Despite the rapid expansion of antiretroviral therapy services, ‘loss to follow-up’ is a significant public health concern globally. Loss to follow-up of individuals from ART has a countless negative impact on the treatment outcomes. There is, however, limited information about the incidence and predictors of loss to follow-up in our study area. Thus, this study aimed to determine the incidence rate and predictors of loss to follow-up among adult HIV patients on ART.TM Version 14.0 software. The Nelson-Aalen cumulative hazard estimator was used to estimate the hazard rate of loss to follow-up, and the log-rank test was used to compare the survival curve between different categorical variables. Both bivariable and multivariable Cox-proportional hazard regression models were fitted to identify predictors of LTFU.A retrospective cohort study was undertaken using 484 HIV patients between January 30, 2008, and January 26, 2018, at Debre Markos Referral Hospital. All eligible HIV patients who fulfilled the inclusion criteria were included in this study. Data were entered into Epi-data Version 4.2 and analyzed using STATAAmong a cohort of 484 HIV patients at Debre Markos Referral Hospital, 84 (17.36%) were loss their ART follow-up. The overall incidence rate of loss to follow-up was 3.7  per 100 adult-years. The total LTFU free time of the participants was 2294.8 person-years. In multivariable Cox-regression analysis, WHO stage IV , having no cell phone , and rural residence  were significant predictors of loss to follow-up.The incidence of loss to ART follow-up in this study was low. Having no cell phone and WHO clinical stage IV were causative predictors, and rural residence was the only protective factor of loss to follow-up. Therefore, available intervention modalities should be strengthened to mitigate loss to follow-up by addressing the identified risk factors. Loss to follow-up is a serious public health concern throughout the world. In Ethiopia, there is limited information regarding incidence and predictors of loss to follow-up amongst adult HIV patients on ART. Therefore, this retrospective cohort study estimates the incidence and predictors of loss to follow-up among adult HIV patients on ART at Debre Markos referral hospital. About 484 samples were included in the study. Data analysis was carried out using STATA Version 14.0 statistical software. The cox-proportional hazard regression model was used to identify the predictors of loss to follow-up. In this study, the incidence of loss to follow-up in this study was high. Having no cell phone, rural residence, and WHO clinical stage IV were the predictors of loss to follow-upThe acquired immunodeficiency syndrome (AIDS) pandemic remains the most serious of infectious disease obstacles to public health [c health . On the c health –4. Sincec health , 6. In ac health .Loss to follow-up (LTFU) is a significant cause for treatment failure and threatens the enhancement of HIV treatment outcomes among patients on ART . LTFU isThe incidence of LTFU ranged from 7.1 per 100 person-year in India , 10.3 peMortality, after loss to follow-up in African countries, ranges from 12 to 87% . LTFU alCurrently, the Ethiopian government targeted 90% HIV viral load suppression rate for people on ART care and treatment for ending the AIDS epidemic by 2020 . To succA hospital-based retrospective cohort study in Debre-Markos Referral Hospital (DMRH) at Debre Markos Town was undertaken. Debre Markos Town is located 299 km far from Addis Ababa, the capital city of Ethiopia, and 265 km far from Bahir-Dar, the capital city of Amhara Regional State. Debre Markos referral hospital serves more than 3.5 million people of the East Gojjam Administrative Zone and neighboring areas. More than twelve thousands of HIV-positive adults have been initiated antiretroviral therapy (ART) care and treatment in Debre Markos Referral Hospital since 2005. As 2018 annual report, the hospital was providing ART care and treatment services to 3716 adult HIV patientsAll adult HIV patients initiated on ART from January 30, 2008, to January 26, 2018, at Debre Markos Referral Hospital in the ART clinic were our source population. All adult HIV patients initiated on ART from January 30, 2008, to October 30, 2017, at Debre Markos Referral Hospital in the ART clinic were our study population. Therefore, those adult HIV patients who had at least 1-month follow-up after ART initiation were included. On the other hand, we excluded HIV patients who had incomplete baseline records, whose medical charts were not available during the data collection period and patients transferred to Debre Markos referral hospital.TM Version 14.2 statistical software to calculate the sample size by considering the following assumptions: the level of significance (α) = 5%, Za/2 , power of 80%, and hazard ratio (HR = 2.05 taken from a previous study) [The minimum required sample size was calculated using a survival analysis formula. We used a STATAs study) . We applThe outcome variable was the time to loss to follow-up, whereas the predictors were socio-demographic characteristics . Baseline clinical and laboratory characteristics .This study aimed to ascertain the incidence of loss to follow-up using 10-year data retrieved from medical record charts. In this study, loss to follow-up was defined as not taking ART refill for 3 months or longer from the last attendance for refill and not yet categorized as dead or transferred-out . ART adhThe data extraction checklist was prepared from the ART entry and follow-up forms. To ensure data quality, before data extraction, the data extraction tool was prepared with care from ART intake and follow-up forms. In addition, we verified consistency between data recording systems and the prepared checklist by randomly selecting and completing 48 sample chart reviews, which resulted in slight amendments to the data extraction checklist. Two BSc nurses who have been working in the ART clinic of Debre Markos Referral Hospital collected the data. Two days of training were given for both data collectors and supervisors to standardize and agree on the way to review medical records. The supervisor and principal investigators supervised the data collectors throughout the entire data collection process.TM Version 4.2 and analyzed using STATA™ Version 14.0 statistical software. At the end of the data extraction period, the outcome of each study subject was dichotomized into censored or event (LTFU). Moreover, to identify the predictor variables, a cox-proportional hazard regression model was fitted. To check the assumption of the Cox-proportional hazard regression model, we used Schoenfeld residual test for continuous variables and Log-Log plot for categorical variables.Data were entered into EpidataFurthermore, we checked the model fitness using a Cox-Snell residual test  was used to declare the presence of a significant association between the explanatory and outcome variables.In univariate analysis, we used the mean with standard deviations to describe normally distributed continuous data and median with interquartile range for skewed continuous data. In another way, the categorical data extracted from HIV-positive patients were described using frequency distribution or percentages. Regarding bivariable analysis, the outcome variable (loss to follow-up) and explanatory variables were entered cox-proportional hazard regression model to select variables for the multivariable cox-proportional hazard regression model. As a result, variables having About 484 HIV-positive patient record charts were retrieved, giving a 100% response rate. The median age of the study participants was 32 years with an inter-quartile range . Two hundred seventy-two (56.20%) of the study participants were from the urban residence. The majority, 406 (83.75%) of HIV-positive patients were married while slightly more than two-thirds (69.21%) of the study participants attended formal education. The majority, 427 (88.41%) of the retrieved HIV-positive patients’ information were Orthodox Christian by religion. During their recruitment for ART, the majority, 420 (86.78%) of the study participants had cell phones. Slightly more than half, 267 (55.17%) of the participants had disclosed their serostatus when they were recruited (Table Two-hundred ninety-three (60.53%) of the study participants were WHO clinical stages of III and IV. Regarding functional status, about three-fourth, 363 (75%) of the study, participants were experiencing working status. The median CD4 count was 161 cells/ml with an inter-quartile range . Nearly half, 239 (49.38%) of the study participants were taking TDF-3TC-EFV ART regimen. In terms of nutritional status, nearly two-thirds, 319 (65.91%), were well nourished . The majority 441 (91.12 %) of the participants had a good ART adherence throughout the follow-up period. Almost two-thirds (67.56%) of the study participants had no experience of regimens change. Slightly less than one-third, 151 (31.46%) of the study participants had developed tuberculosis infection during the follow-up period (Table n = 84). Similarly, the study revealed that the overall incidence rate of loss to follow-up (LTFU) was 3.70  per 100 person-years. During the 10-year follow-up period, slightly less than one-third (30.95%) of the HIV-positive patients had experienced LTFU. The overall incidence rate  was 17.36%  than studies done previously (77.7% and 84%) , 15. SimMoreover, the lower incidence rate of loss to follow-up reported by this study might be due to differences in participant’s residence. This means the proportion of urban residents was only 56.2% in this study but 96.1% in a previous study . Urban rThe current study also identified that the WHO clinical stage IV and having no cell phone were found to increase the rate of loss to follow-up.This study identified WHO clinical stage IV as a predictor of loss to follow-up from ART care and treatment. The risk of experiencing loss to follow-up among HIV-positive patients with WHO clinical stage of IV was 2.8  times higher as compared with WHO clinical stage I. This finding is in line with findings reported in Mizan-Tepi University, Oromia Region, and South Africa , 19, 20.Likewise, this study found that the risk of LTFU among rural residents was reduced by 40%  compared with urban residents. It is in accordance with the study conducted in India . This miFurthermore, ART clients who had no cell phones were nearly two  times more at risk of experiencing LTFU compared with their counterparts (having a cell phone). This finding might be attributable to the cell phone serving as a means of tracing HIV patients who are on ART. Also, a cell phone may increase the relationship between patients and their health professionals. Cell phones also help to keep an appointment with others and health facilities’ appointment period. This scenario compared well with the previous evidence that cell phone improves health care service delivery processes by targeting communication between providers and their patients. Mobile technologies improve clinical management support in settings where there are no specialist clinicians, and they can be used to send patients test results and timely reminders of appointments –38. TherAs a limitation, this study was not considered some essential predictors, like viral load, liver, and renal function tests since it was conducted using secondary data that have missing records. In addition to the above, it might lead to underestimating the incidence rate of loss to follow-up owing to record problems.This study revealed that the incidence rate of loss to follow-up among adult HIV-positive patients at Debre Markos Referral Hospital was low. Having no cell phone, WHO clinical stage IV, and rural residence were the identified predictors of loss to follow-up. Therefore, program leaders and initiatives should strengthen early initiation  of ART care and treatment.Additional file 1: Figure S1. The goodness of fit test for Cox-proportional hazard regression model using cox-Snell residual.


