Little is known about how people living with human immunodeficiency virus (HIV) experience malaria and the concomitant use of anti-malarial treatments with anti-retrovirals (ARVs). An understanding of how patients make sense of these experiences is important to consider in planning and supporting the clinical management and treatment for co-infected individuals.A qualitative study was conducted in Tanzania alongside a clinical trial of concomitant treatment for HIV and malaria co-infection. Focus group discussions were held with people receiving treatment for HIV and/or malaria, and in-depth interviews with health workers responsible for HIV care and members of the clinical trial team. Data were analysed inductively to identify themes and develop theoretical narratives.Results suggest that people living with HIV perceived malaria to be more harmful to them due to their compromised immune status but saw the disease as unavoidable. For those enrolled in the clinical controlled study, taking anti-malarials together with ARVs was largely seen as unproblematic, with health workers’ advice and endorsement of concomitant drug taking influential in reported adherence. However, perceptions of drug strength appeared to compel some people not enrolled in the clinical study to take the drugs at separate times to avoid anticipated harm to the body.Management of HIV and malaria concurrently often requires individuals to cross the domains of different disease programmes. In the context of a trial concerned with both diseases, patients experienced the support of clinicians in guiding and reassuring them about when and how to take drugs concomitantly. This points towards the need to continue to strive for integrated care for patients with HIV. The drugs can affect you because you do not know the dose you are given from the shop is correct or not, yet also you are on ARVs, so when taking them and they go and badly interact and bring problems because you have not done any tests and received assurance from the doctor on how to use the drugs. Further, the stance on proper testing was emphasized by those who believed mixing ARVs with other drugs may cause some problems if proper medical tests and weights described above, were not performed, such as simply purchasing drugs from shops: ..sometimes the drugs’ condition can change… these drugs sometimes bring tiredness” . This situation posed a challenge to participants seeking relief for their symptoms, but who sought to adhere to their perceived responsibility to take medicines only after testing.However, this view of the importance of attending a formal health facility to be tested if experiencing malaria-like symptoms before receiving appropriate treatment was tempered by reports of confusion by participants who had experienced malarial symptoms but subsequently tested negative for the disease. A few people suggested this might be linked to the nature of the ARVs they were taking, which could cause similar symptoms to malaria: “.The view of what ‘should’ be done in the case of malaria-like illness was also contrasted by discussions of self-medication and seeking informal health services, which were woven through accounts of treatment-seeking among both HIV-positive and HIV-negative participants. Such practices are the norm for malaria, and were underpinned by a number of factors, including distance to the health facility, lack of money for transport and fear of stigma for HIV-positive patients. In all these situations, poverty was reported to be a major cause compelling people to self-medicate.… at that moment I was taking nine tablets a day….it reached a point I wanted to stop taking them… And if I was alone until now I would not have felt better, [but] I thank my children and my relatives, they encouraged me. Taking ARVs concomitantly with anti-malarials was spoken about by some in terms of the number of pills, and the challenges faced in taking these. Most respondents articulated the necessity of taking the numerous pills in order to get better, provided that they had been directed by a doctor. A few stated that without such instructions, taking many pills could be poisonous to the body. This reflection of a biomedical discourse was also apparent in descriptions by several participants of the additional medicines they took for conditions over and above HIV and malaria, for example TB or high blood pressure, and how they were ‘used’ to taking numerous drugs. However, there were also accounts of the efforts involved in persevering with regimes, and descriptions of the support needed by some patients – including those who had not participated in the clinical study – to achieve the ‘successful’ taking of all the necessary medicines: “Patients sometimes are worried about the anti-malarial dose quantity especially if they are also taking ARVs’ . As a result, some health workers articulated their perceived duty to provide assurance that the pills would not be problematic. This challenge was particularly associated by health workers with patients who had recently commenced ARVs and the problem was said to be complex for patients taking their pills at home with health workers fearing that they did not complete their doses.Health workers were vocal in expressing their concern over pill burden to patients, arising from contact with patients when prescribing medicines. They reported that patients expressed fear when prescribed what they [patients] thought to be many pills, Facilitator (F): When you take [the drugs] at the same time, what is the problem?Several respondents: There is no problem.R 06: Because you are following the doctor’s advice.” .The notion of timing was also identified as important for taking drugs concomitantly, with some participants highlighting health workers’ role in stipulating the schedule for drug-taking. As such, this echoed a formal biomedical discourse in general, and ARV treatment programme protocols more specifically, whereby medical authority over drug-taking was respected by patients, and appeared – for some participants at least – to be accepted: “arrange[d] themselves when to take the drugs” , these participants did not consider there to be a problem with taking treatments concomitantly.Perceived medical authority also seemed to play a role in some participants’ acceptance of the scheduled timing of taking ARVs and anti-malarials, be it at the same time or separately. Provided doctors had not participated in the clinical study (sub-group C), conveyed a sense of caution towards mixing drugs that was not alleviated by the reassurances of health workers – perhaps because of the non-trial setting. Participants across the study sample perceived that the competing strength, or ‘nguvu’, of medicines such as anti-malarials and ARVs could lead to ‘friction’ or bad interactions. Several participants described this in relation to perceptions of their own health and strength, indicating that possible negative effects of taking drugs at the same time were linked to their individual condition, in some cases suggesting they adjusted the schedules of drugs to accommodate their perceived strength in their own body: F: What time is bad or good for taking drugs at once?R 02:You can use malaria drugs and ARV and this depends on your strength. For me, I can’t take [them] at once, I have to have intervals [between them]. .It is important to separate the immediate combination of the strength from ARVs and the other drug. Because every drug has its own strength. I may have to wait for a certain time, like one hour and a half, the first drug taken would have already be absorbed in its place inside the body. However, a contrasting set of responses, identified predominantly in the FGDs with people who had If I take ARVs at 6 am and I also have to take those of malaria I extend like to wait two up to three hours. This is because even the doctor also told me some drugs can harm you if you take concomitantly so you have to separate time. I take them at the same time because every drug does its work at its section for example there was a time I had to take 20 tablets and I take all because each works differently so I think there is no problem with it. This account of drug strength and importance of separating them also arose within a group of those who participated in the trial. In this group two conflicting perceptions were reported: body fatigue’ and ‘exhaustion’ as a result of taking drugs at the same time. They described the importance of separating the timing of drug-taking to ensure they were able to continue with their work and other daily, household survival strategies:There were also several accounts of strategies adopted by people to separate the timing of taking ARVs and other drugs to limit the perceived anticipated negative impact on their daily lives. Sometimes speaking about medicines in general, rather than anti-malarials specifically, several participants cited experiencing ‘if you do not separate the drugs] after taking the drugs you [need to] find a place and rest… If you haven’t finished your activities, it means you’ll hurt yourself, working while the drugs are also working in your body. .… [This study was conducted to begin to address the paucity of literature on how people respond to HIV and malaria co-infection, two major diseases affecting sub-Saharan Africans, through specific focus on how people make sense of concomitant treatments. Results from this qualitative study show how people with HIV as well as malaria have to straddle two paradigms of treatment – the first as participants of a protocol of care under the attention of dedicated specialists, and the second as active agents seeking care for a familiar disease for which treatment can be found with minimal biomedical expertise required. Making sense of the cross-over of these paradigms through experiences of co-infection, participants of the trial who received careful advice on taking medicines reiterated the acquiescence to biomedical authority also seen in ARV programmes. However, some, including those who had been excluded from the trial, drew on broader experience with self-medication of malaria to consider circumventing this authority in treatment-seeking as well as tinkering with timing of drugs. This potential for deviations attended to both conceptual understandings and practicalities of concomitant medicine taking, and requires further research.In the context of the clinical trial where participants were provided with comprehensive care and advice, as well as small facilitators such as milk for swallowing medicines, many appreciated and accepted the protocols of concomitant medicine taking. This mirrored their acceptance of ARV regimens within treatment programmes, and was reflected in the articulated notion of the ‘lifelong contract’ of receiving ARVs and, in turn, in the general acceptability of mixing drugs when regimens had been ‘authorized’ by doctors. In this setting in Tanzania, this may reflect HIV-positive people’s sense of responsibility towards adherence to treatment regimens, produced through hierarchical relations between health workers and patients  which haThis acceptance of the protocols of treatment, particularly for HIV, stood in contrast to discussions of routine treatment of malaria, which involved more personal experience of self-medication including choice of drugs as well as timings, relating to experiences of the strength of different drugs in one’s own body. While the harmful effect of HIV co-infection on malaria incidence and disease severity is now well documented in the academic literature, including more frequent and more severe malaria illness episodes , we founWhen it came to managing concomitant medicines, the realities of accessing treatment, the number of pills to be taken and their perceived strength meant challenges for many. While some reported adhering to protocols, others spoke of adjusting the timing of taking different drugs in order to accommodate perceptions of potential ‘friction’ between strong drugs, or to accommodate life in general. Given the special sample of participants involved in this study, associated with a clinical trial, these themes require exploration in further research that follows the everyday lives of people living with HIV as they navigate the needs of concomitant medicines. Such research should also investigate further the requirements of patients for stepping across these different paradigms of different disease control programmes. These early findings support the call for health services to be oriented around patient needs rather than the needs of specific disease control programmes  and suppThis research, where acceptance of protocols was rooted in the relationship between patients with health workers providing a good service in a trial, and the authority embedded in HIV treatment programmes, suggests that promoting safe treatment practices of concomitant medicines that are routinely taken as ‘normal’ such as anti-malarials may be challenging. Public health interventions have tended to focus on health promotion through mass communication efforts, informing population groups of lifestyle risks and preventive behaviours that will seal their fates as ‘ill’ or ‘healthy’ . The preAlthough a range of perspectives were sought in this qualitative study, the majority of participants included may have different experiences and perceptions from other people facing HIV and malaria co-infection, due to their previous participation in the clinical study. Despite being observational in design, offering the same treatments as routine care in the surrounding health service, the clinical study led its participants to conceptualize it as offering an enhanced ‘service’ . The eleManagement of HIV and malaria concurrently often requires individuals to cross the domains of different disease programmes. In the context of a trial concerned with both HIV and malaria, patients experienced the support of clinicians in guiding and reassuring them about when and how to take drugs concomitantly. This presented an ‘integrated care’ scenario which appeared to support patient confidence in concomitant medicine taking following biomedical protocols. This points towards the need to continue to strive for integrated care for patients with HIV.


