|Year : 2021 | Volume
| Issue : 5 | Page : 592-600
Assessment of adherence to antiretroviral therapy, associated factors, and relationship to CD4 cell count recovery among HIV-positive adolescents
Nonso Anthony Ogbuefi1, Ebelechuku Francesca Ugochukwu2, Chinyere Ukamaka Onubogu2, Emeka Stephen Edokwe2, Kenneth Nchekwube Okeke2
1 Department of Paediatrics, Nnamdi Azikiwe University Teaching Hospital, Nnewi, Nigeria
2 Department of Paediatrics, Faculty of Medicine, Nnamdi Azikiwe University, Awka and Nnamdi Azikiwe University Teaching Hospital, Nnewi, Nigeria
|Date of Submission||14-Jul-2021|
|Date of Decision||06-Aug-2021|
|Date of Acceptance||31-Aug-2021|
|Date of Web Publication||11-Oct-2021|
Prof. Ebelechuku Francesca Ugochukwu
Department of Paediatrics, Faculty of Medicine, Nnamdi Azikiwe University, Awka and Nnamdi Azikiwe University Teaching Hospital, Nnewi
Source of Support: None, Conflict of Interest: None
Background: Adherence to antiretroviral therapy (ART) in HIV-positive adolescents (HPAs) is an enormous challenge in pediatric HIV management. Suboptimal adherence (OA) encourages treatment failure and HIV transmission. Several factors are inimical to OA, among HPA. Objective: The factors which influence adherence to ART in HPA accessing care in Nnewi, Nigeria, were determined. Subjects and Methods: One hundred and fifty HPAs, aged 10–19 years, who had been on ART for at least 6 months were recruited; 75 each for groups 10–14 and 15–19 years. Sociodemographic data were collected using interviewer-administered questionnaires. Relevant clinical data were retrieved from medical records, and current CD4 cell counts assayed. Results: There were 77 males and 73 females. OA to ART was defined as intake of ≥95% of antiretrovirals over a given period. Using pill count (PC), 74.7% (112/150) had OA. Using the preceding 28-day self-report (P28DSR), 84.0% (126/150) had OA, while for the preceding seven-day self-report (P7DSR), it was 89.3% (134/150). Factors significantly associated with sub-OA were tertiary education, missed clinic visits, travel time to facility ≤1 h, persistent feeling of sadness, and fear of death. Subjects with OA had a significantly higher rate of CD4 cell count recovery compared to those having sub-OA. Conclusion: Sub-OA is common in HPA and can be assessed with a combination of PC and preceding P28DSR, in resource-poor settings. As OA is crucial to CD4 cell count recovery, the latter can be useful in monitoring adherence in HPA.
Keywords: Adherence, antiretroviral therapy, CD4 cell count recovery, HIV-positive adolescents
|How to cite this article:|
Ogbuefi NA, Ugochukwu EF, Onubogu CU, Edokwe ES, Okeke KN. Assessment of adherence to antiretroviral therapy, associated factors, and relationship to CD4 cell count recovery among HIV-positive adolescents. Niger J Med 2021;30:592-600
|How to cite this URL:|
Ogbuefi NA, Ugochukwu EF, Onubogu CU, Edokwe ES, Okeke KN. Assessment of adherence to antiretroviral therapy, associated factors, and relationship to CD4 cell count recovery among HIV-positive adolescents. Niger J Med [serial online] 2021 [cited 2021 Dec 8];30:592-600. Available from: http://www.njmonline.org/text.asp?2021/30/5/592/327949
| Introduction|| |
As of 2015, out of the 3.4 million Nigerians living with HIV, 200,000 were adolescents. The prevalence of HIV in Nigerian adolescents has remained one of the highest in the world despite the high level of knowledge of HIV. Since the introduction of potent combination antiretroviral therapy (ART), there has been a marked improvement in the prevention of HIV transmission, treatment, and progression to AIDS among persons living with HIV/AIDS (PLWHA). About 7.6 million deaths globally, and 4.8 million in Sub-Saharan Africa have been averted. ART targets different points of viral life cycle, thereby altering the natural progression of HIV infection and improving both the health standard and quality of life of PLWHA. Successful maintenance of viral suppression below detection levels is primarily dependent on OA to ART. This in turn limits the destruction of CD4 cells, immunosuppression, and disease progression. However, achieving OA has proven to be a difficult task for both HIV-positive adolescent (HPA) and their health-care providers.
