Long-term HIV care outcomes under universal HIV treatment guidelines: A retrospective cohort study in 25 countries

Ellen Brazier; Olga Tymejczyk; Kara Wools-Kaloustian; Awachana Jiamsakul; Marco Tulio Luque Torres; Jennifer S Lee; Lisa Abuogi; Vohith Khol; Fernando Mejía Cordero; Keri N Althoff; Matthew G Law; Denis Nash; on behalf of the International epidemiology Databases to Evaluate AIDS (IeDEA)

doi:10.1371/journal.pmed.1004367

. 2024 Mar 18;21(3):e1004367. doi: 10.1371/journal.pmed.1004367

Long-term HIV care outcomes under universal HIV treatment guidelines: A retrospective cohort study in 25 countries

Ellen Brazier ^1,^2,^*, Olga Tymejczyk ¹, Kara Wools-Kaloustian ³, Awachana Jiamsakul ⁴, Marco Tulio Luque Torres ⁵, Jennifer S Lee ⁶, Lisa Abuogi ⁷, Vohith Khol ⁸, Fernando Mejía Cordero ⁹, Keri N Althoff ⁶, Matthew G Law ¹⁰, Denis Nash ^1,²; on behalf of the International epidemiology Databases to Evaluate AIDS (IeDEA)^¶

Editor: Marie-Louise Newell¹¹

¹City University of New York, Institute for Implementation Science in Population Health (ISPH), New York, New York, United States of America

²City University of New York, Graduate School of Public Health and Health Policy, New York, New York, United States of America

³Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, United States of America

⁴The Kirby Institute, University of New South Wales, Sydney, Australia

⁵Department of Pediatrics, Instituto Hondureño de Seguridad Social and Hospital Escuela Universitario, Tegucigalpa, Honduras

⁶Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America

⁷Department of Pediatrics, University of Colorado School of Medicine and Children’s Hospital Colorado, Aurora, Colorado, United States of America

⁸National Center for HIV/AIDS, Dermatology and STDs, Phnom Penh, Cambodia

⁹Instituto de Medicina Tropical Alexander von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru

¹⁰The Kirby Institute, University of New South Wales, Sydney, Australia

¹¹University of Southampton, UNITED KINGDOM

KNA receives royalties from Coursera. She was a consultant to the All of Us Research Program. She serves on the scientific advisory board for TrioHealth Inc. DN received consulting fees from Abbvie and Gilead and is the PI of a research grant from Pfizer to his institution’. All other authors have declared that no competing interests exist.

¶ Membership of IeDEA is provided in S1 Text.

^✉

* E-mail: ellen.brazier@sph.cuny.edu

Roles

Ellen Brazier: Formal analysis, Methodology, Writing – original draft, Writing – review & editing

Olga Tymejczyk: Conceptualization, Methodology, Writing – review & editing

Kara Wools-Kaloustian: Data curation, Funding acquisition, Writing – review & editing

Awachana Jiamsakul: Data curation, Writing – review & editing

Marco Tulio Luque Torres: Data curation, Writing – review & editing

Jennifer S Lee: Data curation, Writing – review & editing

Lisa Abuogi: Data curation, Writing – review & editing

Vohith Khol: Data curation, Writing – review & editing

Fernando Mejía Cordero: Data curation

Keri N Althoff: Data curation, Funding acquisition, Writing – review & editing

Matthew G Law: Data curation, Funding acquisition, Writing – review & editing

Denis Nash: Conceptualization, Funding acquisition, Methodology, Writing – review & editing

Marie-Louise Newell: Academic Editor

PMCID: PMC10962811 PMID: 38498589

Abstract

Background

While national adoption of universal HIV treatment guidelines has led to improved, timely uptake of antiretroviral therapy (ART), longer-term care outcomes are understudied. There is little data from real-world service delivery settings on patient attrition, viral load (VL) monitoring, and viral suppression (VS) at 24 and 36 months after HIV treatment initiation.

Methods and findings

For this retrospective cohort analysis, we used observational data from 25 countries in the International epidemiology Databases to Evaluate AIDS (IeDEA) consortium’s Asia-Pacific, Central Africa, East Africa, Central/South America, and North America regions for patients who were ART naïve and aged ≥15 years at care enrollment between 24 months before and 12 months after national adoption of universal treatment guidelines, occurring 2012 to 2018. We estimated crude cumulative incidence of loss-to-clinic (CI-LTC) at 12, 24, and 36 months after enrollment among patients enrolling in care before and after guideline adoption using competing risks regression. Guideline change–associated hazard ratios of LTC at each time point after enrollment were estimated via cause-specific Cox proportional hazards regression models. Modified Poisson regression was used to estimate relative risks of retention, VL monitoring, and VS at 12, 24, and 36 months after ART initiation. There were 66,963 patients enrolling in HIV care at 109 clinics with ≥12 months of follow-up time after enrollment (46,484 [69.4%] enrolling before guideline adoption and 20,479 [30.6%] enrolling afterwards). More than half (54.9%) were females, and median age was 34 years (interquartile range [IQR]: 27 to 43). Mean follow-up time was 51 months (standard deviation: 17 months; range: 12, 110 months). Among patients enrolling before guideline adoption, crude CI-LTC was 23.8% (95% confidence interval [95% CI] 23.4, 24.2) at 12 months, 31.0% (95% CI [30.6, 31.5]) at 24 months, and 37.2% (95% [CI 36.8, 37.7]) at 36 months after enrollment. Adjusting for sex, age group, enrollment CD4, clinic location and type, and country income level, enrolling in care and initiating ART after guideline adoption was associated with increased hazard of LTC at 12 months (adjusted hazard ratio [aHR] 1.25 [95% CI 1.08, 1.44]; p = 0.003); 24 months (aHR 1.38 [95% CI 1.19, 1.59]; p < .001); and 36 months (aHR 1.34 [95% CI 1.18, 1.53], p < .001) compared with enrollment before guideline adoption, with no before–after differences among patients with no record of ART initiation by end of follow-up. Among patients retained after ART initiation, VL monitoring was low, with marginal improvements associated with guideline adoption only at 12 months after ART initiation. Among those with VL monitoring, VS was high at each time point among patients enrolling before guideline adoption (86.0% to 88.8%) and afterwards (86.2% to 90.3%), with no substantive difference associated with guideline adoption. Study limitations include lags in and potential underascertainment of care outcomes in real-world service delivery data and potential lack of generalizability beyond IeDEA sites and regions included in this analysis.

Conclusions

In this study, adoption of universal HIV treatment guidelines was associated with lower retention after ART initiation out to 36 months of follow-up, with little change in VL monitoring or VS among retained patients. Monitoring long-term HIV care outcomes remains critical to identify and address causes of attrition and gaps in HIV care quality.

Using real-world service delivery data, Ellen Brazier and team examine long-term HIV care outcomes under universal HIV treatment guidelines across 25 countries.

Author summary

Why was this study done?

Although universal HIV treatment recommendations have been adopted in national HIV treatment guidelines, longer-term HIV care outcomes under such guidelines are poorly documented and largely limited to single-country studies with short follow-up times.
No multicountry studies using real-world service delivery data have examined long-term HIV care outcomes associated under universal HIV treatment guidelines.

What did the researchers do and find?

With data on 66,963 patients enrolling in HIV care at 109 clinics participating in the International epidemiology Databases to Evaluate AIDS (IeDEA) research consortium across 25 countries where universal HIV treatment guidelines were adopted, we estimated the hazard ratios of loss-to-clinic (LTC) at 12, 24, and 36 months after enrollment, comparing those enrolling in HIV care after guideline adoption to those enrolling before guideline adoption.
Among 57,615 patients with documented initiation of antiretroviral therapy (ART), we also estimated the relative risks of clinic retention, viral load (VL) monitoring, and viral suppression (VS) at 12, 24, and 36 months after ART initiation, comparing those enrolling after versus before national adoption of universal treatment guidelines.
Compared with patients enrolling in HIV care and initiating HIV treatment before national adoption of universal treatment guidelines, those enrolling and initiating treatment after guideline adoption had higher risk of being LTC at 12 months, 24 months, and 36 months after enrollment.
Among patients retained in care after ART initiation, those enrolling in HIV care after the adoption of universal HIV treatment guidelines were more likely to have VL monitoring at 12 months after ART initiation and less likely at 36 months, with no difference at 24 months.
VS was high at each time point among patients enrolling before and after the adoption of universal HIV treatment guidelines, with no substantive change associated with guideline adoption.

What do these findings mean?

Our results raise concerns about long-term retention of patients after ART initiation, as well as the capacity of HIV programs to provide essential aspects of HIV care, including annual VL monitoring for timely identification of adherence problems and treatment failure.
Our findings that patient retention in care at the clinic where ART was initiated decreased after the adoption of universal HIV treatment guidelines and that there has been no improvement in annual VL monitoring among patients retained in care should motivate efforts to identify and address factors associated with attrition among patients enrolling in HIV care, as well as barriers to routine VL testing in the era of universal treatment of all people living with HIV.
Study limitations include potential underascertainment of patient outcomes in real-world service delivery data, lags in the availability of real-world service delivery data, and the nonrepresentativeness of the clinics and countries reflected in IeDEA datasets available for analysis.

Introduction

The World Health Organization (WHO)’s 2015 recommendation for universal treatment for all people living with HIV (PLWH) [1]—known as “Treat-All”—eliminated an important barrier to initiation of antiretroviral therapy (ART) in many settings [2]. While a few high-income countries had universal HIV treatment guidelines in place prior to WHO’s 2015 recommendation, most countries around the globe adopted expanded treatment guidelines subsequently, with an estimated 70% of low- and middle-income countries adopting universal HIV treatment guidelines by the end of 2017 [3].

