Skip to main content
Download PDF

Factors affecting optimal adherence to antiretroviral therapy and viral suppression amongst HIV-infected prisoners in South Ethiopia: a comparative cross-sectional study

AIDS Research and Therapy volume 19, Article number: 5 (2022) Cite this article

Abstract

Background

Maintaining optimal adherence and viral suppression in people living with HIV (PLWH) is essential to ensure both preventative and therapeutic benefits of antiretroviral therapy (ART). Prisoners bear a particularly high burden of HIV infection and are highly likely to transmit to others during and after incarceration. However, the level of treatment adherence and viral suppression in incarcerated populations in low-income countries is unknown. This study aimed to determine factors affecting optimal adherence to antiretroviral therapy and viral suppression amongst HIV-infected prisoners in South Ethiopia.

Methods

A comparative cross-sectional study was conducted between June 1, 2019 and May 31, 2020 to compare the level of adherence and viral suppression between incarcerated and non-incarcerated PLWH. Patient information including demographic, socio-economic, behavioral, and incarceration-related characteristics were collected using a structured questionnaire. Medication adherence was assessed according to self-report and pharmacy refill. Plasma viral load measurements undertaken within the study period were prospectively extracted to determine viral suppression. Univariate and multivariate logistic and fractional regression models were used to analyse data.

Results

Seventy-four inmates living with HIV (ILWH) and 296 non-incarcerated PLWH participated in the study. While ILWH had a significantly higher pharmacy refill adherence compared to non-incarcerated PLWH (89 vs 75%), they had a slightly lower dose adherence (81% vs 83%). The prevalence of viral non-suppression was also slightly higher in ILWH (6.0%; 95% confidence interval (CI): 1.7–14.6%) compared to non-incarcerated PLWH (4.5%; 95%CI: 2.4–7.5%). Overall, missing ART appointments, dissatisfaction with ART services, inability to comply with a specified medication schedule, and types of methods used to monitor the schedule (e.g., news time on radio/TV or other social cues) were significantly associated with non-adherence according to self-report. In ILWH specifically, accessing ART services from a hospital, inability to properly attend clinic appointments, depressive symptoms, and lack of social support predicted NA. Viral non-suppression was significantly higher in males, people of age 31to 35 years and in those who experienced social stigma, regardless of their incarceration status.

Conclusions

Sub-optimal dose adherence and viral suppression are generally higher in HIV-infected prisoners in South Ethiopia compared to their non-incarcerated counterparts. A multitude of factors were found to be responsible for this requiring multilevel intervention strategies focusing on the specific needs of prisoners.

Background

Although there has been a steep decline in the number of new HIV infections and associated deaths in the general population worldwide, key populations such as prisoners remain disproportionately affected by the epidemic, accounting for more than half of all new infections [1]. There is considerably higher HIV prevalence in the prisons of sub-Saharan Africa (SSA), reaching up to 35% in some countries [2]. An HIV prevalence of greater than 4% has been documented in Ethiopian prisons [3], which is more than four times higher than the prevalence in the general Ethiopian population, and one of the highest HIV prevalences in prison populations in SSA relative to the general population [1].

Antiretroviral therapy (ART) is associated with significantly reduced HIV-associated morbidities and mortality. ART is also believed to prevent HIV transmission by suppressing viral load in infected individuals [4,5,6]. To ensure the public health benefits, people living with HIV (PLWH) need to have optimal treatment adherence and achieve viral suppression [7]. Given the overall high prevalence of HIV in prisoners, most of whom will reintegrate into the general community, poor treatment outcomes in such populations may facilitate onward transmission [1, 8].

Promising outcomes have been reported amongst prisoners in both high- and low-income countries (including those in SSA) regarding ART adherence and viral suppression in prisons where standard HIV care is implemented [9,10,11,12]. However, the prevalence of non-adherence (NA) and viral non-suppression remains high in prisons of many countries, with more than half of inmates living with HIV (ILWH) having sub-optimal treatment outcomes in some settings [13,14,15]. Several institutional, psychosocial and personal factors have previously been reported to affect optimal adherence and viral suppression in prisoners. Cooperativeness of security systems [16,17,18], type of ART service delivery approach (for example, provision of ART via directly observed therapy (DOT) or accessing care from external ART sites) [18, 19], the nature of inmate-health care provider relationships [19, 20], and food supply insufficiency (particularly in resource limited countries) [18, 21, 22] have been reported to be the main institutional circumstances to affect ART outcomes.

Psychosocial factors such as social support, stigma and depression have been found to influence ART adherence in prisoners. It has been shown that ILWH who are able to receive social support (be it material, emotional or information support), either from inside or outside of a prison, are more likely to be adherent to ART than those who are not [13, 14, 20]. In contrast, social stigma perpetuated by prison staff and fellow inmates negatively affects adherence [17,18,19]. Further, ILWH often have a high prevalence of depression [23] which may have substantial adverse effects on their ART adherence and viral suppression [9, 13, 15].

With only limited data existing regarding personal factors affecting ART adherence and viral suppression in prisoners, it has been reported that self-perceptions of HIV status, the health benefits of ART as well as its potential adverse consequences have been associated with NA. For instance, ILWH who perceive that ART is inefficient and has side-effects are less likely to adhere to ART [14, 24]. The odds of NA is higher in ILWH who have experienced more frequent antiretroviral (ARV) side-effects [15, 21, 25] and other underlying disease symptoms [14, 26]; and non-adherent ILWH are, in turn, less likely to achieve viral suppression [14]. Having a history of injecting drug use is the only major behavioural factor that has been statistically confirmed to be negatively associated with ART adherence and viral suppression in ILWH [14, 26, 27]. Other personal characteristics reported to be associated with NA and viral non-suppression are younger age (below 35 years) and being male [9, 22, 27, 28].

There have been reports showing that ILWH have limited access to HIV care in many SSA prisons [2, 18, 29], however it remains unclear to what extent incarceration affects optimal ART adherence and viral suppression in such settings. No published studies have quantitatively investigated ART outcomes in the prison systems of Ethiopia previously, although there have been suggestions that population groups commonly referred to as ‘Most at Risk Groups’ (MARPS) for HIV (including prisoners) remain with restricted access to care [30]. This study therefore aimed to determine factors affecting optimal adherence to antiretroviral therapy and viral suppression amongst HIV-infected prisoners in South Ethiopia relative to their non-incarcerated counterparts.

Methods

Study design and setting

A comparative cross-sectional study was conducted between June 1, 2019 and May 31, 2020 to compare the outcomes of ART between HIV-infected incarcerated and non-incarcerated individuals in South Ethiopia. We have provided a detailed description of the study setting elsewhere [31]. In brief, approximately one quarter of the correctional facilities (six of 23 prisons) in South Ethiopia and public health care facilities offering ART services for the prisoners were involved in the study. Prisons with a high load of inmates were chosen purposively to obtain adequate number of prisoners that bear socio-cultural diversity. The prisons are located in the central part of Ethiopia and accommodate people originating from diverse areas of the region and the country, including rural areas.

Participant recruitment

All HIV-infected prisoners who were on ART during the study enrolment period were eligible for participation as a risk group. The comparison groups included HIV-infected non-incarcerated people who were receiving care from the same ART clinics and had similar ART history as the prisoners. Patients who had initiated ART before the beginning of the study and new patients who started ART within the first six months of the study were included. Figure 1 shows the process of participant recruitment. As the population pool for non-incarcerated people was much larger than incarcerated people, a simple random sampling technique [32] was used to recruit a sample of non-incarcerated participants that quadrupled the number of prisoner participants. To assist in this, a list of clients in ART registers served as a sampling frame to select potential participants using a table of random numbers.

