Predictors of loss to follow-up among youths living with HIV after transition from pediatric to adult care in Gambella, southwest Ethiopia

Predictors of loss to follow-up among youths living with HIV after transition from pediatric to adult care in Gambella, southwest Ethiopia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predictors of loss to follow-up among youths living with HIV after transition from pediatric to adult care in Gambella, southwest Ethiopia Akello Dorgi, Abayneh Tunje, Mulugeta Shegaze Shimbre, Abebe Gedefaw Belete, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6566568/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Feb, 2026 Read the published version in BMC Infectious Diseases → Version 1 posted 27 You are reading this latest preprint version Abstract Introduction The transition from pediatric to adult healthcare represents a crucial period for adolescents and youth living with HIV. When this healthcare transition is not properly managed, it can result in negative outcomes such as loss of follow-up, increased morbidity, and mortality. Therefore, it is essential to investigate the factors associated with loss of follow-up among youth living with HIV who have transitioned to adult care, especially given the limited evidence available in the study area. Methods A retrospective cohort study was conducted at Gambella General Hospital with 452 HIV-positive youths enrolled in HIV care between January 1, 2019, and December 30, 2022. Data were extracted from patient charts using the Kobo Toolbox. The Kaplan-Meier survival curve was used to estimate the survival time and Log-rank tests were used to compare the survival probabilities. Bivariable and multivariable Cox proportional hazard regression models were fitted to identify predictors of loss to follow-up among youth living with HIV who have transitioned to adult care. Adjusted Hazard Ratio with 95% confidence intervals was used to assess the strength of association and statistical significance. Results The cohort followed for 1252.51 person-years of observation (PYO), exhibited an overall LTFU of 4.1 (95% CI: 3.1, 5.4) per 100 PYO. Predictors of LTFU included engaging in daily labor (AHR = 3.64; 95% CI: 1.84, 7.22), ambulatory/bedridden functional status (AHR = 2.51; 95% CI: 1.27, 4.95), suboptimal adherence to ART (AHR = 2.48; 95% CI: 1.30, 4.73), CD4 counts below 200 cells/mm3 (AHR = 3.59; 95% CI: 1.73, 7.43), and CD4 counts between 200–350 cells/mm3 (AHR = 2.85; 95% CI: 1.29, 6.32). Conclusion The study underscores LTFU as a significant public health concern among youths who transitioned to adult care. Daily labor, ambulatory/bedridden status, suboptimal ART adherence, and low CD4 counts emerged as predictors of LTFU. Therefore, interventions such as message reminders, early tracing, and targeted health education are crucial, especially for youths with poor clinical profiles. Clinical trial number not applicable. youths LTFU loss to follow-up transition predictors Ethiopia Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Adolescents and young people constitute an increasing proportion of individuals living with HIV globally. In 2019, approximately two out of every seven new HIV infections occurred among individuals aged 15 to 24 years ( 1 ). By 2023, an estimated 360,000 young people within this age group were newly infected with the virus ( 2 ). In Ethiopia, the HIV epidemic remains a significant public health concern, with 605,238 people living with HIV in 2023. Youths are among the most affected demographic groups in the country. According to the HIV Related Estimates and Projections in Ethiopia for the years 2023–2024, 54,455 Ethiopian youth are living with HIV ( 3 ). Adolescents and youths face unique challenges in their HIV care, including issues related to testing and access to care, parental permission laws, disclosure during their teenage years, the transition from dependent to independent care, and the shift to adult HIV care ( 4 ). Several teenagers struggle with adherence and retention in care, which compromises their health ( 5 , 6 ). Adolescents with chronic medical conditions have historically encountered significant challenges during the healthcare transition (HCT), which is defined as a purposeful planned movement of adolescents and young adults with chronic physical and medical conditions from child-centered to adult-oriented healthcare systems ( 7 ). In December 2020, UNAIDS established the 95-95-95 targets to be achieved by 2025. These goals aim to ensure that 95% of individuals living with HIV know their status, 95% of those diagnosed are on antiretroviral therapy (ART), and 95% of those receiving treatment attain viral suppression. Meeting these interconnected milestones would result in at least 86% of the global population living with HIV achieving viral suppression by 2025 ( 8 ). Reaching these ambitious targets requires sustained adherence to treatment and uninterrupted care. Facilitating a smooth transition from pediatric HIV care is crucial for ensuring successful treatment outcomes and long-term well-being and addressing broader public health concerns as the population of adolescents receiving antiretroviral medication (ART) continues to grow and mature into adulthood ( 9 ). When HCT is chaotic and poorly managed, adolescents living with HIV (ALWH) may lose from follow-up, increasing their risk of forward transmission as well as morbidity and death ( 10 , 11 ). Emerging data on outcomes among youth after transition are concerning, with most studies revealing low retention and high mortality rates ( 5 , 7 ). According to a recent study by the US HIV Research Network, 19.8% of youths were lost to follow-up after transitioning to adult HIV care ( 12 ). Similarly, studies done in Ethiopia and Tanzania showed that 13.7% and 42.2% of individuals were lost from follow-up respectively( 13 , 14 ). Previous studies have identified several factors associated with loss to follow-up (LTFU) among individuals receiving HIV care. These include age, level of educational attainment, clinical stage of the disease as classified by the World Health Organization (WHO), presence of opportunistic infections, and nutritional status ( 13 – 15 ). In recent years, numerous studies and related reviews have focused on the transition of young people living with HIV from pediatric to adult health care services. However, there remains a significant gap in the literature regarding the post-transition outcomes of youth, particularly in regions with a high HIV burden. Gambella, a region with one of the highest HIV prevalence rates in Ethiopia ( 3 ), presents a unique context where young people are especially vulnerable to both HIV infection and loss of follow-up after initiating antiretroviral therapy. Despite the critical importance of ensuring continuity of care during this transition period, no prior studies have specifically examined the rate and predictors of loss to follow-up among youth living with HIV in this setting. Therefore, this study was conducted to assess the rate of loss to follow-up and identify associated factors among youth after transitioning to adult HIV care in Gambella. Findings from this study are expected to inform targeted interventions aimed at improving retention in care and treatment outcomes for this high-risk population. METHODS AND MATERIALS Study design, period, and setting A retrospective cohort study was carried out from January 1, 2019, to December 30, 2022. The study was conducted at Gambella General Hospital in Gambella Town. Gambella is the capital of the Gambella region, and the city is 777 kilometers from Addis Ababa. Data from the Gambella Regional Health Bureau showed that there are 106 health posts, 35 health centers (31 governmental and 4 NGOs), and 5 hospitals in the area (four primary hospitals and one general hospital). Currently, 800 Hiv positive youths have active follow-ups at this hospital. Population All youths (aged 15–24) living with HIV who transitioned from pediatric to adult-oriented care at Gambella General Hospital were the source population. The study population comprised all youths living with HIV who transitioned from pediatric to adult-oriented care at Gambella General Hospital from January 1, 2019, to December 30, 2022. Youths who had transitioned from pediatric to adult care but were subsequently transferred to another healthcare facility, as well as those with incomplete records of indicators of the outcome variable, were excluded from the study. Sample size determination and sampling procedure The sample size was determined by using STATA version 14 software by considering the following assumptions: 95% confidence level, a power of 80%, a covariate of interest of 0.5, a hazard ratio (HR = 0.58), and an overall probability of event (LTFU) at the end of the study = 0.32 taken from a previous study performed in Ethiopia ( 16 ). After adding a 10% withdrawal probability, the total sample size becomes 409. At Gambella General Hospital there were a total of 467 youths who live with HIV and transitioned to adult-oriented care from January 1, 2019, to December 30, 2022. Of them, 15 individuals were excluded from the study (9 were found with incomplete records and 6 were transferred to another health facility during the follow-up period). Even though our calculated total sample size was 409, we included all the eligible youths (n = 452) who transitioned between January 1, 2019, and December 30, 2022, in our study (Fig. 1 ). Study variables The outcome variable was LTFU (dichotomized as an event, coded as “1”, and censored, coded