Observational studies have shown that national adoption of universal treatment guidelines has led to greater uptake and more rapid initiation of ART across diverse country settings [4,5]. Improved treatment uptake and more timely initiation of ART are promising for the reduction of HIV morbidity and mortality in patients, as well as preventing onward transmission of the virus (i.e., treatment as prevention) [6–8]. However, longer-term HIV care outcomes, such as retention in care and timely and sustained viral suppression (VS), under universal treatment guidelines are underresearched and largely limited to small single-country studies with short follow-up times of 6 to 12 months [9–14]. While a community cluster-randomized controlled trial in Uganda and step-wedged randomized trial in Eswatini have reported higher 12-month retention and combined 12-month retention/VS rates among patients initiating treatment under universal treatment guidelines, compared with standard initiation practices [15,16], several observational studies have reported no improvement in care retention—or lower retention—among patients initiating treatment in the era of universal treatment [10–13].

Using data from the International epidemiology Databases to Evaluate AIDS (IeDEA) research consortium, we aimed to estimate loss-to-clinic (LTC) at 12, 24, and 36 months after enrollment in HIV care, comparing those enrolling before and after country-level adoption of universal HIV treatment guidelines. Additionally, we aimed to estimate clinic retention, viral load (VL) testing, and VS at 12, 24, and 36 months after ART initiation.

Methods

Data sources

The IeDEA consortium pools observational clinical data on more than 2 million PLWH ever enrolling in HIV care at approximately 400 care and treatment sites in 44 countries [17]. Our study population was drawn from IeDEA’s cohorts in the Asia-Pacific, Central Africa, East Africa, the Caribbean, Central and South America, and North America regions, which agreed to the use of their data for this retrospective cohort study, based on a concept proposal approved by IeDEA’s executive committee. Deidentified patient data from participating IeDEA cohorts were standardized in accordance with IeDEA data definitions [18]. The research was approved by the City University of New York (CUNY) University Institutional Review Board (#2018–0809).

For each country in participating regional cohorts, we identified the date universal ART eligibility was extended to all adult patients, based on policy documents, literature, and inputs from in-country experts, as described elsewhere [19]. Patients were eligible if they were at least 15 years of age and ART naïve at the time of enrollment in HIV care at an IeDEA site, enrolled in care in the 24 months immediately before national adoption of universal treatment guidelines or in the first 12 months thereafter, and enrolled in care at least 12 months before database closure (i.e., submission of data to IeDEA’s regional data centers for processing in accordance with IeDEA’s data exchange standards [18]). For cohorts that deidentify patient data by shifting patient encounter dates by <30 days, we excluded all patients enrolling in care within +/− 30 days of the date of guideline adoption. Cohorts where all patient enrollment dates are shifted to a midyear date (i.e., July 1) were excluded, along with clinics where no patients had any records of VL monitoring. Additionally, we excluded patients with missing data for sex or age at enrollment in HIV care, and missing date of death if recorded as deceased. The years of data used in this study ranged from 2010 to 2021.

Exposure

The exposure of interest was enrollment in HIV care before or after the official date of national adoption of universal HIV treatment guidelines, which, depending on the country, occurred between 2012 and 2018.

Outcomes

Among all patients who were ART naïve, who enrolled in HIV care in the 24 months before or 12 months after national adoption of universal HIV treatment guidelines, and who had sufficient follow-up time between enrollment and database closure, our primary outcome of interest was LTC by 12, 24, and 36 months after enrollment (Fig 1). Across all regional cohorts, LTC was defined as no evidence of contact (e.g., visits, laboratory testing, or medication pickups) with the clinic of enrollment for at least 12 months prior to database closure [20]. The date of LTC was set at 90 days after the last clinic contact, with patients considered LTC if their date of LTC was before censoring at 12, 24, and 36 months, respectively. Patients not documented as having died or transferred and not classified as LTC by the censoring date were considered retained in care at the clinic.

Among the subset of patients with evidence of ART initiation and sufficient follow-up time between ART initiation and database closure, we examined clinic retention at 12, 24, and 36 months after ART initiation, with retention defined as no documentation of death or transfer to another site of care, and not LTC (as defined above). Among patients on ART and retained in care, we examined VL monitoring, defined as any VL test at 12, 24, and 36 months (+/−3 months) after ART initiation, and among patients with VL monitoring at these time points, we examined VS, defined as VL <1,000 copies/mL. ART, antiretroviral therapy; LTC, loss-to-clinic; VL, viral load; VS, viral suppression.

Covariates

Patient-level characteristics included sex (male or female); age at enrollment in HIV care (categorized as 15 to 19 years, 20 to 24 years, 25 to 34 years, and >34 years; CD4 count within 90 days (+/−) of enrollment and no more than 30 days after ART initiation (categorized as: ≤200 cells/μL; 201 to 350 cells/μL; 351 to 500 cells/μL; >500 cells/μL; or unknown/missing); ART initiation was defined as the start of a combination antiretroviral treatment regimen before censoring at 12, 24, and 36 months after enrollment, and initial ART regimens were categorized as non-nucleoside reverse transcriptase inhibitor (NNRTI)-based regimens, protease inhibitor–based regimens, integrase inhibitor–based regimens, other/unknown regimens, or none).

Clinic-level characteristics included location (rural/mostly rural versus urban/mostly urban); facility type (i.e., health center, district hospital, regional/university referral hospital, or other); and country income level in 2018 (low, lower-middle, upper-middle, and high income) as reflected in World Bank databases [21].

Statistical analysis

We used descriptive statistics to compare the characteristics of patients enrolling before and after adoption of universal HIV treatment guidelines, along with LTC at each time point after enrollment, and retention, VL monitoring, and VS after ART initiation. We also described the number and proportion of patients recorded as having transferred or died by each time point after enrollment.

We estimated the crude cumulative incidence (i.e., risk) of LTC at 12, 24, and 36 months after enrollment, stratified by the timing of enrollment relative to the date of guideline adoption and ART initiation status by the censoring time point (i.e., on ART or not by 12, 24, and 36 months after enrollment) via competing risks regression using the Aalen–Johansen estimator [22]. Multivariable cause-specific Cox proportional hazards regression [23] was used to estimate the association between universal treatment guideline adoption and LTC at 12, 24, and 36 months after enrollment, adjusting for the above covariates, and the clustering of patients within clinics was accounted for when fitting the Cox proportional hazards models through the use of a robust sandwich estimator for the covariance matrix. We considered death as a competing risk for LTC, with transfer treated as a censoring variable. We tested for statistical interactions between enrollment period (before versus after guideline adoption) and ART initiation status by censoring time points and stratified results by ART initiation status (i.e., on ART or not on ART).

Among the subset of patients with evidence of ART initiation, we estimated relative risks of the binary outcomes of clinic retention, VL monitoring, and VS at 12, 24, and 36 months after ART initiation via modified Poisson regression models, comparing those enrolling in care after versus before universal HIV treatment adoption. Multivariable models were adjusted for patient and clinic characteristics (sex, age group, enrollment CD4, initial regimen type, clinic location, facility type, and country income level), using generalized estimating equations (GEEs) to account for clustering within clinics.

In a sensitivity analysis, we compared relative hazards of LTC at 12 months after enrollment and relative risks of care retention, VL monitoring, and VS at 12 months after ART initiation among patients enrolling in HIV care in the 12 months after adoption of universal HIV treatment guidelines versus the 13 to 24 months before guideline adoption (i.e., excluding patients enrolling in care during the 12 months immediately before guideline adoption whose outcome ascertainment window was entirely in the period after guideline adoption) (Fig 2).

An initial concept proposal for this analysis (S1 Concept proposal) outlined exposures, types of outcomes of interest, participating IeDEA cohorts, and general analytic approach and was approved by the IeDEA Executive Committee in August 2018. The use of competing risks and cause-specific hazards regression to examine LTC was not prespecified, with these methods introduced a priori to avoid potential bias that might result from failure to account for competing events in outcome estimation. In a second sensitivity analysis, performed in response to reviewer feedback, we excluded countries where universal treatment guidelines were adopted in 2017 and 2018, to assess whether our estimates of HIV care outcomes at 24 and 36 months could have been affected by the Coronavirus Disease 2019 (COVID-19) pandemic in countries with late adoption of universal HIV treatment guidelines.

All statistical analyses were performed using SAS 9.4 (SAS Institute, Cary, NC). A p-value less than 0.05 was considered statistically significant. This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist).

Results

Of 132,776 patients in the initial data submission from participating IeDEA regions, 54,961 did not meet eligibility criteria related to age, ART-naïve status, or timing of enrollment, and 10,689 were excluded because they were in care at clinics with no VL testing or in cohorts where patient data are anonymized by shifting dates of care (Fig 3). Additionally, 163 patients were excluded because of missing data related to age, sex, or death date. After these exclusions, there were 66,963 patients who were ART naïve and aged ≥15 years at enrollment in HIV care at 109 clinics in 25 countries (Argentina, Australia, Brazil, Burundi, Cambodia, Cameroon, Canada, Chile, Congo, Democratic Republic of Congo, Haiti, Honduras, Hong Kong, Japan, Kenya, Malaysia, Mexico, Peru, Rwanda, South Korea, Tanzania, Thailand, Uganda, United States, and Vietnam). The sample included 46,484 (69.4%) who enrolled in the 24 months prior to national adoption of universal HIV treatment guidelines and 20,479 (30.6%) who enrolled in the 12 months after guideline adoption (Table 1). Mean follow-up time was 51 months (standard deviation: 17 months; range: 12, 110 months).

Table 1. Baseline characteristics among patients with at least 12 months of potential follow-up time after enrollment, by period of enrollment (before vs. after adoption of universal HIV treatment guidelines).