Human immunodeficiency virus attacked an immune cell and the CD4 cell which is responsible for the body’s immune to infectious agents. Acquired immunodeficiency syndrome is one of the major public health problems in Sub-Saharan Africa including Ethiopia. The main objective of this study to identify the determinants of CD4 cell count among antiretroviral therapy attendants of infected adults follow up in Gonder teaching referral hospital, Gonder, Ethiopia implemented by SAS version 94.A retrospective cohort study was conducted on 216 regular follow up patients whose age greater than 14 years from December 1, 2012, to December 30, 2017. A multilevel model was used to identify the factors of CD4 cell count of patients and it considered variability between and within patients.The mean with a standard deviation of weight, and a hemoglobin level of patients were 55.48 (10.21), and 18.25 (33.028) respectively. This study concluded that the variation for CD4 cell count existed between patients was 63% and the remaining 37% of variation existing within patients. In this study, the random coefficient time-varying covariate model was well fitted which shows weight and hemoglobin level were statistically significant predictors at a 5% level of significance for the log of CD4 cell count of patients.This study shows the hemoglobin level and weight of patients were statistically significant for the log of CD4 cell count of patients follow up in Gonder teaching referral hospital, Gonder, Ethiopia. Moreover, the result of the study shows that the log of CD4 count of patients increased when hemoglobin level and weight of patients increased. Hence, intervention should be given the ways to increase weight and hemoglobin levels of patients during follow up of antiretroviral therapy. Acquired Immune Deficiency Syndrome (AIDS) is caused by HIV (Human Immune Deficiency Virus) which reduces a person’s ability to fight infection. HIV attacks an immune cell and the CD4 cell is responsible for the body’s immune response to infectious agents. An uninfected individual has around 1100 CD4 cells per milliliter of blood. These CD4 cells, decrease in number with time from HIV, so that an infected person’s CD4 cell count can be used to monitor the progression of the disease [Women account for nearly half of the 40 million people living with HIV-1 worldwide which has a higher proportion existing in developing countries and the human immunodeficiency virus–type 1 (HIV-1) is epidemic for more than 20 years .Sub-Saharan Africa remains most severely affected, with nearly 1 in every 20 adults (4.9%) living with HIV and accounting for 69% of the people living with HIV in this worldwide. Although the regional prevalence of HIV infection is nearly 25 times higher in sub-Saharan Africa than in Asia, almost 5 million people are living with HIV in South, South-East and East Asia combined. After sub-Saharan Africa, the region most heavily affected the Caribbean and Eastern Europe and Central Asia, where 1.0% of adults were living with HIV in 2011 (UNAID 2012).3. Virologic failure as plasma viral load above 1000 copies/ml based on two consecutive viral load measurements after 3 months, with adherence support [Immunological failure as CD4 count falls to the baseline or persistent CD4 levels of below 100 cells/mm support . The WorA retrospective cohort study design was used to collect relevant information’s from ART chart in order to address the objective of this study. Those HIV positive patients who are greater than 14 years old and start ART since December 1, 2012 and who has base line and at least three follow up period until December 30, 2017 included in this study.This study conducted at Gondar Teaching Referral Hospital in North-Western Ethiopia, Amhara Region. The population of this study included HIV positive adults who attend antiretroviral therapy at Gondar Teaching Referral Hospital.This study obtained data from a retrospective cohort study based on ART electronic data base and from the review of patient charts which contains socio-demography, laboratory and clinical information of all HIV patients under ART follow-up including a detailed antiretroviral therapy history from Gondar Teaching Referral Hospital among Antiretroviral Therapy (ART) follow up study. The target population was adults of HIV positive patients whose age greater than 14 years old who initiated on ART from December 1, 2012 to December 30, 2017 GC.The data quality was controlled by data collectors from ART section of the hospital. The controllers were taken intensive training by the Minister of Health for the different services.The longitudinal response variables for this study were CD4 cell count. Therefore, CD4 count has count variable nature due to this reason generalized linear mixed model with the link function log is performed. In addition the predictor variables were used in this study shows on Table Inclusion criteria:- Patients whose age was above 14 years old that are attending a minimum of three visit of HAART treatment in ART clinic for refilling their prescription and who were initiated on ART from December 1, 2012 to December 30, 2017 GC at Gondar Teaching Referral Hospital would be included in the study.Exclusion criteria:- Patients whose age was below 14 years old that are attending HAART treatment in ART clinic for refilling their prescription, patients who are not registered in the ART clinic and who are not initiated on ART were not included in this study. In addition patients out of the study period are not included.This study was used secondary data extracted from patient chart follow-up format and analyzed by SAS version 9.4 statistical software. But the chart prepared by national health organization.One of the biggest problems in longitudinal studies is missing data. However, multilevel analysis is no need to have complete dataset. Therefore, multilevel analysis is very flexible in handling missing data .To assess the changes of outcome (s) over time to associated risk factors by using multilevel longitudinal data. But analyzing multilevel longitudinal data is complicated. For analyzing of this longitudinal data, both descriptive and inferential statistics were used. Multilevel analysis is (probably) the most robust and flexible of the three techniques . Thus, iGrowth curve analysis is offers a statistical framework for analyzing longitudinal data. Growth curve analyses are used to describe the patterns of change over time and to determine the number of visit time. More specifically, growth curve analysis in longitudinal data can estimate a best-fit line or curve to each individual’s responses over time .There are few strict requirements for the types of data that might be analyzed using growth model. First, adequate at least 100 sample sizes are needed to reliably estimate then growth models are often preferred. Second, growth models typically require at least three repeated measures per individual. Third, for the typical method of estimation called maximum likelihood, it is assumed that the repeated measures are continuous and normally distributed.According , separatLongitudinal data are one example of a hierarchical structure; series of repeated measures over time at the lowest level is nested with the individual persons at the highest level. Such nested structures are typically strong hierarchies because there is much more variation between individuals in general than occasions within individuals .These reThis analysis is used for exploring an individual’s variability on longitudinal data for responses in such random effects or multilevel modeling allows investigate two level of variability. Therefore, within and between subjects variability were analyzed for CD4count at each individual i = 1, 2… n . These mThis is the simplest case of hierarchical model in which there are no explanatory variables at all. Then model has only an intercept term and variances at the measurement and individual level. Since the model does not contain a slope, the true individual change is a horizontal line with y-intercept β0i.The model expressed as:-Where, the Greek letters π and β indicate first and second level parameter respectively.By substituting, we get0i is the intercept for individual i for each response; β00is the mean intercept over all individuals, and u0i are the deviation of individual-specific residual; finally, e0i is the time-specific residual. Now, proportion variance or the intra class correlation (ICC) refers to a set of coefficients representing the relationship between variables of the same individuals decomposes into two independent components . Thus, ICC explained by the individuals (level-2) in the population is given byπ0 and u0 for each response are different and ICCmeasures = 1 − ICCindividual.Where, eA random intercepts model is a model in which intercepts are allowed to vary, and therefore, the scores on the dependent variable for each repeated measurement are predicted by the intercept that varies across patients. The prior models are sometimes called unconditional (intercept only) model; because there are no measured covariates used to predict the random effect.pi, and whose value not changes over time called time invariant predictor Zsi.Now, based on , the modTime invariant covariates Z inter the equation at the second level.By substituting, we get a uni-variate random intercept model:-00 is the overall average intercept for each response, β10 is the slop of time varying covariates, β01 is the slop of time invariant covariates, β11 is the mean difference change between time varying covariates and time invariant covariates, and lastly e0i and u0i are still the within and between individual error term of the intercept. Therefore, in this model β00 + β10Xpi + β01Zsi + β11ZsiXpi are the fixed part, because the coefficients are fixed.Where, β0i + e0i