Fig. 1
figure 1

Participant recruitment process

Full size image

Inclusion criteria

Study participants included HIV-infected persons who were able to provide written informed consent, aged 18 years and above, and were receiving HIV care for at least six months at one of the selected ART clinics in South Ethiopia. Six or more months adherence measurement was used in order to rule out a particular risk of poor adherence during the earlier months of ART [33].

Exclusion criteria

Individuals who were seriously unwell (as determined by ART service providers) and unable to provide complete information were excluded. Non-incarcerated PLWH with a previous history of imprisonment and ILWH who had already developed non-adherence and/or had viral non-suppression before incarceration (as confirmed through clinical chart reviews) were also excluded. ILWH were also required to remain imprisoned for at least one month.

Sample size determination

The smallest difference in the proportion of NA between incarcerated and non-incarcerated people was considered to determine the minimum sample size required to identify an estimated prevalence ratio. A formula for unmatched cross-sectional studies [32]; assuming 95% of level of confidence, 80% power, a 5% level of significance and unexposed to exposed group sample ratio of four was used to calculate the sample size. Considering a proportion of 24.4% NA in the general population in Ethiopia [34], and a prevalence ratio of 1.67 in NA in the incarcerated population [22], a minimum sample of 74 inmates was required. As four times the number of incarcerated participants was required compared to the non-incarcerated group, a final sample size of 370 participants was recruited from both populations.

Data collection procedure

ART service providers at the participating public health care facilities invited potential participants to see a trained research assistant in a separate room. The invitation occurred when PLWH made their regular clinic visit. The research assistants were certified HIV counsellors who had a tertiary qualification in health-related disciplines. Participants underwent Paper and Pencil Interviewing (PAPI) about their background information and self-reported adherence to medication once they gave consent for participation to the research assistant.

To minimise the effect of the language barrier on the accuracy of PAPI data, the questionnaire, which was initially prepared in English language, was translated into Amharic, a commonly spoken language in the study area. Completed questionnaires were then translated back into English at the end of the data collection process. Pre-testing was conducted to ensure context validity (i.e. clarity, meaningfulness and difficulty) of questionnaire items with a group of participants representing five percent of the study sample size; using incarcerated and non-incarcerated PLWH at ART clinics remote to the study sites. As lay experts [35], one ART service provider from each study health facility evaluated the face validity of the questionnaire. To perform this, the ART service providers were provided with the questionnaire ahead of the data collection process. Although some items of the questionnaire were obtained from previously validated instruments (as described below), newly developed items were tested for internal consistency using Cronbach’s α [36], and corrections were made by removing less consistent items based on the ‘α’ values of the pre-test data.

Variables and measurements

Background information

In the questionnaire, participants were asked about their sociodemographic, psychosocial (social support, stigma and depression), behavioural, and incarceration related characteristics. The core components of social support including emotional, informational, tangible, comradeship and positive social interactions [37] were assessed using nine items, part of which were adapted from a multi-item scale developed by White et al. [24], which had internal consistency (α) of 0.79. The items were further checked for contextual reliability and showed an acceptable Cronbach’s α value (α = 0.66). The four manifestations of HIV-related social stigma: internalised (negative self-image), enacted (personalised), perceived (concern with public attitude) and concerns with status disclosure [38] were measured using a shortened version [39] of the 40-item scale by Berger et al. [38] (α > 0.7). Non-specific psychological distress was assessed using a six-item scale developed by Kessler et al.[40] (α = 0.89). Participant responses were graded using a five-point Likert scale ranging from 1, “Strongly disagree” to 5, “Strongly agree” for social support and stigma measurements, and a four-category scale (most of the time, some of the time, a little of the time, none of the time) for depression.

Knowledge and attitudes of HIV and ART, as well as self-efficacy in medication use were assessed using items generated from the literature review. The knowledge scale consisted of eight items, and the attitude and self-efficacy scales each consisted of three-item questions. The scales showed sufficient Cronbach’s α values; 0.78 and 0.65 for the knowledge and attitude constructs, respectively. Whereas responses for the knowledge items were scored by assigning one point for every correct response and zero for an incorrect answer, a five-point response scale was used for the attitude and self-efficacy items. In each measurement scale, scores were summed to determine the overall score, and the interquartile range was calculated to categorise results.

Adherence to ART

Adherence was measured using the participants’ self-report and pharmacy refill records to offset the limitations of one method by the other [41,42,43]. Participants were asked once to self-report their medication use in the previous four days, an ideal time interval to minimise possible recall and social desirability bias [43]. The percentage of adherence was then determined by calculating the proportion of pills taken of the number of pills prescribed:

$$ Self - reported\, adherence = \frac{number\, of\, pills\, taken\, in\, the\, last\, four\, days }{{number\, of\, pills\, prescribed\, for\, the\, last\, four\, days }}x100 $$

Participants were also assessed once for their six-monthly (180 days) pharmacy refill adherence to any prescribed ARV drugs. Variation in the medication possession ratio (MPR) was determined by dividing the number of days a patient was late for pharmacy refills by the total days on ART regardless of left-over medications, and then subtracting this proportion from 100% [44, 45]. i.e.

$$ MPR = 1 - \frac{number\, of\, days\, late\, for\, ARV\, pick - up}{{Total\, number\, of\, days\, between\, the\, two\, most\, recent\, ARV\, pick - ups}}x100 $$

For both self-report and pharmacy refill methods, patients with an adherence percentage of < 95% were considered as non-adherent [46, 47]. Participants were also asked to self-report on their adherence to dose schedules and medication instructions in the previous four days or more and complete a brief survey on potential risk factors for NA.

Viral suppression

The South Ethiopian Regional Public Health Laboratory (RPHL) performs viral load tests using plasma samples for HIV patients six months from ART initiation and every 12 months thereafter. Of investigations undertaken within 12 months of the study period, the most recent ones were prospectively extracted from the laboratory registers using patient medication identification numbers. Although the lowest detection limit of the testing machine was 40 copies/mL, viral non-suppression in this study was defined as viral load above 1000 copies/mL, which is partly adapted from World Health Organization (WHO) definitions [42].

Data analysis

Data were manually checked for completeness, consistency and cleanness, entered into an EpiData (version 4.6) template, and then exported to Stata (StataCorp. 2019. Stata Statistical Software: Release 16. College Station, TX: StataCorp LLC.) for analysis [48]. Participant characteristics were initially described in terms of frequencies and percentages for categorical variables, while summary statistics such as means, medians, standard deviations and quartiles were calculated for continuous variables. Bivariate associations amongst categorical variables were estimated using Chi-square (Chi2) test [49] and mean and median differences in continuous variables between the target and comparison populations were determined using T-test and Mann–Whitney U-test respectively [50].

Adherence was analysed in two ways: as a continuous outcome restricted to the interval between 0 and 1, and a binary outcome categorised as adherence and non-adherence (NA). In the first case, as the data included the upper and lower bounds [0, 1], a fractional regression model was used to estimate the results with logit as a link function [51]. Covariates of NA and viral non-suppression were determined using a logistic regression model [52].

The models were developed through a purposeful selection approach [52]. Each variable was initially independently tested using a bivariate regression model to identify eligible variables for a multivariate regression model. A relatively less stringent inclusion criterion (P < 0.2) was used to ensure the consideration of all potentially important covariates in the analyses. Variables which did not contribute to the model at the traditional significance level (P < 0.05) as well as those which did not appear to have a significant confounding effect (β < 20%) were eliminated [52].

Interaction terms were considered when they were found to have a statistically significant effect [53]. Multicollinearity between continuous covariates was tested thorough scatterplots and a bivariate linear regression, whereas a correlation coefficient as well as a variance inflation factor was determined for both continuous and categorical variables [53]. All the covariates included in the models had a variance inflation factor value of less than 1.5.

