Therapeutic failures are still observed in patients living with HIV despite the considerable benefits of antiretroviral therapy. This study aimed to describe the long-term evolution of immunological and virological parameters in patients undergoing treatments for HIV-1 at the Day Hospital of Bobo-Dioulasso in Burkina Faso.a retrospective descriptive and analytical study covering 10 years from 2009 was conducted at the Sourô Sanou University Hospital Center (CHUSS) in Bobo-Dioulasso. HIV-1-positive patients with at least two viral load measurements and two CD4 T cell counts were included in this study. Excel 2019 and RStudio were used to analyze the data.a total of 265 patients were included in this study. The mean age of the patients was 48 ± 8.98 years and women accounted for 77.7% of the study population. A considerable decrease in the number of patients with TCD4 lymphocytes below 200 cells/μl from year 2 of treatment and a progressive increase in those with TCD4 lymphocytes above 500 cells/μl were observed in the study. Regarding the evolution of viral load, an increase in the proportions of patients with an undetectable viral load and a decrease in those with a viral load greater than 1000 copies/ml were noticed in years 2, 5, 6, and 8 of the follow-up. However, a decrease in the proportions of patients with undetectable viral load and an increase in those with viral load above 1000 copies/ml were observed in the years 4, 7, and 10 of follow-up.this study highlighted the different trends of viral load and LTCD4 evolution over 10 years of antiretroviral treatment. It showed a good immunovirological response was shown at the beginning of antiretroviral therapy, and then, a poor evolution of these markers at certain periods during the follow-up of HIV-positive patients. The progression of Human Immunodeficiency Virus (HIV) infection leads to a weakened immune system in HIV-infected patients and increases their vulnerability to opportunistic infections . The conMany countries have, therefore, developed control strategies such as access to effective antiretroviral (ARV) drugs for people living with HIV (PLWHIV). The ARV treatments aim to make the patient's plasma viral load (PVL) undetectable in order to ensure the restoration of immune responses and break the chain of transmission of the virus ,4.et al. reported 9.1% virological failure while in Nigeria Anude et al. 23.4% virological failure, 22.7% immunological failure and 33% immunovirological discordance among PLWHIV [Unfortunately, therapeutic failures are still observed in patients living with HIV despite the considerable benefits of antiretroviral therapy the quality of life of people living with HIV (PLWHIV). This can result in poor outcomes of the evolution of immunological and virological markers. In Indonesia, Fibriani g PLWHIV ,6.In Burkina Faso rate of 11.9% immunological failure, 8% virological failure and 9.5% immunovirological discordance were highlighted in one study . The pooAlthough some studies were carried out on the evolution of immunovirological markers in Burkina Faso, those on the long-term follow-up of people living with HIV remain scarce and not well-documented. This study aimed to describe the long-term evolution of immunological and virological parameters in patients undergoing treatments for HIV-1 at the Day Hospital of Bobo-Dioulasso in order.Design of the study: a retrospective descriptive and analytical study was conducted at the Sourô Sanou University Hospital Center (CHUSS) in Bobo-Dioulasso. It is one of the six (06) university hospital centers in Burkina Faso. Data was collected from November 1, to December 31, 2019 at the Day Hospital, which provides outpatient care for people living with HIV. The study protocol was approved by the head of the Infectious Diseases Department and the Immunology-Virology Laboratory. Informed consent forms were sought from all patients. The anonymity and confidentiality of the data collected were guaranteed and only the Evaluation and Operational Monitoring of ESTHER Programs (ESOPE) numbers were mentioned on the forms.Study population: HIV-1 infected patients under antiretroviral treatment from 2009 to 2019 with at least two plasma viral loads and two CD4 counts were included in this study. Patients co-infected with HIV-1 and HIV-2, HIV-positive patients lost to follow-up, transferred or deceased HIV-positive patients were excluded in the study.Sample size: a total of two hundred and sixty-five (265) subjects were randomly enrolled in this study without consideration to their age and gender.Variables: the variables collected were the socio-demographic characteristics of the patients  and biological data .Data collection: these social, demographic and biological data were retrieved from the computerized databases ESOPE  and the