as “0”). The independent variables are sociodemographic factors including age, sex, education, religion, occupation, marital status, and residence; clinical, treatment, and behavioral factors including CD4 count, clinical staging, opportunistic infections, viral load, functional status, TB infection, adherence, disclosure status, and substance use. Operational definitions Adherence to antiretroviral therapy : is categorized as follows: good adherence, when the percentage of missed doses falls below 5% (less than two out of 30 doses or less than three out of 60 doses), as determined by the ART physician or clinician; fair adherence when the ART physician or clinician notes that the percentage of missed doses ranges between 85% and 94% (equivalent to three to five missed doses out of 30 doses or three to nine missed doses out of 60 doses); and poor adherence when the percentage of missed doses is less than 85% (equivalent to more than six missed doses out of 30 doses or more than nine missed doses out of 60 doses)( 17 ). Censored Patients who were actively on follow-up at the end of the study or those who were dead. HIV status disclosure HIV status disclosure among youth refers to the process through which individuals within the youth demographic group (typically ranging from 15 to 24 years of age) inform others (sexual partners, families, friends) about their HIV-positive status( 17 ). Loss to follow-up (Event) If the patient had been followed up at least once after ART initiation but had not had contact with the clinic for 90 consecutive days or more since their last recorded expected return date ( 16 ). Tuberculosis infection (TB) is defined as individuals who have been diagnosed with HIV infection and who have a documented history of tuberculosis infection, encompassing both active and latent TB infections at any time in their lives( 17 ). Transition The World Health Organization (WHO) defines the HIV care transition as a purposeful planned movement from pediatric to adult care services ( 18 ). Viral load A viral load of less than 1000 RNA copies/mL was defined as low, while a viral load greater than or equal to 1000 RNA copies/mL after at least six months of first-line ART treatment was considered high ( 19 ). Substance use was defined as alcohol consumption or cigarette smoking. Youth individuals aged 15–24 years ( 4 ). Data collection tools, and procedure The data were extracted from patient charts by using an electronic data extraction checklist on the Kobo Toolbox. The standardized ART entry, thorough transition, and follow-up forms used by the ART clinic were the basis for the development of the data extraction checklist ( 20 ). Four data collectors with a bachelor's degree in nursing, trained in HIV management, and/or working in ART clinics were hired, along with two supervisors who had a BSc in Public Health. All the data collectors and supervisors underwent three days of training on the study objectives, data extraction methods, and the importance of maintaining mutual understanding and confidentiality. Data quality control The data collection instrument was pretested on a 5% sample size in the study area before the actual date of data collection, and necessary corrections were made based on the findings. Three day of training was given to all the data collectors and supervisors. For proper labeling and categorization of the data, the principal investigator and the supervisors regularly reviewed the consistency and completeness of the data being collected. The completed information formats were cross-checked with the source data whenever it appeared that the recording was incomplete or ambiguous. Following the completion of the data collection, the data were exported into computer software to be cleaned, coded, analyzed for frequencies, and checked for consistency. Data processing and analysis The data were collected using the Kobo Toolbox and then exported to STATA version 14 for further data management and analysis. Exploratory data analysis was performed to determine the levels of missing values, normality, and the presence of significant outliers. Descriptive statistics (frequency, percentage, mean with standard deviation, median, and IQR) were computed. The incidence rate of LTFU was calculated over the study period by person-years of observation (PYO) (incidence rate of LTFU = [number of LTFU/cumulative PYO]*100). To determine whether there was a difference in the incidence of loss to follow-up between the groups, Kaplan‒Meier survival curves and the log-rank test were used. A life table was used to estimate the cumulative probability of survival at different time intervals. To discover relationships between dependent and independent variables, the Cox proportional hazards model was used. In the bivariable Cox proportional hazards model, variables with a P value < 0.25 were selected as candidates for the multivariable regression model. In the multivariate Cox proportional hazards model, variables with a P value < 0.05 were identified as independent predictors of loss to follow-up. The post-estimation command "estat phtest" was used to test the proportional hazard assumptions using the Schoenfeld residuals test (phtest) and found a p-value = 0.2173. The strength of the association and statistical significance were assessed using the hazard ratio with a 95% confidence interval and P value. The goodness of fit of the model was checked by the Cox-Snell residual plot, which showed that the hazard function followed the 45-degree curve very closely over time. Multi-collinearity between variables was checked using a variance inflation factor with a cut-off point with a median VIF < 5, and a mean VIF of 2.22 is observed in our study. Ethical Clearance The IRB at Arba Minch University approved a study involving patient charts without physical contact, granting ethical approval. The study was supported by the School of Public Health and patient consent was obtained. Data was kept confidential and conducted according to the Helsinki Convention. RESULTS Sociodemographic characteristics The charts of 452 HIV-positive patients were reviewed and analyzed. The median age at the transition from pediatrics to adult units of care was 20 years, with an interquartile range (IQR: 19–21). Approximately half, 229 (50.7%) of the study participants, made the transition when they were between 20 and 24 years old. Of the total youths included in the study, 241 (53.3%) were female. Regarding the patients' educational backgrounds, the majority, 354 (78.3%), had a secondary education, while 69 (15.3%) had a tertiary education. A total of 407 (90.0%) study participants lived in urban areas, and the majority were single (Table 1 ). Table 1 Sociodemographic characteristics of HIV-positive youth living with HIV after the transition from pediatric care to the adult unit of care at Gambella General Hospital, Southwest Ethiopia, 2023 ( n = 452). Characteristics Categories Frequency(n = 452) Percentage (%) Age at transition (years) ≤ 19 20–24 223 229 49.3 50.7 Sex Female Male 241 211 53.3 46.7 Marital status Married Single 63 389 13.9 86.1 Occupation Student 323 71.1 Merchant 27 6.7 Day labourer 52 11.5 Other # 50 11.0 Religion Protestant Orthodox Muslim Other* 227 137 69 16 50.6 30.5 15.3 3.6 Educational status Primary ( 1 – 8 ) Secondary( 9 – 12 ) College and above(12+) 29 354 69 6.4 78.3 15.3 Residence Rural Urban 45 407 10.0 90.0 # Unemployed, Farmer, Housewife * Catholic, Apostolic, Non-religious Clinical, treatment, and behavioral-related characteristics Of all the participants, 94 (20.8%) had a history of tuberculosis (TB) infection, and 311 (68.8%) had at least one opportunistic infection other than TB in their lifetime. Sixty-two percent of the youths were in WHO clinical stages I or II. At baseline, 378 study participants (83.6%) had working functional status, while 74 (16.4%) had ambulatory or bedridden functional status. A total of 305 (67.5%) patients had a CD4 count of 350 cells/mm3 or above, while the majority of participants, 386 (85.4%), exhibited good adherence to ART. A total of 390 (86.3%) youths disclosed their HIV status to their family or other relatives, while the remaining participants kept it a secret and did not disclose their status to anyone else (Table 2 ). Table 2 Clinical, treatment, and behavioral-related characteristics of HIV-positive youth living with HIV after the transition from pediatric care to the adult unit of care at Gambella General Hospital, Southwest Ethiopia, 2023 (n = 452). Characteristics Categories Frequency (n = 452) Percentage (%) TB infection Positive Negative 94 358 20.8 79.2 Complete TB treatment Yes No 72 22 76.6 23.4 Opportunistic infections (OIs) Yes No 311 141 68.8 31.2 CD4 ≤ 200 200–350 > 350 76 71 305 16.8 15.7 67.5 Baseline functional status Working Ambulatory/bedridden 378 74 83.6 16.4 WHO Stage I/II Stage III/IV 281 171 62.2 37.8 Viral load Low viral load High viral load 357 95 79.0 21.0 Disclosure status Yes No 390 62 86.3 13.7 Adherence Good Fair/Poor 386 66 85.4 14.6 Substance use (N = 96) Yes No 41 55 42.7 57.3 The incidence rate of loss to follow-up A total of 452 patients who transitioned from pediatric to adult care were followed for at least one year and at most four years during the follow-up period. The median follow-up period was 3 years (IQR: 2–4). The total follow-up length was 1,252.51 person-years of observation (PYO), and the mean survival time was 3.75 (95% CI: 3.68, 3.82) years. Fifty-two patients (11.5%) were lost to follow-up, 6 (1.3%) passed away, and 394 (87.2%) were still actively being followed up at the