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Predictors of Non-Adherence to Antiretroviral Therapy among Adolescents Living with HIV in the Centre Region of Cameroon

Alice Ketchaji*, Felix Assah, Joseph Fokam, Elvis Asangbeng Tanue, Francisca Monebenimp, Marcelin Ngowe Ngowe

Department of Public Health and Hygiene, Faculty of Health Sciences, University of Buea, Buea, Cameroon

Abstract: Introduction: In spite of progress in antiretroviral therapy (ART) programs, adolescents remain largely vulnerable to poor ART outcomes, due to non-adherence. In the frame of limited evidence on ART adherence during adolescence in resource-constrained settings, we aimed at evaluating the rate of adherence to ART among adolescents and associated factors in Cameroon. Methods: A cross-sectional study was conducted among 401 adolescents receiving ART in 13 health facilities of the Centre Region of Cameroon, from April through August 2018. Adherence was evaluated using a composite of both self-reported and pill count assessments. Risk factors of non-adherence were assessed using the socio-ecological model and p<0.05 were considered statistically significant. Results: Mean age was 14.63 (±2.89) and 55.9 % (224) were female. Rate of adherence was 25.2% (composite-assessment), 38.2% (pill-count) and 60.6% (self-reported). Following the composite-assessment, non-adherence was significantly higher in: vertically vs. horizontally infected adolescents (OR 4.24; 95% CI: 2.16-8.33, p<0.001); facilities with combined adult/adolescent vs. specialized adolescent care (0.32; 95% CI: 0.20-0.52, p<0.001); living beyond 5 km from the heath facility (OR 1.99; 95% CI: 1.26-3.15, p=0.003; inconvenient clinic appointments (OR 3.03; 95% CI: 1.78-5.16, p<0.001); Following multivariate analysis, non-adherence was associated with “living beyond 5 km from the heath facility” (OR 1.84, 95% CI: 1.01-3.33, p=0.045); “adolescents taking medication in the same service with adult” (OR 0.11, 95% CI: 0.03-0.35, p<0.001), managed at a rural health facility (OR 4.29, 95% CI: 1.84-9.96, p=0.001) and not counseled regularly (OR 0.02, 95% CI: 0.01-0.36, p=0.007). Conclusion: In the Centre region of Cameroon, about three-quarters of adolescents might be non-adherent to ART. Interventions towards improved adherence should focus on adolescents managed at the rural health facility and with vertical HIV-infection. Furthermore, convenient clinic appointments, creation of friendly adolescent healthcare centres and decentralising HIV-adolescent care in rural settings would improve adherence to ART program.

Keywords: adolescents, HIV/AIDS, ART, predictors, non-adherence and centre region of yaounde

Received June 12, 2019; Revised July 15, 2019; Accepted July 24, 2019

Citation: Alice Ketchaji, Felix Assah, Joseph Fokam, Elvis Asangbeng Tanue, Francisca Monebenimp, Marcelin Ngowe Ngowe, Predictors of Non-Adherence to Antiretroviral Therapy among Adolescents Living with HIV in the Centre Region of Cameroon. American Journal of Public Health Research, 7 (4). 126-136. January 2019. doi:10.12691/ajphr-7-4-1

1. Introduction

Of the estimated 36.3 million people living with HIV (PLHIV) worldwide, about 2.1 million adolescents are adolescents [1,2,3,4,5], of whom the majority (1.7 million) are living in sub‐Saharan Africa (SSA) [6,7]. Globally, HIV/AIDS is the second leading cause of adolescent mortality and the very first cause of mortality in Africa [7,8]. Of note, while AIDS‐related deaths has decreased by 30%, mortality among adolescents has increased up to 50%, making HIV/AIDS a major health concern during adolescence, especially in low and middle income countries (LMICs) that are disproportionately burdened by the pandemic[8,9].