Adolescence, a period of physical and psychosocial transition, is a vulnerable stage characterized by risk-taking behaviors, concrete thinking, autonomy, feeling of invincibility, and decreased parental supervision. Poor adherence largely noted in HPA compared to other age groups leads to treatment failures and HIV-related morbidity and mortality among them.
There are various medical and psychosocial challenges associated with HIV-positive children and their families in Sub-Saharan Africa. In adolescents, these may pose major problems in addition to the normal developmental challenges. These problems have been found to limit adherence to ART.
Assessment of adherence to ART is a vital process in identifying HPA at risk of treatment failure from poor viral suppression. Despite there being no gold standard in adherence assessment, various direct and indirect methods have been found useful. This study appraised the use of some indirect assessment methods, namely, pill count (PC) and self-report, found to be useful in resource-poor settings.
Adherence status has been identified as one of the major determinants of CD4 cell count response. OA is associated with a 5.8-fold increase in the achievement of adequate CD4 cell count recovery.
Therefore, it behoves health-care providers to identify the limitations peculiar to the vulnerable adolescent age group and institute appropriate measures to ensure OA.
| Subjects and Methods|| |
The study was conducted at Special HIV clinics of the Nnamdi Azikiwe University Teaching Hospital (NAUTH), Nnewi. NAUTH is a tertiary hospital, one of the designated free HIV clinics in the country, accessed by patients from all over Anambra and neighboring states. Nnewi is the biggest industrial city and one of the urban centers in Anambra State, South-East Nigeria.
The clinics, which care for PLWHA, have pediatric and adult units. The pediatric unit cares for HIV-exposed and infected persons <15 years, while the adult unit cares for those aged 15 years and above. Analysis of enrollment records as of November 30, 2016 showed that the pediatric unit had about 220 HPAs (10 to <15 years) in its care with an average of eight new cases per month and an average attendance of 103 per month, while the adult unit had about 213 HPAs (15–19 years) enrolled with an average of five new cases monthly and an average attendance of 97 per month. There is a dedicated pharmacy, a reference laboratory, and an adherence unit. The clinics offer free medical services. PLWHA are followed up on a 1–3 monthly basis. The adherence unit counsels the clients at every visit, identifies their adherence status, re-emphasizes the need for OA, identifies possible drawbacks in achieving OA, and undertakes unannounced PCs. The reference laboratory performs the CD4 cell counts, viral load assessment, and HIV DNA PCR testing. The services were initially sponsored by the Institute of Human Virology, under the auspices of the President's Emergency Plan For AIDS Relief (PEPFAR), which was later in March 2013, taken over by Family Health International (FHI 360). This it does in collaboration with the Federal Government of Nigeria who provides other ancillary services.
This was a cross-sectional, hospital-based, descriptive study to assess adherence to ART and its relationship with CD4 cell count recovery in HPA accessing care.
This included all HPA aged 10–19 years who presented to the HIV clinics of NAUTH, Nnewi.
- HPA aged 10–19 years
- Subjects who had been on ART for a minimum of six months before the study
- Adolescents who gave an assent (if <18 years of age and aware of their status) with a signed informed consent obtained from their parents/caregivers or adolescents ≥18 years of age who gave a signed informed consent.
Data and specimen collection were done from September 8 to November 10, 2017.
All eligible subjects were enrolled in the study consecutively.
Relevant information was collected from the subjects (either directly, through caregivers or both) using an interviewer-administered questionnaire. The number of missed medication doses in the preceding seven days and 28 days was ascertained. The total number of pills in each subject's possession on the last day of ART refill (last follow-up visit ranging from one to three months) was retrieved from the pharmacy records, and the count of remaining pills at the point of recruitment was obtained for each subject.
The baseline CD4 cell count before the commencement of ART and details of intercurrent infections were retrieved from medical records. Current CD4 levels were determined by flow cytometric immunofluorescence using a Partec Cyflow Counter (Sysmex Partec GmbH ® in Germany).
Data were analyzed using the IBM SPSS version 21. Descriptive statistics was employed for the mean and standard deviation of continuous variables and frequencies for categorical variables. Categorical variables were tested for association with adherence using Pearson's Chi-Square (and Fisher's Exact test as appropriate) for bivariate analysis. For multivariate analysis, logistic regression was used. Student's t-test was used to compare the difference in mean duration of ART between subjects with OA and subjects with sub-OA. All tests of association and comparisons were performed at 5% significance level.
Adherence assessment (self-report)
This was done by determining the number of doses the patient had taken, as a proportion of the expected doses, in the preceding 7 and 28 days from the point of the interview. The adherence level was expressed as a percentage and calculated as shown below;
Number of doses taken in the past 7 days = Number of doses expected to be taken minus number of missed doses.
