS Rep. 2012;9(3):259-266. 
28. 
Sterling TR, Villarino ME, Borisov AS, et al. Three months of rifapentine and isoniazid for latent 
tuberculosis infection. The New England journal of medicine. 2011;365(23):2155-2166. 
29. 
Ena J, Valls V. Short-course therapy with rifampin plus isoniazid, compared with standard therapy with 
isoniazid, for latent tuberculosis infection: a meta-analysis. Clinical infectious diseases : an official 
publication of the Infectious Diseases Society of America. 2005;40(5):670-676. 
30. 
Hahn JA, Dobkin LM, Mayanja B, et al. Phosphatidylethanol (PEth) as a biomarker of alcohol 
consumption in HIV-positive patients in sub-Saharan Africa. Alcohol Clin Exp Res. 2012;36(5):854-862. 
31. 
Mave V, Chandanwale A, Kinikar A, et al. Isoniazid Hair Concentrations in Children with Tuberculosis: A 
Proof of Concept Study. International Journal of Tuberculosis and Lung Diseases 2016;In press. 
32. 
Eisenhut M, Thieme D, Schmid D, Fieseler S, Sachs H. Hair Analysis for Determination of Isoniazid 
Concentrations and Acetylator Phenotype during Antituberculous Treatment. Tuberc Res Treat. 
2012;2012:327027. 
33. 
Linas BP, Wong AY, Freedberg KA, Horsburgh CR, Jr. Priorities for screening and treatment of latent 
tuberculosis infection in the United States. Am J Respir Crit Care Med. 2011;184(5):590-601. 
34. 
Bradley KA, Bush KR, Epler AJ, et al. Two brief alcohol-screening tests From the Alcohol Use Disorders 
Identification Test (AUDIT): validation in a female Veterans Affairs patient population. Archives of 
internal medicine. 2003;163(7):821-829. 
35. 
Bush K, Kivlahan DR, McDonell MB, Fihn SD, Bradley KA. The AUDIT alcohol consumption questions 
(AUDIT-C): an effective brief screening test for problem drinking. Ambulatory Care Quality 
Improvement Project (ACQUIP). Alcohol Use Disorders Identification Test. Archives of internal 
medicine. 1998;158(16):1789-1795. 
36. 
Baliunas D, Rehm J, Irving H, Shuper P. Alcohol consumption and risk of incident human 
immunodeficiency virus infection: a meta-analysis. International journal of public health. 
2010;55(3):159-166. 


37. 
Shuper PA, Neuman M, Kanteres F, Baliunas D, Joharchi N, Rehm J. Causal considerations on alcohol 
and HIV/AIDS--a systematic review. Alcohol and alcoholism. 2010;45(2):159-166. 
38. 
Amoakwa K, Martinson NA, Moulton LH, Barnes GL, Msandiwa R, Chaisson RE. Risk factors for 
developing active tuberculosis after the treatment of latent tuberculosis in adults infected with human 
immunodeficiency virus. Open forum infectious diseases. 2015;2(1):ofu120. 
39. 
Volkmann T, Moonan PK, Miramontes R, Oeltmann JE. Tuberculosis and excess alcohol use in the 
United States, 1997-2012. The international journal of tuberculosis and lung disease : the official 
journal of the International Union against Tuberculosis and Lung Disease. 2015;19(1):111-119. 
40. 
Shayo GA, Moshiro C, Aboud S, Bakari M, Mugusi FM. Acceptability and adherence to Isoniazid 
preventive therapy in HIV-infected patients clinically screened for latent tuberculosis in Dar es Salaam, 
Tanzania. BMC Infect Dis. 2015;15:368. 
41. 
Mosimaneotsile B, Mathoma A, Chengeta B, et al. Isoniazid tuberculosis preventive therapy in HIV-
infected adults accessing antiretroviral therapy: a Botswana Experience, 2004-2006. J Acquir Immune 
Defic Syndr. 2010;54(1):71-77. 
42. 
Saukkonen JJ, Cohn DL, Jasmer RM, et al. An official ATS statement: hepatotoxicity of antituberculosis 
therapy. American journal of respiratory and critical care medicine. 2006;174(8):935-952. 
