Viral load suppression after enhanced adherence counseling and its associated factors among clients on antiretroviral therapy

doi:10.21203/rs.3.rs-5003439/v1

Background

Maintaining long-term client adherence and viral suppression requires ongoing increased adherence counseling and follow-up care. Promoting enhanced adherence counseling (EAC) is advised for those whose viral load is greater than or equivalent to 1000 copies/ml. However, there is still a lack of knowledge in the study region about the degree of viral load suppression that occurs after enhanced adherence counseling is implemented.

Objective

The study was to determine the magnitude of Viral load suppression after enhanced adherence counseling and its associated factors among Clients on Anti Retro Viral Therapy follow up in public health facilities in Debre Berhan Regio-Politian City, North Shewa, Ethiopia.

Methods

From July 2018 to June 2023, an institutionally based retrospective follow-up research was conducted. A total of 409 HIV-positive individuals who were enrolled in EAC were included in the study. Additionally, a straightforward random sampling method was used to choose them. Kobo Toolbox was used to collect the data, and STATA version 14.1 was used for analysis. Other variables and the extent of viral load suppression were summarized using descriptive analysis. Utilizing binary logistic regressions, the contributing components were found. Finally, every independent variable in the bivariable analysis with a p-value less than 0.25 was included in the multivariable logistic regression. Eventually, an OR with 95% CI was employed and a p-value of less than 0.05 was deemed statistically significant.

Result

Clients who successfully reduced their viral load were 64.3% of the participants, with a 95% confidence interval of (59.3–68.5). The likelihood of experiencing poor drug adherence (AOR 0.211, 95% CI = 0.117–0.411), being a single client (AOR 0.330, 95% CI = 0.179–0.631), living in a rural area (AOR 0.351, 95% CI = 0.202–0.601), having a history of alcohol consumption (AOR 0.531, 95% CI 0.286–0.985), and chewing khat (AOR 0.223, 95% CI = 0.09–0.545) were less likely to be significantly linked to suppressing viral load.

Conclusion

The target of 70% recommended by the World Health Organization was not achieved in terms of suppressing virus loads. Lower levels of viral load suppression have been associated with variables such alcohol usage, khat chewing, marital status, poor adherence, and site of residence. Thus, it is recommended that ART case managers, adherence counselors, and multidisciplinary teams at medical facilities prioritize clients with low adherence and address behavioral variables in order to optimize viral load suppression.

Viral Load Suppression

Antiretroviral Treatment

Adherence

Counseling

Ethiopia

One measure of treatment outcome that's utilized to assess whether or not patients receive follow-up therapy is viral load suppression. Consequently, the World Health Organization suggested that testing for viral loads be done every year after six and twelve months. Based on the outcome, after at least six months on antiretroviral therapy (ART), those who still have a high viral load (VL) (> 1000 cells/mL of blood) are given enhanced adherence counseling (1, 2). Approximately 68% of the 38.4 million people living with HIV worldwide in 2021 have successfully suppressed their virus. Antiretroviral therapy (ART) has been widely available, and this has contributed to a decrease in the death rate associated with AIDS (3). Viral load testing is recommended by the World Health Organization (WHO) as the gold standard for tracking a patient's reaction to antiretroviral therapy (ART). Unsuppressed viral loads among users of antiretroviral medication are a major concern in the ART treatment program. The United Nations on HIV/AIDS (UNAIDS) developed the three 95s (95-95-95) strategy, which states unequivocally that 95% of all individuals on ART should have a suppressed viral load and end the HIV epidemic by 2030 (2, 4). In 2022, there were a projected 25.6 million HIV positive individuals in the Africa area, and 20.9 million antiretroviral medication recipients, of which only 76% had a suppressed viral load (5) By 2020, the 90-90-90 end HIV/AIDS epidemic target could suppress 72% of the infectious agent in Ethiopia; nevertheless, it will be difficult to achieve the third 95's viral load reduction objective (6–8). An estimated 38.4 million people worldwide are impacted by the HIV/AIDS pandemic, and 66% of those individuals have viral suppression. Over 20.6 million AIDS-related deaths occurred in Eastern Africa as of 2021, according to UNADS estimates, of the 16.6 million persons receiving ART in Eastern and Southern Africa the rates of viral suppression are remained well below the worldwide aim, despite the targets being missed. Numerous African nations fall well short of the three ninety-five targets(9).

Low CD4 count, advanced WHO staging, and the presence of opportunistic infections like as pneumonia and bacterial tuberculosis, adherence to antiretroviral therapy (ART) all have a substantial impact on viral suppression. These variables significantly raise the risk of death and unsuppressed viral load in HIV-positive persons (10–12).

Magnitude of Viral Load Suppression

There are 37.9 million HIV-positive individuals worldwide, and 53% of them have suppressed virus loads. There were 7.7 million more people to reach the goal of 73% of PLHIV having a suppressed viral load (13).In Uganda, the percentage of adults and children living with HIV who have achieved viral load suppression is 39.3% and 59.6%, respectively. In Ethiopia, 82% of those on antiretroviral therapy (ART) have achieved viral suppression, falling short of the 2020 target North-western Nigeria and Ethiopia revealed that 90.4% and 72% of the Clients had viral load suppression. Respectively (14–17). Ethiopia continues to have a high HIV/AIDS rate. In 2020, there were roughly 620,000 HIV/AIDS clients. resulting in 13,000 deaths from HIV. In addition, 480,000 patients were on antiretroviral medication (ART), and in 2020, 72.5% of those patients had achieved viral suppression (18).