Number of doses taken in past 28 days = Number of doses expected to be taken minus number of missed doses. Result of ≥95% was termed OA, while <95% was termed sub-OA.
Adherence assessment (pill count)
The patient's level of adherence assessed by PCs (expressed as a percentage) was calculated as follows;
Number of pills utilized in the interval = Number of pills last dispensed minus number of pills remaining.
Interval = Number of days between the last day of ART supply (last clinic visit) and the day of adherence assessment (next pick-up date).
OA was termed ≥95% while sub-OA was termed <95%. Subjects with sub-OA were adequately counseled on the problems associated with sub-OA, enlightened on the benefits of maintaining OA, and subsequently referred to an adherence counselor.
CD4 cell count recovery
CD4 cell count recovery was determined for participants whose baseline CD4 cell counts were <500 cells/mm3. Current CD4 cell count ≥500 cells/mm3 at ≥4 years of ART was deemed adequate. For duration <4 years, adequate recovery was taken as gaining a minimum of 50 cells/mm3 per year or 5 cells/mm3 per month. Inadequate CD4 cell count recovery did not meet any of these criteria.
Ethical approval was obtained from Ethics Review Committee, NAUTH, Nnewi, before the commencement of the study (NAUTH/CS/66/VOL. 10/2017/013).
Recruitment was voluntary and followed a written informed consent. Confidentiality was maintained throughout the study.
| Results|| |
A total of 150 adolescents aged 10–19 years were recruited. There were 77 (51.3%) males and 73 (48.7%) females. Majority (90.7%) of the subjects (136/150) were students, and 74.0% (111/150) had secondary education. Fourteen students (9.3%) belonged to the upper socioeconomic class, 70 (46.7%) to the middle, and 66 (44.0%) to the lower [Table 1].
Seventy-one (47.3%) subjects were managed on NVP/AZT/3TC, 43 (28.7%) on TDF/EFV/3TC, 12 (8.0%) on ABC/EFV/3TC, 10 (6.7%) on TDF/3TC/LPV/r, 5 (3.3%) on ABC/AZT/3TC, 4 (2.7%) on TDF/3TC/ATV/r, 3 (2.0%) on EFV/3TC/AZT, and 2 (1.3%) on ABC/3TC/LPV/r. One hundred and thirty-four subjects were on first-line regimen, while 16 subjects were on second line.
Information was obtained from subjects alone in 70 (46.7%), primary caregivers alone in 30 (20.0%), and a combination of both in 50 (33.3%).
The mean age of the study subjects was 14.2 ± 2.6 years. Within the younger adolescent age group (10–14 years), more females (42/75 [56.0%]) were enrolled than males (33/75 [44.0%]), whereas in the older group (15–19 years), more males (44/75 [58.7%]) were enrolled than females (31/75 [41.3%]).
Adherence rates and associated factors
OA rates were 89.3% (134/150) for P7DSR, 84.0% (126/150) for P28DSR, and 74.7% (112/150) for PC. There was no significant difference in OA between subjects aged 10–14 years (65/75 [86.7%]) and those aged 15–19 years (61/75 [81.3%]) using P28DSR (χ2 = 0.794, P = 0.373). With the use of PC, there was no significant difference in OA when subjects aged 10–14 years (58/75 [77.3%]) were compared with those aged 15–19 years (54/75 [72.0%]). χ2 = 0.564, P = 0.453.
Gender and age group
There was no significant difference in adherence between all-male and all-female subjects using both P28DSR and PC [Table 2]. Within the younger group (10–14 years), females had higher OA than males. However, this was not significant using either P28DSR or PC. Within the older group (15–19 years), males had a higher OA rate than females, but the differences were not significant for either P28DSR or PC.
Of the 150 subjects studied, 70 (46.7%) self-administered ART. Male subjects who self-administered ART were noted to have slightly higher rate of OA than their female counterparts using both P28DSR (males = 32/40 [80.0%], females = 23/30 [76.7%]) and PC (males = 27/40 [67.5%], females = 19/30 [63.3%]). However, these differences were not significant (P28DSR [χ2 = 0.113, P = 0.737], PC [χ2 = 0.132, P = 0.716]).
Using P28DSR and PC, subjects in the upper SEC were noted to have the lowest rates of OA compared to others. Nevertheless, these differences were not significant.
The rate of OA in subjects in tertiary institutions was lowest using both tools of assessment.