laboratory database.Laboratory analyses: the LTCD-4 count in whole blood was determined using the BD FACSCount machine. Viral ribonucleic acid (RNA) was extracted from the plasma samples manually or semi-automatically using the Abbott m2000 Real Time extraction kit and the Viral NA Extraction® kit, respectively. The semi-automatic viral RNA extraction was performed on the Arrow® extractor using. Amplification and real-time detection of the viral RNA were performed on the m2000rt or CFx96 thermal cyclers depending on the availability of reagents.Data analysis: Excel 2019 and R were used to analyze the data. Univariate regression analysis was carried out to identify the factors associated with low CD4 count and high viral load level, p<0.05 was considered as statistically significant.A total of 265 patients were included in this study.Socio-demographic characteristics of the patients: the socio-demographic characteristics of the study population are shown in Evolution of the proportions of patients per CD4 lymphocyte count/μl during the monitoring the proportions of patients per CD4 lymphocyte count/μl during follow-up period were indicated in the figure below  [et al. (62.3%) [et al. in their study on the analysis of motivations for the choice of ARVs prescribed to naive infected patients in 2016, reported that the category most affected was unmarried people (41%) [The majority of the patients in the study were not in school and represented 43.5% of the study population followed by those with primary school level 30.2%). This high proportion of uneducated people in our study population could be due to the lack of information about HIV infection, which promotes risky behaviours among them. The study population was consisted of 63.4% unemployed patients and 13.2% people. These proportions of unemployment are greater than that of Ouédraogo %. This h (62.3%) . In contle (41%) .et al. on the profile of routine monitoring biological parameters during follow-up of adolescents on long-term triple therapy in Abidjan in 2018. These results could indirectly reflect the effectiveness of antiretroviral therapy. Indeed, one of the objectives of ART is to restore the immune system of patients but only the viral load remains the indicator of its effectiveness [During the follow-up period, we observed a decrease in the number of patients with CD4 < 200 cells/μl and an increase in the number of those with CD4 > 500 cells/μl from the second year of follow-up. Similar data were reported by Yapo tiveness .Regarding the evolution of plasma viral load, our study reported an increase in the proportion of patients with undetectable viral load from the second year after initiation of ARV treatment. Our results are similar to that of Konaté on the rate of virological failures in adult patients undergoing ARV treatment at Sikasso Hospital in Mali in 2018 who reported a proportion of 74.4% of patients with an undetectable viral load at the second year of treatment . Indeed,However, at years 4, 7 and 10, an increase in the proportion of patients with a viral load above 1000 copies/ml was observed. Our results are comparable to the data from Konaté in Mali 2018 who mentioned 63% of patients with a viral load above 1000 copies/ml followed up for more than 3 years . These rGiven its retrospective nature, this study had some limitations including the lack of data on treatment lines and patient compliance. Non-adherence and changes in treatment lines could explain the variations in viral loads at certain periods of follow-up. This could lead to an underestimation of the factors associated with low CD4 count and high viral load level.This study highlighted the different trends of plasma viral load and LTCD4 evolution over 10 years of antiretroviral treatment. It showed a good immunovirological response was shown at the beginning of antiretroviral therapy, and then, a poor evolution of these markers at certain periods during the follow-up of HIV-positive patients. Although treatment is beneficial in the long term there is a poor immunological and virological response that can lead to a high rate of virological and immunological failure. For better patient management, it would be necessary to carry out a large-scale prospective study to better evaluate the factors that may be associated with these failures.The main objective of antiretroviral treatment is to suppress the plasma viral load, which allows better immune restoration;The monitoring of HIV-infected patients is mainly based on the measurement of the main immunological and virological markers, i.e. the TCD4 lymphocyte count and the plasma viral load.At the Day Hospital of Bobo Dioulasso (Burkina Faso), the immunovirological characteristics of patients infected with HIV-1 and followed up on a long-term basis were documented.