Associated Factors for Viral Suppression

Socio-Demographic and Related Characteristics

Age, gender, educational level, length of time on ART and initial viral load count, poor nutritional status, sex, and place of residence were the variables that were statistically linked to viral load suppression (1, 4, 11, 19–22) (1, 23, 24)

Clinical and Related characteristics

CD4 cell count (20, 21, 25).Baseline WHO clinical staging and baseline opportunistic infections, and baseline CD4 are the predictors of viral load suppression (20, 22, 26–28)

Treatment and Related characteristics

Adherence to ART is another predictor for viral load suppression (29, 30). Participants who adhered poorly at enrollment had a minimum 50% reduced chance of obtaining VL suppression following EAC compared to those who adhered well (31–33). Another factor that predicts viral load reduction is the kind of regimen. For example, EFV-based ART regimens demonstrated a nearly 90% and 77.7% lower chance of VL suppression, respectively, compared to PI-based regimens (34, 35). Those who had only attended one or two EAC sessions had a about 70% decreased probability of achieving VL suppression (1, 36, 37)

Psychosocial, Mental and Behavioral Related Characteristics (dup: abstract ?)

Strong evidence linking positive mental health screening results to virus suppression was found in the South African study. Disruptive behavior disorder was found to be substantially correlated with an unsuppressed viral load at a threshold of more than > 1000 copies/ml in both the New York City trial and the Vietnam study. Social and psychological variables are also linked to HIV VL that is not suppressed (20, 25). According to a US study, variations in the duration of viral suppression were not significantly correlated with depression or the use of antidepressants (38–40)

The purpose of this study was to evaluate viral load suppression following EAC and to provide evidence about the main context-specific factors of viral load suppression in the research area.

OBJECTIVE

General Objective

To asses viral load suppression after enhanced adherence counseling and its associated factors among HIV/ADIS Clients at public health facilities of in Debre Berhan regio-Politian city, North Shoa, Ethiopia, 2024.

Specific Objective

To determine the magnitude of Clients with viral load suppression after enhanced adherence counseling among Clients who initiated ART.
To identify factor associated with viral load suppression among Clients who initiated ART.

Study Area

The investigation was carried out in five public ART health facilities in the Ethiopian Amhara region's Debre Berhan regio-Politian city. The distances are 130 km to Addis Ababa, the capital, and 695 km to Bahir Dar, a regional center. The study was conducted in three of the city's five ART health facility sub-cities, which total 39 kebeles. The total population of Debre Berhan City is 209,011, according to the 2016 Ethiopian calendar population projection. Of them, 98,934 were men and 110,077 were women. Twelve health posts, eight health centers, and two hospitals are located in the city. As of June 2023, 3,623 ART customers were enrolled in six ART health facilities under the sub-city, according to the DHIS2 report. Four HFs employ nine case managers and three adherence support staff. Due to inadequate documentation, one private hospital and two health centers that were not ART sites were removed from the study.

Study Design and Period

An institutional-based retrospective follow up study was conducted from July 2018- June 2023.

Population

The source population for this study was all HIV-positive individuals who tested positive for the virus after six months of antiretroviral therapy (ART) and who enrolled in the EAC program in Debre Berhan regio-Politian city, Ethiopia. The study population consisted of HIV-positive individuals who tested positive for the virus at least once and who had at least one repeat result following an Enhanced Adherence counseling session between July 2018 and June 2023 in the designated health facilities. The study subjects were those HIV-positive individuals who were randomly selected and who had viral loads > 1000 copies/ml and at least one repeat result following an Enhanced Adherence counseling session between July 2018 and June 2023 in the designated Health facilities.

Eligibility criteria

Inclusion and exclusion Criteria

All ART- clients whose documented viral load was more than 1000 copies/ml were enrolled in the EAC. In contrast, patients who were lost to follow-up, transferred before finishing all EAC, or had no second virus load result were not included in this research.

Sample size Determination

Sample size determination by using first objective

Sample size was determined by using single population proportion formula by considering the following assumption.

Where:

Z = Standard normal distribution value at 95% CI, which is 1.96;

P = 40.9% (P = 0.409) the proportion of viral load suppression after EAC from study done in Public Hospitals of Hawassa City Administration, Ethiopia (1).

d = the margin of error, taken as 5%.

Accordingly, the sample size were calculated as,

n= (1.96)2 0.409(1-0.409)/ (0.05)2 = 371 by adding 10% for none response, the total sample size is 409 (Table 1)

Table 1

Sample size calculation for the second objective of the study
Factors	Ratio (Unexposed : exposed)	% in Unexposed group	Adjust Odds ratio	% in exposed group	sample size	Reference
Initial CD4 count	1.4	41%	0.149	9.40%	74	(1)
Adherence	1.33	42%	0.504	26.70%	337	(1)
Education	2.54	37.5%	0.317	16%	190	(4)
Duration on ART	0.55	26.9%	2.315	46%	241	(4)

Finally, the sample size for the second objective found to be less than the first objective and the sample size of the first objective, 409

Sampling Technique and Procedure

There are five public ART HFs within the Debre Berhan Town Zonal Health Department; all of these public health facilities were included in the study. Total number of cohort clients who meet the requirements for inclusion Clients who had finished their EAC between July 2018 and June 2023 were gathered from the viral load registration books of the five public health facilities, and the necessary sample size was distributed among them in a proportionate manner. Lastly, use the basic random sampling technique of RAND BETWEEN excel to select the sample at random from the viral load registration book of each of the five public health facilities (Fig. 1).

Study Variables

Dependent Variables

Viral load status (suppressed/unsuppressed).

Independent Variables

Socio-demographic and Related characteristics; Age, sex, Educational status, Marital status, Occupation, Residence
Nutrition status; Normal, Under nutrition
Treatment and Related characteristics; ART adherence, Type of ART Regimen, daily drug dosage, EAC sessions, ART duration, Other medication
Clinical and Related characteristics; WHO clinical stage, CD4 count and opportunistic infections
Psychosocial, Mental and Behavioral Related Characteristics; Mental disorder, Alcohol drinking, Khat Chewig

Operational Definition

Viral load suppression

refers to a viral load below the detection threshold using viral assays. Less than or equal to 1000 copies/mL after 3–6 month of EAC (14).