end of the follow-up period. As a result, the total incidence of loss to follow-up was calculated to be 4.15 (95% CI: 3.16, 5.45) per 100 PYO. The cumulative survival probabilities at the end of 12, 24, and 36 months of follow-up were 96.3%, 91.6%, and 84.7%, respectively. The Kaplan‒Meier survival curve decreased stepwise. However, it did not cross the survival function at a probability of 0.5 (Fig. 2 ). A log-rank test was used to assess the equality of survival function among different categories of different predictors. Hence, the following variables were identified to have a significant difference in survival function among their categories with a log-rank test of p-value less than 0.05. These predictors are functional status, TB status, adherence, WHO clinical stage, age, and attendance at HIV-related health education and counseling. The mean survival time for youths transitioning to adulthood who had a history of tuberculosis was 3.35 (95% CI: 3.13, 3.57) years, and for those with no history of TB, it was 3.86 (95% CI: 3.80, 3.92) years. The mean survival time for participants with working-type functional status was 2.31 (95% CI: 1.56, 3.42) years, and for those with ambulatory or bedridden functional status, it was 1.58 (95% CI: 10.80, 22.97) years. The mean survival time for youths who transitioned to adulthood with good ART adherence was 2.58 (95% CI: 1.78, 3.73) years, and for those with poor ART adherence, it was 14.52 (95% CI: 9.73, 21.66) years (Fig. 3 ). Predictors of loss to follow-up Cox regression models with both bivariate and multivariable analyses were used to evaluate the predictors of loss to follow-up. Predictors with p values less than or equal to 0.25 according to the bivariate Cox regression analysis were deemed candidate variables for the multivariate Cox regression analysis. These predictors are age, CD4, occupation, baseline functional status, level of adherence, TB status history, and WHO stage. Finally, according to the multivariate Cox regression analysis, occupational status, functional status, adherence level, and CD4 count were identified as significant predictors of loss to follow-up (Table 3 ). In this study, patients who were daily laborers had a threefold higher hazard of being lost from follow-up (AHR = 3.64; 95% CI: 1.84, 7.22) than patients who were students. About baseline functional status, patients who were classified as ambulatory or bedridden had a 2.5 times higher hazard of being lost to follow-up compared to those with a working functional status (AHR = 2.51; 95% CI: 1.27–4.95). Youths who demonstrated suboptimal adherence to antiretroviral therapy (classified as fair or poor adherence) had a 2.48 times higher hazard of being lost to follow-up compared to those with good adherence (AHR = 2.48; 95% CI: 1.30, 4.73). Youths on antiretroviral therapy (ART) with CD4 + T-cell counts of 200 cells/mm³ or less had a significantly higher hazard of being lost to follow-up compared to those with CD4 + counts of 350 cells/mm³ or more (AHR = 3.59; 95% CI: 1.73, 7.43). Similarly, those with CD4 + counts between 200 and 350 cells/mm³ were also at increased risk of loss to follow-up (AHR = 2.85; 95% CI: 1.29, 6.32) (Table 3 ). Table 3 Predictors of loss to follow-up among youth living with HIV after the transition from pediatric care to the adult unit of care at Gambella General Hospital, southwestern Ethiopia, 2023 (n = 452). Variables Categories Survival status IR/100 PYOs (95% CI) CHR (95% CI) AHR (95% CI) P -value LTFU Censored Age group (years) ≤ 19 20–24 18 34 204 195 2.6 (1.7, 4.2) 6.0 (4.3, 8.4) 1 2.57 (1.44, 4.57) 1 1.48 (0.78, 2.80) 0.230 Occupation Student Merchant Day laborer Other 21 6 20 5 302 21 32 45 2.23 (1.5, 3.4) 8.7 (3.9, 19.4) 15.3 (9.9, 23.7) 4.5 (1.9, 10.7) 1 4.43 (1.78, 11.01) 7.40 (4.01, 13.67) 2.45 (0.93, 6.65) 1 2.46 (0.92, 6.54) 3.64 (1.84, 7.22) 1.65 (0.60, 4.52) 0.072 < 0.001 0.329 TB history Positive Negative 29 23 65 335 11.9 (8.2, 17.1) 2.3 (1.5, 3.4) 5.45 (3.14, 9.43) 1 1.49 (0.62, 3.60) 1 0.376 Baseline functional status Working Ambulatory/Bedridden 25 27 353 47 2.3 (1.6, 3.4) 15.8 (10.8, 23.0) 1 7.72 (4.68, 13.35) 1 2.51 (1.27, 4.95) 0.008 Adherence Poor/fair Good 24 28 42 358 14.5 (9.7, 21.7) 2.6 (1.8, 3.7) 5.95 (3.45, 10.28) 1 2.48 (1.30, 4.73) 1 0.006 CD4 count ≤ 200 200–350 ≥ 350 23 14 15 53 57 290 12.4 (8.2, 18.6) 7.5 (4.4, 12.6) 1.7 (1.0, 2.8) 8.17 (4.25, 15.71) 4.56 (2.19, 9.47) 1 3.59 (1.73, 7.43) 2.85 (1.29, 6.32) 1 < 0.001 0.010 WHO stage Stage I/II stage III/IV 17 35 264 136 2.2 (1.4, 3.5) 7.4 (5.3, 10.3) 1 3.41 (1.91, 6.09) 1 1.84 (0.97, 3.52) 0.064 AHR: Adjusted hazard ratio; CHR: Crude hazard ratio; IR: Incidence rate; PYOs: Person-Years Observation DISCUSSION The main obstacle to successful treatment is loss of follow-up, which also makes evaluating HIV care and treatment programs challenging. The primary objective of this study was to determine the incidence rate of loss to follow-up and to identify its associated factors among youths living with HIV who were receiving antiretroviral therapy and had transitioned from pediatric to adult care services. The incidence of loss to follow-up in this investigation was 11.5%, with an incidence rate of 4.15 (95% CI: 3.1–5.4) per 100 person-years of observation (PYOs). This result is in line with the studies performed in Zimbabwe, which reported a rate of 4.92 per 100 person-years ( 21 ). However, the findings of this study showed a notable contrast with previous research conducted in Thailand, where the loss to follow-up rate among perinatally infected youth was reported to be 2.9 per 100 person-years of observation (PYO), which is significantly lower than the rate observed in this study ( 22 ). This variance may stem from challenges commonly encountered in developing countries in implementing tracing mechanisms, such as weak healthcare systems, limited resources, and inadequate data systems. Nonetheless, the observed rate was still lower than that reported in studies conducted outside of Ethiopia. For instance, in Kenya, rates of 10.2 and 52.9 per 100 PYO were reported in separate studies. Similarly, the loss to follow-up rate in Malawi was 19.3 per 100 PYO, while another study conducted in Thailand reported a rate of 10.8 per 100 PYO ( 22 – 25 ). This variance may be caused by differences in the study setting, study time, follow-up period, sample size, and sociodemographic characteristics of the study participants. In this study, patients attending ART clinics who were daily laborers had a threefold greater risk of being lost to follow-up than patients who were students. It is similar to previous studies done in Ethiopia, and South Africa ( 26 – 28 ). One possible explanation for the greater loss of follow-up among daily laborers could be attributed to their need to travel from their residence to their place of work, potentially causing difficulties in attending follow-up appointments. In contrast, students who typically remain at home may find it easier to adhere to their follow-up schedules because they are not engaged in daily labor activities essential for their livelihood. Additionally, students may attend their follow-up appointments more willingly and without the psychological stress often associated with workplace environments, potentially contributing to their better adherence to the follow-up schedule. Regarding baseline functional status, patients classified as ambulatory/bedridden had a higher risk of being lost to follow-up than that of their counterparts. The findings of the current study are in line with research conducted in Nigeria ( 29 ). Additionally, a study performed in Godar, Ethiopia, reported a similar result, showing that participants with ambulatory functional status had twice the hazard of being lost to follow-up ( 16 ). This could be attributed to the economic, financial, and social impacts faced by bedridden or ambulatory patients, potentially affecting their retention in care. Youths who were suboptimally (fairly or poorly) adherent to ART treatment had a higher risk of being lost to follow-up than that of their counterparts. This finding is supported by studies performed in Vietnam ( 30 ) and Nigeria ( 31 ), as well as in Ethiopia ( 16 , 32 , 33 ). Suboptimal treatment adherence can lead to AIDS progression, opportunistic infection flare-ups, treatment failure, and the emergence of antiretroviral drug-resistant viral strains, ultimately resulting in loss to follow-up and death ( 34 ). In this study, youth with a CD4 count less than 350 were more likely to be lost to follow-up than those with a CD4 count greater than 350. This finding aligns with research from Ethiopia, where the risk of loss to follow-up increases with decreasing CD4 counts( 13 ). This may be due to the fact that patients with lower CD4 + T-cell counts are more likely to be sick and experience side effects from antiretroviral therapy (ART), making it challenging for them to attend clinic appointments. CONCLUSION The incidence of loss to follow-up was high in the study setting, with predictors including daily labor, ambulatory/bedridden functional status, suboptimal (fair or poor) adherence to ART treatment, and a CD4 count lower than 350 cells/mm 3 . To minimize loss to follow-up, emphasis should be given to youth with adverse clinical characteristics, including ambulatory/bedridden functional status, suboptimal (fair or poor) adherence, and low CD4 counts. Delivering message reminders and implementing early tracing strategies should be given to them. Furthermore, conducting a prospective follow-up study could provide insights into additional