To assess the role of drugs used in dual therapy (DT), as cART simplification, over the risk of treatment failure.Patients starting DT regimen composed by a boosted protease inhibitor (PI/r): darunavir (DRV/r), lopinavir (LPVr) or atazanavir (ATV/r) plus a second drug: raltegravir (RAL), maraviroc (MRV) etravirine (ETR), lamivudine (3TC) or tenofovir (TDF), this one generally used in HBV co-infected patients, were included. The effect of each drug as well as other clinical and virological cofactors over treatment failure was assessed using survival analysis.Overall, 480 patients from six reference Italian centres were included: all switched to DT with HIV-RNA <500 cp/µL, 376 of them at <50 cp/µL. Patients who switched at <50 cp/µL showed a significant lower risk of treatment failure , thus the analysis was focused on this subgroup. Among the patients who switched at <50 cp/µL, the proportion of drug used in DT was: DRV/r 63.0%, RAL 53.7%, ETR 19.4%, ATV/r 18.4%, MRV 17.3%, LPV/r 12.8%, TDF 6.4% and 3TC 5.9%; DRV/r-RAL was the most widely used combination: 32.5%. Treatment failure was observed in 78 patients, of whom 38 virological and 35 for toxicity/intolerance, one patient died during follow-up and four patients interrupted for personal decision with undetectable HIV-RNA. At Cox Model, adjusted by gender, age, non-Italian origin, AIDS diagnosis, time on cART, number of regimens, CD4 nadir, baseline CD4, all the drugs had a positive effect on probability of failure , howeverOur analysis suggest that using PI/r-based DL is highly effective if switching from HIV-RNA <50 cp/µL; DL should be used with caution in patients with low CD4 count and longer history of treatment; DRV/r is the best compromise among PI/r, ATV/r is effective but is associate with frequent interruption by toxicity; RAL showed high tolerability so that its use is related to the lowest risk of failure as second drug.


Studies have shown high initial mortality in Antiretroviral Therapy (ART) programs from resource-limited settings. However, there is dearth of evidence on treatment outcomes and associated determinant factors in public hospitals. Therefore, the objective of this study is to assess survival and identify predictors of death in adult HIV-infected patients initiating ART at a public hospital in Eastern Ethiopia.A retrospective cohort study was conducted by reviewing baseline and follow-up records of patients who started ART between December 1, 2007 and December 31, 2011 at Kharamara hospital. Time to death was the main outcome measure. Kaplan-Meier models were used to estimate mortality and Cox proportional hazards models to identify predictors of mortality.A total of 784 patients  were followed for a median of 60 months. There were 87 (11.1%) deaths yielding an overall mortality rate of 5.15/100 PYO (95% CI: 4.73-6.37). The estimated mortality was 8.4%, 9.8%, 11.3%, 12.7% and 14.1% at 6, 12, 24, 36 and 48 months respectively. The independent predictors of death were single marital status , a bedridden functional status , advanced WHO stage , BMI < 18.5 Kg/m2 , CD4 count < 50 cells/µL , severe anemia , and TB co-infection .Improved survival was observed in patients taking ART in Somali region of Ethiopia. The risk for death was higher in patients with advanced WHO stage, low CD4 count, low Hgb, low BMI, and concomitant TB infection. Intensive case management is recommended for patients with the prognostic factors. Optimal immunologic and weight recoveries in the first 6 months suggest increased effort to retain patients in care at this period. According to the 2012 UNAIDS report, there are an estimated 34 million People Living with HIV (PLHIV) worldwide, Sub-Saharan Africa (SSA) accounting for 69%, with nearly 1 in every 20 adults (4.9%) living with HIV; Around 1.7 million people died from AIDS-related causes worldwide, 70% occurred in SSA , 6. RepoStudy setting: the study was conducted at Kharamara hospital, in Jijiga town from March to May 2013. Jijiga, the administrative capital of Somali region, is located 614 km to the east of Addis Ababa, Ethiopia's capital. The hospital provides general outpatient and inpatient services, surgical and obstetric emergency, and regional referral hospital for ophthalmologic service. In the comprehensive HIV care and treatment service at the hospital, there are 9,208 patients enrolled, of which 1,501 ever-started ART, and 1,432 currently on ART.Study design: a retrospective cohort study was conducted to assess survival and determinants of mortality among PLHIV receiving antiretroviral therapy. A total of 784 PLHIV aged ≥15 years, initiating ART at Kharamara hospital between December 1, 2007 and December 31, 2011 were included in the study. The independent variables were socio-demographic characteristics, baseline clinical, laboratory and ART information. The main outcome measure in the study was follow-up time till Death. The second outcome measures were median changes in CD4 count and weight.Participants: the source population was adult HIV/AIDS patients who started ARV and were on follow up at the ART clinic during the study period. Patients who initiated treatment outside Kharamara hospital, women who were pregnant and lactating mothers on PMTCT, and patients with competing causes of death  were excluded. The sample size was determined by taking the mortality rates in two groups of PLHIV on ART based on their WHO clinical stage as exposure status. The mortality rate among exposed (WHO stage IV) is 0.3% and among non-exposed (WHO stage II-III), 0.1% [I), 0.1% . The stuData collection and quality control: to compile the required information a data collection form was developed from the national ART intake and follow-up forms. The data were collected by reviewing pre-ART register, ART intake form, laboratory request, monthly cohort form, and follow up form. The most recent laboratory results before starting ART were used as a baseline value. If there were no pre-treatment laboratory tests, results obtained within one month of ART initiation were used as baseline. If two results were obtained within a month, their mean would be used. Death from all AIDS related causes was ascertained by reviewing patients’ medical records in the hospital, or registrations reported by adherence supporters. The data were collected by two experienced ART nurses who were trained on comprehensive HIV care and who are working in the ART clinic at Kharamara hospital. A supervisor supervised the data collection process. The data collectors and the supervisor were trained on the data collection procedures for two days. The investigator oversaw the overall process. All completed data collection forms were examined for completeness, consistency and clarity during data management, storage, and analysis.Data analysis: data exploration was carried out to check for any inconsistencies, coding error, out of range, and missing values and appropriate corrections were made. Descriptive analyses of the continuous and categorical data describing the cohort's characteristics at baseline and during follow-up were made. Patients were censored on the date of any one of the following events, whichever occurred first: if lost to follow-up, if transferred to another health facility, or if alive at the end of follow up. The outcomes of each patient were dichotomized into censored or dead. Kaplan-Meier model was be used to assess survival functions stratified by baseline and follow up variables, and the log-rank test was used to assess statistical difference among groups . Cox-proportional Hazards model was used to identify prognostic factors of death, and to calculate their bivariate and adjusted hazard ratios. All analyses were conducted using SPSS version 16.0 for windows .2 and 311 (39.7%) developed active TB infection. With regard to chemoprophylaxis, the majority 658 (83.9%) were given cotrimoxazole and only 61 (7.8%) received INH. The median (IQR) weight, CD4 count and hemoglobin of the cohort were 50kg (44-56), 146 cells /µL (102-196) and 11.5 g/dL (10-13) respectively (see The study cohort included 784 patients with a mean age of 34 (SD±10). Out of the total study population, 485 (58.4%) were females. Three hundred twenty five (41.5%) were married and 455 (58%) had dependent children at home. Four hundred eighty seven (62.1%) had at least completed primary education, and 359 (45.8%) had no occupation. The majority 431 (55%) had good ART adherence see . At the vely see .A total of 87 (11.1%) patients died during the five year follow-up period, with majority of deaths 49(56.3%) occurring in the first 3 months (HR: 0.022). One hundred fifty seven (20.0%) patients were transferred to other facility and 193 (24.6%) were lost-to-follow up. The remaining 344 (43.9%) were active until the last censoring date. The median survival time for event (death) was 20.7 months . The overall mortality rate in the cohort during the 1,608 person-years of observation (PYO) was 5.15/100 PYO (95% CI: 4.73-6.37). The estimated mortality was 8.4%, 9.8%, 11.3%, 12.7% and 14.1% at 6, 12, 24, 36 and 48 months respectively see .In bivariate Cox regression analysis, sex, marital status, education level, functional status, WHO clinical stage, BMI, CD4 count, Anemia, cotrimoxazole prophylaxis, INH prophylaxis, TB co-infection, and ART adherence were all associated with survival (P < 0.05). In the multivariate Cox regression analysis, the independent, significant predictors of mortality in PLHIV on ART at Kharamara hospital were single marital status , a bedridden functional status , advanced WHO stage , BMI < 18.5 Kg/m2 , CD4 count < 50 cells/µL , severe anemia , and TB co-infection  see .Weight and CD4 recovery were used as supplementary indicators for comprehensive treatment outcomes. At the start of ART, the median weight of the cohort was 50.0 Kg. During the follow-up period, the median weight (IQR) at 6, 12, 24, 36, 48 and 60 months were 54 (48-61), 55 (48-64), 56 (50-64), 56 (49-64), 57 (50-65) and 57 (50-64), Kg., respectively . SimilarIn this historical longitudinal study, there were 87 deaths in 1,608 PYO, yielding an incidence density of 5.15/100 PYO (95%CI: 4.73-6.37). The independent predictors of mortality include single marital status, being illiterate, bedridden functional status, advanced WHO status, low BMI, low CD4 count, severe anemia and TB co-infection. The estimated survival probability of the cohort at 6, 12, 24, 36 and 48 months was 91.6%, 90.2%, 88.7%, 87.3% and 85.9%, respectively. This shows a better survival compared to other studies in Africa. According to a study in a Malawian cohort, the probability of being alive on ART at 6, 12 and 18 months was 89.8%, 83.4% and 78.8% respectively . On the 2 was more than two times higher  compared to those with a BMI < 18.5kg/m2. Study conducted in rural Malawi showed individuals who were severely malnourished (BMI < 16kg/m2) had six times higher risk of dying in the first three months than those with a normal nutritional status [The result from this study shows patients with a CD4 count of < 50 cells/mL have a higher risk of mortality  compared to those with a CD4 count of ≥ 200 cells/mL. Majority of previous studies also found twice or more risk of mortality in patients with lower CD4 count compared to those with a CD4 count of ≥ 200 cells/mL , 14. Pated BMI < kg/m2 hadl status . In the l status . Other sl status , 20. Higl status . This mal status .Different studies conducted in African countries, including Ethiopia, have shown a progressive change in CD4 count and weight after initiation of ART , 22. TheImproved survival was observed in patients taking ART in Somali region of Ethiopia. However, the risk for death was higher in patients with bed-ridden functional status, advanced WHO stage, low CD4 count, severe anemia, low BMI, and concomitant TB infection. Intensive clinical and nutritional rehabilitation is recommended during the earliest follow up periods on ART for patients with these prognostic factors. Providing screening and chemoprophylaxis for TB to patients on ART is insurmountable in deterring the infection and decreasing mortality. Optimal immunologic and weight recoveries occurred in the first six months indicating the need for patient retention, esp. during this crucial period.