AIDS continues to be a serious global public health issue. It targets CD4 cells and immunological cells, which are in charge of the body's resistance against pathogenic pathogens. In situations with limited resources, CD4 cell measurement is essential for assessing treatment responses and clinical judgments in HIV-infected children receiving Anti-Retroviral Therapy (ART). The volatility of CD4 cells during ART follow-up is still largely uncharacterized, and there are few new datasets on CD4 cell changes over time. Therefore, the purpose of this analysis was to identify the factors that were predictive of CD4 cell count changes over time in children who started ART at Mekelle General Hospital in northern Ethiopia.A retrospective follow-up study was done. 437 patients in Mekelle general hospital, northern Ethiopia, from 2014–2016 were involved. All patients who have started anti-retrieval treatment (ART) and measured their CD4 cell count at least twice, including the baseline and those who initiated ART treatment, were included in the study population. An exploratory data analysis and linear mixed model analysis were used to explore the predictors of CD4 cell count change in patients and consider variability within and between patients.This study found the correlation variation explained in cells accounted for between patients was 61.3%, and the remaining 38.7% variation existed within. This indicates that there is a substantial change in random slope and intercept between and within patients. WHO clinical stage IV , co-infection HIV/TB , children aged 2–5 , and 6–14 years , non-opportunistic infection , and bedridden functional status  were predictors of cell changes over time.This study found that patients receiving ART experienced a significant change in CD4 cells over time. Because 61.3% of the variation in CD4 cells explained between patients and the remaining 38.7% within patients, such nested data structures are often strong correlation evidence. Co-infection of HIV/TB, functional status, age category of children, WHO clinical stage, and opportunistic infections are potential predictors of CD4 cells count change.Hence, special guidance and attention is also required, especially for those patients who have an opportunistic infections, higher WHO clinical stages, co-infections with HIV and TB, and bedridden functional status.The online version contains supplementary material available at 10.1186/s12887-023-04401-7. Acquired Immune Deficiency Syndrome (AIDS) is caused by the Human Immunodeficiency Virus (HIV), which decreases a person’s ability to fight infection by reducing CD4 cell count and attacking an immune cell that is responsible for the body’s immune response to infectious agents –3. HIV/AEthiopia is one of the SSA countries most affected by HIV/AIDS in all its manifestations . There wTreatment of HIV-infected children with ART leads to immune cell reconstitution, as shown by the increase in CD4 cell counts, decreased risk of opportunistic infection, and improved survival . MoreoveThere are many factors that anti-retrieval therapy will affect that act as related predictors of changes in CD4 cell count. Investigations were conducted in Ethiopia to find predictors of changes in CD4 cell count among children receiving ART –13. ThesFor the analysis of patient responses to therapy and clinical decision-making in resource-constrained locations, it is strongly advised to monitor clinical and diagnostic progression as well as analyze CD4 cell counts of patients on ART over time follow-up . HoweverTherefore, the objective of this analysis was to identify the factors that were predictive of CD4 cell count changes over time in children who began ART at Mekelle General Hospital in northern Ethiopia.An institutional-based retrospective follow-up study design was carried out among HIV-infected children (1–14 years of age) who started ART follow-up over time from 2014–2016.The study was carried out in Mekelle General Hospital, Tigray Regional State, northern Ethiopia. Mekelle is the capital city of the Tigray national Regional State, which is 783 km away from Addis Ababa, the capital of Ethiopia. Besides, Mekelle General Hospital has initiated ART service delivery since September 2004, and until now, more than 10,000 HIV-infected participants have been followed-up over time with their ART services.The study population included HIV-positive children (1–14 years) who initiated anti-retrieval treatment in the Mekelle general hospital ART clinic.Patients whose age was 1–14 years old who are attending a minimum of two visits of ART treatment in the ART clinic for refilling their prescription and who were started on ART from January 1, 2014, to January 30, 2016 at Mekelle General Hospital would be included in the study. However, patients who initiated ART and whose information is incomplete, unreadable, or their manual record is lost, as well as patients who have not had at least two follow-up CD4 cell count measures, were excluded from the study.All HIV-positive children who started ART follow-up over time in the General Hospital from January 1, 2014, to January 30, 2016 were included in the study. In this study, 437 HIV-infected children who had two or more CD4 cell count measurements were used in the linear mixed model analysis. Using the pediatric ART registration book, we have created a sampling frame for the general hospital using the list of all children on ART follow-up and aged 1–14 years in order to select the samples using a systematic random sampling procedure.Data were extracted using a structured checklist built according to it, which was developed and adapted from studying different relevant literature , 11. A fFour health professional nurses working at the ART clinic were employed as data collectors and extracted the dataset after they had been trained for 2 days. The data collection process was supervised by two supervisors. The data processing was carried out in private rooms. The collected data set was checked for completeness and consistency and corrected daily by the supervisors and the principal investigator.CD4 cell count measurements were the response variable for this study.The independent variables were selected based on a review of previous literature , 7, 17. Base line data: refers to the data before antiretroviral therapy started .Refers to patients living with HIV and also developing coexistent TB infection .Good Adherence level to medications: Children living with HIV/AIDS on antiretroviral therapy recorded to have taken 95% or higher of their prescribed antiretroviral therapy medication or missed <  = 3 doses as to their agreement with health care provider , 11.Poor adherence level to medications: level of adherence below 95% of their prescribed antiretroviral therapy medication or missed > 3 doses as to their agreement with health care provider , 11.HIV-infected child on antiretroviral therapy developed one or more recorded history of opportunistic infections within follow up time.Advanced WHO clinical stage: are clinical stage III and IV baseline stages of HIV-infected children throughout registration to antiretroviral therapy .Are clinical stage I and II baseline clinical stages of HIV-infected children within antiretroviral therapy registration .Refers to the CD4 cell count change within follow-up time of patients.p-value < 0.05 were found to be statistically significant in a multivariable random S-I of LMM analysis. Moreover, the maximum likelihood parameter estimation approach was used.Data were extracted, entered, cleaned, decoded, and analyzed using the statistical software R version 3.6.3. Descriptive statistics such as frequency, percentages, and figures were used to describe children’s characteristics. A square root transformation was implemented to get rid of skewness in the CD4 cell count data set, and all analyses were performed using the transformed result data. Moreover, to determine the model that best fits the data set, exploratory data analysis was employed first, especially by assessing individual CD4 cell counts over time using individual profile plots and average profile plots. Therefore, individual profile plots of patients were constructed for the first 50 patients to provide a rough image of how patients changed and to provide explanations for variation within and between subjects. Similarly, the average profile plots were constructed to explain the overall mean shift in CD4 cell count for HIV-infected children. To assess the predictors associated with CD4 cell count changes, all predictors were considered in the multivariable linear mixed regression model analysis with a random effect. Variables with a The most common types of covariance structures in repeated measures are unstructured, independent, compound symmetry, and identity, and the magnitude of residual errors was also considered in model selection. The covariance structure model was employed to determine the predictors associated with CD4 cell count over time. As a result, a model with the least within-individual variation when compared to other models’ residual variability was selected. Moreover, these covariance structures will reduce the probability of model misspecification .In this study, these longitudinal models were fitted based on either of the following three model mechanisms –16.I.Random slopes and random intercepts modelT is the dependent variable,This model analysis used both random intercepts and random slopes, in which both random intercepts and slopes are allowed to vary. So, the scores on the outcome variable for each repeated measurement are predicted by the random intercepts and random slopes that vary across individual patients. These models were performed to measure covariates and predict the random effect at the same time. Besides, we identify the variations explained by within-subjects and between-subjects random intercept and random slope model analysis. The general structure for LMM is expressed as;i is the error terms and bi is a random intercept are assumed to be mutually independent and ICCmeasures = 1 − ICCindividual [Now, the Intra-Class Correlation coefficient (ICC) is defined as a set of coefficients representing the relationship between variables of the same individuals that decompose into two independent components. Thus, the ICC explained by the individuals in the population is given by this formula:mentclass2pt{minimdividual .II.Random intercept only model0.This is the simplest example of hierarchical model analysis, in which intercepts are allowed to vary and there are no predictor variables at all. It has only an intercept term and variances at the measurement and individual levels. Since the model doesn’t contain a random slope, the true individual change is a horizontal line with y-intercept bThe model can be expressed as: -bi0 is the random effect representing between-subjects variation, he error .III.Random slope modelA random slopes model is a longitudinal model analysis in which slopes are allowed to vary. Thus, the scores on the response variable for each repeated measurement are predicted by the slope that varies between patients and subjects. The previous models are sometimes called unconditional (intercept only) models because there are no measured covariates to predict the random effect. Now, when occasions vary, we have different sets of measurements taken at different points over time for different individuals –16. SuchIn order to select the best and final model that appropriately fits the given longitudinal data set, it is essential to compare the different linear mixed models using different techniques and methods. Therefore, Akaki Information Criteria (AIC) and Bayesian Information Criteria (BIC) that are calculated from deviance based on the number of estimated parameters k are also most convenient at a 5% level of significance. Accordingly, a model with a smaller value of AIC/BIC was selected as the preferable model , 19, 20.3 for every 6 months of follow-up after ART start. On the subject of WHO clinical stages, 16.7%, 38.22%, 29.52%, and 15.56% of HIV-infected children were in stages I, II, III, and IV, respectively. Of the children’s patients, 204 (46.68%) were at a good adherence level; 291 (66.59%) had working functional status; and 160 (36.61%) had opportunistic infections during ART start. Regarding co-infection with HIV and TB, 33.64% of HIV-infected children were TB positive  were male participants. The estimated mean change in CD4 cell count of the participants was 