Prevalence and factors associated with pediatric HIV therapy failure in a tertiary hospital in Asmara, Eritrea: A 15-year retrospective cohort study

Samuel Tekle Mengistu; Ghirmay Ghebrekidan Ghebremeskel; Oliver Okoth Achila; Miriam Berhane Abrehe; Samuel Fisseha Tewelde; Mahmud Mohammed Idris; Tsegereda Gebrehiwot Tikue; Araia Berhane Mesfin

doi:10.1371/journal.pone.0282642

. 2023 Mar 9;18(3):e0282642. doi: 10.1371/journal.pone.0282642

Prevalence and factors associated with pediatric HIV therapy failure in a tertiary hospital in Asmara, Eritrea: A 15-year retrospective cohort study

Samuel Tekle Mengistu ^1,^2,^*, Ghirmay Ghebrekidan Ghebremeskel ^1,², Oliver Okoth Achila ³, Miriam Berhane Abrehe ⁴, Samuel Fisseha Tewelde ⁴, Mahmud Mohammed Idris ^2,⁴, Tsegereda Gebrehiwot Tikue ^2,⁴, Araia Berhane Mesfin ⁵

Editor: Carmen María González-Domenech⁶

PMCID: PMC9997912 PMID: 36893200

Abstract

Introduction

Treatment failure (TF) in HIV infected children is a major concern in resource-constrained settings in Sub-Saharan Africa (SSA). This study investigated the prevalence, incidence, and factors associated with first-line cART failure using the virologic (plasma viral load), immunologic and clinical criteria among HIV-infected children.

Methods

A retrospective cohort study of children (<18 years of age on treatment for a period of > 6 months) enrolled in the pediatric HIV/AIDs treatment program at Orotta National Pediatric Referral Hospital from January 2005 to December 2020 was conducted. Data were summarized using percentages, medians (± interquartile range (IQR)), or mean ± standard deviation (SD). Where appropriate, Pearson Chi-Squire (χ2) tests or Fishers exacts test, Kaplan–Meier (KM) estimates, and unadjusted and adjusted Cox-proportional hazard regression models were employed.

Results

Out of 724 children with at least 24 weeks’ follow-up 279 experienced therapy failure (TF) making prevalence of 38.5% (95% CI 35–42.2) over a median follow-up of 72 months (IQR, 49–112 months), with a crude incidence of failure of 6.5 events per 100- person-years (95% CI 5.8–7.3). In the adjusted Cox proportional hazards model, independent factors of TF were suboptimal adherence (Adjusted Hazard Ratio (aHR) = 2.9, 95% CI 2.2–3.9, p < 0.001), cART backbone other than Zidovudine and Lamivudine (aHR = 1.6, 95% CI 1.1–2.2, p = 0.01), severe immunosuppression (aHR = 1.5, 95% CI 1–2.4, p = 0.04), wasting or weight for height z-score < -2 (aHR = 1.5, 95% CI 1.1–2.1, p = 0.02), late cART initiation calendar years (aHR = 1.15, 95% CI 1.1–1.3, p < 0.001), and older age at cART initiation (aHR = 1.01, 95% CI 1–1.02, p < 0.001).

Conclusions

Seven in one hundred children on first-line cART are likely to develop TF every year. To address this problem, access to viral load tests, adherence support, integration nutritional care into the clinic, and research on factors associated with suboptimal adherence should be prioritized.

1. Introduction

The World Health Organization estimated that in 2021, about 1.7 million children aged 0–14 years were living with HIV infection worldwide, 90% of whom were from sub-Saharan Africa (SSA) [1]. In the focus countries, the number of new infections in pediatrics declined, from 240,000 [160,000–380,000] in 2010 to 130,000 [87,000–210,000] in 2021 [1]. According to this report, in these countries, 53% [36–64%] of children aged 0–14 years living with HIV were on combined antiretroviral therapy (cART) [1] resulting in a marked decline in HIV/AIDS-related hospitalization and deaths. However, cART coverage for children living with HIV (CLHIV) in SSA remains behind that of adults [2]. Highlighting these concerns, a 2019 Joint United Nations Programme on HIV/AIDS (UNAIDS) noted that CLHIV are largely overlooked in HIV treatment scale-up programs and are not promptly treated and diagnosed early enough to prevent HIV-related morbidity and mortality [3]. In Eritrea, a 2019 Spectrum modeling estimated that the magnitude of people living with HIV/AIDS (PLWHA) was 14, 000 (0.36% of the total population) and 8,956 (73%) patients are currently cART.

CLHIV (<18 years) make up 4% of PLWHA.

Multiple factors have been invoked to explain the treatment gap in children. In particular, there is a consensus among investigators that existing diagnostic and treatment approaches for children are complex and difficult to implement in resource-limited settings. Other lingering difficulties include unavailability of an age-appropriate treatment regimen; frequent co-infections; the wide use of drugs with low-genetic barriers to resistance; variable pharmacokinetics; suboptimal adherence; limited real-time viral load (VL) monitoring; high frequency (>10%) of pretreatment HIV drug resistance mutations (PDRMs) [4]; drug-related adverse events and drug stock-outs due to breaking downs in supply chains [5–9]. Besides, complex psychosocial problems such as caregiver support, over-centralization, and/or inappropriate integration of HIV/AIDS services into the broader child health platform further aggravate the problem [3]. Lastly, data suggest that a significant number of children (>50%) are exposed to suboptimal regimens, potentially leading to sub therapeutic drug concentrations [10].

An upshot of this catalog of barriers is a high drug failure rate in children. In general, studies in Low- and Middle-income Countries (LMIC) have reported TF rates of 10–34% among children after 2–3 years of cART [11]. In SSA, estimates of virologic failure (VF), with or without resistance mutations in children, range between 13–56% [12–15]. Also, delays in detecting early TF and subsequent switching to second-line therapy may compromise overall treatment outcomes [7, 16]. This is particularly relevant for children where such delays are common and are associated with increased risk of clinical progression to AIDS and higher morbidity and mortality [8].

At present, identifying and managing drivers of TF in children is a continuing concern [17]. However, the problem is not well studied in most resources constrained countries including Eritrea. Therefore, this study explores the frequency of pediatric HIV TF and its associated factors in one of the largest treatment centers in Eritrea.

2. Materials and methods

2.1 Study design and setting

We conducted a retrospective cohort study at the pediatric HIV/AIDs follow-up clinic in National Pediatric Referral Hospital (NPRH). HIV/AIDs follow-up clinic in NPRH was commissioned in 2005, making it the first institution in Eritrea to offer cART to CLHIV. Before the decentralization of services to other zones (2010), NPRH (in the Maekel zone) was the only institution offering cART to CLHIV in the country. In total, 822 children under the age of 18 years received service/or have been enrolled at the clinic since its inception.

Ethical approval was obtained from the Ministry of Health (MOH) research Ethics and Protocol review committee with a letter of reference: 21/09/20. All data collected from the records of respondents will be kept confidential at all times. Consent to participate was not obtained from the patients or guardians because the study was based on anonymized patient records and this was waived by the ethical committee. All procedures of the study also followed the recommendation of the Declaration of Helsinki Convention.

2.2 Study cohort description

All individuals ≤ 18 years old living with HIV/AIDS who attended the NPRH HIV follow-up clinic from 2005–2020 were enrolled in the study. Those with a follow-up duration of < 6 months missing key follow-up data, unknown follow-up duration, and those with no therapy outcome endpoints, were excluded (Fig 1). Unknown duration of follow-up refers to patients who don’t have last point of their show-up in the clinic after being enrolled. It is treated differently from those who are known to be lost to follow-up. As those who are lost to follow-up could be censored according to their therapy outcome till the last point they were known to be in follow up. All eligible children and adolescents received free treatment for HIV–according to the national ART Guidelines. The guidelines endorsed the use of two Nucleoside Reverse Transcriptase Inhibitors (NRTIs) and a Non-Nucleoside Reverse Transcriptase Inhibitors NRTI (NNRTI) as the standard first-line regimen and use of protease inhibitors as second-line regimens. All Children were prescribed fixed-dose combination tablets. Assessment of drug adherence was routinely conducted by monitoring missed doses.

2.3 Data collection

Relevant data were extracted from an existing database and patients’ clinical cards. Accordingly, all clinical cards were reviewed for demographic information, clinical, laboratory, and anthropometric data. The process was undertaken by trained health professionals from 12^th September 2020 to 2^nd February 2021 and was closely monitored by the principal investigator and supervisor.

2.4 Operational definitions

A case of TF is defined as, a patient who fulfills any definition of TF and/or has been switched to a second line due to TF with adherence support [6].
- ○ Clinical TF: New or recurrent clinical event indicating advanced or severe immunodeficiency (WHO clinical stage 3 and 4 clinical conditions except for TB) after 6 months of treatment.
- ○ Immunologic TF: In children <5 years, persistent CD4⁺ levels <200 cells/mm³ (count less than the threshold for two consecutive measures 6 months apart) and in children >5 years, persistent CD4⁺ levels <100 cells/mm³ despite treatment for 6 months.
- ○ Virologic TF: plasma VL >1000 copies/ml based on two consecutive VL measurements of 3 months interval.
B. Adherence was assessed by pill count at each follow-up visit as good, fair, and poor if a child missed <5%, 5 to <10%, and >10% doses respectively of the expected monthly doses. Suboptimal adherence comprises of any patient with at least one record of fair or poor adherence. Adherence was categorized as maladherent or good adherent due the fact that each patient has separate column of adherence record for each follow up. Adherence data was available as a separate column for each follow up. Therefore, it was possible to summarize adherence profile from multiple time points.
C. Immunodeficiency is classified as mild, advanced, and severe according to the WHO 2016 thresholds [16].
D. WHO standard deviations (SD) for growth monitoring were calculated to define nutritional status of participants. Accordingly; Undernutrition was defined as “underweight” (weight-for-age <–2SD); “stunting” (height-for-age <–2SD); “wasting” (weight-for-height <–2SD).
E. Adolescents are individuals whose age is greater than 10 and less than 18 years. Children are those less than 10 years.

2.5 Endpoints definition

For TF analysis, the period of follow-up was from cART initiation up to the earliest detection of TF. Children without TF were censored at the date of death, lost to follow-up (defined as missing follow-up visits for more than 6 months), transferred to another clinic, or the date record of any last event in the clinic.

2.6 Statistical analyses

All analyses were conducted using SPSS version 26 (SPSS Inc., Chicago, Illinois, USA.) and Stata version 12.0 (Stata Corporation, College Station, TX). Where appropriate, demographic and HIV-related characteristics of patients were summarized using percentages, medians (± interquartile range (IQR)), or mean ± standard deviation (SD). Descriptive analyses were stratified by therapy outcome in all key variables at baseline using Pearson’s Chi-square test or Fisher’s exact test, and Mann-Whitney U test for continuous data. The incidence rate of TF was calculated by dividing the number of patients with TF by the total number of person-years of follow-up. Kaplan–Meier estimates and log-rank tests were performed to compare the cumulative incidence of TF between different categories of patient-specific characteristics. The confidence interval for incidence rates were calculated in Stata (StataCorp. 2011. Stata: Release 12. Statistical Software. College Station, TX: StataCorp LP) using the following method after defining the data in time to event form: main tab statistics > survival analysis > Summary statistics, tests and tables > Person-time, incidence rates and SMR.

Finally, Cox proportional hazards analysis was conducted to identify the factors associated with VF. The following variables were considered in a multivariate-adjusted Cox proportional hazards model of TF: initial cART treatment regimen, age at treatment initiation, adherence, gender, baseline disease stage, immunodeficiency status, frequency of cART changes, year od cART start and baseline anthropometric values for age z-score [18]. Adjusted Cox proportional hazards models were used to determine the odds of TF. Log-likelihood ratio was used to create these adjusted models, with a variable being included in the model if it resulted in an improvement in the model fit. Two-sided p-values < 0.05 were accepted as statistically significant.