We built a cohort study of HIV patients taking long-term first-line Antiretroviral Therapy in 2003. In this assay, we focused on the development of primary drug resistance mutations against Non-Nucleoside Reverse Transcriptase Inhibitor (NNRTI), K103N, Y181C and G190A.The cohort study was built in Henan province, China. We used Single Genome Amplification (SGA) to analyze the frequency of K103N, Y181C and G190A in serial plasma samples of three individual patients. We also performed standard genotype HIV drug resistance assay in 204 patients of this cohort study to analyze the frequency of these mutations.In the SGA sequences, the K103N decreased and vanished, while the frequency of Y181C and G190A increased in individual patient receiving long-term Antiretroviral Therapy (ART). In the sequences of standard genotype HIV drug resistance assay, the frequency of K103N, Y181C and G190A had the similar pattern with that in SGA sequences. Among these patients, the viral suppression were still sufficient after receiving ART for 72 months, and 78.6% (160/204) patients could have their CD4 count over than 200cells/ul.In some patients, first-line ART had the possibility to provide sufficient treatment effect for over than 72 months, but in long-term treatment, the dominant NNRTI drug resistance mutation K103N could reduced, while the proportion of variants with mutation Y181C or G190A may increased. This result was not similar with that in vitro study, which state that variant with K103N or Y181C had an equal viral fitness with wild type. There were 46 patients receiving ART for over 100 months (22.5%). Among the 204 patients, the main age range was 30–45 years (56.4%), maximum for 63, minimum for 24. Male female ratio was 0.6:1.The frequencies of three primary NNRTI drug resistance mutations, K103N, Y181C and G190A, fluctuated during long-term ART. More specifically, K103N decreased, while Y181C and G190A showed increasing trend Table .Table 2In the patient HENDRC593, the frequency of K103N showed a decreasing trend, especially from 44.8 months, the K103N vanished in SGA sequences. On the contrary, G190A showed a dramatic increasing trend from the initiation of ART.In patients ANHDRC106, after taking ART for 23.9 months, K103N and Y181C became dominant mutations (100%), while the frequency of G190A was 10%. In the follow-up time points, K103N showed a decreasing trend and G190A showed an increasing trend, while Y181C maintained dominant.In patients HENDRC622, K103N showed a decreasing trend from 20.5 to 61.5 months, while G190A kept 100% in quasispecies. The Y181C increased slightly from 0.0% to 31.6% from receiving ART for 44.7 months to 61.5 monthsThe research of Jiong Wang  stated tAll drug resistance assays were done by 20 December 2012. All the 204 patients took NVP from the initiation of ART treatment and never changed to another NNRTI drug. We collected the treatment and drug resistance results in 4 time points of the 204 patients: 1.ART for 6-18 months; 2. ART for 30-42 months; 3. ART for 54-67 months; 4. ART for 72-102 months. Then we calculated and analyzed the frequency of K103N, Y181C and G190A in the four time points .At the time point of receiving ART for 6–18 months, 84.8% patients had their CD4 cells counts over 200 cells/ml. At the time point of receiving ART for over 72 months, there were still 78.6% (143/182) patients had their CD4 counts over 200 cells/ml.For viral load, there were 39.1% patients failed to suppress their viral load in initial period (6–18 months). After receiving ART for over 72 months, 44.5% patients failed to suppress their viral load; it indicated that the first-line ART had the possibility to provide treatment effect in 55.5% patients  were listed here in Tables Among K103N, Y181C and G190A, some correlations could be found, but they were not always positively correlated, the correlated mutations network changed with the time of ART. For example, in standard genotype sequencing dataset when receiving ART for 12 months Y181C, G190A and H221Y were positively correlated with K103N, but when receiving ART for over 60 months, Y181C and H221Y were positively correlated with K103N, while G190A was not.Besides the mutations conferring drug resistance effect, there were a number of accessory mutations or compensatory mutations that correlated with the three NNRTI mutations, more specifically, 5 mutations for G190A in 12 months 14 ones for K103N, and 9 for Y181C. In the dataset of receiving ART for over60 months, there were 35 mutations that correlated with G190A, 20 mutations for K103N, and 26 mutations for Y181C. It indicated that with the ART extended, more accessory mutations would emerged.This research was based on a cohort study including 204 patients taking first-line ART therapy since early stage infection. By the end of 2012, the median treatment duration was 86.4 months (77.6-98.0 months), including 46 patients received ART for over 100 months (22.5%). In our research, after the ART treatment for over 72 months, 55.5% patients had their viral load suppressed and 78.6% patients kept CD4 counts over 200 cells/ml. It seems that the first-line ART could still possibly be effective after long-term treatment.Though the first-line ART is potent method to fight against HIV, but the development of drug resistance could make ART less effective –27. NNRTIn our research, the result was different with that in vitro study. We found that during the long-term ART treatment, the frequency of K103N, Y181C and G190A were not kept steady, the fact is the frequency of quasispecies with K103N decreased, and could even vanish in SGA sequences, while the proportion of Y181C and G190A could increase from 0% to nearly 100%. This result was also identified in a 204-patinets long-term cohort study.Based on the results, we estimated that among the three strong NNRTI drug resistance mutations, K103N, Y181C and G190A, there may be some competition process in long-term ART. In the initial period of NVP-containing ART, K103N, Y181C and G190A emerged in different rate among quasispecies which K103N and G190A had a higher rate than Y181C . ConsideThe result of cKa/Ks ratio could provide another support for the competition hypothesis. Among the K103N, Y181C and G190A, they were not always positively correlated with each other, but may turn to negative correlation with the time of receiving ART. It indicated that although in vitro study, the three mutations all had high replication rate and less viral fitness reduction than other NNRTI mutations, variants with these mutations were not so strong in vivo when existed in plasma at same time, they were not always favored by variant under ART pressure. Some competition effect among them may change the frequency of these mutations.Here is a question according to the research of Jiong Wang , the redThe further mechanism and detail of the competition process were remaining unclear; we may need next-generation sequencing and other techniques to go deep in this question. At least we believe this changing of NNRTI mutations is crucial in long-term ART treatment, especially for the country with limited antiviral drugs.


Studies are limited on pegylated interferon (Peg-IFN) therapy for chronic hepatitis B (CHB) patients who failed or relapsed on previous antiviral therapy.We aimed to investigate the effect of Peg-IFN therapy in treatment-experienced CHB patients.A total of 57 treatment-experienced CHB patients at two medical centers were enrolled. All of the patients were treated with Peg-IFN α-2a at 180 μg weekly for 24 or 48 weeks. The hepatitis B serological markers and viral loads were tested every 3 months until 1 year after stopping Peg-IFN therapy. The endpoints were HBV DNA <2000IU/mL, hepatitis B e antigen (HBeAg) seroconversion, and a hepatitis B surface antigen (HBsAg) loss at 12 months post-treatment.10 IU/mL at 12 weeks of therapy were factors associated with treatment response. In HBeAg-negative patients, 9.1%, 15.2%, and 6.1% of the patients achieved undetectable HBV DNA, HBV DNA <2000 IU/mL, and an HBsAg loss, respectively, at 12 months post-treatment. No factor was significantly associated with the treatment response in the HBeAg-negative patients. The median HBsAg level declined from 3.4 to 2.6 log10 IU/mL in all the patients, and the 5-year cumulative rate of the HBsAg loss was 9.8% in the HBeAg-negative patients. Overall, none of the patients prematurely discontinued the Peg-IFN therapy.In HBeAg-positive patients, 25.0%, 29.2%, and 12.5% of the patients achieved HBeAg seroconversion, HBV DNA <2000 IU/mL and a combined response, respectively, at 12 months post-treatment. Prior IFN therapy, a high baseline ALT level, a low creatinine