340.9  cells/mmThe visualized pattern of mentclass2pt{minimth to the last month. It is evident that the total mean To sum up, the overall smooth average profile plot in entclass1pt{minimaThe upper triangle Fig. The study used the AIC, BIC, and Log-lik criterion to compare different models. For each model, the value is computed as AIC =  − 2*ln(likelihood) + 2(p + k) and BIC = -2*ln(likelihood) + ln(N)*k. where, k is the number of parameters estimated and N is the number of observations.Based on the following statistical values of the AIC, BIC, and Log-Lik criteria, a linear mixed effect model with a random S-I model was preferable for modelling since the lowest value is preferable. In this study, the modeling analysis of a linear mixed model with random S-I was assumed to compensate for individual variations in mentclass2pt{minimAfter adjusting for possible confounding predictors using the multivariable LMM with random slope and random intercept analysis, it was found that covariates such as WHO clinical stage, co-infection HIV/TB, functional status, opportunistic infections, and age were significantly associated with In children who are HIV-positive, an increase in the predictor variable causes an increase in CD4 cell count, while an increase in the independent variable causes a decrease in CD4 cell count, as shown by the positive (+ ve) sign of the assessed value of parameters  times lower than that of those patients who with WHO clinical stage I.Age was closely associated with CD4 cell count changes, suggesting that the average The standard deviation estimated values for the random slope and intercept were found to be 0.13 and 2.98, respectively. This indicates that there is substantial variability in random slopes and intercepts between and within patients and that the mean change in Moreover, Table SPatients with HIV are now living longer and dying less due to highly active ART. Effective ART had a major impact on people with HIV infection's longevity and ability to avoid opportunistic infections. According to research from the past and the current study, ART is a useful treatment for HIV that can reduce viral load to undetectable levels.The aim of this study was to identify the rate of CD4 cell count change over time and determine its associated predictors among HIV-infected children who started antiretroviral therapy. The individual profile plots indicated the presence of variation in The results of LMM for random effects indicated that there was a significant variation in the change in mentclass2pt{minimAccording to this study, children's age and CD4 cell count changes had negative associations. The CD4 cell count square root odds were 0.43 and 1.02 times lower than the reference group. This finding is consistent with those of studies carried out in northwest Ethiopia , 21. It Children with opportunistic infections had major negative impacts on CD4 cell count changes over time. This indicated that children with no opportunistic infections had higher Ethiopia , 22. TheChildren with advanced HIV stages have poorer immunological recovery than those with early HIV stages because opportunistic infections are more common in patients with advanced HIV stages .The present CD4 cell count of patients who had opportunistic infections was lower than that of their counterparts. This study is in line with a study conducted in Ethiopia . This maCo-infection HIV and TB are also highly associated with a decrease in CD4 cell count over time. The finding is consistent with previous studies in Ethiopia , 19, whiFunctional status since the initiation of ART in patients was also found to be a statistically significant predictor of CD4 cell count change over time. Therefore, bedridden patients have a low rate of recovery of CD4 cell count changes. Otherwise, children patients who are in working functional status can take the prescribed medication by themselves at the time given by the health professionals, which leads to a good recovery of CD4 cell count. This result is consistent with another study done in Ethiopia, which also reported a significant association between functional status and changes in CD4 cell count over time .However, there are additional variables besides HIV that can affect CD4 cell numbers. Rural living  and lackFinally, the shift in CD4 cell count was significantly impacted negatively by WHO clinical stage-IV. As a result, when compared to patients in stage I, the CD4 squared root chances for patients in stage IV reduced by 1.30. This conclusion is in line with research done in Ethiopia , 25.To account for the patients with/between variations in children, this study used the most appropriate statistical linear mixed model analysis . Additionally, this research will help policymakers create a better plan for managing HIV-positive people. Additionally, the investigation will serve as a starting point for future scholars. The final drawback of this study is that, due to the retrospective follow-up nature of the results, which did not include all the crucial predictor variables like viral load, additional clinical parameters due to a lack of materials, or technical issues, the causal association cannot be precisely well-defined. Additionally, the impact of CD4 cell count can differ due to a lack of high-quality and accessible datasets. These could be regarded as study gaps.This study found that patients receiving ART experienced a significant change in CD4 cells over time. Because 61.3% of the variation in CD4 cells explained between patients and the remaining 38.7% within patients, such nested data structures are often strong correlation evidence. Co-infection of HIV/TB, functional status, age category of children, WHO clinical stage, and opportunistic infections are potential predictors of CD4 cells count change.Therefore, special guidance and attention is also required, especially for those patients who have an opportunistic infections, higher WHO clinical stages, co-infections with HIV and TB, and bedridden functional status.Additional file 1: S1 Table. Selection of correlation structure for ART data. S2 Table. Random parameter estimates and random effects models comparisons with smallest AIC, BIC, and LogLik for ART data. Appendix. R-commands for data analysis.


