Prevalence and determinants of adherence to antiretroviral treatment among HIV patients on first-line regimen: a cross-sectional study in Dakar, Senegal

Prevalence and determinants of adherence to antiretroviral treatment among HIV patients on first-line regimen: a cross-sectional study in Dakar, Senegal

Mouhamed Abdou Salam Mbengue^1,2,&, Serigne Omar Sarr³, Aissatou Diop³, Cheikh Tidiane Ndour^4,5, Bara Ndiaye^3,5, Souleymane Mboup¹

 

¹Institut de Recherche en Santé, de Surveillance Epidémiologique et de Formations (IRESSEF), Dakar, Senegal, ²University of the Witwatersrand, Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Heath Sciences, Johannesburg, South Africa, ³Department of Pharmacy, Faculty of Medicine and Pharmacy Cheikh Anta Diop University, Dakar, Senegal, ⁴Department of Infectious Diseases, Faculty of Medicine and Pharmacy Cheikh Anta Diop University, Dakar, Senegal, ⁵Centre de Formation et de Recherche Clinique de Fann Dakar, Senegal

 

 

^&Corresponding author
Mouhamed Abdou Salam Mbengue, Institut de Recherche en Santé, de Surveillance Epidemiologique et de Formations (IRESSEF), Dakar, Senegal| Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Heath Sciences, Johannesburg, South Africa

 

 

 

 

Successful and long-term positive impact of antiretroviral therapy requires high rates of adherence. There is a clear relationship between adherence to ARV treatment, viral load suppression, acquired drug resistance and treatment failure [1, 2]. In the long term, non-adherent patients on tri-therapy are more likely to die than adherent patients on the same therapy [3, 4]. In developing countries where older first-line therapies are being used, the development and transmission of drug-resistant strains of HIV will cause the switch to costly second line antiretroviral treatment and limit the treatment options available. Successful antiretroviral treatment requires sustaining high rates of adherence and is a complex behavior which is influenced by a wide range of factors. These factors have been previously categorized into socio-demographic, condition-related, treatment-related, patient-related, and interpersonal factors [5-8]. However, there is only few studies on the prevalence and the main determinants of adherence to antiretroviral treatments in the context of routine clinical care at the public health facilities. Since 1998, Senegal has launched an ambitious antiretroviral treatment programme with a free access to antiretroviral treatment and care for all HIV infected patients [9]; in 2010, there was more than 15,000 patients on antiretroviral treatment in more than 100 health facilities across the country and currently, the coverage rate for ARV among HIV adult patients in Senegal is estimated to be at 75% [10]. Since 2012, the country has adopted the WHO/UNAIDS "90-90-90" treatment target [11]. Thus, Senegal's National AIDS program will need to initiate nearly 36,450 HIV positive patients on ART in order to attain the objective of 90-90-90 during the next following years [10] because of this increase in the number of persons initiated on ART in the upcoming years, monitoring adherence will be the key strategy for the country to attain the third goal of the "90-90-90" approach and globally for the success of the ART program across the country. In Senegal, there is a lack of study investigating adherence to antiretroviral treatment in the context of routine public health care system. Most of the studies that have assessed adherence to ARVs among infants and adult patients have been implemented alongside clinical trials and have mainly recruited patients from highly specialized research centers. Thus, few of them have assessed adherence to antiretroviral treatment in the context of routine clinical care [12-15]. The aim of this study is to assess the factors contributing to ARV adherence after initiating ARVs at six public hospitals in Dakar, Senegal.

 

 

Design and settings: this is a cross-sectional study of HIV positive adult patients recruited from six public hospitals from October 2015 to January 2016. Patients were attending clinic for repeat prescriptions on first line regimen in Dakar, Senegal. Data were collected during a face-to-face structured interview during their medical visits. The study sites included: centre de recherche pour la prise en charge clinique (CRFC-Fann Center), Centre hospitalier Roi Baudoin de Guédiawaye, Institut d'Hygiène Social (IHS), Centre de Santé de Rufisque, Centre de Santé de Diamniadio and Centre de Sante de Ouakam. In these settings, the clinical staff provides care according to Senegalese Guidelines on Antiretroviral treatment defined by the Ministry of Health and the National AIDS programme [16]. Currently, more than 15,000 patients are enrolled in care in these six (6) sites with more than 2,000 having had initiated ART. The treatment programme provides patients with access to counselling, antiretroviral treatment viral load monitoring and psychosocial support.