level, undetectable HBV DNA at 12 weeks and a decline in HBV DNA >2 logPeg-IFN re-treatment is effective for a proportion of HBeAg-positive treatment-experienced patients; it has limited efficacy for HBeAg-negative treatment-experienced patients. Peg-IFN might facilitate HBsAg loss in HBeAg-negative treatment-experienced patients. Chronic hepatitis B (CHB) is a global health problem, with an estimated 350 million people being chronically infected. CHB is tThe current recommended first-line antiviral therapies for CHB include pegylated-interferon (Peg-IFN) α-2a, entecavir (ETV) and tenofovir disoproxil fumarate (TDF). Compared with nucleos(t)ide analogue (NUC) therapy, Peg-IFN therapy has the advantages of a finite duration, an absence of resistance and higher rates of anti-HBe seroconversion with 12 months of therapy. Peg-IFN therapy has the disadvantages of having a moderate antiviral effect, inferior tolerability and a risk of adverse events. Although Peg-IFN therapy has higher rates of anti-HBe seroconversion with 12 months of therapy, the efficacy is unsatisfactory. For Hepatitis B e antigen (HBeAg)-positive patients, one year of Peg-IFN therapy achieved a 22–27% HBeAg seroconversion rate at the end of treatment, which increased to 35% 1–2 years after stopping treatment. AlthoughRe-treatment of CHB is a continuing concern in a substantial proportion of patients after antiviral therapy. Limited studies have been reported on Peg-IFN treatment for patients who failed or relapsed on previous antiviral therapy. We aimed to investigate the efficacy of Peg-IFN therapy in treatment-experienced CHB patients and the factors associated with treatment efficacy.3 and hemoglobin <12 mg/dL; a platelet count <90,000 cells/m3); evidence of alcoholism or drug abuse; and any other known disease for which Peg-IFN therapy was not suitable. The HBeAg-positive patients received Peg-IFN α-2a at180 μg weekly for 24  or 48 weeks (as per the patient’s willingness); the HBeAg-negative received the therapy for 48 weeks.A total of 57 treatment-experienced CHB patients at 2 medical centers who had failed or relapsed after previous antiviral therapy were enrolled in this study. The inclusion criteria included HBV DNA >20,000 IU/mL and alanine aminotransferase (ALT) levels between 1–10 fold the upper limit of normal (ULN) for the HBeAg-positive patients and HBV DNA >2000 IU/mL and ALT levels between 1–10-fold the ULN for the HBeAg-negative patients. The exclusion criteria included the following: HBV antiviral therapy within 6 months of enrollment; hepatitis C virus or human immunodeficiency virus co-infection; decompensated liver disease (a Child—Pugh score ≥ 7); pregnant or breast-feeding women; cytopenia (a white blood count <1500 cells/m8IU/mL). The HBV genotype was determined using a restriction fragment length polymorphism (RFLP) method, as described earlier.: 177.5 [148–268] U/L vs. 88.5 [70.3–144.4] U/L, p = 0.009). Four of the 8 patients (50%) with prior IFN therapy achieved HBeAg seroconversion at 12 months off therapy compared to 2 of the 16 patients (12.5%) with prior NUC therapy (p = 0.129).The rates of HBeAg loss were 33.3% (8/24), 33.3% (8/24), and 33.3% (8/24) at the end of therapy, 6 months and 12 months post-treatment, respectively. The treatment responses of Peg-IFN therapy in the HBeAg-positive patients are shown in The rates of achieving HBV DNA <2000 IU/mL were 41.7% (10/24), 33.3% (8/24), and 29.2% (7/24) at the end of therapy, 6 months post-treatment, and 12 months post-treatment, respectively. Three of the 10 HBeAg-positive patients who had HBV DNA <2000 IU/mL at the end of therapy experienced a virological relapse (HBV DNA >2000 IU/mL). One of the 3 patients who had an HBeAg seroconversion at end of treatment had an HBeAg seroreversion and clinical relapse (HBV DNA >2000 IU/mL ± ALT >2x ULN) 6 months after discontinuation of Peg-IFN therapy. Of two HBeAg-positive patients with a virological relapse, one developed HBeAg-negative CHB and one had persistent positive HBeAg. None of the three relapsers had an HBsAg loss. Prior IFN therapy , a baseline ALT level greater than 5-fold ULN , and undetectable HBV DNA at 12 weeks of therapy  were significant factors associated with HBV DNA <2000 IU/mL at 12 months off therapy.10 IU/mL at 12 weeks of therapy were significant factors associated with HBeAg seroconversion plus HBV DNA <2000 IU/mL at 12 months post-treatment . , 12.5% (3/24), and 12.5% (3/24) achieved HBeAg seroconversion plus HBV DNA <2000 IU/mL at the end of therapy, 6 months post-treatment, and 12 months post-treatment, respectively. Female gender, prior IFN therapy, a baseline high ALT level, a low creatinine level, undetectable HBV DNA at 12 weeks and a decline in HBV DNA >2 logtively). At the end of therapy, 69.7% (23/33) and 93.9% (31/33) of the HBeAg-negative patients achieved undetectable HBV DNA levels and HBV DNA<2000 IU/mL, respectively.  The rate10 IU/mL in all the patients, from 3.8 to 2.7 log10 IU/mL in the HBeAg-positive patients, and from 3.2 to 2.5 log10 IU/mL in the HBeAg-negative patients. , the 5-year cumulative HBsAg loss rate was 9.8%. (HBsAg loss was achieved in 1 (3.0%), 2 (6.1%), and 2 (6.1%) patients at the end of therapy, 6 months post-treatment, and 12 months post-treatment, respectively.  One of tas 9.8%. None of the patients prematurely discontinued Peg-IFN α-2a therapy, and the dosage was not modified in any of the patients. Fifty-four patients (94.7%) completed 12 months of post-treatment follow-up. Three patients (5.3%) did not complete the follow-up because of liver decompensation related to a hepatitis B flare at 2, 6, and 7 months post-treatment, and they began NUC therapy immediately, with subsequent normalization of liver function.This study demonstrated that Peg-IFN therapy for treatment-experienced CHB was effective for HBeAg-positive patients in terms of HBeAg seroconversion (25%) and the virological response (29%). Peg-IFN retreatment was unsatisfactory for combined serological and virological responses (12.5%) in HBeAg-positive patients and for the virological response (15%) in HBeAg-negative patients. Peg-IFN therapy was well tolerated in treatment-experienced CHB and might facilitate HBsAg loss in HBeAg-negative CHB patients.The registration trial of Peg-IFN α-2a for HBeAg-positive CHB demonstrated that 32% and 14% of patients achieved HBeAg seroconversion and HBV DNA <400 copies/mL 6 months post-treatment, respectively. A recentMarcellin P. et al. reported the initial phase III study of Peg-IFN therapy in patients with HBeAg-negative CHB and demonstrated that 48 weeks of Peg-IFN Alfa-2a mono therapy achieved HBV DNA<400 and <20,000 copies/mL 24 weeks post-treatment in 19% and 43% of patients, respectively. Our stud10 IU/mL at week 12 was recently reported, which predicts treatment response in HBeAg-negative patients with Peg-IFN therapy.[10 IU/mL at week 12 is significantly associated with the treatment response in the HBeAg-positive patients (p = 0.042) and not in the HBeAg-negative patients (p = 0.643). Further study is needed to clarify the predictive value of an on-treatment decline in HBV DNA in HBeAg-positive and -negative patients.A decline in HBV DNA >2 log therapy. In our sSerum HBsAg levels have been associated with treatment response to Peg-IFN therapy. On-treatment declines in HBsAg level could predict treatment response or failure in HBeAg-positive patients.,17 FurthNone of our patients developed HCC during the follow up period, regardless of whether the treatment failed or succeeded. The patients who failed the Peg-IFN therapy might be more prone to developing HCC in succeeding years and should have a strict follow-up.One limitation of this study was its relatively small sample size. Whether our results could be extrapolated to treatment-experienced patients in general remains to be studied. Another limitation of this study was that a major proportion of the HBeAg-positive patients received only 24 weeks of Peg-IFN therapy, which might have a negative effect on the response rate. A prospective study with a larger cohort of patients treated with 48 weeks of Peg-IFN is warranted. Given the unmet need for the retreatment of CHB patients and the limited literature data, our results provide important information regarding the role of finite therapy with Peg-IFN in treatment-experienced patients.The retreatment of CHB with Peg-IFN was effective for HBeAg-positive patients in terms of HBeAg seroconversion and the virological response, particularly for patients with prior IFN treatment experience. Although the virological response to the re-treatment of HBeAg-negative patients was unsatisfactory, Peg-IFN might facilitate HBsAg loss in the clinical setting.


