Lower rates of ART initiation and decreased retention among ART-naïve patients who consume alcohol enrolling in HIV care and treatment programs in Kenya and Uganda

Ioannis Patsis; Suzanne Goodrich; Constantin T Yiannoutsos; Steven A Brown; Beverly S Musick; Lameck Diero; Jayne L Kulzer; Mwembesa Bosco Bwana; Patrick Oyaro; Kara K Wools-Kaloustian

doi:10.1371/journal.pone.0240654

. 2020 Oct 23;15(10):e0240654. doi: 10.1371/journal.pone.0240654

Lower rates of ART initiation and decreased retention among ART-naïve patients who consume alcohol enrolling in HIV care and treatment programs in Kenya and Uganda

Ioannis Patsis ¹, Suzanne Goodrich ², Constantin T Yiannoutsos ^3,^*, Steven A Brown ⁴, Beverly S Musick ⁴, Lameck Diero ⁵, Jayne L Kulzer ⁶, Mwembesa Bosco Bwana ⁷, Patrick Oyaro ⁸, Kara K Wools-Kaloustian ²

Editor: Joel Msafiri Francis⁹

¹Department of Hygiene and Epidemiology, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece

²Division of Infectious Diseases, Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, United States of America

³Department of Biostatistics, Indiana University Fairbanks School of Public Health, Indianapolis, Indiana, United States of America

⁴Department of Biostatistics, Indiana University School of Medicine, Indianapolis, Indiana, United States of America

⁵School of Medicine, College of Health Sciences, Moi University, Eldoret, Kenya

⁶Department of Medicine, University of California San Francisco, San Francisco, California, United States of America

⁷Department of Medicine, Mbarara University of Science and Technology, Mbarara, Uganda

⁸Centre for Microbiology Research, Kenya Medical Research (KEMRI), Nairobi, Kenya

⁹University of the Witwatersrand, SOUTH AFRICA

Competing Interests: The authors have declared that no competing interests exist.

^✉

* E-mail: cyiannou@iu.edu

Roles

Ioannis Patsis: Formal analysis, Methodology, Writing – original draft, Writing – review & editing

Suzanne Goodrich: Conceptualization, Investigation, Project administration, Supervision, Validation, Writing – original draft, Writing – review & editing

Constantin T Yiannoutsos: Funding acquisition, Methodology, Supervision, Writing – original draft, Writing – review & editing

Steven A Brown: Data curation, Writing – review & editing

Beverly S Musick: Conceptualization, Data curation, Methodology, Writing – original draft, Writing – review & editing

Lameck Diero: Investigation, Resources, Supervision, Writing – review & editing

Jayne L Kulzer: Investigation, Resources, Supervision, Validation, Writing – review & editing

Mwembesa Bosco Bwana: Resources, Supervision, Validation, Writing – review & editing

Patrick Oyaro: Resources, Supervision

Kara K Wools-Kaloustian: Conceptualization, Funding acquisition, Resources, Supervision, Validation, Writing – original draft, Writing – review & editing

Joel Msafiri Francis: Editor

PMCID: PMC7584184 PMID: 33095784

Abstract

Objectives

Almost 13 million people are estimated to be on antiretroviral therapy in Eastern and Southern Africa, and their disease course and program effectiveness could be significantly affected by the concurrent use of alcohol. Screening for alcohol use may be important to assess the prevalence of alcohol consumption and its impact on patient and programmatic outcomes.

Methods

As part of this observational study, data on patient characteristics and alcohol consumption were collected on a cohort of 765 adult patients enrolling in HIV care in East Africa. Alcohol consumption was assessed with the AUDIT questionnaire at enrollment. Subjects were classified as consuming any alcohol (AUDIT score >0), hazardous drinkers (AUDIT score ≥8) and hyper drinkers (AUDIT score ≥16). The effects of alcohol consumption on retention in care, death and delays in antiretroviral therapy (ART) initiation were assessed through competing risk (Fine & Gray) models.

Results

Of all study participants, 41.6% consumed alcohol, 26.7% were classified as hazardous drinkers, and 16.0% as hyper drinkers. Depending on alcohol consumption classification, men were 3–4 times more likely to consume alcohol compared to women. Hazardous drinkers (median age 32.8 years) and hyper drinkers (32.7 years) were slightly older compared to non-hazardous drinkers (30.7 years) and non-hyper drinkers (30.8 years), (p-values = 0.014 and 0.053 respectively). Median CD4 at enrollment was 330 cells/μl and 16% were classified World Health Organization (WHO) stage 3 or 4. There was no association between alcohol consumption and CD4 count or WHO stage at enrollment. Alcohol consumption was associated with significantly lower probability of ART initiation (adjusted sub-distribution hazard ratio aSHR = 0.77 between alcohol consumers versus non-consumers; p-value = 0.008), and higher patient non-retention in care (aSHR = 1.77, p-value = 0.023).

Discussion

Alcohol consumption is associated with significant delays in ART initiation and reduced retention in care for patients enrolling in HIV care and treatment programs in East Africa. Consequently, interventions that target alcohol consumption may have a significant impact on the HIV care cascade.

Introduction

In 2017, there were 19.6 million [17.5 million– 22.0 million] people with HIV (PWH) in Eastern and Southern Africa [1]. As the number of patients accessing HIV care has rapidly increased in the past ten years, nearly 12.9 million people [11.4 million- 13.4 million] in Eastern and Southern Africa are now on antiretroviral therapy (ART) [1]. However, retention in care remains a significant challenge [2]. For example, in East Africa only about 69% of patients initiating ART remain in care at the clinic of their initial enrollment after two years[3]. Patients who are disengaged from care have significantly higher mortality [2, 4, 5] and HIV transmission rates compared to those who remain engaged in care [6, 7]. Consequently, the investigation of factors that impact patterns of ART initiation and retention in care for patients enrolling in these programs is of particular significance.

Though the association between alcohol consumption and poor health outcomes in HIV infection is well established [8–10], the impact of alcohol consumption on patients’ initiation of ART and retention in care has yet to be thoroughly examined. Nevertheless, there is some recent evidence that drinking is associated with poor retention in care, in both resource-replete and resource-limited settings [11, 12]. Heavy drinking and frequent binge drinking are associated with worse retention in HIV care in patients observed at seven U.S. HIV clinical sites [11], and findings from a systematic review suggest that low-income and middle-income countries (as classified by the World Bank) face issues similar to those of high-income countries [12]. Despite that, only a few evidence-based interventions specifically target problematic alcohol consumption [12]. In addition, measurement of alcohol exposure and retention measures varies among studies so that generalizability of their findings is an issue. Moreover, adjusting for all possible confounders in such studies is challenging, and causality cannot be inferred [11]. In Africa, 42.6% of the population is estimated to consume alcohol [13]. Thus, the impact of alcohol on the course of HIV infection and the effectiveness of care and treatment programs, which rely on rapid initiation of patients on ART and patient retention into care for their success, may be significantly affected by ambient alcohol consumption patterns, particularly in resource-limited areas with high HIV prevalence like sub-Saharan Africa.

The purpose of this observational study was to determine the prevalence of alcohol consumption in ART-naïve patients initiating HIV care and to assess whether alcohol consumption is associated with time to ART initiation, mortality, and patient retention in care.

Methods

Study design

This prospective observational study was approved by the Indiana University Institutional Review Board and the ethical bodies affiliated with each participating site: The Academic Model Providing Access to Healthcare (AMPATH): Moi University College of Health Sciences and MOI Teaching and Referral Hospital’s Institutional Research and Ethics Committee; Family AIDS Care and Education Services (FACES): Kenya Medical Research Institute/National Ethics Review Committee; Mbarara Immune Suppression Syndrome (ISS) Clinic: Mbarara University of Science & Technology Institutional Review Committee. Participant written informed consent was obtained at the time of enrollment into study.