Place of residence
There was no significant difference in OA between subjects living in the rural and urban areas.
Persons primarily responsible for the administration of antiretroviral therapy
There was no significant difference in OA when persons primarily responsible for the administration of ART were compared.
Loss of primary caregivers
There was no significant difference in OA when subjects who had lost primary caregivers were compared to subjects who had not.
Subject's primary caregivers
There was no significant difference in OA when subject's primary caregivers were compared.
Awareness of status
All the older adolescents were aware of their status, while 47 out of 75 younger adolescents knew their status. There was no significant difference in OA between subjects aware and those unaware of status.
Experience of stigmatization/discrimination
Out of the 150 subjects, only 7 who had disclosed their status to nonfamily members experienced stigmatization. However, this did not affect adherence.
Duration on antiretroviral therapy
The mean duration on ART of the study population was 6.7 ± 3.7 years. There was no significant difference in duration on ART and rate of OA.
Other selected factors
For both P28DSR and PC, significantly higher OA rates were noted in subjects with no history of a missed clinic visit(s), persistent feeling of sadness, and persistent fear of death when compared with those who had.
Reasons for missed visits
Thirty-six subjects (24%) missed clinic appointment visits. The common reasons were school exams/activities (10/36 [27.8%]) and unavailability of caregiver (10/36 [27.8%]). Others were the subject's ill health (6/36 [16.7%]), family function (4/36 [11.1%]), lack of transportation fare (3/36 [8.3%]), and subject travelling for holiday (3/36 [8.3%]).
Reasons for missed doses
Fifty-nine subjects (39.3%) missed doses. Some subjects had more than one reason for missing medications. The most common reason was forgetfulness (56/59 [94.9%]). Others were subject's travelling/staying away from home (21/59 [35.6%]), presence of persons unaware of subject's status (4/59 [6.8%]), refusal due to being tired of taking medications/dislike of the medications (3/59 [5.1%]), exhaustion of the drugs (3/59 [5.1%]), unavailability of caregiver (2/59 [3.4%]), and subject's illness (1/59 [1.7%]).
Use of reminders
Half of the subjects used reminders, out of which 76.0% (57/75) used phone alarms, 16.0% (12/75) clock alarms, 5.3% (4/75) wristwatch alarms, while the remaining 2.7% (2/75) used radio news time. Out of the 56 subjects that forgot to take pills, 55.4% (31/56) did not use a reminder while 44.6% (25/56) did.
Adjusted odds ratio for sub-optimal adherence assessed by P28DSR using logistic regression
Using P28DSR, multivariate analysis showed that significant factors with higher odds for sub-OA were history of missed clinic visits (OR = 4.592, P < 0.05), tertiary education (OR = 14.627, P < 0.05), persistent feeling of sadness (OR = 3.562, P < 0.05), and persistent fear of death (OR = 5.394, P < 0.05).
Adjusted odds ratio for suboptimal adherence assessed by PC using logistic regression
Using PC, multivariate analysis showed that significant factors with greater odds for sub-OA were travel time to facility ≤1 h (OR = 9.936, P < 0.05), missed clinic visits (OR = 5.491, P < 0.05), and persistent feeling of sadness (OR = 6.664, P < 0.05).
Immunological staging of HIV in the study subjects
The median baseline CD4 cell count was 345.5 cells/mm3 with an interquartile range (IQR) of 454, while the median current CD4 cell count (as at the time of the study) was 631.5 cells/mm3 with an IQR of 469. There was a decrease in the number of study subjects with severe immunosuppression from 49 (32.7%) at baseline to 11 (7.3%) at the time of the study and an increase in the number of subjects with none/insignificant immunosuppression from 52 (34.7%) at baseline to 102 (68.0%) as at the time of the study. This is shown in [Table 3].
|Table 3: Comparative immunologic staging of human immunodeficiency virus in subjects|
Click here to view
Relationship between adherence and CD4 cell count recovery
CD4 cell count recovery was assessed in 98 subjects whose baseline CD4 cell counts were below 500 cells/mm3. When CD4 cell count recovery was related to adherence, significant differences were noted with the use of P28DSR and PC. Subjects who were OA had a higher adequate CD4 cell recovery (66/84 [78.6%]) than subjects with sub-OA (6/14 [42.9%]) using P28DSR [Table 4]. With the use of PC, adequate CD4 cell recovery was significantly higher in subjects with OA (62/77 [80.5%]) than in those with sub-OA (10/21 [47.6%]).