A significance of associations between covariates and the outcome variables was determined at a P < 0.05 with 95% confidence interval (CI). Goodness-of-fit for the logistic regression models was assessed using Hosmer and Lemeshow Chi2 test [52] whereas that of the fractional regression analyses was checked using a generalised linear model applying logit as a link function [54, 55]. In all cases, the models fit the data well.

Missing values were observed within several dependent and independent variables in the dataset. For variables included in the analysis models, Little’s test was used to check whether the missingness occurred completely at random (MCAR), independent of observed and unobserved values [56]. The test identified that the pattern of missingness varied across the variables and violated the assumption of MCAR. Thus, the multiple imputation technique (m = 20) was applied to take into account the effect of missing values, in which the results obtained from each completed-data analysis were combined to produce a single multiple-imputation result. The fit of the imputation models was checked using a graphical method [57]. The distribution of the observed and completed values appeared to be comparable.

Results

Participant characteristics

One hundred and twenty-two ILWH were identified in the six selected correctional facilities. Of these, 24 (19.7%) ILWH did not participate due to their release from prison ahead of their clinic appointment at which the consenting process would have been conducted. Ten ILWH (8.2%) failed to meet the study eligibility criteria. Of the remaining 88 ILWH, 74 agreed to participate in the study, which gives a response rate of 84.1%. During the study period, there was a total of 3806 non-incarcerated adult PLWH who were receiving ART services at the six selected public health care facilities; of whom, 296 were randomly selected for participation in this study as comparators.

Characteristics of participants are described in Table 1. The majority 66 (89%) of ILWH participants were male, as were only 139 (47%) non-incarcerated PLWH participants. Both groups were of comparable age; the median age of ILWH was 34 years (Interquartile range (IQR): 28–40 years) whereas that of non-incarcerated PLWH was 35 years (IQR: 30–40 years). Ninety-eight (33%) non-incarcerated PLWH participants reported having completed high school and above education whereas 13 (18%) ILWH participants reported attaining similar educational level (P = 0.015). ILWH were more likely to be farmers or daily labourers prior to incarceration. Most 67 (23%) non-incarcerated PLWH were housewife. Half of the ILWH reported urban areas as their last residence before incarceration whereas 204 (69%) non-incarcerated PLWH were urban residents (P = 0.013). Thirteen (18%) ILWH reported experiencing a homelessness.

Table 1 Characteristics of incarcerated and non-incarcerated people living with HIV in South Ethiopia (N = 370)
Full size table

Non-adherence to ART

The median duration of ART use was 44 months (IQR: 24–68 months) for prisoners and 48 months (IQR: 23–72 months) for non-incarcerated clients. Sixty-four (17%) participants had non-adherence (NA) by self-report and 82 (22%) by pharmacy refill methods. While prisoners had a significantly lower pharmacy refill non-adherence (11%) compared to non-incarcerated clients (15%) (P = 0.009), they had a slightly higher dose non-adherence (see Table 2).

Table 2 Clinical characteristics of incarcerated and non-incarcerated people living with HIV in South Ethiopia (N = 370)
Full size table

Viral non-suppression

Seventeen (4.7%) participants were found to have viral non-suppression. Prisoners had a slightly higher viral non-suppression compared to non-incarcerated people—four out of 67 (6%) ILWH participants had viral non-suppression (see Table 2).

Factors associated with ART non-adherence

Various factors were identified as determinants of overall dose-NA in a multivariate logistic regression analysis (see Table 3). The analysis indicated that missing a clinic appointment increases the odds of dose-NA. For instance, the odds of being adherent to ART decreased by 94% among patients who missed at least one clinic appointment compared to patients who didn't miss their clinic appointment (Adjusted odds ratio (AOR): 0.06; 95%CI: 0.02–0.22). The ability to strictly adhere to a specific medication schedule was a determinant of dose adherence in incarcerated and non-incarcerated clients. Accordingly, the odds of dose adherence was 99% lower in those who were able to keep their medication schedule most of the time rather than all of the time (AOR: 0.01; 95%CI: 0.002–0.13) and 99.8% lower in those who never followed their medication schedule (AOR: 0.002; 95%CI: 0.0001–0.05). Methods that participants used to manage their medication schedule also appeared to affect dose adherence. Clients who used news time on radio/TV or other social cues, such as sunlight or departure time to school/church/mosque were less likely to comply with doses relative to those who were able to use one or more time monitoring devices such as mobile phones, wristwatches, etc. (AOR: 0.08; 95%CI: 0.01–0.53 vs AOR: 0.07; 95%CI: 0.01–0.67). In addition, the risk of dose-NA was more than seven times higher in clients who had poor satisfaction with ART services (AOR: 0.14; 95%CI: 0.03–0.63), which was higher in incarcerated ART clients than their non-incarcerated counterparts (see Table 3).

Table 3 Logistic regression model of factors associated with self-reported ART non-adherence amongst incarcerated and non-incarcerated ART clients in South Ethiopia (incarcerated = 74; non-incarcerated = 296)
Full size table

We specifically assessed predictors of non-adherence to doses and pharmacy refill in prisoners using a multivariate fractional regression analysis (see Tables 4 and 5). Prisoners who were accessing ART services from a hospital were 75% less likely to comply with scheduled doses (AOR: 0.25; 95%CI: 0.07–0.90) compared to prisoners who were accessing the services from a health centre. The risk of dose-NA increased by 93% when prisoners missed a single ART appointment (AOR: 0.07; 95%CI: 0.01–0.67) and by 99% when they missed two or more appointments (AOR: 0.01; 95%CI: 0.002–0.08). Inmates with depressive symptoms had a 74% lower likelihood of dose adherence than those without depressive symptoms (AOR: 0.26; 95%CI: 0.07–0.88) (see Table 4).

Table 4 Fractional regression model of factors associated with self-reported dose adherence amongst incarcerated people living with HIV in South Ethiopia (N = 74)
Full size table

Similar to dose adherence, accessing ART services from a hospital decreased the inmates’ pharmacy refill adherence by 95% compared to accessing the services from a health centre (AOR: 0.05; 95%CI: 0.02–0.13). Prisoners who had viral non-suppression were more than two times less likely to comply with pharmacy refill (AOR: 0.38; 95%CI: 0.20–0.73). Moreover, the likelihood of pharmacy refill adherence was 86% lower in inmates who reported lacking ILWH-roommates (AOR: 0.14; 95%CI: 0.05–0.40) (see Table 5).

Table 5 Fractional regression model of factors associated with pharmacy refill adherence amongst incarcerated people living with HIV in South Ethiopia (N = 74)
Full size table

Factors associated with viral non-suppression

A multivariate logistic regression identified predictors of overall viral non-suppression in incarcerated and non-incarcerated ART clients. The estimation was made based on a complete case analysis and multiple imputation of variables with missing values (see Table 6). Sociodemographic factors such as gender, age and social stigma appeared to be determinants of viral non-suppression in both analyses. In the complete case analysis, the risk of viral non-suppression was 97% higher in males than females (AOR: 0.03; 95%CI: 0.003–0.41) whereas 96% higher in multiple imputation (AOR: 0.04; 95%CI: 0.003–0.41). ART clients in the age group of 31 to 35 years had more than fourteen times the risk of having viral non-suppression relative to those who were > 35 years old in the complete case analysis (AOR: 14.10; 95%CI: 2.35–84.57) and about thirteen times higher risk in the multiple imputation (AOR: 13.05; 95%CI: 2.10–81.16). Experiencing social stigma increased the risk of viral non-suppression more than tenfold both in the complete case analysis and multiple imputation (AOR: 10.59; 95%CI: 1.81–62.03 vs AOR: 10.19; 95%CI: 1.77–58.57).