Study setting and standard of care

The study took place in five clinics within the East Africa International epidemiology Databases to Evaluate AIDS (EA-IeDEA) consortium. EA-IeDEA is one of seven regional consortia supported by the National Institutes of Health to consolidate, curate and analyze HIV care and treatment data in order to evaluate the outcomes of people living with HIV/AIDS [14]. Study sites included the FACES clinics at Lumumba sub-County Hospital, (Kisumu Kenya) and Suba sub-County Hospital (Sindo, Kenya); two AMPATH clinics based at Moi Teaching and Referral Hospital (Eldoret, Kenya); and the Mbarara ISS Clinic (Mbarara, Uganda). The sites in Eldoret and Kisumu are primarily urban, while Sindo is rural and Mbarara is semi-urban. Each clinic provides comprehensive HIV care including CD4 and HIV viral load testing, provision of ART, diagnosis and treatment of opportunistic infections, as well as management of common non-communicable diseases.

Individuals enrolling in care have blood drawn for a CD4 count and undergo World Health Organization (WHO) disease staging by a clinician [15]. Patients started on trimethoprim-sulfa prophylaxis, if no contraindication existed. Eligibility criteria for ART was assessed as per WHO guidelines during the study period, hence individuals were eligible for ART if they had a CD4 count below 350 cells/μl and/or a WHO stage of >2 [16].

Patients not meeting ART eligibility criteria returned to clinic at a frequency determined by the clinic, but usually at a maximum of every six months, for care and continued monitoring of their CD4 count for ART eligibility. For patients initiating ART, CD4 counts were repeated at 6–12 months depending on the clinics testing strategy. Routine viral load testing was not always available during the study period.

Study population

Participants were recruited from January 25, 2013 to June 25, 2014. Participants met eligibility criteria for inclusion in this study if they were at least 18 years of age, newly enrolling in HIV-care at one of the five participating clinics, and ART-naïve. Potential participants were excluded if they did not meet any of the above criteria or were unable or unwilling to provide consent for the study. All participants were educated about the study and provided written consent prior to enrollment.

The Alcohol Use Disorder Identification Test (AUDIT) questionnaire, which has been validated in a number of primary care medical settings, was used by a trained research assistant to collect patient’s alcohol use data over the past one year [17–20]. The AUDIT version utilized is comprised of 10 questions covering three domains: hazardous alcohol use, dependence symptoms, and harmful alcohol use. The total AUDIT score is calculated by summing the values from each question (range 0–4). RAs attempted to minimize under-reporting of a participant's alcohol use by asking in-depth questions about the types of alcohol consumed, the volume (facilitated by showing participants a large collection of glasses and bottles in varying shapes and sizes) and frequency of consumption. Using a spreadsheet with the known alcohol content of commercially available drinks and estimates of the alcohol content of local (non-commercial) brews [21], calculations were made to determine the number of standard drinks (one drink defined as 10 grams of alcohol) each participant consumed.

Additional information was collected as part of routine care including demographics (e.g., age, gender, civil status, HIV disclosure), and clinical data (e.g., CD4 count and WHO disease stage at enrollment). Locator information including addresses and telephone numbers were obtained as part of clinic enrollment and then verified and updated as needed by study personnel. Participants who failed to return for a scheduled visit for more than two months were traced by trained outreach personnel. The first attempt was made by telephone, and, if unsuccessful, community tracing was attempted. Once the participant or a reliable close informant was found, vital status and engagement in care were documented.

Statistical methods

Definition of the outcomes of interest

The three outcomes of interest were time from enrollment in care to ART initiation, mortality, and retention in care. Retention in care was measured by rates of patients actually disengaged from care as ascertained by tracing patients in the community who failed to return to clinic within two months from their last clinic visit to ensure they had not died or were receiving care elsewhere [22, 23]. In this manner, we were able to estimate patient retention in care from the perspective of the patient rather than that of the program. On the basis of tracing and community outreach, participants were classified as silent transfers (undocumented transfer to another care facility), or as having relocated, deceased, disengaged, or missing (untraceable). Within these analyses, censoring occurred at the date patients were traced or, if not located, at the last clinic visit for patients who were determined to be silent transfers and participants who had relocated, because these outcomes were not considered adverse outcomes from a programmatic perspective and were classified as being retained in care. By contrast, patients who were untraceable (missing) or who were found to have disengaged from care were considered as having adverse programmatic outcomes and so were classified as not having been retained in care.

Predictors used in the analyses

Utilizing WHO criteria binary categories based on AUDIT scores were developed: non-alcohol consumers (AUDIT = 0) versus alcohol consumers (AUDIT > 0); people engaged in hazardous drinking (AUDIT ≥ 8) versus not (AUDIT < 8); and people engaged in high alcohol consumption (combined categories of harmful drinking and risk for dependence; AUDIT ≥16) versus not (AUDIT <16) [13].

The covariates of interest included gender, age (18–24, 25–34, 35–44, 45+), civil status (legally married versus not), CD4 cell count at enrollment (<50, 50–99, 100–199, 200–349, 350–499, 500+), WHO stage (1/ 2 or 3/ 4) and HIV status disclosure (whether the participant has disclosed HIV carrier status to anyone) at the time of enrollment. The five participating clinics were grouped under their parent programs: FACES, AMPATH and Mbarara. Missing values for CD4 cell count, WHO stage, civil status, and HIV disclosure multiple imputation was utilized using iterative chained equations (ICE) under the assumption that the missing values were missing at random (MAR) [24].

Statistical modeling

Descriptive statistics were used to calculate the prevalence of alcohol consumption. The association between age, CD4 count at enrollment and alcohol consumption was examined by the Mann-Whitney test. The Pearson chi-square test was used to examine the possible association between categorical variables like gender, WHO stage, age and the AUDIT alcohol consumption categories.

To examine the impact of alcohol use on delays in ART initiation, retention in care and mortality, survival analysis techniques were used. Study participants were at risk of experiencing more than one outcome. However, observation of one outcome such as death or non-retention, prior to another, such as, for example, ART initiation, would preclude observing the other outcome. As such, all events other than the event of interest were considered competing risks [25]. To assess the impact of alcohol consumption on each event of interest, in the presence of all competing events and other explanatory factors, we used the method of Fine and Gray in the analysis [25]. We performed three separate analyses, each investigating the possible effect of different levels of alcohol consumption (i.e., any alcohol use, hazardous and higher or harmful alcohol consumption, the exposure) on the likelihood (sub-distribution hazard) of each of the three events of interest (ART initiation, death, and non-retention in care). In all cases we adjusted for all relevant patient-level characteristics. With regard to the event of ART initiation, the competing risks were non-retention in care and death, whereas for mortality, the corresponding competing risks were non-retention and ART initiation. The competing risk for the event of non-retention was death.

Time-to-event analyses were conducted under the assumption of independent censoring. This means that the failure rate for subjects within a subgroup (e.g., people engaged in hazardous alcohol consumption) who had relocated or silently transferred is assumed to be equal to the failure rate for subjects in that subgroup who remained on observation and had not experienced any event until that time-point [26]. Associations with corresponding p-values <5% were designated as statistically significant, and as possibly significant if between 5% and 10%. Data analyses were conducted using the statistical program Stata version 13 (StataCorp, College Station, Texas).

Results

Participant characteristics

A total of 765 patients with median age of 31.2 years at enrollment participated in the study (Table 1); more than half were women (61%). At enrollment, the median CD4 count was 330 cells/μl (IQR: 137–513) and 84% (n = 591) of study participants with a known WHO stage had a WHO stage 1 or 2 disease. About two thirds of the participants with a known civil status were legally married (66%, n = 361) and just over half of those with a known disclosure status had disclosed their HIV status (56%, n = 269). As classified by AUDIT scores, 41.6% of study participants consumed alcohol, with 26.7% scoring at least in the hazardous range and 16.0% in the harmful drinking range or being at risk for dependency.

Table 1. Characteristics of the study cohort.