3. Results

3.1 Demographic and clinical characteristics

A total of 822 CLHIV were screened for eligibility and 724(88%) fulfilled the inclusion criteria (Fig 1). Those eligible, with at least 24 weeks of follow-up, were followed for a total of 3913 person-years. In this period, 34/724 (4.7%) died, 105(14.5%) were lost to follow-up, and 403 (55.7%) were transferred out. The median age at enrollment to the clinic was 78 months (IQR, 38.5–114.5 months). Females comprise 47.2% of the population. Half of the children (50.1%) initiated cART before 2010, whereas 34.7% initiated between 2011 and 2015, and 15.2% in 2016 and onwards. More than half of the children (52%) had advanced HIV disease (WHO clinical stage 3 and 4). The median duration of follow-up was 79 months (IQR, 49–112 months).The excluded 97 children were majorly due missing key time to event data. Those eligible were more likely to be from the Maekel zone (p-value = 0.03), in an advanced WHO clinical stage (p-value < 0.001), and on zidovudine (AZT) + lamivudine (3TC) backbone than their counterparts. Furthermore, the fewer of the study participants were wasted (weight for height, Z<-2) (p-value = 0.02), acutely malnourished (weight for age Z <-3) (p-value <0.001), and severely immunosuppressed (p-value = 0.037) when compared to eligible participants (Table 1).

Table 1. Baseline characteristics of HIV-infected children and adolescents in the in National Referral Pediatric Follow-up Clinic, Asmara, Eritrea (2005–2020).

Characteristics^†	Included (n = 724)	Excluded (n = 97)	Total n (%)
Gender
Male	376 (52.7)	63 (57.7)	439 (53.4)
Female	337 (47.3)	46 (42.3)	383 (46.6)
Year of birth	2003 (1999–2005)	2002 (1999–2006)
Address
Maekel	523 (73.3)	69 (63.8)	592 (72)
Outside Maekel	190 (26.7)	40 (36.6)	230 (28)
cART initiation year	2011 (2008–2014)	2010 (2007–2014)
2005–2009	274 (38.5)	42 (39.2)	316 (38.6)
2010–2014	275 (38.6)	44 (41.1)	319 (62.2)
2015–2019	162 (22.7)	21 (19.6)	183 (22.4)
Age at cART initiation	105 (59–142)	101 (68–135)
< 67 months	174 (24.4)	31 (28.4)	205 (25.1)
67–102 months	189 (26.5)	19 (17.4)	205 (25.1)
103–136 months	176 (24.7)	30 (27.5)	207 (25.3)
> 136 months	172 (24.1)	29 (26.6)	201 (24.6)
Clinical stage^§
Early Stage	291 (41.3)	25 (23.1)	316 (38.9)
Advanced Stage	413 (58.7)	83 (76.9)	496 (61.1)
TB Status
Not Symptomatic	636 (97.5)	88 (97.7)	724 (97.6)
Symptomatic	16 (2.5)	2 (2.3)	18 (2.4)
cART backbone
AZT + 3TC	539 (75.8)	41 (37.9)	580 (70.7)
ABC + 3TC	89 (12.5)	17 (15.7)	106 (12.9)
D4T + 3TC	49 (6.8)	45 (41.6)	94 (11.5)
TDF + FTC	34 (4.7)	5 (4.6)	39 (4.8)
NNRTI/PI
NVP	360 (50.7)	63 (58.8)	423 (51.7)
EFV	349 (48.3)	44 (41.2)	393 (48)
Height for age, z-score
Z ≤ -2	470 (67.5)	66 (74.1)	536 (68.3)
Z > -2	226 (32.4)	23 (25.9)	249 (31.7)
Weight for height, z-score
Z ≤ -2	40 (25.6)	13 (56.5)	53 (29.6)
Z > -2	116 (74.4)	10 (44.5)	126 (70.4)
Weight for age, z-score
Z ≤ -2	309 (67.7)	48 (92.3)	357 (69.6)
Z > -2	148 (32.3)	8 (7.7)	156 (30.4)
cART changes
Yes	580 (79.4)	21 (19.2)	601 (73.1)
No	124 (16.9)	72 (66)	196 (23.8)
Unknown	9 (1.2)	16 (16.8)	25 (3)
Immunosuppression
Mild	87 (14.3)	9 (11.3)	96 (13.9)
Advanced	201 (33.1)	17 (21.5)	224 (32.4)
Severe	319 (52.5)	53 (67)	372 (53.8)

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Abbreviations: ABC, abacavir; AZT, zidovudine, cART, combined antiretroviral therapy, CI, confidence interval; d4T, Stavudine; EFV, Efavirenz; IQR, interquartile range; LPV/r; NVP, nevirapine; TB, tuberculosis; 3TC, lamivudine; Z-score, NCHS standard deviation.

Superscripts

^†Presented as n (%) for categorical data and median (interquartile range) for continuous data

^§WHO clinical early and advanced refer to stage 1 & 2 and stages 3 &4, respectively.

Description: cART change: Change constitutes changes of cART regimen among the first line options due a reason other than therapy failure e.g., due to toxicity, drug-drug interaction, among other.

3.2 Crude-incidence of cART failure

Crude incidence rates by key characteristics are provided in Table 2. A total of 279 TF events occurred. The prevalence of failure was 38.5% (95% CI 35–42.2). The crude incidence of TF was 6.5 events per 100-persons years of follow up (95% CI 5.8–7.3). The median time to TF was 4 years (IQR 2–7 years) among those who failed the first-line of therapy. Virologic failure (VF) occurred alone in 167/279 (23.1% [95% CI 20–26.3]) patients, immunologic failure (IF) alone was found in 19 (2.6% [95% CI 1.6–4.1]) cases, 82 (11.3% [95% CI 9.1–13.9]) had only clinical failure (CF) and 11 (1.5% [95% CI 0.8–2.7]) children had concomitant virologic, clinical and immunologic failures. Among all the children with TF, only 88 (31.5%) were switched to second-line treatments with the median time to cART switch being 19 months (IQR 11.3–49.7).

Table 2. Crude incidence and associated factors of therapy failure among children and adolescents in National Referral Pediatric Follow-up Clinic, Asmara, Eritrea (2005–2020).

Characteristics^†	Person time (years)	Events	Crude Incidence Rate (95% CI)	Rate Ratio (95% CI)	p-value^‡
Gender
Male	2203.3	156	7.1 (6.1–8.3)	1 (Ref) 0.84 (0.65–1.1)	0.07
Female	2051.41	120	5.8 (4.9–7)	1 (Ref) 0.84 (0.65–1.1)	0.07
Year of birth
≤ 2003	2602.8	175	6.7 (5.8–7.8)	1 (Ref) 0.91 (0.7–1.2)	0.24
2004+	1628.8	100	6.1 (5–7.5)	1 (Ref) 0.91 (0.7–1.2)	0.24
Address
Maekel	3270.3	197	6 (5.2–6.9)	1 (Ref) 1.3 (1–1.8)	0.01
Outside Maekel	984.4	79	8 (6.4–10)	1 (Ref) 1.3 (1–1.8)	0.01
Cohort Year
≤ 2010	2685	144	5.4 (4.5–6.3)	1 (Ref) 1.6 (1.2–2)	< 0.001
2011+	1546.6	131	8.4 (7.1–10)	1 (Ref) 1.6 (1.2–2)	< 0.001
Clinical stage^§
Early	1615.6	92	5.6 (4.6–7)	1 (Ref) 1.2 (1–1.6)	0.05
Late	2592.4	180	6.9 (6–8)	1 (Ref) 1.2 (1–1.6)	0.05
Immunosuppression
Mild/Advanced	1708.5	99	5.8 (4.7–7)	1 (Ref) 1.2 (0.9–1.5)	0.1
Severe	2007.3	136	6.8 (5.7–8)	1 (Ref) 1.2 (0.9–1.5)	0.1
Weight for age
z > -2	1405.9	70	5 (3.9–6.3)	1 (Ref) 1.5 (1.1–1.9)	0.003
z ≤ -2	2848.8	206	7.2 (6.3–8.3)	1 (Ref) 1.5 (1.1–1.9)	0.003
Height for age
z > -2	1514.8	82	5.4 (4.4–6.7)	1 (Ref) 1.3 (1–1.7)	0.02
z ≤ -2	2739.8	194	7.1 (6.2–8.2)	1 (Ref) 1.3 (1–1.7)	0.02
Weight for height
z > -2	3473.2	218	6.3 (5.5–7.2)	1 (Ref) 1.2 (0.9–1.6)	0.13
z ≤ -2	781.5	58	7.4 (5.7–9.6)	1 (Ref) 1.2 (0.9–1.6)	0.13
cART Backbone
AZT + 3TC	3615	216	6 (5.2–6.8)	1 (Ref) 1.5 (1.1–2.1)	0.002
AZT+3TC, ELSE^¶	639.7	60	9.4 (7.3–12.1)	1 (Ref) 1.5 (1.1–2.1)	0.002
NNRTI
EFV	1819	128	7 (5.9–8.4)	1 (Ref) 0.9 (0.7–1.1)	0.1
NVP	2424.3	145	6 (5.1–7)	1 (Ref) 0.9 (0.7–1.1)	0.1
Sub-optimal adherence record
No	3705	75	5.4 (4.7–6.2)	1 (Ref) 2.5 (1.9–3.2)	< 0.001
Yes	549.7	201	13.6 (10.9–17.1)	1 (Ref) 2.5 (1.9–3.2)	< 0.001

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Abbreviations: AZT, zidovudine; 3TC, lamivudine; cART, combined antiretroviral therapy; CI, confidence interval; EFV, efavirenz; LPV/r, lopinavir/ritonavir; NNRTI, Non-nucleoside Reverse Transcriptase Inhibitors; NVP, nevirapine; TB, tuberculosis; Z-score, NCHS standard deviation.

Superscripts

^†Characteristics were evaluated at baseline, unless otherwise specified

^‡Compares the difference of crude incidence of failure per patients’ characteristics^,

^§WHO clinical early and advanced refer to stage 1 & 2 and stages 3 &4, respectively

^¶abacavir + lamivudine, stavudine + lamivudine or tenofovir + emtricitabine.

3.3 Prevalence and factors associated with TF

The children who failed on a first-line cART as compared to those who didn’t were more likely to be in advanced WHO clinical stage (p = 0.005), have lower baseline CD4⁺ lymphocyte count (p = 0.004), be stunted (P = 0.01), have had a longer duration of follow up (p = 0.003), have suboptimal adherence (p<0.001), have a record of cART change (p = 0.002) and have a higher frequency of regimen change (p = 0.001) (Table 3).

Table 3. Characteristics of the study participants stratified by therapy outcome in National Referral Pediatric Follow-up Clinic, Asmara, Eritrea (2005–2020).