Ongoing intercurrent infection and adherence
In this study, 21 subjects had ongoing intercurrent infections comprising 8 malaria cases, 6 of upper respiratory tract infection, 3 of fungal dermatitis, 2 of pulmonary tuberculosis, 1 of scabies, and 1 of cellulitis of the foot. When ongoing intercurrent infection was related to adherence, there was no significant difference in OA between subjects with and without ongoing intercurrent infections.
Ongoing intercurrent infection and CD4 cell count recovery
Subjects without ongoing intercurrent infection had a higher rate of CD4 cell recovery (63/84 [75.0%]) than those with (9/14 [64.3%]), but this difference was not significant (P = 0.513 [Fisher's exact]).
| Discussion|| |
The adherence rate to ART in HPA accessing care in NAUTH, Nnewi, was assessed using three different tools – PC, P28DSR, and P7DSR. Of these, P7DSR (10.7%) identified fewer subjects with sub-OA than P28DSR (16.0%). This may be explained by the phenomenon of “white coat adherence” causing a higher rate of OA with the use of P7DSR following a tendency for subjects and their caregivers to be more adherent close to clinic visits., The differences observed between the recall methods and PC can be explained by the fact that the recall methods are subjective, not readily verifiable and tend to overestimate adherence.
The 74.7% obtained by PC is comparable to the rates in other studies in Nigeria,, Botswana, and Tanzania. In contrast, a higher rate of 85% was reported in a South African study. This may be attributed to the fact that the South African subjects were aged 6 months to 13 years with the administration of ART mainly dependent on the primary caregivers. The South African study was also prospective with adherence assessed on monthly basis and an average taken at the end of the study period. Furthermore, monthly adherence support counseling was offered to the subjects and caregivers when adherence level fell below 95%.
The P28DSR rate of 84.0% is analogous with the rates obtained in similar Nigerian studies.,, However, some studies carried out in Southeast Nigeria, and elsewhere,, recorded a disparity in the rates. This may be due to variation in the ages sampled and study design. The lower rate by Cruz et al. is most likely due to the definition of sub-OA in the study as <100%. This would have put a smaller number of the participants as OA when compared with the index study that classified sub-OA as <95%. Higher rates compared to the index study were recorded in Abuja and Kano, both in northern Nigeria, with 95.4% and 90.5%, respectively. The study done in Kano involved the same age group as the index study but assessed adherence using a previous three-month recall. This may have been responsible for the higher rate as the subjects/caregivers would likely have experienced difficulties in recalling missed doses over a prolonged period, leading to the overestimation of adherence. In the study done in Abuja, subjects who had been on ART for ≥1 month were recruited. Owing to the fact that this is a long-term therapy, this cutoff point may not be adequately reflective of the adherence rate as subjects tend to take medications better at onset with the belief of cure or fear of worsening state of ill health. Furthermore, subjects were given drug charts at the time of dispensing ART to take home and record both the time of administration and the number of tablets taken each time daily. This is amenable to manipulations and the rate may not be reflective of the true adherence level of the study subjects. On the other hand, subjects may also have been positively influenced by participating in their adherence monitoring, leading to an improved adherence.
There was no significant difference in optimal adherence (OA) between the younger and older adolescents in this study. This is consistent with studies carried out in Botswana and Uganda but at variance with those in Zimbabwe and the United States. The Zimbabwean study was conducted in a rural setting and documented a higher rate of poor adherence in older than younger adolescents. Poor adherence was attributed to the political instability at the time of the study (resulting in financial difficulties in paying for transportation and forceful movement out of the district). The instability may have led to reduced parental/caregiver supervision and thus caused irregular clinic visits to pick up ART. The older adolescents may have also been fending for themselves and thus suboptimally adherent due to the pressure of day-to-day activities. In the US study, a significantly poorer adherence in younger adolescents was noted. This may be due to insufficient understanding of the disease and the benefits of OA and possibly the dedication of the primary caregivers toward the administration of ART.
There was no significant gender difference in adherence. This was analogous to findings in some other Nigerian,, and non-Nigerian,, studies. In the study done in Enugu, Nigeria, females had significantly higher sub-OA than males. In contrast, the studies conducted in Botswana and Uganda reported that males had significantly higher sub-OA than females.
SEC had no significant relationship with adherence. Similar findings were reported in some Nigerian,, and Indian studies. In contrast, a study done in Cross River State, Nigeria documented that sub-OA was associated with low SEC, as subjects with an average household monthly income of less than ₦19,000 ($118.75) significantly had sub-OA compared to subjects with ≥ ₦19,000 ($118.75).