	Overall (n:765)	no alcohol consumption	alcohol consumption¹	non-hazardous alcohol consumption	Hazardous alcohol consumption²	Non-harmful drinking	Harmful drinking³
Overall (n:765)		447 (58.4)	318 (41.6)	561(73.3)	204 (26.7)	643 (84.1)	122 (16.0)
IeDEA program p-value^*		<0.001		0.062		<0.001
AMPATH	264 (34.5)	166 (37.1)	98 (30.8)	184 (32.8)	80 (39.2)	209 (32.5)	55 (45.1)
Mbarara	264 (34.5)	130 (29.1)	134 (42.1)	207 (36.9)	57 (27.9)	245 (38.1)	19 (15.6)
FACES	237 (31.0)	151 (33.8)	86 (27.0)	170 (30.3)	67 (32.8)	189 (23.4)	48 (39.3)
Total	765	447	318	561	204	643	122
Gender [N (%)] p-value^*		<0.001		<0.001		<0.001
Male	295 (38.6)	120 (26.9)	175 (55.0)	164 (29.2)	131 (64.2)	213 (33.1)	82 (67.2)
Female	470 (61.4)	327 (73.2)	143 (45.0)	397 (70.8)	73 (35.8)	430 (66.9)	40 (32.8)
Total	765	447	318	561	204	643	122
Age [median (IQR)] p-value^**		NS		0.014		0.053
	31.2 (26.1, 39.5)	30.8 (25.8, 39.8)	32.0 (26.9, 39.4)	30.7 (25.8, 38.9)	32.8 (27.6, 40.5)	30.8 (25.8, 39.4)	32.7 (27.8, 40.7)
Age (categorical) [N (%)] p-value^*		NS		NS		NS
18–24	147 (19.2)	97 (21.7)	50 (15.7)	120 (21.4)	27 (13.2)	134 (20.8)	13 (10.7)
25–34	338 (44.2)	186 (41.6)	152 (47.8)	243 (43.3)	95 (46.6)	279 (43.4)	59 (48.4)
35–44	182 (23.8)	107 (23.9)	75 (23.6)	128 (22.8)	54 (26.5)	147 (22.9)	35 (28.7)
>44	98 (12.8)	57 (12.8)	41 (12.9)	70 (12.5)	28 (13.7)	83 (12.9)	15 (12.3)
WHO stage at enrollment [N (%)] p-value^*		NS		NS		NS
1–2	591 (84.0)	341 (84.2)	250 (83.6)	437 (85.2)	154 (80.6)	500 (84.6)	91 (80.5)
3–4	113 (16.1)	64 (15.8)	49 (16.4)	76 (14.8)	37 (19.4)	91 (15.4)	22 (19.5)
Total (non-missing)	704	405	299	513	191	591	113
Missing	61 (8.0)	42 (9.4)	19 (6.0)	48 (8.6)	13 (6.4)	52 (8.1)	9 (7.4)
CD4 at enrollment [median (IQR)] p-value^**		NS		NS		NS
	330 (137, 513)	316 (148, 534)	342 (127, 487)	325 (150, 525)	342 (115, 479)	329 (141, 515.5)	330 (124, 469)
Missing [N (%)]	197 (25.8)	130 (29.1)	67 (21.1)	142 (25.3)	55 (27.0)	159 (24.7)	38 (31.1)
CD4 count (categorical) [N (%)]
[0–49]	67 (11.8)	37 (11.7)	30 (12.0)	47 (11.2)	20 (13.4)	56 (11.6)	11 (13.1)
[50–99]	45 (7.9)	23 (7.3)	22 (8.8)	32 (7.6)	13 (8.7)	38 (7.9)	7 (8.3)
[100–249]	119 (21.0)	73 (23.0)	46 (18.3)	92 (22.0)	27 (18.1)	102 (21.1)	17 (20.2)
[250–349]	70 (12.3)	39 (12.3)	31 (12.4)	53 (12.7)	17 (11.4)	59 (12.2)	11 (13.1)
[350–499]	117 (20.6)	57 (18.0)	60 (23.9)	79 (18.9)	38 (22.5)	97 (20.0)	20 (23.8)
≥500	150 (26.4)	88 (27.8)	62 (24.7)	116 (27.7)	34 (22.8)	132 (27.3)	18 (21.4)
Total	568	317	251	419	149	484	84
Civil status [N (%)] p-value^*		NS		NS		NS
Not married	189 (34.4)	101 (32.8)	88 (36.4)	139 (34.3)	50 (34.5)	165 (35.2)	24 (29.6)
Married	361 (65.6)	207 (67.2)	154 (63.6)	266 (65.7)	95 (65.5)	304 (64.8)	57 (70.4)
Total (non-missing)	550	308	242	405	145	469	81
Missing	215 (28.1)	139 (31.1)	76 (23.9)	156 (27.8)	59 (28.9)	174 (27.1)	41 (33.6)
HIV status disclosure [N (%)] p-value^*		NS		NS		NS
Not disclosed	208 (43.6)	135 (44.9)	73 (41.5)	148 (43.8)	60 (43.2)	165 (43.4)	43 (44.3)
Disclosed	269 (56.4)	166 (55.2)	103 (58.5)	190 (56.2)	79 (56.8)	215 (56.6)	54 (55.7)
Total (non-missing)	477	301	176	338	139	380	97
Missing	288 (37.6)	146 (32.7)	142 (44.7)	223 (39.8)	65 (31.9)	263 (40.9)	25 (20.5)

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¹AUDIT score of >0

²AUDIT score of ≥8

³AUDIT score of ≥16

NS: non-significant

*Chi-square test

**Mann-Whitney test.

Men were more likely than women to consume alcohol regardless of the categorization of alcohol consumption (Table 1). In fact, the odds of belonging to the higher alcohol consumption category were between 3-fold and 4-fold higher for men compared to women. Older participants were more likely to belong to higher alcohol consumption categories when compared to younger participants. However, no age-by-gender interaction was identified. There was no association between enrollment CD4 count, WHO stage, or HIV disclosure status and alcohol consumption (Table 1).

Initially, 25% (n = 190) of participants were identified as lost to program. After tracing, 38% were found to be silent (unreported) transfers, 29% disengaged from care, 7% deceased, 12% relocated and 13% untraceable (missing). During the study period, 28 (3.7%) participants died.

Competing-risk analysis of the events of interest

Consumption of alcohol was strongly associated with a higher (sub-distribution) hazard of not being retained in care in a competing-risk analysis with death as the competing event. Adjusted for all relevant factors, patients who consumed any amount of alcohol had a 77% higher hazard of being non-retained in care (adjusted sub-distribution hazard rate–aSHR– 1.77, p-value = 0.023) compared to those not consuming alcohol. Given the low rates of non-retention (10.6%, Table 2), this is approximately equal 77% higher rate of non-retention. Other alcohol exposure classifications (i.e., hazardous versus non-hazardous and harmful versus non-harmful alcohol consumption) were not associated with patient retention (Table 3).

Table 2. Events used in the survival analyses.