predictors of loss to follow-up, such as financial problems, mental health disorders, and societal and health system-related factors. Abbreviations ALWH Adolescent Living with HIV ADYA Adolescents and Young Adults LTFU Loss to Follow-Up HCT Health Care Transition HIV Human Immunodeficiency Virus OI Opportunistic Infection PYOs Person Year of Observation VL Viral Load WHO World Health Organization Declarations Competing interests The authors declare that they have no competing interests. Availability of data and materials The datasets used during the current study are available from the corresponding author upon reasonable request. Consent for publication Not applicable Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Authors' contributions AD, AT, and MSS developed the concept and did the investigation. AD, AT, MSS, AGB, TG, BOA, BBB, AM, MD, and WM did data curation, methodology, supervision, format analysis, project administration, and validation. All authors review and edit the manuscript. ACKNOWLEDGMENT The authors would like to thank Arba Minch University University for its technical help. Gratitude is also given to the study participants and data collectors for their efforts towards the success of this research. References UNAIDS. Young people and HIV. 2021:1-20. UNICEF. Adolescent HIV prevention - UNICEF DATA [Available from: https://data.unicef.org/topic/hivaids/adolescents-young-people/. Institute EPH. HIV related estimates and projections in Ethiopia for the year 2021–2022. The Ethiopian Public Health Institute Addis Ababa, Ethiopia; 2022. Ajiboye AS, Eshetu F, Lulseged S, Getaneh Y, Tademe N, Kifle T, et al. 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BMC Research Notes. 2019;12:1-7. EFMoH. National Comprehensive HIV Prevention, Care and Treatment Training for Healthcare Providers In: Health-Ethiopia Mo, editor. Addis Ababa2022. p. 1-585. Organization WH. HIV and adolescents: guidance for HIV testing and counselling and care for adolescents living with HIV: recommendations for a public health approach and considerations for policy-makers and managers: World Health Organization; 2013. Atnafu GT, Moges NA, Wubie M, Gedif G. Incidence and predictors of viral load suppression after enhanced adherence counseling among HIV-positive adults in West Gojjam Zone, Amhara Region, Ethiopia. Infection and drug resistance. 2022:261-74. FMOH. National consolidated guidelines for comprehensive HIV prevention, care and treatment. Addis Ababa: Fmoh. 2018:1-238. Kranzer K, Bradley J, Musaazi J, Nyathi M, Gunguwo H, Ndebele W, et al. Loss to follow‐up among children and adolescents growing up with HIV infection: age really matters. African Journal of Reproduction and Gynaecological Endoscopy. 2017;20(1). Teeraananchai S, Puthanakit T, Kerr SJ, Chaivooth S, Kiertiburanakul S, Chokephaibulkit K, et al. Attrition and treatment outcomes among adolescents and youths living with HIV in the Thai National AIDS Program. Journal of virus eradication. 2019;5(1):33-40. Mburu C, Njuguna I, Neary J, Mugo C, Moraa H, Beima-Sofie K, et al. Mortality and Loss to Follow-Up Among Adolescents and Young Adults Attending HIV Care Programs in Kenya. AIDS Patient Care and STDs. 2023;37(7):323-31. Ojwang’ V, Penner J, Blat C, Agot K, Bukusi E, Cohen C. Loss to follow-up among youth accessing outpatient HIV care and treatment services in Kisumu, Kenya. AIDS care. 2016;28(4):500-7. Weigel R, Estill J, Egger M, Harries AD, Makombe S, Tweya H, et al. Mortality and loss to follow-up in the first year of ART: Malawi national ART programme. Aids. 2012;26(3):365-73. Mberi MN, Kuonza LR, Dube NM, Nattey C, Manda S, Summers R. Determinants of loss to follow-up in patients on antiretroviral treatment, South Africa, 2004-2012: a cohort study. BMC Health Serv Res. 2015;15:259. Megerso A, Garoma S, Eticha T, Workineh T, Daba S, Habtamu Z, et al. Predictors of loss to follow-up in antiretroviral treatment for adult patients in the Oromia region, Ethiopia. HIV/AIDS - Research and Palliative Care. 2016. Teshale AB, Tsegaye AT, Wolde HF. Incidence and predictors of loss to follow up among adult HIV patients on antiretroviral therapy in University of Gondar Comprehensive Specialized Hospital: A competing risk regression modeling. PLoS One. 2020;15(1):e0227473. Odafe S, Idoko O, Badru T, Aiyenigba B, Suzuki C, Khamofu H, et al. Patients’ demographic and clinical characteristics and level of care associated with lost to follow‐up and mortality in adult patients on first‐line ART in Nigerian hospitals. African Journal of Reproduction and Gynaecological Endoscopy. 2012;15(2). Tran DA, Ngo AD, Shakeshaft A, Wilson DP, Doran C, Zhang L. Trends in and determinants of loss to follow up and early mortality in a rapid expansion of the antiretroviral treatment program in Vietnam: findings from 13 outpatient clinics. PloS one. 2013;8(9):e73181. Agbaji O, Abah I, Falang K, Ebonyi A, Musa J, Ugoagwu P, et al. Treatment discontinuation in adult HIV-infected patients on first-line antiretroviral therapy in Nigeria. Current HIV research. 2015;13(3):184-92. Bantie B, Seid A, Kerebeh G, Alebel A, Dessie G. Loss to follow-up in “test and treat era” and its predictors among HIV-positive adults receiving ART in Northwest Ethiopia: Institution-based cohort study. Frontiers in Public Health. 2022;10:876430. Guyo TG, Toma TM, Haftu D, Kote M, Merid F, Kulayta K, et al. Proportion of Attrition and Associated Factors Among Children Receiving Antiretroviral Therapy in Public Health Facilities, Southern Ethiopia. HIV/AIDS-Research and Palliative Care. 2023:491-502. Organization WH. Consolidated guidelines on HIV prevention, testing, treatment, service delivery and monitoring: recommendations for a public health approach: World Health Organization; 2021. Additional Declarations No competing interests reported. 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16:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6566568/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6566568/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12879-026-12596-0","type":"published","date":"2026-02-02T15:57:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82353997,"identity":"17e948a3-95c7-4b9c-802c-fd4565b7ce48","added_by":"auto","created_at":"2025-05-09 11:09:05","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":27479,"visible":true,"origin":"","legend":"\u003cp\u003eSampling procedures for predictors of loss to follow-up among youths living with HIV who transitioned from pediatric to adult-oriented care at Gambella General Hospital from January 1, 2019, to December 30, 2022, Southwest Ethiopia\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6566568/v1/48daaff28de69e412eb13f23.jpg"},{"id":82358649,"identity":"1f5ec60e-3c58-4893-9bcd-d13182c06805","added_by":"auto","created_at":"2025-05-09 11:25:05","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":397213,"visible":true,"origin":"","legend":"\u003cp\u003eThe overall Kaplan–Meier estimate of the loss to follow-up among youths living with HIV after the transition from pediatric care to the adult unit of care at Gambella General Hospital, Southwest Ethiopia, 2023.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6566568/v1/1378e73f05db830df1e89205.jpg"},{"id":82354000,"identity":"27772e2f-da79-492c-bd11-e66672f5510a","added_by":"auto","created_at":"2025-05-09 11:09:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":32970,"visible":true,"origin":"","legend":"\u003cp\u003eThe Kaplan–Meier estimate of the LTFU by major predictor variables among youth attending the ART clinic at Gambella General Hospital, Western Ethiopia, 2023\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6566568/v1/6f17c7ae29ca7064e1bc5941.png"},{"id":102234942,"identity":"7662c25f-5e62-4b1e-96a5-f43edc7e8c74","added_by":"auto","created_at":"2026-02-09 16:14:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1438640,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6566568/v1/059904f9-2e40-459d-be78-458aa6a7a10f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictors of loss to follow-up among youths living with HIV after transition from pediatric to adult care in Gambella, southwest Ethiopia","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAdolescents and young people constitute an increasing proportion of individuals living with HIV globally. In 2019, approximately two out of every seven new HIV infections occurred among individuals aged 15 to 24 years (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). By 2023, an estimated 360,000 young people within this age group were newly infected with the virus (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In Ethiopia, the HIV epidemic remains a significant public health concern, with 605,238 people living with HIV in 2023. Youths are among the most affected demographic groups in the country. According to the HIV Related Estimates and Projections in Ethiopia for the years 2023\u0026ndash;2024, 54,455 Ethiopian youth are living with HIV (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdolescents and youths face unique challenges in their HIV care, including issues related to testing and access to care, parental permission laws, disclosure during their teenage years, the transition from dependent to independent care, and the shift to adult HIV care (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Several teenagers struggle with adherence and retention in care, which compromises their health (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Adolescents with chronic medical conditions have historically encountered significant