High Viral load

refers to when viral load result is greater than 1000 copies/mL(14).

Virological Failure

is experienced when VL is above 1000 copies/ml based on two consecutive VL measurements within 3–6 months. A PLHIV who has Virological failure will receive adherence support following the first VL test(46).

Good adherence

equal to or greater than 95% adherence i.e., missing only 1 out of 30 doses or missing 2 from the 60 doses implies good adherence(14).

Fair adherence

85–94% adherence, i.e., missing 2–4 doses out of 30 doses or 4 to 9 doses from 60 doses(14).

Poor adherence

less than 85% adherence, i.e. missing > 5 doses out of 30 doses or > 10 doses from 60 doses implies poor adherence(14).

A mental disorder is characterized by a clinically significant disturbance in an individual’s cognition, emotional regulation, or behavior(47).

Nutritional status for Adolescent

Normal BMI for age greater than or equal to -2 SD and + 1SD and Under Nutrition BMI for age < -3 SD(48).

Nutritional status for Adult

Normal BMI for adult 18.5–25 and undernutrition less than 18.5(48).

Data Collection Methods.

Using a pretested structured checklist, the data was gathered from the following sources: the client's chart, the EMR ART Smart Care, the viral load registration book, the increased adherence counseling page, and the laboratory request. As a result, every chart that included comprehensive data regarding clients receiving ART was examined. The data that was most current to the commencement date of increased adherence counseling sessions were deemed baseline data if clinical parameters and laboratory findings (CD4 count and WHO clinical stage) were not found at that time. Five certified BSc degree nurses were chosen to gather data, and two ART data managers with extensive expertise served as supervisors and also engaged in the process. Chart abstraction and sociodemographic and clinical characteristics were both included in the data collection form. The most recent and comprehensive data for incomplete charts were also obtained by using various registers, follow-up forms, and laboratory findings. Expert content reviews and internal consistency checks were used to verify the authenticity and reliability of the tests.

Data quality management, process and analysis

Designing appropriate data gathering materials ensured the quality of the collected data. A one-day instruction on the goal of the study, data extraction, and recording from the client's chart and registration books was given to supervisors and data collectors. A supervisor was tasked with making sure no data was overlooked throughout the data gathering phase. The primary investigator of the study was in charge of all overall operations. Ten percent of participants were chosen at random from the Debresina Health Center to take a pretest. Any challenges were redesigned after the pretest in light of the experience gained from it. The tools are validated for every data extraction sheet during the development of Kobo data variables, and data entry, frequency, and cross-tabulation are performed for outliers and missing data thereafter. The Kobo Collect Toolbox was used to gather the data, which was then exported to Excel for analysis using Stata version 14 and validated for completeness. Based on the characteristics of the variables, descriptive statistics were performed, and the results were displayed in a table and graph. To determine whether there was a rough correlation between the independent and outcome variables, bi-variable logistic regression analysis was carried out. Candidates for multivariable analysis were variables with a p-value of less than 0.25 in the bivariable analysis. Following EAC sessions, the percentage of viral load suppression was displayed. The independent predictors of viral suppression and associated factors among HIV patients undergoing therapy were identified using a multivariable logistic regression model. Statistical significance was defined as P-values less than 0.05. Variables with a standard error (SE) or variance inflation factor (VIF less than 10) greater than two were eliminated from the analysis and those without a collinearity effect were added to a binary logistic regression model to examine its relationship with the outcome variables. The Hosmer-Lem show test (> 0.05) or goodness of fit test (estat gof) were used to assess the model's fitness. The model's value was 0.28.

Socio-demographic Characteristics

There were 409 study participants in all, yielding a 100% response rate. Of the 409 study participants, 244 (59.6%) were female and 129 (31.5%) were in the 40–49 age range. Among them, 189 (46.2%) were illiterate and 170 (41.5%) were married. In terms of religion, the participants were 91.4% orthodox followers, 240 worked daily (58.6%), and 280 lived in urban areas (68.4%) (Table 2).

Table 2

Socio Demographic Characteristics among Study Participants from July 2018- June 2023, Debre Birehan Regio- Politian City, Amhara, Ethiopia, 2024 (n = 409)
Variables	Category	Frequency	Percent (%)
Age	10–19 years	17	4.16
	20–29 years	66	16.14
	30–39 years	98	23.96
	40–49 years	129	31.54
	> 50 years	94.1	24.21
Sex	Male	165	40.3
Sex	Female	244	59.6
Marital status	Married	170	41.5
	Separated	40	9.7
	Single	110	26.8
	Widowed	37	9.05
	Divorced	52	12.7
Current Education Level	Illiterate	189	46.2
	Elementary	117	28.6
	secondary school	96	23.4
	college and higher	7	1.7
Religion	Orthodox	374	91.4
	Protestant	10	2.4
	Muslim	22	5.3
	Catholic	3	0.7
Occupation	Student	34	8.3
	Merchant	41	10.02
	Farmer	63	15.4
	NGOs	1	0.2
	Gov’t Employee	30	7.3
	daily laborer	240	58.6
Residence	Rural	129	31.5
Residence	Urban	280	68.4

Treatment and Related Characteristics

A treatment supporter was present for every participant, and 318 (77.7%) of them did not experience any side effects. Of the participants, about 174 (42.5%) chose their medication on an ad hoc basis, and 85% of them waited more than a year for the viral load to be determined. Furthermore, 304 patients, or 74.3%, have been receiving antiretroviral medication (ART) for longer than five years. When it came to the frequency of drug use, 335 clients (81.9%) took their medication once daily, and 303 participants (74%), did not use any other medications than ART. Furthermore, 164 individuals (24.9%) had poor adherence, whereas 47 individuals (11.9%) had fair adherence. Only 97 participants (23.7%) attended the EAC session after three to six months, and 321 patients (78.4%) were on first-line medication.