43. 
Livertox: Isoniazid. Clinical and Research Information on Drug-Induced Liver Toxicity  
http://livertox.nih.gov/Isoniazid.htm. 
44. 
Latent Tuberculosis Infection: A Guide for Primary Health Care Providers. Atlanta: Centers for Disease 
Control and Prevention;2013. 
45. 
Kopanoff DE, Snider DE, Jr., Caras GJ. Isoniazid-related hepatitis: a U.S. Public Health Service 
cooperative surveillance study. The American review of respiratory disease. 1978;117(6):991-1001. 
46. 
Comstock GW. Prevention of tuberculosis among tuberculin reactors: maximizing benefits, minimizing 
risks. Jama. 1986;256(19):2729-2730. 
47. 
Fountain FF, Tolley E, Chrisman CR, Self TH. Isoniazid hepatotoxicity associated with treatment of latent 
tuberculosis infection: a 7-year evaluation from a public health tuberculosis clinic. Chest. 
2005;128(1):116-123. 
48. 
LoBue PA, Moser KS. Use of isoniazid for latent tuberculosis infection in a public health clinic. American 
journal of respiratory and critical care medicine. 2003;168(4):443-447. 
49. 
Tedla Z, Nyirenda S, Peeler C, et al. Isoniazid-associated hepatitis and antiretroviral drugs during 
tuberculosis prophylaxis in HIV-infected adults in Botswana. American journal of respiratory and critical 
care medicine. 2010;182(2):278-285. 
50. 
Tedla Z, Nguyen ML, Sibanda T, et al. Isoniazid-associated hepatitis in adults infected with HIV receiving 
36 months of isoniazid prophylaxis in Botswana. Chest. 2015;147(5):1376-1384. 
51. 
Menzies D, Long R, Trajman A, et al. Adverse events with 4 months of rifampin therapy or 9 months of 
isoniazid therapy for latent tuberculosis infection: a randomized trial. Annals of internal medicine. 
2008;149(10):689-697. 
52. 
Getahun H, Chaisson RE, Raviglione M. Latent Mycobacterium tuberculosis Infection. The New England 
journal of medicine. 2015;373(12):1179-1180. 
53. 
Parsons JT, Rosof E, Mustanski B. The temporal relationship between alcohol consumption and HIV-
medication adherence: a multilevel model of direct and moderating effects. Health Psychol. 
2008;27(5):628-637. 
54. 
Braithwaite RS, McGinnis KA, Conigliaro J, et al. A temporal and dose-response association between 
alcohol consumption and medication adherence among veterans in care. Alcohol Clin Exp Res. 
2005;29(7):1190-1197. 
55. 
Duraisamy K, Mrithyunjayan S, Ghosh S, et al. Does Alcohol consumption during multidrug-resistant 
tuberculosis treatment affect outcome?. A population-based study in Kerala, India. Annals of the 
American Thoracic Society. 2014;11(5):712-718. 


56. 
Namuwenge PM, Mukonzo JK, Kiwanuka N, et al. Loss to follow up from isoniazid preventive therapy 
among adults attending HIV voluntary counseling and testing sites in Uganda. Transactions of the Royal 
Society of Tropical Medicine and Hygiene. 2012;106(2):84-89. 
57. 
Munseri PJ, Talbot EA, Mtei L, Fordham von Reyn C. Completion of isoniazid preventive therapy among 
HIV-infected patients in Tanzania. The international journal of tuberculosis and lung disease : the 
official journal of the International Union against Tuberculosis and Lung Disease. 2008;12(9):1037-
1041. 
58. 
Durovni B, Cavalcante SC, Saraceni V, et al. The implementation of isoniazid preventive therapy in HIV 
clinics: the experience from the TB/HIV in Rio (THRio) study. AIDS (London, England). 2010;24 Suppl 
5:S49-56. 
59. 
Guidelines on the management of latent tuberculosis infection. Geneva: World Health 
Organization;2015. 
60. 
Williams AB, Amico KR, Bova C, Womack JA. A proposal for quality standards for measuring medication 
adherence in research. AIDS and behavior. 2013;17(1):284-297. 