Adolescence (10-19 years) is known as the phase of physical growth and development, accompanied by sexual maturation, intimate relationships, changes in sexuality [6,10,11,12], mental capacity [13], self-efficacy and independence. In this transition phase of life, adolescents are highly vulnerable to HIV/AIDS and other sexually transmitted infections (STIs), and those infected from birth encountered challenging in the continuum of care [14]. Of note, adherence to ART, known as the footprint for therapeutic success, remains a major setback for adolescents receiving ART [14]. Therefore, in order to meet the third pillar of the UNAIDS 90‐90‐90 targets (90% of PLHIV know their status, 90% of those who know their status are on treatment, and 90% of those on treatment are virally suppressed) among adolescents living with HIV (ALHIV) [15], it is essential to implement efficient measures for adherence assessment and to identify areas of specific interventions for the improved management and monitoring of adolescents receiving ART [14].

As a key component of adherence to ART program, retention in care has been extensively evaluated in several African countries, including Cameroon [16,17,18,19]. Nonetheless, there are fewer evidence on adherence levels among ALHIV, as well as methods and tools to ensure a thorough monitoring in routine clinical practice [14].

At country level, there is an urgent need of generating information on the number of ALHIV (10-19 years) in care and factors affecting their response to treatment, considering early and advanced age adolescence, as well as other socio-demographic, environmental and clinical factors [20,21]. Cameroon has endorsed in current UNAIDS 90-90-90 goals [19,20,21], with a preliminary report of 36% adherence rate among adolescents in an urban site of Yaoundé, which therefore call for informed policy-implementation that would contribute to the success of the ART program [22,23,24,25,26]. We therefore sought to ascertain rate of adherence to ART of adolescents and factors associated with poor adherence in a typical Cameroonian context.

2. Materials and Methods

2.1. Study Design and Population

Using a quantitative approach and a socio-ecological model, a cross-sectional study was conducted in the period ranging from April through August 2018 among ALHIV receiving ART in 13 health facilities (Mother-Child Centre of the Chantal BIYA Foundation, Yaounde Health Centre, Yaoundé Jamot Hospital, National Welfare Social Insurance Hospital, Ayos Regional Hospital, Cite Verte District Hospital, Efoulan District Hospital, Obala District Hospital, Olembe District Hospital, Mbalmayo District Hospital, Bikop Health Centre, Health and Social Animation Centre of Nkoldongo, Nkomo Medical Centre), coming from 8 health districts of the Centre Region of Cameroon.

2.2. Description of the Study Settings

The Centre region of Cameroon has the national capital city, has 30 health districts for a total of 3,724,000 inhabitants. This region also has reference paediatric health facilities and the highest number of PLHIV on ART, including those at adolescence (25% national coverage) [22]. In the entire region, a total of 105 health facilities provide ART of which 99 had adolescent healthcare services.

2.3. Sampling Method and Procedure

Based on a stratified random sampling from the total 99 adolescent healthcare facilities in the Centre region, 26 health facilities meet the eligibility criteria of having a minimum of 30 ALHIV receiving ART. From these 26 eligible sites, 50% (13) were randomly selected as study sites to ensure the regional representativeness.

In every study site, adolescents were enrolled based on the following criteria: aged 10-19 years, on ART for at least 6 months, registered for ART monitoring in the study site, and whose consent/assent was provided.

For the entire study, the minimum sample size was calculated based on the following statistical formula (Eq1):

With P=0.36 (rate of 36% adherence in the aforementioned study conducted among adolescents in the city of Yaoundé in Cameroon) [26], Z=1.96 at 95% confidence interval (CI), with D=0.05 error rate. This resulted to a minimum sample size “n” of 354 participants to be enrolled in the study.

2.4. Data Collection and Measurements

Prior to implementation, data collection tool was pretested and validated in one health facility. After pretesting, data collection was done by trained study investigators during routine clinic attendance of adolescents in each of the study site. Briefly, a structured questionnaire was administered eligible to each eligible ALHIV. This form entailed socio-demographic data as well as individual factors, health system factors, family and psychosocial factors, treatment history, biological parameters, and clinical factors that could influence adherence. Additionally, medical data were retrieved from existing medical records.