Nutrition Status

After undergoing a nutritional screening, 225 participants, or 55% of the total, exhibited normal nutritional status.

Clinical and Related Characteristics

Of the individuals, 306 (75%) were defined as being in WHO clinical stage one, and 71 (17%) had infections that were opportunistic. Furthermore, 157 (17.3%) of them had a CD4 count of less than 350 at baseline.

Psychosocial, Mental and Behavioral Related Characteristics

Taking into account their individual habits, 220 (53.4%) and 62 (15%) of the individuals drank alcohol and used khat. However, 395 (96%) of the individuals did not receive a mental health diagnosis (Table 3).

Table 3

Clinical, Treatment, Nutritional, Mental And Behavioral Characteristics Among Study Participants From July 2018- June 2023, Debre Berhan Regio- Politian City, Amhara, Ethiopia, 2024 (N = 409)
Variables	Category	frequency	Percent (%)
WHO clinical stage	Stage one	306	74.8
	Stage two	59	14.4
	Stage three	27	6.6
	Stage four	4	0.9
Opportunistic infection	Yes	71	17.3
Opportunistic infection	No	338	82.6
Duration on ART	< 1 Years	5	1.2
	1–3 years	16	3.9
	3–5 years	84	20.5
	> 5 years	304	74.3
Other medication other than ART	Cotrimoxazole	20	4.8
	Anti-Depression INH	14 72	3.4 17.6
	NA	303	74
Adherence assessment	Good	260	63.5
	Poor	102	24.9
	Fair	47	11.49
Side effects	Yes	91	22.2
Side effects	No	318	77.7
Daily drug dosage intake	Once	335	81.9
	Twice	68	16.6
	Three times	6	1.4
Frequently drug intake style	Unscheduled	174	42.5
Frequently drug intake style	Scheduled	235	57.4
Number of EAC sessions attended	1–3 months	312	76.2
Number of EAC sessions attended	3–6 months	97	23.7
Treatment supporter	Yes	409	100
Treatment supporter	No	0	0
Duration on ART before VL done	1–6 months	21	5
	6–12 months	40	9.7
	> 12months	348	85
Baseline CD4	< 350	157	17.3
	350–500	71	38.3
	> 500	181	44.2
Current ART regimen	First Line Drugs	321	78.4
	Second Line Drugs	77	18.8
	Third Line Drugs	11	2.6
Diagnosed with Mental disorder	Yes	14	3.4
Diagnosed with Mental disorder	No	395	96
Alcohol Drink	Yes	220	53.7
Alcohol Drink	No	189	46.2
Nutritional status	Normal	225	55
	Underweight	152	37
	Obesity	32	7.8
Khat Chewing	Yes	62	15.1
Khat Chewing	No	347	84.8

Viral Load Status

Among the participants, clients who effectively suppressed their viral load was 64.3%, with a 95% CI of (59.5–68.8) (Fig. 2).

Factors Associated with Viral Load Suppression

Following bi-variable analysis, candidate variables for multivariable logistic regression were identified with P < 0.25, including sex, marital status, adherence, initial CD4 count, residence, opportunistic infection, current ART regimen, other medication, daily drug dosage intake, duration of ART, diagnosis of mental disorder, drinking alcohol, and khat chawing. According to the multivariable analysis, those with poor treatment adherence were 79% less likely than those with good drug adherence to have suppressed viral load during the EAC session (AOR 0.211 (95% CI = 0.117–0.411)). Compared to married individuals, the likelihood of a suppressed viral load was 67% lower in singles [AOR 0.333 (95% CI = 0.179–0.631)]. Compared to those who live in urban areas, the odds of having a suppressed viral load were 67% lower in rural areas [AOR 0.351 (95% CI = 0.202–0.601)].

Alcohol drinkers had a 47 percent lower chance of suppressing their viral load compared to abstainers [(AOR 0.531, 95% CI= (0.826–0.985)]. Additionally, customers who used Khat Chawing were 78% less likely to have a suppressed viral load than those who did not (AOR 0.223, 95% CI = 0.09–0.545) (Table 4).

Table 4.Bi-variat And Multivariable Analysis of Factors Associated With Viral Load Suppression Among High Viral Load HIV Infected People After Enhanced Adherence Counselling (EAC) Session In Debre Brehan Regio Politian City, North Shoa, Ethiopia, 2024 (n=409).