Table 6 Logistic regression model of factors associated with virological failure amongst incarcerated and non-incarcerated ART clients in South Ethiopia
Full size table

Discussion

This study aimed to identify factors affecting optimal adherence to ART and viral suppression in prisoners in South Ethiopia. Prisoners had a level of viral suppression (94%) which is close to the third goal of the Joint United Nations Programme on HIV/AIDS (UNAIDS), i.e. achieving viral suppression in 95% of treated individuals by 2030 [58]. They also had a lower prevalence of non-adherence (NA) and viral non-suppression than that commonly reported in SSA general populations [59, 60] including that of Ethiopia [33, 61], as well as in prison populations internationally [9, 10, 14, 15, 62]. However, NA and viral non-suppression prevalence amongst prisoners in this study was higher relative to the local non-incarcerated population. The recent and rapid expansion of ART services in Ethiopia might have contributed to the positive treatment outcomes in this study [7, 63], but the discrepancy between incarcerated and non-incarcerated individuals may suggest an inequitable access to standard HIV care between community- and correctional facility-based populations. The findings also indicate the importance of a patient’s compliance with specified doses for achieving viral suppression [64, 65], which predicts HIV-related morbidities and mortality, as well as further transmission [4,5,6].

Various structural, psychosocial, individual and clinical factors were identified to influence ART adherence and viral suppression in ILWH relative to non-incarcerated PLWH. While missing ART appointments was an important factor affecting adherence in both incarcerated and non-incarcerated populations, it appeared to be more critical in ILWH. Regular clinic visits are essential for ART clients in order to receive ongoing adherence counselling and support services, as well as clinical assessment and further prescription of ART [42]. Omission of such appointments, therefore, subsequently leads to sub-optimal adherence and facilitation of community transmission [8, 42, 47, 66]. Prisoners in low-income countries often access ART services from external public health care facilities, which presents serious of institutional barriers (e.g. a lack of transport facilities and uncooperative security system) [18, 67]. We recommend implementation of standard HIV care package in the prison system as supported by international guidelines [23, 68, 69].

A significantly lower level of satisfaction with ART services was observed in ILWH than non-incarcerated PLWH. This is important because the results revealed an 86% lower likelihood of dose adherence in clients who had low satisfaction. Furthermore, the odds of dose adherence were 75% lower in ILWH who had received ART services from a hospital compared to those who were receiving ART services from a health centre. The importance of good health care provider-patient relationships for enhancing adherence is well recognised both in prison [19, 20, 26] and community-based populations [70, 71]. However, health care provider- and health facility-related issues (e.g., long waiting time) are amongst the most frequently reported barriers to ART adherence in SSA [72]. The findings therefore suggest a need for decentralisation of ART services to primary health care facilities including prison clinics. Training of health care providers in HIV care provision is pivotal to achieve this, in addition to reinforcing collaboration between prison and community healthcare systems [73].

Our study showed a significant decrease in the odds of adherence in prisoners with depressive symptoms and in those who lacked social support. Although depression strongly predicts NA in the community-based populations as well [74,75,76], ILWH often feel depressed due to concerns related to imprisonment [23] and HIV infection itself [77]. The positive impact of social support on prisoners’ ART adherence and the likely increase in the risk of NA when ILWH suffer from social isolation is well recognised [14, 20, 78]. Thus, in addition to enhancing peer support programs in prison settings, integration of HIV care and treatment of medically diagnosed depression is likely to be essential for maintaining ART adherence in prisoners.

Among the individual level factors assessed in this study, the ability to comply with a specified medication schedule determined dose adherence in incarcerated and non-incarcerated ART clients. Our study also signified that the type of methods clients used to manage their medication schedule affected dose adherence. For example, dose adherence significantly decreased in clients who used news time on radio/TV or other social cues compared to those who used more direct methods, such as mobile phones and/or wristwatches. Research shows that patients’ ability to comply with medication instructions generally increases when they perceive good efficacy and safety of ART [14, 24, 71]. In addition, the use of reminder devices such as telephone reminders, clocks and alarms has been associated with a significant increase in ART adherence [79,80,81]. Adapting such interventions to prison context and the specific needs of prisoners is required.

In the current study, ILWH who experienced viral non-suppression had a significantly lower MPR adherence. Prior studies have shown that having NA lessens the likelihood of viral suppression in both prison- [14] and community-based populations [34, 82,83,84]. However, the current study provided no evidence regarding such a relationship, which might be due to the small number of participants who had developed the clinical outcomes. Nonetheless, people with a higher plasma viral load [85,86,87] and other disease symptoms [14, 26] often find it challenging to consistently use their medication. This could be related to a high pill burden and potential drug interactions that are likely to occur during the advanced stages of HIV infection due to opportunistic infections [59, 88, 89]. The finding underscores the importance of early HIV treatment for achieving optimal adherence in prisoners.

This study identified a significantly higher likelihood of viral non-suppression in males, people in the age group of 31 to 35 years and in those who encountered or perceived social stigma, irrespective of their incarceration status. Prior studies also showed higher odds of viral suppression in female prisoners than male prisoners [90]. With limited evidence available regarding the mechanism of how gender influences viral suppression, females often conform better to ART in the community settings [33, 59, 86], which might have also facilitated their adherence during incarceration.

Younger age (below 35 years) has been frequently reported to be associated with a higher risk of NA and viral non-suppression in both incarcerated [22, 27] and non-incarcerated populations [33, 59, 60]. People in this age group are generally more likely to adopt substance misuse behaviours and often encounter social stigma and discrimination [91]. Young adult males predominate prison populations in South Ethiopia [92] and around the world [93, 94], and they have a high prevalence of HIV infection compared to other age groups [95, 96]. Group specific HIV care intervention strategies including provision of adequate educational information about HIV and the importance of a consistent use of ART, are highly recommended.

The significant positive association between social stigma and viral non-suppression in this study may reflect the adverse effect of alienation on a patient’s appropriate use of medication [88, 97, 98], which is particularly profound in prison populations [17,18,19, 26]. Nonetheless, there existed no statistically significant association between social stigma and self-reported or pharmacy refill adherence in this study, which may represent a lower specificity of both methods in detecting adherence relative to plasma viral load measurement [41, 43, 99, 100]. Educational interventions are required to reduce this health related social stigmatisation by improving a general understanding of HIV amongst prison staff and prisoners [101].

This study had a few limitations. Approximately one quarter of correctional facilities present in South Ethiopia were included in the study based on the size of their prison populations. While there was no variation in treatment outcomes based on the type of correctional facility, it is still possible that ILWH who were in other prisons may have had different outcomes. A nationally representative study is required to draw conclusions that are illustrative of the prison populations in Ethiopia. Given the high turnover amongst prisoners and the high prevalence of sub-optimal ART outcomes in recidivists [102, 103], the prevalence of NA and viral non-suppression might have been underestimated in incarcerated people. Factors that affect ART outcomes throughout the incarceration cycle (during arrest, stay in jail, stay in prison and after release) should be longitudinally investigated by examining individuals at each stage of incarceration.

The participants’ true compliance to medication might have been over- or under-estimated as adherence in this study was measured using self-report and pharmacy refill methods [99, 100]. Self-reported adherence is likely to be threatened by recall and social desirability bias [100]. To minimise the effect of recall bias, short term (the previous four days) adherence was measured so that the participants’ memories about doses would be clearer. Strategies that could reduce the participants’ perceptions of the possible consequences of reporting adherence or non-adherence (such as reinforcing the importance of reporting both adherence and non-adherence for the research project, and reassurance that the information provided would not affect their care) were used to minimise social desirability bias. The pharmacy refill method of adherence measurement does not guarantee that clients could not obtain drugs from sources other than the reporting pharmacy, or provide information about when and how they take the medication [100]. Nonetheless, public health care facilities in the study area were almost exclusively providing ART services, which might have minimised an oversupply of drugs as only such institutions were involved in this study. In addition, when self-report and pharmacy refill methods are used in conjunction, the weakness of one approach could be offset by the strength of the other [100].

