Virologic and Immunological Failure and Associated Factors Among Adults Living With HIV on HAART at Jimma Medical Center,Southwest Ethiopia

Abstract

Background

Antiretroviral therapy reduces HIV morbidity, mortality, viral replication, and transmission, and viral load testing is the preferred method for monitoring effectiveness. This study assessed virologic and immunologic failure and associated factors among adults living with HIV on HAART at Jimma Medical Center, southwest Ethiopia.

Methods

An institution-based cross-sectional study was conducted from February to March 2019 among 272 adults on HAART. Data were collected from medical records and structured interviews. CD4+ T-cell counts and plasma viral loads were measured using standard laboratory procedures. Data were analyzed using SPSS version 24. Descriptive statistics summarized the data, and bivariate and multivariate logistic regression analyses were performed to identify associated factors. Multicollinearity was assessed before the final model. Adjusted odds ratios (AORs) with 95% confidence intervals were reported, and a P-value <.05 was considered statistically significant.

Results

Of the participants, 60.7% were female, with a mean age of 36.6 years and a median ART duration of 96 months. Immunologic failure occurred in 8.1% and virologic failure in 10.3%. Immunologic failure was associated with tuberculosis co-infection and poor ART adherence. Virologic failure was associated with poor adherence, baseline viral load ≥1000 copies/mL, and CD4+ count <250 cells/mm³.

Conclusion

Adherence support remains essential for viral suppression.

Keywords

HIV immunological failure virologic failure HAART

Introduction

The pandemic caused by the Human Immunodeficiency Virus (HIV), leading to acquired immunodeficiency syndrome (AIDS), has posed a significant medical and public health challenge globally. As of 2022, it is estimated that approximately 39.0 million individuals worldwide were living with HIV. The epidemic has a disproportionately severe impact on Sub-Saharan Africa (SSA), which carries the largest share, representing nearly 65% of all people living with HIV (PLWHA) across the globe.¹

Despite considerable progress in diagnosing, treating, and preventing HIV/AIDS, SSA continues to face a disproportionately high burden of the epidemic, with persistently high rates of new infections and deaths related to HIV. Ethiopia ranks among the countries in SSA that are most impacted by HIV/AIDS, with approximately 609,349 individuals living with HIV in 2022. This ongoing issue is indicative of underlying structural and systemic problems, such as barriers to healthcare access, disparities in service provision, and inadequate resources for prevention and treatment initiatives.²

The introduction of highly active antiretroviral therapy (HAART) has revolutionized HIV care by significantly reducing HIV-related morbidity and mortality, transforming HIV infection from a fatal disease into a manageable chronic condition.^3,4 HAART works by suppressing viral replication, restoring immune function, and reducing the incidence of opportunistic infections and AIDS-related complications. In parallel, the international scale-up of antiretroviral therapy (ART) has become a major global public health priority, particularly in resource-limited settings where the burden of HIV remains highest. Expanding access to ART in these settings has been central to improving survival, enhancing quality of life, and reducing HIV transmission at both individual and population levels.^5,6,7 In Ethiopia, ART coverage has increased significantly over the past decade, and the country is on the right path to achieve the UNAIDS 95–95–95 targets, making progress. According to a recent UNAIDS report, 90% of PLWHA are aware of their status, 94% of those diagnosed are receiving ART, and 96% of those who received ART have achieved viral suppression.^8,9

The emergence and transmission of HIV drug resistance (HIVDR) undermine the success of ART programs. HIVDR refers to the ability of HIV to replicate in the presence of drugs that commonly suppress its replication, leading to virologic and immunologic treatment failure.¹⁰ Apart from harming individual health, this also increases treatment costs and creates a reservoir for the transmission of resistant viruses to newly infected individuals.¹¹

Virologic failure is defined by the World Health Organization (WHO) and national guidelines as a plasma viral load ≥1000 copies/mL on 2 consecutive measurements taken 3 months apart, following adherence support after the first elevated result, among individuals receiving ART for at least 6 months. Immunologic failure is indicated when the CD4+ T-cell count is ≤250 cells/mm³ following clinical failure or persistent CD4 levels <100 cells/mm³, in the absence of concomitant or recent infections that may cause transient CD4 decline. Although immunologic criteria have historically been used to monitor treatment response, they have limited sensitivity and positive predictive value for detecting virologic failure, which may lead to delayed recognition of treatment failure.^12,13,14 Routine viral load monitoring is highly recommended as the preferred approach to monitor treatment response among PLWHA, with the associated development of a treatment monitoring algorithm to identify those who need to switch to second-line treatment if drug resistance is suspected; however, in many parts of Ethiopia, especially in rural areas, access to regular testing remains limited.¹⁴

In southwestern Ethiopia, including the Jimma zone, major challenges that affect the ART outcomes are low treatment adherence, limited access to viral load testing, stigma, and socio-economic disparities. Additionally, the high prevalence of HIV-1 subtype C in Ethiopia may influence patterns of resistance and treatment response.^15,16 Conducting research on virological and immunological failure among HIV patients at Jimma Medical Center (JMC), the only referral hospital in Southwest Ethiopia, is vital to determine existing HIV treatment status and to identify effective measures for preventing and controlling HIV treatment failure. This study aimed to determine virologic and immunological failure and associated factors among young and adult people with HIV at JMC.

Methods and Materials

Ethical Considerations

The study was conducted per the principles of the Declaration of Helsinki and the International Council on Harmonization Guidelines for Good Clinical Practice. Ethical approval was obtained from the Jimma University Institutional Ethical Review Board of Institute of Health (Ref. No. IHRPGD/640/2019) on January 30, 2019. Permission to conduct the study was sought from JMC. Written informed consent was obtained from the study participants and parents or caregivers, respectively, after the benefits and risks of the study were described. Any information concerning the participants was kept confidential, and the samples collected from the participants were analyzed only for the intended purposes.

The Aims, Design, and Setting of the Study

This study aimed to determine virologic and immunological failure and associated factors among adult PLWHA on ART at JMC in southwest Ethiopia. An institution-based cross-sectional study was conducted from February to March 2019. JMC is one of the oldest public medical centers in the country. Currently, it is the only tertiary, specialized, and teaching medical center under Jimma University in the southwestern part of the country, providing specialized clinical services for more than 20 million people in the catchment area and comprehensive HIV/AIDS services for more than 5000 PLWHA per year.