Adherence had no significant relationship with place of residence unlike the findings in a Ugandan study, in which subjects in urban areas significantly had a higher adherence rate than those in the rural. This was attributed to a lack of transport fare to the health facilities with its attendant loss to follow-up in the rural regions and better adolescent-friendly services with peer support groups in the urban. A South African study also reported that subjects living in the rural areas had a higher rate of sub-OA due to a lack of finance and poor access to health facilities.
Subjects in tertiary institutions had the least OA rate compared to other educational levels. They were approximately 15 times more likely to have sub-OA than subjects with other educational levels. They were also more likely not to take ART in the presence of persons unaware of their status. These findings are probably at best anecdotal considering the very low number (four) of subjects in that category.
The loss of a primary caregiver was not significantly associated with adherence in this study. This is in contrast with other studies, where the loss of a parent–caregiver was associated with sub-OA. In the index study, loss of primary caregivers included caregivers other than a parent and this may have resulted in the variation. Majority of the subjects in one center were dependent on a primary caregiver for ART administration. This probably affected the outcome which differed from the current study. In the second center, loss of the caregiver was only related to death from HIV/AIDS, and thus may have varied from the current study.
The finding that subjects with a history of missed clinic visits were approximately five times more likely to have sub-OA corroborates the reports of other workers., Furthermore, the five-fold increase in the odds of sub-OA associated with a persistent fear of death is similar to reports by Lawan et al. in Kano, Nigeria and Cruz et al. in Brazil. Analysis with respect to persistent feeling of sadness showing higher odds of sub-OA is comparable to the findings of Lawan et al. in Kano, Nigeria, Peltzer et al. in South Africa, Amberbir et al. in Ethiopia, and Fawzi et al. in a rural area of Rwanda, where subjects with depression were found to be associated with sub-OA.
Contrary to expectation, subjects with longer travel time to the health facility had better OA rates. Several ART centers are located around the study site, such that subjects who choose to travel longer possibly did so to avoid unwanted disclosure and stigmatization in centers close to them, hence they were probably more motivated to adhere. Uzochukwu et al. in a study conducted in Enugu (an urban area in Nigeria) also reported similar findings where OA was significantly higher in subjects who lived at a longer distance from the health facility (>20 km) than those who lived closer to the facility (≤20 km). In contrast, studies done in Nepal (South Asia) showed that sub-OA was significantly associated with long distance and travel time of >1 h to a health facility., These studies were conducted in rural areas where few ART facilities were situated and far away from the dwellers. The long-distances, transportation problems, and high cost of transportation fares probably hindered pickup of the medications and subsequently led to sub-OA.
Duration on ART had no significant relationship with adherence in this study. This was similar to the finding by Ugwu et al. A different outcome was noted by Akahara et al. who reported that sub-OA was significantly associated with a longer duration of therapy. In contrast, a report from rural Nepal showed that subjects who had been on ART for >24 months significantly had a higher OA than subjects who had been on therapy for ≤24 months. This emphasizes the role of sustained and adequate adherence counseling.
Stigmatization/discrimination was not significantly related to adherence in the present study. This varied from other studies where stigmatized or discriminated subjects were significantly associated with sub-OA.,, The absence of a noteworthy association between stigmatization and adherence may be attributed to the low prevalence of the former (4.7%). This tended to play down the anticipated effect of stigmatization.
Subjects with OA had significantly higher adequate CD4 cell count recovery than subjects with sub-OA. A study among adolescents conducted in South Africa reported that lower CD4 cell count recovery observed in adolescents was significantly associated with sub-OA. Abrogoua et al. in a prospective study done in Cote d'Ivoire noted a significant relationship between adherence and CD4 cell recovery, OA increasing the likelihood of achieving adequate CD4 cell count recovery by 5.8 folds. These have shown the importance of assessing CD4 cell count recovery as a means of immunologic monitoring of adherence to ART in HPA in a resource-limited setting where routine virologic monitoring may not be feasible.
Ongoing intercurrent infections did not have a significant relationship with adherence unlike the study by Ugwu et al. where comorbidity was significantly associated with sub-OA. The variation from the index study may probably be attributed to the fact that the study by Ugwu et al. was conducted in children aged 5 months to 17 years with those <10 years accounting for a greater percentage of the study population (168/213 [78.9%]). The severity of their illness coupled with the pressure on the caregivers to cater for the sick child in this younger age group may have resulted in sub-OA to ART.
| Conclusion|| |
The OA rate to ART in HPA accessing care in NAUTH, Nnewi is 74.7% using PC. With the use of P28DSR, the adherence rate is 84.0%, while the rate is 89.3% with the use of P7DSR. These rates fall short of the expected 95% and above.