are called the random part of the model. Where, Xpi, p = 1, 2,…, P denotes the P time varying covariates that were included in the analysis, and Zsi, s = 1, 2… S denotes the s invariant covariates that were included in the analysis.The remaining uThis random coefficients model is a model in which slopes are allowed to vary in addition to intercepts for each uni-variate response. The relationship between an explanatory variable and the response is different across all patients with their intercept and slope by considering time varying covariate and time invariant covariate.Time invariant covariates Z inter the equation at the second level.By substituting, we get uni-variate random coefficient model:-1i the random slope of time varying covariates for response.Where, the only additional term in this model is un + 1 − tn is a constant for all n [The most common covariance structures in repeated measure are: First, Simple structure species that the observations are independent, even on the same patient, and have homogeneous variance. Second, Compound symmetric structures were the observations on the same patient has homogeneous covariance and homogeneous covariance. Third, unstructured structure species no patterns in the covariance matrix, and is more appropriate for balanced data nature. Fourth, Autoregressive (order 1) covariance structure species homogeneous variance; and more appropriate for unbalanced data and equally spaced measurement times such that tor all n . In addior all n .In order to select variables to be included in multi-variable analysis, forward variable selection was used. The first step in this selection is to fit a univariate multilevel model for each covariate at the 25% level. Next univariate model is fitted that contains all covariates that are significant in univariable analysis.In order to select the best and final model which is appropriately fits with the given longitudinal data, it is necessary to compare the different models by using different techniques and methods. Hence, Akaki information criteria (AIC) and Bayesian information criteria (BIC) that calculated from deviance based on number of estimated parameter p is also most convenient at 5% level of significance, but for multilevel model deviance information criteria (DIC) is appropriate. After all, Deviance compares Chi squared distribution with degrees of freedom equal to the difference (p) in the number of parameters fitted under the two models smaller values is better .Parameter estimates of multilevel model were derived for both fixed components and random components. To estimate this component, there is different of parameter estimation technique. Among that, maximum likelihood estimation is most commonly used estimation method in multilevel model .t test statistic is commonly used to test significance of individual parameter regression coefficients for each independent variable. Second, the deviance based test, or likelihood ratio test is a general principle for testing fixed multi-parameter and for testing about the random part of the model in applications of a hierarchical linear model.Once a model has been developed through different techniques in estimating the model parameters, there were several mechanisms involved in assessing the appropriateness, adequacy and usefulness of the model. First For multilevel analysis making inference about the model depends on whether the data met the required assumptions or not. In this hierarchical regression models some graph and other techniques were used to assess peculiarities or the distinctive features of the model with regards to the data. Therefore the assumptions in this hierarchical regression models were linearity, normality, heteroscedasticity and residuals nature using usual manner.There were a total of 1408 visits from 216 subjects; the numbers of visits per subject were minimum 3 and a maximum of 10 visits with an equally 6-month interval for all patients. The sample sizes at the six consecutive time points were 216, 216, 196, 167, 142, 125, 105, 93, 80 and 68. Therefore, the measurements were sharply increasing the degree of missing over time due to dropouts, missed clinic visits and transfers.From socio-demographic and clinical related covariates results were shown in Table As can be seen from Table Table The visualized the pattern of CD4 count measurements of the patient’s overtime and the overall individual plots were considered. Figure Table Besides residuals plot shown in Fig. From the fitted univariate multilevel model was modeled with sets of covariates that include fixed effect parameters age, marital status, weight, hemoglobin level, functional status, WHO stage, TB screen, opportunistic infection, and ART adherence. Among those covariates, there was significance difference by intercept, weight and hemoglobin level for a CD4 count of patients. According to results intercepts, weight and hemoglobin levels were positively associated with a CD4 count of patients. Therefore, this study shows that CD4 cell count of patients increased as weight and hemoglobin levels increase .Table 6PHighly active antiretroviral therapy has greatly reduced morbidity and mortality in patients with human immunodeficiency virus. The effective antiretroviral therapy was important intervention in terms of improving longevity and preventing opportunistic infections in patients with human immunodeficiency virus infection. The literature and this study shows antiretroviral therapy is effective treatment for human immunodeficiency virus to reduce the viral load to undetectable levels. The studied patients received the same type of antiretroviral due to the therapeutic effectiveness and adherence. Antiretroviral drug were associated with weight gain and hemoglobin level. Human immunodeficiency virus has damaged immune system and reduced CD4 count. Highly active antiretroviral therapy will strength immune system, extend life and increased CD4 count. Weight and hemoglobin level increased due to patients received antiretroviral therapy.Based on different well-organized literature, some discussions were organized as follows:-In this study, the random coefficient time-varying covariate model was well fitted because generalized Chi square/df was 8.49 which closed to one. From the multilevel analysis, more than half percent of the variation was explained from between patients; and the remaining is explained by within patients. The finding is consistent with , 16. LasThis study used a series of repeated measurements over time at the lowest level is nested with the individual patients at the highest level. Such nested structures are typically strong hierarchies because 63% of the variation for CD4 count existed between patients and the remaining 37% of variation existing within patients. Therefore, multilevel modeling considered variability between, and within patients. This study determined the determinants of CD4 cell count among antiretroviral therapy attendants of HIV infected adults follow up in Gonder teaching referral hospital, Gonder, Ethiopia.From this study, hemoglobin level and weight of patients were statistically significant at a 5% level of significance for the log of CD4 count of patients follow up in Gonder teaching referral hospital, Gonder, Ethiopia. Moreover, the result of the study shows that the log of CD4 count of patients increased when hemoglobin level and weight of patients increased.


Antiretroviral treatment (ART) adherence is highlighted in management of patients living with human immunodeficiency virus. In South Korea, ART medication research has rarely been conducted due to the low economic burden associated with government-funded treatment. This cross-sectional study aimed to compare the pill burden impact between ART regimen compliance and HIV-RNA viral load suppression. Data were collected from 2008 to 2016 at a general hospital in South Korea. A total of 210 HIV/AIDS treatment-naïve patients were grouped as follows: single-tablet regimen , mild pill burden (two-four tablets/day), and heavy pill burden (≥ five tablets/day). Patients were analyzed according to gender, age at index date, medical insurance type, comorbidities, depression, HIV/AIDS disease burden as indicated by HIV-RNA viral load and CD4, and laboratory variables. In a multivariate logistic regression model, the STR group demonstrated adherence 5.10 times more often than the heavy pill burden group. Females and patients with an initial viral load of 500,000 or more were 0.090- and 0.040-fold less adherent to the ART regimen. Among these patients, 95% or more of the MPR group were 7.38 times more likely to have a lower limit of detection (LLOD) of viral load suppression. The highest initial viral load group was 0.090-fold less likely to have an LLOD than the reference group. These results suggest that a single-tablet regimen could improve medication adherence and the clinical virologic outcome. Therefore, general population research on ART adherence and polypharmacy is needed. The Uni80 years . It is w80 years .Despite the importance of compliance with an ART regimen, adherence rates are less than optimal, ranging from 60% to 80% , 6. It iDuring the course of our study three Korean-recommended HIV/AIDS guidelines were released—in 2011, 2013, and 2015. These were developed in accordance with the European AIDS Clinical Society and IAS-USA panel strategies –10. The In May 2012, TDF/FTC became available for prescription in HIV/AIDS treatment, and lamivudine (3TC)/abacavir (ABC) became available in August 2012, after its addition to the drug formulary. These simplified ART regimens have made it much easier for patients to take ART medication once a day; this is composed of two tablets of TDF/FTC in addition to efavirenz. Treatment has been further simplified with the addition to the drug formulary of single-tablet