challenges during the healthcare transition (HCT), which is defined as a purposeful planned movement of adolescents and young adults with chronic physical and medical conditions from child-centered to adult-oriented healthcare systems (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn December 2020, UNAIDS established the 95-95-95 targets to be achieved by 2025. These goals aim to ensure that 95% of individuals living with HIV know their status, 95% of those diagnosed are on antiretroviral therapy (ART), and 95% of those receiving treatment attain viral suppression. Meeting these interconnected milestones would result in at least 86% of the global population living with HIV achieving viral suppression by 2025 (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Reaching these ambitious targets requires sustained adherence to treatment and uninterrupted care. Facilitating a smooth transition from pediatric HIV care is crucial for ensuring successful treatment outcomes and long-term well-being and addressing broader public health concerns as the population of adolescents receiving antiretroviral medication (ART) continues to grow and mature into adulthood (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen HCT is chaotic and poorly managed, adolescents living with HIV (ALWH) may lose from follow-up, increasing their risk of forward transmission as well as morbidity and death (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Emerging data on outcomes among youth after transition are concerning, with most studies revealing low retention and high mortality rates (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). According to a recent study by the US HIV Research Network, 19.8% of youths were lost to follow-up after transitioning to adult HIV care (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Similarly, studies done in Ethiopia and Tanzania showed that 13.7% and 42.2% of individuals were lost from follow-up respectively(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Previous studies have identified several factors associated with loss to follow-up (LTFU) among individuals receiving HIV care. These include age, level of educational attainment, clinical stage of the disease as classified by the World Health Organization (WHO), presence of opportunistic infections, and nutritional status (\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn recent years, numerous studies and related reviews have focused on the transition of young people living with HIV from pediatric to adult health care services. However, there remains a significant gap in the literature regarding the post-transition outcomes of youth, particularly in regions with a high HIV burden. Gambella, a region with one of the highest HIV prevalence rates in Ethiopia (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), presents a unique context where young people are especially vulnerable to both HIV infection and loss of follow-up after initiating antiretroviral therapy. Despite the critical importance of ensuring continuity of care during this transition period, no prior studies have specifically examined the rate and predictors of loss to follow-up among youth living with HIV in this setting. Therefore, this study was conducted to assess the rate of loss to follow-up and identify associated factors among youth after transitioning to adult HIV care in Gambella. Findings from this study are expected to inform targeted interventions aimed at improving retention in care and treatment outcomes for this high-risk population.\u003c/p\u003e"},{"header":"METHODS AND MATERIALS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design, period, and setting\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eA retrospective cohort study was carried out from January 1, 2019, to December 30, 2022. The study was conducted at Gambella General Hospital in Gambella Town. Gambella is the capital of the Gambella region, and the city is 777 kilometers from Addis Ababa. Data from the Gambella Regional Health Bureau showed that there are 106 health posts, 35 health centers (31 governmental and 4 NGOs), and 5 hospitals in the area (four primary hospitals and one general hospital). Currently, 800 Hiv positive youths have active follow-ups at this hospital.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePopulation\u003c/h3\u003e\n\u003cp\u003eAll youths (aged 15\u0026ndash;24) living with HIV who transitioned from pediatric to adult-oriented care at Gambella General Hospital were the source population. The study population comprised all youths living with HIV who transitioned from pediatric to adult-oriented care at Gambella General Hospital from January 1, 2019, to December 30, 2022. Youths who had transitioned from pediatric to adult care but were subsequently transferred to another healthcare facility, as well as those with incomplete records of indicators of the outcome variable, were excluded from the study.\u003c/p\u003e\n\u003ch3\u003eSample size determination and sampling procedure\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe sample size was determined by using STATA version 14 software by considering the following assumptions: 95% confidence level, a power of 80%, a covariate of interest of 0.5, a hazard ratio (HR\u0026thinsp;=\u0026thinsp;0.58), and an overall probability of event (LTFU) at the end of the study\u0026thinsp;=\u0026thinsp;0.32 taken from a previous study performed in Ethiopia (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). After adding a 10% withdrawal probability, the total sample size becomes 409. At Gambella General Hospital there were a total of 467 youths who live with HIV and transitioned to adult-oriented care from January 1, 2019, to December 30, 2022. Of them, 15 individuals were excluded from the study (9 were found with incomplete records and 6 were transferred to another health facility during the follow-up period). Even though our calculated total sample size was 409, we included all the eligible youths (n\u0026thinsp;=\u0026thinsp;452) who transitioned between January 1, 2019, and December 30, 2022, in our study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eStudy variables\u003c/h3\u003e\n\u003cp\u003eThe outcome variable was LTFU (dichotomized as an event, coded as \u0026ldquo;1\u0026rdquo;, and censored, coded as \u0026ldquo;0\u0026rdquo;). The independent variables are sociodemographic factors including age, sex, education, religion, occupation, marital status, and residence; clinical, treatment, and behavioral factors including CD4 count, clinical staging, opportunistic infections, viral load, functional status, TB infection, adherence, disclosure status, and substance use.\u003c/p\u003e\n\u003ch3\u003eOperational definitions\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003eAdherence to antiretroviral therapy\u003c/b\u003e: is categorized as follows: good adherence, when the percentage of missed doses falls below 5% (less than two out of 30 doses or less than three out of 60 doses), as determined by the ART physician or clinician; fair adherence when the ART physician or clinician notes that the percentage of missed doses ranges between 85% and 94% (equivalent to three to five missed doses out of 30 doses or three to nine missed doses out of 60 doses); and poor adherence when the percentage of missed doses is less than 85% (equivalent to more than six missed doses out of 30 doses or more than nine missed doses out of 60 doses)(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCensored\u003c/strong\u003e \u003cp\u003ePatients who were actively on follow-up at the end of the study or those who were dead.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHIV status disclosure\u003c/strong\u003e \u003cp\u003eHIV status disclosure among youth refers to the process through which individuals within the youth demographic group (typically ranging from 15 to 24 years of age) inform others (sexual partners, families, friends) about their HIV-positive status(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLoss to follow-up (Event)\u003c/strong\u003e \u003cp\u003eIf the patient had been followed up at least once after ART initiation but had not had contact with the clinic for 90 consecutive days or more since their last recorded expected return date (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eTuberculosis infection (TB)\u003c/strong\u003e \u003cp\u003eis defined as individuals who have been diagnosed with HIV infection and who have a documented history of tuberculosis infection, encompassing both active and latent TB infections at any time in their lives(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eTransition\u003c/strong\u003e \u003cp\u003eThe World Health Organization (WHO) defines the HIV care transition as a purposeful planned movement from pediatric to adult care services (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eViral load\u003c/strong\u003e \u003cp\u003eA viral load of less than 1000 RNA copies/mL was defined as low, while a viral load greater than or equal to 1000 RNA copies/mL after at least six months of first-line ART treatment was considered high (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSubstance use\u003c/strong\u003e \u003cp\u003ewas defined as alcohol consumption or cigarette smoking.