61. 
Marcellin F, Spire B, Carrieri MP, Roux P. Assessing adherence to antiretroviral therapy in randomized 
HIV clinical trials: a review of currently used methods. Expert Rev Anti Infect Ther. 2013;11(3):239-250. 
62. 
Simoni JM, Huh D, Wang Y, et al. The Validity of Self-Reported Medication Adherence as an Outcome in 
Clinical Trials of Adherence-Promotion Interventions: Findings from the MACH14 Study. AIDS Behav. 
2014;18(12):2285-2290. 
63. 
Kagee A, Nel A. Assessing the association between self-report items for HIV pill adherence and 
biological measures. AIDS care. 2012;24(11):1448-1452. 
64. 
Marrazzo JM, Ramjee G, Richardson BA, et al. Tenofovir-based preexposure prophylaxis for HIV 
infection among African women. The New England journal of medicine. 2015;372(6):509-518. 
65. 
Gandhi M, Ameli N, Bacchetti P, et al. Atazanavir concentration in hair is the strongest predictor of 
outcomes on antiretroviral therapy. Clinical infectious diseases : an official publication of the Infectious 
Diseases Society of America. 2011;52(10):1267-1275. 
66. 
Gandhi M, Ameli N, Bacchetti P, et al. Protease inhibitor levels in hair strongly predict virologic 
response to treatment. AIDS (London, England). 2009;23(4):471-478. 
67. 
Gandhi M, Greenblatt RM. Hair it is: the long and short of monitoring antiretroviral treatment. Ann 
Intern Med. 2002;137(8):696-697. 
68. 
Podsadecki TJ, Vrijens BC, Tousset EP, Rode RA, Hanna GJ. "White coat compliance" limits the reliability 
of therapeutic drug monitoring in HIV-1-infected patients. HIV clinical trials. 2008;9(4):238-246. 
69. 
Beumer JH, Bosman IJ, Maes RA. Hair as a biological specimen for therapeutic drug monitoring. 
International journal of clinical practice. 2001;55(6):353-357. 
70. 
Liu AY, Yang Q, Huang Y, et al. Strong relationship between oral dose and tenofovir hair levels in a 
randomized trial: hair as a potential adherence measure for pre-exposure prophylaxis (PrEP). PLoS One. 
2014;9(1):e83736. 
71. 
van Zyl GU, van Mens TE, McIlleron H, et al. Low lopinavir plasma or hair concentrations explain 
second-line protease inhibitor failures in a resource-limited setting. J Acquir Immune Defic Syndr. 
2011;56(4):333-339. 
72. 
Baxi SM, Greenblatt RM, Bacchetti P, et al. Nevirapine Concentration in Hair Samples Is a Strong 
Predictor of Virologic Suppression in a Prospective Cohort of HIV-Infected Patients. PLoS One. 
2015;10(6):e0129100. 
73. 
Koss CA, Natureeba P, Mwesigwa J, et al. Hair concentrations of antiretrovirals predict viral 
suppression in HIV-infected pregnant and breastfeeding Ugandan women. AIDS (London, England). 
2015;29(7):825-830. 
74. 
Prasitsuebsai W, Kerr SJ, Truong KH, et al. Using Lopinavir Concentrations in Hair Samples to Assess 
Treatment Outcomes on Second-Line Regimens Among Asian Children. AIDS research and human 
retroviruses. 2015;31(10):1009-1014. 


75. 
Huang Y, Gandhi M, Greenblatt RM, Gee W, Lin ET, Messenkoff N. Sensitive analysis of anti-HIV drugs, 
efavirenz, lopinavir and ritonavir, in human hair by liquid chromatography coupled with tandem mass 
spectrometry. Rapid communications in mass spectrometry : RCM. 2008;22(21):3401-3409. 
76. 
Huang Y, Yang Q, Yoon K, et al. Microanalysis of the antiretroviral nevirapine in human hair from HIV-
infected patients by liquid chromatography-tandem mass spectrometry. Analytical and bioanalytical 
chemistry. 2011;401(6):1923-1933. 
77. 