regimens such as elvitegravir/cobicistat/emtricitabine/tenofovir  or dolutegravir/abacavir/lamivudine (listed in March 2016). Therefore, we compared the medication possession ratio (MPR) according to sequential groupings into heavy pill burden (≥ five tablets/day), mild pill burden (two-four tablets/day), and single-tablet regimen  by chronological drug formulary changes.We set out to compare ART regimen adherence with pill burden depending on the drug formulary listing of new HIV/AIDS medications. Consequently, the objective of this study was to investigate medication compliance by medication possession ratio (MPR) according to pill burden, as well as by viral load suppression by medication compliance according to MPR.We extracted electronic medical record (EMR) data from a general hospital in South Korea. We selected patients who were at least 20 years of age and had been prescribed an ART regimen between January 01, 2008, and December 31, 2016. A total of 242 patients were initially identified. Of these, the ART-naïve patients were defined based on their transfer from a local public health center or a local clinic and no ART regimen prescription. So, 32 subjects were excluded because their status was not naïve in treatment for HIV/AIDS. The remaining 210 subjects were ART-naïve patients who had been referred from a local clinic to the general hospital for HIV/AIDS treatment. They would be considered for the final analysis. Patients were categorized into three groups by ART pill count. The magnitude of the pill burden group was measured with the number of tablets according to index date as follows: 1) heavy pill burden group ; 2) mild pill burden group (two-four tablets/day), after TDF/ FTC had been added to the drug formulary; and 3) single-tablet regimen group (STR), after the first co-formulated ART, elvitegravir/cobicistat/emtricitabine/tenofovir, had been listed in the drug formulary. The index date was defined as the first date the patient with an HIV/AIDS-related diagnosis  codes B20-B24) was dispensed a highly active antiretroviral treatment (HAART) regimen. For each group, the number of pills that the patient needed to take per day was counted separately for each antiretroviral treatment. The ART regimens that had been prescribed for a minimum of 30 days after the index date and maintained during the 12–24 week period were assessed. If an ART regimen was changed during the study period, the antiretroviral pill burden was determined to be the average pill number.We analyzed two outcomes: the degree of ART regimen adherence and viral load suppression. Adherence to the ART regimen was measured with the MPR, which is a proxy for medication compliance by pharmacy refill data. The MPR was calculated as the total number of days from the index date to the date of the viral load result and was used as the denominator. During that period, the number of days each patient had the ART available was used as a numerator , 12. ThePatients with an MPR of 95% or higher were considered adherent to the medication regimen –15. The After ART medication was initiated, confirmation of the HIV-RNA viral load between week12 and week24 was recommended , 10. TheThe Anderson model has been evaluated as suitable for analyzing predictive factors for the use of medical and social services. Therefore, we referenced several covariates to account for patient characteristics  that was frequently cited in past HIV/AIDS articles , 20. TheThis was a cross-sectional study. The patients’ demographic and clinical characteristics were compared across the three pill burden groups using the χ2 test, Fisher’s exact test, or analysis of variance, as appropriate. Logistic regression models were performed to identify the factors associated with the MPR and to evaluate the difference in viral load suppression between MPR 95% and less than according to pill burden groups and baseline covariates. All analyses were performed using STATA 15 .This study was approved by the Institutional Review Board of Inha University Hospital (INHAUH 2018-11-011-002). All information that could identify the subject, such as name and registration number, was deleted from the electronic medical records for data confidentiality and the subject’s anonymity. The extracted data was generated and managed by password-protected files, to which only one researcher had access. All data, including statistical analyses and results, were stored in one designated computer set with a security number, which was blocked from the external network. An informed consent waiver was approved by the Institutional Review Board of Inha University Hospital.A total of 242 HIV/AIDS patients were prescribed antiretroviral therapy in the years 2008–2016. Of these, 32 subjects were excluded because they were referred from other general hospitals with a non-naïve HIV/AIDS status. The remaining 210 subjects were ART-naïve patients and were included in the final analysis. The baseline characteristics of these 210 subjects are presented in Males predominated in all three different pill burden groups at an average of 93.2% to 96%. No statistically significant differences were found among the three groups for pre-index viral load, presence of depression, comorbidities, and all clinical laboratory data , LDL, and HDL). In the case of pre-index viral load, there was one patient without a pre-index HIV-RNA PCR, but their HIV/AIDS positive result was indicated only by the ELISA method (reference negative) . In addiAmong the three groups, the heavy pill burden group of 47 patients (54.7%) had a CD4 count below the 200 required for prophylactic antibiotic treatment, whereas only seven (8.1%) patients had a CD4 count >350. However, in the single-tablet regimen group, the proportion of CD4 count below the 200 was 34%, and the CD4 count over the 350 was 28%, relatively (p = 0.017). These results may reflect guideline changes, including the offer of antiretroviral treatment to all patients regardless of their CD4+ T cell counts in the 2013 guidelines. Of the three groups, the total of syphilis-infected patients was 33 patients; the heavy pill group had 18 patients (20.5%), eight patients (11.1%) were in the mild pill burden group, and seven patients (14%) were in STR, respectively. The proportion of patients who were prescribed antidepressants or anti-anxiety medications for depression was similar, between 17% and 18% among the three groups, regardless of duration.Univariate and multivariate logistic analysis indicated that gender, pre-index viral load, and antiretroviral treatment pill burden were associated with the MPR, as shown in A binary logistic regression analysis on viral load suppression revealed that the pre-index viral load and medication compliance of 95% or higher were important factors, as shown in In the unadjusted analysis, a group with 95% or higher MPR was 7.74 times more likely to have viral load suppression . In contrast, a high pre-index viral load  was 0.060-fold less likely to have virologic suppression . Such results were parallel with the multivariate logistic analysis. Patients with a 95% or higher MPR were 7.38 times more likely to have viral load suppression than those with an MPR of lower than 95% . The group with a pre-index viral load of 500,000 or more was 0.09-fold less likely to have viral load suppression  than that with a pre-index viral load of less than 10,000. In contrast, unlike the results of the univariate logistic regression analysis, the antiretroviral treatment pill burden group did not show a statistically significant association with the viral load suppression in multivariate logistic regression analysis .We investigated the relation between pill burden and medication adherence and viral load suppression. In particular, we assessed whether a gradual change in pill burden, based on drug formulary changes from more than five tablets a day (heavy pill burden) to two-four tablets (mild pill burden) or a single-tablet level, was associated with a higher rate of medication adherence and virologic suppression.Our findings showed that a lower pill burden, a lower HIV-RNA level, and male patients were associated with higher medication adherence. Patients who had a higher initial viral level had a lower MPR. In addition, the MPR of the lower pill burden group as STR was higher than that of the heavy pill burden group. These are parallel with the results of other global studies comparing the MPR of STR and MTR for drug compliance , 24, 25.This study has several limitations. First, we analyzed a small sample of 210 individuals, which is too small to represent the general Korean population. From 2008 to 2016, an average number of 20–30 HIV/AIDS-naïve patients were enrolled in the institution from which our data were extracted, excluding patients who had transferred while already in HIV/AIDS treatment. Considering that 1,191 new HIV/AIDS subjects were registered in 2017 across Korea, this means that our study sample accounted for only approximately 2.5% of all newly-enrolled HIV/AIDS patients nationwide . FurtherDespite these limitations, our study provides a significant finding regarding the relationship between pill burden and ART adherence. According to the results, the lower pill burden represented by taking STR and the lower HIV/AIDS disease burden represented by the lower initial viral load were associated with better medication adherence and virologic suppression. These results suggest that the STR could improve medication adherence and clinical virologic outcomes, as we expected. As HIV/AIDS patients continue to live longer, it is becoming increasingly important to manage medication adherence and concomitant medications effectively; therefore, further research on ART adherence and polypharmacy is needed for the general population.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.