Conclusions

This study revealed that sub-optimal dose adherence and viral suppression are generally higher in HIV-infected prisoners in South Ethiopia compared to their non-incarcerated counterparts. Structural, psychosocial, personal and clinical factors contributed to sub-optimal ART outcomes for prisoners. A discouraging institutional context hindered inmates from attending clinic visits, which increased the likelihood of dose-NA. While a lack of satisfaction with ART services predicted dose-NA in both incarcerated and non-incarcerated PLWH, prisoners were significantly less likely to be satisfied with ART services provided by external health care facilities. Experience of psychiatric distress and a lack of social support were found to be important psychosocial determinants of adherence in prisoners. Adherence to medication schedules, which itself was strongly influenced by the type of methods used to monitor time, predicted dose-adherence in both populations. Regardless of an incarceration status, males, people in the age group of 31 to 35 years and those who encountered social stigma were more likely to have viral non-suppression. The findings suggest a need for multilevel interventional approaches that focus on the specific needs of prisoners to alleviate these multiple barriers.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

ART:

Antiretroviral therapy

ARV:

Antiretroviral

AOR:

Adjusted odds ratio

CD4:

Cluster of differentiation-4

Chi2 :

Chi-square

CI:

Confidence interval: COR: Crude odds ratio

DOT:

Directed observed therapy

ILWH:

Inmates living with HIV

MARPS:

Most at risk groups

MCAR:

Missing completely at random

MPR:

Medication possession ratio

NA:

Non-adherence

PAPI:

Paper and pencil interviewing

PLWH:

People living with HIV

RHB:

Regional Health Bureau

RPHL:

Regional Public Health Laboratory

SBREC:

Social and Behavioural Research Ethics Committee

SNNPR:

Southern Nations, Nationalities and People’s Region

SR:

Self-report

SSA:

Sub-Saharan Africa

TV:

Television

UNAIDS:

The Joint United Nations Programme on HIV/AIDS

USD:

United States dollars

WHO:

World Health Organization

References

  1. Joint United Nations Programme on HIV/AIDS (UNAIDS). Data for 2020. Geneva: UNAIDS; 2020.

    Google Scholar 

  2. Telisinghe L, Charalambous S, Topp SM, Herce ME, Hoffmann CJ, Barron P, et al. HIV and tuberculosis in prisons in sub-Saharan Africa. Lancet. 2016;388(10050):1215–27.

    PubMed  PubMed Central  Google Scholar 

  3. United Nations Office on Drugs and Crime (UNODC). HIV and AIDS Prevention, Care, Treatment Support in Prison Settings sub-Saharan Africa: Final Project Report (2017) Pretoria: UNODC; 2017.

  4. Kato DM, Granich VR, Bui BD, Tran RH, Nadol RP, Jacka RD, et al. The potential impact of expanding antiretroviral therapy and combination prevention in Vietnam: towards elimination of HIV transmission. J Acquir Immune Defic Syndr. 2013;63(5):e142–9.

    PubMed  PubMed Central  Google Scholar 

  5. Tanser F, Barnighausen T, Grapsa E, Zaidi J, Newell M-L. High coverage of ART associated with decline in risk of HIV acquisition in rural KwaZulu-Natal, South Africa. Science. 2013;339(6122):966.

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Granich R, Gupta S, Hersh B, Williams B, Montaner J, Young B, et al. Trends in AIDS deaths, new infections and ART coverage in the top 30 countries with the highest AIDS mortality burden: 1990–2013. PLoS ONE. 2015;10(7):e0131353.

    PubMed  PubMed Central  Google Scholar 

  7. World Health Organization (WHO). Guideline on when to start antiretroviral therapy and on pre-exposure prophylaxis for HIV. Geneva: WHO; 2015.

    Google Scholar 

  8. World Health Organization (WHO). Consolidated guidelines on HIV prevention, diagnosis, treatment and care for key populations : 2016 update. Geneva: WHO; 2016.

    Google Scholar 

  9. Meyer JP, Cepeda J, Wu J, Trestman RL, Altice FL, Springer SA. Optimization of human immunodeficiency virus treatment during incarceration: viral suppression at the prison gate. JAMA Intern Med. 2014;174(5):721–9.

    PubMed  PubMed Central  Google Scholar 

  10. Lucas KD, Eckert V, Behrends CN, Wheeler C, MacGowan RJ, Mohle-Boetani JC. Evaluation of routine HIV opt-out screening and continuum of care services following entry into eight prison reception Centers-California, 2012. Morb Mortal Wkly Rep. 2016;65(7):178–81.

    Google Scholar 

  11. Mpawa H, Kwekwesa A, Amberbir A, Garone D, Divala OH, Kawalazira G, et al. Virological outcomes of antiretroviral therapy in Zomba central prison, Malawi; a cross-sectional study. J Int AIDS Soc. 2017;20(1):21623.

    PubMed  PubMed Central  Google Scholar 

  12. Telisinghe L, Hippner P, Churchyard GJ, Gresak G, Grant AD, Charalambous S, et al. Outcomes of on-site antiretroviral therapy provision in a South African correctional facility. Int J STD AIDS. 2016;27(13):1153–61.

    CAS  PubMed  Google Scholar 

  13. Uthman OA, Oladimeji O, Nduka C. Adherence to antiretroviral therapy among HIV-infected prisoners: a systematic review and meta-analysis. AIDS Care. 2017;29(4):489–97.

    PubMed  Google Scholar 

  14. Ines SM, Moralejo L, Marcos M, Fuertes A, Luna G. Adherence to highly active antiretroviral therapy in HIV-infected inmates. Curr HIV Res. 2008;6(2):164–70.

    CAS  PubMed  Google Scholar 

  15. Soto Blanco JM, Ruiz Pérez I, De Labry Lima AO, Castro Recio JM, Girela López E, Antón Basanta JJ. Adherence to antiretroviral treatment in prisons. AIDS Res Hum Retroviruses. 2005;21(8):683–8.

    Google Scholar 

  16. Culbert G. Violence and the perceived risks of taking antiretroviral therapy in US jails and prisons. Int J Prison Health. 2014;10(2):94–110.

    PubMed  PubMed Central  Google Scholar 

  17. Small W, Wood E, Betteridge G, Montaner J, Kerr T. The impact of incarceration upon adherence to HIV treatment among HIV-positive injection drug users: a qualitative study. AIDS Care. 2009;21(6):708–14.

    PubMed  Google Scholar 

  18. Shalihu N, Pretorius L, van Dyk A, Vander Stoep A, Hagopian A. Namibian prisoners describe barriers to HIV antiretroviral therapy adherence. AIDS Care. 2014;26(8):968–75.

    PubMed  Google Scholar 

  19. Roberson DW, White BL, Fogel CI. Factors influencing adherence to antiretroviral therapy for HIV-infected female inmates. J Assoc Nurses AIDS Care. 2009;20(1):50–61.

    PubMed  PubMed Central  Google Scholar 

  20. Mostashari F, Riley E, Selwyn PA, Altice FL. Acceptance and adherence with antiretroviral therapy among HIV-infected women in a correctional facility. J Acquir Immune Defic Syndr Hum Retrovirol. 1998;18(4):341–8.

    CAS  PubMed  Google Scholar 

  21. Seyed Alinaghi SA, Farhoudi B, Mohraz M, Alipour A, Golrokhy R, Hosseini M, et al. Adherence to antiretroviral therapy and tuberculosis treatment in a prison of Tehran, Iran. Infect Disord Drug Targets. 2016;16(3):199–203.