Hickey MD, Salmen CR, Tessler RA, et al. Antiretroviral concentrations in small hair samples as a 
feasible marker of adherence in rural Kenya. J Acquir Immune Defic Syndr. 2014;66(3):311-315. 
78. 
Gandhi M, Mwesigwa J, Aweeka F, et al. Hair and plasma data show that lopinavir, ritonavir, and 
efavirenz all transfer from mother to infant in utero, but only efavirenz transfers via breastfeeding. J 
Acquir Immune Defic Syndr. 2013;63(5):578-584. 
79. 
Baxi SM, Liu A, Bacchetti P, et al. Comparing the Novel Method of Assessing PrEP Adherence/Exposure 
using Hair Samples to other Pharmacologic and Traditional Measures. J Acquir Immune Defic Syndr. 
2014. 
80. 
Cohan D, Natureeba P, Koss CA, et al. Efficacy and safety of lopinavir/ritonavir versus efavirenz-based 
antiretroviral therapy in HIV-infected pregnant Ugandan women. AIDS (London, England). 2014. 
81. 
Hahn JA, Emenyonu. N I, Fatch R, et al. Declining and rebounding unhealthy alcohol consumption 
during the first year of HIV care in rural Uganda, using phosphatidylethanol to augment self-report. 
Addiction. In press 2015. 
82. 
Carrico AW, Hunt PW, Emenyonu NI, et al. Unhealthy Alcohol Use is Associated with Monocyte 
Activation Prior to Starting Antiretroviral Therapy. Alcohol Clin Exp Res. 2015. 
83. 
Geijo MP, Herranz CR, Vano D, Garcia AJ, Garcia M, Dimas JF. [Short-course isoniazid and rifampin 
compared with isoniazid for latent tuberculosis infection: a randomized clinical trial]. Enfermedades 
infecciosas y microbiologia clinica. 2007;25(5):300-304. 
84. 
Spyridis NP, Spyridis PG, Gelesme A, et al. The effectiveness of a 9-month regimen of isoniazid alone 
versus 3- and 4-month regimens of isoniazid plus rifampin for treatment of latent tuberculosis infection 
in children: results of an 11-year randomized study. Clinical infectious diseases : an official publication 
of the Infectious Diseases Society of America. 2007;45(6):715-722. 
85. 
Sharma SK, Sharma A, Kadhiravan T, Tharyan P. Rifamycins (rifampicin, rifabutin and rifapentine) 
compared to isoniazid for preventing tuberculosis in HIV-negative people at risk of active TB. The 
Cochrane database of systematic reviews. 2013;7:Cd007545. 
86. 
Johnson JL, Okwera A, Hom DL, et al. Duration of efficacy of treatment of latent tuberculosis infection 
in HIV-infected adults. AIDS (London, England). 2001;15(16):2137-2147. 
87. 
Keeler E. Decision analysis. Jama. 1977;237(22):2380. 
88. 
Gupta S, Abimbola T, Date A, et al. Cost-effectiveness of the Three I's for HIV/TB and ART to prevent TB 
among people living with HIV. The international journal of tuberculosis and lung disease : the official 
journal of the International Union against Tuberculosis and Lung Disease. 2014;18(10):1159-1165. 
89. 
Pho MT, Swaminathan S, Kumarasamy N, et al. The cost-effectiveness of tuberculosis preventive 
therapy for HIV-infected individuals in southern India: a trial-based analysis. PLoS One. 
2012;7(4):e36001. 
90. 
Smith T, Samandari T, Abimbola T, Marston B, Sangrujee N. Implementation and Operational Research: 
Cost-Effectiveness of Antiretroviral Therapy and Isoniazid Prophylaxis to Reduce Tuberculosis and 
Death in People Living With HIV in Botswana. J Acquir Immune Defic Syndr. 2015;70(3):e84-93. 
91. 
Rose DN. Short-course prophylaxis against tuberculosis in HIV-infected persons. A decision and cost-
effectiveness analysis. Annals of internal medicine. 1998;129(10):779-786. 
92. 
Hahn JA, Fatch R, Wanyenze RK, et al. Decreases in self-reported alcohol consumption following HIV 
counseling and testing at Mulago Hospital, Kampala, Uganda. BMC Infect Dis. 2014;14:403. 