Variables	Categories	Viral load Status		COR (CI 95%	AOR (CI 95%)	P- value
Variables	Categories	Suppressed (%)	Unsuppressed (%)	COR (CI 95%	AOR (CI 95%)	P- value
Sex	Male	91 (55%)	74(45%)	1	1	0.230
Sex	Female	172(70%)	72(30%)	1.942 (1.286 - 2 .933)	1.932((0.810 -2.393)	0.230
Marital status	Married	128(75%)	42(25%)	1	1	0.953
	Separated	22(55%)	18(45%)	2.49(1.221 -5.091)	1.025(0.442-2.377)	0.953
	Single	54(49%)	56(51%)	0.316(0.189 - 0.527)	0.330(0.179-0.631)	0.001*
	Widowed	23(62%)	14(38%)	1.85(0.876 - 3.928)	0.571(0.232-1.403)	0.222
	Divorced	36(69%)	16(31%)	0.738(0.3724- 1.463)	1.052(0.445-2.487)	0.908
Adherence	Good	202(78%)	58(22%)	1	1	0.496
	Fair	24(51%)	23(49%)	0.299(0.157-0.569)	0.744(0.318-1.740)	0.496
	Poor	37(36%)	65(64%)	0.16(0.099 -0.268)	0.211(0.117-0.411)	0.001*
initial CD4	>500	162(89%)	19(11%)	1	1
	350-500	47(66%)	24(34%)	0.689(0.336 – 1.412)	1.168(0.486-2.804)	0.728
	<350	54(35%)	103(65%)	0.553(0.310 - 0.986)	1.136(0.559-2.310)	0.723
Residence	Urban	196(70%)	84(30%)	1	1
Residence	Rural	67(52%)	62(48%)	0.463(0.301 - 0.711)	0.351(0.202-0.601)	0.001*
Opportunistic Infection	No	238(70%)	100(30%)	1	1	0.969
Opportunistic Infection	Yes	25(35%)	46(65%)	0.228(0.133 - 0.391)	1.022(0.337-3.097)	0.969
Current ART regimen	First line	238(73%)	89(27%)	1	1	0.247
Current ART regimen	Second &Third Line	31(41%)	44(59%)	0.258(0.154 -0.433)	0.551(0.201-1.510)	0.247
Other Medication	No	219(72%)	84(28%)	1	1
Other Medication	Yes	44(42%)	62(58%)	0.272(0.171- 0.431)	0.620(0.313-1.229)	0.171
Daily Drug Dosage	Once	239(71%)	96(29%)	1	1
Daily Drug Dosage	Twice	24(32%)	50(68%)	0.219(0.126 -0.380)	0.830(0.285-2.418)	0.733
Mental disorder	No	259(52%)	236(48%)	1	1
Mental disorder	Yes	4(29%)	10(71%)	0.233 (0.070 - 0.771)	0.858(0.188-3.913)	0.844
Drinking Alcohol	No	149(79%)	40(21%)	1	1
Drinking Alcohol	Yes	114(52%)	106(48%)	0.288(0.186- 0.447)	0.531(0.286-0.985)	0.045*
Khat Chawing	No	248(71%)	99(29%)	1	1
Khat Chawing	Yes	15(24%)	47(76%)	0.127(0.068-0.238)	0.223(0.09-0.545)	0.001*

COR =Crude odd ratio; AOR = adjusted odd ratio; ART =Antiretroviral therapy; WHO=World Health Organization. ∗ P < 0.05

Based on the study findings, 64.3% of ART clients who were involved in enhanced adherence counseling (EAC) had suppressed viral load. Once potential confounding variables were taken into account, there was reduced likelihood of a meaningful correlation between suppressed viral load and marital status, poor adherence, place of residence, alcohol consumption, and khat chawing. The percentage of viral load suppression (64.3%) in this investigation was comparatively similar to that of a study done at North Wollo Zone Public Hospital (66.4%) (4). This can be because of the study design that was employed and the similarity in the healthcare institutions' levels of delivery. Our study's results, however, were greater than those of the study carried out in the west Gojam zone (51.73%),31.2% I Harare, Zimbabwe (2, 36). The observed disparity may stem from variations in sociodemographic characteristics, the methodologies employed in the research, and the accessibility of health services for the HIV client. Consequently, a high viral load has been linked to accelerated disease progression in HIV infection, immune system damage, an increased risk of opportunistic infection, and other complications. Furthermore, this study's findings were significantly higher than those of studies done in West Gojam zone health facilities (51.73%) and public hospitals run by the Hawassa City administration (40.9%) yet a greater suppression rate showed that the research carried out in northwest Nigeria's Amhara Public Health Institute (73%), (90.4%),96% in Tanzania (41–43). This level of viral load suppression is also less than the 70% WHO target, with the exception of northwest Nigeria and the Amara Public Health Institute studies (23).

This variance may be explained by variations in the standard of medical care, data collection, and documentation methods. Further investigation is necessary to determine whether barriers at the facility or client level account for the overall lower incidence of viral suppression. The number of clients at the healthcare institution may make the time allotted for counseling sessions insufficient, and the experience and education of the counselor may have an impact on how well the message is received. Numerous psychosocial factors can affect adherence at the client level; if these issues are not addressed, they may contribute to the reported ineffectiveness of counseling (44–46).

The overall impression is that patients with high viral loads may not receive adequate care, which could delay the identification of therapy failure. When it came to the marital status variable, single customers had a 67% lower chance of having a suppressed viral load than married clients. This suggests that unmarried clients may receive less spousal support than married clients, and that people with unsuppressed viral loads are more likely to spread the virus to others through unprotected sex. Furthermore, a single client may be more vulnerable to psychological, social, and economic variables. This could have an impact on their motivation and capacity to follow a treatment plan. However this finding is not supported by other similar studies (47)

Individuals who adhered poorly to antiretroviral treatment were 79% less likely to have a suppressed viral load than those who did so well. This result is consistent with research from eastern Uganda (30). Additionally, there have been other studies that show a strong correlation between poor adherence and viral load suppression. This suggests that a variety of barriers to consistent medication adherence, such as fear of discrimination or disclosure, the burden of ART medication in the form of pills, mental health conditions like depression or substance abuse, a lack of social support system, or other psychosocial challenges, may contribute to poor ART adherence behaviors (48).

This study found a high correlation between alcohol consumption and the suppression of viral loads; however, other investigations did not find a statistically significant relationship between alcohol consumption and viral load suppression. This suggests that alcohol use may impact drug metabolism directly or decrease medication adherence, which could compromise the efficacy of antiretroviral treatment (ART). This may result in less effective viral suppression, raise the possibility of drug-resistant virus strains emerging, and make medication forgetfulness more likely (49)