Main event	Competing event(s)	Alcohol consumption			Hazardous drinking			Harmful drinking
Main event	Competing event(s)	No	Yes	Total	No	Yes	Total	No	Yes	Total
Total		447	318	765	561	204	765	643	122	765
*Non-retention* [N(%)]		41 (9.2)	40 (12.6)	81 (10.6)	55 (9.8)	26 (12.8)	81 (10.6)	63 (9.8)	18 (14.8)	81 (10.6)
	Death [N(%)]	14 (3.1)	14 (4.4)	28 (3.7)	16 (2.9)	12 (5.9)	28 (3.7)	22 (3.4)	6 (4.9)	28 (3.7)
	Censored [N(%)]	392 (87.7)	264 (83.0)	656 (85.8)	490 (87.3)	166 (81.4)	656 (85.8)	558 (86.8)	98 (80.3)	656 (85.8)
*Death* [N(%)]		14 (3.1)	14 (4.4)	28 (3.7)	16 (2.9)	12 (5.9)	28 (3.7)	22 (3.4)	6 (4.9)	28 (3.7)
	Non-retention [N(%)]	41 (9.2)	40 (12.6)	81 (10.6)	55 (9.8)	26 (12.8)	81 (10.6)	63 (9.8)	18 (14.8)	81 (10.6)
	Censored [N(%)]	392 (87.7)	264 (83.0)	656 (85.8)	490 (87.3)	166 (81.4)	656 (85.8)	558 (86.8)	98 (80.3)	656 (85.8)
*ART initiation* [N(%)]		313 (70.0)	208 (65.4)	521 (68.1)	386 (68.8)	135 (66.2)	521 (68.1)	441 (68.6)	80 (65.6)	521 (68.1)
	Non-retention [N(%)]	21 (4.7)	26 (8.2)	47 (6.1)	29 (5.2)	18 (8.8)	47 (6.1)	34 (5.3)	13 (10.7)	47 (6.1)
	Death [N(%)]	5 (1.1)	3 (0.9)	8 (1.1)	5 (0.9)	3 (1.5)	8 (1.1)	6 (0.9)	2 (1.6)	8 (1.1)
	Censored [N(%)]	108 (24.2)	81 (25.5)	189 (24.7)	141 (25.1)	48 (23.5)	189 (24.7)	162 (25.2)	27 (22.1)	189 (24.7)

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Table 3. Results of the Fine & Gray competing events survival models, adjusting for age, gender, CD4 count and WHO stage at enrollment, disclosure of HIV status, marital status and site of enrollment; aSHR: adjusted sub-distribution hazard.

		Any alcohol use		Hazardous alcohol consumption		Harmful alcohol consumption
Main event	Competing risk	aSHR [95% CI]	(p-value)	aSHR [95% CI]	(p-value)	aSHR [95% CI]	(p-value)
Non-retention	Death	1.766 [1.083, 2.879]	0.023	1.380 [0.737, 2.389]	0.250	1.480 [0.817, 2.681]	0.195
Death	Non-retention	1.164 [0.580, 2.334]	0.669	1.736 [0.799, 3.773]	0.163	1.245 [0.464, 3.345]	0.663
ART initiation	Non-retention Death	0.770 [0.636, 0.933]	0.008	0.841 [0.680, 1.040]	0.111	0.814 [0.623, 1.064]	0.132

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Consumption of alcohol was also associated with a reduced likelihood (sub-distribution hazard) of ART initiation. In the adjusted analysis, patients who consumed any alcohol were approximately 25% less likely to initiate ART as compared to persons who do not consume alcohol (aSHR: 0.77; p-value = 0.008). No association was observed between the other alcohol consumption classifications and the likelihood of ART initiation (Table 3).

Alcohol consumption, regardless of classification, was not associated with an increased hazard of mortality (Table 3).

Discussion

This study found a strong association between patient’s alcohol consumption in the past one year and retention in HIV care. Association between higher levels of alcohol consumption and patient retention in care was less clear, suggesting that it may be the consumption of alcohol per se which may be associated with patient retention rather than the amount of consumed alcohol. These results are largely consistent with a large U.S.-based study [11]. However, that study, which included more men (82%) who were identified as heavy drinkers (25% vs. 16% in our study), found a stronger association (adjusted OR 0.78) between heavy alcohol consumption and non-retention, something that our study failed to establish. A systematic review also identified the adverse impact of alcohol use on the HIV care cascade (diagnosis, linkage to care and retention) but only 4 out of 53 studies in that review specifically focused on retention in care and only one was set in a low-income country [12].

Our analysis showed that patients who consumed alcohol had a lower likelihood of initiating ART. In a systematic review [12] three out of six studies looking specifically at ART initiation had similar findings, with the others failing to show a significant association between alcohol consumption and delay in ART initiation. Given that alcohol consumption did not have an effect on ART eligibility in our data (analysis not shown), lower rates of ART initiation among patients that consume alcohol may have occurred because these patients may not have met assessment criteria suggesting readiness to start ART and to maintain adherence to the ART regimen. As a result, providers may have been reluctant to initiate patients who consume alcohol on ART, thus delaying the start of therapy. Interestingly, a study from Uganda [27] showed a majority of patients who were previously consuming alcohol and started on ART subsequently abstained from alcohol over three years of follow-up. While providers may perceive that delay in ART initiation is advantageous, ART start may actually improve abstinence to alcohol and allow for the public health benefit of decreasing patients’ viral load.

Alcohol consumption was not associated with higher mortality in our cohort. Mortality was generally a rare event in our study given the short follow-up time (less than one year for most participants) and the fact that the majority of our patients were relatively healthy at enrollment as determined by CD4 count and WHO stage (Table 1). Consequently, with less than 4% of study subjects dying during the study, our study was not powered to detect any potential effects of alcohol consumption on mortality.

We found no association between alcohol consumption and HIV disease severity in patients enrolling in care based on WHO stage and CD4 cell count. These results are consistent with prior studies in which alcohol dependence was associated with lower CD4 counts, but moderate alcohol consumption was not [28–31]. Moreover, given the small proportion of high alcohol consumption in our study, such an effect might be difficult to detect.

Our inability to establish a clear association between the highest levels of alcohol consumption and adverse patient and programmatic outcomes may be due to inaccurate patient reporting despite the significant effort made by our study staff to determine the actual number of standardized drinks consumed. It is possible that some participants attempted to offer more socially acceptable descriptions of their alcohol consumption patterns, which may have resulted in an underestimation of the number of heavy drinkers. In addition, the alcohol content of domestically produced brews varies and thus is difficult to assess. Combined, these limitations in our data collection may account for finding an association of alcohol consumption with decreased retention in care and delays in ART initiation, but not confirming the same findings among those at the highest alcohol consumption classifications.

Conclusion

Our results show that alcohol consumption, irrespective of amount, is strongly associated both with lower rates of ART initiation and lower patient retention in care (respectively the second and the critical third component of the WHO’s 90-90-90 target [1]). Consequently, its evaluation appears to be a significant target for intervention in HIV care. With a high prevalence of alcohol consumption in sub-Saharan Africa, alcohol use is a factor that programs should consider addressing as they implement and employ universal testing and treatment for all people living with HIV [16].

Data Availability

Regarding data sharing, complete data for this study cannot be publicly shared because of legal and ethical restrictions. The principles of collaboration under which IeDEA was founded and the regulatory requirements of the different countries’ IRBs and other legislative and regulatory bodies, require the submission and approval of a project concept sheet by investigators, both within and outside of IeDEA, which has to be approved by the individual sites and the IeDEA Regional Executive Committee. Proposals to individual regions are governed by similar processes (see https://www.ccasanet.org/collaborate/ for helpful documents and processes governing the Central, South America and the Caribbean Network, one of the seven IeDEA regions as well the concept proposal form for the East Africa IeDEA region, from where this manuscript originated). The website (www.iedea-ea.org) includes all information and provides access to forms that researchers in the community can use to request data from IeDEA East Africa. Requests can be directed to our project coordinator, Ms. Yee Yee Kuhn (ykuhn@iu.edu).

Funding Statement

CTY, KWK, LD, JK, MB and BSM were funded by grant number AI069911 provided by the National Institutes of Allergy and Infectious Diseases SG work was funded by a supplement to this grant provided by the National Institute of Drug Abuse. IP was funded by a graduate scholarship offered by the National and Kapodistrian University of Athens Greece.

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PLoS One. doi: 10.1371/journal.pone.0240654.r001

Decision Letter 0

Joel Msafiri Francis

18 Dec 2019

PONE-D-19-24950

Prevalence and Impact of Alcohol Use in Patients Enrolling in HIV Care in East Africa

PLOS ONE

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Reviewer #1: Thank you very much for sending in this review as well as thank the author for the great work.