After the scale-up of antiretroviral therapy (ART) for HIV infected people, increasing numbers of patients have pretreatment drug resistance (PDR). In this study, the prevalence of PDR was evaluated in adults initiating antiretroviral therapy in China.Blood samples were obtained from 1943 patients who initiated antiretroviral therapy (ART) in 2017 from 13 provinces or cities in China. Pol sequences were used to analyze drug resistance and construct transmission networks. Logistic regression model was used to estimate the potential factors associated with PDR.OR) = 2.64, 95% CI: 1.57–4.44) and individuals from Liangshan, Dehong, and Lincang . In total, 754 sequences were used to generate 164 transmission networks. Five transmission networks had two or three sequences containing the same mutations, two networks contained subjects from Liangshan, and one network contained subjects from Dehong.In total, 1711 eligible patients  were included, of which 117 (6.8%) had PDR. The highest rates of PDR were 12.2% in Liangshan Prefecture of Sichuan and 9.3 and 8.9% in Dehong and Lincang Prefecture of Yunnan. A multivariate logistic regression analysis revealed that PDR was significantly higher among intravenous drug users (adjusted Odds Ratio (aOverall, the PDR prevalence was moderate, with a particularly high prevalence in areas with severe HIV epidemics. These results indicate the importance of continuous PDR monitoring in patients initiating antiretroviral therapy. Highly active antiretroviral therapy has decreased mortality rates and prolonged the lives of people living with HIV or AIDS (PLWHA) –4. In ChIn China, the NFATP was initiated at a nationwide scale in 2003  and all This was a cross-sectional study to estimate the prevalence of PDR in adults initiating antiretroviral therapy in China. The method of HIVDR test was used to explore the level of PDR in part of regions in China. The study design was performed according to the 2014 WHO protocol for PDR . China hThe patients’ recruitment and specimens collection were performed from January 2017 to June 2017 (6 months). Two clinics were randomly selected in each study area. Patients were recruited at the selected clinics during the study period depending on the enrollment order. And the recruitment was based on sequential sampling of patients who received ART treatment at the clinics [Baseline variables were obtained for all patients, including socio-demographic and behavioural characteristics. The variables of date of birth, sex, marital status, ethnicity, years of education and route of HIV infection were achieved from the national epidemiology database of HIV infection . Historypol region following an in-house method [https://hivdb.stanford.edu/hivdb/by-sequences/).Whole blood was sampled before the initiation of ART. CD4 cells were counted by flow cytometry at local CDCs within 12 h of sample collection. Plasma was isolated and sent under cold-chain conditions to the laboratory at NCAIDS, China CDC. RNA was extracted from 200 μl of plasma, amplified, and used to sequence the HIV e method , 24. Thee method . The clae method . PDR wasPR and RT associated with drug resistance mutations were removed according to the most recent update of major HIV-1 drug resistance mutations [To avoid potential biases due to convergent evolution, 43 codons in utations . Finallyutations , 27 wereutations .OR) and adjusted odds ratio (aOR) with 95% confidence interval (95% CI) were reported. All tests were two tailed and a P-value < 0.05 was considered statistically significant. Statistical analyses were performed using SAS V9.4 .In this cross-sectional study, we collected baseline data and obtained subtypes after sequencing analysis of participants. Univariate and multivariate logistic regression models were used to estimate the potential factors associated with pretreatment drug resistance. We adjusted age, sex, marital status, ethnicity, year of education, route of infection, CD4 cell counts before ART, subtype, and region for each participant. We constructed multivariate logistic regression model in a stepwise manner to select variables independently associated with drug resistance. Odds ratio  sequences. Twenty participants were excluded, including fourteen participants who were aged below 18 years and six without any information. Finally, 1711 participants were included in the study , V179D , and E138A  were the most common mutations in the reverse transcriptase (RT) region. For NRTIs, the most frequent PDR drug was D4T , followed by AZT . The most frequent mutations were K65R  and D67N  in the RT region. All 10 patients with PDR to PIs were resistant to LPV/r and the most common mutations were I47V , I50V , I54V , and V82A/L/F  in the protease (PR) region.In total 6.8% (117/1711) of patients had PDR. Among them, 79 (4.6%) had PDR to NNRTI, 38 2.2%) to NRTI, and 10 (0.6%) to PI  and that for Liangshan, Dehong, and Lincang versus other regions was 2.30 (95% CI: 1.57–3.35). A multivariate logistic regression model showed that IDUs and region  were important factors, with aORs of 2.64 (95% CI: 1.57–4.44) and 2.04 (95% CI: 1.26–3.30), respectively.Risk factors associated with HIV PDR are listed in Table pol sequences were obtained to construct genetic transmission networks. We constructed transmission networks with a genetic distance threshold of 0.0125, which identified the maximum number of clusters. In total, 754 (44.1%) sequences were used to generate 164 transmission networks . Three transmission networks contained two PDR sequences with the K103N mutation and they were from Liangshan prefecture of Sichuan province, Dehong Prefecture of Yunnan Province, and Liuzhou City of Guangxi Province. In addition, three PDR sequences with E138Q and V179D mutations comprised a transmission network and they were from Liangshan prefecture of Sichuan province. Another transmission network included two PDR sequences with the L210W mutation from Shandong Province.Overall, 43 sequences with PDR were included in the networks and the degrees between PDR and no PDR in the networks were not statistically significant. The 43 sequences with PDR were dispersed among 23 transmission networks; shared mutations yielded five transmission networks Fig. . The mosAmong the 754 individuals included in the transmission networks, 49 were infected via IDU. Among these 49 individuals, five had PDR in five different networks. No transmission of the virus with PDR was observed among individuals who inject drugs.CI: 6.5–7.1%). According to the WHO, low, moderate, and high levels of HIV drug resistance are defined as < 5%, 5–15%, and > 15% [CI: 4.4–4.8%) in this study, which is below the 10% threshold to change the recommended first-line ART regimen [We performed a cross-sectional study of HIV PDR in China and obtained an overall PDR prevalence of 6.8%  or stavudine (D4T) + lamivudine (3TC) + nevirapine (NVP) in 2005  and D4T Transmission networks were constructed to explore whether the HIVDR related cluster was existed among PLWHA before the initiation of ART. There were no significant differences between the degrees of PDR and no PDR. However, there were five transmission networks with two or three same DRMs. Two transmission networks contained subjects from Liangshan, and one contained subjects from Dehong. Another contained subjects from Liuzhou. All these cities are in regions with the most severe HIV epidemics. One additional transmission network contained subjects from Shandong, with moderate HIV rates. There was a HIVDR related cluster contained three same DRMs in Liangshan, especially. These results suggest that PDR could be transmitted among high-risk populations for HIV infection in the future.The study had the following limitations. First, China has seven regions . This pilot study survey did not include all these regions and was not representative of all individuals living with HIV or AIDS in China. We will expand the PDR study to all provinces in inland China next year. Second, the lack of drug-resistant virus transmission among IDUs in this study could be related to the sampling density. IDUs were not infected in local areas or related individuals were not recruited in this study.The present results demonstrate that the overall prevalence of PDR is moderate in China. However, in Liangshan prefecture of Sichuan, it exceeds 10%. There was no ongoing transmission of resistance-associated mutations in the networks. However, regional differences and complex epidemic trends suggest that the continuous surveillance of PDR is essential for estimating the level of PDR and ensuring the effectiveness of current ART regimens in China.