93. 
Bajunirwe F, Haberer J, Boum Y, et al. Comparison of self-reported alcohol consumption to 
phosphatidylethanol measurement among HIV infected patients initiating antiretroviral treatment in 
south western Uganda. PLoS ONE. 2014. 
94. 
Santos GM, Emenyonu NI, Bajunirwe F, et al. Self-reported alcohol abstinence associated with ART 
initiation among HIV-infected persons in rural Uganda. Drug and alcohol dependence. 2014;134:151-
157. 
95. 
Kruse GR, Bangsberg DR, Hahn JA, et al. Tobacco use among adults initiating treatment for HIV 
infection in rural Uganda. AIDS and behavior. 2014;18(7):1381-1389. 
96. 
Hahn JA, Fatch R, Kabami J, et al. Self-Report of Alcohol Use Increases When Specimens for Alcohol 
Biomarkers Are Collected in Persons With HIV in Uganda. J Acquir Immune Defic Syndr. 2012;61(4):e63-
64. 
97. 
Woolf-King S, Steinmaus C, Reingold A, Hahn JA. An update on alcohol use and risk of HIV infection in 
sub-Saharan Africa: Meta-analysis and future research directions. International Journal of Alcohol and 
Drug Research. 2013;2(1):99-110. 
98. 
Wanyenze RK, Kamya MR, Fatch R, et al. Abbreviated HIV counselling and testing and enhanced 
referral to care in Uganda: a factorial randomised controlled trial. The Lancet. Global health. 
2013;1(3):e137-145. 
99. 
Woolf-King SE, Fatch R, Emenyonu N, et al. Development and validation of the East Africa Alcohol 
Expectancy Scale (AFEXS). Journal of studies on alcohol and drugs. 2015;76(2):336-343. 
100. 
Sundararajan R, Wyatt MA, Woolf-King S, et al. Qualitative Study of Changes in Alcohol Use Among 
HIV-Infected Adults Entering Care and Treatment for HIV/AIDS in Rural Southwest Uganda. AIDS and 
behavior. 2014. 
101. 
Asiimwe SB, Fatch R, Emenyonu NI, et al. Comparison of Traditional and Novel Self-Report Measures to 
an Alcohol Biomarker for Quantifying Alcohol Consumption Among HIV-Infected Adults in Sub-Saharan 
Africa. Alcohol Clin Exp Res. 2015;39(8):1518-1527. 
102. 
Bartlett JA. Addressing the challenges of adherence. J Acquir Immune Defic Syndr. 2002;29 Suppl 1:S2-
10. 
103. 
Getahun H, Matteelli A, Abubakar I, et al. Management of latent Mycobacterium tuberculosis infection: 
WHO guidelines for low tuberculosis burden countries. Eur Respir J. 2015;46(6):1563-1576. 
104. 
Swaminathan S, Subbaraman R, Venkatesan P, et al. Tuberculin skin test results in HIV-infected 
patients in India: implications for latent tuberculosis treatment. The international journal of 
tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and 
Lung Disease. 2008;12(2):168-173. 
105. 
Sterling TR, Villarino ME, Borisov AS, et al. Three months of rifapentine and isoniazid for latent 
tuberculosis infection. The New England journal of medicine. 2011;365(23):2155-2166. 
106. 
Belknap R, A/ B, Holland D, et al. Adherence to Once-Weekly Self-Administered INH and Rifapentine for 
Latent TB: iAdhere. Conference on Retroviruses and Opportunistic Infections; 2015; Seattle. 
107. 
Maartens G. What more is required to use rifamycin regimens to prevent TB in people living with HIV in 
resource constrained settings? HIV/TB Research Frontiers Meeting in conjunction with Conference on 
Retroviruses and Opportunistic Infections; 2015; Seattle. 
108. 
Gupta RK, Lucas SB, Fielding KL, Lawn SD. Prevalence of tuberculosis in post-mortem studies of HIV-
infected adults and children in resource-limited settings: a systematic review and meta-analysis. AIDS 
(London, England). 2015;29(15):1987-2002. 