Sample Size Determination

The sample size was determined using the single population proportion formula by taking the proportion of virologic failure of the analytical hypothesis as 5.3% from a previous study conducted in Jimma.¹⁶ Because the anticipated prevalence was low (5.3%), using a conventional absolute precision of 5% would have produced an imprecise estimate, as the margin of error would be nearly equal to the prevalence itself. Therefore, we applied 50% relative precision (d = 2.65%), an accepted methodological approach for estimating small proportions, ensuring that the confidence interval width remains proportionate to the expected prevalence.

The sample size was calculated as follows:

n = \frac{{(Z α / 2)}^{2} P (1 - P)}{d^{2}} = \frac{{(1.96)}^{2} 0.053 (1 - 0.053)}{{0.0256}^{2}} = 275

Because the source population (young and adults eligible for inclusion) was 3,067, which is less than 10,000, a finite population correction (FPC) was applied:

n_{f} = \frac{n}{(1 + \frac{n}{N})} = n_{f} = \frac{275}{(1 + \frac{275}{3067})} = 253

To account for potential non-response or ineligible participants, a 10% contingency was added, resulting in a final required sample size of 278 participants, of whom 272 were enrolled. Participants were recruited using convenience sampling from eligible ART clinic attendees during the study period. Consecutive patients meeting the inclusion criteria were screened until the required sample size was reached. Of 278 planned participants, 272 were included in the final analysis; 6 were excluded due to invalid results (4 viral load and 2 CD4 count) (Figure 1).

Figure 1.

STROBE flowchart showing overall work flow of the study.

STROBE flowchart shows screening, exclusions, ineligible cases, and the final analytic sample (Figure 1).

Operational Definition

Baseline viral load: The plasma HIV-1 RNA level measured at the time of ART initiation, prior to commencement of ART.

Immunological failure: CD4 count at or below 250 cells/mm³ following clinical failure or persistent CD4 levels below 100 cells/mm³, in the absence of concomitant or recent infection that could cause a transient CD4 decline. Although WHO acknowledges that immunologic criteria have limited sensitivity and positive predictive value for detecting virologic failure, no validated alternative immunologic definition has been proposed.

Virological failure: Virological failure was defined as a plasma viral load ≥1000 copies/mL on 2 consecutive viral load measurements taken at least 3 months apart, with adherence support provided after the first elevated result in accordance with national ART guidelines for patients on treatment for at least 6 months. All participants had 2 viral load measurements: the first was obtained from medical records, and the second during the study period. Only participants with a ≥3 months interval between measurements were included.

Adherence: Number of dose regimens of ART taken × 100

Number of prescribed regimen doses of ART

100% Good adherence > 95%, fair adherence 85% to 95% and poor adherence < 85% doses taken.

Eligibility Criteria

Inclusion Criteria

PLWHA who have been on ART for at least 6 months.

PLWHA who have given written informed consent if they are older than 18 or a parent or guardian has given informed consent if they are younger than 18.

Exclusion Criteria

PLWHA who were critically ill and unconscious.

PLWHA with incomplete baseline socio-demographic (age, sex, educational level, occupation, marital status, residence, and socioeconomic status), clinical data (duration on ART, Corimoxazole prophylaxis, TB status, therapeutic switching, level of ART adherence, body mass index, and WHO clinical stage), or laboratory data (base line viral load and CD4+ T-cell count).

PLWHA with follow-up viral load < 3 months.

Data Collection

Questionnaire

Clinical data were collected retrospectively from patients’ cards starting from HAART commencement (baseline characteristics and other clinical information) and from participant interviews via a structured questionnaire.

Blood Specimen Collection and Processing

Blood sample collection and processing were conducted in accordance with standard operating procedures (SOPs). Each participant provided 5 mL of venous blood in a vacutainer tube containing EDTA for immunological investigation (CD4+ T-cell count and others). Plasma was isolated from whole blood and subsequently stored at −80 °C until it was further tested for HIV viral load.

Immunological Analysis

CD4⁺ T-cell enumeration was performed using EDTA-anticoagulated whole blood samples on the BD FACSPresto™ Near-Patient CD4 Counter (Becton Dickinson), a point-of-care flow cytometry-based system designed for rapid and accurate quantification of CD4⁺ T lymphocytes. Prior to analysis, blood samples were gently inverted to ensure proper mixing and prevent clot formation. Approximately 25 µL of the well-mixed whole blood was dispensed onto a manufacturer-preloaded, single-use test cartridge, which was then securely capped and incubated at ambient room temperature for approximately 30 min to allow adequate staining and cell stabilization. Following incubation, the cartridge was inserted into the BD FACSPresto™ instrument, which automatically performed cell counting and analysis, providing both absolute CD4⁺ T-cell counts (cells/µL) and CD4⁺ T-cell percentages. The system utilizes integrated fluorescence detection and automated gating algorithms to ensure consistency and reliability of results. To maintain analytical accuracy and instrument performance, internal quality control procedures were conducted on a daily basis using manufacturer-supplied control cartridges with known target values. In addition, the laboratory actively participated in the national external quality assessment program coordinated by the Ethiopian Public Health Institute, ensuring ongoing performance monitoring, inter-laboratory comparison, and adherence to national and international quality standards.^18–19

Viral Load Testing

The HIV viral load was determined via the Roche COBAS^® Ampliprep^® and COBAS^® TaqMan^® systems. The COBAS^® Ampliprep/COBAS^® TaqMan HI2CAP test uses nucleic acid amplification to quantify HIV-1 RNA in plasma. The test employs COBAS^® Ampliprep instruments for automated sample processing and a COBAS^® TaqMan analyzer for automated nucleic acid amplification and detection. The test comprises 3 key processes: specimen preparation to isolate HIV-1 RNA, reverse transcription of the target RNA to create complementary DNA (cDNA), and simultaneous PCR amplification of target cDNA and detection via a cleaved dual-labelled oligonucleotide detection probe unique to the target. Check the AMPLINK software results window or printouts for flags and comments to ensure that the batch is valid.²⁰

Data Analysis

All the data were entered into EpiData 4.6 (EpiData Association, Denmark) and checked for completeness and consistency. The data were subsequently analyzed via SPSS version 24 software (IBM, USA). Descriptive statistics were used to present sociodemographic, clinical, and treatment-related characteristics. Bivariate and multivariate logistic regression, multicollinearity assessment, and adjusted odds ratios (AORs) were calculated to determine whether there was a statistically significant association between the dependent and independent variables. All variables with a P-value ≤ .2 in the bivariate analysis were included in the multivariable model. A P-value < .05 was considered statistically significant.