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eYouth\u003c/strong\u003e \u003cp\u003eindividuals aged 15\u0026ndash;24 years (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData collection tools, and procedure\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe data were extracted from patient charts by using an electronic data extraction checklist on the Kobo Toolbox. The standardized ART entry, thorough transition, and follow-up forms used by the ART clinic were the basis for the development of the data extraction checklist (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Four data collectors with a bachelor's degree in nursing, trained in HIV management, and/or working in ART clinics were hired, along with two supervisors who had a BSc in Public Health. All the data collectors and supervisors underwent three days of training on the study objectives, data extraction methods, and the importance of maintaining mutual understanding and confidentiality.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData quality control\u003c/h3\u003e\n\u003cp\u003eThe data collection instrument was pretested on a 5% sample size in the study area before the actual date of data collection, and necessary corrections were made based on the findings. Three day of training was given to all the data collectors and supervisors. For proper labeling and categorization of the data, the principal investigator and the supervisors regularly reviewed the consistency and completeness of the data being collected. The completed information formats were cross-checked with the source data whenever it appeared that the recording was incomplete or ambiguous. Following the completion of the data collection, the data were exported into computer software to be cleaned, coded, analyzed for frequencies, and checked for consistency.\u003c/p\u003e\n\u003ch3\u003eData processing and analysis\u003c/h3\u003e\n\u003cp\u003eThe data were collected using the Kobo Toolbox and then exported to STATA version 14 for further data management and analysis. Exploratory data analysis was performed to determine the levels of missing values, normality, and the presence of significant outliers. Descriptive statistics (frequency, percentage, mean with standard deviation, median, and IQR) were computed. The incidence rate of LTFU was calculated over the study period by person-years of observation (PYO) (incidence rate of LTFU = [number of LTFU/cumulative PYO]*100). To determine whether there was a difference in the incidence of loss to follow-up between the groups, Kaplan‒Meier survival curves and the log-rank test were used. A life table was used to estimate the cumulative probability of survival at different time intervals. To discover relationships between dependent and independent variables, the Cox proportional hazards model was used. In the bivariable Cox proportional hazards model, variables with a P value\u0026thinsp;\u0026lt;\u0026thinsp;0.25 were selected as candidates for the multivariable regression model. In the multivariate Cox proportional hazards model, variables with a P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were identified as independent predictors of loss to follow-up. The post-estimation command \"estat phtest\" was used to test the proportional hazard assumptions using the Schoenfeld residuals test (phtest) and found a p-value\u0026thinsp;=\u0026thinsp;0.2173. The strength of the association and statistical significance were assessed using the hazard ratio with a 95% confidence interval and P value. The goodness of fit of the model was checked by the Cox-Snell residual plot, which showed that the hazard function followed the 45-degree curve very closely over time. Multi-collinearity between variables was checked using a variance inflation factor with a cut-off point with a median VIF\u0026thinsp;\u0026lt;\u0026thinsp;5, and a mean VIF of 2.22 is observed in our study.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEthical Clearance\u003c/h2\u003e \u003cp\u003eThe IRB at Arba Minch University approved a study involving patient charts without physical contact, granting ethical approval. The study was supported by the School of Public Health and patient consent was obtained. Data was kept confidential and conducted according to the Helsinki Convention.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSociodemographic characteristics\u003c/h2\u003e \u003cp\u003eThe charts of 452 HIV-positive patients were reviewed and analyzed. The median age at the transition from pediatrics to adult units of care was 20 years, with an interquartile range (IQR: 19\u0026ndash;21). Approximately half, 229 (50.7%) of the study participants, made the transition when they were between 20 and 24 years old. Of the total youths included in the study, 241 (53.3%) were female. Regarding the patients' educational backgrounds, the majority, 354 (78.3%), had a secondary education, while 69 (15.3%) had a tertiary education. A total of 407 (90.0%) study participants lived in urban areas, and the majority were single (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic characteristics of HIV-positive youth living with HIV after the transition from pediatric care to the adult unit of care at Gambella General Hospital, Southwest Ethiopia, 2023 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;452).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency(n\u0026thinsp;=\u0026thinsp;452)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at transition (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;19\u003c/p\u003e \u003cp\u003e20\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e223\u003c/p\u003e \u003cp\u003e229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.3\u003c/p\u003e \u003cp\u003e50.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e241\u003c/p\u003e \u003cp\u003e211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.3\u003c/p\u003e \u003cp\u003e46.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003cp\u003e389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003cp\u003e86.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMerchant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDay labourer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003csup\u003e\u003cb\u003e#\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReligion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtestant\u003c/p\u003e \u003cp\u003eOrthodox\u003c/p\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003cp\u003eOther*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e227\u003c/p\u003e \u003cp\u003e137\u003c/p\u003e \u003cp\u003e69\u003c/p\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.6\u003c/p\u003e \u003cp\u003e30.5\u003c/p\u003e \u003cp\u003e15.3\u003c/p\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary (\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eSecondary(\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eCollege and above(12+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003cp\u003e354\u003c/p\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003cp\u003e78.3\u003c/p\u003e \u003cp\u003e15.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003cp\u003e407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003cp\u003e90.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e\u003cb\u003e#\u003c/b\u003e\u003c/sup\u003eUnemployed, Farmer, Housewife \u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e Catholic, Apostolic, Non-religious\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eClinical, treatment, and behavioral-related characteristics\u003c/h2\u003e \u003cp\u003eOf all the participants, 94 (20.8%) had a history of tuberculosis (TB) infection, and 311 (68.8%) had at least one opportunistic infection other than TB in their lifetime. Sixty-two percent of the youths were in WHO clinical stages I or II. At baseline, 378 study participants (83.6%) had working functional status, while 74 (16.4%) had ambulatory or bedridden functional status. A total of 305 (67.5%) patients had a CD4 count of 350 cells/mm3 or above, while the majority of participants, 386 (85.4%), exhibited good adherence to ART. A total of 390 (86.3%) youths disclosed their HIV status to their family or other relatives, while the remaining participants kept it a secret and did not disclose their status to anyone else (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical, treatment, and behavioral-related characteristics of HIV-positive youth living with HIV after the transition from pediatric care to the adult unit of care at Gambella General Hospital, Southwest Ethiopia, 2023 (n\u0026thinsp;=\u0026thinsp;452).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency (n\u0026thinsp;=\u0026thinsp;452)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94\u003c/p\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.8\u003c/p\u003e \u003cp\u003e79.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplete TB treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72\u003c/p\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76.6\u003c/p\u003e \u003cp\u003e23.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpportunistic infections (OIs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e311\u003c/p\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.8\u003c/p\u003e \u003cp\u003e31.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;200\u003c/p\u003e \u003cp\u003e200\u0026ndash;350\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026thinsp;350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76\u003c/p\u003e \u003cp\u003e71\u003c/p\u003e \u003cp\u003e305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003cp\u003e15.7\u003c/p\u003e \u003cp\u003e67.