Adherence measurement was based on three different approaches: (1) self-report by the patient, (2) pharmacy pill count, and (3) composite of both self-reported and pill count. For the self-reported adherence, the Center for Adherence Support Evaluation (CASE) adherence index tool was used, consisting of three questions that summed to a total score of 19 points. Participants with a CASE Index score ≥10 were classified as good adherence compared to those with a CASE Index score <10 with poor adherence [23]. Pharmacy pill count was calculated by subtracting the number of pills returned by a patient from the expected number of pills to be available. Good adherence based on pill count was defined as a score ≥95% of drug intake (i.e. corresponding to 29 out of 30 days intake for a single pill daily prescription). Finally, the composite adherence assessment was based on the combination of both methods (CASE index score and pill count), and good adherence by the composite method was defined as both a CASE Index score ≥10 and pill count ≥95%.

2.5. Data Analysis

Statistical analyses were done using STATA version 10.1. Chi-square test was used to evaluate differences in frequency distribution. Analysis of variance (ANOVA) was used to assess differences between group means. All p-values <0.2 from bivariate analyses were included in the multivariate logistic regression to determine the association of independent factors associated with adherence to ART. P-value less than 0.05 were considered as statistically significant at 95 % confidence level.

2.6. Ethical and Administrative Considerations

Ethical Clearance for the study was obtained both from the Regional Delegation of Public Health of the Centre region - CE02243N°/CRERSHC/2017 and the Institutional Review Board of the Faculty of Health Sciences of the University of Buea - 2018/024/UB/SG/IRB/FHS. An administrative authorization was obtained from the Regional Delegation of Public Health, Centre region - 1902/L/MINSANTE/SG/DRSPC. A written informed consent was obtained each guardian as well as informed assent from each participant. For purpose of confidentiality and privacy, data were managed using specific identifiers and stored in a password-protected computer.

3. Results

3.1. Socio-demographic Characteristics of the Study Population

A total of 401 adolescents were enrolled from the 13 study health facilities in the Centre region of Cameroon. The mean age was 14.63 (±2.89) and 55.9% (224) were female; the majority [243(60.6%)] of the adolescents were at the secondary level of education. Almost all the adolescents [394(98.3%)] were single and more than half of them (56.6%) living with their parents at the time of study. A non-negligible number [49 (12.2%)] of these adolescents reported to be alcohol consumers and 13 (3.2%) were cigarette smokers. About half them lived around their respective health facilities (Table 1).

Table 1. Socio-demographic characteristics of study population

Figure 1. Adherence status of Adolescence initiated on ART

3.2. Rate of Adherence to ART in the Study Participants

According to each adherence assessment method, the rate of adolescents reported with good adherence was 60.6%, 38.2% and 25.2% by the self-reported, the pill count and the composite methods, respectively (Figure 1).

3.3. Factors Associated of Non-adherence to ART among Study Participants

3.3.1. Socio-demographic Factors

According to marital status, single adolescents were 19 times more at risk of non-adherence compared their married peers (OR 18.88; 95% CI: 2.25-158-84; p=0.007). Living more than 5 kilometers away from the health facility had 2 times more at risk of poor adherence as compared to those who lived less than 5 kilometers away (OR 1.99; 95% CI: 1.26-3.15; p=0.003), and those involved in sexual activities had two times less risk of non-adherence (OR 0.50; 95% CI: 0.31-0.83; p=0.007), as shown in Table 2.

3.3.2. Health Facilities Factors

Compared to faith-based centres, adolescents receiving ART in private health facilities were about 21 times more at risk of non-adherence (OR 20.87; 95% CI: 2.57- 169.40; p=0.004), those in parastatal health facilities had 3 times more risk of non-adherence (OR 33.06; 95% CI: 1.446- 6.44; p=0.003). Adolescents taking their treatment at the same centres with adults were at higher risk of non-adherence score as compared to those in the centres without adult care (OR 0.32; 95% CI: 0.20-0.52; p<0.001), as shown in Table 3.

Table 2. Socio-demographic factors associated to non-adherence to ART

Table 3. Health facilities factors associated to non-adherence to ART

Table 4. Clinical factors associated to non-adherence to ART

3.3.3. Clinical Factors

According to transmission mode, vertically infected adolescents had 4 times more risk of non-adherence compared to those infected horizontally (OR 4.24; 95% CI: 2.16-8.33; p<0.001); those on ART beyond 12 months had 3-10 times more risk of non-adherence (see Table 4). Disclosure of HIV status before 10 years of age had 3 times more risk of non-adherence compared to disclosure between at 15-19 years old. Unsuppressed viral load had 5 times more risk of non-adherence compared to those with suppressed viral load result (OR 4.99; 95% CI: 2.56-9.73; p<0.001) in Table 4.