Factors that negatively influence adherence to ART in these HPA include being in a tertiary institution, history of missed clinic visits, travel time to facility ≤1 h, persistent feeling of sadness, and persistent fear of death.
A higher rate of adequate CD4 cell recovery was identified in subjects with OA in comparison with subjects with sub-OA and thus, is a useful tool in monitoring adherence in these HPA.
Limitations of the study
HIV viral load testing was not done due to financial constraints. Subject's adherence level would have been related to viral suppression for better assessment. Furthermore, it was difficult to use subjects' results of free viral load testing done by the HIV Care unit since it was done on yearly basis and would not have been factored into the time frame of the study.
Despite the tools of assessment identifying factors that influenced adherence, they were still prone to distortion. There may be unreported cases of pill dumping which may affect PC. There was a possibility of overestimated adherence with the use of self-report method.
Determination of adherence level was only done in relation to number of tablets taken. Timing and dietary requirements were not factored into the assessment.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest
| References|| |
Battles HB, Wiener LS. From adolescence through young adulthood: Psychosocial adjustment associated with long-term survival of HIV. J Adolesc Health 2002;30:161-8.
Onubogu CU, Ugochukwu EF. A 17 year experience of attrition from care among HIV infected children in Nnewi South-East Nigeria. BMC Infect Dis 2021;21:409.
Lawan UM, Amole G, Jahun MG, Abute JE. Psychosocial challenges and adherence to antiretroviral therapy among HIV-positive adolescents attending an ART center in Kano, Northwestern Nigeria. Int J Med Sci Public Health 2015;4:1439-44.
World Health Organisation. Adherence to Long-Term Therapies: Evidence for Action. Geneva: World Health Organisation; 2003. p. 1-151.
Abrogoua DP, Kablan BJ, Kamenan BA, Aulagner G, N'guessan K, Zohoré C. Assessment of the impact of adherence and other predictors during HAART on various CD4 cell responses in resource-limited settings. Patient Prefer Adherence 2012;6:227-37.
Shigdel R, Klouman E, Bhandari A, Ahmed LA. Factors associated with adherence to antiretroviral therapy in HIV-infected patients in Kathmandu District, Nepal. HIV AIDS Res Palliat Care 2014;6:109-16.
Centres for Disease Control and Prevention. Evaluation of Adherence to ART in Children in Nigeria; 2008. p. 1-45.
Kim KH, Yi J, Lee SH. The CD4 slope can be a predictor of immunologic recovery in advanced HIV patients: A case-control study. Korean J Intern Med 2015;30:705-13.
Cramer JA, Scheyer RD, Mattson RH. Compliance declines between clinic visits. Arch Intern Med 1990;150:1509-10.
Chapp-Jumbo A, Onyire N, Orji M, Onwe O, Ojukwu J. Assessment of rate of adherence to highly active antiretroviral therapy (HAART) among HIV infected children attending the infectious disease clinic of federal teaching hospital Abakaliki (FETHA), Ebonyi State. J Child Adolesc Behav 2016;4:8-11.
Bello S. HIV/AIDS patients' adherence to antiretroviral therapy in Sobi Specialist Hospital, Ilorin, Nigeria. Glob J Med Res 2011;11:16-25.
Ndiaye M, Nyasulu P, Lowenthal ED, Gross R. Risk factors for suboptimal antiretroviral therapy adherence in HIV-infected adolescents in Gaborone, Botswana : A pilot cross-sectional study. J Paediatr Child Health 2013;7:891-5.
Nyogea D, Mtenga S, Henning L, Franzeck FC, Glass TR, Letang E, et al.
Determinants of antiretroviral adherence among HIV positive children and teenagers in rural Tanzania: A mixed methods study. BMC Infect Dis 2015;15:28.
Smith C, Gengiah TN, Yende-Zuma N, Upfold M, Naidoo K. Assessing adherence to antiretroviral therapy in a rural paediatric cohort in KwaZulu-Natal, South Africa. AIDS Behav 2016;20:2729-38.
Mukhtar-Yola M, Adeleke S, Gwarzo D, Ladan ZF. Preliminary investigation of adherence to antiretroviral therapy among children in Aminu Kano Teaching Hospital, Nigeria. Afr J AIDS Res 2006;5:141-4.