    CAS  PubMed  Google Scholar 

  22. Paparizos V, Kourkounti S, Leuow K, Georgoulas S, Kyriakis K, Antoniou C. Adherence to antiretroviral therapy among HIV-infected prisoners. Infez Med. 2013;21(3):189–93.

    PubMed  Google Scholar 

  23. Fazel S, Baillargeon J. The health of prisoners. Lancet. 2011;377(9769):956–65.

    PubMed  Google Scholar 

  24. White BL, Wohl DA, Hays RD, Golin CE, Liu H, Kiziah CN, et al. A pilot study of health beliefs and attitudes concerning measures of antiretroviral adherence among prisoners receiving directly observed antiretroviral therapy. AIDS Patient Care STDS. 2006;20(6):408–17.

    PubMed  Google Scholar 

  25. Rodrguez-Daz CE, Rivera-Negrn RM, Clatts MC, Myers JJ. Health care practices and associated service needs in a sample of HIV-positive incarcerated men in puerto rico: implications for retention in care. J Int Assoc Provid AIDS Care. 2014;13(6):492–6.

    Google Scholar 

  26. Farhoudi B, Alipour A, Ghodrati S, Seyedalinaghi S, Zanganeh M, Mohraz M. Barriers to adherence to antiretroviral treatment among inmates of a prison in Tehran, Iran: a qualitative study. Arch Clin Infect Dis. 2018. https://doi.org/10.5812/archcid.57911.

    Article  Google Scholar 

  27. Uthman OA, Oladimeji O, Nduka C. Adherence to antiretroviral therapy among HIV-infected prisoners: a systematic review and meta-analysis. Aids Care-Psychol Socio-Med Aspects Aids/Hiv. 2017;29(4):489–97.

    Google Scholar 

  28. Meyer JP, Cepeda J, Taxman FS, Altice FL. Sex-related disparities in criminal justice and HIV treatment outcomes: a retrospective cohort study of HIV-infected inmates. Am J Public Health. 2015;105(9):1901–10.

    PubMed  PubMed Central  Google Scholar 

  29. Todrys KW, Amon JJ, Malembeka G, Clayton M. Imprisoned and imperiled: access to HIV and TB prevention and treatment, and denial of human rights, in Zambian prisons. J Int AIDS Soc. 2011;14:8.

    PubMed  PubMed Central  Google Scholar 

  30. Federal Democratice Republic of Ethiopia Ministry of Health (FMH). National guideline for comprehensive HIV prevention, care and treatment. Addis Ababa: FMH; 2017.

    Google Scholar 

  31. Fuge TG, Tsourtos G, Miller ER. Various structural factors influenced early antiretroviral therapy initiation amongst HIV infected prisoners: a qualitative exploration in South Ethiopia. BMC Public Health. 2021;21(1):1463.

    PubMed  PubMed Central  Google Scholar 

  32. Kelsey JL. Methods in observational epidemiology. 2nd ed. Thompson WD, Evans AS, editors. New York, US: Oxford University Press; 1986. 366 p.

  33. Tegegne AS, Ndlovu P, Zewotir T. Factors affecting first month adherence due to antiretroviral therapy among HIV-positive adults at Felege Hiwot Teaching and Specialized Hospital, north-western Ethiopia; a prospective study. BMC Infect Dis. 2018;18(1):83.

    PubMed  PubMed Central  Google Scholar 

  34. Bezabhe WM, Chalmers L, Bereznicki LR, Gee P, Peterson GM. Antiretroviral adherence and treatment outcomes among adult Ethiopian patients. AIDS Care. 2016;28(8):1018–22.

    PubMed  Google Scholar 

  35. Boateng G, Neilands T, Frongillo E, Melgar-Quinonez H, Young S. Best practices for developing and validating scales for health, social, and behavioral research: a primer. Front Public Health. 2018;6:149.

    PubMed  PubMed Central  Google Scholar 

  36. Tavakol M, Dennick R. Making sense of Cronbach’s alpha. Int J Med Educ. 2011;2:53.

    PubMed  PubMed Central  Google Scholar 

  37. Sherbourne CD, Stewart AL. The MOS social support survey. Soc Sci Med. 1991;32(6):705–14.

    CAS  PubMed  Google Scholar 

  38. Berger BE, Ferrans CE, Lashley FR. Measuring stigma in people with HIV: psychometric assessment of the HIV stigma scale. Res Nurs Health. 2001;24(6):518–29.

    CAS  PubMed  Google Scholar 

  39. Reinius M, Wettergren L, Wiklander M, Svedhem V, Ekstrom A, Eriksson L. Development of a 12-item short version of the HIV stigma scale. Health Qual Life Outcomes. 2017;15(1):115.

    PubMed  PubMed Central  Google Scholar 

  40. Kessler RC, Andrews G, Colpe LJ, Hiripi E, Mroczek DK, Normand SLT, et al. Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychol Med. 2002;32(6):959–76.

    CAS  PubMed  Google Scholar 

  41. Henegar C, Westreich D, Maskew M, Brookhart M, Miller W, Majuba P, et al. Comparison of pharmacy-based measures of adherence to antiretroviral therapy as predictors of virological failure. AIDS Behav. 2015;19(4):612–8.

    PubMed  PubMed Central  Google Scholar 

  42. World Health Organization (WHO). Consolidated guidelines on the use of antiretroviral drugs for treating and preventing HIV infection: recommendations for a public health approach. Geneva, Switzerland: WHO; 2013.

    Google Scholar 

  43. Reynolds RN, Sun NJ, Nagaraja LH, Gifford WA, Wu AA, Chesney AM. Optimizing measurement of self-reported adherence with the ACTG adherence questionnaire: a cross-protocol analysis. J Acquir Immune Defic Syndr. 2007;46(4):402–9.

    PubMed  Google Scholar 

  44. Musumari P, Wouters E, Kayembe P, Kiumbu Nzita M, Mbikayi S, Suguimoto S, et al. Food insecurity is associated with increased risk of non-adherence to antiretroviral therapy among HIV-infected adults in the Democratic Republic of Congo: a cross-sectional study. PLoS ONE. 2014;9(1):e85327.

    PubMed  PubMed Central  Google Scholar 

  45. Masa R, Chowa G, Nyirenda V. Barriers and facilitators of antiretroviral therapy adherence in rural Eastern province, Zambia: the role of household economic status. Afr J AIDS Res. 2017;16(2):91–9.

    PubMed  PubMed Central  Google Scholar 

  46. De Boer MI, Prins MJ, Sprangers AGM, Nieuwkerk TP. Using different calculations of pharmacy refill adherence to predict virological failure among HIV-infected patients. J Acquir Immune Defic Syndr. 2010;55(5):635–40.

    PubMed  Google Scholar 

  47. World Health Organization (WHO). Adherence to long-term therapies: evidence for action. Human immunodeficiency virus and acquired immunodeficiency syndrome. Geneva: WHO; 2003.

    Google Scholar 

  48. StataCorp. Stata Statistical Software. Release. 16th ed. College Station, TX: StataCorp LLC.; 2019.

    Google Scholar 

  49. Ugoni A, Walker B. The Chi square test: an introduction. Comsig Rev. 1995;4(3):61–4.

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Arora PN. Biostatistics. Rev. ed. Malhan PK, editor. Mumbai, India: Himalaya Pub. House; 2010.

  51. Leslie EP, Jeffrey MW. Econometric Methods for fractional response variables with an application to 401 (K) plan participation rates. J Appl Economet. 1996;11(6):619–32.