Patient characteristics	N (%)	Before guideline adoption	After guideline adoption	p-Value^a
All patients	66,963	46,484 (69.4)	20,479 (30.6)
Sex
Male	30,233 (45.1)	20,872 (44.9)	9,361 (45.7)	0.053
Female	36,730 (54.9)	25,612 (55.1)	11,118 (54.3)
Age (in years)
Median age (IQR)	34 (27, 43)	34 (27, 43)	34 (27, 43)	0.006^b
15–19 years	2,939 (4.4)	2,085 (4.5)	854 (4.2)	0.046
20–24 years	9,203 (13.7)	6,469 (13.9)	2,734 (13.4)
25–34 years	21,729 (32.4)	15,050 (32.4)	6,679 (32.6)
>34 years	33,092 (49.4)	22,880 (49.2)	10,212 (49.9)
CD4 count at enrollment
No CD4 count at enrollment	38,009 (56.8)	24,455 (52.6)	13,554 (66.2)	<0.001
Any CD4 count at enrollment	28,954 (43.2)	22,029 (47.4)	6,925 (33.8)
Median CD4 count (IQR)	304 (136, 497)	302 (136, 492)	315 (138, 518)	<0.001^b
<200 cells/μl	9,972 (34.4)	7,637 (34.7)	2,335 (33.7)	<0.001
200–349 cells/μl	6,492 (22.4)	5,005 (22.7)	1,487 (21.5)
350–499 cells/μl	5,359 (18.5)	4,104 (18.6)	1,255 (18.1)
> = 500 cells/μl	7,131 (24.6)	5,283 (24.0)	1,848 (26.7)
Initiation of ART by censoring endpoint (12 months)
Not on ART	12,575 (18.8)	10,041 (21.6)	2,534 (12.4)	<0.001
On ART	54,388 (81.2)	36,443 (78.4)	17,945 (87.6)
Mean time in days (SD) to ART initiation among patients initiating ART within 12 months of enrollment	33.5 (64.3)	42.3 (71.2)	15.7 (42.0)	<0.001^c
Median time in days (IQR) to ART initiation among patients initiating ART within 12 months of enrollment	7 (0, 32)	14 (0, 44)	0 (0, 14)	<0.001^b
Clinic characteristics
Location
Urban/mostly urban	52,737 (78.8)	36,467 (78.5)	16,270 (79.4)	0.004
Rural/mostly rural	14,226 (21.2)	10,017 (21.5)	4,209 (20.6)
Facility type
Health center	16,321 (24.4)	11,152 (24.0)	5,169 (25.2)	<0.001
District hospital	16,559 (24.7)	11,618 (25.0)	4,941 (24.1)
Regional, provincial or university hospital	30,301 (45.3)	20,941 (45.0)	9,360 (45.7)
Other^d	3,782 (5.6)	2,773 (6.0)	1,009 (4.9)
Country income level (2018)
Low income	24,469 (36.5)	16,600 (35.7)	7,869 (38.4)	<0.001
Lower-middle income	31,277 (46.7)	21,887 (47.1)	9,390 (45.9)
Upper-middle income	2,419 (3.6)	1,725 (3.7)	694 (3.4)
High income	8,798 (13.1)	6,272 (13.5)	2,526 (12.3)
Geographic region
Asia-Pacific	952 (1.4%)	580 (1.2%)	372 (1.8%)	<0.001
Central/South America	9,421 (14.1%)	6,335 (13.6%)	3,086 (15.1%)
Central Africa	7,019 (10.5%)	4,946 (10.6%)	2,073 (10.1%)
East Africa	42,093 (62.9%)	29,354 (63.1%)	12,739 (62.2%)
North America	7,478 (11.2%)	5,269 (11.3%)	2,209 (10.8%)
Year of national adoption of universal HIV treatment guidelines
2012–2015^e	8,633 (12.9)	6,091 (13.1)	2,542 (12.4)	<0.001
2016^f	50,471 (75.4)	34,705 (74.7)	15,766 (77.0)
2017–2018^g	7,859 (11.7)	5,688 (12.2)	2,171 (10.6)

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ART, antiretroviral therapy; IQR, interquartile range; SD, standard deviation.

^aChi-squared test.

^bKruskal–Wallis test.

^ct Test (Satterthwaite).

^dOther facility types are sites that report data as part of a network comprising clinics and hospitals.

^eArgentina, Australia, Brazil, Canada, South Korea, Mexico, Thailand, United States.

^fBurundi, Cambodia, Cameroon, Haiti, Hong Kong, Japan, Kenya, Rwanda, Uganda.

^gChile, Congo, Democratic Republic of Congo, Honduras, Malaysia, Peru, Tanzania, Vietnam.

Among the full sample of patients with at least 12 months of follow-up time between enrollment and database closure, 54.9% were female, and the median age was 34 years (interquartile range [IQR]: 27, 43), with little difference by period of enrollment (before vs. after adoption of universal treatment guidelines). A smaller proportion of patients enrolling in care after adoption of universal treatment guidelines had CD4 testing results recorded at enrollment (33.8% after vs. 47.4% before; p < 0.001); however, among those with any enrollment CD4 test, median CD4 counts were clinically similar among those enrolling before and after adoption of universal treatment guidelines (302 cells/μL [IQR: 136, 492] vs. 315 cells/μL [IQR: 138, 518]). The proportion of patients who had initiated ART by 12 months after enrollment was higher among those enrolling in the year after guideline adoption, compared with those enrolling before (87.6% vs. 78.4%; p < 0.001), and among those initiating ART within 12 months of enrollment, the median time from enrollment to treatment initiation decreased from 14 days (IQR: 0, 44) to 0 days (IQR: 0, 14).

The majority of patients (78.8%) were in care at clinics in urban/mostly urban settings, and almost half (45.3%) were at tertiary hospitals, with 24.4% at health centers and 24.7% at district hospitals. Most patients (83.2%) were from low- and lower-middle-income countries and countries that adopted universal HIV treatment guidelines in 2016 (75.4%), with small minorities of patients in countries with earlier (2012 to 2015) or later adoption of such guidelines (2017 to 2018).

Of patients enrolling before national adoption of universal HIV treatment guidelines, 45,098 (97.0%) had at least 24 months of follow-up time before database closure, and 42,859 (92.2%) had at least 36 months of follow-up time. Among patients enrolling after guideline adoption, 19,012 (92.8%) had at least 24 months of follow-up time and 11,392 (55.6%) had at least 36 months of follow-up time. The distribution of patient and clinic characteristics among those with 24 and 36 months of follow-up time after enrollment was similar to those of patients with 12 months of follow-up time, with few appreciable differences between patients enrolling after versus before adoption of universal treatment guidelines (S1 Table).

Among all patients with at least 12 months of follow-up time after enrollment, 69.2% were retained in care at 12 months after enrollment, 24.2% were LTC, 3.3% were documented as deceased, and 3.4% were documented transfers (Table 2). Compared with patients enrolling before adoption of universal treatment guidelines, higher proportions of patients enrolling after guideline adoption were recorded as transfers at 12 months (4.1% versus 3.0%; p < 0.001), 24 months (6.0% versus 4.5%; p < 0.001), and 36 months after enrollment (6.7% versus 5.6%; p < 0.001) or were LTC at each time point (12 months: 25.1% versus 23.8%, p < 0.001; 24 months: 35.9% versus 31.0%, p < 0.001; 36 months: 40.2% versus 37.2%, p < 0.001). Among all patients with a record of ART initiation, 73.6% were retained in care at 12 months after ART initiation, with 62.0% and 53.4%, respectively, retained at 24 and 36 months after ART initiation. Among patients on ART, clinic retention was higher at each time point among those enrolling in care before guideline adoption than after (75.1% versus 70.5% at 12 months, p < 0.001; 64.8% versus 55.8% at 24 months, p < 0.001; and 55.0% versus 48.2% at 36 months, p < 0.001).

Table 2. HIV care outcomes by 12, 24, and 36 months after enrollment, overall and by ART initiation status by censoring time point.

Care outcome	Cohort (N)	Among all patients in cohort	Before guideline adoption	After guideline adoption	p-Value^a
Care outcome	Cohort (N)	n (%)	n (%)	n (%)	p-Value^a
Death
12 months after enrollment	66,963	2,185 (3.3)	1,503 (3.2%)	682 (3.3%)	0.516
24 months after enrollment	64,110	2,623 (4.1)	1,817 (4.0%)	806 (4.2%)	0.219
36 months after enrollment	54,251	2,471 (4.6)	1,978 (4.6%)	493 (4.3%)	0.191
Transfer
12 months after enrollment	66,963	2,249 (3.4)	1,404 (3.0%)	845 (4.1%)	<0.001
24 months after enrollment	64,110	3,177 (5.0)	2,045 (4.5%)	1,132 (6.0%)	<0.001
36 months after enrollment	54,251	3,147 (5.8)	2,380 (5.6%)	767 (6.7%)	<0.001
LTC
12 months after enrollment	66,963	16,191 (24.2)	11,056 (23.8%)	5,135 (25.1%)	<0.001
24 months after enrollment	64,110	20,820 (32.5)	13,997 (31.0%)	6,823 (35.9%)	<0.001
36 months after enrollment	54,251	20,526 (37.8)	15,946 (37.2%)	4,580 (40.2%)	<0.001
Retention in care
12 months after enrollment	66,963	46,338 (69.2)	32,521 (70.0%)	13,817 (67.5%)	<0.001
24 months after enrollment	64,110	37,490 (58.5)	27,239 (60.4%)	10,251 (53.9%)	<0.001
36 months after enrollment	54,251	28,107 (51.8)	22,555 (52.6%)	5,552 (48.7%)	<0.001
Retention in care
12 months after ART initiation	57,615	42,418 (73.6)	29,483 (75.1%)	12,935 (70.5%)	<0.001
24 months after ART initiation	55,416	34,350 (62.0)	24,785 (64.8%)	9,565 (55.8%)	<0.001
36 months after ART initiation	46,850	25,041 (53.4)	20,025 (55.0%)	5,016 (48.2%)	<0.001
VL testing among patients initiating ART and retained in care
12 months after ART initiation	42,418	28,289 (66.7)	18,804 (63.8%)	9,485 (73.3%)	<0.001
24 months after ART initiation	34,350	24,430 (71.1)	17,413 (70.3%)	7,017 (73.4%)	<0.001
36 months after ART initiation	25,041	17,523 (70.0)	14,328 (71.6%)	3,195 (63.7%)	<0.001
VS among retained patients with VL test
12 months after ART initiation	28,289	24,404 (86.3)	16,210 (86.2%)	8,194 (86.4%)	0.671
24 months after ART initiation	24,430	21,413 (87.7)	15,184 (87.2%)	6,229 (88.8%)	0.001
36 months after ART initiation	17,523	15,606 (89.1)	12,721 (88.8%)	2,885 (90.3%)	0.013

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ART, antiretroviral therapy; LTC, lost to clinic; VL, viral load; VS, viral suppression.