Quality Control

For better quality laboratory results, blood was collected following SOP. Blood samples were rejected if there was any hemolysis or clotting, if the tubes were not filled with the minimum volume, or if the samples were improperly labelled. Control materials were used. The results of all laboratory examinations were recorded carefully in a well-standardized report format and attached to the questionnaire according to the subject's unique identification number.

The reporting of this study conforms to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement for cross-sectional studies²¹ (Supplemental File 1).

Results

Sociodemographic Characteristics of the Study Participants

A total of 272 PLWHA on first-line ART voluntarily participated in this study. More than half of the 165 (60.7%) of the study participants were females. The mean age of the participants was 36.6± 8.7 years. One hundred seventeen (43%) of the participants were within the age group of 35-44 years (Table 1).

Table 1.

Socio-Demographic Characteristics of the Study Participants at JMC From February to March 2019.

Variables	Socio-Demographic Characteristics	Frequency	Percent
Sex	Male	107	39.3
Sex	Female	165	60.7
Age in years	15-24 years	19	7.0
	25-34 years	86	31.6
	35-44 years	117	43.0
	≥45 years	50	18.4
Residence	Urban	221	81.3
Residence	Rural	51	18.8
Marital status	Single	35	12.9
	Married	134	49.3
	Divorced	58	21.3
	Widowed	45	16.5
Educational status	Illiterate/Unable to read and write	34	12.5
	Lower grades (Grade 1-8)	115	42.3
	Higher School (Grade 9-12)	73	26.8
	College Diploma and above	50	18.4
Monthly income	<1500 ETB	108	39.7
Monthly income	≥1500 ETB	164	60.3

Clinical and Treatment-Related Characteristics

All participants enrolled in this study were on ART for at least 6 months. At baseline, the majority, 98 (36.0%) of the participants were found on WHO clinical stage III, followed by WHO stage II 96 (35.3%), and 91 (33.5%) were classified as having a BMI > 18.5. One hundred eleven (40.8%) of the participants’ CD4+ T-cell counts were between 100 and 250 cells/mm³. Regarding viral load, 118 (43.4%) and 24 (8.8%) patients had viral loads of 19-999 and 1000 copies/mL, respectively. At the time of data collection, half (50.45%) of the study participants were on ART for 5-10 years. The majority of 235 (86.4%) of them had WHO clinical stage TI, 224 (82.4%) had a CD4+ T-cell count above 250 cells/mm³, 225 (82.7%) had good adherence to ART, and 62 (22.8%) switched their regimen. One hundred twenty-five (46.0%) patients were on the TDF + 3TC + EFV regimen, followed by AZT +3TC + NVP 83 (30.5%). Sixty-three (23.2%) patients were receiving cotrimoxazole prophylaxis (Table 2).

Table 2.

Clinical and Treatment-Related Characteristics of the Study Participants at JMC From February to March 2019.

Variables	Categories	Frequency	Percent
Baseline WHO clinical stage	I	45	16.5
	II	96	35.3
	III	98	36.0
	IV	33	12.1
Current WHO clinical stage	T-I	235	86.4
	T-II	19	7.0
	T-III	18	6.6
Baseline BMI, kg/m²	< 18.5	91	33.5
	18.5-24.99	163	59.9
	≥ 25	18	6.6
Current BMI, kg/m²	<18.5	49	18.0
	18.5-24.99	166	61.0
	≥ 25	57	21.0
Baseline CD4+ T-cell count cells/mm³	<100	61	22.4
	100-250	111	40.8
	>250	100	36.8
Current CD4+ T-cell count, cells/mm³	<100	17	6.3
	100-250	31	11.4
	> 250	224	82.4
Baseline viral load	0-19	130	47.8
	19-999	118	43.4
	≥1000	24	8.8
Current viral load	0-19	151	55.5
	19-999	93	34.2
	≥1000	28	10.3
Duration of ART use	<24 months	15	5.5
	24-59 months	46	16.9
	60-120 months	137	50.4
	>120 months	74	27.2
Regimen change previously	Yes	62	22.8
Regimen change previously	No	210	77.2
ART regime is currently taking	AZT/3TC/NVP[IC]	83	30.5
	TDF/3TC/NVP[If]	17	6.3
	TDF/3TC/EFV[Ie]	125	46.0
	AZT/3TC/EFV[Id]	33	12.1
	Others	14	5.1
Current TB	Yes	14	5.1
Current TB	No	258	94.9
Adherence to ART	Good	225	82.7
	Fair	17	6.3
	Poor	30	11.0
Cotrimoxazole prophylaxis	Yes	63	23.2
Cotrimoxazole prophylaxis	No	209	76.8

Note: Tenofovir (TDF), Lamivudine (3TC), Nevirapine (NVP), Efavirenz (EFV), Zidovudine (AZT]).

Overall Failure

The overall failure rate among PLWHA was 14.07%; of these, 8.08% had immunological failure, 10.3% had virological failure, and 4.04% experienced both virological and immunological failure.

Immunological Failure and Associated Factors

In this study, the overall prevalence of immunological failure was 8.08% (22/272). Bivariate and multivariate logistic regression analyses were conducted to determine risk factors for immunological failure. All variables with a P-value ≤ .20 in the bivariate analysis were considered eligible for inclusion in the final multivariable logistic regression model. In the bivariate logistic regression analysis, monthly income, current body mass index (BMI), current WHO clinical stage, current tuberculosis (TB) coinfection status, current viral load, and adherence status were identified as candidate variables for the multivariable analysis. After controlling for potential confounders, current TB coinfection and adherence status remained independently and significantly associated with immunological failure (P-value < .05). Patients with TB coinfection had an AOR of 11.81 (95% CI: 1.67-86.731) for immunological failure compared to those without TB coinfection. Patients with poor adherence to treatment had an AOR of 9.73 (95% CI: 2.35-40.33) for immunological failure compared to those with good adherence (Table 3).