They set out to determine the prevalence of alcohol consumption in ART naive patients initiating HIV care and assess whether alcohol consumption was associated with time to ART initiation, mortality and patient retention in care in East Africa.

They found that alcohol consumption was associated with lower rates of ART initiation and lower patient retention in care.

The study used data from clinics under the IeDEA consortium with clients enrolled between 2013 and 2014.

What is not clear is why data of a rather old cohort was used and more so the duration of followup is not stated and therefore it is very difficult to ascertain what the advantage of the old cohort was given that the authors do not state this clearly.

The number of subjects enrolled in the study from multiple clinics is 765 participants. whether this number was enough to demonstrate the outcomes was not illustrated and could partly explain the lack of effect of hazardous and hyper drinkers. The authors could help the readers by describing the power the study.

The authors should demonstrate what new knowledge this paper is adding to the body of knowledge and most importantly how to generalize and apply these results in the current test and treat era, where viral loads are available for treatment monitoring and where programs have intensified patient retention activities.

Introduction: Line 22.... The authors quote reference 11 about causality, what is not clear is the applicability of this reference given that the authors are also not inferring any causality.

The study used ART naive patients initiating care although many countries have reached a bigger proportion of their PLHIV and initiated them on ART with the current policy of teats and treat , what is not clear is how this study can be applied in the setting of test and treat. This weakness should be thoroughly discussed in the discussion section including the failure by the authors from obtaining Viral load data should be further discussed.

Methods:Predictors used in the analysis-- The authors should check the definition of Non drinkers and drinkers.

conduct a power analysis or sample size calculation.

The authors did not mention whether only 765 participants were the clients who came for care in the study clinics and indeed enrolled in care in those multiple clinics during the study enrollment period. How many were not enrolled? what were the other reasons for exclusion if any ?

Reviewer #2: See attached word document for reviewer comments to the author which address the above questions and provide feedback on the submission.

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Reviewer #2: Yes: Sarah B. Puryear

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PLoS One. 2020 Oct 23;15(10):e0240654. doi: 10.1371/journal.pone.0240654.r002

Author response to Decision Letter 0

26 Jun 2020

Reviewer # 1

What is not clear is why data of a rather old cohort was used and more so the duration of follow-up is not stated and therefore it is very difficult to ascertain what the advantage of the old cohort was given that the authors do not state this clearly.

We state (see below) that some of the outcomes, particularly mortality, were rather rare in this population, so the power to detect any differences was limited.

The number of subjects enrolled in the study from multiple clinics is 765 participants. Whether this number was enough to demonstrate the outcomes was not illustrated and could partly explain the lack of effect of hazardous and hyper drinkers. The authors could help the readers by describing the power the study.

We state the advantages of using this particular cohort, which have to do with the fact that this is a research study (versus a study from routinely collected data), and we also outline the limitations of using a pre-UTT cohort in the limitations of the study (see below).

Duration of follow-up is not stated

The initial plan was to follow-up subjects for six months after enrollment into the study. The three participating sites had staggered start times based on receipt of regulatory approval. All patients had the same completion date, with the longest follow-up period for a subject being 433 days.

The authors could help the readers by describing the power the study. Conduct a power analysis or sample size calculation.

Power studies and sample-size calculations are helpful at the stage of study design. They are not relevant to be used in retrospect. It is obvious, and was discussed at length in the text, that, due to very few events in some of the analyses (particularly with respect to mortality), the study had inadequate power to deect all but the most extreme differences.

Generalize and apply these results in the current test and treat era, where viral loads are available. …the failure by the authors from obtaining Viral load data should be further discussed

An attempt was made to use viral load in this study but there were insufficient data to support an analysis (just 29 entries). As pointed out in the discussion, the most significant finding is that, from a programmatic point of view, drinkers seem to have a significant lower propensity (hazard) to initiate ART. Whether this is the case with the implementation of new guidelines remains to be seen, probably by conducting a new study utilizing a new cohort. In other words, it remains to be seen whether, in the more simplified environment of ART initiation during the universal-test-and-treat era, the impact of factors which delay treatment initiation persists. Certainly, the impact of other factors (e.g., adherence counseling, availability of laboratory tests at the start of therapy, etc.), which delayed ART initiation even among otherwise eligible patients, has receded in recent months, after adoption of UTT by virtually every country in the world (see Tymejczyk et al., J Inf Dis 2019). We have added this comment in the Discussion.

Introduction: Line 22.... The authors quote reference 11 about causality, what is not clear is the applicability of this reference given that the authors are also not inferring any causality.

Actually, reference 11 (Monroe et al.., JAIDS, 2016) speaks about an association and not causation, so it is similar to our study. In addition, the study of Monroe and colleagues does not address issues of delay of ART initiation and did not ascertain what happened to patients who were lost to the program, which, depending on the reason (unreported death versus transfer versus frank disengagement from care) would lead to possibly different conclusions. Our study, in addition to directly addressing the question of retention, it ensured that patients who were no longer retained at our sites were or were not alive and in care, by tracing those who stopped attending clinic in the community.

The study used ART naive patients initiating care although many countries have reached a bigger proportion of their PLHIV and initiated them on ART with the current policy of teats and treat, what is not clear is how this study can be applied in the setting of test and treat. This weakness should be thoroughly discussed in the discussion section including the failure by the authors from obtaining Viral load data should be further discussed.

We mention this issue in several points throughout the manuscript (see also below). The short answer is that this study cannot address what the nexus between alcohol and ART initiation and retention will be in the era of universal test and treat (UTT). However, this study is useful as further evidence that ignoring alcohol use (and, for that matter, other factors which may affect retention) in the rush to initiate everyone on therapy, may be counterproductive in the long run.

Predictors used in the analysis.

We have added that patient-level characteristics (demographic factors like age and gender, plus disease-related factors like CD4 count, World Health Organization (WHO) HIV disease stage), logistical issues (e.g., distance from clinic) and clinic-level characteristics, such as location of clinic and level of care, were collected by the study and were used in the analyses.

The authors should check the definition of Non drinkers and drinkers.

This is a good point. In our manuscript “non-drinkers (AUDIT > 0) versus drinkers (AUDIT = 0);” -it should be the other way around. We have corrected this in the paper.

The following is an excerpt from the screening log from two of our sites in Kisumu, Kenya. It appears, reassuringly enough, that the majority of exclusions concerned protocol violations, with only a small number of patients who were ultimately not enrolled having done so because of refusal to participate in the study.

Site No Screened No Enrolled Not Enrolled- 63

Kisumu 232 169 Reason Number

Transfer In- 33

Declined Enrollment 12

Known Positives 5

Under the age of 18 12

Very sick with T.B- 1

Suba Site 89 76 Declined Enrollment 5

Under the age of 18 8

Totals

Screened 321

Enrolled 245

Not Enrolled 76

Reasons

Transfer In 33

Declined Enrollment 17

Known Positives 5

Under the age 20

Very sick clients 1

Our site in Uganda reported the following:

Number of patients screened: 490

Number of patients enrolled: 264

The declines had the following issues:

• Not interested

• Declined to give a reason

• Too weak

• Below Age

• Needed approval from partner

• Stigma issues

• Mental issues

• Time bad

• House maid

• Prisoner

Unfortunately, we have not yet been able to ascertain how many of those who declined participation (226/490) were in each of these groups and obviously, some of the issues listed may be associated with alcohol use (e.g., “mental issues”) or even some among those who were “not interested” or “declined to give a reason”, or stated “time [was] bad” may have had their decision to participate influenced by alcohol consumption leading to some bias in the outcome. Nevertheless, to the extent that the experience in Uganda is similar as to that in Kenya, the majority of exclusions were related to logistical issues or protocol violations.

Reviewer # 2

TITLE

The title as written is vague. Suggest clarifying the intended meaning of “impact” in the title, e.g. “Prevalence of Alcohol Use and Impact on HIV Care Outcomes in ART-naïve Adults Enrolling in HIV Care in East Africa”

We have modified the title verbatim.