Moreover, there is a considerable correlation between suppressing viral load and khat chawing. The idea that chewing khat could lessen the effectiveness of ART is suggested by the high connection observed among khat users. There are several possible causes for this association. First, by disrupting sleep cycles and daily patterns, khat's stimulant effect may lead to irregular medication adherence. Moreover, khat use may have an impact on hunger and food consumption, which may further impair immunity. A socioeconomic and psychological component, such as a higher stress level or the presence of other dangerous behaviors, may also play a part in substance use. The necessity of integrated drug use therapies within HIV treatment programs is underscored by these findings. In order to address the possible effects of khat use on ART adherence and effectiveness, healthcare providers should screen for khat users and give counseling and support. with order to effectively decrease the viral load, public health activities should concentrate on informing clients who are HIV positive about the hazards involved with using khat and advocating for better alternatives. Ultimately, client households in rural locations had 65% lower odds of having a reduced viral load than those in urban settings. Conversely, other research revealed that the decrease of viral loads was unrelated to residency status. The variations could be due to infrastructure, older research, fewer health care facilities, more travel times to receive care, or regional variance. Because of this, stigma and prejudice, along with the greater rate of HIV transmission, have an impact on public health and reduce economic productivity due to increased morbidity and mortality. In general, people with unsuppressed viral loads have an increased chance of developing comorbidities, AIDS-defining diseases, and opportunistic infections, among other HIV-related problems. Reduced quality of life and worse overall health outcomes may result from this

Strength and Limitation of The Study

This study makes use of and attempts to evaluate additional variables that were not included previously. The retrospective approach and secondary data evaluation of this study are among its drawbacks. For the purpose of data analysis and interpretation, only the variables that are listed in the patient records may be utilized. Due to the lack of information in secondary data, several important elements, including the distance to the medical institution and other clinically relevant data, may be very important yet go unaddressed.

Clients receiving enhanced adherence counseling (EAC) have viral load suppression that is far lower than the 70% WHO objective. Chewing khat, consuming alcohol, living situation, adherence, and marital status were significantly associated with suppressing viral loads. These variables were shown to have dangers that lessen the possibility of suppressing the viral load. EAC services should be given for clients with poor adherence and behavioral barriers like individual who drink alcohol and khat chawing by trained service provider along with case managers and adherence supporter.

ART Anti-Retroviral Therapy

CD4 Cluster of Differentiation 4

CDC Center for Disease Control and Prevention

HAART Highly Active Antiretroviral Treatment

HIV/AIDS Human Immunodeficiency Virus/Acquired Immune Deficiency Syndrome

PLHIV People Living with HIV

UNAIDS The United Nations Programme on HIV/AIDS

Ethical Clearance and consent to participate

Ethical clearance was obtained from Institutional Review Board (IRB) of the Asrat Woldeyes Health Science campus, Debre Berhan University. Official letters from the city health department were written to the public health facilities for an effective approach in data collection process. Verbal consent was obtained from each health facilities providers and data clerks. The information they provide were used only for the study purpose. Data obtained from them were keep confidentially by using codes instead of using personal identifiers.

Consent for publication: Not Applicable

Clinical Trial number: Not Applicable

Competing of interest: All authors declare that there are no conflicts of interest.

Author's Contribution

TNB was the concept generator, wrote the proposal, took part in gathering information, and produced some of the text.

AE took part in overseeing the overall project, the planning, the execution, and the written work's outcomes. TMD contributed to the results, writing of discussions, analysis, and paper preparation; ADM and AG engaged in the literature, methods, writing, and analysis of the findings.

Funding

This study had no funding source. This was done using the researcher's source of money.

Availability of Data and materials

All data relating to this research work are also incorporated into the research article, in addition to this it is available, and may be provided at any time by the corresponding author upon reasonable request

Acknowledgments

We would like to thank Debre Berhan University Asrat Woldeyes Health Sciences Campus and the Department of Public Health, as well as the health staff of Debre Berhan Comprehensive specialized Hospital as well as the data collectors for their contribution to facilitating the conduct of this research.