^aChi-squared test.

Among all patients retained in care after ART initiation, VL monitoring ranged from 66.7% at 12 months after ART initiation to 71.1% at 24 months and 70.0% at 36 months (Table 2), with higher proportions of patients enrolling after adoption of universal treatment guidelines having VL monitoring at 12 and 24 months (73.2% and 73.4%, respectively), compared with before (63.8% and 70.3%). In contrast, VL monitoring at 36 months was lower among patients enrolling after guideline adoption (63.7%) than before (71.6%, p < 0.001). Among patients retained after ART initiation with VL test results, VS ranged from 86.3% at 12 months to 89.1% at 36 months, with marginal increases in VS at 24 and 36 months among patients enrolling after adoption of universal HIV treatment guidelines (88.8% and 90.3%, respectively) than before (87.2% and 88.8%, respectively).

The crude cumulative incidence of LTC (CI-LTC) at each time point is shown in Fig 4, stratified by timing of enrollment relative to national adoption of universal treatment guidelines. At each time point, CI-LTC was higher among those enrolling after guideline adoption than before, with insubstantial differences at 12 months after enrollment (CI-LTC: 25.1% versus 23.8%), and larger differences at 24 months (CI-LTC: 35.9% versus 31.0%) and 36 months (CI-LTC: 40.2% versus 37.2%). CI-LTC stratified by ART initiation status is shown in Fig 5. Among patients enrolling before adoption of universal treatment guidelines, CI-LTC at each time point was more than twice as high among patients not on ART, compared with those initiated on ART, with smaller differences by ART initiation status among those enrolling after guideline adoption. Among patients already on ART, CI-LTC at each time point was substantially higher among those enrolling after the adoption of universal treatment guidelines than before; among those not yet on ART, CI-LTC at 24 and 36 months did not differ by period of enrollment (before versus after guideline adoption).

Fig 5 — ART, antiretroviral therapy; CI, cumulative incidence; LTC, lost to clinic; 95% CI, 95% confidence interval.

Crude and adjusted hazard ratios (aHRs) of LTC at each time point after enrollment are shown in Table 3, for all patients and stratified by ART initiation status. In the full sample, hazards of LTC at each time point were higher among patients enrolling after adoption of universal treatment guidelines compared with before, but differences were small and not statistically significant. However, the association of universal treatment guidelines with LTC hazards at 24 and 36 months after enrollment differed by ART initiation status. Among patients on ART by 24 and 36 months after enrollment, hazards of LTC were substantially higher among patient enrolling after adoption of universal treatment guidelines (24-month aHR 1.38 [95% CI 1.19, 1.59]; p < 0.001 and 36-month aHR 1.34 [95% CI: 1.18, 1.53]; p < 0.001). In contrast, among patients not on ART by 12, 24, and 36 months after enrollment, the hazards of LTC did not differ by period of enrollment.

Table 3. Risks and hazard ratios of LTC associated with national adoption of universal HIV treatment guidelines.

Care outcome	Enrollment before guideline adoption^* N (%)	Enrollment after guideline adoption N (%)	HR (95% CI)	aHR^†^§ (95% CI; p-value)
LTC
12 months after enrollment^♦	11,056 (23.8)	5,135 (25.1)	1.08 (0.96, 1.21)	1.04 (0.94, 1.15); p = 0.454
24 months after enrollment^‡	13,997 (31.0)	6,823 (35.9)	1.19 (1.05, 1.36)	1.13 (0.99, 1.27); p = 0.056
36 months after enrollment^**	15,946 (37.2)	4,580 (40.2)	1.11 (0.98, 1.27)	1.11 (0.98, 1.25); p = 0.100
LTC among patients on ART before end of follow-up
12 months after enrollment	6,271 (17.2)	3,825 (21.3)	1.30 (1.12, 1.51)	1.25 (1.08, 1.44); p = 0.003
24 months after enrollment	9,038 (24.2)	5,510 (32.4)	1.44 (1.25, 1.67)	1.38 (1.19, 1.59); p < 0.001
36 months after enrollment	11,073 (30.7)	3,823 (36.9)	1.30 (1.13, 1.50)	1.34 (1.18, 1.53); p < 0.001
LTC among patients not on ART before end of follow-up
12 months after enrollment	4,785 (47.7)	1,310 (51.7)	1.17 (1.00, 1.36)	1.16 (0.96, 1.39); p = 0.117
24 months after enrollment	4,959 (63.8)	1,313 (64.7)	1.03 (0.88, 1.19)	0.99 (0.86, 1.15); p = 0.934
36 months after enrollment	4,873 (72.1)	757 (72.6)	1.01 (0.87, 1.16)	1.03 (0.90, 1.18); p = 0.656

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aHR, adjusted hazards ratio; ART, antiretroviral therapy; HR, hazards ratio; LTC, lost to clinic.

*Reference group: Patients enrolling in care before adoption of universal HIV treatment guidelines.

^†Adjusted for sex, age group, enrollment CD4, facility type, clinic location, and country income level.

^§Transfer and death treated as competing events.

^♦p-Value for interaction term between enrollment period and ART status by censoring time point: 0.400.

^‡p-Value for interaction term between enrollment period and ART status by censoring time point: 0.002.

**p-Value for interaction term between enrollment period and ART status by censoring time point: 0.027.

Table 4 presents the proportion of patients retained in care at clinic of enrollment at 12, 24, and 36 months after ART initiation, having a record of VL monitoring and having VS at each time point, along with crude and adjusted relative risks (aRRs) of each outcome by period of enrollment. Adjusting for patient and clinic characteristics, those enrolling in care after adoption of universal treatment guidelines were less likely to be retained in care at 12 months (aRR 0.95 [95% CI: 0.93, 0.98]; p = 0.001); 24 months (aRR 0.88 [95% CI: 0.84, 0.94]; p < 0.001), and 36 months after ART initiation (aRR 0.87 [95% CI: 0.82, 0.92]; p < 0.001). Among patients retained after ART initiation, those enrolling in HIV care after adoption of universal treatment guidelines were more likely to have VL monitoring at 12 months after ART initiation (aRR 1.15 [95% CI: 1.05, 1.26]; p = 0.004) and less likely at 36 months (aRR 0.86 [95% CI: 0.80, 0.92]; p < 0.001), with no difference at 24 months. Among patients with VL monitoring at 12, 24, and 36 months after ART initiation, the likelihood of VS at 24 months was marginally, but not substantively, higher among patients enrolling in HIV care after guideline adoption (aRR 1.03 [95% CI: 1.01, 1.04]; p = 0.001), with no differences at 12 or 36 months.

Table 4. Risks of HIV care outcomes after ART initiation associated with national adoption of universal HIV treatment guidelines.

Care outcome	Enrollment before guideline adoption^* N (%)	Enrollment after guideline adoption N (%)	RR (95% CI)	aRR^† (95% CI); p-value
Retention in care
12 months after ART initiation	29,483 (75.1)	12,935 (70.5)	0.94 (0.91, 0.97)	0.95 (0.93, 0.98); p = 0.001
24 months after ART initiation	24,785 (64.8)	9,565 (55.8)	0.86 (0.81, 0.91)	0.88 (0.84, 0.94); p < 0.001
36 months after ART initiation	20,025 (55.0)	5,016 (48.2)	0.88 (0.82, 0.93)	0.87 (0.82, 0.92); p < 0.001
VL testing among patients initiating ART and retained in care
12 months after ART initiation	18,804 (63.8)	9,485 (73.3)	1.15 (1.04, 1.28)	1.15 (1.05, 1.26); p = 0.004
24 months after ART initiation	17,413 (70.3)	7,017 (73.4)	1.04 (0.99, 1.10)	1.03 (0.99, 1.07); p = 0.166
36 months after ART initiation	14,328 (71.6)	3,195 (63.7)	0.89 (0.80, 0.99)	0.86 (0.80, 0.92); p < 0.001
VS among those retained in care with VL testing
12 months after ART initiation	14,106 (86.0)	7,050 (86.2)	1.00 (0.98, 1.03)	1.01 (0.98, 1.04); p = 0.360
24 months after ART initiation	15,184 (87.2)	6,229 (88.8)	1.02 (1.00, 1.03)	1.03 (1.01, 1.04); p = 0.001
36 months after ART initiation	12,721 (88.8)	2,885 (90.3)	1.02 (1.00, 1.03)	1.01 (0.99, 1.03); p = 0.363

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aRR, adjusted risk ratio; ART, antiretroviral therapy; RR, risk ratio; VL, viral load; VS, viral suppression; 95% CI, 95% confidence interval.