Characteristics^†	Therapy failure cases n = 279	No Therapy failure n = 445	p-value^‡	Total n (%)
Gender
Males	157 (41.1)	225 (58.9)	0.134	382 (52.8)
Females	122 (35.7)	220 (64.4)	0.134	342 (47.2)
Year of birth	2002 (1999–2005)	2003 (1999–2006)	0.114
Address
Maekel	199 (37.5)	332 (62.5)	0.27	531 (73.3)
Outside Maekel	80 (41.5)	113 (58.5)	0.27	193 (26.7)
Age at cART initiation (months)	103.9 (98.4–109.5)	99.3 (94.8–103.7)	0.59
Duration enrollment to cART initiation (months)	6 (1–33)	7 (1–36)	0.77
cART initiation year	2010 (2008–2014)	2011 (2008–2014)	0.71
2005–2009	109 (38.4)	175 (61.6)	0.87	284 (39.3)
2010–2014	110 (39.7)	167 (60.3)		277 (38.4)
2015–2019	60 (39.3)	101 (62.7)		161 (22.3)
Clinical stage ^§
Stage 1 and 2	94 (32.2)	198 (67.8)	0.005	290 (40.6)
Stage 3 and 4	181 (42.7)	243 (57.3)	0.005	425 (59.4)
TB status
Not symptomatic	242 (38.1)	393 (61.9)	0.28	634 (97.5)
Symptomatic	4 (25)	12 (75)	0.28	16 (2.5)
Immunosuppression
Mild	30 (34.5)	57 (65.5)	0.06	87 (14.2)
Advanced	70 (33.8)	137 (66.2)		207 (33.8)
Severe	138 (43.1)	181 (56.9)		319 (52)
Weight for age, z-score
Z≤ -2	117 (37.9)	192 (62.1)	0.07	309 (67.8)
Z > -2	43 (29.3)	104 (70.7)	0.07	147 (32.2)
Height for age, z-score
Z ≤-2	193 (41.2)	276 (58.8)	0.043	469 (67.5)
Z > -2	75 (33.2)	151 (66.8)	0.043	226 (32.5)
Weight for height, z-score
Z ≤ -2	17 (42.5)	23 (57.5)	0.16	40 (25.8)
Z > -2	35 (30.4)	80 (69.6)	0.16	115 (74.2)
cART Backbone
AZT + 3TC	217 (40)	326 (60)	0.14	543 (75.1)
AZT + 3TC, ELSE^¶	61 (33.9)	119 (66.1)	0.14	180 (24.9)
NNRTI/PI
NVP	146 (39.9)	220 (60.1)	0.126	366 (50.6)
EFV	130 (36.6)	225 (63.4)		355 (49.1)
LPV/r	2 (100)	0		2 (0.3)
Suboptimal adherence
Yes	76 (70.7)	30 (28.3)	<0.001	106 (14.6)
No	203 (32.8)	415 (67.2)	<0.001	618 (85.4)
cART Substitution
Yes	243 (41.4)	344 (58.6)	0.002	586 (80.9)
No	32 (24.8)	97 (75.2)		129 (17.8)
Unknown	4 (44.4)	5 (55.6)		9 (1.2)
Frequency of cART change	2 (2–3)	1 (1–3)	< 0.001

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Abbreviations: ABC, abacavir; AZT, zidovudine, cART, combined antiretroviral therapy, CI, confidence interval; d4T, Stavudine; EFV, Efavirenz; IQR, interquartile range; LPV/r; NVP, nevirapine; TB, tuberculosis; 3TC, lamivudine; Z-score, NCHS standard deviation. P values refer to differences between included and excluded patients on baseline characteristics

Superscripts

^†Presented as n (%) for categorical data and median (interquartile range) for continuous data

^‡ The comparisons were performed using Pearson’s Chi-square test or Fisher’s exact test, as appropriate, for categorical data, and Wilcoxon rank sum/Mann Whitney U-test for continuous data

^§WHO clinical early and advanced refer to stage 1 & 2 and stages 3 &4, respectively.

^¶abacavir + lamivudine, stavudine + lamivudine or tenofovir + emtricitabine.

Description: Frequency of cART change: Change constitutes changes of cART regimen among the first line options due a reason other than therapy failure e.g., due to toxicity, drug-drug interaction, among others.

3.4 Relationship between number viral load tests performed and detection of TF

A strong relationship was observed between the number of VL tests performed and TF. A steep drop in the recruitment of infected children into the cART program and an increase in VL testing were also observed in the later years (Fig 2).

3.5 Kaplan-Meier analysis for TF incidence

The Kaplan-Meier estimates (Fig 3) of failure incidence comparing TF for the following factors; adherence, age at treatment initiation, cohort year, and cART backbone using median time to therapy failure. Sub-optimal adherence was associated with a reduced median time to TF (75.8 (95% CI, 65.7–85.96) months vs 117.2 (95% CI, 111.6–122.8) months) (Fig 3A). A significant difference in failure rates was also observed between adolescents and children: children, median = 120.7 (95% CI, 115–126.7) months; and adolescents, median = 78 (95% CI, 71.3–85) months (Fig 3B). Late initiation year were also associated with shorter median time to TF (2005–2009, mean = 124.85 (95% CI, 118.03–131.7) months; 2010–2014, median = 93.24 (95% CI, 88.3–98.1); 2015–2019, median 51.02 (95% CI, 47.1–54.95) (Fig 3C). The median time to TF for AZT+3TC was 114.5 (95%CI, 109.1–119.8) months vs 92.1 (95%CI, 79.7–104.4) months for alternative backbones (Fig 3D).

3.6 Multivariate analysis of independent factors of TF

In the adjusted Cox proportional hazards model independent factors of TF were suboptimal adherence (aHR = 2.9, 95%CI 2.2–3.9, p<0.001), cART backbone other than AZT and 3TC (aHR = 1.6, 95% CI 1.1–2.2, p = 0.01), severe immunosuppression (aHR = 1.5, 95%CI 1–2.4, p = 0.04), wasting or weight for height z ≤ -2 (aHR = 1.5, 95% CI 1.1–2.1, p = 0.02), late cART initiation calendar years (aHR = 1.15, 95%CI 1.1–1.3, p < 0.001), and older age at cART initiation (aHR = 1.01, 95%CI 1–1.02, p<0.001) (Table 4).

Table 4. Cox proportional hazards of cART therapy failure among CLHIV and adolescents in National Referral Pediatric Follow-up Clinic, Asmara, Eritrea (2005–2020).

Characteristics^†	Crude HR (95% CI)	p-value	Adjusted HR (95% CI)	p-value
Gender
Male	1.0 (Reference)
Female	0.90 (0.70–1.30)	0.8
Address
Maekel	1.0 (Reference)		1.0 (Reference)
Outside Maekel	1.30 (0.90–1.60)	0.15	1.30 (0.90–1.70)	0.09
Age at cART initiation	1.01 (1.0–1.02)	<0.001	1.01 (1.0–1.02)	<0.001
cART started calendar year	1.16 (1.10–1.20)	<0.001	1.15 (1.10–1.30)	<0.001
Clinical Stage
Early	1.0 (Reference)
Advanced	1.13	0.4
Immunosuppression
Mild	1.0 (Reference)		1.0 (Reference)
Advanced	1.01 (0.64–1.60)	0.9	1.02 (0.60–1.60)	0.9
Severe	1.40 (0.95–2.30)	0.08	1.50 (1.02–2.40)	0.04
Height for age, z score
Z >-2	1.0 (Reference)
Z <-2	1.14 (0.8–1.1.5)	0.4
Weight for height, z score
Z >-2	1.0 (Reference)		1.0 (Reference)
Z <-2	1.50 (1.05–2.10)	0.02	1.50 (1.10–2.10)	0.02
cART Backbone
AZT + 3TC	1.0 (Reference)		1.0 (Reference)
AZT+3TC, Else^¶	1.60 (1.10–2.40)	0.008	1.60 (1.10–2.20)	0.01
NNRTI/PI
EFV	1.0 (Reference)
NVP	1.16 (0.90–1.60)	0.3
cART change frequency	0.90 (0.67–1.19)	0.46
Suboptimal adherence
No	1.0 (Reference)		1.0 (Reference)
Yes	2.90 (2.10–3.90)	<0.001	2.90 (2.20–3.90)	<0.001

Open in a new tab

Abbreviations: AZT, zidovudine; 3TC, lamivudine; CI, confidence interval; cART, combined antiretroviral therapy; EFV, efavirenz; NVP, nevirapine; d4T, Stavudine; EFV, Efavirenz; z-scores NCHS standard deviations.

Superscripts

^†Characteristics were evaluated at baseline, unless otherwise specified

^‡ The analyses were performed using Cox proportional hazards model

^§ WHO clinical early and advanced refer to stage 1 & 2 and stages 3 &4

^¶abacavir + lamivudine, stavudine + lamivudine or tenofovir+emtricitabine

4. Discussion

This study documented the pediatric HIV TF rate in one of the largest referral facilities in Eritrea. In this cohort, the HIV TF rate was 38.5% (95% CI, 35–42.2) with a median (IQR) time to TF of 48 (IQR, 24–84) months. The results in this study are comparable to first-line NNRTI-based cART failure rate results elsewhere in the sub-continent– 34% in Kenya [9]; 29% (95% CI, 6–33) in Mozambique, and 34% (median of 26.4 months) in Uganda [19]. Perhaps, more importantly, a meta-analysis conducted in specific LMIC reported a pooled TF rate of 26–36% [11]. Admittedly, TF rates vary widely with relatively high and low failure rates reported in some jurisdictions in SSA –60% in the Central Africa Republic [12] vs. 14.1% in Ethiopia [14, 15]. The observed variation is largely influenced by study type, treatment duration, VL thresholds, and CD 4⁺ T cell count thresholds employed, among others [15, 16, 20]. In general, studies deploying low CD4⁺ T cell count (<50 cells/mm³) or VL thresholds (<1000 copies/ml) tend to report high TF rates [14, 15]. In contrast, studies deploying clinical and/or immunologic endpoints as the sole determinants of TF tend to present the converse [14]. As VF tends to precede clinical and immunological failure by approximately 12 months [9, 19], it’s a more sensitive prognosticator of TF [21]. In this cohort, the frequencies of clinical and immunological failure were, indeed, low (19 (6.8%) immunological, and 82 (29.3%) clinical failure). Therefore, the limited and sporadic use of VL testing (particularly in the period preceding 2017) adds the possibility that this study may have underestimated the frequency of TF in this cohort. This, without a doubt, highlights the importance of expediting the ongoing efforts to scale up VL testing in the country.

Aside from the relatively high frequency of TF reported in this study; the data demonstrated that TF was associated with physician documentation of suboptimal adherence; cART backbone other than AZT+3TC: (Abacavir(ABC)+(3TC); Stavudine(d4T)+3TC or Tenofovir(TDF)+Emtricitabine (FTC)); severe immunosuppression; weight-for-age and height-for-age Z-scores; cART drug substitution/holding regimens; late cART initiation calendar years and older age at cART initiation. These findings harmonize well with previous reports [21]. For example, multiple studies in the region have shown that chronic malnutrition, low CD4⁺ cell count, suboptimal adherence, and cART drug substitution are important drivers of first-line cART failure [8, 14, 15]. Our finding that a higher likelihood of first-line cART failure in children who had frequent cART drug substitution is also consistent with others studies [14, 22]. Delays in pill pick-ups, distances to treatment centers, drug stock-outs, and inefficiencies in supply chains have been associated with frequent cART drug substitutions [5] and maybe decisive in this jurisdiction. In another drug-resistance mutations research, they noted that malnutrition is characterized by perverse alterations in body composition and metabolic dysfunction and that these factors may undermine the efficacy of cART [23]. Importantly, children with severe immunosuppression and/or malnourished are more susceptible to gastrointestinal infections (chronic diarrhea), possibly impeding the absorption of cART [23].

Similarities aside, country-level analysis reveals important differences between this study and other studies from the region. For instance, when years of treatment initiation were disaggregated to 2005–2009, 2010–2014, 2015–2019 recent cART initiation was strongly associated with TF. This finding and the data demonstrating that the use of backbones other than 3TC and AZT or that older age at cART initiation is associated with TF are either unique or more common in this setting [8]. The association between the late cART initiation calendar year and TF is probably linked to the modest expansion of VL testing services in the country. Presumably, there is a linear relationship between expanded VL testing and enhanced detection of VF (Fig 2). This finding and the data showing a high failure rate of backbones other than 3TC and AZT; may point at a potential reduction in the efficacy of existing cART due to the emergence of resistance-associated mutations (RAM) as a result of prolonged usage or pre-existence of RAM before initiation of treatment-common NRTI based mutations include M184V, K65R, and four major NNRTI based mutations: 103N, Y181C, G190A, and V106M. These are well-documented possibilities [24]. To address this concern, particularly when Pre-treatment drug-resistance mutations (PDRMs) are ≥10%, the WHO recommends testing for PDRMs before initiating cART or when considering a programmatic switch of 1st-line cART from NNRTI-based regimens. In the absence of RAM, these possibilities are hard to verify. Overall, the finding underscores the importance of research on RAM and its effect on virologic trajectories or overall treatment outcomes. In Eritrea, a previous (2016/17) unpublished survey conducted among ART initiators, estimated that PDRMs to NNRTI in adults was 7.1% (95% CI: 3.8–12.9%)–the most frequently observed mutation was in the K103 position (National CDC). However, data RAM or PDRMs in children on cART is missing. Therefore, the contribution of RAM or PDRM to TF in the country is hard to discern. Altogether, the finding underscores the importance of research on RAM and its effect on Virologic trajectories or overall treatment outcomes.