Anito AA, Lenjebo TL, Woticha E, Solomon F. Magnitude of viral load suppression and associated factors among clients on antiretroviral therapy in public hospitals of Hawassa City Administration, Ethiopia. HIV/AIDS (Auckland, NZ). 2022;14:529.
Atnafu GT, Moges NA, Wubie M, Gedif G. Incidence and predictors of viral load suppression after enhanced adherence counseling among HIV-positive adults in West Gojjam Zone, Amhara Region, Ethiopia. Infection and drug resistance. 2022:261 – 74.
Meintjes G, Moorhouse MA, Carmona S, Davies N, Dlamini S, Van Vuuren C, et al. Adult antiretroviral therapy guidelines 2017. South Afr J HIV Med. 2017;18(1):1–24.
Diress G, Dagne S, Alemnew B, Adane S, Addisu A. Viral load suppression after enhanced adherence counseling and its predictors among high viral load HIV seropositive people in north wollo zone public hospitals, northeast Ethiopia, 2019: retrospective cohort study. AIDS Res Treat. 2020;2020(1):8909232.
Organization WH. The advanced HIV disease research landscape. World Health Organization; 2024.
Assefa Y, Gilks CF, Dean J, Tekle B, Lera M, Balcha TT, et al. Towards achieving the fast-track targets and ending the epidemic of HIV/AIDS in Ethiopia: successes and challenges. Int J Infect Dis. 2019;78:57–64.
Ayana WG, Ayana Hordofa M, Dechasa Yadeta A. Determinants of virologic failure among adult HIV patients on first line antiretroviral treatment in Oromia, Central Ethiopia: 2022 a case-control study. AIDS Res Therapy. 2024;21(1):42.
Wilson D. HIV/AIDS prevention and treatment. Lancet. 2002;360(9326):88–9.
Maheu-Giroux M, Ciaranello AL, Salomon JA, Sohn AH. Refining global HIV estimates for decision‐making: advances in analytic and modelling methods used by the Joint. United Nations Programme on HIV/AIDS. Medknow; 2021. p. e25790.
McMahon JH, Elliott JH, Bertagnolio S, Kubiak R, Jordan MR. Viral suppression after 12 months of antiretroviral therapy in low-and middle-income countries: a systematic review. Bull World Health Organ. 2013;91:377–85.
Byonanebye DM, Semitala FC, Katende J, Bakenga A, Arinaitwe I, Kyambadde P, et al. High viral suppression and low attrition in healthy HIV-infected patients initiated on ART with CD4 above 500 cells/µL in a program setting in Uganda. Afr Health Sci. 2020;20(1):132–41.
Abadiga M, Hasen T, Mosisa G, Abdisa E. Adherence to antiretroviral therapy and associated factors among Human immunodeficiency virus positive patients accessing treatment at Nekemte referral hospital, west Ethiopia, 2019. PLoS ONE. 2020;15(5):e0232703.
Marsh K, Eaton JW, Mahy M, Sabin K, Autenrieth CS, Wanyeki I, et al. Global, regional and country-level 90-90-90 estimates for 2018: assessing progress towards the 2020 target. LWW; 2019. pp. S213–26.
Pius A, Josephine NN, Erick S, Winifred A, Rita M, Silverjoseph O, et al. Influence of intensified adherence counselling on viral load suppression of people receiving antiretroviral therapy at a health centre IV in southwestern Uganda: a qualitative study. AIDS Res Therapy. 2021;18:1–7.
Mitiku AA, Dimore AL, Gach W. Trends of HIV Infection and Progress Towards the 95-95-95 Targets in Gambella Regional State from 2019 to 2023, Southwest ETHIOPIA. HIV/AIDS-Research and Palliative Care. 2024:193–201.
Bain LE, Nkoke C, Noubiap JJN. UNAIDS 90-90-90 targets to end the AIDS epidemic by 2020 are not realistic: comment on Can the UNAIDS 90-90-90 target be achieved? A systematic analysis of national HIV treatment cascades. BMJ global health. 2017;2(2):e000227.
Kerschberger B, Boulle AM, Kranzer K, Hilderbrand K, Schomaker M, Coetzee D et al. Superior virologic and treatment outcomes when viral load is measured at 3 months compared to 6 months on antiretroviral therapy. Afr J Reprod Gynaecol Endoscopy. 2015;18(1).
Organization WH. Health in 2015: from MDGs, millennium development goals to SDGs, sustainable development goals. 2015.
Ali JH, Yirtaw TG. Time to viral load suppression and its associated factors in cohort of patients taking antiretroviral treatment in East Shewa zone, Oromiya, Ethiopia, 2018. BMC Infect Dis. 2019;19:1–6.
Mogosetsi NJ, Mabuza LH, Ogunbanjo G. The prevalence of HIV load suppression and related factors among patients on ART at Phedisong 4 Clinic, Pretoria, South Africa. The Open Public Health Journal. 2018;11(1).
Adeiza MA, Wachekwa I, Nuta C, Donato S, Koomson F, Whitney J et al. Gaps and opportunities in Hiv service delivery in high volume Hiv care centers in Liberia: lessons from the field. Annals Global Health. 2021;87(1).
Ford N, Orrell C, Shubber Z, Apollo T, Vojnov L. HIV viral resuppression following an elevated viral load: a systematic review and meta-analysis. Afr J Reprod Gynaecol Endoscopy. 2019;22(11).
DRuGS A. tHe uSe of ANtIRetRoVIRAL DRuGS foR tReAtING AND PReVeNtING HIV INfeCtIoN. 2013.
Ahmed M, Merga H, Jarso H. Predictors of virological treatment failure among adult HIV patients on first-line antiretroviral therapy in Woldia and Dessie hospitals, Northeast Ethiopia: a case-control study. BMC Infect Dis. 2019;19:1–7.
Rangarajan S, Colby DJ, Bui DD, Nguyen HH, Tou PB, Danh TT, et al. Factors associated with HIV viral load suppression on antiretroviral therapy in Vietnam. J virus eradication. 2016;2(2):94–101.
Erjino E, Abera E, Lemma Tirore L. Time to Viral Load Suppression and Its Predictors Among Adult Patients on Antiretro Viral Therapy in Nigist Eleni Mohammed Memorial Comprehensive Specialized Hospital, Hossana, Southern Ethiopia. HIV/AIDS-Research and Palliative Care. 2023:157 – 71.
Thapa R, Amatya A, Pahari DP, Bam K, Newman MS. Nutritional status and its association with quality of life among people living with HIV attending public anti-retroviral therapy sites of Kathmandu Valley, Nepal. AIDS Res therapy. 2015;12:1–10.
Wakooko P, Gavamukulya Y, Wandabwa JN. Viral load suppression and associated factors among HIV patients on antiretroviral treatment in Bulambuli district, eastern Uganda: a retrospective cohort study. Infect Diseases: Res Treat. 2020;13:1178633720970632.
Haas AD, Technau KG, Pahad S, Braithwaite K, Madzivhandila M, Sorour G, et al. Mental health, substance use and viral suppression in adolescents receiving ART at a paediatric HIV clinic in South Africa. J Int AIDS Soc. 2020;23(12):e25644.