*Reference group: Patients enrolling in care before adoption of universal treatment guidelines.

^†Adjusted for sex, age group, enrollment CD4, initial regimen type, clinic location, facility type, and country income level.

Sensitivity analyses restricted to patients enrolling in the 13 to 24 months before adoption of universal HIV treatment guidelines and the first year afterwards showed consistent results for LTC outcomes at 12 months after enrollment, as well as retention, VL monitoring, and VS at 12 months after ART initiation (S2 Table). Post hoc sensitivity analyses that excluded countries with late adoption of universal HIV treatment guidelines in 2017 and 2018, where 24- and 36-month outcomes windows could have partially coincided with service disruptions related to the COVID-19 pandemic, also yielded results consistent with our main analyses (S3 and S4 Tables).

Discussion

Using real-world service delivery data from 109 clinics across 25 countries, our study found that patient retention at 12, 24, and 36 months after ART initiation decreased among patients enrolling in care after national adoption of universal HIV treatment guidelines. Additionally, while VS was high before and after guideline adoption among patients retained in care and on ART, there was little improvement in annual VL monitoring.

Prior experimental and observational studies examining HIV care outcomes under universal treatment guidelines have focused on outcomes within 12 months after treatment initiation [9–13,15,16] and have reported mixed results in terms of patient attrition and retention. To our knowledge, no other studies using real-world service delivery data have examined longer-term HIV care outcomes at 24 and 36 months after enrollment and after ART initiation in the era of universal HIV treatment.

In accordance with prior research [4,5,11], we found that patients enrolling in care under universal HIV treatment guidelines initiated ART more rapidly, with a lower proportion of patients having no record of ART initiation. While observed improvements in ART initiation are encouraging, our study raises concerns about the capacity of HIV programs to support engagement in care for some patients after ART initiation. Study findings also raise concerns about the quality of HIV care in the era of universal HIV treatment. WHO has recommended annual VL monitoring after ART initiation since 2013 [24]; however, VL monitoring remains suboptimal, with more than one-quarter of patients at each annual time point in our study having no record of VL testing. Among patients enrolling after national adoption of universal treatment guidelines, we found no improvement in VL testing at 24 months, and VL monitoring at 36 months decreased among patients enrolling after guideline adoption. Observed decreases in VL monitoring at 36 months are surprising because the subset of patients enrolling in HIV care after guideline adoption with at least 36 months of follow-up time comprised more patients from high-income countries with greater capacity for VL testing [25]. Additionally, while the COVID-19 pandemic is known to have disrupted HIV-related services in many settings [26,27], most patients in our study (>95%) were in countries adopting universal treatment guidelines in 2016 or earlier, meaning that VL monitoring at 36 months after ART initiation reflected prepandemic VL monitoring practices. In sensitivity analyses that excluded countries where outcomes at 36 months could have coincided with the COVID-19 pandemic findings were consistent with our main analyses. Accordingly, observed gaps in annual VL monitoring among retained patients—particularly with increasing time since ART initiation—reinforce concerns about the adequacy of resource allocation for elements of HIV care that are essential for identifying adherence challenges and drug resistance and for guiding timely regimen switching [28]. Additionally, as other research has shown that patient monitoring is positively associated with retention [29], gaps in VL monitoring may represent missed opportunities for motivating patients to remain engaged in care.

Study limitations include well-known limitations of observational studies for causal attribution, along with possible underascertainment of deaths and transfers to other sites of care in routine HIV service delivery data [30–32]. As tracing studies have reported rates of undocumented (i.e., “silent”) transfer ranging from 4% to 54% among patients lost to follow-up [30–32], true LTC may be overestimated in our study, and deaths and transfers are likely underestimated [33]. Although WHO began recommending the decentralization of HIV care in LMICs in the mid- to late 2000s, well before the adoption of universal HIV treatment guidelines in these settings [34–36] and we used a conservative definition of LTC [37], it is possible that decentralization has accelerated with the rollout of universal treatment policies, resulting more undocumented transfers, particularly among patients at centralized or tertiary care sites who silently transfer to peripheral HIV care facilities [31,32].

While we were able to adjust for selected patient characteristics, there may be important unmeasured differences between patients enrolling in HIV care and initiating treatment before and after the adoption of universal treatment guidelines. Other analyses have shown that patients entering HIV care in the era of universal treatment initiate treatment more rapidly [4,5] and that the expansion of HIV treatment eligibility criteria has resulted in the treatment of patients with earlier stage HIV disease [38]. While earlier treatment initiation is associated with improved clinical outcomes and reduced onward HIV transmission, rapid initiation of treatment after enrollment in HIV care is also associated with lower retention in care [39,40], and qualitative research has suggested that distress and uncertainty about HIV diagnosis, concerns about stigma, fear of lifelong medication, and other patient-level factors may contribute to attrition and lower treatment adherence among those rapidly initiating treatment, particularly among patients with early stage disease who do not feel unwell [41,42]. Our observed decreases in retention after ART initiation in the era of universal treatment may reflect the fact that some patients, who would have been LTC prior to ART initiation before guideline adoption, initiated ART more rapidly.

We had limited data on patient characteristics and note that substantial decreases in CD4 testing after the adoption of universal treatment guidelines [43] constrain our ability to adjust for patient immunological status at the time of care entry and treatment initiation, which may be a source of bias in the estimates we report. We also were unable to adjust for pregnancy status or examine whether longer-term HIV care outcomes among pregnant women—a group that was eligible for immediate treatment and life-long ART prior to the adoption of universal HIV treatment guidelines—differed from those of other patients. Additionally, while we adjusted for clinic characteristics, such as location, facility type, and country income level, there may be important time-varying contextual and health system factors, including supply-side constraints related to increased demand for HIV treatment, which we were unable to adjust for in our analyses. It is noteworthy that for patients in our study who enrolled in HIV care and initiated ART before the adoption of universal HIV treatment guidelines, outcomes at 24 and 36 months occurred after guidelines had changed. While it is unlikely that routine follow-up care of patients established on ART differed by the timing of treatment initiation, practices related to ART readiness and adherence counseling may have differed substantially before and after the adoption of universal HIV treatment guidelines but we were unable to examine or control for such differences.

A further limitation is the nonrepresentativeness of IeDEA sites within countries and regions included in this analysis; almost half of our sample were in care at university and tertiary referral hospitals, and these sites are likely better resourced than other HIV clinics within the same geographic areas. Accordingly, our estimates of VL monitoring before and after the adoption of universal HIV treatment guidelines may be biased upwards. While consistent with other research [44,45], our estimates of VS may also be biased upwards, particularly as those without VL monitoring likely include patients disengaged from care—patients known to have higher rates of viral nonsuppression [46,47]. Additionally, our study findings may not be generalizable to other locations and contexts that were not included in our study. It is also possible that patients enrolling in care and initiating treatment during the first year after the adoption of universal HIV treatment guidelines are not representative of those enrolling during subsequent years, with HIV outcomes improving among patients enrolling 2 to 3 years after the initial period of guideline adoption.

Key strengths of our study include the use of real-world service delivery data from a large sample to examine longer-term programmatic outcomes after the adoption of universal HIV treatment guidelines across diverse country settings. Associations observed in our heterogeneous sample of patients from diverse settings across multiple regions and different years of guideline adoption may be broadly reflective of HIV care outcomes among patients who were ART naïve at enrollment in HIV care during the years surrounding the adoption of universal HIV treatment guidelines in these settings.

While the adoption of universal HIV treatment guidelines has expanded access to and uptake of timely treatment for PLWH, our findings raise concerns about existing service delivery strategies and capacity for longer-term retention of patients in the era of universal treatment. Our findings of increased LTC at 24 and 36 months after enrollment and decreased retention at all time points after ART initiation suggest a risk of worsened patient outcomes. Additionally, while VS rates at all time points are high, our study highlights suboptimal VL monitoring, particularly with increased duration of time in care. Because of lags in the availability of data extracted from patient records and databases, we were only able to examine outcomes among patients enrolling in HIV care and initiating treatment during the first year after the adoption of universal treatment guidelines. However, our findings underscore the critical importance of monitoring long-term HIV care outcomes as additional data become available, as well as examining HIV care outcomes among groups known to be at increased risk of attrition and poor VS, including pediatric patients and pregnant and postpartum women. Equally vital are efforts to identify and address health system, community and patient-level determinants of attrition before and after ART initiation, and barriers to adherence and viral nonsuppression in the era of universal treatment of all PLWH.

Supporting information

S1 Checklist. STROBE Statement.

(DOCX)

pmed.1004367.s001.docx^{(33.6KB, docx)}

S1 Text. Acknowledgments.

(DOCX)

pmed.1004367.s002.docx^{(41.3KB, docx)}

S1 Concept Proposal. Concept sheet: Multiregional analysis.

(PDF)

pmed.1004367.s003.pdf^{(480.9KB, pdf)}

S1 Table. Baseline characteristics among patients enrolling in care at least 24 and 36 months before database closure.

(DOCX)

pmed.1004367.s004.docx^{(33.6KB, docx)}

S2 Table. Sensitivity analyses restricted to patients enrolling 13–24 months before and 12 months after national adoption of universal HIV treatment guidelines.

(DOCX)

pmed.1004367.s005.docx^{(28.4KB, docx)}

S3 Table. Risks and hazards of LTC associated with national adoption of universal HIV treatment guidelines in countries introducing guideline changes before 2017.