Globally, HIV/AIDS is one of the diseases that have a huge burden in terms of cost and health of individuals; and Sub-Sahara Africa is the highly affected region by the pandemic. Tanzania is among the countries that have a higher prevalence of HIV/AIDS-related mortality. This study aimed at using the joint survival model to estimate the association between viral load outcome and survival outcome to death adjusting for age, sex, adherence, and visit date.Secondary data from a retrospective cohort of HIV patients attending health care and treatment centers were used to analyze the association between the longitudinal viral load and time-to-death outcomes. The three-step analysis was based on the individual mixed effects linear model and the Cox proportional hazards models to estimate the significance of the independent outcomes, and the joint survival model as a final model. The joint model was used to estimate the factors affecting the average change in log viral load over time and the risk factors for the survival time of HIV patients. The exposures for both models were ART adherence status, age, male, and visit date whereas the outcome for the LMM was log viral load and the outcome for the Cox PH model was time-to-death in years.The joint survival model results revealed that a 10-year increase in age was associated with a 37% increased risk of death , and being male was associated with a 49% higher risk of death  compared to females. The results also provided evidence of an association between the longitudinal log viral load and the survival time to death ) whereby a unit increase in the log viral load was associated with a 26% increase in the risk of death .The joint survival model analysis provided valuable insights into the associations between time to death and log viral load with adherence to ART, age, visit date, and sex of the patients. This implies that viral load suppression, as well as sex and age-specific interventions, are necessary for reducing HIV/AIDS-related deaths. Globally, HIV/AIDS is one of the diseases which have a huge burden in terms of cost. For example, more than US$500 billion was estimated to be spent on the prevention, care, and treatment of HIV/AIDS globally between 2000 and 2015 . DespiteIn Tanzania, the Tanzania HIV Impact Survey (THIS) 2016–2017 revealed that the prevalence of HIV among individuals aged 15 to 49 years was 4.7% with an annual incidence of 0.27% [Several studies have explored the survival outcome of HIV/AIDS patients using Kaplan-Meir and proportional hazards models –12. In sSecondary data from the National AIDS Control Programme (NACP) for HIV-positive patients attending health care and treatment clinic was used.The study employed a retrospective longitudinal study design. The patient visits data from 1996 to 2020 were used to generate the longitudinal data with the earliest observation being in September 1996 and the latest being observed in November 2020. The analyzed data were from Tanzania’s mainland. Patients aged 15 years and over were selected to be included in the study, whereas patients under the age of 15 were excluded because the study was confined to adults.The outcome variables were logarithmic transformed viral load as a longitudinal trajectory and time to death event in years as a survival outcome. The covariates used to predict the outcomes were age group in years, male sex, patient visit date, and adherence status. These variables were included to determine their effects on the outcome variable. The variable ART regimen was not included because it was not available in the dataset, but its effect can be observed through the viral load. The description of the study variables is elaborated in Table Data processing, management, presentation, and analysis were performed by using STATA. Separate analyses for proportional hazards survival and linear mixed effects were performed to estimate the survival and longitudinal biomarker outcomes respectively. Finally, a joint survival model that combines both the time-to-death survival and the log viral load as longitudinal outcomes was performed. The joint survival model is constructed in three steps which are the proportional hazards (PH) model for the time to event, the linear mixed model (LMM) for the longitudinal trajectory, and the joint survival model that combines both the PH and LMM models.A Cox PH model with a time-varying covariate of the observed viral load biomarker But this model assumes that the viral load biomarker doesn’t change until a new measurement is taken, as such, it ignores measurement error in the biomarker.The longitudinal trajectory outcome model assumes the observed continuous biomarker of the log viral load denoted as Where:In this model, The assumption for this model is The true unobserved longitudinal profile up to time  follows :4\documewhere As observed in Table The outcome of interest was the continuous logarithm-transformed viral load count. The linear mixed model accounts for fixed and random effects, and missing values and allows for unequal spaced time intervals. The mixed effects model was run for 103,536 observations of patients which were nested in 99,433 groups of individual