109. 
Kigozi IM, Dobkin LM, Martin JN, et al. 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. 2009;52(2):280-289. 
110. 
Friedman LM, Furberg CD, DeMets DL. Fundamentals of Clinical Trials. Springer; 2010. 
111. 
Snider DE, Jr. Pyridoxine supplementation during isoniazid therapy. Tubercle. 1980;61(4):191-196. 


112. 
Deschamps AE, De Geest S, Vandamme AM, Bobbaers H, Peetermans WE, Van Wijngaerden E. 
Diagnostic value of different adherence measures using electronic monitoring and virologic failure as 
reference standards. AIDS patient care and STDs. 2008;22(9):735-743. 
113. 
Eby J, Chapman J, Marukutira T, et al. The adherence-outcome relationship is not altered by diary-
driven adjustments of microelectronic monitor data. Pharmacoepidemiology and drug safety. 2015. 
114. 
Berg KM, Arnsten JH. Practical and conceptual challenges in measuring antiretroviral adherence. J 
Acquir Immune Defic Syndr. 2006;43 Suppl 1:S79-87. 
115. 
Ayoade A, Oladipo I. Evaluation of the correlation between self-report and electronic monitoring of 
adherence to hypertension therapy. Blood pressure. 2012;21(3):161-166. 
116. 
Winhusen TM, Kropp F, Lindblad R, et al. Multisite, randomized, double-blind, placebo-controlled pilot 
clinical trial to evaluate the efficacy of buspirone as a relapse-prevention treatment for cocaine 
dependence. The Journal of clinical psychiatry. 2014;75(7):757-764. 
117. 
Amico KR, Fisher WA, Cornman DH, et al. Visual analog scale of ART adherence: association with 3-day 
self-report and adherence barriers. J Acquir Immune Defic Syndr. 2006;42(4):455-459. 
118. 
Feldman BJ, Fredericksen RJ, Crane PK, et al. Evaluation of the single-item self-rating adherence scale 
for use in routine clinical care of people living with HIV. AIDS and behavior. 2013;17(1):307-318. 
119. 
Lu M, Safren SA, Skolnik PR, et al. Optimal recall period and response task for self-reported HIV 
medication adherence. AIDS and behavior. 2008;12(1):86-94. 
120. 
Reid MC, Fiellin DA, O'Connor PG. Hazardous and harmful alcohol consumption in primary care. 
Archives of internal medicine. 1999;159(15):1681-1689. 
121. 
McGinnis KA, Justice AC, Kraemer KL, Saitz R, Bryant KJ, Fiellin DA. Comparing alcohol screening 
measures among HIV-infected and -uninfected men. Alcohol Clin Exp Res. 2013;37(3):435-442. 
122. 
Dhalla S, Kopec JA. The CAGE questionnaire for alcohol misuse: a review of reliability and validity 
studies. Clinical and investigative medicine. Medecine clinique et experimentale. 2007;30(1):33-41. 
123. 
Radloff LS. The CES-D scale a self-report depression scale for research in the general population. 
Applied psychological measurement. 1977;1(3):385-401. 
124. 
Heatherton TF, Kozlowski LT, Frecker RC, FAGERSTROM KO. The Fagerström test for nicotine 
dependence: a revision of the Fagerstrom Tolerance Questionnaire. British journal of addiction. 
1991;86(9):1119-1127. 
125. 
Helander A, Hansson T. [National harmonization of the alcohol biomarker PEth]. Lakartidningen. 
2013;110(39-40):1747-1748. 
126. 
Wurst FM, Thon N, Yegles M, Schruck A, Preuss UW, Weinmann W. Ethanol Metabolites: Their Role in 
the Assessment of Alcohol Intake. Alcohol Clin Exp Res. 2015;39(11):2060-2072. 
127. 
Koenker R. Quantile regression. New York: Cambridge University Press; 2005. 
128. 
Hao L, Naiman DQ. Quantile regression. Thousand Oaks: Sage Publications; 2007. 
129. 
Deschamps AE, Van Wijngaerden E, Denhaerynck K, De Geest S, Vandamme AM. Use of electronic 
monitoring induces a 40-day intervention effect in HIV patients. J Acquir Immune Defic Syndr. 