(DOCX)

pmed.1004367.s006.docx^{(29.1KB, docx)}

S4 Table. Relative risks of HIV care outcomes after ART initiation associated with national adoption of universal HIV treatment guidelines in countries introducing guideline changes before 2017.

(DOCX)

pmed.1004367.s007.docx^{(30.6KB, docx)}

Acknowledgments

We sincerely thank the staff at contributing sites, as well as IeDEA regional data managers. The full list of Acknowledgments can be found in the Supporting information (S1 Text).

Abbreviations

aHR: adjusted hazard ratio
aRR: adjusted relative risk
ART: antiretroviral therapy
CI-LTC: cumulative incidence of loss-to-clinic
COVID-19: Coronavirus Disease 2019
GEE: generalized estimating equation
IeDEA: International epidemiology Databases to Evaluate AIDS
IQR: interquartile range
NNRTI: non-nucleoside reverse transcriptase inhibitor
PLWH: people living with HIV
VL: viral load
VS: viral suppression
WHO: World Health Organization
95% CI: 95% confidence interval

Data Availability

Complete data for this study cannot be posted in a supplemental file or a public repository because of legal and ethical restrictions. The principles of collaboration of IeDEA and the regulatory requirements of the different IRBs of our participating sites (sometimes representing national IRBs of ministries of health) require the submission of a project concept proposal and approval by the IeDEA Executive Committee. To request data, please review IeDEA guidance available at: https://www.iedea.org/resources/multiregional-research-sops-templates/ and contact the Executive Committee (https://www.iedea.org/working-groups/executive-committee/). Signing of a data sharing agreement may also be required.

Funding Statement

The International Epidemiology Databases to Evaluate AIDS (IeDEA) is supported by the U.S. National Institutes of Health’s National Institute of Allergy and Infectious Diseases (https://www.niaid.nih.gov), the Eunice Kennedy Shriver National Institute of Child Health and Human Development (https://www.nichd.nih.gov), the National Cancer Institute (https://www.cancer.gov), the National Institute of Mental Health (https://www.nimh.nih.gov), the National Institute on Drug Abuse (https://nida.nih.gov), the National Heart, Lung, and Blood Institute (https://www.nhlbi.nih.gov), the National Institute on Alcohol Abuse and Alcoholism (https://www.niaaa.nih.gov), the National Institute of Diabetes and Digestive and Kidney Diseases (https://www.niddk.nih.gov), the Fogarty International Center (https://www.fic.nih.gov), and the National Library of Medicine (https://www.nlm.nih.gov): Asia-Pacific, U01AI069907 (AJ, MGL and VK); Caribbean, Central and South America network for HIV epidemiology (CCASAnet), U01AI069923 (MTLT and FMC); Central Africa, U01AI096299 (EB, DN and OT); East Africa, U01AI069911 (KWK and LA); NA-ACCORD, U01AI069918 (KNA and JSL). Informatics resources are supported by the Harmonist project, R24AI124872. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS Med. doi: 10.1371/journal.pmed.1004367.r001

Decision Letter 0

Louise Gaynor-Brook

8 Aug 2023

Dear Dr Brazier,

Thank you for submitting your manuscript entitled "Long-term HIV care outcomes under “Treat-All” guidelines: A retrospective cohort study" for consideration by PLOS Medicine.

Your manuscript has now been evaluated by the PLOS Medicine editorial staff as well as by an academic editor with relevant expertise and I am writing to let you know that we would like to send your submission out for external peer review.

However, before we can send your manuscript to reviewers, we need you to complete your submission by providing the metadata that is required for full assessment. To this end, please login to Editorial Manager where you will find the paper in the 'Submissions Needing Revisions' folder on your homepage. Please click 'Revise Submission' from the Action Links and complete all additional questions in the submission questionnaire.

Please re-submit your manuscript within two working days, i.e. by Aug 10 2023 11:59PM.

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Kind regards,

Louise Gaynor-Brook, MBBS PhD

Senior Editor

PLOS Medicine

PLoS Med. doi: 10.1371/journal.pmed.1004367.r002

Decision Letter 1

Louise Gaynor-Brook

22 Nov 2023

Dear Dr. Brazier,

Thank you very much for submitting your manuscript "Long-term HIV care outcomes under “Treat-All” guidelines: A retrospective cohort study" (PMEDICINE-D-23-02140R1) for consideration at PLOS Medicine.

Your paper was evaluated by a senior editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to three independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

Whilst we will not be able to accept the manuscript for publication in the journal in its current form, we would like to consider a revised version that fully addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

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Sincerely,

Louise Gaynor-Brook, MBBS PhD

Senior Editor, PLOS Medicine

lgaynor@plos.org

plosmedicine.org

-----------------------------------------------------------

Thank you for your patience with a longer assessment process than we anticipated, and apologies for the delay in providing you with an editorial decision.

Comments from the Academic Editor:

Consideration of the national settings and programmes is required, which may underlie some of the results noted and may need to be addressed either by additional analysis and/or points in the Discussion.

The authors need to indicate and justify why there were no Southern Africa/ West Africa data included - those IeDEA data exist and most of HIV infected people live in Southern Africa. This point needs to be addressed as a limitation in the Discussion, and the text needs to be very clear of the settings from which the data come (perhaps even in the Title).

Wealth Index is not a sufficient covariate to indicate differences between countries. Instead there needs to be a more insightful variable that indicates something about access to health care/ access to ART/ or possibly early versus late TA policy adopters - anything that says something about the public health system would be especially important for the countries in Africa.

There seems to be some confusion regarding being in HIV care and being on ART - is the start of the follow up from accessing HIV care eg for testing, or from starting ART, or both? Being in HIV care may say a lot about the individual whereas ART initiation says a lot about the system with adherence then saying something about the individual too.

ART status is taken at censoring points but this then is likely to be strongly correlated with being in care/LTC? Instead, it would be better to allow for timing of ART initiation during the follow up period, or if not available, whether ART was already initiated at the preceding time point (whether that was a clinic visit 3 or 6 months early or the preceding censoring point).

People who started ART before the national TA policy was introduced are different from those starting ART after TA policy - and that is not sufficiently addressed. In particular it would be likely that those initiating before TA already have some positive link with the clinic while after TA, they may have attended a particular clinic for HIV testing (and if positive would have been initiated on ART) but then moved away and access ART elsewhere, as the initial clinic would not be the most convenient for long-term care. This needs to be addressed.

Before TA policies were introduced, pregnant women would already have been HIV tested and if positive would have been initiated on ART for life - this policy was common globally. The health care system would be geared towards those women and their children, even long-term. How would this have been changed by a TA policy? Can this be addressed?

Of course, what is most important from a public health perspective is to retain those who positively want to be on ART in long term care, in a clinic (or within a referral system that allows any move to be visible) that is convenient long term for the individual concerned. The results noted on page 17 are relevant to this point and this point should be explored in the discussion.

Requests from the editors:

General comments:

Please include line numbers in your revised manuscript, ideally not starting from 1 with each new page.

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Please define CI and aHR at first use.

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Please specify ‘months’ after 12 and 24 when discussing hazards of LTC

Please include the important dependent variables that are adjusted for in the analyses.

In the last sentence of the Abstract Methods and Findings section, please describe 2-3 of the main limitations of the study's methodology.

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Results:

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metadata, via entries in the submission form.

Figures:

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Tables:

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References:

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Comments from the reviewers:

Reviewer #1: In this paper, the authors present an analysis of data from the IeDEA consortium, aiming to answer the question of whether people with HIV had better follow-up outcomes following the introduction of Treat-All guidelines. While this analysis does account for the competing risk of death when assessing the effect of the Treat-All guidelines on the outcome of care retention, there are several aspects of this analysis that require clarification and further justification, as described in my comments below.

1. Please complete and include the STROBE checklist (see https://www.equator-network.org/reporting-guidelines/strobe/)

2. Abstract: The regions that the 25 countries were drawn from and the period of time that the data corresponds to should be mentioned.

3. Methods:

a. What statistical program was used to analyse the data? If R, please be sure to cite the relevant packages.

b. It is stated that multivariable cause-specific hazards regression was applied - but what model specifically was assumed? How was clustering within clinic accounted for in the cause-specific hazards regression models? Please provide details.

c. What is meant by "correlates of LTC"? What research question is this analysis aiming to answer? Generally speaking, if interest is in the effect of a covariate or an exposure on an outcome, the researcher must consider all variables that may act as confounders for the relationship between that covariate/exposure on the outcome, and adjust for these in an analysis. Thus, separate analyses may be required for each exposure/covariate and outcome pair. Tables of regression coefficients or hazard ratios as presented in Table 4 are very difficult to interpret, and often do not have a valid causal interpretation and thus should not be included. Please see Westreich D and Greenland S, The Table 2 Fallacy: presenting and interpreting confounder and modifier coefficients. American Journal of Epidemiology, 2013;177:92-298. (https://doi.org/10.1093/aje/kws412)

d. I would think that those patients with ART initiation are likely to have been treated for longer at a clinic than those patients without ART initiation, and thus would have a lower incidence of loss to follow up. This does not appear to have been commented on: it has implications for interpretation of results. The analysis of patients who commenced ART during their follow up is conditioning on a future event; a more appropriate analysis would treat ART initiation as a time-varying exposure. Further, after the Treat-All guidelines were introduced I can imagine that more patients commenced ART earlier; thus the effect of the introduction of the guidelines in those patients who commenced ART cannot be disentangled from the differences between the group of patients who had commenced ART before the introduction of Treat-All and the group who commenced ART after Treat-All. The approach taken requires justification or deletion.

e. Patients who were enrolled in clinics prior to the date of adoption in their country will, depending on their date of enrolment, still be enrolled after the date of adoption. Thus patients could have a time-varying exposure to the Treat-All policy. How was this accounted for in the analysis? What are the impacts of this on conclusions?