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline functional status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWorking\u003c/p\u003e \u003cp\u003eAmbulatory/bedridden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e378\u003c/p\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.6\u003c/p\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage I/II\u003c/p\u003e \u003cp\u003eStage III/IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e281\u003c/p\u003e \u003cp\u003e171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.2\u003c/p\u003e \u003cp\u003e37.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eViral load\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow viral load\u003c/p\u003e \u003cp\u003eHigh viral load\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e357\u003c/p\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.0\u003c/p\u003e \u003cp\u003e21.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisclosure status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e390\u003c/p\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.3\u003c/p\u003e \u003cp\u003e13.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdherence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003cp\u003eFair/Poor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e386\u003c/p\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.4\u003c/p\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubstance use (N\u0026thinsp;=\u0026thinsp;96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.7\u003c/p\u003e \u003cp\u003e57.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eThe incidence rate of loss to follow-up\u003c/h2\u003e \u003cp\u003eA total of 452 patients who transitioned from pediatric to adult care were followed for at least one year and at most four years during the follow-up period. The median follow-up period was 3 years (IQR: 2\u0026ndash;4). The total follow-up length was 1,252.51 person-years of observation (PYO), and the mean survival time was 3.75 (95% CI: 3.68, 3.82) years. Fifty-two patients (11.5%) were lost to follow-up, 6 (1.3%) passed away, and 394 (87.2%) were still actively being followed up at the end of the follow-up period. As a result, the total incidence of loss to follow-up was calculated to be 4.15 (95% CI: 3.16, 5.45) per 100 PYO. The cumulative survival probabilities at the end of 12, 24, and 36 months of follow-up were 96.3%, 91.6%, and 84.7%, respectively. The Kaplan‒Meier survival curve decreased stepwise. However, it did not cross the survival function at a probability of 0.5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA log-rank test was used to assess the equality of survival function among different categories of different predictors. Hence, the following variables were identified to have a significant difference in survival function among their categories with a log-rank test of p-value less than 0.05. These predictors are functional status, TB status, adherence, WHO clinical stage, age, and attendance at HIV-related health education and counseling. The mean survival time for youths transitioning to adulthood who had a history of tuberculosis was 3.35 (95% CI: 3.13, 3.57) years, and for those with no history of TB, it was 3.86 (95% CI: 3.80, 3.92) years. The mean survival time for participants with working-type functional status was 2.31 (95% CI: 1.56, 3.42) years, and for those with ambulatory or bedridden functional status, it was 1.58 (95% CI: 10.80, 22.97) years. The mean survival time for youths who transitioned to adulthood with good ART adherence was 2.58 (95% CI: 1.78, 3.73) years, and for those with poor ART adherence, it was 14.52 (95% CI: 9.73, 21.66) years (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePredictors of loss to follow-up\u003c/h2\u003e \u003cp\u003eCox regression models with both bivariate and multivariable analyses were used to evaluate the predictors of loss to follow-up. Predictors with p values less than or equal to 0.25 according to the bivariate Cox regression analysis were deemed candidate variables for the multivariate Cox regression analysis. These predictors are age, CD4, occupation, baseline functional status, level of adherence, TB status history, and WHO stage.\u003c/p\u003e \u003cp\u003eFinally, according to the multivariate Cox regression analysis, occupational status, functional status, adherence level, and CD4 count were identified as significant predictors of loss to follow-up (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, patients who were daily laborers had a threefold higher hazard of being lost from follow-up (AHR\u0026thinsp;=\u0026thinsp;3.64; 95% CI: 1.84, 7.22) than patients who were students. About baseline functional status, patients who were classified as ambulatory or bedridden had a 2.5 times higher hazard of being lost to follow-up compared to those with a working functional status (AHR\u0026thinsp;=\u0026thinsp;2.51; 95% CI: 1.27\u0026ndash;4.95). Youths who demonstrated suboptimal adherence to antiretroviral therapy (classified as fair or poor adherence) had a 2.48 times higher hazard of being lost to follow-up compared to those with good adherence (AHR\u0026thinsp;=\u0026thinsp;2.48; 95% CI: 1.30, 4.73). Youths on antiretroviral therapy (ART) with CD4\u0026thinsp;+\u0026thinsp;T-cell counts of 200 cells/mm\u0026sup3; or less had a significantly higher hazard of being lost to follow-up compared to those with CD4\u0026thinsp;+\u0026thinsp;counts of 350 cells/mm\u0026sup3; or more (AHR\u0026thinsp;=\u0026thinsp;3.59; 95% CI: 1.73, 7.43). Similarly, those with CD4\u0026thinsp;+\u0026thinsp;counts between 200 and 350 cells/mm\u0026sup3; were also at increased risk of loss to follow-up (AHR\u0026thinsp;=\u0026thinsp;2.85; 95% CI: 1.29, 6.32) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePredictors of loss to follow-up among youth living with HIV after the transition from pediatric care to the adult unit of care at Gambella General Hospital, southwestern Ethiopia, 2023 (n\u0026thinsp;=\u0026thinsp;452).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eSurvival status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIR/100 PYOs (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP -value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLTFU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCensored\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;19\u003c/p\u003e \u003cp\u003e20\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e204\u003c/p\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.6 (1.7, 4.2)\u003c/p\u003e \u003cp\u003e6.0 (4.3, 8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e2.57 (1.44, 4.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1.48 (0.78, 2.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudent\u003c/p\u003e \u003cp\u003eMerchant\u003c/p\u003e \u003cp\u003eDay laborer\u003c/p\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003cp\u003e6\u003c/p\u003e \u003cp\u003e20\u003c/p\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e302\u003c/p\u003e \u003cp\u003e21\u003c/p\u003e \u003cp\u003e32\u003c/p\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.23 (1.5, 3.4)\u003c/p\u003e \u003cp\u003e8.7 (3.9, 19.4)\u003c/p\u003e \u003cp\u003e15.3 (9.9, 23.7)\u003c/p\u003e \u003cp\u003e4.5 (1.9, 10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e4.43 (1.78, 11.01)\u003c/p\u003e \u003cp\u003e7.40 (4.01, 13.67)\u003c/p\u003e \u003cp\u003e2.45 (0.93, 6.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e2.46 (0.92, 6.54)\u003c/p\u003e \u003cp\u003e3.64 (1.84, 7.22)\u003c/p\u003e \u003cp\u003e1.65 (0.60, 4.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65\u003c/p\u003e \u003cp\u003e335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.9 (8.2, 17.1)\u003c/p\u003e \u003cp\u003e2.3 (1.5, 3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.45 (3.14, 9.43)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.49 (0.62, 3.60)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline functional status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWorking\u003c/p\u003e \u003cp\u003eAmbulatory/Bedridden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e353\u003c/p\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.3 (1.6, 3.4)\u003c/p\u003e \u003cp\u003e15.8 (10.8, 23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e7.72 (4.68, 13.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e2.51 (1.27, 4.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdherence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor/fair\u003c/p\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.5 (9.7, 21.7)\u003c/p\u003e \u003cp\u003e2.6 (1.8, 3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.95 (3.45, 10.28)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.48 (1.30, 4.73)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4 count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;200\u003c/p\u003e \u003cp\u003e200\u0026ndash;350\u003c/p\u003e \u003cp\u003e\u0026ge;\u0026thinsp;350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003cp\u003e14\u003c/p\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53\u003c/p\u003e \u003cp\u003e57\u003c/p\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.4 (8.2, 18.6)\u003c/p\u003e \u003cp\u003e7.5 (4.4, 12.6)\u003c/p\u003e \u003cp\u003e1.7 (1.0, 2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.17 (4.25, 15.71)\u003c/p\u003e \u003cp\u003e4.56 (2.19, 9.47)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.59 (1.73, 7.43)\u003c/p\u003e \u003cp\u003e2.85 (1.29, 6.32)\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHO stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage I/II\u003c/p\u003e \u003cp\u003estage III/IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e264\u003c/p\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.2 (1.4, 3.5)\u003c/p\u003e \u003cp\u003e7.4 (5.3, 10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e3.41 (1.91, 6.