Table 3.

Bivariate and Multivariate Logistic Regression Analysis of Factors Associated With Immunological Failure in JMC From February to March 2019.

			Immunological Failure
Variables	Categories	Total	Yesn=22n (%)	Non =250n (%)	Crude Odds Ratio [COR, 95% CI]	P-Value	Adjusted Odds Ratio AOR [95% CI]	P-Value
Sex	Male	107	14 (15.0)	93 (85.0)	2.30 [1.19-7.31]	.019	2.09 [0.59-7.42]	.254
Sex	Female	165	8 (7.3)	157 (92.7)	1
Monthly income	<1500 ETB	108	12 (15.8)	96 (84.2)	1.93[0.81-4.63]	.143	1.02 [0.27-3.79]	.982
Monthly income	≥1500 ETB	164	10 (6.7)	154 (93.3)	1
Current WHO clinical stage	T1	234	12 (16.1)	222 (83.9)	1
	T2	19	1 (16.9)	18 (83.1)	0.97[0.12-7.89]	.980	0.12 [0.004-3.22]	.205
	T3	18	9 (7.1)	9 (92.9)	18.50[6.21-55.09]	.000	3.35 [0.44-25.66]	.245
Current BMI, kg/m²	<18.5	48	11 (7.1)	37 (92.9)	5.35[1.40-20.50]	.014	6.57 [0.81-53.36]	.078
	18.5-24.99	167	8 (28.6)	159 (71.4)	0.91[0.23-3.54]	.742	1.11 [0.14-3.89]	.924
	≥25	57	3 (8.3)	54 (91.3)	1
Current viral load	0-19	151	3 (6.9)	148 (93.1)	1
	19-999	93	8 (6.8)	85 (93.2)	4.64[1.20-17.97]	.260	4.25 [0.87-20.71]	.073
	≥1000	28	11 (45.8)	17 (54.2)	31.92[8.09-125.84]	.000	3.72 [0.50-27.26]	.200
Current TB co-infection	Yes	14	9 (64.3)	5 (35.7)	33.92[9.94-115.75]	.000	11.81 [1.67-86.73]	.015*
Current TB co-infection	No	258	13(5.0)	245 (95.0)	1
Adherence to ART	Good	225	8 (3.6)	217 (96.4)	1
	Fair	17	1 (5.9)	16 (94.1)	1.70[0.20-14.41]	.629	0.90 [0.05-17.11]	.945
	Poor	30	13(43.3)	17 (56.7)	20.74[7.56-56.93]	.000	9.73 [2.35-40.33]	.002*

Abbreviations: COR, crude odds ratio; AOR, adjusted odds ratio; CI, confidence interval; P, significant value; 1, reference category.

*Significant association.

We assessed the interaction between TB co-infection and poor adherence based on a biological and clinical rationale, as TB infection and suboptimal adherence may potentially act synergistically to worsen immunological outcomes in PLWHA. However, the interaction term was not statistically significant (P = .266, OR = 7.25, 95% CI: 0.22-24.46), suggesting no evidence of synergistic effect between these 2 factors in our study population. This indicates that TB co-infection and poor adherence independently contribute to immunological failure rather than acting in combination to amplify risk. Clinically, this finding implies that both factors should be addressed separately in patient management and intervention strategies.

Virologic Failure and Associated Factors

Among the 272 study participants, 10.3% (28/272) had virologic failure (≥1000 RNA copies/mL), after ≥6 months of ARV therapy. Of the 28 study participants with virologic failure, 16 (15.0%) were males and 12 (7.3%) were females. Twenty (9.0%) and 8 (15.7%) were urban and rural indwellers, respectively. With respect to monthly income, 17 (15.8%) earn monthly income less than 1500 ETB, whereas the remaining 11 (6.7%) earn 1500 ETB and above 13 (7.1%). The multivariate logistic regression analysis revealed that the baseline viral load, current CD4+ T-cell count, and adherence status were significantly associated with virologic failure (P-value < .05).

Patients with a baseline viral load ≥1000 RNA copies/mL had significantly higher odds of virologic failure compared to those with < 1000 copies/mL (AOR = 16.734, 95% CI: 3.899-45.928). Patients who had a current CD4 count < 250 cells/mm³ had 6.87 times greater odds of virologic failure than patients who had a current CD4+ T-cell count less than 250 cells/mm³ and above (AOR = 6.87, 95% CI: 2.452-19.236). The odds of virologic failure were approximately 9.56 times greater among patients who had poor adherence to treatment than among those who had good adherence (AOR= 9.56, 95% CI: 2.621-34.902) (Table 4).

Table 4.

Bivariate and Multivariate Logistic Regression Analysis of Factors Associated With Virologic Failure in JMC From February to March 2019.