M184V/I cause high-level lamivudine (3TC) and emtricitabine (FTC) resistance and increased tenofovir disoproxil fumarate (TDF) susceptibility. Nonetheless, 3TC and FTC (collectively referred to as XTC) appear to retain modest activity against human immunodeficiency virus-1 with these mutations possibly as a result of reduced replication capacity. In this study, we determined how M184V/I impacts virus load (VL) in patients failing therapy on a TDF/XTC plus nonnucleoside reverse-transcriptase inhibitor (NNRTI)-containing regimen.We compared VL in the absence and presence of M184V/I across studies using random effects meta-analysis. The effect of mutations on virus reverse-transcriptase activity and infectiousness was analyzed in vitro.10 VL, 0.18; 95% confidence interval, .05–.31). CD4 count was lower both at initiation of antiretroviral therapy and at VF in participants who went on to develop M184V/I. L74I was present in 10.2% of persons with M184V/I but absent in persons without M184V/I (P < .0001). In vitro, L74I compensated for defective replication of M184V-mutated virus.M184I/V was present in 817 (56.5%) of 1445 individuals with virologic failure (VF). Virus load was similar in individuals with or without M184I/V (difference in logVirus loads were similar in persons with and without M184V/I during VF on a TDF/XTC/NNRTI-containing regimen. Therefore, we did not find evidence for a benefit of XTC in the context of first-line failure on this combination. Lamivudine is a cornerstone antiretroviral whose efficacy has been ascribed to high fitness cost of the lamivudine resistance mutation M184V. However, here we demonstrate elevated viral loads in the context of M184V, likely attributable to compensatory mutations such as L74I. See the Editorial Commentary by Kuritzkes, on pages 1067–9.) using a public health approach with limited viral load (VL) monitoring has been accompanied by high prevalence of drug resistance to nonnucleoside reverse-transcriptase inhibitor (NNRTI)-containing regimens among individuals with virological failure (VF) in low- and middle-income countries (LMICs) .The cytosine analogs lamivudine (3TC) and emtricitabine (FTC), collectively referred to as XTC, are components of first- and second-line regimens recommended by the World Health Organization (WHO). However, high-level XTC resistance can be conferred and selected by single amino acid changes at position 184 of reverse transcriptase (RT) in the highly conserved  amino acid domain that includes the active  site of the p66 polymerase subunit of RT . M184V/I10 [Several lines of evidence suggest that in addition to causing high-level reductions in XTC susceptibility in vitro and modestly increased tenofovir disoproxil fumarate (TDF) susceptibility, viruses with these mutations retain some in vivo susceptibility to XTC possibly because of their reduced replication capacity . For exa10 .Therefore, to understand the relationship between M184I/V and VL in the era of tenofovir-based cART in which thymidine analog mutations (TAMs) were not present, and also in the context of limited or no access to VL monitoring, we studied individuals failing the WHO-recommended first-line regimen of TDF/XTC/NNRTI across a range of settings .The study population has previously been described and is presented in Mutations were defined as amino acid differences at positions 1 to 240 between each sequence and the consensus subtype B amino acid reference sequence. Because some individuals may have been exposed to thymidine analogs before TDF-containing regimens , we exclantiretroviral (ARV) naive at baseline. The primary outcome was VL at VF; hence, patients without this outcome were excluded.Each sequence was subtyped as previously described, and sequence quality control measures were taken to identify sequences with APOBEC  G-to-A hypermutation . Duplica10 VLs according to presence of M184I/V mutation both within and across studies. To quantify the impact of M184I/V on VL, we calculated mean log10 VL in each study according to M184I/V. Differences were pooled across studies using random-effects meta-analysis. Estimates of the standard error in each study were calculated by dividing the pooled estimate of the standard deviation by the square root of the number of patients with/without M184I/V in any given study. We repeated this process in subgroups of patients defined by several baseline characteristics: presence of K65R mutation, presence of major NNRTI mutations, choice of NRTI, choice of NNRTI, categories of baseline CD4 count (< and >200 cells/mm3), and categories of baseline VL (< and >100 000 copies per mL). We used the same methods for analyses of CD4 count and treatment failure. To assess whether M184I/V was associated with VL failure independently of other mutations, we performed a separate analysis in which we used a mixed linear regression model adjusting for study as a random effect and other mutations associated with increased VL (which were identified by forward stepwise variable selection). Next, we used Fisher’s exact test to identify mutations associated with M184I/V. We used 2-sided P values and Stata version 15.1 for all statistical analyses.We graphically compared the distribution of logpol sequence was identified with mutations of interest, and the gag-PR-RT-IN region was amplified by polymerase chain reaction with flanking restriction sites inserted into primers. After cloning into an expression plasmid, site-directed mutagenesis was performed to revert isoleucine back to leucine at RT amino acid 74, valine back to methionine at RT amino acid 184, or both. Plasmids expressing gag-pol were cotransfected into 293T cells along with a vesicular stomatitis virus-G envelope expressing plasmid and a vector encoding luciferase expressed from a long terminal repeat promoter as previously described [A patient-derived escribed . Supernaescribed .Among 2873 participants included in the initial group, 1445 from 32 study groups across 15 countries had an available failure VL measurement, and M184I/V was present in 817 (56.5%) of these . Partici10 VL  than patients without M184I/V . When restricting analyses to comparisons of patients within the same study, the estimated difference in VL was nonsignificant in the vast majority of studies   . After s.05–.31) .P < .0001). CD4 count at VF was also lower in patients with M184V/I than those without .We next explored the relationship between detection of M184I/V failure and CD4 count, noting that the duration of VF was likely longer in LMIC regions. Mean baseline CD4 was significantly lower among patients who went on to develop M184I/V by treatment failure compared with those who did not . L74I was observed in 11.7% of subtype C-infected participants with M184I/V at VF and in 14.4% of CRF01_AE participants with M184I/V at VF  of patients with M184I/V, but not in 628 patients in which M184I/V was absent (/V at VF .A previous study reported that L74I can restore replication to a virus with the K65R mutation without conferring drug resistance ; therefoDespite having a low genetic barrier to drug resistance, 3TC has retained importance and a central role in both first- and second-line ART . TherefoOur primary finding that VL was similar in participants with and without M184V/I at the time of VF was robust across baseline CD4 count, baseline VL, gender, and different NNRTI and NRTI drugs in the first-line treatment regimen. We observed lower baseline and VF CD4 counts in individuals with M184V/I, although rate of change of CD4 did not differ based on M184V/I status. Lower baseline CD4 count is known to be associated with higher VF rates and a higher probability of drug resistance at VF , 46. A pWe identified L74I as being specifically enriched in individuals with M184V and not present at all in those without M184V/I. We observed significant prevalence of L74I in subtypes C and CRF01_AE, although limited numbers of participants across subtypes hindered a full understanding of subtype distribution. In vitro experiments demonstrated that L74I restores replication efficiency to a virus with the M184V mutation over a single round of infection, and that enhancement was due to efficiency of HIV reverse transcription in viral particles.The emergence of L74I exclusively in patients with M184V/I suggests an in vivo selection advantage of L74I + M184V replication over M184V alone, at least in some individuals. L74I was first reported as a mutation associated with exposure to abacavir or less commonly tenofovir , 50, andpol gene sequenced in this study, could have similar effects as L74I in participants with M184V/I. Data from our study support the transmission potential of M184V/I-containing viruses in the context of prolonged VF and accumulated coevolved mutations in RT that occurs under “real-world” conditions.Because L74I was observed only in approximately 10% of those with M184V/I, we postulate that alternative mutations, less strongly linked to M184V/I or perhaps outside the region of the Limitations of this study include its retrospective cross-sectional design, absence of drug levels or adherence data, and unknown duration of VF for participants. Our study was not designed to provide a mechanistic understanding of the relationship between M184 and fitness: it was designed to understand the pathogenic potential of M184V-containing viruses in patients treated in the real world. Finally, there was heterogeneity between population groups, and, to account for this, analyses were conducted within study. It should also be noted that stratification by tenofovir or NNRTI resistance resulted in small numbers for subanalyses.In summary, we show that 3TC-resistant and 3TC-susceptible viruses show similar VLs in patients failing NNRTI-based ART containing 3TC, tenofovir, and NNRTI, likely in part due to viral evolution of compensatory changes that maintain replication efficiency of M184V/I-containing viruses. These data reinforce the importance of effective VL monitoring to limit HIV drug resistance and disease progression in the face of suboptimal drug pressure, particularly in low-resource settings. Finally, given that we did not find benefit of 3TC in patients failing first-line treatment, a prospective clinical trial could determine whether there is benefit for including XTC in second-line regimens for the treatment of persons whose viruses develop M184I/V after VF on a first-line treatment regimen.The Journal of Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.Supplementary materials are available at jiz631_suppl_Supplementary_Figure_1Click here for additional data file.jiz631_suppl_Supplementary_Table_1Click here for additional data file.jiz631_suppl_Supplementary_Table_2Click here for additional data file.