3.3.4. Patient and Drug Factors

Failure to take treatment on time had 3 times more risk of being non-adherence to ART (OR 3.71, 95% CI: 1.64-8.40 p=0.002), and not taking medication as prescribed due to side effects also had a slight higher risk of being non-adherent (1.17; 95% CI: 1.08-2.74, p=0.023), as shown in Table 5.

3.3.5. Environmental Factors

No environmental factor was found to be associated with non-adherence, as shown in Table 6.

3.3.6. Healthcare Factors

Regarding healthcare delivery services, having an inconvenient clinic appointment date (OR 3.03, 95% CI: 1.78-5.16, p<0.000) and not attending hospital adherence awareness workshops (OR 2.38, 95% CI: 1.28-4.41, p=0.006) were significantly associated with non-adherence to ART, as detailed in Table 7.

Table 5. Patient and drug factors associated to non-adherence

Table 6. Environmental factors associated with non-adherence

Table 7. Healthcare factors associated with non-adherence

3.4. Multivariate Logistic Regression Model

3.4.1. Multivariate Logistic Regression Model for Socio-demographic Factors Predicting Non-adherence to Treatment

In multivariate analysis, factors significantly associated with non-adherence were (a) “being single” (OR17.59, 95% CI: 1.89-163-67, p=0.012) and (b) “living far from the health facility” (OR1.84, 95% CI: 1.01-3.33, p=0.045), as shown in Table 8.

3.4.2. Multivariate Logistic Regression Model for Health Facility Factors Predicting Non-adherence to Treatment

In multivariate analysis, factors significantly associated with non-adherence were: (a) “being managed in a private health facility” (OR17.59, 95% CI: 181.17-1984.99, p<0.001); (b) “being managed in the same health facility with adults (OR0.11, 95% CI: 0.03-0.35, p<0.001); and (c) being managed at a rural health facility (OR4.29, 95% CI: 1.84-9.96, p=0.001); as detailed in Table 9.

3.4.3. Regression Model for Clinical Factors Predicting Non-adherence to Treatment

In multivariate analysis, factor significantly associated with non-adherence was have a viral load unsuppressed (OR 4.71, 95% CI: 1.88-11.79, p=0.001); as detailed in Table 10.

3.4.4. Regression Model for Medication, Social, Health Care Providers and Environmental Factors Predicting Non-Adherence to Treatment

In multivariate analysis, factor significantly associated with non-adherence was being Not counseled regularly (OR0.02, 95% CI: 0.01-0.36, p=0.007); as detailed in Table 11.

Table 8. Regression model for socio-demographic factors predicting non-adherence to treatment

Table 9. Regression model for health facility factors predicting non-adherence to treatment

Table 10. Regression model for clinical factors predicting non-adherence to treatment

Table 11. Regression model for medication, social, health care providers and environmental factors predicting non-adherence to treatment

4. Discussion

With the goal to contribute in achieving the third pillar of the 90-90-90 targets through improved adherence among the most disproportionally affected population by HIV/AIDS, our present study focused on evaluating adherence to ART and associated factors among adolescents living with HIV/AIDS in the Centre Region of Cameroon.

In this study with a slightly higher proportion of female adolescents (similar to previous reports) [24,25,26,27], in a population of adolescents mainly infected vertically [28], the rate of adherence appeared very low (i.e. one quarter). This finding underscores the fact that adolescents constitute a vulnerable population in the field of HIV/AIDS, and therefore deserve specialised care to ensure their successful transition from pediatric to adult care. Moreover, adherence level varies decreasingly from the self-reported method, pill count, to the combined method. This observation is similar to a systematic review conducted in 2014 reporting a varying range of 16% to 99% adherence rates amongst ALHIV from LMICs [9]. In a study conducted in a single health facility in Yaoundé, 36% of ALHIV were reported adherent to ART following self-reporting [26], as compared to 60% from our present investigation. The discrepancy observed highlights the lack of reliability of self-reported adherence in assessing compliance to ART. Furthermore, the former study was conducted only in an urban health facility while our study was conducted in several health facilities of both rural and urban areas, thereby giving a better appraisal of the local context [9,26].