Variables	Categories	Total	Virologic Failure		Crude Odds Ratio [COR, 95% CI]	P-Value	Adjusted Odds Ratio AOR [95% CI]	P-Value
Variables	Categories	Total	Yesn=28n (%)	No n =244 n (%)	Crude Odds Ratio [COR, 95% CI]	P-Value	Adjusted Odds Ratio AOR [95% CI]	P-Value
Gender	Male	107	16 (15.0)	91(85.0)	2.24[1.02-4.95]	.046	2.15 [0.67-7.6.85]	.197
Gender	Female	165	12 (7.3)	153(92.7)	1
Monthly income	<1500 ETB	108	17(15.8)	91(84.2)	2.60[1.17-5.79]	.020	2.02 [0.66-6.22]	.220
Monthly income	≥1500 ETB	164	11(6.7)	153(93.3)	1
Current WHO stage	TI	235	17 (7.2)	218 (92.7)	1
	TII	19	2 (10.5)	17 (89.5)	1.51[0.32-7.08]	.602	2.51. [0.38-16.43]	.338
	TIII	18	9 (50.0)	9 (50.0)	12.82[4.50-36.55]	.000	2.12 [0.22-20.45]	.515
Current BMI, kg/m²	<18.5	48	7 (14.6)	41 (85.4)	4.44[1.73-11.43]	.022	1.25 [0.078-2.712]	.292
	18.5-24.99	167	18 (10.77)	149 (89.2)	0.85[0.11-6.89]	.845	2.0 [0.55-7.26]	.607
	>25	57	3 (5.3)	54 (94.7)	1
Baseline viral load	0-19	130	9 (6.9)	121 (93.1)	1
	19-999	118	8 (6.8)	110 (93.2)	0.98[0.37-2.62]	.964	0.55 [0.15-2.07]	.376
	≥1000	24	11 (45.8)	13 (54.2)	11.38[3.98-32.52]	.000	16.73 [3.90-45.93]	.000*
Current CD4 count, cells/mm³	≤250	46	14 (30.4)	32 (69.6)	6.63[2.892-15.16]	.004	6.87 [2.45-19.24]	.037*
Current CD4 count, cells/mm³	>250	226	14 (6.2)	212 (93.8)	1
Current TB coinfection	Yes	14	8 (57.1)	6 (42.9)	15.87[5.01-50.24]	.000	4.09 [0.43-38.52]	.219
Current TB coinfection	No	258	20 (7.8)	238 (92.2)	1
Adherence to ART	Good	225	11 (4.9)	214 (95.1)	1
	Fair	17	2 (11.8)	15 (88.2)	2,59[0.53-12.78]	.241	0.53 [0.53-5.20]	.582
	Poor	30	15 (50.0)	15 (50.0)	19.46[7.62-49.69]	.000	9.56 [2.62-34.90]	.001*

Abbreviations: COR, crude odds ratio; AOR, adjusted odds ratio; CI, confidence interval; P, significant value, 1, reference category.

*Significant association.

The potential interaction between TB coinfection and poor adherence was assessed by including an interaction term (TB × poor adherence) in the logistic regression model. The interaction was not statistically significant (P = .703), while poor adherence remained a strong independent predictor of viral failure (OR = 11.69, 95% CI: 4.26-32.12).

Multicollinearity Diagnostics

To assess potential multicollinearity among predictors, variance inflation factors (VIFs) and tolerance values for baseline viral load, current CD4 count, and WHO clinical stage were calculated. The results were as follows: baseline viral load (VIF = 1.067, Tolerance = 0.937), current CD4 count (VIF = 1.088, Tolerance = 0.919), baseline WHO clinical stage (VIF = 1.025, Tolerance = 0.976), and current WHO clinical stage (VIF = 1.116, Tolerance = 0.896). All VIF values were well below 10 and tolerance values above 0.1, indicating no evidence of multicollinearity affecting the regression estimates.

Discussion

This study revealed that the overall prevalence of immunological failure was 8.08% (22/272), which aligns with studies conducted in the same country, which reported rates of 6.5%,²² 11.5%,²³ and 9.5%.²⁴ However, this prevalence is lower than that reported in studies conducted in Adigrat 12.35% ²⁵, Gondar 13.5%,²⁶ Southeast Ethiopia 15%,²⁷ Tanzania 42.8%,²⁸ Nepal 37.17%,²⁹ and China 18.75%.³⁰ These differences might be attributed to variations in study design, diagnostic method, study period, and the immunological failure cut-off point, which was defined as a CD4+ T-cell count < 350 cells/mm³ for studies conducted in Nepal.²⁹

In our study, TB coinfection was significantly associated with immunological failure. Patients with TB coinfection had 11.8-fold higher odds of immunological failure compared with those without TB (AOR = 11.81, 95% CI: 1.67-86.73; P = .015). This finding is consistent with previous studies conducted in northern Ethiopia²² and northwestern Ethiopia.²⁶ The wide confidence interval, likely due to the small number of cases, indicates limited precision and the need for cautious interpretation. This increased risk may be attributed to the ability of TB infection to impair cellular immune responses by inducing apoptosis of CD4⁺ T lymphocytes, thereby reducing overall CD4⁺ T-cell counts and compromising immune recovery.³¹

PLWHA who had poor adherence to ART had significantly higher odds of immunological failure compared with those with good adherence (AOR = 9.73, 95% CI: 2.346-40.330; P = .002). This finding is consistent with results reported in studies conducted by GG Hailu.²² The findings revealed that poor adherence to ART significantly facilitates increased viral replication. Elevated viral loads, in turn, enhance the infection and destruction of CD4⁺ T lymphocytes, the primary target cells of HIV. This ongoing cycle of viral replication and immune cell infection ultimately leads to a progressive decline in CD4⁺ T cell counts.¹⁴

This study revealed that the prevalence of virologic failure was 10.3% (28/272), which is in line with the findings of studies conducted in Ethiopia 10.43%,²⁵ 11.5%,²² 14.7%,²⁶ 10.24%,³² and 15.9%,³³ and comparable rates of virologic failure were reported in studies away from Ethiopia 9.2%,²⁹ 9%,³⁴ 10.59³⁵, and 14.2% ²⁷ The results of the present study were higher than those previously reported in the same study area 5.3%.¹⁷ However, our findings were lower than those of other studies performed in Senegal 19%,³⁶ Kenya 24%³⁷ Uganda 33.3%,³⁸ Malawi 32%,³⁹ Myanmar 57.1%,⁴⁰ and China 41.81%⁴¹ (Table 5). This variation could be attributed to differences in study design, duration of follow-up, diagnostic methods, patient populations, and healthcare contexts, as illustrated in Table 5.

Table 5.

Comparison of Virologic Failure Across Studies.