    Google Scholar 

  52. Hosmer DW. Applied logistic regression. 3rd ed. Lemeshow S, Sturdivant RX, editors. Hoboken, N.J.: Wiley; 2013. 528p.

  53. Fahrmeir L, Kneib T, Lang S, Marx B. Regression: models, methods and applications. Heidelberg, Berlin: Springer; 2013. p. 698.

    Google Scholar 

  54. Raftery AE. Bayesian model selection in social research. Sociol Methodol. 1995;25:111–63.

    Google Scholar 

  55. Akaike H. Information theory and an extension of the maximum likelihood principle. In: Parzen E, Tanabe K, Kitagawa G, editors. Selected papers of Hirotugu Akaike. New York: Springer; 1998. p. 199–213.

    Google Scholar 

  56. Li C. Little’s test of missing completely at random. Stand Genomic Sci. 2018;13(4):795–809.

    Google Scholar 

  57. Eddings W, Marchenko Y. Diagnostics for multiple imputation in stata. Stand Genomic Sci. 2018;12(3):353–67.

    Google Scholar 

  58. Joint United Nations Programme on HIV/AIDS (UNAIDS). Understanding fast-track: accelerating actions to end the AIDS epidemic by 2030. Geneva: UNAIDS; 2015.

    Google Scholar 

  59. Bijker R, Jiamsakul A, Kityo C, Kiertiburanakul S, Siwale M, Phanuphak P, et al. Adherence to antiretroviral therapy for HIV in sub-Saharan Africa and Asia: a comparative analysis of two regional cohorts. J Int AIDS Soc. 2017;20(1):21218.

    PubMed  PubMed Central  Google Scholar 

  60. Semvua SK, Orrell C, Mmbaga BT, Semvua HH, Bartlett JA, Boulle AA. Predictors of non-adherence to antiretroviral therapy among HIV infected patients in northern Tanzania. PLoS ONE. 2017;12(12):e0189460.

    PubMed  PubMed Central  Google Scholar 

  61. Negash E, Wakgari N, Wasie B, Edris M, Bekele G. Adherence to antiretroviral therapy and its associated factors among HIV positive patients in Nekemte public health institutions, West Ethiopia. HIV AIDS Rev. 2016;15(3):116–21.

    Google Scholar 

  62. Davies NE, Karstaedt AS. Antiretroviral outcomes in South African prisoners: a retrospective cohort analysis. PLoS ONE. 2012;7(3):e33309.

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Assefa Y, Gilks CF, Lynen L, Williams O, Hill PS, Tolera T, et al. Performance of the antiretroviral treatment program in Ethiopia 2005–2015: strengths and weaknesses toward ending AIDS. Int J Infect Dis. 2017;60(C):70–6.

    PubMed  Google Scholar 

  64. 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 (Adherence and Viral Load). PLoS ONE. 2010;5(5):e10460.

    PubMed  PubMed Central  Google Scholar 

  65. Wood SE, Hogg RR, Yip VB, Harrigan SGP, O’Shaughnessy SGM, Montaner SGJ. The impact of adherence on CD4 cell count responses among HIV-infected patients. J Acquir Immune Defic Syndr. 2004;35(3):261–8.

    PubMed  Google Scholar 

  66. Fong OW, Ho CF, Fung LY, Lee FK, Tse WH, Yuen CY, et al. Determinants of adherence to highly active antiretroviral therapy (HAART) in Chinese HIV/AIDS patients. HIV Med. 2003;4(2):133–8.

    CAS  PubMed  Google Scholar 

  67. Chakrapani V, Kamei R, Kipgen H, Kh JK. Access to harm reduction and HIV-related treatment services inside Indian prisons: experiences of formerly incarcerated injecting drug users. Int J Prison Health. 2013;9(2):82–91.

    PubMed  Google Scholar 

  68. World Health Organization (WHO)-Regional office for Europe. Prisons and health. Copenhagen: WHO; 2014.

    Google Scholar 

  69. United Nations Office for Drugs and Crime (UNODC). HIV prevention, treatment and care in prisons and other closed settings: a comprehensive package of interventions. Vienna: UNODC; 2013.

    Google Scholar 

  70. Watt MH, Maman S, Golin CE, Earp JA, Eng E, Bangdiwala SI, et al. Factors associated with self-reported adherence to antiretroviral therapy in a Tanzanian setting. AIDS Care. 2010;22(3):381–9.

    PubMed  PubMed Central  Google Scholar 

  71. Croome N, Ahluwalia M, Hughes LD, Abas M. Patient-reported barriers and facilitators to antiretroviral adherence in sub-Saharan Africa. J Acquir Immune Defic Syndr. 2017;31(7):995–1007.

    Google Scholar 

  72. 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-e.

    Google Scholar 

  73. United Nations Office on Drugs and Crime (UNODC). Policy brief: HIV testing and counselling in prisons and other closed settings. Vienna: UNODC; 2009.

    Google Scholar 

  74. Magidson JF, Saal W, Nel A, Remmert JE, Kagee A. Relationship between depressive symptoms, alcohol use, and antiretroviral therapy adherence among HIV-infected, clinic-attending patients in South Africa. J Health Psychol. 2017;22(11):1426–33.

    PubMed  Google Scholar 

  75. Wagner GJ, Slaughter M, Ghosh-Dastidar B. Depression at treatment initiation predicts HIV antiretroviral adherence in Uganda. J Int Assoc Provid AIDS Care. 2017;16(1):91–7.

    PubMed  Google Scholar 

  76. Langebeek N, Gisolf EH, Reiss P, Vervoort SC, Hafsteinsdttir TB, Richter C, et al. Predictors and correlates of adherence to combination antiretroviral therapy (ART) for chronic HIV infection: a meta-analysis. BMC Med. 2014;12(1):142.

    PubMed  PubMed Central  Google Scholar 

  77. Bing EG, Burnam MA, Longshore D, Fleishman JA, Sherbourne CD, London AS, et al. Psychiatric disorders and drug use among human immunodeficiency virus-infected adults in the united states. Arch Gen Psychiatry. 2001;58(8):721–8.

    CAS  PubMed  Google Scholar 

  78. Altice FL, Mostashari F, Friedland GH. Trust and the acceptance of and adherence to antiretroviral therapy. J Acquir Immune Defic Syndr. 2001;28(1):47–58.

    CAS  PubMed  Google Scholar 

  79. Hardy H, Kumar V, Doros G, Farmer E, Drainoni M-L, Rybin D, et al. Randomized controlled trial of a personalized cellular phone reminder system to enhance adherence to antiretroviral therapy. AIDS Patient Care STDS. 2011;25(3):153–61.

    PubMed  PubMed Central  Google Scholar 

  80. Xuan Tran B, Thanh Nguyen L, Hoang Nguyen N, Van Hoang Q. Determinants of antiretroviral treatment adherence among HIV/AIDS patients: a multisite study. Glob Health Action. 2013;6(1):19570.

    Google Scholar 

  81. Kunutsor S, Walley J, Katabira E, Muchuro S, Balidawa H, Namagala E, et al. Using mobile phones to improve clinic attendance amongst an antiretroviral treatment cohort in Rural Uganda: a cross-sectional and prospective study. AIDS Behav. 2010;14(6):1347–52.

    PubMed  Google Scholar 

  82. Boussari O, Subtil F, Genolini C, Bastard M, Iwaz J, Fonton N, et al. Impact of variability in adherence to HIV antiretroviral therapy on the immunovirological response and mortality. BMC Med Res Methodol. 2015;15:10.

    PubMed  PubMed Central  Google Scholar 

  83. Haberer JE, Musinguzi N, Boum Y 2nd, Siedner MJ, Mocello AR, Hunt PW, et al. Duration of antiretroviral therapy adherence interruption is associated with risk of virologic rebound as determined by real-time adherence monitoring in Rural Uganda. J Acquir Immune Defic Syndr. 2015;70(4):386–92.