ABSTRACT

Methods: Would be more specific than “East Africa” as this affects interpretation of alcohol use prevalence; recommend specifying Kenya & Uganda

We have limited geographic references to Kenya and Uganda rather than the entire region of East Africa as suggested.

Methods: Because primary outcomes are measured over time, please include the duration of follow-up

As above, the maximum duration of follow-up was 433 days

Methods: Include the comparison groups for each classification, i.e. non-drinkers for all? Or non-hazardous, non-hyper, etc.?

• Non-drinkers vs drinkers

• Non-hazardous drinkers (which includes non-drinkers) vs hazardous drinkers

• Non-hyper drinkers (which includes non-drinkers and hazardous drinkers) vs. hyper drinkers

Results: Suggest clarifying “any alcohol use was associated..” as hazardous and hyper use were not found to be associated with retention or ART outcomes.

We agree. This was done

Results: Given mortality is a primary outcome of the study, results regarding the association should be included within the abstract

We agree. We have included in the abstract a mention that mortality differences were not observed.

Conclusions: The phrasing “significant delays in ART initiation” implies a period, however this is not provided in the results. I.e. is a significant delay 1 week or 1 year?

The statistical analysis modeled a quantity related to the hazard (or propensity) of initiating ART. This is called the “sub-distribution hazard” (Fine & Gray models) of initiating ART between drinkers and non-drinkers subject to the (competing) risk of death prior to initiating therapy. Since this sub-distribution hazard of initiating ART was significantly lower among people living with HIV who consumed alcohol, the cumulative incidence of initiating treatment (i.e., the probability of starting therapy before a given time) was lower for these individuals. We attempted to demystify this concept for a non-statistical audience by referring to this as a “delay in ART initiation”. We are now referring to “a lower probability of initiating therapy” rather than delays in therapy initiation.

INTRODUCTION

2nd paragraph: “impact on initiation of ART and retention in care yet to be thoroughly examined” is perhaps an oversimplification of the literature. Certainly, there is a gap, but a more nuanced discussion of the data would help to contextualize these findings.

We have pointed out the fact that the impact of alcohol consumption on initiation of ART and retention in care have not been fully examined and stated that this is one in a series of studies on the subject.

Mention of "few EBM interventions” distracts from the point of the paragraph; suggest deleting

Done

In general, the points raised here don’t connect to the study being done: i.e. the study does not introduce new measure of alcohol exposure, new measurement of retention, or adjust for more confounders than past studies

The “novelty” of this study is to assess what the possible impact of alcohol consumption is on the speed of ART initiation and retention in care. We clarify this in the introduction.

METHODS

In general, the ordering of this section is confusing. Recommend that the opening sentence include not only the study design type, but also the study period, study purpose and setting. I.e. “From January 2013 to June 2014, we conducted a prospective observational study to determine X among adults entering HIV care in Uganda and Kenya.” This will clarify and contextualize the subsequent detailed methods section. Furthermore, the subheading “Study Population” should be divided to include a section on “Measures.” Several portions of the text included under “Statistical methods” are descriptions of measures, rather than statistical methods and should be re-organized appropriately.

We have significantly reorganized this section per the reviewer’s comments.

Study Design/Ethics: Was referral for alcohol counseling or treatment offered to hazardous and hyper drinkers?

Yes, all patients were given a brochure with information on HIV and alcohol use regardless of their responses to the AUDIT questionnaire. This brochure had information on how to contact a health provider for assistance or to ask more questions. This was added in the methods.

Study setting and SOC: The study period is mentioned but not defined until later in the manuscript; please add study years with respect to ART eligibility criteria to aid interpretation of the SOC being described. Both Kenya and Uganda updated ART guidelines to expand treatment to all individuals with a CD4<500 in 2014. Was this implemented during the study time period? Were all individuals started on ART based on a cut off of 350 (i.e. were pregnant women or those with a high CD4 and OIs excluded?)

Although the CD4 cutoff was 350 cells/μl at the time of study initiation, the sites were already moving to starting patients on ART who had CD4 counts <500 cells/μl. Women who were pregnant and those with opportunistic infections were also eligible for ART at CD4 counts >500 cells/μl. This was also added to the methods.

Study setting and SOC: Please clarify ART eligibility criteria further (1) how were pregnant women and (2) those with OIs treated if CD4 > threshold?

See above

Study population: Recommend a sub-heading of “Measures” after describing and participants and before describing AUDIT

Sub-headings have been added as part of the overall reorganization of this section.

Study population: Please provide reference for AUDIT validation in sub-Saharan African setting

The following reference has been added in this section:

Chishinga N, Kinyanda E, Weiss HA et al. Validation of brief screening tools for depressive and alcohol use disorders among TB and HIV patients in primary care in Zambia. BMC Psychiatry, 11, 75 (2011). https://doi.org/10.1186/1471-244X-11-75

Study population: You reference the “AUDIT version utilized”: was this not the standard AUDIT tool? If non-standard, please include in the supplemental information. The standardization of procedures used to assess alcohol use are unclear. If additional or supplemental questions were asked and conflicted with answers to the standard 10-question screen, which answer was used in the analysis? While standard to include a chart of types of alcohol and volume to aid classification of alcohol use, additional questions about frequency are not typically standard.

We have indicated that the standard AUDIT test was used and have removed the word “version”, which in retrospect was unfortunate and caused confusion. We also state that a number of visual aids were used to attempt to ascertain the alcohol content of home brews so that they could be entered in the standardized AUDIT tool.

Statistical methods: The definition of “retention in care” is unclear. Firstly, “retention in care,” as defined, seems to measure “retention in care failure” as it is “patients actually disengaged from care”. Second, earlier in the manuscript, it is stated that a normal interval between appointments is up to 6 months; however, retention in care failure is being assessed at 2 months following the last (non-missed?) appointment.

Because follow-up schedules change from time to time, we have defined as a missed visit, leading to a designation as “lost to program” any visit that does not occur within two months from the next scheduled visit. This takes care of varying frequencies in appointments. We have clarified in this in the text. We have defined as “retention in care” from a patient’s rather a program’s perspective as the complement event of a true disengagement from care (or otherwise known as a “gap in care”). So a patient is retained if they are in care; anywhere. Given that this study, unlike most routine protocols, included aggressive patient outreach to establish both patients’ vital status and their access to care, after failing to keep a clinic visit at one of our program facilities, we were able to assess true disengagement from care and, conversely, true retention in care. We have clarified this in the text.

Statistical methods: The sentence “IN this manner, we were able to estimate” is editorial and does not describe methodology. Suggest deleting.

I think that this phrase actually clarifies that it is the retention in care from the perspective of the patient that we try to measure. This is an important feature of this study, in contrast to most similar published research studies. Being able to estimate retention in this manner removes a major limitation present in the vast majority of studies, where retention in care means continuous care at the index program. We have added more forceful language about the fact that it is retention in care from the patient’s perspective that this study was capable of estimating.

Statistical methods: For those who were consistently retained in care (i.e. did not require tracing), how was censoring done?

Out of study subjects who were consistently retained in care, those who at the end of the follow up period did not experience any event were censored.

Statistical methods: How was the mortality outcome ascertained? Were death registers utilized?

Mortality was ascertained in multiple ways. “Passively”, or through reports to the clinic by family members or healthcare providers, or “actively”, by reaching out to the patient’s family, community or village elders, either by telephone or by sending a community worker to the patient’s disclosed location to determine their vital status.

Statistical methods: Please provide a clear definition of the 3rd primary outcome, time from enrollment to ART initiation. This is later referred to as delays in ART initiation: please clearly define the outcome and use consistent terminology. Is ART initiation restricted based on ART eligibility by CD4 measured at the relevant visit?