Another result was the observed link between older age at cART initiation and TF. According to several SSA studies, younger children have a higher likelihood of VF compared to their older counterparts [8, 14]. In contrast, our data demonstrate that children who started treatment when they were older had a higher likelihood of TF. Multiple explanations can be invoked to explain this outcome including the possibility that HIV is still a stigma-laden disease in Eritrea. Difficulties facing adolescents in disclosing HIV status or caregivers/families in disclosing HIV status to their child may undermine enrolment in cART programs. Often, contact with clinicians is prompted by the recurrence of opportunistic infections or severe clinical problems. A consequence of this development is a bias towards more advanced diseases, high VL, or WHO stage-four disease [11, 25]. The data also hints at the residual effect of low attendance of pre-natal clinics by pregnant mothers in previous years. Along with expanding PMTCT coverage and pediatric DNA-based HIV-1 testing, testing strategies for children outside of these programs should be developed.

As discussed above, drug failure in this cohort is associated with several modifiable risk factors. These include malnutrition, suboptimal adherence, and delays in drug switching, or high frequency of drug substitution. According to some studies, optimization of adherence, following the expansion of virologic testing, should be the first approach to addressing TF, when resistance assays are unavailable [9]. In turn, adherence is associated with a variety of factors including drug, social-cultural background, health workers’ and health system factors [8]. Another problem is the fact that physicians have only limited tools to perform reliable diagnoses of poor adherence to cART. Intervention must therefore be multi-pronged and data-driven. For instance, some possible interventions include expansion of access to alternative regimens, fixed-dose combination tablets, and syrups, strengthening drug delivery chains, patient counseling, reduction in contact intervals between patients and clinicians (weekly or bi-weekly) and following missed clinic visits by home visits.

Separately, we evaluated the duration between diagnosis of TF and switching. According to our result, a large number (68.4%) of children with TF were still on a first-line regimen. A relatively long median time to cART switch (19 months (IQR 11.3–49.7)) was also uncovered. Remarkably, long lag-time between diagnosis of TF and switch to second-line regimens is a common phenomenon in many cART programs in SSA [2, 9, 22]. According to a recent WHO report, the continued use of Nevirapine-based regimens despite the high levels of PDRMs to NNRTIs contribute to lower viral suppression among children [24]. There are multiple reasons why clinicians may delay switching. These include unavailability of generic second-line or third-line cART; the complexity of second-line options (particularly appropriate pediatric formulations); concerns regarding adherence and the need to salvage therapy in the face of emerging RAM, among others [2]. The limited range of second-line treatment options, the predominant use of clinical failure as a switch trigger, and adverse drug reactions is a plausible explanation of the reluctance by clinicians in this cohort to switch regimens.

Admittedly, the consequences of modest switching delays remain controversial. According to some investigators, remaining on failing NNRTI-based cART is associated with a heightened risk of drug resistance particularly, thymidine-associated mutations (TAMs) [2, 26]. In contrast, the ARROW study suggested that delays in the switching of up to 2 years may have limited clinical implications [27]. Similar results were reported in the CHER trial where 84% had VL<400 c/mL at the end of 5 years, with only 2.05% completing follow-up switching [28]. The possibility that endpoints of remaining on failing regimens may differ depending on the regimen, has also been suggested [22]. Although these findings have interesting implications for treatment in resource-limited settings such as Eritrea; guidelines suggest that children with VF should be switched promptly to treatment regimens that include pharmacologically boosted PIs, or other drug combinations preferably integrase strand transfer inhibitors (INSTIs) based combinations [5, 22, 29].

5. Conclusion

The catalog of factors uncovered in this report bears a close resemblance to previous reports from the region. In this regard, they highlight existing concerns about the effectiveness of the current pediatric cART program in SSA. Moving forward, widespread virologic testing, prompt switching of regimens, limiting treatment interruptions, better support systems for adherence, and resistance surveillance should be emphasized. Moreover; the integration of nutritional support to tackle the impact and high magnitude of undernutrition in this population should be worked upon. Altogether, it’s our considered opinion that implementing these measures is crucial in the country’s pursuit of the UNAIDS 90-90-90 goals.

This study has several strengths as well as limitations. The relatively long duration of follow-up (15 years), large study population, and robust clinical data are major strengths. Nevertheless, it has several limitations. First, we may have underestimated the incidence of TF because not all patients performed VL tests. Second, retrospective studies are associated with missing covariate data. Further, critical socioeconomic data on parents or guardians and programmatic data were not collected. The approach used for adherence may also be limiting.

Supporting information

S1 File

(SAV)

Click here for additional data file.^{(63KB, sav)}

Acknowledgments

The authors would like to thank the clinical staff who supported this work at Orotta Pediatric National Referral Hospital. We are grateful to National Communicable Disease Control Division, Eritrean Ministry of Health, and ART Health Management Information System (HMIS) developers and technicians. We also thank the families and participants in the study. Our sincere appreciation goes to Dr. Ariam Mebrahtu, Dr. Natnael Belay, Dr. Yafet Tekle, Dr. Yonathan Tesfaldet, and Dr. Simon Tesfay who helped us in data collection.

List of abbreviations

3TC: Lamivudine
ABC: Abacavir
AIDs: Acquired Immunodeficiency syndrome
AZT: Zidovudine
cART: Combined Anti-Retroviral Therapy
CD4: Cluster Designation
d4T: Stavudine
DNA: Deoxyribose Nucleic Acid
EFV: Efavirenz
HIV: Human Immunodeficiency Virus
FTC: Emtricitabine
HMIS: Health Management Information System
HR/AHR–Hazard Rate: Adjusted Hazard Rate
LTFU: Lost To Follow Up
NNRTI: Non-Nucleoside Reverse Transcriptase Inhibitors
NVP: Nevirapine
PYFU: person-year follow-up
SD: Standard deviation
TF: Therapy failure
VL: Viral load
WHO: World Health Organization

Data Availability

All relevant data are within the paper and its Supporting Information file.

Funding Statement

The author(s) received no specific funding for this work.

References

1.UNAID. HIV/AIDS 2021 fact sheet [Internet]. https://www.unaids.org/en/resources/fact-sheet. 2021. Available from: https://www.unaids.org/en/resources/fact-sheet accessed on 22nd of October 22, 2022
2.Fitzgerald F, Penazzato M, Gibb D. Development of antiretroviral resistance in children with HIV in low-and middle-income countries. J Infect Dis. 2013;207(suppl_2): S85–92. doi: 10.1093/infdis/jit115 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.UNAIDS. Start Free Stay Free AIDS Free, 2019. Report. [Google Scholar]
4.Gupta RK, Gregson J, Parkin N, Haile-Selassie H, Tanuri A, Forero LA, et al. HIV-1 drug resistance before initiation or re-initiation of first-line antiretroviral therapy in low-income and middle-income countries: a systematic review and meta-regression analysis. Lancet Infect Dis. 2018;18(3):346–55. doi: 10.1016/S1473-3099(17)30702-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Cissé A-M, Laborde-Balen G, Kébé-Fall K, Dramé A, Diop H, Diop K, et al. High level of treatment failure and drug resistance to first-line antiretroviral therapies among HIV-infected children receiving decentralized care in Senegal. BMC Pediatr. 2019;19(1):1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
6.WHO. Consolidated Guidelines on the Use of Antiretroviral Drugs for Treating and Preventing HIV Infection. Recommendations for a Public Health approach, 2nd edition—2017, 131 p. [Google Scholar]
7.Haile GS BA. Predictors of treatment failure, time to switch, and reasons for switching to second-line antiretroviral therapy in HIV-infected children receiving first-line antiretroviral therapy at a Tertiary Care Hospital in Ethiopia. BMC Pediatr. 2019;19(37). [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Muri L, Gammell A, Ntamatungiro AJ, Glass TR, Luwanda LB, Battegay M, et al. Development of HIV drug resistance and therapeutic failure in children and adolescents in rural Tanzania: an emerging public health concern. 2017;0(May 2016). doi: 10.1097/QAD.0000000000001273 [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Wamalwa D, Lehman DA, Benki-Nugent S, Gasper M, Gichohi R, Maleche-Obimbo E, et al. Long-term virologic response and genotypic resistance mutations in HIV-1 infected Kenyan children on combination antiretroviral therapy. J Acquir Immune Defic Syndr. 2013;62(3):267. doi: 10.1097/QAI.0b013e31827b4ac8 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Rogo T, DeLong AK, Chan P, Kantor R. Antiretroviral treatment failure, drug resistance, and subtype diversity in the only pediatric HIV clinic in Rhode Island. Clin Infect Dis. 2015;60(9):1426–35. doi: 10.1093/cid/civ058 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Boerma RS, Boender TS, Bussink AP, Calis JC, Bertagnolio S, Rinke de Wit TF, et al. Suboptimal viral suppression rates among HIV-infected children in low-and middle-income countries: a meta-analysis. Clin Infect Dis. 2016;ciw645. doi: 10.1093/cid/ciw645 [DOI] [PubMed] [Google Scholar]
12.High levels of virological failure with major genotypic resistance mutations in HIV-1-infected children after 5 years of care according to WHO-recommended 1st-line and 2nd-line antiretroviral regimens in the Central African Republic: a cross-sectional study. Vol. 96, Medicine. [DOI] [PMC free article] [PubMed]
13.Predictors of treatment failure and time to detection and switching in HIV-infected Ethiopian children receiving first-line antiretroviral therapy. Vol. 12, BMC infectious diseases. p. 1–8. [DOI] [PMC free article] [PubMed]
14.Asgedom SW, Maru M, Berihun B, Gidey K, Niriayo YL AT, Asgedom SW, et al. Immunologic and Clinical Failure of Antiretroviral Therapy in People Living with Human Immunodeficiency Virus within Two Years of Treatment. Hindawi BioMed Res Int. 2020;2020:8. doi: 10.1155/2020/5474103 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Lenjiso GA, Endale BS BY. Clinical and immunological failure among HIV-positive adults taking first-line antiretroviral therapy in Dire Dawa, eastern Ethiopia. BMC Public Health. 2019;19(771). doi: 10.1186/s12889-019-7078-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
16.WHO. Consolidated Guidelines on the Use of Antiretroviral Drugs for Treating and Preventing HIV Infection: Recommendations for a Public Health Approach. 2nd. Geneva; 2016. 131 p. [PubMed]
17.Irvine C, Armstrong A, Nagata JM, Rollins N, Schaaf D, Doherty M, et al. Setting Global Research Priorities in Pediatric and Adolescent HIV Using the Child Health and Nutrition Research Initiative (CHNRI) Methodology. 2018;78:3–9. doi: 10.1097/QAI.0000000000001742 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Juergen Dr., Prof. Micheal Golden, Dr. John DO. ENA for SMART [Internet]. 2020. Available from: www.nutrisurvey.net/ena/ena.html [Google Scholar]
19.Costenaro P, Penazzato M, Lundin R, Rossi G, Massavon W, Patel D, et al. Predictors of Treatment Failure in HIV-Positive Children Receiving Combination Antiretroviral Therapy: Cohort Data From Mozambique and Uganda. 2015;4(1):39–48. doi: 10.1093/jpids/piu032 [DOI] [PubMed] [Google Scholar]
20.Crowell CS, Maiga AI, Sylla M, Taiwo B, Kone N OA, et al. High Rates of Baseline Drug Resistance and Virologic Failure Among ART Naïve HIV-Infected Children in Mali. Pediatr Infect Dis J. 2017;36(11):e258–e263. [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Rutherford GW, Anglemyer A, Easterbrook PJ, Horvath T, Vitoria M, Penazzato M, et al. Predicting treatment failure in adults and children on antiretroviral therapy: a systematic review of the performance characteristics of 2010 WHO immunologic and clinical criteria for virologic failure. Aids. 2014;28:S161–9. [DOI] [PubMed] [Google Scholar]
22.Patten G, Schomaker M, Davies M-A, Rabie H, Van Zyl G, Technau K EB, et al. What Should we do When HIV-Positive Children Fail First-Line Combination Antiretroviral Therapy? A Comparison of 4 ART Management Strategies. Pediatr Infect Dis J. 2019;38(4):400–405. [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Rossouw TM, Feucht UD, Melikian G, Van Dyk G, Thomas W, du Plessis NM, et al. Factors associated with the development of drug resistance mutations in HIV-1 infected children failing protease inhibitor-based antiretroviral therapy in South Africa. PLoS One. 2015;10(7):e0133452. doi: 10.1371/journal.pone.0133452 [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Beck IA, Levine M, McGrath CJ, Bii S, Milne RS, Kingoo JM SI, et al. Pre-treatment HIV-drug resistance associated with the virologic outcome of first-line NNRTI-antiretroviral therapy: A cohort study in Kenya. EclinicalMedicine. 2020;18. [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Bertagnolio S, De Luca A, Vitoria M, Essajee S, Penazzato M, Hong SY, et al. Determinants of HIV drug resistance and public health implications in low-and middle-income countries. Antivir Ther. 2012;17(6):941. doi: 10.3851/IMP2320 [DOI] [PubMed] [Google Scholar]
26.Team P-1 S. First-line antiretroviral therapy with a protease inhibitor versus non-nucleoside reverse transcriptase inhibitor and switch at higher versus low viral load in HIV-infected children: an open-label, randomized phase 2/3 trial. Lancet Infect Dis. 2011;11(4):273–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Szubert AJ, Prendergast AJ, Spyer MJ, Musiime V, Musoke P, Bwakura-Dangarembizi M, et al. Virological response and resistance among HIV-infected children receiving long-term antiretroviral therapy without virological monitoring in Uganda and Zimbabwe: Observational analyses within the randomized ARROW trial. PLoS Med. 2017;14(11):e1002432. [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Cotton M. VA, Gibb D, Otwombe K, Josipovic D, Panchia R, Jean-Philippe P, et al. Early ART followed by Interruption Is Safe and Is Associated with Better Outcomes than Deferred ART in HIV+ Infants: Final Results from the 6-Year Randomized CHER Trial. In: 19th Conference on Retroviruses and Opportunistic Infections. Atlanta; 2012. p. Paper 28LB.
29.Meyers T, Sawry S, Wong JY, Moultrie H, Pinillos F, Fairlie L, et al. Virologic failure among children taking lopinavir/ritonavir-containing first-line antiretroviral therapy in South Africa. Pediatr Infect Dis J. 2015;34(2):175. doi: 10.1097/INF.0000000000000544 [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0282642.r001