    CAS  PubMed  PubMed Central  Google Scholar 

  84. Mekuria LA, Nieuwkerk PT, Yalew AW, Sprangers MA, Prins JM. High level of virological suppression among HIV-infected adults receiving combination antiretroviral therapy in Addis Ababa, Ethiopia. Antivir Ther. 2016;21(5):385–96.

    CAS  PubMed  Google Scholar 

  85. Anoje C, Agu K, Oladele E, Badru T, Adedokun O, Oqua D, et al. Adherence to on-time ART drug pick-up and its association with CD4 changes and clinical outcomes amongst HIV infected adults on first-line antiretroviral therapy in Nigerian hospitals. AIDS Behav. 2017;21(2):386–92.

    PubMed  Google Scholar 

  86. Fonsah JY, Njamnshi AK, Kouanfack C, Qiu F, Njamnshi DM, Tagny CT, et al. Adherence to Antiretroviral Therapy (ART) in yaounde-cameroon: association with opportunistic infections, depression, ART Regimen and side effects. PLoS ONE. 2017;12(1):e0170893.

    PubMed  PubMed Central  Google Scholar 

  87. Gebrezgabher BB, Kebede Y, Kindie M, Tetemke D, Abay M, Gelaw YA. Determinants to antiretroviral treatment non-adherence among adult HIV/AIDS patients in northern Ethiopia. AIDS Res Ther. 2017;14:16.

    PubMed  PubMed Central  Google Scholar 

  88. Denison JA, Koole O, Tsui S, Menten J, Torpey K, van Praag E, et al. Incomplete adherence among treatment-experienced adults on antiretroviral therapy in Tanzania, Uganda and Zambia. AIDS (London, England). 2015;29(3):361–71.

    CAS  Google Scholar 

  89. Letta S, Demissie A, Oljira L, Dessie Y. Factors associated with adherence to Antiretroviral Therapy (ART) among adult people living with HIV and attending their clinical care, Eastern Ethiopia. BMC Int Health Hum Rights. 2015;15:33.

    PubMed  PubMed Central  Google Scholar 

  90. Fuge TG, Tsourtos G, Miller ER. A systematic review and meta-analyses on initiation, adherence and outcomes of antiretroviral therapy in incarcerated people. PLoS ONE. 2020;15(5):e0233355.

    CAS  PubMed  PubMed Central  Google Scholar 

  91. United Nations Children’s Fund (UNICEF). Young people and HIV/AIDS: opportunity in crisis Geneva. Switzerland: UNICEF; 2002.

    Google Scholar 

  92. Fuge TG, Ayanto SY. Prevalence of smear positive pulmonary tuberculosis and associated risk factors among prisoners in Hadiya Zone prison, Southern Ethiopia. BMC Res Notes. 2016;9(1):201.

    PubMed  PubMed Central  Google Scholar 

  93. Blankenship KM, Del Rio Gonzalez AM, Keene DE, Groves AK, Rosenberg AP. Mass incarceration, race inequality, and health: expanding concepts and assessing impacts on well-being. Soc Sci Med. 2018;215:45–52.

    PubMed  PubMed Central  Google Scholar 

  94. Meyer JP, Cepeda J, Taxman FS, Altice FL. Sex-related disparities in criminal justice and HIV treatment outcomes: a retrospective cohort study of HIV-infected inmates. Am J Public Health 2015; 105(9): 1901–1910. Date of Publication: 01 Sep 2015.; 2015.

  95. Govender K, Beckett SE, George G, Lewis L, Cawood C, Khanyile D, et al. Factors associated with HIV in younger and older adult men in South Africa: findings from a cross-sectional survey. BMJ Open. 2019;9(12):e031667.

    PubMed  PubMed Central  Google Scholar 

  96. Hoenigl M, Chaillon A, Morris SR, Little SJ. HIV infection rates and risk behavior among young men undergoing community-based testing in San Diego. Sci Rep. 2016;6(1):25927.

    CAS  PubMed  PubMed Central  Google Scholar 

  97. Kidia K, Machando D, Bere T, Macpherson K, Nyamayaro P, Potter L, et al. “I was thinking too much”: experiences of HIV-positive adults with common mental disorders and poor adherence to antiretroviral therapy in Zimbabwe. Trop Med Int Health. 2015;20(7):903–13.

    PubMed  PubMed Central  Google Scholar 

  98. Dewing S, Mathews C, Lurie M, Kagee A, Padayachee T, Lombard C. Predictors of poor adherence among people on antiretroviral treatment in Cape Town, South Africa: a case-control study. AIDS Care. 2015;27(3):342–9.

    PubMed  PubMed Central  Google Scholar 

  99. Kapiamba G, Masango T, Mphuthi D. Antiretroviral adherence and virological outcomes in HIV-positive patients in Ugu district, KwaZulu-Natal province. Afr J AIDS Res. 2016;15(3):195–201.

    PubMed  Google Scholar 

  100. Williams A, Amico K, Bova C, Womack J. A proposal for quality standards for measuring medication adherence in research. AIDS Behav. 2013;17(1):284–97.

    PubMed  PubMed Central  Google Scholar 

  101. Zack B. HIV prevention: behavioral interventions in correctional settings. In: Greifinger RB, editor. Public Health Behind bars; from prisons to communities. New York: Springer; 2007. p. 156–73.

    Google Scholar 

  102. Stephenson BL, Wohl DA, Golin CE, Tien H-C, Stewart P, Kaplan AH. Effect of release from prison and re-incarceration on the viral loads of HIV-infected individuals. Public Health Rep. 2005;120(1):84–8.

    PubMed  PubMed Central  Google Scholar 

  103. Meyer JP, Cepeda J, Springer SA, Wu J, Trestman RL, Altice FL. HIV in people reincarcerated in Connecticut prisons and jails: an observational cohort study. Lancet HIV 2014; 1(2): e77–e84. Date of Publication: 2014.; 2014.

Download references

Acknowledgements

We would like to thank the authorities of the participating health care facilities and prisons for allowing their institutions to take part in the study. We would also like to thank all study participants for providing the information.

Funding

Not applicable.

Author information

Authors and Affiliations

  1. College of Medicine and Public Health, Flinders University, Adelaide, Australia

    Terefe Gone Fuge, George Tsourtos & Emma R. Miller

Authors
  1. Terefe Gone Fuge
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. George Tsourtos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Emma R. Miller
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

TGF conceived and designed the study; supervised data collection; analysed and interpreted data; drafted the manuscript. GT and ERM participated in the subsequent revisions of the manuscript. All authors read and approved the final paper.

Corresponding author

Correspondence to Terefe Gone Fuge.

Ethics declarations

Ethics approval and consent to participate

This study received ethical approvals from Flinders University, Social and Behavioural Research Ethics Committee (SBREC) (Project Number: 8362) and Ethical Review Board of SNNPR Health Bureau. Formal permissions were obtained from the SNNPR State Prison Administration and Regional Health Bureau (RHB), and consent was obtained from each correctional and health care facility authority. All participants gave written consent to confirm voluntary participation.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Fuge, T.G., Tsourtos, G. & Miller, E.R. Factors affecting optimal adherence to antiretroviral therapy and viral suppression amongst HIV-infected prisoners in South Ethiopia: a comparative cross-sectional study. AIDS Res Ther 19, 5 (2022). https://doi.org/10.1186/s12981-022-00429-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12981-022-00429-4

Keywords

  • Incarceration
  • Antiretroviral therapy
  • Adherence
  • Viral suppression
  • South Ethiopia

AIDS Research and Therapy

ISSN: 1742-6405

Contact us