4. Results

a. How complete was the dataset? Were all patient-level characteristics available for all patients in the dataset, or was the analysis restricted to those patients with complete data? Missing CD4 count at enrolment is mentioned, but what about other characteristics? If there was missingness in other characteristics, what implications might this have for the results and their interpretation?

b. In several places on Page 7, results relating to proportions of patients experiencing outcomes are mentioned without any accompanying proportions or indications of where readers may find these. For example, in the first paragraph: where are the proportions of patients with transfers at each of the time points mentioned in the second sentence?

c. The modern usage of p-values favours a more holistic interpretation of results, rather than simply assessing whether a p-value happens to be small. When a large amount of data is analysed, small differences can have very small p-values: statistical significance may not equate to statistical significance. The American Statistical Association's statement on p-values, available at https://doi.org/10.1080/00031305.2016.1154108 provides further context and guidance. An assessment of the differences between groups guided by clinical rather than statistical significance, with consideration of variability, would be far more appropriate here.

d. As indicated in my comments on the Statistical Analysis section, it is extremely difficult to interpret the results presented in Table 4 in a useful way. Thus this table and associated discussion should be deleted.

5. Discussion

a. For those countries with national adoption dates occurring in 2017-2018, the disruption caused by the COVID-19 pandemic and associated restrictions could have had a large impact on outcomes at 24 to 36 months. Although this is mentioned in the discussion, a sensitivity analysis where data from 2017 and 2018 is excluded is required.

b. Given my concerns above regarding the analysis of the patients who had initiated ART, the conclusions regarding decreased retention after ART initiation need to be further justified.

Reviewer #2: This is a large, retrospective cohort analysis by the IeDEAL group, using data from 109 clinics in 25 countries to study the impact of the Treat-All (TA) policy on retention on ART, viral load (VL) coverage and VL suppression. The manuscript is very well written, the complex methodology has been explained clearly and the results are nicely displayed in graphs and tables. There are no ethical issues.

Major points

1. The main benefit of TA is higher uptake of ART and prevention of complications through earlier treatment. While uptake of ART among persons diagnosed and enrolled in care will be higher after TA, this benefit could be reduced or negated by higher attrition from care. In other words, of persons enrolled in care, the net percentage of those who started ART and were then retained on ART at the 3 time points, should be compared between the 2 time periods. Does the study provide this insight? The authors have stratified LTC between those starting ART and not starting ART, and compared retention among those who started ART, but this is not the same. Can the authors explain?

2. All before/after analyses have challenges with confounding by impacts that vary over time. This is also relevant in this study with its broad geographical scope and variable calendar time of the intervention. I believe that the authors should pay more attention to this in the Discussion. How do the authors assess the impact of external factors, including the C19 pandemic, the transition to INSTIs and possible other changes that may have happened in tandem with the TA policy, on the findings of their study?

3. The authors state that "our heterogeneous sample of patients from diverse settings across multiple regions and different years of Treat-All adoption" is a strength of the study. However, it seems difficult to compare the effects of the transition to TA in the USA to those in Africa several years later, with the vastly different health systems and populations. In my view, adjustment for income level of the country does not fully address this. For instance, readers may be concerned that the overall outcomes and conclusions could be less relevant for Africa due to being driven by results from North America and Asia. Could the authors elaborate on this?

4. Enrolment of the post TA cohort was directly after the introduction of the change in policy. Could disruptions in health systems due to the change in policy have a temporary effect on the study outcomes? An example of this is supply challenges due to increased demand. If so, participants enrolled in the period immediately after the TA policy introduction may not be representative of individuals started on ART later.

Reviewer #3: This paper reports a comprehensive analysis of retrospective data on retention, viral suppression and other outcomes in a large multi-national cohort of patients enrolling in HIV care. The data come from the IdEA consortium and include information on nearly 67,000 patients from 109 clinics in 25 countries. The main objective of the analyses is to compare outcomes in patients enrolling before and after national guidelines changed to a Treat-All policy in each country.

This is a very well written paper. The rationale, methods and results are generally clearly presented and there is a good discussion of the policy implications of the findings. Strengths of the study include the availability of service delivery data from a very large sample of patients in a wide range of settings, with follow-up to 36 months after enrolment in HIV care. Previous studies have mostly been based on more restricted geographical settings and shorter follow-up periods, so this study makes an important contribution to the field. The findings of lower retention and little improvement in viral load monitoring in patients enrolling after the change to Treat-All have clear implications for policy and practice.

An important limitation is that there are no study sites in Southern Africa, where a large proportion of HIV patients live, although this reflects the coverage of the IdEA consortium, over which the authors have little control. Furthermore the study only includes data on patients enrolling in care during the first year after the guideline change. It usually takes time for guidelines to "bed in" and for health services to adapt to the new context, and especially the challenge of serving a larger number of patients on ART. It will be important to carry out further analyses once the new guideline has been in place for a longer time. These points are not discussed.

Apart from these observations, I only had a few comments which are listed below.

1. Some readers may be confused by the fact that WHO made the recommendations for Treat-All in 2015 and yet some sites included in the study changed to Treat-All as early as 2012. A brief comment on the reasons for this might be helpful.

2. A key issue is that this is an observational analysis, and that the groups of patients enrolling before and after the guideline change may differ in ways that would affect the outcomes being measured, leading to selection bias. Table 1 shows that there are few differences in basic demographic and clinical variables, and the effect measures are adjusted for these variables. However, there may be differences in other unmeasured variables. For example, a Treat-All policy might encourage a wider range of patients to present for care, possibly including a higher proportion of those with intrinsically lower health-seeking behavior, and consequently lower retention and/or adherence to treatment. I note that hazard ratios are considerably attenuated after the limited adjustment that is possible, and might be further attenuated if it were possible to adjust for additional variables. This is mainly a point for discussion.

3. Some of the analyses are stratified by whether or not patients had initiated ART before end of follow-up. But it is not clear how long they took to initiate ART, so how much of their follow-up period was while they were on ART. We are told that 87.6% of those enrolling in HIV care after the guideline change were on ART by 12 months, but some more information on the delay in initiating ART might be helpful.

4. A smaller proportion of patients enrolling after the guideline change had CD4 counts at baseline, which is not surprising as ART initiation was no longer dependent on the CD4 count value. However, this lack of data compromises the ability to control for an important clinical variable. Again, this is mainly a point for discussion.

5. Figs 3 and 4: The legend for these figures needs to explain that 95% CIs are shown and also define what is meant by "significant". Also, it would be possible to display the confidence intervals on the diagrams.

6. Table 3 does NOT show the "hazards" of LTC at each time-point. The first two columns show (I think) the cumulative incidence or risk of LTC by each time-point. It is in columns 3 and 4 I think that hazard ratios are shown. Could this be clarified please?

7. Table 4: A minor point, but with a covariate with several categories (e.g. age) it would be usual to show an LRT p-value for the overall evidence of any effect of that variable on the outcome, in addition to the effects and CIs for the individual categories.

8. Supplement Table 1: Please check the footnotes, which should define both HR and RR as these are both shown in the table.

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. 2024 Mar 18;21(3):e1004367. doi: 10.1371/journal.pmed.1004367.r003

Author response to Decision Letter 1

14 Dec 2023

Attachment

Submitted filename: Response to Reviewers.docx

pmed.1004367.s008.docx^{(93.6KB, docx)}

PLoS Med. doi: 10.1371/journal.pmed.1004367.r004

Decision Letter 2

Louise Gaynor-Brook

19 Jan 2024

Dear Dr. Brazier,

Thank you very much for re-submitting your manuscript "Long-term HIV care outcomes under universal HIV treatment guidelines: A retrospective cohort study in 25 countries" (PMEDICINE-D-23-02140R2) for consideration at PLOS Medicine.

I have discussed the paper with our academic editor and it was also seen again by three reviewers. I am pleased to tell you that, provided the remaining editorial and production issues are fully dealt with, we expect to be able to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

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Please let me know if you have any questions, and we look forward to receiving the revised manuscript.

Sincerely,

Richard Turner PhD, for Louise Gaynor-Brook, MBBS PhD

Consulting Editor, PLOS Medicine

plosmedicine@plos.org

------------------------------------------------------------

Requests from Editors:

In the abstract and main text, we ask you to quote p values alongside 95% CI, where available.

At line 38 (abstract), we suggest "More than half ..." or similar.

At lines 85-86, we ask you to remove "25%" and "more than 30%", bearing in mind that these are relative increases. You may quote the relevant HR and CI if you wish.

We ask you to list the 25 countries, e.g., early in the Results section (main text).

At line 468 we suggest "While the adoption ... has ...".

Noting information in the tables, please quote exact p values or "p<0.001" for the smaller values.

In the reference list, please use the journal name abbreviation "PLoS ONE".

Reference 3 may be missing page numbers or a PII: please add additional information as needed.

To reference 18 and any other preprints, please add "[preprint]", or update the citation with the relevant published article.

Comments from academic editor:

My comment regarding pregnant women and the provision of universal ART for this group from first antenatal visit which has been available for 2 decades (and from before Universal treatment guidelines for all became available) is not really covered by the sentence in the final paragraph of the discussion. This is not an issue of 'crowding out' but a concern as to how changes in this group contribute to the findings. This would be most likely in the African and South American context - but it would be nice to see whether there is any evidence of retention in care in this group was improved after treatment for all policy became the norm. I do not know if pregnancy status data were available in this dataset.

Comments from Reviewers:

*** Reviewer #1:

I thank the authors for their responses to my comments on the previous version of this manuscript. I have a few follow-up questions and comments.