Akahara C, Nwolisa E, Odinaka K, Okolo S. Assessment of antiretroviral treatment adherence among children attending care at a tertiary hospital in Southeastern Nigeria. J Trop Med 2017;2017:3605850.
Nwankwo V, Nduka O, Ilodigwe E, Ogbonna B, Uzodinma U, Okonta J. Assessment of highly active antiretroviral therapy (HAART) adherence among HIV patients in a tertiary health institution in Nigeria. Afr J Pharm Pharmacol 2014;8:1192-9.
Ukwe CV, Ekwunife OI, Udeogaranya PO, Iwuamadi UI. Self-reported adherence to HAART in South-eastern Nigeria is related to patients' use of pill box. J Soc Aspects HIV AIDS 2010;7:10-5.
Ugwu R, Eneh A. Factors influencing adherence to paediatric antiretroviral therapy in Port Harcourt, South-South Nigeria. Pan Afr Med J 2013;16:1-8.
Oku AO, Owoaje ET, Oku OO, Monjok E. Prevalence and determinants of adherence to highly active anti- retroviral therapy amongst people living with HIV/AIDS in a rural setting in South-South Nigeria. Afr J Reprod Health 2014;18:133-44.
Cruz ML, Cardoso CA, Darmont MQ, Souza E, Andrade SD, D'Al Fabbro MM, et al.
Viral suppression and adherence among HIV-infected children and adolescents on antiretroviral therapy: Results of a multicenter study. J Pediatr (Rio J) 2014;90:563-71.
Mohammed M, Sarki R. Adherence to antiretroviral drugs in North-Central Zone of Nigeria. East Cent Afr J Pharm Sci 2004;7:52-5.
Nabukeera-Barungi N, Elyanu P, Asire B, Katureebe C, Lukabwe I, Namusoke E, et al.
Adherence to antiretroviral therapy and retention in care for adolescents living with HIV from 10 districts in Uganda. BMC Infect Dis 2015;15:520.
Bygrave H, Mtangirwa J, Ncube K, Ford N, Kranzer K, Munyaradzi D. Antiretroviral therapy outcomes among adolescents and youth in rural Zimbabwe. PLoS One 2012;7:e52856.
Murphy D, Belzer M, Durako S. Longitudinal antiretroviral adherence among adolescents infected with human immunodeficiency virus. Arch Paediatr Adolesc Med 2005;159:764-70.
Murphy DA, Sarr M, Durako SJ, Moscicki AB, Wilson CM, Muenz LR, et al.
Barriers to HAART adherence among human immunodeficiency virus-infected adolescents. Arch Pediatr Adolesc Med 2003;157:249-55.
Govind M, Gabhale N, Shah D, Jadhav V, Nath K, Manglani M. Adherence to antiretroviral therapy and factors affecting adherence among paediatric HIV patients. Int J Contemp Paediatr 2017;4:1962-8.
Peltzer K, Preez NF, Ramlagan S, Anderson J. Antiretroviral treatment adherence among HIV patients in KwaZulu-Natal, South Africa. BMC Public Health 2010;10:1-10.
Shumba C, Atuhaire L, Imakit R, Atukunda R, Memiah P. Missed doses and missed appointments: Adherence to ART among adult patients in Uganda. AIDS 2013;2013:1-7.
Amberbir A, Woldemichael K, Getachew S, Girma B, Deribe K. Predictors of adherence to antiretroviral therapy among HIV-infected persons: A prospective study in Southwest Ethiopia. BMC Public Health 2008;8:265.
Smith Fawzi MC, Ng L, Kanyanganzi F, Kirk C, Bizimana J, Cyamatare F, et al.
Mental health and antiretroviral adherence among youth living with HIV in Rwanda. Pediatrics 2016;138:e20153235.
Uzochukwu BS, Onwujekwe OE, Onoka AC, Okoli C, Uguru NP, Chukwuogo OI. Determinants of non-adherence to subsidized anti-retroviral treatment in southeast Nigeria. Health Policy Plan 2009;24:189-96.
Wasti SP, Simkhada P, Randall J, Freeman JV, van Teijlingen E. Factors influencing adherence to antiretroviral treatment in Nepal: A mixed-methods study. PLoS One 2012;7:e35547.
Nachega JB, Hislop M, Nguyen H, Dowdy DW, Chaisson RE, Regensberg L, et al.
Antiretroviral therapy adherence, virologic and immunologic outcomes in adolescents compared with adults in southern Africa. J Acquir Immune Defic Syndr 2009;51:65-71.
[Table 1], [Table 2], [Table 3], [Table 4]