Country	Year	Study Design	VL Threshold (copies/mL)	Failure Rate (%)	First-Line ART Regimen
Ethiopia	2010-2012	Prospective cohort	≥1000	5.3%	NNRTI-based (TDF/3TC/EFV)
Senegal	2018	Cross-sectional	≥1000	19%	Mixed NNRTI-based (d4T/AZT+3TC+NVP/EFV)
Kenya	2008-2009	Cross-sectional	≥400	24%	NNRTI-based (TDF+3TC+EFV/NVP)
Uganda	2004-2011	Prospective cohort	≥1000	33.3%	NNRTI-based (TDF+3TC+EFV)
Malawi	2015-2017	Observational cohort	≥1000	32%	NNRTI-based (TDF+3TC+EFV)
Myanmar	2003-2017	Retrospective cohort	≥1000	57.1%	NNRTI-based (TDF+3TC+EFV)
China	2016-2017	Population-based	≥1000	41.81%	NNRTI-based

In our study, virologic failure was defined using a plasma viral load threshold of ≥1000 copies/mL, consistent with WHO recommendations. In contrast, the study in Kenya employed a lower threshold of ≥400 copies/mL,³⁷ which increases sensitivity for detecting failure and partially explains the higher reported prevalence. The Uganda study³⁸ was a prospective cohort with extended follow-up over several years, inherently capturing cumulative treatment failures, whereas our cross-sectional design provides only a single time-point assessment. The study in Malawi³⁹ specifically enrolled hospitalized patients, a population with more advanced disease and higher comorbidity burden, which likely contributed to its higher failure rate. Differences in healthcare delivery models, the maturity of national ART programs, adherence monitoring practices, and the provision of adjunctive support such as nutritional interventions in Ethiopia¹⁷ further introduce non-comparable heterogeneity. Therefore, although all studies evaluated virologic failure among PLWHA on first-line NNRTI-based ART, the observed variation in reported rates is more likely a reflection of methodological and contextual differences rather than true epidemiological disparities between countries.

In this study, HIV patients with a high baseline viral load (≥1000 RNA copies/mL) had significantly higher odds of virologic failure compared to those with a baseline viral load <1000 copies/mL (AOR = 16.73, 95% CI: 3.90-45.92, P < .001). This finding is interpreted strictly as an association. While a higher pre-treatment viral burden may be related to delayed viral suppression and an increased likelihood of treatment failure, consistent with previous studies.^{^36,42,43}

PLWHA who had a current CD4+ T-cell count of <250 cells/mm³ were 7 times more likely to develop virologic failure compared to those with CD4+ T-cell counts ≥250 cells/mm³ (AOR = 6.87, 95% CI: 2.45-19.24, P=.037). This finding was consistent with those of studies conducted in Ethiopia (AOR=2.81, 95% CI: 1.05-7.51, P = .04),²¹ (AOR = 24.88; 95% CI: 11.73-52.81),³² and (AOR=7.51, 95% CI: 3.985-14.14).⁴⁴ It is widely recognized that the CD4 T-cell count is inversely related to viral replication and load. As patients’ immune status is suppressed, the rate of viral replication increases in comparison with immune-competent responses. Moreover, immunosuppressed clients are more exposed to different opportunistic infections that promote the detrimental cycle of immune dysfunction and viral replication.¹⁴

The risk of virologic failure was 9.56 times greater among patients who had poor adherence to treatment than among those who had good adherence (AOR= 9.56, 95% CI: 2.62-34.90, P=.001). This finding was supported by studies in Ethiopia (AOR= 6.73, 95% CI: 93.29-13.76),³² (AOR = 4.54; 95% CI: 2.09-9.87),³³ and (AOR=2.99, 95% CI: 1.33-6.73).⁴³ The underlying reason is that individuals who fail to take at least 3 doses of ART per month are especially vulnerable to the development of drug-resistant viral strains and progressive immunosuppression. Insufficient adherence results in subtherapeutic drug concentrations, which are inadequate to fully suppress viral replication. This incomplete suppression allows the virus to replicate more rapidly, which leads to virologic failure.¹⁴

In this study, although TB coinfection was significantly associated with immunological failure, the interaction between TB and poor adherence was not statistically significant for either immunological (P=.266) or virological failure (P=.703). The wide confidence intervals observed for the interaction terms may reflect limited sample size within specific subgroups, reducing statistical power to detect effect modification. Nevertheless, poor adherence consistently demonstrated a strong independent association with both immunological (OR = 7.56, 95% CI: 2.24-25.45, P = .001) and virological failure (OR = 11.69, 95% CI: 4.26-32.12, P < .001), underscoring its central role in treatment outcomes. These findings suggest that while TB coinfection contributes to immunological deterioration, it does not significantly modify the effect of adherence on treatment failure.

In this study, sociodemographic factors such as gender, age, residence, and marital status were not significantly associated with virological or immunological failure. This finding is inconsistent with some previous studies. The observed differences may be explained by variations in study settings, sample size, patient characteristics, and healthcare access, as well as contextual differences in adherence support and ART service delivery.

Regarding the pattern of failure, only a small proportion (4.04%) of participants had concurrent virological and immunological failure, while others had discordant outcomes. This inconsistency highlights the limitation of using immunological criteria alone as a surrogate marker for virological failure, as CD4 count changes may not accurately reflect viral suppression status in all patients.

Finally, this study has several limitations. Its single-center design may limit generalizability to other settings. The cross-sectional nature of the study also prevents establishing causal relationships between predictors and outcomes. In addition, adherence data were self-reported, which may introduce recall or social desirability bias. In addition, the absence of genotypic resistance testing restricted the ability to determine the underlying mechanisms of virologic failure.

Conclusion

In the present study, a baseline viral load of ≥1000 RNA copies/mL, a current CD4 count of less than 250 cells/mm³, and poor adherence to ART were significantly associated with virologic failure. In addition, TB coinfection and poor adherence were significantly associated with immunological failure. These findings highlight the critical role of sustained treatment adherence and clinical monitoring in improving treatment outcomes among PLWHA.

Recommendation

Targeted strategies to improve adherence to ART should be strengthened, including enhanced adherence counseling, patient education, and psychosocial support. Routine viral load monitoring should be maintained to enable early detection of virologic failure. Furthermore, better integration of TB and HIV services, along with community-based adherence support interventions, may improve overall treatment outcomes.

However, before implementing intensified monitoring strategies for high-risk groups, further studies are recommended to assess cost-effectiveness, feasibility, and programmatic implications. Future research should also incorporate genotypic resistance testing and periodic resistance surveillance to differentiate between treatment failure due to poor adherence and that due to drug resistance.