1. (This comment relates to the authors' response to my comment 3(b) on the previous version.) Please state in the Methods section that clustering of patients within clinics was accounted for when fitting the Cox proportional hazards models through the use of a robust sandwich estimator for the covariance matrix.

2. Were the assumptions of proportional hazards valid here?

3. In Table 1, rather than (or at least in addition to) including p-values to assess differences between the characteristics of patients enrolled before and after guideline adoption, I would recommend the calculation of standardised differences - these do not suffer from the issue of small difference + large sample size issue of p-values, and instead give some idea of the magnitude of the differences between groups.

4. In Table 1, rather than 95% confidence intervals for the mean time in days to ART initiation, reporting means and standard deviations and medians and lower and upper quartiles would be more informative, since characteristics of the sample are being described.

5. The issue of the interpretation of p-values raised in point 4(c) of my review of the previous version of this manuscript does not appear to have been thoroughly addressed. For example, on page 9: lines 325-326 ("small, but significant"); line 332 ("significantly higher"); line 347 ("but differences did not reach statistical significance"). I recommend that these sentences be re-worded in terms of clinical significance of observed differences and the range of values supported by the data through the confidence intervals for estimates.

6. In Table 3, rather than noting which care outcomes had interaction terms with p-values below some arbitrary cut-off, please provide all interaction term p-values.

*** Reviewer #2:

My comments and the extensive, very good comments from colleague reviewers, including those related to statistical issues, have been addressed well by the authors

Important limitations of the study remain but these have been acknowledged clearly in the Discussion

The conclusions are sufficiently supported by the data and are highly relevant for HIV care and treatment programs

In my view, the manuscript is ready for publication in PLoS Medicine

*** Reviewer #3:

I have carefully reviewed the revised version of the manuscript as well as the authors' responses to the reviews and editorial comments. I would like to commend the authors for the care with which they have revised the paper. I am very satisfied with the changes they have made in response to my comments. I only have the following final comments and suggestions but do not need to see the paper again:

1. I note that a suggestion was made to the authors to report more p-values for comparisons in the Abstract or Text. However, I do not agree with this suggestion. I take the same view as the authors, that while there is continuing debate on this issue, current best practice is to de-emphasize the reporting of p-values, especially in studies (like this one) where very large sample sizes mean that even the smallest observed differences are deemed "statistically significant". As the authors note, the Confidence Intervals provide much more useful information about the size and precision of the estimates of interest.

2. I commented previously that the term "hazard" is often used in the paper when what is meant is really "hazard ratio" - that is, an effect estimate (comparing different exposure groups). However, there are still instances (e.g. Line 344 and Table 3) where greater clarity would be achieved by using the term "hazard ratio".

3. The lower proportion of patients with a viral load measure in the group enrolled after the guideline change is a surprising and concerning result that conflicts with the findings at 12m and 24m. I note that a much smaller number of patients in this group have data available at 36m, and suspect that they may be biased towards those enrolled at earlier time periods. If this is so, and if completeness of VL monitoring has been increasing over time, might this be an explanation for these contrasting results?

***

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. 2024 Mar 18;21(3):e1004367. doi: 10.1371/journal.pmed.1004367.r005

Author response to Decision Letter 2

7 Feb 2024

Attachment

Submitted filename: Response to reviewers.docx

pmed.1004367.s009.docx^{(72.2KB, docx)}

PLoS Med. doi: 10.1371/journal.pmed.1004367.r006

Decision Letter 3

Louise Gaynor-Brook

22 Feb 2024

Dear Dr Brazier,

On behalf of my colleagues and the Academic Editor, Marie-Louise Newell, I am pleased to inform you that we have agreed to publish your manuscript "Long-term HIV care outcomes under universal HIV treatment guidelines: A retrospective cohort study in 25 countries" (PMEDICINE-D-23-02140R3) in PLOS Medicine.

I appreciate your thorough responses to the reviewers' and editors' comments throughout the editorial process. We look forward to publishing your manuscript, and editorially there are only a few remaining minor stylistic/presentation points that should be addressed prior to publication. We will carefully check whether the changes have been made. If you have any questions or concerns regarding these final requests, please feel free to contact me at aschaefer@plos.org.

Please see below the minor points that we request you respond to:

1) l.39: Please report the interquartile range together with the median (“median age was 34 years.”).

2) Please throughout the main text (including the abstract), report statistical information as follows to improve clarity for the reader “22% (95% CI [13%,28%]; p</=)”. Please note the use of commas to separate upper and lower bounds, as opposed to hyphens as these can be confused with reporting of negative values.

3) We understand that in the last revision you were given the option to include the data in the Author Summary, but after further discussion with the editorial team, we feel that the Author Summary should be understandable to the lay reader and should not include data or technical language/jargon. We apologize for any inconvenience this may have caused. Editorial suggestion: Compared with patients enrolling in HIV care and initiating HIV treatment before national adoption of universal treatment guidelines, those enrolling and initiating treatment after guideline adoption had a higher chance of LTC at 12 months, 24 months and 36 months after enrollment.

3) Please check again carefully for the appropriate use of "hazard ratio" instead of "hazard" (e.g. l.44/l.86/l.351-360).

4) In the references, please replace the word “cited” with “accessed”.

5) Please add the following statement, or similar, to the Methods: "This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist)."

6) To help us extend the reach of your research, please provide any X (formerly known as Twitter) handle(s) that would be appropriate to tag, including your own, your co-authors’, your institution, funder, or lab. Please enter in the submission form any handles you wish to be included when we post about this paper.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. Please be aware that it may take several days for you to receive this email; during this time no action is required by you. Once you have received these formatting requests, please note that your manuscript will not be scheduled for publication until you have made the required changes.

In the meantime, please log into Editorial Manager at http://www.editorialmanager.com/pmedicine/, click the "Update My Information" link at the top of the page, and update your user information to ensure an efficient production process.

PRESS

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Thank you again for submitting to PLOS Medicine. We look forward to publishing your paper.

Sincerely,

Alexandra Schaefer, PhD

On behalf of:

Louise Gaynor-Brook, MBBS PhD

Senior Editor

PLOS Medicine

Associated Data

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

Supplementary Materials

S1 Checklist. STROBE Statement.

(DOCX)

pmed.1004367.s001.docx^{(33.6KB, docx)}

S1 Text. Acknowledgments.

(DOCX)

pmed.1004367.s002.docx^{(41.3KB, docx)}

S1 Concept Proposal. Concept sheet: Multiregional analysis.

(PDF)

pmed.1004367.s003.pdf^{(480.9KB, pdf)}

S1 Table. Baseline characteristics among patients enrolling in care at least 24 and 36 months before database closure.

(DOCX)

pmed.1004367.s004.docx^{(33.6KB, docx)}

S2 Table. Sensitivity analyses restricted to patients enrolling 13–24 months before and 12 months after national adoption of universal HIV treatment guidelines.

(DOCX)

pmed.1004367.s005.docx^{(28.4KB, docx)}

S3 Table. Risks and hazards of LTC associated with national adoption of universal HIV treatment guidelines in countries introducing guideline changes before 2017.

(DOCX)

pmed.1004367.s006.docx^{(29.1KB, docx)}

S4 Table. Relative risks of HIV care outcomes after ART initiation associated with national adoption of universal HIV treatment guidelines in countries introducing guideline changes before 2017.

(DOCX)

pmed.1004367.s007.docx^{(30.6KB, docx)}

Attachment

Submitted filename: Response to Reviewers.docx

pmed.1004367.s008.docx^{(93.6KB, docx)}

Attachment

Submitted filename: Response to reviewers.docx

pmed.1004367.s009.docx^{(72.2KB, docx)}

Data Availability Statement

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PERMALINK

Long-term HIV care outcomes under universal HIV treatment guidelines: A retrospective cohort study in 25 countries

Ellen Brazier

Olga Tymejczyk

Kara Wools-Kaloustian

Awachana Jiamsakul

Marco Tulio Luque Torres

Jennifer S Lee

Lisa Abuogi

Vohith Khol

Fernando Mejía Cordero

Keri N Althoff

Matthew G Law

Denis Nash

Roles

Abstract

Background

Methods and findings

Conclusions

Author summary

Why was this study done?

What did the researchers do and find?

What do these findings mean?

Introduction

Methods

Data sources

Exposure

Outcomes

Fig 1. Study populations and windows for primary outcome ascertainment.

Covariates

Statistical analysis

Fig 2. Sensitivity analysis: Study populations and windows for outcome ascertainment.

Results

Fig 3. Sample flow diagram.

Table 1. Baseline characteristics among patients with at least 12 months of potential follow-up time after enrollment, by period of enrollment (before vs. after adoption of universal HIV treatment guidelines).

Table 2. HIV care outcomes by 12, 24, and 36 months after enrollment, overall and by ART initiation status by censoring time point.

Fig 4. Crude CI (95% CIs) of LTC at 12, 24, and 36 months after enrollment before vs. after adoption of universal HIV treatment guidelines.

Fig 5. Crude CI (95% CIs) of LTC at 12, 24, and 36 months after enrollment before vs. after adoption of universal HIV treatment guidelines, by treatment initiation status (on ART vs. not on ART) at ascertainment time point.

Table 3. Risks and hazard ratios of LTC associated with national adoption of universal HIV treatment guidelines.

Table 4. Risks of HIV care outcomes after ART initiation associated with national adoption of universal HIV treatment guidelines.

Discussion

Supporting information

Acknowledgments

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Louise Gaynor-Brook

Roles

Decision Letter 1

Louise Gaynor-Brook

Roles

Author response to Decision Letter 1

Decision Letter 2

Louise Gaynor-Brook

Roles

Author response to Decision Letter 2

Decision Letter 3

Louise Gaynor-Brook

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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