patients with repeated measures.The mixed effects model results in Table The intra-class correlation (ICC) post-estimation test result for LMM was 0.388. This shows that the hierarchical LMM was an appropriate model and that 39% of the variability in the log viral load was explained by variables and clustering by repeated measures of the patient.The Cox PH analysis is a commonly used statistical technique to assess the association between risk factors and survival outcomes. The relationship between HIV viral load and patients’ mortality has been extensively studied, as it plays a crucial role in determining the progression and management of HIV/AIDS. In this model, the findings of a Cox proportional hazards (PH) analysis focused on examining the independent effect of adherence status, male sex, age group, and calendar time (visit date) on the risk of death. The variable visit date was excluded from the analysis because it resulted in the violation of the proportional hazards assumption. The Cox PH model was employed to determine the independent effect of HIV viral load on the risk of death while controlling for other relevant factors. The proportional hazard model results showed that, at a 1% level of significance, adherence status, male sex, age group, and visit date were significantly associated with patients’ survival outcome.The Cox PH results as shown in Table The post-estimation test for the proportional hazards assumption indicated the proportional hazards assumption was not violated at a 1% level of significance since the Chi-square test statistic was not significant (P = 0.089). Therefore, the Cox proportional hazards model was appropriate for the analysis of time-to-event data.The joint survival model is a statistical approach that allows for the simultaneous analysis of time-to-event outcomes and longitudinal data. In this study, researchers utilized a joint survival model to estimate the relationships between time to death and log viral load adjusting for adherence status, male sex, age group, and patient visit date. The parameter estimates and corresponding confidence intervals were derived to quantify these associations. The hazard rates for the survival part were obtained by exponentiating the parameter estimates.The results of the joint survival model estimates as observed in Table For the longitudinal part, all variables were significant at a 1% level of significance. Being poor adherent to ART was associated with 1.234 increase in the log viral load , being male was associated with an increase in the log viral load by 0.228 , and for 10 years increase in age the log viral load was decreased by 0.118 . A day change in visits was associated with a decrease in the log viral load by 0.001 .The joint survival model estimated a positive association between the log viral load and time to death as a unit increase in the log viral load was associated with a 26% increase in the risk of death with a hazard rate estimate of 1.262 . A study by Mchunu et al.  had simiThe joint survival model’s estimates for the association between time to death and age group revealed that a 10-year increase in age was associated with a 37% increase in the risk of death. These results are similar to those of other studies conducted in different countries using the joint survival model , 16. ThiBeing male was associated with an increase in the risk of dying by 49% compared to females. These results are similar to the study conducted in Ethiopia by Khorashadizadeh et al. , which sThe joint survival model analysis provided valuable insights into the associations between time to death and viral with the age and sex of the patients. The positive association between log viral load and mortality highlights the critical role of effective viral load suppression in improving patient outcomes. This underscores the importance of regular monitoring and timely intervention to achieve and maintain viral load suppression. The consistent association between age and mortality reinforces the need for age-specific care and interventions to address the unique needs of older patients. Understanding the factors that influence time to death in patients is crucial in improving patient care. Healthcare providers should consider age as a key factor in treatment decision-making and follow-up care.The study limitations include the unavailability of an important ART regimen variable which is a potential confounder hence its effect could not be captured. Also, the presence of missing values is another limitation as it resulted in the reduction of the observations during the analysis. Another limitation is that earlier years’ viral load measurements were few; however, this was compensated for in this analysis by including other covariates. Hence, we suggest further studies that account for missing values and biased data as well as accounting for ART regimen. Finally, the use of secondary institutional data has some limitations because some variables that could be important confounders were not available; future studies could use a well-designed cohort but over a longer period because the events of interest can be very few for a smaller sample size of participants.