 

Eligibility criteria: we included HIV-positive patients who were on ART following the Senegalese antiretroviral treatment guidelines. Eligible subjects were patients ≥18 years of age who initiated standard government first-line ART regimens of Tenofovir (TDF) or Zidovudine (AZT) with lamivudine (3TC) and either Efavirenz (EFV) or Nevirapine (NVP). We excluded patients who were referred from another facility or were hospitalized at the time of the study. Thus, our study only included out-patients who initiated on ART and who came for their scheduled antiretroviral (ARV) pickup or medical visits during the time of the study. For each patient, eligibility criteria were ascertained by a clinical pharmacist at each site.

 

Sample size determination: based on a literature review of previous studies, we hypothesized that the true proportion of HIV-positive patients highly adherent to antiretroviral treatment (Po) was at most 55%. Our study wanted to identify correctly with a power of 90% (zβ = 1.28) and for two-sided test and 5% significance (zα = 1.6449) a difference of at least 10% (P1: 60%), given that we used more stringent criteria which combine three self-reported adherence methods into on single multiple adherence measurement tool. Hence, we applied the Woodward formula [17]. Thus, the total patients that should be sampled was 173 (n = 173).

 

Measures and adherence assessment: self-report questionnaire, Visual Analogue Scale (VAS) and the Simplified Medication Adherence Questionnaire (SMAQ) were used. Data on each of the three tools were collected by a final year student in Pharmacy. Each of the three methods have been validated in previous studies and extensively used in routine health care settings [18].

 

Self-report questionnaire: in the self-report questionnaire, there are four questions on which the patient responded with either "yes" or "no". A patient who answers "no" to all four questions was recorded as highly adherent, but the one whose answer is "yes" to one of the items is recorded as moderately adherent. When a patient responds "yes" to two (2) or more questions, he or she was rated as poorly adherent [18].

 

Visual analog acale (VAS): each patient was asked to mark on a scale of measurement from 0 to 100%, his or her adherence to the medication over the past 4 weeks. The results were converted to an adherence level expressed as a proportion (%) and classified into three (3) categories of adherence. Patients with result above 95% were classified as adherent and those with a result equal to or below 95% were classified as poorly adherent to antiretroviral treatment [18].

 

Simplified medication adherence questionnaire (SMAQ): the SMAQ was used to collect information on adherence over the previous 3 months period. The SMAQ score ranged from 0 to 7 with 0 corresponding to 100% adherence. A patient was considered as positive or non-adherent when a positive response was given to one of the questions, or the patient did not take any medicine over the past weekend, or had missed taking the medicine for more than 2 days over the past 3 months [19].

 

Multi-method approach: World Health Organization (WHO) recommends a multi-approach method when measuring adherence to antiretroviral treatment [19-21] . The multi-method approach tool included self-reports combined with VAS and the SMAQ. Overall adherence assessment with the multi-method approach was rated into two (2) categories: high and low. A high level of adherence corresponds to a patient who reported "no" to all questions with self-reporting, had a VAS score "yes" 95% and who is adherent following the SMAQ method. A patient who did not meet the above-mentioned criteria was classified as poorly adherent with the multi-method approach.

 

Study variables: the outcome of interest was poor adherence to ARV treatment. We considered a multi-method approach by further categorizing overall adherence as adherent or non-adherent. A patient was categorized as adherent if they answered "no" to the all the self-report questions, reported 90% VAS or more and knew the dose, time and instructions. Where responses to self-report, VAS or pill identification were less than optimal (e.g. moderately or poorly adherent), overall adherence was categorized as non-adherent. We evaluated the relationship between adherence to antiretroviral therapy, socio-demographic and economic factors. We used the cut-off values reported in previous studies and the diagnostic prediction models found in the literature data on socio-demographic and economic factors were collected as part of the interview for each patient. The socio-demographic variables included: age, gender, nationality, religion, marital status, education respondent, tobacco use, alcohol consumption, treatment duration and ART sites. The social support and economic factors included variables such as employment, ability to pay his/her medical care, having shared the HIV status, received support from family or friend, being a member of an association of persons living with HIV, participated to educative sessions on ARVs or visited a traditional healer recently or in the past. These variables were selected following a literature review and from previous studies that assessed factors associated with adherence to ARV [6, 8, 11, 15, 22, 23]. For each variable included in our analysis, we used the cut-off values known from previous studies and models found in the literature.