Hansana V, Sanchaisuriya P, Durham J, Sychareun V, Chaleunvong K, Boonyaleepun S, et al. Adherence to antiretroviral therapy (ART) among people living with HIV (PLHIV): a cross-sectional survey to measure in Lao PDR. BMC Public Health. 2013;13:1–11.
Bezabhe WM, Chalmers L, Bereznicki LR, Peterson GM. Adherence to antiretroviral therapy and virologic failure: a meta-analysis. Medicine. 2016;95(15):e3361.
Bulage L, Ssewanyana I, Nankabirwa V, Nsubuga F, Kihembo C, Pande G, et al. Factors associated with virological non-suppression among HIV-positive patients on antiretroviral therapy in Uganda, August 2014–July 2015. BMC Infect Dis. 2017;17:1–11.
Maina E, Mureithi H, Adan A, Muriuki J, Lwembe R, Bukusi E. Incidences and factors associated with viral suppression or rebound among HIV patients on combination antiretroviral therapy from three counties in Kenya. Int J Infect Dis. 2020;97:151–8.
Lakoh S, Jiba DF, Vandy AO, Poveda E, Adekanmbi O, Murray MJ, et al. Assessing eligibility for differentiated service delivery, HIV services utilization and virologic outcomes of adult HIV-infected patients in Sierra Leone: a pre-implementation analysis. Global health action. 2021;14(1):1947566.
Duong T, Judd A, Collins IJ, Doerholt K, Lyall H, Foster C, et al. Long-term virological outcome in children on antiretroviral therapy in the UK and Ireland. Aids. 2014;28(16):2395–405.
Bvochora T, Satyanarayana S, Takarinda KC, Bara H, Chonzi P, Komtenza B, et al. Enhanced adherence counselling and viral load suppression in HIV seropositive patients with an initial high viral load in Harare, Zimbabwe: operational issues. PLoS ONE. 2019;14(2):e0211326.
Etoori D, Ciglenecki I, Ndlangamandla M, Edwards CG, Jobanputra K, Pasipamire M, et al. Successes and challenges in optimizing the viral load cascade to improve antiretroviral therapy adherence and rationalize second-line switches in Swaziland. J Int AIDS Soc. 2018;21(10):e25194.
Bucek A, Leu C-S, Benson S, Warne P, Abrams EJ, Elkington KS, et al. Psychiatric disorders, antiretroviral medication adherence and viremia in a cohort of perinatally HIV-infected adolescents and young adults. Pediatr Infect Dis J. 2018;37(7):673–7.
Gokhale RH, Weiser J, Sullivan PS, Luo Q, Shu F, Bradley H. Depression prevalence, antidepressant treatment status, and association with sustained HIV viral suppression among adults living with HIV in care in the United States, 2009–2014. AIDS Behav. 2019;23:3452–9.
Mellins CA, Elkington KS, Leu C-S, Santamaria EK, Dolezal C, Wiznia A, et al. Prevalence and change in psychiatric disorders among perinatally HIV-infected and HIV-exposed youth. AIDS Care. 2012;24(8):953–62.
Berihun H, Bazie GW, Beyene A, Zewdie A, Kebede N. Viral suppression and associated factors among children tested for HIV viral load at Amhara Public Health Institute, Dessie Branch, Ethiopia: a cross-sectional study. BMJ open. 2023;13(1):e068792.
Abdullahi SB, Ibrahim OR, Okeji AB, Yandoma RI, Bashir I, Haladu S, et al. Viral suppression among HIV-positive patients on antiretroviral therapy in northwestern Nigeria: an eleven-year review of tertiary care centre records, January 2009–December 2019. BMC Infect Dis. 2021;21:1–8.
Mboggo E, Mtisi E, Mushy SE, Mkawe S, Ngalesoni F, Muya A, et al. Determinants of viral load suppression failure among HIV adults on ARV attending health care facilities: a retrospective study in Tanga region, Tanzania. BMC Infect Dis. 2024;24(1):312.
Mushy SE, Mtisi E, Mkawe S, Mboggo E, Ndega J, Yahya-Malima KI, et al. Barriers to viral load suppression among HIV-positive adolescents in antiretroviral therapy. Tanzania: a retrospective cohort study in Tanga; 2022.
Nasuuna E, Kigozi J, Babirye L, Muganzi A, Sewankambo NK, Nakanjako D. Low HIV viral suppression rates following the intensive adherence counseling (IAC) program for children and adolescents with viral failure in public health facilities in Uganda. BMC Public Health. 2018;18:1–9.
Lukyamuzi Z, Etajak S, Katairo T, Mukunya D, Tetui M, Ssenyonjo A, et al. Effect and implementation experience of intensive adherence counseling in a public HIV care center in Uganda: a mixed-methods study. BMC Infect Dis. 2021;21(1):1168.
Leplingard F, Borne S, Martinelli C, Leclère C, Lopez T, Guérin J, et al. editors. FWM-assisted Raman laser for second-order Raman pumping. Optical Fiber Communication Conference; 2003: Optica Publishing Group.
Mundamshimu JS, Malale K, Kidenya BR, Gunda DW, Bwemelo L, Mwashiuya M, et al. Failure to Attain HIV Viral Suppression After Intensified Adherence Counselling-What Can We Learn About Its Factors? Infect Drug Resist. 2023;16:1885–94.
Desai N, Burns L, Gong Y, Zhi K, Kumar A, Summers N, et al. An update on drug–drug interactions between antiretroviral therapies and drugs of abuse in HIV systems. Expert Opin Drug Metab Toxicol. 2020;16(11):1005–18.

No competing interests reported.

Viral load suppression after enhanced adherence counseling and its associated factors among clients on antiretroviral therapy

Status:

Version 1

Abstract

Background

Objective

Methods

Result

Conclusion

Figures

Introduction

Magnitude of Viral Load Suppression

Associated Factors for Viral Suppression

Socio-Demographic and Related Characteristics

Clinical and Related characteristics

Treatment and Related characteristics

Psychosocial, Mental and Behavioral Related Characteristics (dup: abstract ?)

OBJECTIVE

General Objective

Specific Objective

Methods and Study settings

Study Area

Study Design and Period

Population

Eligibility criteria

Inclusion and exclusion Criteria

Sample size Determination

Sample size determination by using first objective

Sampling Technique and Procedure

Study Variables

Dependent Variables

Independent Variables

Operational Definition

Data quality management, process and analysis

Result

Socio-demographic Characteristics

Treatment and Related Characteristics

Nutrition Status

Clinical and Related Characteristics

Psychosocial, Mental and Behavioral Related Characteristics

Viral Load Status

Factors Associated with Viral Load Suppression

Discussion

Strength and Limitation of The Study

Conclusion and Recommendation

Abbreviation/Acronyms

Declarations

References

Additional Declarations

Status:

Version 1