Please see above. We have refrained from referring to lower incidence in ART initiation as a “delay”, particularly since the data are moot on this issue. Our use of the term “delay of ART initiation” emanates from the fact that, during this period of time, patients could meet eligibility criteria to start ART, but the actual decision to start was based by the clinician’s or the patient’s assessment of “ART readiness”, i.e., the patient’s ability to take ART medications on a daily basis. Patients may have had extenuating circumstances (such as alcohol use) that could hinder their ART readiness which resulted in them delaying ART initiation until barriers to being fully compliant to the drugs before starting were eliminated. This manifests in the data as a longer time until ART initiation (and, by extension, a lower incidence of ART start in these patients). Nevertheless, there is no data value that explicitly encodes a decision to defer ART initiation, resulting in the less explicit reference to this as a decreased incidence of initiation of ART.

Statistical methods: For all primary outcomes, what was the timeline from enrollment to outcome measurement? This is critical for interpretation of the outcomes; retention in care over 3 months is vastly different than over 2 years, for example.

The maximum length of follow-up was 433 days.

Predictors used in the analyses: For the alcohol use categories, were non-drinkers (AUDIT=0) included in the groups non-hazardous drinkers? In non-hyper drinkers?

Yes. Categories were dichotomized according to level of drinking. So non-hazardous drinkers and non-hyper drinkers would include non-alcohol users. We suspect that this may have been the reason for the equivocal results observed in these groups, as complete abstention from alcohol appeared to be strongly related to earlier ART initiation (or higher cumulative incidence of treatment initiation after enrollment). Having sequestered the non-alcohol use category (in, for example, a three-way analysis of no alcohol, non-hyper drinking (among alcohol users).

Predictors used in the analyses. Regarding the terminology “hyper-drinkers”, is this used elsewhere in the literature? If yes, please provide a reference. If not, what was the rationale for combining standard categories, given mention in the introduction that non-standard definitions of alcohol use limit comparison across studies?

This is our choice of terminology, in an attempt to differentiate hazardous drinking from excessive drinking (without using socially or morally fraught terminology). These categories correspond to standard Audit score cutoffs for the highest level of alcohol consumption.

RESULTS

Participant characteristics: How many patients were screened and excluded based on the criteria outlined in methods? Distribution of reasons for exclusion?

See above.

What percent met eligibility for ART at enrollment? At subsequent visits? What was the mean follow up time and number of visits for participants?

A significant proportion of subjects was eligible at initiation (263 out of 765 or 34.4%). The median number of visits was 6, with a range of 1 to 17.

“Initially 25% lost”: When is initially? Is this the follow up interval?

This refers to the number of study participants who were lost to program. These subjects were thus lost from a programmatic perspective. In a routine analysis, like those in the majority of similar publications in the literature, this would effectively be the proportion of patients not retained in care (again, from the perspective of the program). However, as we delineate in the manuscript, this estimate would significantly underestimate retention in care (and, by extension, risk factors for non-retention) as this group includes people who were truly disengaged from care, others who were deceased and still others who were in care elsewhere (i.e., had an undocumented or “silent” transfer to another facility).

DISCUSSION

In general, the null findings for hazardous and hyper-drinkers seem most like attributable to a small sample size with a rare outcome that is unable to detect significance. The discussion should include attention to this possibility.

We have addressed this as part of the limitations of the study. Another possibility is that, once one gets away from the alcohol use/no alcohol use dichotomy, the comparison groups may become more variable as well.

A more robust discussion of the studies limitations, including the assumptions of the statistical model, small sample size, observational nature/incomplete data, etc., is merited.

We have attempted to make a fuller accounting of the limitations of the study in the Discussion. Please see also previous comment.

CONCLUSION

The conclusion is over-stated. The study did not find that all amounts of alcohol use were association with lower rates of ART initiation and lower retention in care.

With respect, we don’t want to get too far (become to equivocal) with respect to the conclusion that alcohol use (including higher levels of use) was associated with challenges in ART initiation and retention. As I point out earlier in this response to comments, low power and heterogeneous comparison groups may have been the “culprits” for the negative results with respect to higher levels of drinking. In addition, given that the study was based on self-reported alcohol use levels (another limitation explicitly stated in the Discussion), there may also be greater heterogeneity in what constitutes “high” levels of alcohol use, to a much greater extent than someone stating whether they consume alcohol or not.

The 3rd component of 90-90-90 is viral suppression, not retention in care; furthermore, retention in care is an intermediary step preceding suppression, but does not serve as a proxy for viral suppression

Noted. We have removed references to retention in care as the third pillar of the 90-90-90 target.

The conclusion that “its evaluation appears to be a significant target for intervention is overstated; however fair to conclude that screening for alcohol use in ART naïve patients enrolling in care may be able to identify patients at higher risk for delaying ART start despite eligibility and not being retained in care.

We have revised this sentence per reviewer recommendation.

Table 2:

Change in the ordering of columns adds confusion. Please move binary drinkers to the first column to compare with Tables 1 & 3.

Done

Attachment

Submitted filename: PONE-D-19-24950-1r-response-to-reviewers.docx

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PLoS One. doi: 10.1371/journal.pone.0240654.r003

Decision Letter 1

Joel Msafiri Francis

13 Aug 2020

PONE-D-19-24950R1

Lower rates of ART initiation and decreased retention among ART-naïve patients who consume alcohol enrolling in HIV care and treatment programs in Kenya and Uganda

PLOS ONE

Dear Dr. Yiannoutsos,

Please submit your revised manuscript by Sep 27 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

Joel Msafiri Francis, MD, MS, PhD

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

**********

6. Review Comments to the Author

Reviewer #1: The authors have commendably addressed most of the comments highlighted at the previous review.

There are no line numbers to facilitate quick review however below are some suggestions to tighten the message and make it easier for the audience to understand.

Methods :

1. Study setting and standard of care

Paragraph two: ......Individuals enrolling in care have a CD4 count drawn ...... : could this be rephrased to read "Individuals enrolling in care have blood drawn for CD4 cell count "?

2. Study procedures

a) The Alcohol Use Disorder Screening Test (AUDIT) ......> Change word "screening" to "Identification"

b)Please provide the period of recall for alcohol use in this section - 1 yr?

3. Statistical analyses

Paragraph two ......different dichotomous level of alcohol consumption (i.e., binary, hazardous and hyper drinking, the exposure)..... "Binary " changed to word "drinkers" or "any drinking"

Results

Competing risk analysis

4....... Given the low rates of non-retention (10.6%, Table 2), this is approximately equal 76.6% higher odds or risk of non-retention.....

a) Improve sentence to read 77%( similar visually to the aSHR 1.77) instead 76.6%

b) Review the use of the words " higher odds or risk". The author's reference 26 [Practical recommendations for reporting Fine‐Gray model analyses for competing risk data] recommends that the SHR is best reported as a rate rather that risk since the aSHR does not in and of itself quantify the magnitude of the effect of "any drinking" on the Cumulative incidence function of non retention.

Discussion

5) sentence 1 . This study found a strong association between alcohol consumption and non-retention in

HIV care. - Suggestion to rephrase as "This study found a strong association between alcohol consumption within the last *** insert recall period) and non-retention in HIV care.

6) Consequently, with less than 4% of study subjects dying during the study, there was very small power to detect any potential effects of alcohol consumption on mortality. Remove wording very small power and could possibly rephrase to " Consequently, with less than 4% of

study subjects dying, our study was not powered to detect any potential effects of alcohol consumption on mortality.

7) Rephrase sentence " In addition, dichotomizing the

data inCombined, these limitations in our data collection may account for finding an association

of alcohol consumption with decreased retention in care and delays in ART initiation, but not

confirming the same findings among those at the highest alcohol consumption classifications."

**********

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Reviewer #1: No

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2020 Oct 23;15(10):e0240654. doi: 10.1371/journal.pone.0240654.r004

Author response to Decision Letter 1

28 Aug 2020

Reviewer # 2

METHODS

Study setting and standard of care

Paragraph two: ......Individuals enrolling in care have a CD4 count drawn ...... : could this be rephrased to read "Individuals enrolling in care have blood drawn for CD4 cell count "?