Switching to a rilpivirine, tenofovir and emtricitabine (RTE) single-tablet regimen (STR) has been evaluated in a limited number of virologically suppressed patients. The aim of this study was to describe clinical outcomes in HIV-positive patients switched from a suppressive antiretroviral regimen to RTE STR in routine clinical practice.In this retrospective study of antiretroviral-treated patients with <50 copies of HIV RNA/mL switched to RTE STR, virological failure (VF) was defined as two consecutive measurements of ≥50 copies/mL or a single measurement of ≥50 copies/mL followed by any change in treatment. Treatment failure (TF) was defined as VF or discontinuation of the STR for any reason. Univariate mixed-linear models were used to identify differences in laboratory parameters over time.p=0.027), the CD4+/CD8+ ratio (p=0.0001), and Hb (p=0.024), alanine amino transferase (ALT) (p=0.009), total bilirubin (p<0.0001), indirect bilirubin (p<0.0001), total cholesterol (p<0.0001) and triglyceride (p<0.0001) levels. There was also a slight but statistically significant increase in serum creatinine (p=0.0004), aspartate amino transferase (AST) (p=0.001) and liver fibrosis index (FIB-4) (p=0.002), and a decrease in eGFRcreat (p<0.0001) and high-density lipoprotein (HDL) cholesterol (p<0.0001) values. The study limitations include its retrospective design, the relatively short follow-up, and the absence of data concerning the severity of clinical adverse events; however, it does provide new information concerning the laboratory changes that occur in patients switching from PI-based or PI-sparing regimens to RTE STR.The analysis involved 307 patients  with a median age of 45.8 years (interquartile range (IQR 39.3–50.9), who were followed up for a median of 7.4 months (IQR 4.6–10.9). VF occurred in three patients (1%) switched from a protease inhibitor (PI)-based regimen, after a median of 2.6 months (IQR 1.6–3.0), and TF in 34 patients (11%) after a median of three months (IQR 1.4–5.8), 24 of whom (71%) were receiving a PI-based regimen at baseline. Overall, there was a slight but statistically significant improvement in the mean monthly change from baseline in CD4+ cell counts (The study findings confirm the efficacy and safety in clinical practice of switching to RTE STR in virologically suppressed patients receiving other antiretrovirals. The combination of rilpivirine, tenofovir and emtricitabine (RTE) proved to be effective safe and well tolerated in registration trials in patients starting a first-line regimen ,2. Howev90) for several weeks after EFV discontinuation, and RPV exposure was in the range observed in phase III studies within approximately two weeks of the switch [Studies of a switch to this STR from non-nucleoside reverse transcriptase inhibitor (NNRTI)-based regimens concentrated on the efficacy data because pharmacokinetic issues raise concerns in switching from first-generation NNRTIs to rilpivirine. Rilpivirine is a substrate of the CYP3A4 subunit of cytochrome p450, and efavirenz (EFV) and nevirapine (NVP) are inducers of this metabolic pathway. It has been shown that exposure to rilpivirine after switching from EFV is initially lower than that observed when rilpivirine is started without previous exposure to EFV in healthy HIV-negative adults . One cliSwitching to RTE STR has only been evaluated in a limited number of virologically suppressed patients on stable treatment. The aim of this study was to describe clinical outcomes in HIV-positive patients switched from suppressive antiretroviral regimens to RTE STR in routine clinical practice in order to provide useful information for everyday patient management.creat) was calculated using the CKD-EPI formula [This retrospective study involved all of the patients with <50 copies of HIV RNA/mL receiving antiretroviral treatment at the Department of Infectious Diseases at San Raffaele Scientific Institute  who were switched to RTE STR in the context of routine clinical practice. VF was defined as two consecutive measurements of ≥50 copies/mL or a single measurement of ≥50 copies/mL followed by any change in anti-retroviral treatment. Treatment failure (TF) was defined as VF or discontinuation of the STR for any reason. Follow-up began from the start of the RTE STR (baseline) and finished at the time of its discontinuation or data freezing (23 July 2014), whichever came first. The estimated glomerular filtration rate ) or frequencies and percentages as appropriate. Changes from baseline in laboratory values were evaluated after six months and throughout the rest of the follow-up; the subjects who had been observed for less than six months after the switch were not analysed for change from baseline to month six.The patients’ baseline characteristics were compared using Wilcoxon's rank-sum test for continuous and the chi-squared or Fisher's exact test for categorical variables. Univariate mixed-linear models were used to estimate unadjusted mean monthly changes  from baseline in laboratory parameters over time and identify differences between baseline PI-including versus PI-sparing regimens.All of the statistical tests were two-sided at 5% level, and were performed using SAS software, release 9.2 (SAS Institute).p=0.003). VF occurred in three patients (1%) switched from a PI-based regimen after a median of 2.6 months (1.6–3.0), two of whom had a history of resistance to NRTIs and NNRTIs: previous genotype resistance tests in one of the three patients had shown the 184V, 74V and 190E mutations, and the same mutations were detected at VF; in the second, previous tests had shown the 184V, 67N, 70R and 215F mutations (no test results were available for the time of VF); the drug history of the third was not known, but no drug resistance mutation was found at the time of VF.The median follow-up was 7.4 months (4.6–10.9): 6.7 months (4.3–10.0) in the case of patients switched from a PI regimen, and 8.6 months (5.3–13.2) in the case of those switched from a PI-sparing regimen  and neurological toxicity .TF was observed in 34 patients (11%) after a median of three months (1.4–5.8), 24 of whom (71%) were receiving a PI-based regimen at baseline. The most frequent causes of discontinuation were gastrointestinal toxicity , followed by a reduction in eGFRcreat, high-density lipoprotein (HDL) cholesterol, aspartate amino transferase (AST) and FIB-4 values. The patients switched from a PI-based regimen showed a slight but statistically significant improvement in the CD4+/CD8+ ratio, and in Hb, ALT, total bilirubin, indirect bilirubin, direct bilirubin, total cholesterol and triglyceride levels, and a slight but statistically significant worsening in eGFRcreat, HDL cholesterol, AST and FIB-4 values. The patients switched from a PI-sparing regimen showed a slight but statistically significant improvement in the CD4+/CD8+ ratio, and in ALP, glucose, total cholesterol and triglyceride levels, and a slight but statistically significant worsening in eGFRcreat, direct bilirubin and HDL cholesterol values.The VF and treatment discontinuation results of this study are similar to (or better than) those reported in prospective clinical trials. In the SPIRIT study, 90% of the patients switched from a PI/r-based regimen to an RTE STR maintained <50 copies/mL at week 48 at the snapshot analysis, and VFs were observed in 2.5% of patients . All 32 Although we do not have any supporting pharmacokinetic data, our results seem to indicate that switching from EFV or NVP to rilpivirine is safe: the absence of VFs in this group suggests that EFV or NVP maintain higher than minimally effective concentrations  during the period before rilpivirine reaches effective steady state concentrations.Two of the three VFs in our study occurred in patients with a history of drug resistance to NRTIs and NNRTIs: this was not unexpected and underlines that switching from a high to a low genetic barrier regimen should only be offered to patients who have never failed on NRTIs or NNRTIs. The third VF occurred in the absence of drug resistance, which suggests poor adherence to treatment.We found that the switch to RTE was associated with an improvement in total cholesterol and triglyceride levels regardless of whether the patients were switched from a PI-based or PI-sparing regimen; the study design does not allow us to ascertain whether these changes in lipid profiles are mainly due to the withdrawal of the PI or the introduction of TDF. It must also be underlined that HDL cholesterol levels decreased and the total/HDL cholesterol ratio did not significantly change: a longer follow-up may clarify the net benefit of the switch in terms of lipid profiles.The significant reduction in plasma bilirubin concentrations observed in the patients switched from PIs is clearly due to the withdrawal of atazanavir. However, the slight but statistically significant increase in direct bilirubin in the patients switched from a PI-sparing regimen, and the slight but statistically significant increase in FIB-4 values in those switched from a PI-based regimen deserve attention and suggest the need for a careful follow-up; the design of the study does not allow us to conclude that these changes were due to the switch, but this possibility cannot be ruled out. These findings have not been reported in previous studies, mainly because these did not specifically investigate changes in bilirubin or FIB-4.Patients starting first-line antiretroviral therapy with NNRTI-based regimens typically experience a smaller increase in CD4+ cell counts than those starting with PI-based regimens . There acreat was expected, as it is known that rilpivirine increases serum creatinine levels by inhibiting the OCT2 tubular transporter, which reduces the tubular secretion of creatinine [The reduction eGFReatinine . The redeatinine ,2.The limitations of this study include its retrospective design and relatively short follow-up. However, it does provide new information concerning the laboratory changes  that occur in patients switching from PI-based or PI-sparing regimens to RTE.n=3), it highlights the risk of using this treatment strategy in patients with a history of resistance to NRTIs or with rilpivirine-associated resistance mutations.The study results confirm the efficacy and safety in clinical practice of switching to RTE STR in virologically suppressed patients receiving other antiretrovirals. However, although the number of VFs was low (


South African HIV treatment guidelines call for patients who fail first-line antiretroviral therapy (ART) to be switched to second-line ART, yet logistical issues, clinician decisions and patient preferences make delay in switching to second-line likely. We explore the impact of delaying second-line ART after first-line treatment failure on rates of death and virologic failure.We include patients with documented virologic failure on first-line ART from an observational cohort of 9 South African clinics. We explored predictors of delayed second-line switch and used marginal structural models to analyze rates of death following first-line failure by categorical time to switch to second-line. Cox proportional hazards models were used to examine virologic failure on second-line ART among patients who switched to second-line.3. Among these patients, marginal structural models showed increased risk of death (adjusted HR for switch in 6–12 months vs. 0–1.5 months = 1.47 (95% CI: 0.94–2.29), and Cox models showed increased rates of second-line virologic failure despite the presence of survivor bias (adjusted HR for switch in 3–6 months vs. 0–1.5 months = 2.13 (95% CI: 1.01–4.47)).5895 patients failed first-line ART, and 63% switched to second-line. Among patients who switched, median time to switch was 3.4 months (IQR: 1.1–8.7 months). Longer time to switch was associated with higher CD4 counts, lower viral loads and more missed visits prior to first-line failure. Worse outcomes were associated with delay in second-line switch among patients with a peak CD4 count on first-line treatment ≤100 cells/mmEven small delays in switch to second-line ART were associated with increased death and second-line failure among patients with low CD4 counts on first-line. There is opportunity for healthcare providers to switch patients to second-line more quickly. South Africa has the largest number of people living with HIV/AIDS worldwide, with roughly 5.9 million adults living with HIV in 2013 [According to national treatment guidelines switch from first-line ART (2 nucleoside reverse transcriptase inhibitors (NRTIs) plus 1 non-nucleoside reverse transcriptase inhibitor (NNRTI)) to second-line (2 NRTIs plus 1 protease inhibitor (PI)) should occur immediately after two consecutive failing viral loads >1000 copies/mL on first-line, assuming a patient is adhering to treatment . PatientPoor ART adherence has been consistently shown to predict