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27 Apr 2022

PONE-D-21-19949

Prevalence and predictors of pediatric HIV therapy failure in a tertiary hospital in Asmara, Eritrea: A 15-year retrospective cohort study

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The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Summary of the research

Pediatric HIV remains a concern, particularly in sub-Saharan setting. The authors addressed the incidence and correlates of antiretroviral treatment failure defined by virologic, immunologic or clinical criteria. The crude incidence of failure was 6.5 events per 34 100- person-years (95% CI 5.8-7.3). Factors associated with TF were treatment adherence assess by pills count, ART backbone other than AZT and 3TC, severe immune, suppression, wasting, late ART initiation in calendar years and older age at ART initiation. Study data are important for the medical care of infants with HIV. I have some comments on the paper. These are provided below, split into minor and major.

Minor comments

The introduction and discussion part may be shortened.

Line 52: Space between 2018 and reference 1

Line 59: Do authors mean prevalent cases instead of prevalence.

Line 95: Provide ethical approval number.

Line 100: The study includes children and adolescents. Please specify this .

Line 103: Is LPV/r not a recommended first line ART treatment in children in Eritrea under 3 years?

Line 130: Please define SD

Line 167: Please explain why you provide a p-value without any comparison.

Table 4: For some odds ratios, you presented decimal number with one figure after the decimal others two, plesese present them the same way.

Line 384: The limitation section should be in the discussion section.

Major comments

- You excluded 12% of your cohort without any justification. Can you provide the details why they were excuded. As stated in table 1, excluded participants were mostly in advanced stage of the disease and lived outside of Maekel. The exclusion of these participants is source of selection bias which is not addressed in the manuscript. For the survival analyze , we suggest you include all participants and censored those lost to follow up. In addition, presenting percentages in line makes it difficult to read table 1. It would be better to present the percentages in colons.

- The study design is a retrospective study yet you report TF prevalence. In retrospective studies you can not evaluate the prevalence . You also reported a cumulative incidence and incidence rate. Can the authors clarify this in the manuscript.

- Adherence was assessed upon each follow-up visit based on missed doses. This variable changes with time and so this should be mentionned in the model. In addition, this variable was operationally defined as a categorial variable, but in table 4 it was instead used as a quantitative variable.

- The denominator of TF is not clear from line 184 to line 187 ( how did you calculate the percentages)?. In table 2 there is no reference for the rate ratio.

- The bivariate analysis in table 3 is not appropriate. Please remove this section.

- ART change during follow up is probably related to TF or vice versa, so it would not be appropriate to use this variable as a predictor.

**********

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Reviewer #1: No

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PLoS One. 2023 Mar 9;18(3):e0282642. doi: 10.1371/journal.pone.0282642.r002

Author response to Decision Letter 0

23 Jun 2022

We would like to thank Academic Editor of PLOS ONE and our reviewer for their invaluable inputs and constructive comments that are helpful to massively improve the quality of the manuscript. After careful consideration of the points raised, the point-by-point response are as follows:

Minor Points raised or amendments requested by reviewer #1 Authors’ Response are beneath of each reviewer's comments

2. Line 52: Space between 2018 and reference 1

The comment has been addressed

3. Line 59: Do authors mean prevalent cases instead of prevalence.

The statement has been overhauled to “In Eritrea, a 2019 Spectrum modeling estimated that the magnitude of people living with HIV/AIDS (PLWHA) is 14, 000 (0.36%). Among these, children (<15 years) make up 4% and 8,956 (73%) patients are currently cART.”

4.Line 95: Provide ethical approval number

Directive noted and the number is provided.

5.Line 100: The study includes children and adolescents. Please specify this.

The comment has been addressed and further description of the age breakdown is stated in the methodology section called operational definition.

6. Line 103: Is LPV/r not a recommended first line ART treatment in children in Eritrea under 3 years?

Protease inhibitors (LPV/r) are second line regimens in Eritrea as per local guidelines due the absence of syrup based formulations. So the LPV/r tablets are similarly used as second-line regimens as in adults.

7.Line 130: Please define SD.

SD is defined in the beginning of the statement as “Standard deviation (SD)”

8. Line 167: Please explain why you provide a p-value without any comparison.

The comparison is now being placed and mainly compares the included vs excluded cases.

9. Table 4: For some odds ratios, you presented decimal number with one figure after the decimal others two, please

Present them the same way.

The comment has been addressed.

10. Line 384: The limitation section should be in the discussion section

Necessary changes have been undertaken.

11.Discussion may be shortened

Some sentences have been deleted.

Major Revision

Major points raised or amendments requested by Reviewer #1 Authors’ response

1. You excluded 12% of your cohort without any justification. Can you provide the details why they were excluded? As stated in table 1, excluded participants were mostly in advanced stage of the disease and lived outside of Maekel. The exclusion of these participants is source of selection bias which is not addressed in the manuscript.

The detailed justification for exclusion are specified in figure 1. 97 cases were excluded out of which, 54 patients were excluded due to follow-up less than 6 months.

In studies of this kind [See Costenaro P, Penazzato M, Lundin R, Rossi G, Massavon W, Patel D, et al. Predictors of Treatment Failure in HIV-Positive Children Receiving Combination Antiretroviral Therapy : Cohort Data From Mozambique and Uganda. 2015;4(1):39–48.], treatment response is evaluated at 6 months following treatment initiation. The reasoning behind this cut-off is that treatment response cannot be defined prior to this duration. An additional 42 cases were excluded due to key missing follow up data: unknown follow up duration and those with no therapy outcome endpoints. Missing data is a common problem in retrospective studies of this kind. Consequently, this statement has been in cooperated in the manuscript.

2. For the survival analysis, we suggest you include all participants and censored those lost to follow up.

This has been addressed in the methodology subsection, end-point definition as “For TF analysis, the period of follow-up was from cART initiation up to the earliest detection of TF. Children without TF were censored at the date of death, lost to follow-up (defined as missing follow-up visits for more than 6 months), transferred to another clinic, or the date record of any last event in the clinic.” So, yes we included all participants and censored those lost to follow up.

3. Presenting percentages in line makes it difficult to read table 1. It would be better to present the percentages in colons.

The comment has been addressed. All the percentages are across columns.

4. The study design is a retrospective study yet you report TF prevalence. In retrospective studies you cannot evaluate the

Prevalence. You also reported a cumulative incidence and incidence rate. Can the authors clarify this in the manuscript?

From epidemiologic literatures (Biostatistics and epidemiology: a primer for health and biomedical prefessionals / by SylviaWassertheil-Smoller. —3rd ed.page 92-93), our understanding of prevalence is:

= Number of persons with a condition / Total number of persons in population at risk for the condition at a particular point in time

In our case, our population is children living with HIV receiving cART in the study setting and the condition is therapy failure.

On the other hand, incidence means:

= Number of new cases of a disease per unit of time / Total number at risk in beginning of this time period

In our opinion, so long as patient’s information is well documented, period prevalence of a specific disease in a population can be calculated.

Overall, it our submission that period prevalence can be calculated using retrospective data so long as the data is well collected and the time period is specified.

The below listed references used similar approach to determine the burden (prevalence) of TF.

A. Solomon Weldegebreal Asgedom et al., Immunologic and Clinical Failure of Antiretroviral Therapy in People Living with Human Immunodeficiency Virus within Two Years of Treatment. Hindawi BioMed Research International Volume 2020, Article ID 5474103, 8 pages

B. Dow et al.: Durability of antiretroviral therapy and predictors of virologic failure among perinatally HIV-infected children in Tanzania: a four-year follow-up. BMC Infectious Diseases 2014 14:567

C. Isaac O. Abah et al: Antiretroviral Therapy-associated Adverse Drug Reactions and their Effects on Virologic Failure- A Retrospective Cohort Study in Nigeria. Current HIV Research, 2018, 16, 436-446

D. Bacha et al.: Predictors of treatment failure and time to detection and switching in HIV-infected Ethiopian children receiving first line anti-retroviral therapy. BMC Infectious Diseases 2012 12:197.

5. Adherence was assessed upon each follow-up visit based on missed doses. This variable changes with time and so this

Should be mentioned in the model.

The definition of suboptimal adherence comprises of at least one record of fair or poor adherence. Hence patients were classified as either having a record of suboptimal adherence or no record throughout their follow up period.

6. In addition, this variable was operationally defined as a categorical variable, but in Table 4 it was instead used as a quantitative variable.

This comment has been addressed.

7. The denominator of TF is not clear from line 184 to line 187 (how did you calculate the percentages)?

Absolute numbers have been included. e.g. 279/724 for a prevalence of 38%. The denominator is total number ofCLHIV whereas the numerators utilized are number Virologic failure alone to calculate VF, Immunologic failure alone to calculate IF, clinical failure alone to calculate CF and number of patients who had virologic, immunologic and clinical failure at the same time.