Done

Study procedures

a) The Alcohol Use Disorder Screening Test (AUDIT) ......> Change word "screening" to "Identification"

Done

b) Please provide the period of recall for alcohol use in this section - 1 yr?

The term “over the past one year” was added to the sentence “The Alcohol Use Disorder Identification Test (AUDIT) questionnaire, which has been validated in a number of primary care medical settings, was used by a trained research assistant to collect patient’s alcohol use data over the past one year.”

Statistical analyses

Paragraph two ......different dichotomous level of alcohol consumption (i.e., binary, hazardous and hyper drinking, the exposure)..... "Binary " changed to word "drinkers" or "any drinking"

We have removed all accurate but, admittedly, awkward terms like “binary” or “dichotomous” drinking in favor of more colloquial terminology like “any alcohol use”. For example, the above paragraph was changed to “We performed three separate analyses, each investigating the possible effect of different levels of alcohol consumption (i.e., any alcohol use, hazardous and hyper drinking, the exposure) on the likelihood (sub-distribution hazard) of each of the three events of interest (ART initiation, death, and non-retention in care).” (changes bolded).

Results

Competing risk analysis

Given the low rates of non-retention (10.6%, Table 2), this is approximately equal 76.6% higher odds or risk of non-retention ... [i]mprove sentence to read 77%( similar visually to the aSHR 1.77) instead 76.6%

Done

Review the use of the words " higher odds or risk". The author's reference 26 [Practical recommendations for reporting Fine‐Gray model analyses for competing risk data] recommends that the SHR is best reported as a rate rather that risk since the aSHR does not in and of itself quantify the magnitude of the effect of "any drinking" on the Cumulative incidence function of non retention.

Done. For example, the above sentence now reads “Given the low rates of non-retention (10.6%, Table 2), this is approximately equal 77% higher rate of non-retention. Other alcohol exposure classifications (i.e., hazardous versus non-hazardous…” (change bolded).

Discussion

Sentence 1 . This study found a strong association between alcohol consumption and non-retention in

HIV care. - Suggestion to rephrase as "This study found a strong association between alcohol consumption within the last *** insert recall period) and non-retention in HIV care.

Done with a reference to the one-year recall applied to the one-year recall

Consequently, with less than 4% of study subjects dying during the study, there was very small power to detect any potential effects of alcohol consumption on mortality. Remove wording very small power and could possibly rephrase to " Consequently, with less than 4% of study subjects dying, our study was not powered to detect any potential effects of alcohol consumption on mortality.

Done

Rephrase sentence " In addition, dichotomizing the data in

Combined, these limitations in our data collection may account for finding an association

of alcohol consumption with decreased retention in care and delays in ART initiation, but not

confirming the same findings among those at the highest alcohol consumption classifications."

Done

Attachment

Submitted filename: PONE-D-19-24950-2r-response-to-reviewers.docx

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PLoS One. doi: 10.1371/journal.pone.0240654.r005

Decision Letter 2

Joel Msafiri Francis

30 Sep 2020

PONE-D-19-24950R2

Lower rates of ART initiation and decreased retention among ART-naïve patients who consume alcohol enrolling in HIV care and treatment programs in Kenya and Uganda

PLOS ONE

Dear Dr. Yiannoutsos,

Please submit your revised manuscript by Nov 14 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Joel Msafiri Francis, MD, MS, PhD

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #3: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: No

Reviewer #3: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Reviewer #1: (No Response)

Reviewer #3: This manuscript is well written and clear. The data analyses sufficiently support the conclusions.

I recommend minor edits, specifically to terminology used throughout the manuscript to describe persons with HIV and persons who drink.

Consistent with current literature, the authors should consider using 'persons with HIV (PWH)' in place of 'people living with HIV (PLWH)'.

The authors should also reconsider their descriptions of persons who consume alcohol as well as their classification of levels of alcohol use/drinking. Consistent with current literature, the use of the word 'drinkers' throughout the manuscript should be changed to 'persons who consume/drink alcohol', or 'persons who engage in alcohol consumption', etc. Along the same lines, levels of alcohol use may be described as 'heavy alcohol use or drinking' and 'very heavy alcohol use or drinking' to match the corresponding levels described in the manuscript as 'hazardous' and 'hyper'. 'Harmful drinking' and 'alcohol dependent/dependency' are terms that are not in line with current terminology, which is: 'at risk for alcohol use disorders'. The use of the term 'hyper' in particular is not standard. The use of the term 'non-drinkers' should be reconsidered and perhaps replaced with 'abstainers' instead.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #3: Yes: Nneka I. Emenyonu

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2020 Oct 23;15(10):e0240654. doi: 10.1371/journal.pone.0240654.r006

Author response to Decision Letter 2

30 Sep 2020

We have revised the terminology changing virtually all references to "drinking" as "alcohol consumption", "hazardous drinking" as "engagement in hazardous alcohol consumption", "hyper drinking" as "harmful alcohol consumption" (per AUDIT designation) wherever possible. We did not change a reference to "heavy drinkers" as this was the designation from another study. We have changed PLWHIV to PWH for people with HIV as requested by the reviewer.

PLoS One. doi: 10.1371/journal.pone.0240654.r007

Decision Letter 3

Joel Msafiri Francis

1 Oct 2020

Lower rates of ART initiation and decreased retention among ART-naïve patients who consume alcohol enrolling in HIV care and treatment programs in Kenya and Uganda

PONE-D-19-24950R3

Dear Dr. Yiannoutsos,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

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Kind regards,

Joel Msafiri Francis, MD, MS, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0240654.r008

Acceptance letter

Joel Msafiri Francis

16 Oct 2020

PONE-D-19-24950R3

Lower rates of ART initiation and decreased retention among ART-naïve patients who consume alcohol enrolling in HIV care and treatment programs in Kenya and Uganda

Dear Dr. Yiannoutsos:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Joel Msafiri Francis

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

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Submitted filename: Review comments.docx

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Attachment

Submitted filename: PONE-D-19-24950-1r-response-to-reviewers.docx

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Data Availability Statement

[pone.0240654.ref001] 1.(UNAIDS) JUNPoHA. 90-90-90: An ambitious treatment target to help end the AIDS epidemic. Geneva: Joint United Nations Programme on HIV/AIDS (UNAIDS), 2014. [Google Scholar]

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PERMALINK

Lower rates of ART initiation and decreased retention among ART-naïve patients who consume alcohol enrolling in HIV care and treatment programs in Kenya and Uganda

Ioannis Patsis

Suzanne Goodrich

Constantin T Yiannoutsos

Steven A Brown

Beverly S Musick

Lameck Diero

Jayne L Kulzer

Mwembesa Bosco Bwana

Patrick Oyaro

Kara K Wools-Kaloustian

Roles

Abstract

Objectives

Methods

Results

Discussion

Introduction

Methods

Study design

Study setting and standard of care

Study population

Statistical methods

Definition of the outcomes of interest

Predictors used in the analyses

Statistical modeling

Results

Participant characteristics

Table 1. Characteristics of the study cohort.

Competing-risk analysis of the events of interest

Table 2. Events used in the survival analyses.

Table 3. Results of the Fine & Gray competing events survival models, adjusting for age, gender, CD4 count and WHO stage at enrollment, disclosure of HIV status, marital status and site of enrollment; aSHR: adjusted sub-distribution hazard.

Discussion

Conclusion

Data Availability

Funding Statement

References

Decision Letter 0

Joel Msafiri Francis

Roles

Author response to Decision Letter 0

Decision Letter 1

Joel Msafiri Francis

Roles

Author response to Decision Letter 1

Decision Letter 2

Joel Msafiri Francis

Roles

Author response to Decision Letter 2

Decision Letter 3

Joel Msafiri Francis

Roles

Acceptance letter

Joel Msafiri Francis

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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