treatment failure, viremia, and increase in drug resistance mutations –12. EvenWe investigate the impact of delaying second-line ART initiation on patient survival and second-line treatment outcomes. We also explore possible reasons for delays in second-line ART.We conducted an observational cohort study using medical record data from the Right to Care cohort, with patients from nine South African HIV clinics . Each clinic used the same electronic medical record system, which captured patient demographics, clinical information, visit dates, lab results, diagnoses, and drug regimens. Retrospective analysis of the Right to Care cohort was approved by the Human Research Ethics Committee of the University of the Witwatersrand. Boston University provided permission for analysis of de-identified data.The study population included treatment naïve adults (≥18 years old) who started a standard first-line ART regimen after April 2004 with confirmed first-line virologic failure. A standard regimen was defined as two NRTIs (stavudine (d4T), zidovudine (AZT) or tenofovir (TDF), plus lamivudine (3TC) or emtricitabine (FTC)), and one NNRTI (efavirenz (EFV) or nevirapine (NVP)). First-line failure was defined as two consecutive viral loads >1000 copies/mL between two weeks and 12 months apart, with the first failing viral load after at least three months on treatment. Patients who switched to second-line before documented treatment failure were excluded. Second-line was defined as switch from NNRTI to PI plus a change in at least one NRTI. The dataset closed in April 2014, so we only included subjects who experienced first-line failure before April 2013 to ensure that all patients had at least 12 months of potential follow-up after first-line failure to allow time for switch to second-line.First, we used Cox models predicting days to switch after first-line failure to explore reasons for delayed switch to second-line. Relevant predictors were identified based on strength and precision of the hazard ratio. We considered variables related to clinic attendance  and disease progression , adjusting for clinic, year of failure, age and sex. We then categorized the population into mutually exclusive groups by time from first-line failure  until second-line switch  to examine descriptive characteristics.Next, we investigated the impact of delayed switch through two outcomes: (1) death after first-line failure, and (2) confirmed virologic failure on second-line . All patients who failed first-line, including those who never switched, were included in analyses for death through the use of marginal structural models –34, whilIn marginal structural models all patients who failed first-line contributed person-time to each time to switch exposure group until they failed to follow the definition of that exposure group, at which point they were censored. As a result, person-time and outcomes for patients who never switched were included, and time-dependent confounding was addressed. To create the marginal structural model, observations for each subject were created in half-month time units beginning at first-line ART failure. Each observation was matched to the most recent date of time-dependent covariates including BMI, hemoglobin, CD4 count and viral load, which were carried forward up to 12 months until the date of second-line switch. Person-time ended at death, dataset closure, or when an individual failed to follow routine viral load monitoring, defined as >12 months without a viral load measurement. The dataset was then expanded to make 6 copies of each individual’s follow-up time, one for each exposure group: switch in <1.5 months, 1.5–3 months, 3–6 months, 6–12 months, >12 months, or never  and exposure history. The denominator model estimated the probability a subject had their observed exposure given baseline (as above), time varying confounder values (CD4 count and BMI) and exposure history. The resulting weights had a mean of 1.02 and a range from 0.30 to 4.19.Artificial censoring in these models created selection bias that was accounted for by weighting by the inverse probability of remaining uncensored. Probability of remaining uncensored is equivalent to the probability of starting (or not starting) second-line at a given time point for each exposure group . ProbabiMarginal structural Cox model parameters were estimated by fitting a weighted pooled logistic regression to estimate hazard of death by time of switch to second-line group. Robust standard errors were used to account for weighting. We adjusted for variables included in the numerator of the weight model and considered other confounders that were common causes of time to switch and death. Models were stratified by several potential effect measure modifiers related to patient health on first-line, including peak CD4 count from ART initiation through first-line failure, viral load at first-line failure, and viral suppression on first-line ART.For analyses with second-line virologic failure as the outcome, we examined categorical time to switch to second-line  using Cox proportional hazards models. Individuals who never switched were not included. Person-time began at time of second-line switch, and ended at the outcome of interest or when a patient failed to follow routine virologic monitoring on second-line. Outcomes for a single elevated viral load and virologic failure after 3 months on second-line were included in sensitivity analyses. Inverse probability of censoring weights were used in models for both death and second-line failure to explore the impact of patients lost to follow-up after switch to second-line .We included 5895 patients who failed first-line ART. 370 patients were excluded because they switched to second-line before failure, and 991 patients were excluded because they failed treatment <12 months before the dataset closure. Median time between failing viral loads on first-line was 2.9 months (IQR: 1.9–4.9). Median time on first-line until failure was 17.4 months (IQR: 10.2–30.9). There were 2189 patients (37%) who never switched to second-line, and only 28% of these had evidence of a suppressed viral load within 12 months after failure. Among the 3706 who switched to second-line, median time to switch after first-line failure was 3.4 months (IQR: 1.1–8.7 months). Median follow-up on second-line among patients who switched was 16.8 months (IQR: 8.5–28.9 months).After switch to second-line, 14% experienced confirmed virologic failure on second-line, 42% remained in care with regular viral load monitoring, 12% remained in care but had missed viral load monitoring visits, 30% stopped attending the clinic (had their last clinic attendance date >6 months prior to the dataset closure), and 2% died. Among those who stopped attending the clinic, 42% were recorded as transfers, and 8% later died. 3 was 1.32 (95% CI: 1.14–1.52).Patients who did not switch or switched in >12 months tended to have lower viral loads and higher CD4 counts at first-line failure than those who switched more quickly , suggestDelayed switch was also related to patient behavior. Missing visits on first-line before failure was more common for patients with longer time to switch . In Cox 3 . Among patients with a peak CD4 count >100 cells/mm3, delayed switch to second-line was associated with slightly elevated death rates only among the longest time and never switch groups, while patients switching <6 months after first-line failure had similar hazards of death as patients who switched within 1.5 months  patients who had person-time end at time of death. Remaining patients were lost to follow-up (3555 (60.3%)), or remained in care (2042 (34.6%)). For 152 patients, death occurred after loss to follow-up, and they were considered lost and not a death. Models were adjusted for proportion of missed visits prior to first-line failure, which is predictive of time to switch and death, and all variables included in the numerator of the weights other than clinic due to small strata. We found that longer time to switch groups had slightly elevated hazards of death  . After s5 months .Marginal structural models stratified by other indicators of patient health on first-line  similarly demonstrated that delaying switch was most harmful for sickest patients . When ex3) did not return to the clinic after 3 months following first-line failure, compared to 17% (319/1877) with a peak CD4 count >100 cells/mm3.When examining second-line treatment failure, individuals who did not switch could not be included. As a result, there was survivor bias in the Cox models for second-line treatment failure because patients in longer time to switch groups survived longer after first-line failure by definition. A substantial proportion of non-switching patients were lost from care soon after first-line failure, and the loss of sicker patients more quickly after first-line failure emphasizes the likely presence of survivor bias. Among patients who never switched, 34% (100/294) of those with a low peak CD4 count Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 TableAdjusted marginal structural models for hazard ratios of death after first-line failure (a) and adjusted Cox proportional hazards ratios for confirmed failure on second-line ART (b), with weighting by inverse probability of censoring after second-line switch to account for loss to follow-up.(DOCX)Click here for additional data file.


With more people receiving antiretroviral treatment (ART), the need to detect treatment failure and switch to second-line ART has also increased. We assessed CD4 cell counts (as a marker of treatment failure), determined the rate of switching to second-line treatment and evaluated mortality related to treatment failure among HIV-infected patients in Guinea-Bissau.In this retrospective cohort study, adult patients infected with HIV-1 receiving ≥6 months of ART at an HIV clinic in Bissau were included from June 2005 to July 2014 and followed until January 2015. Treatment failure was defined as 1) a fall in CD4 count to baseline (or below) or 2) CD4 levels persistently below 100 cells/µL after ≥6 months of ART. Cox hazard models, with time since six months of ART as the time-varying coefficient, were used to estimate the hazard ratio for death and loss to follow-up.We assessed 1,591 HIV-1-infected patients for immunological treatment failure. Treatment failure could not be determined in 594 patients (37.3%) because of missing CD4 cell counts. Among the remaining 997 patients, 393 (39.4%) experienced failure. Only 39 patients (9.9%) with failure were switched from first- to second-line ART. The overall switching rate was 3.1 per 100 person-years. Mortality rate was higher in patients with than without treatment failure, with adjusted hazard rate ratios (HRRs) 10.0 (95% CI: 0.9–107.8), 7.6 (95% CI: 1.6–35.5) and 3.1 (95% CI: 1.5–6.3) in the first, second and following years, respectively. During the first year of follow-up, patients experiencing treatment failure had a higher risk of being lost to follow-up than patients not experiencing treatment failure .We found a high rate of treatment failure, an alarmingly high number of patients for whom treatment failure could not be assessed, and a low rate of switching to a second-line therapy. These factors could lead to an increased risk of resistance development and excess mortality. With the rapid scale-up of antiretroviral treatment (ART) availability in sub-Saharan Africa, the need for appropriate treatment monitoring has also increased . As moreIn accordance with WHO guidelines, most HIV-1-infected patients in Africa initiate ART with two nucleoside/nucleotide reverse transcriptase inhibitors (NRTIs) and one non-nucleotide reverse transcriptase inhibitor (NNRTI), with the NNRTI being either nevirapine (NVP) or efavirenz (EFV) .In Bissau, the capital of the West African country Guinea-Bissau, the prevalence of HIV-1 infection has been increasing (4.4% in 2006), and the prevalence of HIV-2 (4.4%) is the highest of any country in the world. A small proportion of individuals are dually infected with both HIV-1 and HIV-2 0.5%) . The aim of this study was (1) to assess immunologic failure rates in patients completing six or more months of ART, (2) to determine the rate of treatment switching in patients with or without immunologic failure and (3) to assess the mortality rate of patients, with or without treatment failure.We included patients from the HIV clinic at the Hospital National Simão Mendes (HNSM) in Bissau. The clinic is the base of the Bissau HIV Cohort, and the study aims and characteristics of the cohort have been described in detail previously . The stuAt the first visit to the clinic, HIV testing was performed and, if the result was positive, basic demographic information was collected and patients were given a requisition for laboratory analyses . Blood sampling was usually performed at