8. In table 2 there is no reference for the rate ratio.

This comment has been addressed.

9. The bivariate analysis in table 3 is not appropriate. Please remove this section.

Table analyzed factors associated with the prevalence of TF. While table 2 analyses factors as per subcategories of the variables’ incidence.

However, and based on your opinion, we can either retain the table (after revising the title – e.g we have amended the title to factors associated with prevalence TF) as is generally the case in studies of this kind or attach it as a supplementary file.

We can also omit it altogether.

10. ART change during follow up is probably related to TF or vice versa, so it would not be appropriate to use this variable as a predictor. We have substituted the word cART change with the word, cART substitution. cART substitution is not necessarily associated TF but can be linked to stock outs, adverse drug reactions and emerging of preferable regimens. cART switching is definitely due TF. Our variable merely captured cART substitution.

Attachment

Submitted filename: Response to reviewer.docx

Click here for additional data file.^{(27.2KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0282642.r003

Decision Letter 1

Maria Elisabeth Johanna Zalm

19 Sep 2022

PONE-D-21-19949R1Prevalence and predictors of pediatric HIV therapy failure in a tertiary hospital in Asmara, Eritrea: A 15-year retrospective cohort studyPLOS ONE

Dear Dr. Mengstu,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Your revised manuscript has been assessed by three peer-reviewers, including a statistical reviewer, and their reports are appended below. The reviewers comment that the analyses reported in the study could be improved upon and that they could be put into context rather than report risk factors only. In addition, the reviewers comment that some of the references reported in this study should be updated to reflect the, now-published, 2021 data. Could you please revise the manuscript to carefully address the concerns raised?

Please submit your revised manuscript by Oct 31 2022 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,

Maria Elisabeth Johanna Zalm, Ph.D

Editorial Office

PLOS ONE

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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 #2: All comments have been addressed

Reviewer #3: (No Response)

Reviewer #4: (No Response)

Reviewer #5: All comments have been addressed

**********

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

Reviewer #2: Yes

Reviewer #3: Partly

Reviewer #4: Yes

Reviewer #5: Yes

**********

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

Reviewer #2: Yes

Reviewer #3: No

Reviewer #4: Yes

Reviewer #5: Yes

**********

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

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

**********

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

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

**********

6. Review Comments to the Author

Reviewer #2: The authors addressed all the comments. However, I still see a few typos: eg, line 162, something is missing between CLHIV and the next sentence; title of Table 1. Please check for similar errors.

Reviewer #3: General comments:

I had some questions about the analyses and I think they could be improved. Also, if at all possible, I encourage the authors to try to put their analyses in context better rather than just reporting risk factors. For instance, attributable risks might be nice though maybe not feasible.

Specific comments:

1. (lines 28, 251, etc.) The term "predictors" is used in this manuscript. I believe the authors are treating this as a synonym to "factors" and "independent variables" but I would encourage the authors to not use the term "predictors". The authors have not built a predictive model, they are looking at associations between these variables and the outcomes.

2. (lines 123-4) Does "persistent" mean CD4+ levels below the threshold for six months for every measurement?

3. (lines 151-156) Some of the covariates in the Cox model appear to be time-dependent. How were those handled? Would it be better to switch to a multiple endpoint Cox model, such as an Anderson-Gill model? Also, since treatment failure probably does not happen at the time of the survey, i.e., it happens between two surveys, was the outcome treated as interval censored?

4. (lines 156-158) Stepwise variable selection procedures, which include backward selection, usually do not do a good job of finding the most appropriate model (e.g., https://doi.org/10.1002/sim.3943). Stepwise procedures and any p-value based selection have quite a bit of evidence suggesting that they are poor at selecting the appropriate variables. For a decent summary, see the link above. It's better to select based on more robust criteria, especially measures which assess the fit of the model or, better yet, a penalized estimator such as lasso or lars.

5. (lines 157-158) Usually stepwise variable selection procedures select based on the p-value of the predictor variable, which is not a measure of model fit. If you have used something other than the default, that is not mentioned here. Regardless, I strongly recommend not using stepwise selection procedures.

6. (Table 1) Significance testing in these situations is generally frowned upon because a non-significant p-value does not indicate that groups are the same. For info on the topic in relation to baseline imbalance in randomized trials see Altman, https://doi.org/10.2307/2987510 and Senn, https://doi.org/10.1002/sim.4780131703. I believe the same logic extends to your table 1. My recommendation is to remove the significance testing from table 1 and use standardized difference to assess differences (see Austin, https://doi.org/10.1080/03610910902859574).

7. (Table 2) How were the CIs calculated for these rates? I don't believe I saw that in the statistical methods section.

8. (line 251) Although I know the phrase "independent predictors" is used quite a bit, I don't understand the usage of "independent" in a multivariable model since the effect is dependent on the other predictors in the model. I recommend not using that phrasing and something along the lines of "after controlling for other factors".

Reviewer #4: Thank you for the opportunity to review this paper looking at treatment failure on first line ART in Eritrea. Overall, the methods are appropriate to address the research question and results are generally clearly presented. I have several comments below – most of these individually are very minor. Main overall comment relates to the ordering of the results:

The authors look at the outcome in 3 ways – a prevalence (which does not take into account the follow up time), crude incidence rates (giving estimates of TF per PYs of follow up ) and then using KM an cox models to look at cumulative incidence of TF. I would suggest considering reordering these and presenting table 3 first. While this table provides a summary of characteristics in those who failed and those who did not, it doesn’t take into account the duration of follow up and to me this would make sense to come first. Next the crude incidence rates (table 2) could follow and then the KM and cox modelling.

Other comments:

Abstract results

Line 33 - Prevalence for TF is provided, this is the rate across all follow-up – it would be useful to also quote here the duration of follow-up. E.g., “prevalence was 38.5% () over a median follow up of XX [XX-XX]”

line 42 – missing word (seven in one hundred)

Introduction

Line 48 - UNAIDS estimates are provided from 2019 in the first paragraph. Data for 2021 have now been released – some of these references could be updated.

Line 59 – number of PLWHA in 2019 WAS 14000 (not IS, as now in the past). This figure is also quoted with a % - presumably, this is the % of the total population? This should be stated to be clear.

Line 60 – this sentence starts with providing information on children then quotes the number on ART, which appears to relate to the total population, including adults. This could be reordered as this is not immediately clear.

Line 76 – “ranging” – should be “range”

Materials and methods

Line 91 – states 822 children <15 were seen be the clinic (this is the total shown in the flow diagram). Line 100 states all individuals <18 were included. Is the criteria to be seen in the clinic being <15 years, but pts were included in the analysis as long as they initiated ART by 18 years? Or were they only followed up to 18 years? Please clarify why the age cut offs differ

Line 102 – How is it possible to have an unknown duration of follow up, and why is this treated differently from being LTFU? If a pt was seen in clinic and followed for a certain time, couldn’t they be censored at the last point they were known to be in follow up?

Line 108 – were missed ART doses self reported by the pts? This should be stated

Line 123 – ‘persistent’ should be defined. Eg, does this mean 2 CD4s below the specified levels over a certain time? When was the failure deemed to have occurred? At the time of the first measurement?

Line 128 – “<10%” should be “5 to <10%”. I note the previous reviewer also queried the inclusion of adherence in the model. If this was recorded at every follow up visit, the authors could consider inclusion as a time update variable in the Cox model, rather than the binary suboptimal adherence variable used.

Line 134 – height-for-age is missing a “-”

Line 153 – year of CART start was included in the model and not listed here

Line 155 – cART change frequency – please specify what constitutes a change.

Results

Line 169 – Before comparing those included/included it would be useful to state in text that 97 were excluded due to …..

Line 172 – authors state the majority of those included were wasted etc compared to those excluded. I think this should be “fewer” of the participants included were stunted, wasted etc. Eg only 25.6% of those included had weight for age <-2 compared to 56.5% of those excluded.

Line 188 – the median time to TF is quoted, presumably this is median time in those who failed? This should be specified to distinguish between other estimates of median time to TF estimated using KM later in the results.

Line 194 – table 2. Why do the number of events in table 2 differ from table 3? EG 156 males with TF in table 2 and 157 in table 3.

Line 204 – table 3 presents row percentages, for example showing what proportion of pt with WHO mild, moderate, and advance stage had TF. The text starting line 204 describes characteristics of those who failed compared to those who didn’t. While in the end the differences are equivalent/follow on from each other, from the text, I would expect to see column percentages in table 3 so the trends described can easily be seen (or else the text should be amended to better reflect the table format).

Line 238 – at first use should specify the ‘median’ quoted is ‘median time to failure’.

Line 240 – ‘mean’ is given for some years. Further in line 242 its referred to as ‘average’ time to failure. Consistent language should be used (and assume it should be median throughout?)

Discussion

Line 273 – the prevalence’s quoted in other countries do not read well, please review sentence structure

Line 340 – ‘delays in drug switching’ – where/how was this assessed?

Reviewer #5: In this study the authors aimed to investigate the prevalence, incidence, and predictors of first-line cART failure using the virologic (plasma viral load), immunologic and clinical criteria among children and adolescents living with HIV (CLHIV) on cART. They found that the prevalence of therapy failure was 279/724 (38.5% (95% CI 35-195 42.2) and they analyzed the factors that might have been associated with the TF. The idea to perform the study is interesting, it's methodology is correct. The number of patients is high. The manuscript is well prepared, however several minor stylistic or language corrections are required. The authors have answered all the queries raised at the first revision. Thus, I have no further queries.

**********

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 #2: No

Reviewer #3: No

Reviewer #4: No

Reviewer #5: No

**********

PLoS One. 2023 Mar 9;18(3):e0282642. doi: 10.1371/journal.pone.0282642.r004

Author response to Decision Letter 1

10 Nov 2022

See attached document - point by point response.doc

Attachment

Submitted filename: Point by point response.docx

Click here for additional data file.^{(26.7KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0282642.r005

Decision Letter 2

Carmen María González-Domenech

6 Dec 2022

PONE-D-21-19949R2

Prevalence and factors associated with pediatric HIV therapy failure in a tertiary hospital in Asmara, Eritrea: A 15-year retrospective cohort study

PLOS ONE

Dear Dr. Mengstu,

Please submit your revised manuscript by Jan 20 2023 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,

Dr. CM González-Domenech

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

Dear Dr. Mengstu,

Thank you for submitting your reviewed manuscript to PLOS ONE. This has improved considerably addressing the suggestions and concerns from the reviewers. Anyway, one out of the three reviewers still has some comments which you can find below. In addition, I also have few just minor comments (comment 7 from Reviewer 3 related to CI calculation is not included in the Method section yet; comments 2 and 19 from Reviewer 4 were answered but not included in the corresponding sections, Method and Discussion, respectively; line 100 of reviewed version: an “and” is missing (between duration, “and” unknown); line 226: One of the p for the p-values is differently in capital letter).

If applicable, we recommend that you deposit or indicate how the raw data could be accessed, as the reviewer 4 pointed.

#Reviewer 4

The authors have addressed/responded to previous comments adequately. Below are some additional minor comments.

Authors state all relevant data are contained within the paper and supporting files. The raw data are not, so could be clarified if/how data can be accessed

Abstract

Line 22 - Typo in first line – (should be “treatment failure IN”)

Line 29 – Data are plural, so should be “Data were”

Line 41 – score is missing “weight for height z-score” . AHR has a capital A, previously was aHR

Line 45 – The authors state seven in one hundred children likely to develop TF. The results is per 100 person years – so the conclusion should clarify its each year.

Introduction

Line 54 – what year does the 53% refer to? Reference is recent UNAIDS report but previous sentence refers to 2018 so this isn't clear. And is the 53% in the focus countries, or globally?