 

Data analysis and statistical methods: continuous variables were described as mean and standard deviation or median and interquartile range and were compared by t-test or Wilcoxon rank sum test when appropriate. Categorical variables were described by frequencies and percentages and were analyzed by chi-square test. For multivariate analysis, we considered the outcome category "none" as baseline/reference category. We considered socio-demographic characteristics as potentials confounding variables and we used the log-likelihood ratio test to select the independent variables for the multivariate model. The selection of independent variables for the model relied on their ability to improve the general model. For each variable, the results of the multinomial logistic regression were expressed as adjusted odds ratio (AoR) with its corresponding 95% confidence interval (CI) and p-values were calculated with an alpha level of 0.05. Data was captured and entered Excel spreadsheet and transferred to STATA 14 for descriptive, bivariate analysis and multivariate regression. A p-value < 0.05 was considered statistically significant.

 

 

Sample characteristics: 150 patients were analyzed. For the number of participants who accepted the study in detail see the flow chart below (Figure 1). Of the 150 patients in the study population, more than three quarter were females and 82.67% were aged above 35 years old. Education level ranged from beyond individuals with no education (45.33%) to secondary school (28.0%). Of the 150 patients 82.67% were Senegalese while the rest were from neighboring countries. Among the patients, only small proportions (6.67%) smoked in the past or were still smoking. 33% of patients were enrolled in CRFC-Fann Center and only 20% of patients had a treatment duration below one year. There were minimal missing values (Table 1).

 

Social support and socio-economic characteristics: patients were predominantly unemployed (62.67%) (Table 1). Most of the patients were prescribed TDF-3TC-EFC (81%) as first line ARV regimen. One-third (37.77%) could pay for their medical care was and 68% of the patients had shared their HIV status. Additionally, patients who participated in the activities with an association of person living with HIV (PLWHIV) were 19.33%. However, most of the patients have at least once participated to a therapeutic education session on ARVs. Finally, only 5.33% of patients have visited a traditional healer after the diagnostic of HIV infection.

 

ART adherence: a total of 150 patients had their adherence assessed using the 30-days visual analog scale (VAS) and most of them (86.7%) had a score above 95% (70.67%) in the month prior to the survey (Table 2). Results from the self-reporting adherence questionnaire, found that only 11.33% were adherent and with the SMAQ the proportion of patients classified as being highly adherent increased again to 30.6%. In the multi-method approach which combines visual analogue scale, self-reporting and SMAQ, of a total of 150 patients, 26.67% were found to be highly adherent.

 

Determinants of ART adherence: in bivariate analysis, factors associated with poor adherence were tobacco use and being involved into the activities of associations of persons living with HIV (Table 3). Results from the multivariate regression analysis (Table 4) indicates that there was a decrease in the odds of having high adherence level depending on patient's demographic and psychosocial support. Patients classified as widowed (Aor: 0.49 95% CI: 0.03-0.94. p value: 0.043) were less likely to be highly adherent as compared with patients who reported they were single at the time of the study. Additionally, patients who declared that they participate in the activities within an association of person living with VIH (PLWHIV) were 2.34 times more likely to be adherent as compared to patient who did not belong to any association of persons living with HIV (Aor:2.89 95% CI:1.04-7.99 p value: 0.041) (Table 4).

 

 

Recent estimates state that 6 million HIV-positive people have initiated ART in sub-Saharan Africa and this figure is likely to increase in the next year following the implementation of the "test and treatment initiative" and the "90-90-90" approach [16-24]. Initiating all the HIV-positive patients onto care will be a remarkable success for the National AIDS programs. However, one of the main challenges will be keeping these patients on care and highly adherent to antiretroviral treatment first line regimen. In this context, a thorough understanding of factors associated with ART adherence allows for targeted interventions that can be implemented within the context of health care services in public hospitals areas in order to keep patients in care and adhering to treatment in the early stages of ART [25]. It is therefore necessary to continue to tackle the issue of adherence and identify patients at risk of poor clinical outcomes who need therapeutic education and support. In our study, approximately 30% of patients were found to be adherent to ARV treatment. This finding is consistent with previous reports from industrialized countries, documenting 25%-44% of adherent patients [22]. However, this finding is different from previous studies in Sénégal [12-15]. In a study implemented in a cohort of patients who were on ART from 1998 to 2000, Laniece et al. [13] showed that long term adherence among HIV patients in Dakar was very high with nearly more than 95% being adherent This situation may be explained by the fact that in our study, we used a more stringent criteria to assess adherence with the combining of three self-reported adherence as recommended by WHO [26]. Additionally, these previous studies were conducted from 1998-2000 in a highly specialised research centres with a specific cohort of HIV positive patients in the context of clinical trials with where patients had access to different services including an adherence counselling sessions and psychological support combined with regular clinical consultation. Assessing the determinants of adherence to antiretroviral treatment is critical because of its potential to target patients who need adherence counselling and support during medical visits. Our results show that patients who participated in activities with an association for persons living with HIV were more likely to achieve higher adherence level to ART compared with patients who did not participate in such activities. This finding is in line with previous studies in Africa that highlighted the positive impact and the role of peer-counselling sessions or adherence clubs for improving and maintaining high adherence levels among persons living with HIV [4, 23, 27, 28]. Our findings suggest that, participating to peer education activities and to different support groups activities could potentially help them to cope with the psychological effects of the long-term antiretroviral therapies.