Modelling of longitudinal biomarkers and time-to-event data are important to monitor disease progression. However, these two variables are traditionally analyzed separately or time-varying Cox models are used. The former strategy fails to recognize the shared random-effects from the two processes while the latter assumes that longitudinal biomarkers are exogenous covariates, resulting in inefficient or biased estimates for the time-to-event model. Therefore, we used joint modelling for longitudinal and time-to-event data to assess the effect of longitudinal CD4 count on mortality.We studied 4014 patients from the Centre for the AIDS Programme of Research in South Africa (CAPRISA) who initiated ART between June 2004 and August 2013. We used proportional hazards regression model to assess the effect of baseline characteristics (excluding CD4 count) on mortality, and linear mixed effect models to evaluate the effect of baseline characteristics on the CD4 count evolution over time. Thereafter, the two analytical approaches were amalgamated to form an advanced joint model for studying the effect of longitudinal CD4 count on mortality. To illustrate the virtues of the joint model, the results from the joint model were compared to those from the time-varying Cox model.Using joint modelling, we found that lower CD4 count over time was associated with a 1.3-fold increase in the risk of death, . Whereas, results from the time-varying Cox model showed lower CD4 count over time was associated with a 1.2-fold increase in the risk of death, .Joint modelling enabled the assessment of the effect of longitudinal CD4 count on mortality while correcting for shared random effects between longitudinal and time-to-event models. In the era of universal test and treat, the evaluation of CD4 count is still crucial for guiding the initiation and discontinuation of opportunistic infections prophylaxis and assessment of late presenting patients. CD4 count can also be used when immunological failure is suspected as we have shown that it is associated with mortality. Until the era of test-and-treat, CD4 count was by far the most widely used biological marker for antiretroviral therapy (ART) eligibility and HIV (human immunodeficiency virus) progression . HoweverAlthough CD4 count testing is no longer recommended for stable virologically suppressed patients in South Africa, however, the CD4 count still plays an important role in stratifying the risk of death among patients with low CD4 count who are failing first line ART regimen . The couIn our setting, it has been shown that patients who started ART with low CD4 count have an increased risk of death  and it iJoint models of longitudinal and time-to-event data have received much attention in the literature dating back to the past two decades –16, and In view of the shortcomings of time-varying Cox models and separate analyses of longitudinal and time-to-event processes, our objective is to use a joint modelling strategy to assess the effect of longitudinal CD4 count on mortality among patients initiated on ART.In this analyses we use data from the Centre for the AIDS Programme of Research in South Africa (CAPRISA). The CAPRISA AIDS Treatment (CAT) programme enrolled HIV positive patients and initiated them on ART between June 2004 and August 2013. Eligibility criteria was in accordance with the Department of Health guidelines throughout. Males and females at least 14 years of age from urban (eThekwini) and rural (Vulindlela) sites were enrolled. Routine demographic and clinical data were recorded at baseline and at follow-up visits. Laboratory safety assessments and CD4 counts and viral loads were conducted at baseline and every 6 months or as clinically indicated. Patients were regarded as lost to follow-up if they missed 3 consecutive scheduled visits and if all attempts to track them telephonically and physically had failed. Information on the deaths was based on hospital chart notes, death certificates or oral reports from patient’s relatives.The eThekwini and Vulindlela sites initiated the first patient on ART in October 2004 and June 2004 respectively. Patients at the eThekwini site were recruited from the Prince Cyril Zulu Clinic of Communicable Disease which is the chest clinic adjacent to the CAPRISA clinic and sometimes patients presented themselves for HIV testing. Patients at the Vulindlela site were recruited from the Mafakatini clinic which is situated near that site or similarly presented themselves seeking health care.In our setting it has been shown that gender is associated with both CD4 count and mortality . Therefo10 viral load and tuberculosis (TB) status. A square root transformation was applied to the data to normalize the CD4 count.Baseline predictors of mortality were assessed through both univariable and multivariable proportional hazards regression. The linear mixed effects models were used to assess the effect of baseline characteristics on the CD4 count evolution post ART initiation, where the individual patient and time post ART initiation were used as random effects. All multivariable models were adjusted for the following baseline covariates: gender , age (in years), clinic site , logProportionality was assessed by the Schoenfeld proportional hazards test which provides proportional hazards test for individual covariates and a global test for the model with all variables combined. Variables that violated the proportional hazards assumption were not included in the proportional hazards model.Thereafter the longitudinal and time-to-event models were coupled to form a joint model. To illustrate the virtues of the joint model we compared it to the time-varying Cox model. Analyses were conducted using SAS, version 9.4  and R version 3.5.1. The JM package by  was usedFormulating a standard joint modelling framework, follows a typical setup where you have a linear mixed-effects (LME) model for the longitudinal data and a Cox proportional hazards (PH) model for the time-to-event data, with the two models sharing some random effects . This isbi∼N thenAssume εij is taken as independent of the random intercept bi0 and slope bi1.with mutually independent error The Cox proportional hazards model with a covariate described by the random effects model given above is written asEstimates for this semi-parametric approach are obtained by the Expectation-maximization (EM) algorithm .tij, that is independent of the random effects and covariate history, the joint likelihood Assuming non-informative censoring and a measurement schedule f(bi|Σb) are the densities of yij and N respectively andwhere with 3, p <0.001) . Out of the 4014, 2557 (63.7%) were females. TB prevalence was higher in men compared to women (32.1% vs. 19.7%). Moreover, women initiated ART with slightly higher CD4 count than men  of 1.7 (0.7-3.1) years, men and women were lost to follow-up at a median (IQR) of 1.5 (0.5–2.9) and 1.9 (0.9–3.2) years respectively (p = 0.118).There were 161  and 235 (9.2%) men and women lost to follow-up respectively, with men having higher lost to follow-up rates in our cohort in keeping with reports from published literature . MoreoveOut of the 4014 patients, 190 had only one CD4 measurement and the baseline mean CD4 count among these patients was 196.3 compared to a baseline mean CD4 count of 137.1 among patients with more than one CD4 count measurement. Of the 190 patients with only one CD4 count measurement, 34 (17.9%) died soon after this measurement was taken at a median time to death of 0.66 years. Among patients with only one CD4 count measurement, 27  presented with WHO clinical stage 1, 56 (29.5) presented with WHO clinical stage 2, 89 (46.8%) presented with WHO clinical stage 3 and 18 (9.5%) presented with WHO clinical stage 4. Among patients with more than one CD4 count measurement, 552/3799 (14.5%) presented with WHO clinical stage 1, 750/3799 (19.7%) presented with WHO clinical stage 2, 2057/3799 (54.15%) presented with WHO clinical stage 3 and 440/3799 (11.6%) presented with WHO clinical stage 4.β= -1.90, S.E= 0.22, p <0.001) when compared to women. However, their CD4 count evolution was not significantly different and thus the interaction term between gender and time was excluded in the model .Men started ART with low CD4 count  and 4.3 (95% CI: 3.8- 4.9) per 100 person-years (p-y), respectively; mortality rate ratio (MRR): 1.54 (1.27-1.88) p <0.001. In addition, patients from the rural site had higher mortality rates compared to those from the urban site, 4.8 per 100 p-y, (95% CI: 4.3-5.4) vs. 5.6 per 100 p-y, (95% CI: 4.7-6.6); MRR: 1.16, (95% CI: 0.95-1.42), p= 0.005. In addition, patients who started ART with a higher baseline viral load had a significantly higher risk of death HR: 1.57, (95% CI: 1.26-1.96), p= 0.004 , p= 0.004 .The joint model finds a significantly strong association between the CD4 count and the risk of death, with a unit decrease in the square root CD4 count corresponding to a 1.3-fold increase in the risk of death . These results are statistically significant indicating that indeed CD4 count is a good predictor of mortality and in fact confirms that an increase in CD4 counts is associated with better survival , showed no statistical difference between the urban and rural sites in terms of the CD4 count improvement over time, with patients from the urban site having a higher rate of change. This finding reaffirms the results obtained by , 26, 27.Results from the survival sub-model  showed that patients from the urban site had a higher survival prognosis compared to those from the rural site, however this was not significant at 5% level of significance. Patients presenting without TB at ART initiation had an elevated risk of dying compared to those with prevalent TB. These results reaffirms the results obtained by . PrevaleThe joint model was advantageous for answering multivariate questions at the same time . The most appealing feature of joint models is its ability to capture or take into consideration the association between the survival time and repeated measurement of a risk factor variable . The joiThe joint model for longitudinal and time-to-event data has several advantages especially in clinical trials. In a survival analysis setting, where the covariate of interest is time-dependent, either the entire history of the covariate for every subject, or, minimally, measurements of the covariate at each time of disease occurrence for all subjects in the corresponding risk set, are necessary. This extensive measurement of covariate is rarely, if ever, executed and the values obtained are typically subject to measurement error. Thus by modelling the covariates over time, we can enhance the survival analysis since we can interpolate covariate values between the observed measurements to the specific times of disease occurrence, with the use of the entire covariate history of the subjects. Furthermore, according to , after aWe found that after ART initiation the CD4 count increases and is influenced by measured covariates such as age, gender TB status and baseline viral load. Furthermore, gender and baseline viral load were found to be significant predictors of all-cause mortality. The joint model found a strong association between CD4 count measurement process and mortality which means that the full CD4 count history is a predictor of mortality. These results are in consonant with previous research , 33, 34.Joint modelling enabled the assessment of the effect of longitudinal CD4 count on mortality while correcting for shared random effects between longitudinal and time-to-event models. In biomedical research where measurements of various outcomes are taken over a time period in an attempt to understand patients’ health or the risk of an event occurring, the joint modelling approach will be the most useful tool to consider in an effort to link the longitudinal measurement process and time-to-event outcomes. In the era of universal test and treat, the evaluation of CD4 count is still crucial for guiding the initiation and discontinuation of opportunistic infections prophylaxis and assessment of late presenting patients. The CD4 count can also be used when immunological failure is suspected as we have shown that it is associated with mortality.The joint modelling approach is likelihood based and assumes that the data is missing at random. Further work will involve sensitivity analysis to determine the impact of departures from this assumption.Additional file 1 Supplementary tables.