the clinic on the following day. Patients receiving ART were normally seen on a monthly or bimonthly basis and were scheduled to have a CD4 cell count performed every six months. VL measurements were not available in Guinea-Bissau during the study period. Patients on ART were considered lost to follow-up (LTFU) if they had not visited the clinic for six months. Information on death and transfer to other ART centres was collected by personal information, telephone calls with contact persons or from the hospital wards .Patients were eligible for ART in accordance with WHO guidelines . At the In this study, treatment failure was based on immunological markers defined according to the WHO 2013 guidelines  as follo® SL_3 .HIV screening was conducted with a rapid test , and confirmation and discrimination were performed using the SD Bioline HIV 1/2 3.0 rapid test . During 2012, the SD Bioline HIV 1/2 3.0 was gradually replaced by the First Response HIV card 1-2.0 . CD4 cell counts were measured by flow cytometry using Partec CyFlowBaseline characteristics of patients with and without treatment failure and those with unassessable treatment failure were compared using the chi-square test for categorical variables and the Kruskal–Wallis test for continuous variables . Median and interquartile range (IQR) were determined for continuous variables. The rate of treatment failure was determined by following patients from six months of ART exposure until the development of treatment failure. Cox hazard models, with time since six months of ART as the time-varying coefficient, were used to estimate the hazard ratio for death and loss to follow-up. The use of a time-varying coefficient allowed us to make comparisons among patients alive after 1, 2 or ≥3 years, respectively. The Cox model was adjusted for potential confounders . Differences in mortality rate ratio over time were compared with the Wald test.Patients were followed from the date of six months of ART and until the patient's death, transfer to another clinic, LTFU or end of the study. Patients LTFU were censored at their last visit at the clinic. Patients who transferred to other ART centres were censored at the date of transfer. Other patients were censored on January 1, 2015, when all participants had been followed for at least six months. All statistical analyses were carried out using Stata IC 13.1 .Prior to enrolment, all patients in the Bissau HIV Cohort voluntarily provided signed and dated informed consent or a fingerprint if they were unable to read or sign. We received approval to use data from patient files as long as patient confidentiality was respected. The study was approved by the UCEPS, the National Ethics Committee of Guinea-Bissau (Parecer NCP/No.15/2007).By July 1, 2014, a total of 6,311 HIV-infected adults had attended the clinic . Only HIV-1 mono-infected patients were included in this study. Patients already receiving ART at enrolment were excluded (74 patients), as were patients who did not start ART during the study period . Furthermore, patients not retained in care after at least six months of ART were excluded, leaving 1,591 patients for further analyses, representing 64% of all HIV-1-infected patients who initiated ART for the first time at the clinic .Median time from HIV diagnosis at the clinic until the start of ART was 17 days (IQR: 8–57 days). The majority of patients  were started on NNRTI-based treatment regimens while 194 (12%) and 13 patients (1%) were started on PI-based and triple NRTI regimens, respectively. The most common ART combinations were AZT/3TC/NVP (40%), AZT/3TC/EFV (22%), TDF/FTC/EFV (12%), D4T/3TC/NVP (7%) and AZT/3TC/LPV/r (6%).Among 1,591 patients who remained in care after at least six months of treatment, 997 patients (63%) had a CD4 cell count measurement before and at least six months after starting ART, making it possible to assess immunological treatment failure . TreatmePatients were followed from having received six months of ART and contributed to 3,850 person-years of observation. The median number of CD4 cell count measurements after receiving six months of ART was 2 (IQR: 1–4). With a median follow-up time of 34 months (IQR: 21–56 months), patients had a CD4 cell count measurement every fifteenth month (IQR: 11–21 months), with the most frequent measurements done in patients experiencing treatment failure . The medImmunological treatment failure was detected in 393 patients (24.7%). For 183 patients (11.5%), failure was detected based on CD4 cell counts persistently below 100 cells/µL, whereas 210 patients (13.2%) were classified as having treatment failure because their most recent CD4 cell count was lower than their baseline CD4 cell count before receiving ART. The rate of treatment failure among patients remaining in care after at least six months of ART was 20.3 per 100 person-years  among patients for whom treatment failure was assessable. After exclusion of patients not starting on an NNRTI-based treatment regimen, the rate of treatment failure was 19.6 per 100 person-years (95% CI: 11.6–33.2). The characteristics of these patients are presented in A total of 127/1591 patients (8.0%) were switched to second-line treatment during the follow-up period. Reasons for switching to second-line treatment included treatment failure , ART out of stock , reason not specified , interaction with tuberculosis therapy  and adverse events . Of 1,383 patients, 58 (4.2%) treated with NNRTIs switched to PIs while 26 (1.9%) switched to triple NRTIs. Of 194 patients, 17 (8.8%) treated with PIs switched to NNRTIs, and 16 (8.3%) switched to triple NRTIs. Among 13 patients initially treated with triple NRTIs, four (30.8%) and six (46.2%) switched to NNRTIs or PIs, respectively.Of the 127 patients, 36 (28.3%) later switched their ART drugs again before the end of the study. This group mainly consisted of patients switched back to their initial ART after a period during which this particular ART was out of stock but later became available again.Only 39 (9.9%) of the 393 patients who experienced immunological treatment failure were in the group of patients who were switched to second-line therapy whereas 51 (8.6%) of the 594 patients for whom immunological treatment failure could not be assessed were switched. The rate of switching to second-line therapy because of immunological failure as defined in this study was 3.1 per 100 person-years (95% CI: 2.2–4.4). Because some patients were switched from an NNRTI to a PI, whether they had treatment failure or not, the overall switch rate was 2.7 per 100 person-years (95% CI: 2.2–3.3).p=0.42).Of 1591 patients, 125 (7.9%) died, 71 patients (4.5%) were transferred to other ART centres and 478 were LTFU (30.0%), leaving 917 patients (57.6%) alive during follow-up at the end of this study. Fifty-five of the deaths occurred among patients for whom treatment failure was assessable. Mortality during the first, second and following years of follow-up in the study is presented in During the first year of follow-up, patients experiencing treatment failure had a higher risk of being LTFU than patients not experiencing treatment failure (adjusted hazard rate ratio 4.4 [95% CI: 1.7–11.8]).The three major findings of this study are that 1) one third of all patients were not assessed for treatment failure, 2) one third of those who were assessed experienced treatment failure and 3) only one in ten patients experiencing treatment failure were switched to second-line treatment. Consequently, patients who experienced treatment failure had increased mortality.The strength of this study is the inclusion of all HIV-1-infected patients attending the largest HIV clinic in Guinea-Bissau. Furthermore, the study represents the real-life situation of one of the poorest countries in the world. This study is, however, limited by lack of data on adherence and clinical outcome. Treatment failure, whether immunological or virological, should be confirmed with a second measurement after assessing adherence, which was not the case in our study. This gap could have led to an overestimation of the true prevalence of treatment failure. On the other hand, it was not possible to assess treatment failure because CD4 cell count measurements were lacking in more than one third of patients. This lack probably led to an underestimation of the prevalence of treatment failure since patients for whom failure was not assessable may be a subgroup with poor healthcare-seeking behaviour. The HIV clinic in Bissau has a high rate of LTFU , and morIn the present study, we included only HIV-1-infected patients. We have previously shown that the SD Bioline HIV 1/2 3.0 rapid test is not optimal for discriminating HIV types ,13. HoweDifferent definitions of immunological treatment failure have been proposed by the WHO, making comparisons between studies difficult ,10. In aThe high rate of treatment failure in our study could be explained by a low adherence among patients at the clinic and was not affected by whether the patients received a PI or a NNRTI-based treatment regimen. A cross-sectional study among patients at the clinic showed that only 16% had remembered to take more than 80% or more of their medicine during the last month , which mAmong patients in this study, 37.7% had no CD4 cell count measurement before initiating ART or after at least six months of ART. Because of an unstable reagent supply and machine breakdowns, CD4 cell count measurements were not available for patients at several time points during the study period. Furthermore, Guinea-Bissau has been considered politically unstable for many years, and coup attempts have occasionally closed down the HIV clinic and the laboratory. All patients should have access to CD4 cell count testing to optimize ART management and reduce disease progression and mortality ,29.An analysis of 11 ART programmes in sub-Saharan Africa found that the rate of switching was 2.2 per 100 person-years . The incPatients who switched treatment in our study probably reflect only the most obvious cases of treatment failure because of our inability to accurately determine when a change in treatment regimen was indicated, given that VL testing is not available. The low rate of switching, even among patients in whom immunological treatment failure was detected, calls for an increased awareness of treatment failure. It may also indicate that ART programmes in many low-resource settings with similar conditions suffer from a lack of optimal care, in particular but not limited to the lack of availability of VL testing. Clinicians may doubt the immediate benefit of second-line therapy because of the higher cost, difficulties in ensuring patient treatment adherence due to the higher pill burden of PI-based regimens, absence of fixed-dose drug combinations and the fear that no further treatment options will be available if subsequent failure on second-line regimens occurs ,34. We hFew data are available on the mortality of patients who meet the WHO criteria for treatment failure but do not switch to second-line treatment. However, an analysis of 11 ART programmes in sub-Saharan Africa showed that the adjusted mortality rate ratios were 1.64 (95% CI: 0.84–3.22) in patients who switched and 3.29 (95% CI: 1.85–5.84) in patients failing first-line therapy compared with patients without treatment failure who remained on a first-line regimen . These vA limitation of this analysis is that the assessment of treatment failure depends on the availability of measurements, which is dependent on healthcare-seeking behaviour and a clinical evaluation of the need for a CD4 measurement. There could be a higher chance of a patient being classified as experiencing treatment failure if the patient comes to the clinic frequently because treatment failure would be more likely to be detected in those who had more CD4 cell count measurements. However, this factor could lead to both under- and overestimation of the mortality rate among the treatment failure group. Patients failing treatment were more often LTFU, and a large proportion of patients LTFU may have died ; thus, tOur results highlight the need for better methods to detect treatment failure and a stronger focus on switching patients with treatment failure to second-line treatments in Guinea-Bissau. There are probably many reasons for the lack of awareness of treatment failure, including problems with the delivery of ART because of inadequate drug supplies, loss to follow-up, lack of healthcare workers and inadequate laboratory services . This laIn conclusion, we identified a high rate of treatment failure, but an even more alarmingly high number of patients for whom treatment failure could not be assessed and patients experiencing treatment failure who continued their first-line regimen. This scenario could lead to an increased risk of resistance development and excess mortality.