 

Data from industrialized countries showed that younger patients with poorer education and those with low socio-economic status [29, 30] were less likely to being adherent, but there is little evidence from resource-limited settings. Surprisingly, we did not detect a relationship between adherences to antiretroviral therapies and socio-economic factors. This difference may be explained by the smaller sample size in our pilot study but also by the fact that in these previous studies, adherence was assessed in a long-term manner with a prospective cohort study. Additionally, in our study there was no association between income or socio-economic levels and adherence to ART. This situation may be explained by the fact that ART is free for all HIV positive patients regardless of their socio-economic level in Sénégal since 2002 [10]. In our study, there was a significant relationship between marital status and adherence to antiretroviral treatments. Widowed patients were less likely to be highly adherent as compared to single patients. Our findings confirm previous studies in Senegal, West Africa and in Uganda who have reported a similar pattern among HIV-positive patients [15-31] . Although other studies have reported mixed results regarding the role of marital status [32, 33] on adherence, this situation may be explained by the fact that single patients may have access to better social support as they may only share their status with the medical staff for HIV antiretroviral therapy. For widowed HIV positive patients, HIV/AIDS represents a shame and they may fear discrimination and reject from the community who may already know their HIV status. Our findings should be considered within the context of the study's monitoring. First, because there is no gold standard method to assess adherence to antiretroviral treatment, thus our study used data from three self-reported adherence measures as recommended by WHO [34]. Therefore, our results may not be completely comparable to previous studies who used a single adherence questionnaire. Second, the use of self-report medication adherence was a limitation since patients had to recall the number of missed doses or the number of days during which the medication was not taken, thus there was a possibility for the participants to overestimate their adherence level. Fourth, we did not have access to ARVs medical records and informations on the availability of ARVs drugs at the health facilities. Therefore, there was no possibility to assess the impact of drug stock out in adherence levels or to correlate the adherence levels with viral load which represents the gold standard when assessing adherence to antiretroviral treatment. Finally, since this was a cross-sectional survey, the outcome and exposure were collected at the same time and therefore no inference or causalities association can be concluded. However, even with these limitations, the analysis of data collected from hospitals provides important insights into the factors influencing adherence to antiretroviral treatment in Sénégal and can be useful in guiding policy implementation strategies.

 

 

The adherence rate found in this study seems to be low. The use of three different adherence indicators was important for reducing bias through self-reporting and therefore strengthened the indicator. For HIV positive patients living alone particularly those who are widowed, additional supports may be needed to ensure that they obtain their medication and they comply with antiretroviral regimen. Additionally, activities such as peer to peer group-based models or adherence clubs should be implemented to assist in improving adherence and virologic suppression amongst HIV-positive patients in Sénégal.

 

 

The authors declare no competing interests.

 

 

Mouhamed Abdou Salam Mbengue and Serigne Omar Sarr designed the work, drafted, corrected and validated the manuscript. Mouhamed Abdou Salam Mbengue performed the statistical analysis. Mouhamed Abdou Salam Mbengue wrote the first draft of the manuscript; Aissatou Diop conducted survey in hospitals, entered the data and revised the first draft of the manuscript; Cheikh Tidiane Ndour, Bara Ndiaye corrected and validated the manuscript interpretation of data and revised the first draft of the manuscript. Souleymane Mboup revised and corrected the first draft of the manuscript and critically reviewed scientific content and supervised the research. All the authors have read and agreed to the final manuscript.

 

 

We thank all the patients who consented to participate as well as the site coordinators (Dr Mame Basty Koita Fall et Dr Ndeye Amy Sarr) for accepting to participate to this study and providing pharmacy data for this study. We are grateful to the National AIDS Program in Senegal (CNLS) who granted access to different hospitals in Dakar.