Methods

Line 99 – the authors previously elaborated on what was meant by unknown follow up duration. However, in the manuscript I think this might still be unclear to the reader (“Additional exclusion considerations included unknown follow up duration, unknown therapy outcome endpoints (Fig 1)”. The figure describes this as “Key missing follow up data”. Should this be “Missing key follow up data”? Could you simply state in the manuscript – Unknown treatment outcome? Or even “missing key follow up data”? The issue is you don’t have the data to determine if/when the outcome occurred, but this isn’t really clear.

Line 120 – “6 months of effective treatment” – what is meant by effective? Or do you just mean after 6 months of being on treatment (as for the immunological definition).

Line 124 – does the 3 months refer to time on treatment, or the space between VLs (could be clarified in text).

Results

Line 184 – p<0.03 – should be p = 0.03

Table 1: should p for weight for age be p<0.001 instead of p<0.005?

Table 2 and 3 – the table numbers are the wrong way round in the table titles.

Line 213 – table 2 is referred to after the median time to cART switch – but that is not the information presented in the table. Table 2 should be properly introduced in the text. Eg “Crude incidence rates by key characteristics are provided in table 2”

Table 4: The authors explained in their response what was meant by cART change frequency, but I think would be helpful to include this in the manuscript – e.g. in the table footnote.

Discussion

Paragraph 1 – some studies give a time frame for duration of follow up – is it possible to say anything about the others? Its difficult to interpret rates without information on timepoint/duration

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

Reviewer #4: (No Response)

**********

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

Reviewer #2: Yes

Reviewer #3: (No Response)

Reviewer #4: Yes

**********

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

Reviewer #2: Yes

Reviewer #3: (No Response)

Reviewer #4: Yes

**********

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

Reviewer #2: Yes

Reviewer #3: (No Response)

Reviewer #4: No

**********

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

Reviewer #2: Yes

Reviewer #3: (No Response)

Reviewer #4: Yes

**********

6. Review Comments to the Author

Reviewer #2: All comments are adequately addressed and I am satisfied with the responses. No further comment from my end.

Reviewer #3: Thank you for your thoughtful consideration of my comments. To follow up on one of them,

1. (comment 6) Yes, I recommend removing the p-value column from table 1.

2. (comment 7) I'm not familiar enough with Stata to know how this is calculated. If possible, I recommend including a methodological citation for how the CIs were calculated. This will depend on whether the Stata help has a citation.

Reviewer #4: The authors have addressed/responded to previous comments adequately. Below are some additional minor comments.

Authors state all relevant data are contained within the paper and supporting files. The raw data are not, so could be clarified if/how data can be accessed

Abstract

Line 22 - Typo in first line – (should be “treatment failure IN”)

Line 29 – Data are plural, so should be “Data were”

Line 41 – score is missing “weight for height z-score” . AHR has a capital A, previously was aHR

Line 45 – The authors state seven in one hundred children likely to develop TF. The results is per 100 person years – so the conclusion should clarify its each year.

Introduction

Line 54 – what year does the 53% refer to? Reference is recent UNAIDS report but previous sentence refers to 2018 so this isn't clear. And is the 53% in the focus countries, or globally?

Methods

Line 120 – “6 months of effective treatment” – what is meant by effective? Or do you just mean after 6 months of being on treatment (as for the immunological definition).

Line 124 – does the 3 months refer to time on treatment, or the space between VLs (could be clarified in text).

Results

Line 184 – p<0.03 – should be p = 0.03

Table 1: should p for weight for age be p<0.001 instead of p<0.005?

Table 2 and 3 – the table numbers are the wrong way round in the table titles.

Table 4: The authors explained in their response what was meant by cART change frequency, but I think would be helpful to include this in the manuscript – e.g. in the table footnote.

Discussion

**********

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PLoS One. 2023 Mar 9;18(3):e0282642. doi: 10.1371/journal.pone.0282642.r006

Author response to Decision Letter 2

1 Jan 2023

Minor Points raised or amendments requested by Academic Editor (Dr. CM González-Domenech)

1. 1. Comment 7 from Reviewer 3 related to CI calculation is not included in the Method section yet These CI were generated by STATA v 12 thru the tab of statistics > survival analysis > Summary statistics, tests and tables > Person-time, incidence rates and SMR after defining the data in time to event form.

Generally, these are equivalent to unadjusted HR with CI calculated in regard to their respective SE.

This statement has been incorporated in the third round of revision.

2. Comments 2 and 19 from Reviewer 4 were answered but not included in the corresponding sections, Method and Discussion, respectively Will be addressed

3. Line 100 of reviewed version: an “and” is missing (between duration, “and” unknown); line 226: One of the p for the p-values is differently in capital letter. The needed amendments have been carried out.

4. If applicable, we recommend that you deposit or indicate how the raw data could be accessed, as the reviewer 4 pointed. The raw data has been depositied along with reseach materials

#Reviewer 4 Authors’ Response

Abstract

1. Line 22 - Typo in first line – (should be “treatment failure IN”) Addressed

2. Line 29 – Data are plural, so should be “Data were” Addressed

3. Line 41 – score is missing “weight for height z-score”. AHR has a capital A, previously was aHR Addressed

4. Line 45 – The authors state seven in one hundred children likely to develop TF. The results is per 100 person years – so the conclusion should clarify its each year. Addressed

Introduction

1. Line 54 – what year does the 53% refer to? Reference is recent UNAIDS report, but previous sentence refers to 2018 so this isn't clear. And is the 53% in the focus countries, or globally? The statement has been overhauled. The 53% represents the number of children covered by cART in focus countries.

2. Comments 2 and 19 from Reviewer 4 were answered but not included in the corresponding sections, Method and Discussion, respectively Will be addressed

4. If applicable, we recommend that you deposit or indicate how the raw data could be accessed, as the reviewer 4 pointed. The raw data has been depositied along with reseach materials

#Reviewer 4 Authors’ Response

Abstract

1. Line 22 - Typo in first line – (should be “treatment failure IN”) Addressed

2. Line 29 – Data are plural, so should be “Data were” Addressed

3. Line 41 – score is missing “weight for height z-score”. AHR has a capital A, previously was aHR Addressed

4. Line 45 – The authors state seven in one hundred children likely to develop TF. The results is per 100 person years – so the conclusion should clarify its each year. Addressed

Introduction

1. Line 54 – what year does the 53% refer to? Reference is recent UNAIDS report but previous sentence refers to 2018 so this isn't clear. And is the 53% in the focus countries, or globally? The statement has been overhauled. The 53% represents the number of children covered by cART in focus countries.

Methods

1. Line 99 – the authors previously elaborated on what was meant by unknown follow up duration. However, in the manuscript I think this might still be unclear to the reader (“Additional exclusion considerations included unknown follow up duration, unknown therapy outcome endpoints (Fig 1)”. The figure describes this as “Key missing follow up data”. Should this be “Missing key follow up data”? Could you simply state in the manuscript – Unknown treatment outcome? Or even “missing key follow up data”? The issue is you don’t have the data to determine if/when the outcome occurred, but this isn’t really clear. Statement has been incorporated to make it clear

2. Line 120 – “6 months of effective treatment” – what is meant by effective? Or do you just mean after 6 months of being on treatment (as for the immunological definition). The word “effective” is erased as it’s a deplicate information as outcome with adherence support was already mentioned in line 117.

3. Line 124 – does the 3 months refer to time on treatment, or the space between VLs (could be clarified in text). overhauled

Results

1. Line 184 – p<0.03 – should be p=0.03 overhauled

2. Table 1: should p for weight for age be p<0.005? overhauled

3. Table 2 and 3 – the table numbers are the wrong way round in the table titles. overhauled

4. Line 213 – table 2 is referred to after the median time to cART switch – but that is not the information presented in the table. Table 2 should be properly introduced in the text. Eg “Crude incidence rates by key characteristics are provided in table 2” True. Changes has been made

5. Table 4: The authors explained in their response what was meant by cART change frequency, but I think would be helpful to include this in the manuscript – e.g. in the table footnote. Added

Discussion

1. Paragraph 1 – some studies give a time frame for duration of follow up – is it possible to say anything about the others? It’s difficult to interpret rates without information on time point/duration True, as the results are from different studies of different methodology, duration of follow-up couldn’t be obtained from all of them. Some are only cross-sectional, evaluating prevalence of TF while others are cohorts, evaluating both prevalence and incidences.

Reviewer #3 Authors’ Response

1. (comment 6) Yes, I recommend removing the p-value column from table 1. Its removed.

Attachment

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PLoS One. doi: 10.1371/journal.pone.0282642.r007

Decision Letter 3

Carmen María González-Domenech

6 Jan 2023

PONE-D-21-19949R3Prevalence and factors associated with pediatric HIV therapy failure in a tertiary hospital in Asmara, Eritrea: A 15-year retrospective cohort studyPLOS ONE

Dear Dr. Samuel Tekle Mengistu,

==============================

ACADEMIC EDITOR:

Almost all the comments from Reviewers have already been addressed but some minor mistakes still remained. Please, amend them before accepting for publication:

Cite 23. The title of the article is wrong. “No TPredicting” must be replaced by “Predicting […]”

Table 2 and 3, p-value with "V" in lowcase.

Line 40. AHR is still in capital letter (Reviewer 4´s comment not addressed).

Table 1. Label “Excluded” has dissappeared in the column but the corresponding data are remaining

==============================

Please submit your revised manuscript within one week (12th of January 2023). 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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Kind regards,

Carmen María González-Domenech, PhD

Academic Editor

PLOS ONE

Journal Requirements:

Additional Editor Comments (if provided):

Almost all the comments from Reviewers have already been addressed but some minor mistakes still remained. Please, amend them before accepting for publication:

Cite 23. The title of the article is wrong. “No TPredicting” must be replaced by “Predicting […]”

Table 2 and 3, p-value with "V" in lowcase.

Line 40. AHR is still in capital letter (Reviewer 4´s comment not addressed).

Table 1. Label “Excluded” has dissappeared in the column but the corresponding data are remaining

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

Reviewers' comments:

PLoS One. 2023 Mar 9;18(3):e0282642. doi: 10.1371/journal.pone.0282642.r008

Author response to Decision Letter 3

18 Feb 2023

To PLOS ONE Editorial Office

We would like to thank staff Editor of PLOS ONE and our reviewers for their invaluable inputs and constructive comments that are helpful to massively improve the quality of the manuscript. After careful consideration of the points raised, the point-by-point response to the fourth round of revision are as follows:

1. Cite 23 is misspelled: Addressed.

2. P-values in table 2 and 3 should be in low case: The letters are changed to low case now.

3. Line 40: AHR, ‘A’ still in capital letter: The term adjusted hazards ratio has been abbreviated as aHR consistently throughout the document now.

4. Table 1. The label “Excluded” has been removed but the corresponding data still exists: The table is checked for any missing labels of the column, excluded but it still holds.

Attachment

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PLoS One. doi: 10.1371/journal.pone.0282642.r009

Decision Letter 4

Carmen María González-Domenech

21 Feb 2023

Prevalence and factors associated with pediatric HIV therapy failure in a tertiary hospital in Asmara, Eritrea: A 15-year retrospective cohort study

PONE-D-21-19949R4

Dear Dr. Mengistu,

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.

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Kind regards,

Carmen María González-Domenech, Ph.D.

Academic Editor

PLOS ONE

PLoS One. doi: 10.1371/journal.pone.0282642.r010

Acceptance letter

Carmen María González-Domenech

27 Feb 2023

PONE-D-21-19949R4

Prevalence and factors associated with pediatric HIV therapy failure in a tertiary hospital in Asmara, Eritrea: A 15-year retrospective cohort study

Dear Dr. Mengistu:

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.

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

Kind regards,

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on behalf of

Dr. Carmen María González-Domenech

Academic Editor

PLOS ONE

Associated Data

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

Supplementary Materials

S1 File

(SAV)

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Data Availability Statement

All relevant data are within the paper and its Supporting Information file.