Adolescents thus face several challenges in accepting their status and in gaining autonomy towards treating their infection, as most of them are in denial, afraid, misinformed or lack familial or social [9]. Thus, to ensure a successful transition to adult care, identifying risk factors of non-adherence are essential for ALHIV receiving ART.

Regarding socio-demographic features, neither sex nor age ranges of adolescents were not found to be associated with adherence. However, single adolescents were about 18 times at risk of non-adherence when compared to those who were married. This is similar to the study [7], likely due to the fact that married adolescents have support towards adherence from their partners, coupled to adherence motivated by the needs to avoid infecting their partner and to achieving suppressed viremia as a prerequisite for procreation [7].

Beyond 12 months after ART initiation, adherence becomes very concerning, and is known as the driven factor of the high mortality amongst ALHIV, with varying adherence in Africa as compared to the western world [25,29,30,31]. This suggests the need for active patient involvement mediated by healthcare provider’s support [32].

Adolescents receiving ART in faith-based health facilities were more adherent compared to their peers in parastatal and private facilities. This should be due to the fact that in religious health facilities, prayer, as a form of psychosocial support through spiritual belief, is an important and integral part of patient care. Adolescents taking their treatment at the same centers with adults were more likely to be non-adherent rather than those in centers without adult care. This should be due to the fact that adolescents still feel belonging to the pediatric population and, coupled to their aforementioned peculiarity, need of a separate service for clinical monitoring. Nonetheless, familiarising adolescents gradually with adult care service, could help in facilitating transition to adult care, pending findings on effective implementation strategies.

It has been found that adolescents taking their medication in the facilities located in the rural zone were 5 times likely to be non-adherent rather than those in the urban zone. This should be due to the fact that most of the activities relating to the care of adolescents living with HIV are centred at the level of health facilities.

On a separate note, adolescents with unsuppressed viral load were 5 times at risk of being non-adherent compared to those with suppressed viral load. This supports the use of viremia as an indicator of adherence in the absence of HIV drug resistance [7,16].

Among the health care delivery factors, not counselled regularly by health care providers, was predictors of non-adherence to ART, suggesting the need for caregivers to pay attention to patient’s need/preference [3].

Although our investigation contributes to knowledge on ART adherence and associated factors during adolescence in the Centre region of Cameroon, the current design was limited to quantitative data analysis. Qualitative assessments are henceforth needed for in-depth understanding of barriers to adherence and mitigating factors.

5. Conclusions

This study identified 7 predictors of non-adherence to ART among adolescents across 13 health facilities. Following the composite approach, the adherence rate appears very low, and varies between self-reported and pill-count measurements. Thus, in the Centre region of Cameroon, about three quarter of adolescents might be non-adherent to ART. Interventions towards improved adherence should focus on adolescents living more than 5 km from the health facility, in rural areas, and within the private and public health facilities. Furthermore, creation of adolescent healthcare centres and enhance counselling would improve adherence to ART program in resource-constrained countries facing similar challenges like Cameroon.

Funding

The study was conducted with support from the investigative team.

Authors’ Contributions

AK, FA, FM and MNN: designed and implemented the study; AK collected the data; AK, EA and JF: analysed and interpreted the data; AK: initiated the manuscript; FA and JF: revised the initial version of the manuscript; All authors: revised and approved the final version of the manuscript.

Acknowledgements

Our gratitude goes to the HIV/AIDS adolescents and their parents/caregivers for their voluntary participation and collaboration throughout the study. We also thank all the Directors and staff of the health facilities in the Centre Region who took part in the study, for their technical collaboration. We also acknowledge Pr Zoung-Kanyi Bissek Anne Cecile, Dr Zeh Kakanou Florence, Dr Elat Nfetam Jean Bosco, Dr Nelly Kamgaing, M. Kembou Etienne, Dr Amando Frank, Dr Andreas Frambo, M. Choffo Eugène, Forteh Emmanuela and Ms. Takam Arrey for providing assistance in the better implementation of this study.

Conflict of Interest

The authors have no potential conflict of interest to declare.

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