2006;43(2):247-248. 
130. 
Tishbirani R. Regression shrinkage and selection via the lasso. . J Royal Statist Soc. 1996;58(1):267-288. 
131. 
James G, Witten D, Hastie T, Tibshirani R. An Introduction to Statisical Learning with Applications in R. 
Springer; 2013. 
132. 
Little RJA, Rubin DB. Statistical Analysis with Missing Data. Vol 2nd edition. New York: John Wiley; 
2002. 
133. 
Rubin D. Multiple Imputation for Nonresponse in Surveys. New York: Wiley; 1987. 
134. 
Hedeker D, Gibbons RD. Application of random-effects pattern-mixture models for missing data in 
longitudinal studies. Psychological Methods. 1997;2(1):64-78. 
135. 
Oni T, Burke R, Tsekela R, et al. High prevalence of subclinical tuberculosis in HIV-1-infected persons 
without advanced immunodeficiency: implications for TB screening. Thorax. 2011;66(8):669-673. 


136. 
Cox JA, Lukande RL, Nelson AM, et al. An Autopsy Study Describing Causes of Death and Comparing 
Clinico-Pathological Findings among Hospitalized Patients in Kampala, Uganda. PLoS ONE. 
2012;7(3):e33685. 
137. 
Hoffmann CJ, Charalambous S, Thio CL, et al. Hepatotoxicity in an African antiretroviral therapy cohort: 
the effect of tuberculosis and hepatitis B. AIDS. 2007;21(10):1301-1308. 
138. 
Bliven-Sizemore EE, Sterling TR, Shang N, et al. Three months of weekly rifapentine plus isoniazid is less 
hepatotoxic than nine months of daily isoniazid for LTBI. Int J Tuberc Lung Dis. 2015;19(9):1039-1044. 
139. 
Sterling TR, Villarino ME, Borisov AS, et al. Three Months of Rifapentine and Isoniazid for Latent 
Tuberculosis Infection. New England Journal of Medicine365. 2011;365(23):2155-2166. 
140. 
Guo N, Marra Ca, Marra F, Moadebi S, Elwood RK, FitzGerald JM. Health state utilities in latent and 
active tuberculosis. Value in Health. 2008;11(7):1154-1161. 
141. 
Kirenga BJ, Worodria W, Massinga-Loembe M, et al. Tuberculin skin test conversion among HIV 
patients on antiretroviral therapy in Uganda. The International Journal of Tuberculosis and Lung 
Disease. 2013;17(3):336-341. 
142. 
Swaminathan S, Ramachandran R, Baskaran G, et al. Risk of development of tuberculosis in HIV-
infected patients. The International Journal of Tuberculosis and Lung Disease. 2000;4(9):839-844. 
143. 
Whalen CC, Johnson JL, Okwera A, et al. A Trial of Three Regimens To Prevent Tuberculosis in Ugandan 
Adults Infected With the Human Immunodeficiency Virus. New England Journal of Medicine. 
1997;337(12):801-808. 
144. 
Fitzgerald DW, Desvarieux M, Severe P, Joseph P, Johnson Jr WD, Pape JW. Effect of post-treatment 
isoniazid on prevention of recurrent tuberculosis in HIV-1-infected individuals: a randomised trial. The 
Lancet. 2000;356(9240):1470-1474. 
145. 
Golub JE, Cohn S, Saraceni V, et al. Long-term Protection From Isoniazid Preventive Therapy for 
Tuberculosis in HIV-Infected Patients in a Medium-Burden Tuberculosis Setting: The TB/HIV in Rio 
(THRio) Study. Clinical Infectious Diseases. 2015;60(4):639-645. 
146. 
Golub JE, Pronyk P, Mohapi L, et al. Isoniazid preventive therapy, HAART and tuberculosis risk in HIV-
infected adults in South Africa: a prospective cohort. AIDS (London, England). 2009;23(5):631-636. 
147. 