[pone.0282642.ref001] 1.UNAID. HIV/AIDS 2021 fact sheet [Internet]. https://www.unaids.org/en/resources/fact-sheet. 2021. Available from: https://www.unaids.org/en/resources/fact-sheet accessed on 22nd of October 22, 2022

[pone.0282642.ref002] 2.Fitzgerald F, Penazzato M, Gibb D. Development of antiretroviral resistance in children with HIV in low-and middle-income countries. J Infect Dis. 2013;207(suppl_2): S85–92. doi: 10.1093/infdis/jit115 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282642.ref003] 3.UNAIDS. Start Free Stay Free AIDS Free, 2019. Report. [Google Scholar]

[pone.0282642.ref004] 4.Gupta RK, Gregson J, Parkin N, Haile-Selassie H, Tanuri A, Forero LA, et al. HIV-1 drug resistance before initiation or re-initiation of first-line antiretroviral therapy in low-income and middle-income countries: a systematic review and meta-regression analysis. Lancet Infect Dis. 2018;18(3):346–55. doi: 10.1016/S1473-3099(17)30702-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282642.ref005] 5.Cissé A-M, Laborde-Balen G, Kébé-Fall K, Dramé A, Diop H, Diop K, et al. High level of treatment failure and drug resistance to first-line antiretroviral therapies among HIV-infected children receiving decentralized care in Senegal. BMC Pediatr. 2019;19(1):1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282642.ref006] 6.WHO. Consolidated Guidelines on the Use of Antiretroviral Drugs for Treating and Preventing HIV Infection. Recommendations for a Public Health approach, 2nd edition—2017, 131 p. [Google Scholar]

[pone.0282642.ref007] 7.Haile GS BA. Predictors of treatment failure, time to switch, and reasons for switching to second-line antiretroviral therapy in HIV-infected children receiving first-line antiretroviral therapy at a Tertiary Care Hospital in Ethiopia. BMC Pediatr. 2019;19(37). [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282642.ref008] 8.Muri L, Gammell A, Ntamatungiro AJ, Glass TR, Luwanda LB, Battegay M, et al. Development of HIV drug resistance and therapeutic failure in children and adolescents in rural Tanzania: an emerging public health concern. 2017;0(May 2016). doi: 10.1097/QAD.0000000000001273 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282642.ref009] 9.Wamalwa D, Lehman DA, Benki-Nugent S, Gasper M, Gichohi R, Maleche-Obimbo E, et al. Long-term virologic response and genotypic resistance mutations in HIV-1 infected Kenyan children on combination antiretroviral therapy. J Acquir Immune Defic Syndr. 2013;62(3):267. doi: 10.1097/QAI.0b013e31827b4ac8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282642.ref010] 10.Rogo T, DeLong AK, Chan P, Kantor R. Antiretroviral treatment failure, drug resistance, and subtype diversity in the only pediatric HIV clinic in Rhode Island. Clin Infect Dis. 2015;60(9):1426–35. doi: 10.1093/cid/civ058 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282642.ref011] 11.Boerma RS, Boender TS, Bussink AP, Calis JC, Bertagnolio S, Rinke de Wit TF, et al. Suboptimal viral suppression rates among HIV-infected children in low-and middle-income countries: a meta-analysis. Clin Infect Dis. 2016;ciw645. doi: 10.1093/cid/ciw645 [DOI] [PubMed] [Google Scholar]

[pone.0282642.ref012] 12.High levels of virological failure with major genotypic resistance mutations in HIV-1-infected children after 5 years of care according to WHO-recommended 1st-line and 2nd-line antiretroviral regimens in the Central African Republic: a cross-sectional study. Vol. 96, Medicine. [DOI] [PMC free article] [PubMed]

[pone.0282642.ref013] 13.Predictors of treatment failure and time to detection and switching in HIV-infected Ethiopian children receiving first-line antiretroviral therapy. Vol. 12, BMC infectious diseases. p. 1–8. [DOI] [PMC free article] [PubMed]

[pone.0282642.ref014] 14.Asgedom SW, Maru M, Berihun B, Gidey K, Niriayo YL AT, Asgedom SW, et al. Immunologic and Clinical Failure of Antiretroviral Therapy in People Living with Human Immunodeficiency Virus within Two Years of Treatment. Hindawi BioMed Res Int. 2020;2020:8. doi: 10.1155/2020/5474103 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282642.ref015] 15.Lenjiso GA, Endale BS BY. Clinical and immunological failure among HIV-positive adults taking first-line antiretroviral therapy in Dire Dawa, eastern Ethiopia. BMC Public Health. 2019;19(771). doi: 10.1186/s12889-019-7078-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282642.ref016] 16.WHO. Consolidated Guidelines on the Use of Antiretroviral Drugs for Treating and Preventing HIV Infection: Recommendations for a Public Health Approach. 2nd. Geneva; 2016. 131 p. [PubMed]

[pone.0282642.ref017] 17.Irvine C, Armstrong A, Nagata JM, Rollins N, Schaaf D, Doherty M, et al. Setting Global Research Priorities in Pediatric and Adolescent HIV Using the Child Health and Nutrition Research Initiative (CHNRI) Methodology. 2018;78:3–9. doi: 10.1097/QAI.0000000000001742 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282642.ref018] 18.Juergen Dr., Prof. Micheal Golden, Dr. John DO. ENA for SMART [Internet]. 2020. Available from: www.nutrisurvey.net/ena/ena.html [Google Scholar]

[pone.0282642.ref019] 19.Costenaro P, Penazzato M, Lundin R, Rossi G, Massavon W, Patel D, et al. Predictors of Treatment Failure in HIV-Positive Children Receiving Combination Antiretroviral Therapy: Cohort Data From Mozambique and Uganda. 2015;4(1):39–48. doi: 10.1093/jpids/piu032 [DOI] [PubMed] [Google Scholar]

[pone.0282642.ref020] 20.Crowell CS, Maiga AI, Sylla M, Taiwo B, Kone N OA, et al. High Rates of Baseline Drug Resistance and Virologic Failure Among ART Naïve HIV-Infected Children in Mali. Pediatr Infect Dis J. 2017;36(11):e258–e263. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282642.ref021] 21.Rutherford GW, Anglemyer A, Easterbrook PJ, Horvath T, Vitoria M, Penazzato M, et al. Predicting treatment failure in adults and children on antiretroviral therapy: a systematic review of the performance characteristics of 2010 WHO immunologic and clinical criteria for virologic failure. Aids. 2014;28:S161–9. [DOI] [PubMed] [Google Scholar]

[pone.0282642.ref022] 22.Patten G, Schomaker M, Davies M-A, Rabie H, Van Zyl G, Technau K EB, et al. What Should we do When HIV-Positive Children Fail First-Line Combination Antiretroviral Therapy? A Comparison of 4 ART Management Strategies. Pediatr Infect Dis J. 2019;38(4):400–405. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282642.ref023] 23.Rossouw TM, Feucht UD, Melikian G, Van Dyk G, Thomas W, du Plessis NM, et al. Factors associated with the development of drug resistance mutations in HIV-1 infected children failing protease inhibitor-based antiretroviral therapy in South Africa. PLoS One. 2015;10(7):e0133452. doi: 10.1371/journal.pone.0133452 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282642.ref024] 24.Beck IA, Levine M, McGrath CJ, Bii S, Milne RS, Kingoo JM SI, et al. Pre-treatment HIV-drug resistance associated with the virologic outcome of first-line NNRTI-antiretroviral therapy: A cohort study in Kenya. EclinicalMedicine. 2020;18. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282642.ref025] 25.Bertagnolio S, De Luca A, Vitoria M, Essajee S, Penazzato M, Hong SY, et al. Determinants of HIV drug resistance and public health implications in low-and middle-income countries. Antivir Ther. 2012;17(6):941. doi: 10.3851/IMP2320 [DOI] [PubMed] [Google Scholar]

[pone.0282642.ref026] 26.Team P-1 S. First-line antiretroviral therapy with a protease inhibitor versus non-nucleoside reverse transcriptase inhibitor and switch at higher versus low viral load in HIV-infected children: an open-label, randomized phase 2/3 trial. Lancet Infect Dis. 2011;11(4):273–83. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282642.ref027] 27.Szubert AJ, Prendergast AJ, Spyer MJ, Musiime V, Musoke P, Bwakura-Dangarembizi M, et al. Virological response and resistance among HIV-infected children receiving long-term antiretroviral therapy without virological monitoring in Uganda and Zimbabwe: Observational analyses within the randomized ARROW trial. PLoS Med. 2017;14(11):e1002432. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0282642.ref028] 28.Cotton M. VA, Gibb D, Otwombe K, Josipovic D, Panchia R, Jean-Philippe P, et al. Early ART followed by Interruption Is Safe and Is Associated with Better Outcomes than Deferred ART in HIV+ Infants: Final Results from the 6-Year Randomized CHER Trial. In: 19th Conference on Retroviruses and Opportunistic Infections. Atlanta; 2012. p. Paper 28LB.

[pone.0282642.ref029] 29.Meyers T, Sawry S, Wong JY, Moultrie H, Pinillos F, Fairlie L, et al. Virologic failure among children taking lopinavir/ritonavir-containing first-line antiretroviral therapy in South Africa. Pediatr Infect Dis J. 2015;34(2):175. doi: 10.1097/INF.0000000000000544 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Prevalence and factors associated with pediatric HIV therapy failure in a tertiary hospital in Asmara, Eritrea: A 15-year retrospective cohort study

Samuel Tekle Mengistu

Ghirmay Ghebrekidan Ghebremeskel

Oliver Okoth Achila

Miriam Berhane Abrehe

Samuel Fisseha Tewelde

Mahmud Mohammed Idris

Tsegereda Gebrehiwot Tikue

Araia Berhane Mesfin

Roles

Abstract

Introduction

Methods

Results

Conclusions

1. Introduction

2. Materials and methods

2.1 Study design and setting

2.2 Study cohort description

Fig 1. Flow diagram of study recruitment and outcomes in children receiving cART in National Referral Pediatric Follow-up Clinic, 2005–2020.

2.3 Data collection

2.4 Operational definitions

2.5 Endpoints definition

2.6 Statistical analyses

3. Results

3.1 Demographic and clinical characteristics

Table 1. Baseline characteristics of HIV-infected children and adolescents in the in National Referral Pediatric Follow-up Clinic, Asmara, Eritrea (2005–2020).

3.2 Crude-incidence of cART failure

Table 2. Crude incidence and associated factors of therapy failure among children and adolescents in National Referral Pediatric Follow-up Clinic, Asmara, Eritrea (2005–2020).

3.3 Prevalence and factors associated with TF

Table 3. Characteristics of the study participants stratified by therapy outcome in National Referral Pediatric Follow-up Clinic, Asmara, Eritrea (2005–2020).

3.4 Relationship between number viral load tests performed and detection of TF

Fig 2. Relationship among viral load tests performed in the clinic and therapy failure detection in National Referral Pediatric Follow-up Clinic, Asmara, Eritrea (2005–2020).

3.5 Kaplan-Meier analysis for TF incidence

Fig 3.

3.6 Multivariate analysis of independent factors of TF

Table 4. Cox proportional hazards of cART therapy failure among CLHIV and adolescents in National Referral Pediatric Follow-up Clinic, Asmara, Eritrea (2005–2020).

4. Discussion

5. Conclusion

Supporting information

Acknowledgments

List of abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Lorena Verduci

Roles

Author response to Decision Letter 0

Decision Letter 1

Maria Elisabeth Johanna Zalm

Roles

Author response to Decision Letter 1

Decision Letter 2

Carmen María González-Domenech

Roles

Author response to Decision Letter 2

Decision Letter 3

Carmen María González-Domenech

Roles

Author response to Decision Letter 3

Decision Letter 4

Carmen María González-Domenech

Roles

Acceptance letter

Carmen María González-Domenech

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

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