Supplemental Material

sj-doc-1-jia-10.1177_23259582261453642 - Supplemental material for Virologic and Immunological Failure and Associated Factors Among Adults Living With HIV on HAART at Jimma Medical Center, Southwest Ethiopia

Supplemental material, sj-doc-1-jia-10.1177_23259582261453642 for Virologic and Immunological Failure and Associated Factors Among Adults Living With HIV on HAART at Jimma Medical Center, Southwest Ethiopia by Rahel Tamrat Teshome, Mulatu Gashaw, Aster Wakjira Garedo, Tesfaye Deme, Wondimagegn Adissu, Daniel Yilma, Zeleke Mekonnen and Lealem Gedefaw Bimerew in Journal of the International Association of Providers of AIDS Care (JIAPAC)

Supplemental Material

sj-docx-2-jia-10.1177_23259582261453642 - Supplemental material for Virologic and Immunological Failure and Associated Factors Among Adults Living With HIV on HAART at Jimma Medical Center, Southwest Ethiopia

Supplemental material, sj-docx-2-jia-10.1177_23259582261453642 for Virologic and Immunological Failure and Associated Factors Among Adults Living With HIV on HAART at Jimma Medical Center, Southwest Ethiopia by Rahel Tamrat Teshome, Mulatu Gashaw, Aster Wakjira Garedo, Tesfaye Deme, Wondimagegn Adissu, Daniel Yilma, Zeleke Mekonnen and Lealem Gedefaw Bimerew in Journal of the International Association of Providers of AIDS Care (JIAPAC)

Footnotes

Abbreviations

Acknowledgments

We would like to thank Jimma University, the data collectors, and all the study participants.

ORCID iDs

Rahel Tamrat Teshome

Aster Wakjira Garedo

Wondimagegn Adissu

Lealem Gedefaw Bimerew

Ethical Considerations

Author Contributions

Rahel Tamrat: writing—original draft, methodology, investigation, project administration, data curation, conceptualization. Mulatu Gashaw: writing—original draft, validation, methodology, conceptualization. Aster Wakjira Gredo: writing—review & editing, validation, software, methodology. Tesfaye Deme : writing—review & editing, methodology, data curation. Wondimagegn Adissu: writing—review & editing, validation, methodology, data curation, visualization. Daniel Yilma: writing—review & editing, resources, formal analysis. Zeleke Mekonen: writing—review & editing, supervision, formal analysis. Lealem Gedefaw: writing—review & editing, validation, supervision, project administration, funding acquisition.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Availability of Data and Materials

Readers who will require the data and materials of the current study can communicate and obtain the data from the corresponding author upon reasonable request.

Supplemental Material

Supplemental material for this article is available online.

References

UNAIDS. Global HIV & AIDS statistics fact sheet 2023. UNAIDS; 2023.

Ethiopian Public Health Institute (EPHI). HIV estimates report 2022. EPHI; 2022.

Woldegeorgis

Asgedom

Habte

Kassie

Badacho

. Highly active antiretroviral therapy is necessary but not sufficient: A systematic review and meta-analysis of mortality incidence rates and predictors among HIV-infected adults receiving treatment in Ethiopia. BMC Public Health. 2024;24(1):1735.

Getaneh

Ning

, et al. Survival and predictors of mortality among adults initiating highly active antiretroviral therapy in Ethiopia: A retrospective cohort study (2007–2019). Biomed Res Int. 2022;2022:5884845.

Eggleton

Nagalli

. Highly active antiretroviral therapy (HAART). In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing, 2023.

Chun

Dirlikov

Cox

, et al. Vital signs: Progress toward eliminating HIV as a global public health threat through scale-up of antiretroviral therapy and health system strengthening supported by the U.S. President's emergency plan for AIDS relief - worldwide, 2004–2022. MMWR Morb Mortal Wkly Rep. 2023 Mar 24;72(12):317–324.

Girón-Callejas

Lorenzana

Pickles

, et al. High HIV viral suppression among adults receiving WHO-recommended first-line dolutegravir-based antiretroviral therapy in low- and middle-income countries: A systematic review and meta-analysis of programmatic evidence. AIDS Res Ther. 2025 Sep 26;22(1):91.

UNAIDS up date :Impact of the pause of U.S. of foreign assistance in Ethiopia Feb,2025.

Mitiku

Dimore

Gach

. Trends of HIV infection and progress towards the 95-95-95 targets in Gambella Regional State from 2019 to 2023, southwest Ethiopia. HIV AIDS (Auckl). 2024;16:193–201.

10.

World Health Organization. HIV drug resistance report 2017. WHO; 2017.

11.

Bertagnolio

De Luca

Vitoria

, et al. Determinants of HIV drug resistance and public health implications in low- and middle-income countries. Antivir Ther. 2012;17(6):941–953.

12.

World Health Organization. Consolidated Guidelines on HIV Prevention, Testing, Treatment, Service Delivery and Monitoring: Recommendations for a Public Health Approach. WHO; 2023.

13.

WHO. Consolidated guidelines on the use of antiretroviral drugs for treating and preventing HIV infection: Recommendations for a public health approach- 2nd ed. WHO; 2016.

14.

Federal Ministry of Health. National consolidated guidelines for comprehensive HIV prevention, care and treatment. Ministry of Health; 2018.

15.

Demas

Gebremariam

Kebede

Ayele

. Adherence to antiretroviral treatment and associated factors among seropositive people received treatment in Jimma town public health facilities, Ethiopia. J Int Assoc Provid AIDS Care. 2022 Jan-Dec;21:23259582221121096.

16.

Balew

Abeje

Mekonnen

Degu

. Prevalence of HIV drug resistance among patients experiencing first-line treatment failure in Ethiopia: A systematic review and meta-analysis. BMC Public Health. 2025 Jun 3;25(1):2059.

17.

Abdissa

Yilma

Fonager

, et al. Drug resistance in HIV patients with virological failure or slow virological response to antiretroviral therapy in Ethiopia. BMC Infect Dis. 2014 Dec 4;14(1):181.

18.

BD Biosciences. BD FACSPresto™ near-Patient CD4 Counter :Instructions for Use. Becton Dickinson; 2014.