Immunological markers (CD4 count) are used in developing countries to decide on initiation of antiretroviral therapy and monitor HIV/AIDS disease progression. HIV is an incurable chronic illness, making quality of life paramount. The direct relationship between quality of life and CD4 count is unclear. The purpose of this study is to determine the relationship between change in CD4 count and quality of life measures in a Ugandan cohort of people living with HIV.We prospectively assessed quality of life among 1274 HIV patients attending an HIV clinic within a national referral hospital over a period of 6 months. Quality of life was measured using an objective measure, the Medical Outcomes Study HIV health survey summarized as Physical Health Score and Mental Health Score and a subjective measure, the Global Person Generated Index. Generalized estimating equations were used to analyze the data. The primary predictor variable was change in CD4 count, and the outcome was quality of life scores. We controlled for sociodemographic characteristics, clinical factors and behavioral factors. Twenty in-depth interviews were conducted to assess patient perception of quality of life and factors influencing quality of life.Of the 1274 patients enrolled 1159 had CD4 count at baseline and six months and 586 (51 %) received antiretroviral therapy. There was no association found between change in CD4 count and quality of life scores at univariate and multivariate analysis among the study participants whether on or not on antiretroviral therapy. Participants perceived quality of life as happiness and well-being, influenced by economic status, psychosocial factors, and health status.Clinicians and policy makers cannot rely on change in immunological markers to predict quality of life in this era of initiating antiretroviral therapy among relatively healthy patients. In addition to monitoring immunological markers, socioeconomic and psychosocial factors should be underscored in management of HIV patients. There is an established relationship between immunological and virological outcomes as important markers of HIV disease progression and treatment failure , 2. CD4 Some studies have looked at factors associated with QoL, including CD4 count in developed and developing count –12. CD4 The studies that have been carried out to specifically assess the relationship between immunological and virological outcomes and QoL have reported conflicting outcomes. For example, in a randomized double-blind controlled study carried out over a period of 24 months, an association between change in QoL and change in CD4 count was reported, and change in CD4 count was a stronger long-term predictor of QoL compared with change in viral burden . AnotherVery few studies in resource-limited settings have assessed immunological markers (CD4 count) and QoL over time, yet immunological markers and QoL change over time . Addition = 640), and the other group was not eligible for ART (CD4 >350 cells/μL) and received basic care and no ART (n = 634). Patients were excluded if they did not consent or were acutely ill and required medical or surgical treatment or admission to the hospital.A cohort of people living with HIV was drawn from a population of HIV patients attending the HIV clinic at the Mulago National Referral Hospital. The inclusion criteria for the study were adult patients ages ≥18 years; none were on ART at baseline. One group was ready to initiate therapy and started ART on the day of the baseline interview  and the mean change in PHS per week was 0.09 (SD 0.10). The change in 24 weeks was postulated at 2.16 and hence the mean PHS estimated to be 51.73 [We used PASS 13 software  for sample size calculations. A sample size of 107 achieves 80 % power to detect a difference of −2.2 between the null hypothesis of mean 49.5 and the alternative mean of 51.7. Estimated sample size for one-sample comparison of mean to hypothesized value: a sample size of 150 is required to detect a change in PHS of 2.16 assuming alpha =0.05, power =90 %.This was a prospective cohort study. Participants were enrolled consecutively as they registered per clinic day. Enrollment began in April 2012 and was completed in December 2013. World Health Organization (WHO) guidelines were used to initiate ART in patients with a CD4 count less than 350, or co-infected with TB regardless of CD4 count, or co-infected with hepatitis B regardless of CD4 count . We apprAt enrollment, we collected data using a questionnaire and structured interview on sociodemographic characteristics such as age, gender, marital status, socioeconomic status , behavioral characteristics  and smoking), clinical characteristics , and depression (see below for measure details)), and history of opportunistic infection. Blood was drawn at the baseline visit for CD4 counts. If a CD4 blood test had been done within 14 days before the enrollment day, it was not repeated and the CD4 count was retrieved from the clinic records. At 6 months, blood was drawn for another CD4 count and the results given to the patient on their next clinic visit by their primary care provider. Interviews were conducted in person by the first author and two supervised research assistants at baseline, 3 months and 6 months. To maintain data quality, the first author reviewed a sample of the data collection forms to ensure correctness. Double data entry in Epidata v3.1 was done by the two assistants and the first author checked and compared these data with the original scripts if there were discrepancies. At the end of the study, we reviewed the records of patients lost to follow-up to determine if there were any differences in the reasons for loss between the two groups (ART and non-ART).We used two measures to assess QoL so as to capture the subjective and objective aspects of QoL. The objective measure used was the Medical Outcomes Study HIV (MOS-HIV) health survey and the subjective measure was the Global Person Generated Index (GPGI). The objective measure mainly captures levels of functioning and symptoms and gives a clinician an indication of how the patient is progressing. However, this measure ignores individual patient perception and what may be important to them regarding their QoL. The subjective measure allows the individual to express what their QoL is like , therebyThe MOS-HIV tool is preferred because it is an HIV disease-specific measure and among the most widely used patient-reported outcome measures , 29. It The GPGI score is a subjective measure that has been validated in other developing countries . The GPGThe Center for Epidemiological Depression Scale (CES-D)  was usedThe CAGE questionnaire was used to screen for alcoholism. CAGE has been shown to have cross-cultural validity because it does not focus on the specifics of alcohol use . A cutofART adherence was measured using a 3-day self-report and a cutoff of 95 % was used to determine adherence (≥95 %) and non-adherence (<95 %). Self-reporting has been shown to have adherence levels not different from unannounced pill count, visual analog scale and electronic drug monitoring . A cutofp-value <0.05. The interviews were transcribed and translated for coding and thematic analysis. Data were analyzed manually using a framework approach to thematic analysis.The main outcome was QoL using the subscales from the MOS-HIV as PHS and MHS, and global QoL measured by the GPGI. The MOS-HIV was scored using the guidelines obtained from the Mapi Research Trust, and missing data were handled as per the measurement guidelines , 29, 33.The study was approved by Makerere University College of Health Sciences Higher Degrees Research and Ethics Committee and the Uganda National Council for Science and Technology . All participants provided written, informed consent prior to participation in the study. Participants were informed that refusal to participate would not affect their care services in any way. No personal identifiers such as names were collected. Confidentiality was maintained throughout the study, from the data collection process to storage and analysis of the data.Of the 1274 PLHIV enrolled in the study, 115 (9 %) were lost to follow-up by the 6-month visit. Of the patients lost to follow-up, 54 (47 %) were on ART; there were no differences in the attrition factors  between patients on ART and those not on ART. The total used for this analysis was 1159 patients who had both baseline and 6-month CD4 counts. The majority of these were women 823 (71 %), and 586 (51 %) were on ART. The majority of the patients were less than 30 years old 482 (41 %). Only 322 (28 %) of the total 1159 participants reported using alcohol; 184 (57 %) were probable alcohol abusers with a CAGE score ≥2. Table The median baseline CD4 count was 396 cells/μL IQR 260–634) and 501 at 6 months (IQR 381–635). Among individuals on ART, 545 (93 %) had an adherence score of ≥95 % at the 3- and 6-month visits. Overall, 313 (27 %) participants had an opportunistic infection at baseline and 220 (19 %) at 6 months. Details of other clinical characteristics are given in Table 60–634 anThe MOS-HIV subscales used to construct the PHS and MHS at baseline, month 3 and month 6 ranged from pain with a mean of 26.4 (SD 23.4) to social functioning with a mean of 93.0 (SD 18.8), pain 21.2 (18.6) to social functioning 96.0 (13.5), pain 20.4 (17.9) to social functioning 96.1 (31.6) at baseline, month 3 and month 6 respectively. Table p = 0.440, MHS p = 0.245 and GPGI score OR = 1 p = 0.030) indicating similar patterns of change over time in both groups. Table The baseline mean PHS was 46.6 (SD 4.2), MHS 46.6 (SD 4.2) and median GPGI 71.7 (IQR 56.7–85.0) Table There was no significant association between change in CD4 count and PHS, MHS and GPGI QoL scores in the univariate analysis and after adjusting for potential confounders of PHS and GPGI scores among patients on ART. Confounders included baseline CD4 count, study visit, gender, age, education level, marital status, alcohol consumption, monthly income, smoking status, diagnosis of depression, social support, presence of opportunistic infection, WHO disease stage, religion, and employment status. For MHS, the potential confounders were as above, but without the depression screening score. Change in CD4 count and PHS, MHS and GPGI after adjusting for potential confounders were not significant. Low education level, probable depression and WHO stage III and IV were associated with low PHS, females had lower MHS and MHS improved with time and alcohol consumption and probable depression were also related to low GPGI. Detailed results are presented in Table Among patients not receiving ART, there was no significant association between change in CD4 count and PHS and MHS in the univariate and multivariate analyses. There was a negative association between GPGI score and change in CD4 count with OR = 0.9991 in the univariate analysis, and OR = 0.9990 in the multivariate analysis for high GPGI with a unit change in CD4. The associations were very small  in univariate analysis and 0.1 %) in multivariate analysis. Older age, probable depression were associated with low PHS, low income and presence of opportunistic infection were associated with low MHS and older age, low education and probable depression were also negatively associated with GPGI Table .Table 6CThe in-depth interviews revealed other factors to be important for a good QoL in addition to good health, such as having money, building a house, family relations with spouses and children. The themes identified were “liveabilty of environment (work and finances), Life ability , utility of life  and appreciation of life (happiness) . HoweverOur evaluation provides evidence that there is no significant association between changes in CD4 count and changes in PHS, MHS and GPGI QoL scores among patients on ART and those not on ART. The change in GPGI score was too small for change in CD4 count to be considered a predictor of change in QoL . ContrarThe lack of association between CD4 change and QoL scores is possibly because the majority of the participants were asymptomatic with relatively high CD4 counts, with only 104 9 %) of patients with counts less than 100 cells/ μL at baseline and 27 (2 %) at 6 months. Therefore, because most participants had a high CD4 count, they had a good QoL. There may be insufficient change in their CD4 count to make a significant change in their QoL. In earlier studies  % of pat, so therAnother possible reason for our results is that QoL is a subjective construct and there are many other factors that come into play other than immunological markers. Patients have goals and expectations for QoL that change with time, and clinicians cannot assume that an increase in CD4 count will automatically lead to a better QoL for patients. Our qualitative data showed that QoL as well as general well-being were influenced by income, relationships, emotional well-being and health status . There mOur finding of weak to no correlation between change in CD4 count and QoL scores is similar to findings of other studies carried out in sub-Saharan Africa , 18. TheThis study has limitations. The 6-month follow-up for chronic illness may be rather short; a longer follow-up time is recommended. This was not a randomized study, and viral load testing could have enriched this study as a more accurate measure of disease burden. The large sample size may have caused a weak association with GPGI.Nevertheless, the strengths of this study include the large sample size and longitudinal design to specifically assess the association between immunological markers and QoL. To our knowledge this is the first study to look at this in sub-Saharan Africa. Most importantly, confounders of QoL were statistically controlled for. The results of the qualitative sub-study, which provided insight into the context of participants’ lives, help to explain why CD4 count alone cannot be used to predict QoL.These data suggest that an increase or decrease in CD4 may not translate to better or worse QoL within a period of 6 months among PLHIV with relatively high CD4 count. It may therefore be paramount for clinicians and policy makers to consider other factors, such as income and psychosocial support, in monitoring QoL among HIV/AIDS patients and not to rely on change in immunological markers alone. HIV/AIDS is now an incurable chronic condition; therefore, a good QoL together with prolonged survival is paramount.In addition to monitoring immunological markers, other psychosocial and socioeconomic factors should be underscored in management of HIV patients in order to optimize treatment outcomes.A similar study with longer follow-up period would further enrich the findings of this study.