International Union Against Tuberculosis Committee on P. Efficacy of various durations of isoniazid 
preventive therapy for tuberculosis: five years of follow-up in the IUAT trial. Bulletin of the World 
Health Organization. 1982;60(4):555-564. 
148. 
Organization GHOWH. Tuberculosis Country Profiles: Uganda. 2015. 
149. 
Nakagawa F, May M, Phillips A. Life expectancy living with HIV: recent estimates and future 
implications. Current Opinion in Infectious Diseases. 2013;26(1):17-25. 
150. 
Organization GHOWH. Life Tables by Country: Uganda. 2014. 
151. 
Mills EJ, Bakanda C, Birungi J, et al. Mortality by baseline CD4 cell count among HIV patients initiating 
antiretroviral therapy: evidence from a large cohort in Uganda. AIDS. 2011;25(6):851-855. 
152. 
Lewden C, Chêne G, Morlat P, et al. HIV-Infected Adults With a CD4 Cell Count Greater Than 500 
Cells/mm3 on Long-Term Combination Antiretroviral Therapy Reach Same Mortality Rates as the 
General Population. JAIDS Journal of Acquired Immune Deficiency Syndromes. 2007;46(1):72-77. 
153. 
Badri M, Lawn SD, Wood R. Short-term risk of AIDS or death in people infected with HIV-1 before 
antiretroviral therapy in South Africa: a longitudinal study. The Lancet.368(9543):1254-1259. 
154. 
Masiira B, Baisley K, Mayanja BN, Kazooba P, Maher D, Kaleebu P. Mortality and its predictors among 
antiretroviral therapy naïve HIV-infected individuals with CD4 cell count ≥350 cells/mm(3) compared to 
the general population: data from a population-based prospective HIV cohort in Uganda. Global Health 
Action. 2014;7:10.3402/gha.v3407.21843. 
155. 
Bendavid E, Grant P, Talbot A, Owens DK, Zolopa A. Cost-effectiveness of antiretroviral regimens in the 
World Health Organization's treatment guidelines: a South African analysis. AIDS. 2011;25(2):211-220. 
156. 
Jelsma JMEHJTXDM. An investigation into the health-related quality of life of individuals living with HIV 
who are receiving HAART. AIDS Care. 2005;17(5):579-588. 


157. 
Friend-du Preez N, Peltzer K. HIV Symptoms and Health-Related Quality of Life Prior to Initiation of 
HAART in a Sample of HIV-Positive South Africans. AIDS Behav. 2010;14(6):1437-1447. 
158. 
Shea KM, Kammerer JS, Winston CA, Navin TR, Horsburgh CR. Estimated Rate of Reactivation of Latent 
Tuberculosis Infection in the United States , Overall and by Population Subgroup. 2013(6). 
159. 
Durovni B, Saraceni V, Moulton LH, et al. Effect of improved tuberculosis screening and isoniazid 
preventive therapy on incidence of tuberculosis and death in patients with HIV in clinics in Rio de 
Janeiro, Brazil: a stepped wedge, cluster-randomised trial. The Lancet Infectious Diseases. 
2013;13(10):852-858. 
160. 
Tan CH, Hartman JC. Sensitivity Analysis in Markov Decision Processes With Uncertain Reward 
Parameters. Journal of Applied Probability. 2011;48(4). 
161. 
Bounthavong M, Hsu DI, Okamoto MP. Cost-effectiveness analysis of linezolid vs. vancomycin in 
treating methicillin-resistant Staphylococcus aureus complicated skin and soft tissue infections using a 
decision analytic model. International journal of clinical practice. 2009;63(3):376-386. 
162. 
Frey HC, Patil SR. Identification and review of sensitivity analysis methods. Risk Anal. 2002;22(3):553-
578. 
163. 
Baucells M, Borgonovo E. Invariant Probabilistic Sensitivity Analysis. Management Science. 
2013;59(11). 
164. 
UNAIDS Country Report, Uganda. 2014; 
http://www.unaids.org/en/regionscountries/countries/uganda. Accessed 01/11/2016, 2016. 
165. 
Global Status Report on Alcohol and Health. Geneva: World Health Organization;2014. 
166. 
Global Tuberculosis Report 2015. Wolrd Health Organization;2015.