 

 

Table 1: demographic characteristics of HIV positive patients

Table 2: adherence to antiretroviral therapy among adults

Table 3: factors associated with adherence to antiretroviral therapy

Table 4: determinants of adherence to antiretroviral therapy

Figure 1: flowchart of the participants in the cross-sectional study. The flowchart indicates how the final eligible study total participants was arrived at Participants who were pregnant, or on second line antiretroviral treatment study (n=4) were excluded as their management is different

 

 

1. Ford N, Darder M, Spelman T, Maclean E, Mills E, Boulle A. Early adherence to antiretroviral medication as a predictor of long-term HIV virological suppression: five-year follow up of an observational cohort. PLoS ONE. 2010; 5(5): 3-6. PubMed | Google Scholar


2. Bangsberg DR. Less than 95% adherence to non-nucleoside reverse-transcriptase inhibitor therapy can lead to viral suppression. Clin Infect Dis. 2006 Oct; 43(7): 939-41. PubMed | Google Scholar


3. Mannheimer SB, Morse E, Matts JP, Andrews L, Child C, Schmetter B et al. Sustained benefit from a long-term antiretroviral adherence intervention; results of a large randomized clinical trial. J Acquir Immune Defic Syndr. 2006; 43 Suppl 1: S41-S47. PubMed | Google Scholar


4. Paterson DL, Swindells S, Mohr J, Brester M, Vergis EN, Squier C et al. Adherence to protease inhibitor therapy and outcomes in patients with HIV infection. Ann Intern Med. 2000 Jul 4; 133(1): 21-30. PubMed | Google Scholar


5. Langebeek N, Gisolf EH, Reiss P, Vervoort SC, Hafsteinsdóttir TB, Richter C et al. Predictors and correlates of adherence to combination antiretroviral therapy (ART) for chronic HIV infection: a meta-analysis. 2014 Aug 21; 12: 142. PubMed | Google Scholar


6. Reda AA, Biadgilign S. Determinants of Adherence to Antiretroviral Therapy among HIV-Infected Patients in Africa. AIDS Res Treat. 2012; 2012: 574656. PubMed | Google Scholar


7. Katz IT, Ryu AE, Onuegbu AG, Psaros C, Weiser SD, Bangsberg DR et al. Impact of HIV-related stigma on treatment adherence: systematic review and meta-synthesis. J Int AIDS Soc. 2013; 16(3 Suppl 2). PubMed | Google Scholar


8. Heestermans T, Browne JL, Aitken SC, Vervoort SC, Klipstein-Grobusch K. Determinants of adherence to antiretroviral therapy among HIV-positive adults in sub-Saharan Africa: a systematic review. BMJ Glob Health. 2016; 1(4): e000125. PubMed | Google Scholar


9. Desclaux A, Ciss M, Taverne B, Sow PS, Egrot M, Faye MA et al. Access to antiretroviral drugs and AIDS management in Senegal. AIDS. 2003; 17(Suppl 3): S95-101. PubMed | Google Scholar


10. Conseil National de Lutte Contre le SIDA (CNLS). Rapport de situation sur la riposte nationale a l'épidemie de VIH/SIDA/Senegal:2015-2016. SEN_narrative_report_2015.pdf. Accessed May 30 2017.


11. UNAIDS. 90-90-90: An ambitious treatment target to help end the AIDS epidemic. Accessed May 30 2017.


12. Etard J-F, Lanièce I, Fall M, Cilote V, Blazejewski L, Diop K et al. A 84-month follow up of adherence to HAART in a cohort of adult Senegalese patients. 2007; 12(10): 1191. PubMed | Google Scholar


13. Laniece I, Ciss M, Desclaux A, Diop K, Mbodj F, Ndiaye B et al. Adherence to HAART and its principal determinants in a cohort of Senegalese adults. AIDS. 2003; 17(Suppl 3): S103-S108. PubMed | Google Scholar


14. Bastard M, Fall MB; groupe d’étude de la Cohorte ANRS 1215. Long term adherence to HAART in Senegal. Bull Soc Pathol Exot. 2014; 107(4): 241-243. PubMed


15. Sow PG, Toure K, Coume M, Dia AT, Traore I. Predictors of ART adherence among HIV infected individuals in Dakar Senegal. J Med Sci. 2012; 3(4): 212-216. Google Scholar


16. 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 Glob Health. 2017 Mar 7; 2(2): e000227. PubMed | Google Scholar


17. Woodward M. Epidemiology: Study design and Data Analysis. Third Edit, Florida: Chapman and Hall/CRC. 2013. Google Scholar


18. Steel Gavin, Nwokike Jude, Joshi MP. Development of a Multi-Method Tool to Measure ART Adherence in Resource-Constrained Settings: The South Africa Experience 2007. Accessed May 30 2017.