Undernutrition is a major public health problem in HIV patients in sub-Saharan Africa. To address the problem of malnutrition, the Ethiopian Ministry of Health implemented a therapeutic feeding program, which is the provision of nutritional treatment, care, and support for undernourished individuals. However, little is known about the outcome of a therapeutic feeding program. Therefore, this study aimed to assess nutritional recovery and its predictors among undernourished HIV patients enrolled in a therapeutic feeding program in Northwest Ethiopia.  An institutional-based retrospective cohort study was conducted among 376 randomly selected adult undernourished HIV patients enrolled in the therapeutic feeding program from July 2010 to January 2017 at Finote-Selam General Hospital. Data were collected by reviewing patients' charts, follow-up cards, and undernutrition treatment registration books using a pretested structured checklist. The main outcome variable was nutritional recovery, defined based on body mass index. Bivariable and multivariable log-binomial regression models were used to identify the predictors of nutritional recovery.  From total undernourished HIV patients enrolled in the therapeutic feeding program, 61.2% were recovered with a median recovery time of 12 weeks (IQR 9–17 weeks) for moderate acute malnutrition and 25 weeks (IQR 22–31 weeks) for severe acute malnutrition. Rural residence (adjusted risk ratio (ARR) = 0.53, 95% CI: 0.27–0.85), no formal education , poor ART adherence level , and WHO clinical stage III or IV  decrease the probability of nutritional recovery.  Nutritional supplementation plays a critical role in the nutritional care and treatment of malnourished patients. Healthcare providers should give more attention to persons with poor adherence levels, advanced WHO clinical stage, rural residence, and low educational status. Future prospective follow-up studies should be performed to assess important variables such as family income, food sharing at the household level, and distance to health institutions. In mid-2017, 20.9 million people living with HIV were taking antiretroviral therapy (ART) globally . Sub-SahThere is a significant association between HIV infection and undernutrition. People with HIV are exposed to nutritional deficiencies, and their nutritional status is a strong prognostic factor for the progress of the disease, survival, and functioning levels in the course of the disease. Malnutrition significantly increases the mortality risk for HIV-infected individuals regardless of treatment status. Thus, nutritional support is often identified as one of the most immediate and critical needs for people living with HIV , 8.Nutrition counseling and interventions can slow or reverse the process that leads to weight loss and wasting in people living with HIV/AIDS. In addition, the provision of nutritional treatment, care, and support for those undernourished PLHIVs is important to prevent morbidity and mortality . Several2) are provided with two sachets of ready-to-use therapeutic food (RUTF) daily until recovery from malnutrition or for a maximum of three months. Those with severe acute malnutrition (BMI < 16 kg/m2) are provided with four sachets daily until recovery or for a maximum of six months [In Ethiopia, the prevalence of undernutrition in HIV-infected people was significantly high 12–25%) , 11, 12.–25% , 11x months .Despite the nutritional therapy program being implemented in all hospitals in Ethiopia, evidence on the outcome of nutritional therapeutic feeding interventions among adult HIV patients is still limited. The Ethiopian Ministry of Health and other stakeholders need evidence on the progress of the nutritional intervention program to improve the national guidelines focusing on nutritional intervention and to improve patients' quality of life . TherefoA retrospective cohort study was conducted in Finote-Selam Hospital, which is located in Northwest Ethiopia, and it is 375 km far from Addis Ababa, the capital city of Ethiopia, and 176 km away from Bahir Dar, the capital city of the Amhara regional state.All recorded adult (≥18 years) HIV-infected people who were malnourished (MAM and SAM) and enrolled in the therapeutic feeding program between July 2010 and January 2017 in Finote-Selam Hospital.All malnourished adult HIV patients who enrolled in a therapeutic feeding program between July 2010 and January 2017 in Finote-Selam Hospital and had recorded nutritional treatment outcomes are included.All malnourished adult HIV-infected people with documented nutritional treatment outcomes are included.Pregnant women and patients with body swelling (edematous patients) were excluded from the study because BMI measurement is not accurate for these groups of the population.The required sample size was calculated using Epi Info, version 7, statistical software with the following assumptions: 95% confidence interval, 80% power, and 1 : 1 exposed-to-nonexposed ratio. Sex was taken as an exposure variable because it yields the largest sample size and the percentage of recovery in the nonexposed group  is 67% from a recent study performed at Mekelle Hospital, Northern Ethiopia . The finUndernourished adult HIV-infected people who fulfill the inclusion criteria were selected from the available list of food by prescription registration booklet to develop a sampling frame and then the samples were selected using a simple random sampling method from available records.Data were collected using a pretested structured checklist that was adapted from the WHO nutritional treatment guideline, Ethiopia Federal Ministry of Health food by prescription program registration book, patient chart, and follow-up form. The patient chart, food by prescription registration book, and HIV follow-up card were the source of data. The checklist includes sociodemographic data, clinical and laboratory data, ART-related data, and follow-up data. Data collectors (BSc Nurses) were trained on the objective of the study, the contents of the checklist, and how to extract data from different data sources.The outcome variable was nutritional recovery (recovered/not recovered), which was defined as a participant reached a BMI of 18.5 or more for two consecutive visits within three months for moderate acute malnutrition and six months for severe acute malnutrition patients.The independent variables were age, sex, religion, educational status, occupation, residence, ARV regimen, duration of ART, ART level of adherence, WHO clinical stage, opportunistic infection (OI), CD4 count, and functional status.2–18.5 kg/m2, and severe acute malnutrition is defined as BMI < 16.5 kg/m2. In this study, baseline clinical data means was at the time of malnutrition diagnosis. The level of ART adherence was classified based on the percentage of ARV drugs taken  [Moderate acute malnutrition is defined as BMI 16.5 kg/mcribed.) .Data were entered using Epi data, version 3.1, statistical software and analyzed using SPSS, version 20. Univariate analysis was used to describe the patient's baseline characteristics. The log-binomial regression model was used to identify predictors of nutritional recovery. In this study, we preferred log-binomial regression over the logistic regression model to report the adjusted risk ratio because the incidence of outcome  was greater than 10%.In this study, the mean age of participants was 34 years (±9.2 SD) and the majority (60.2%) were females. Around 45.4% of the participants were married, and 62.0% were educated .μL. The mean BMI was 16.86 (SD + 1.16) at baseline and 18.27 (SD + 1.29) at the end of the study , 61.2% patients were considered recovered after they enrolled in the therapeutic feeding program. An overall total of 146 of 376 (38.8%) patients did not recover from undernutrition, 58 (15.4%) of whom died and 88 (23.4%) not responded. The nutritional recovery rate was 0.081 persons per month with a median recovery time of 12 weeks (IQR 9–17 weeks) for moderate acute malnutrition and 25 weeks (IQR 22–31 weeks) for severe acute malnutrition.In the current study, residence, educational status, ART adherence level, and WHO clinical stage were significant predictors of nutritional recovery at multivariable analysis.It was found that participants with rural residences had a 47% lower probability of recovery when compared with urban (ARR = 0.53 (95% CI: 0.27–0.85)). Not attending any formal education was associated with a decreased probability of nutritional recovery . The probability of nutritional recovery was 86% lower for participants who had poor ART adherence compared with those who had a good adherence level . Similarly, the probability of nutritional recovery was 62% lower for participants who were WHO clinical stage III or IV at baseline as compared with those who were stage I or II  .This study revealed that from the total patients who were on the therapeutic feeding program, nearly two-thirds of patients were recovered from undernutrition, which is comparable with a study done in Mekelle Hospital, Ethiopia , but it Our findings indicated that residence, educational status, ART adherence level, and WHO clinical stage were the predictors of nutritional recovery. A rural residence is described as a barrier to nutritional recovery. This was in concordance with other studies performed in different parts of Africa , 21. TheIn the current study, the ART level of adherence was the other clinical predictor of nutritional recovery. The finding is in agreement with other studies , 22, sugThe study has several limitations. First, the nature of the retrospective study design limits the ability to gather all relevant factors. The data were collected by documentary review, and hence, the analysis and interpretation of the data are restricted to only those variables that are captured in the patient records. Some of the important variables  were not accessible. Second, there could have been a selection bias arising from the fact that the samples were restricted only to patients with recorded nutritional outcomes. Lost to follow-up patients may have different characteristics than regular follow-up patients.In this study, about 61.2% of the malnourished patients recovered after the therapeutic feeding program. This implies that nutrition plays a critical role in comprehensive care, support, and treatment of PLHIV. Therefore, incorporating nutrition interventions into HIV/AIDS programs is essential. Rural residence, no educational status, poor ART adherence level, and WHO clinical stage III or IV were the barriers for nutritional recovery for patients on the therapeutic feeding program. The finding of this study recommends that the therapeutic feeding program could improve the probabilities of nutritional recovery in malnourished HIV patients. In addition, the current finding suggests that a therapeutic feeding program should give more attention to persons with a poor adherence level, advanced WHO clinical stage, rural residence, and low educational status. Future research should be performed by including important variables such as socioeconomic status, food sharing at the household level, and distance to health institutions.