19.

Ethiopian Public Health Institute (EPHI). Guidelines for the Implementation of Point-of-Care CD4 Testing Technologies in Ethiopia. FMOH/EPHI, 2013.

20.

Roche Diagnostics. COBAS ® AmpliPrep/COBAS ® TaqMan ® HIV-1 test : package insert. 2007;1–36.

21.

von Elm

Altman

Egger

Pocock

Gøtzsche

Vandenbroucke

. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: Guidelines for reporting observational studies. Br Med J. 2007;335(7624):806–808.

22.

Hailu

Hagos

Wasihun

Dejene

. Virological and immunological failure of HAART and associated risk factors among adults and adolescents in Tigray region, northern Ethiopia. PLoS One. 2018;13(5):e0196259.

23.

Yirdaw

Hattingh

. Prevalence and predictors of immunological failure among HIV patients on HAART in southern Ethiopia. PLoS One. 2015;10(5):e0125826.

24.

Kiros

Taye

Workineh

, et al. Immuno-virological status and its associated factors among HIV-positive patients receiving highly active antiretroviral therapy at delgi primary hospital, northwest Ethiopia, 2020/2021: A cross-sectional study. Heliyon. 2022;8(8):e10169.

25.

Demsie

Bantie

Allene

Alema

Gebrie

. Antiretroviral treatment failure among HIV-positive adults taking first-line therapy and associated risk factors at adigrat general hospital, adigart, Ethiopia 2019: A cross sectional study. Int J Surg Open. 2020 Jan 1;26:16–21.

26.

Ayele

Tessema

Amsalu

Ferede

Yismaw

. Prevalence and associated factors of treatment failure among HIV/AIDS patients on HAART attending University of Gondar Referral Hospital Northwest Ethiopia. BMC Immunol. 2018;19(1):1–13. 10.1186/s12865-018-0278-4

27.

Mamo

Assefa

Negash

, et al. Virological and immunological antiretroviral treatment failure and predictors among HIV positive adult and adolescent clients in southeast Ethiopia. HIV/AIDS - Res Palliat Care. 2022;14:73–85.

28.

Chomboko

Kabyemera

Kidenya

Kamugisha

. Prevalence and predictors of immunological treatment failure among HIV infect-ed adults on the first-line antiretroviral therapy in Mbeya Region, Tanzania. M J HIV. 2018;3(1):017.

29.

Ojha

Shakya

Dumre

. Virological and immunological Status of the people living with HIV/AIDS undergoing ART treatment in Nepal. Biomed Res Int. 2016;2016:6817325.

30.

Niu

Tang

Chen

, et al. Treatment failure and associated factors among people living with HIV on highly active antiretroviral therapy in mainland China: A systematic review and meta-analysis. PLoS One. 2023;18(55):e0284405.

31.

Glassman

Campbell

, et al. Host cell death and modulation of immune response against Mycobacterium tuberculosis infection. Int J Mol Sci. 2024 6;25(11):6255.

32.

Derseh

Shewayerga

Mekuria

Basha

. Virological treatment failure among adult HIV/AIDS patients from selected hospitals of North Shoa Zone, Amhara region, Ethiopia. Infect Drug Resist. 2020;13:4417–4425.

33.

Wendie

Workneh

. Prevalence and predictors of virological failure among adults living with HIV in south wollo zone, northeast Ethiopia: A retrospective cohort study. HIV/AIDS - Res Palliat Care. 2020;12:393–402.

34.

Kiweewa

Esber

Musingye

, et al. HIV Virologic failure and its predictors among HIV-infected adults on antiretroviral therapy in the African cohort study. PLoS One. 2019;14(2):1–16.

35.

Samizi

Panga

Malugu

Gitige

Mmbaga

. Rate and predictors of HIV virological failure among adults on first-line antiretroviral treatment in Dar Es Salaam, Tanzania. J Infect Dev Ctries. 2021 Jun 30;15(6):853–860.

36.

Sembene

, et al. Prevalence and factors associated with virologic failure among people living with HIV monitored in a decentralized health care facility. Adv Infect Dis. 2019;09(03):226–237.

37.

Hassan

Nabwera

Mwaringa

, et al. HIV-1 virologic failure and acquired drug resistance among first-line antiretroviral experienced adults at a rural HIV clinic in coastal Kenya: A cross-sectional study. AIDS Res Ther. 2014;11(1):9.

38.

Kazooba

Mayanja

Levin

Masiira

Kaleebu

. Virological failure on first-line antiretroviral therapy; associated factors and a pragmatic approach for switching to second line therapy evidence from a prospective cohort study in rural South-Western Uganda, 2004-2011. Pan Afr Med J. 2018;29:191.

39.

Gupta-Wright

Fielding

van Oosterhout

, et al. Virological failure, HIV-1 drug resistance, and early mortality in adults admitted to hospital in Malawi: An observational cohort study. Lancet HIV. 2020 Sep 1;7(9):e620–e628.

40.

Mesic

Spina

Mar

, et al. Predictors of virological failure among people living with HIV receiving first line antiretroviral treatment in Myanmar: Retrospective cohort analysis. AIDS Res Ther. 2021;18(1):1–12.

41.

Yuan

Liu

Jia

, et al. Prevalence and determinants of virological failure, genetic diversity and drug resistance among people living with HIV in a minority area in China: A population-based study. BMC Infect Dis. 2020;20(1):1–10.

42.

Chen

Han

Song

, et al. Very high baseline HIV viremia impairs efficacy of nonnucleoside reverse transcriptase inhibitor-based ART: A long-term observation in treatment-naïve patients. Infect Dis Poverty. 2020 Dec 22;9(1):75.

43.

Aytenew

Asferie

Ejigu

, et al. Virological failure and associated factors among patients receiving anti-retroviral therapy in Ethiopia: A systematic review and meta-analysis. BMJ Open. 2024;14(11):e087569.

44.

Meshesha

Nigussie

Asrat

Mulatu

. Determinants of virological failure among adults on first-line highly active antiretroviral therapy at public health facilities in Kombolcha town, Northeast, Ethiopia: A case–control study. BMJ Open. 2020;10(7):1–9.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.09 MB

0.03 MB