19. Knobel H, Alonso J, Casado JL, Collazos J, González J, Ruiz I et al. Validation of a simplified medication adherence questionnaire in a large cohort of HIV-infected patients: the GEEMA Study. AIDS Lond Engl. 2002; 16(4): 605-13. PubMed | Google Scholar


20. Williams AB, Amico KR, Bova C, Womack JA. A proposal for quality standards for measuring medication adherence in research. AIDS Behav. 2013 Jan 1; 17(1): 284-97. PubMed | Google Scholar


21. Evans D, Fox MP. When and how should we be measuring adherence to antiretroviral therapy in resource-limited settings. J Clin Res HIV AIDS Prev. 2013; 1(2): 24-30. Google Scholar


22. Mills EJ, Nachega JB, Bangsberg DR, Singh S, Rachlis B, Wu P et al. Adherence to HAART: a systematic review of developed and developing nation patient-reported barriers and facilitators. PLoS Med. 2006 Nov 21; 3(11): e438. PubMed | Google Scholar


23. Buregyeya E, Naigino R, Mukose A, Makumbi F, Esiru G, Arinaitwe J et al. Facilitators and barriers to uptake and adherence to lifelong antiretroviral therapy among HIV infected pregnant women in Uganda: a qualitative study. BMC Pregnancy Childbirth. 2017 Dec; 17: 94 Google Scholar


24. UNAIDS. Global AIDS. 2016. Accessed May 30 2017.


25. 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 May 1; 91(5): 377-385E. PubMed | Google Scholar


26. Berg KM, Arnsten JH. Practical and conceptual challenges in measuring antiretroviral adherence. J Acquir Immune Defic Syndr. 2006 Dec 1; 43 Suppl 1: S79-87. PubMed | Google Scholar


27. Haberer JE, Sabin L, Amico KR, Orrell C, Galárraga O, Tsai AC et al. Improving antiretroviral therapy adherence in resource-limited settings at scale: a discussion of interventions and recommendations: Haberer JE et al. J Int AIDS Soc. 2017; 20(1): 21371.. PubMed | Google Scholar


28. Luque-Fernandez MA, Van Cutsem G, Goemaere E, Hilderbrand K, Schomaker M, Mantangana N et al. Effectiveness of Patient Adherence Groups as a Model of Care for Stable Patients on Antiretroviral Therapy in Khayelitsha, Cape Town, South Africa. PLoS ONE. 2013 Feb 13; 8(2): e56088. PubMed | Google Scholar


29. Burch LS, Smith CJ, Anderson J, Sherr L, Rodger AJ, O'Connell R et al. Socioeconomic status and treatment outcomes for individuals with HIV on antiretroviral treatment in the UK: cross-sectional and longitudinal analyses. Lancet Public Health. 2016 Nov; 1(1): e26-36. PubMed | Google Scholar


30. Nachega JB, Uthman OA, Peltzer K, Richardson LA, Mills EJ, Amekudzi K et al. Association between antiretroviral therapy adherence and employment status: systematic review and meta-analysis. Bull World Health Organ. 2015 Jan 1; 93(1): 29-41. PubMed | Google Scholar


31. Esther B, Rose N, Aggrey M, Fred M, Godfrey E, Jim A et al. Facilitators and barriers to uptake and adherence to lifelong antiretroviral therapy among HIV infected pregnant women in Uganda: a qualitative study. BMC Pregnancy Childbirth. 2017; 17: 94. PubMed | Google Scholar


32. Chime OH, Arinze-Onyia SU, Obionu CN. Do peer support groups influence medication adherence: a study among people living with HIV/AIDS in Enugu State, Nigeria. Proc Singap Healthc. 2018 Mar 4; 27(4). Google Scholar


33. Ware NC, Wyatt MA, Tugenberg T. Social relationships, stigma and adherence to antiretroviral therapy for HIV/AIDS. AIDS Care. 2006 Nov 1; 18(8): 904-10. PubMed | Google Scholar


34. Paterson DL, Swindells S, Mohr J, Brester M, Vergis EN, Squier C, Wagener MM, Singh N. Adherence to anti-HIV therapy and the outcome of treatment. Ann Intern Med. 2000 Jul 4; 133(1): 21.