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1.84 (0.97, 3.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eAHR: Adjusted hazard ratio; CHR: Crude hazard ratio; IR: Incidence rate; PYOs: Person-Years Observation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe main obstacle to successful treatment is loss of follow-up, which also makes evaluating HIV care and treatment programs challenging. The primary objective of this study was to determine the incidence rate of loss to follow-up and to identify its associated factors among youths living with HIV who were receiving antiretroviral therapy and had transitioned from pediatric to adult care services. The incidence of loss to follow-up in this investigation was 11.5%, with an incidence rate of 4.15 (95% CI: 3.1\u0026ndash;5.4) per 100 person-years of observation (PYOs). This result is in line with the studies performed in Zimbabwe, which reported a rate of 4.92 per 100 person-years (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). However, the findings of this study showed a notable contrast with previous research conducted in Thailand, where the loss to follow-up rate among perinatally infected youth was reported to be 2.9 per 100 person-years of observation (PYO), which is significantly lower than the rate observed in this study (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). This variance may stem from challenges commonly encountered in developing countries in implementing tracing mechanisms, such as weak healthcare systems, limited resources, and inadequate data systems. Nonetheless, the observed rate was still lower than that reported in studies conducted outside of Ethiopia. For instance, in Kenya, rates of 10.2 and 52.9 per 100 PYO were reported in separate studies. Similarly, the loss to follow-up rate in Malawi was 19.3 per 100 PYO, while another study conducted in Thailand reported a rate of 10.8 per 100 PYO (\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). This variance may be caused by differences in the study setting, study time, follow-up period, sample size, and sociodemographic characteristics of the study participants.\u003c/p\u003e \u003cp\u003eIn this study, patients attending ART clinics who were daily laborers had a threefold greater risk of being lost to follow-up than patients who were students. It is similar to previous studies done in Ethiopia, and South Africa (\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). One possible explanation for the greater loss of follow-up among daily laborers could be attributed to their need to travel from their residence to their place of work, potentially causing difficulties in attending follow-up appointments. In contrast, students who typically remain at home may find it easier to adhere to their follow-up schedules because they are not engaged in daily labor activities essential for their livelihood. Additionally, students may attend their follow-up appointments more willingly and without the psychological stress often associated with workplace environments, potentially contributing to their better adherence to the follow-up schedule.\u003c/p\u003e \u003cp\u003eRegarding baseline functional status, patients classified as ambulatory/bedridden had a higher risk of being lost to follow-up than that of their counterparts. The findings of the current study are in line with research conducted in Nigeria (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Additionally, a study performed in Godar, Ethiopia, reported a similar result, showing that participants with ambulatory functional status had twice the hazard of being lost to follow-up (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). This could be attributed to the economic, financial, and social impacts faced by bedridden or ambulatory patients, potentially affecting their retention in care.\u003c/p\u003e \u003cp\u003eYouths who were suboptimally (fairly or poorly) adherent to ART treatment had a higher risk of being lost to follow-up than that of their counterparts. This finding is supported by studies performed in Vietnam (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) and Nigeria (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), as well as in Ethiopia (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Suboptimal treatment adherence can lead to AIDS progression, opportunistic infection flare-ups, treatment failure, and the emergence of antiretroviral drug-resistant viral strains, ultimately resulting in loss to follow-up and death (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, youth with a CD4 count less than 350 were more likely to be lost to follow-up than those with a CD4 count greater than 350. This finding aligns with research from Ethiopia, where the risk of loss to follow-up increases with decreasing CD4 counts(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). This may be due to the fact that patients with lower CD4\u0026thinsp;+\u0026thinsp;T-cell counts are more likely to be sick and experience side effects from antiretroviral therapy (ART), making it challenging for them to attend clinic appointments.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe incidence of loss to follow-up was high in the study setting, with predictors including daily labor, ambulatory/bedridden functional status, suboptimal (fair or poor) adherence to ART treatment, and a CD4 count lower than 350 cells/mm\u003csup\u003e3\u003c/sup\u003e. To minimize loss to follow-up, emphasis should be given to youth with adverse clinical characteristics, including ambulatory/bedridden functional status, suboptimal (fair or poor) adherence, and low CD4 counts. Delivering message reminders and implementing early tracing strategies should be given to them. Furthermore, conducting a prospective follow-up study could provide insights into additional predictors of loss to follow-up, such as financial problems, mental health disorders, and societal and health system-related factors.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALWH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdolescent Living with HIV\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eADYA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdolescents and Young Adults\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLTFU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLoss to Follow-Up\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHealth Care Transition\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHIV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHuman Immunodeficiency Virus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOpportunistic Infection\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePYOs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePerson Year of Observation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eViral Load\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets used during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eAD, AT, and MSS developed the concept and did the investigation. AD, AT, MSS, AGB, TG, BOA, BBB, AM, MD, and WM did data curation, methodology, supervision, format analysis, project administration, and validation. All authors review and edit the manuscript. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eACKNOWLEDGMENT\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Arba Minch University University for its technical help. Gratitude is also given to the study participants and data collectors for their efforts towards the success of this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eUNAIDS. Young people and HIV. 2021:1-20.\u003c/li\u003e\n\u003cli\u003eUNICEF. Adolescent HIV prevention - UNICEF DATA [Available from: https://data.unicef.org/topic/hivaids/adolescents-young-people/.\u003c/li\u003e\n\u003cli\u003eInstitute EPH. HIV related estimates and projections in Ethiopia for the year 2021\u0026ndash;2022. The Ethiopian Public Health Institute Addis Ababa, Ethiopia; 2022.\u003c/li\u003e\n\u003cli\u003eAjiboye AS, Eshetu F, Lulseged S, Getaneh Y, Tademe N, Kifle T, et al. Predictors of HIV testing among youth 15\u0026ndash;24 years in urban Ethiopia, 2017\u0026ndash;2018 Ethiopia population-based HIV impact assessment. PloS one. 2023;18(7):e0265710.\u003c/li\u003e\n\u003cli\u003eDahourou DL, Gautier‐Lafaye C, Teasdale CA, Renner L, Yotebieng M, Desmonde S, et al. Transition from paediatric to adult care of adolescents living with HIV in sub‐Saharan Africa: challenges, youth‐friendly models, and outcomes. Journal of the International AIDS Society. 2017;20:21528.\u003c/li\u003e\n\u003cli\u003eKariminia A, Law M, Davies MA, Vinikoor M, Wools‐Kaloustian K, Leroy V, et al. Mortality and losses to follow‐up among adolescents living with HIV in the Ie DEA global cohort collaboration. Journal of the International AIDS Society. 2018;21(12):e25215.\u003c/li\u003e\n\u003cli\u003eKakkar F, Van der Linden D, Valois S, Maurice F, Onnorouille M, Lapointe N, et al. Health outcomes and the transition experience of HIV-infected adolescents after transfer to adult care in Quebec, Canada. BMC pediatrics. 2016;16:1-7.\u003c/li\u003e\n\u003cli\u003eFrescura L, Godfrey-Faussett P, Feizzadeh A A, El-Sadr W, Syarif O, Ghys PD, et al. Achieving the 95 95 95 targets for all: a pathway to ending AIDS. PLoS One. 2022;17(8):e0272405.\u003c/li\u003e\n\u003cli\u003eHaghighat R, Toska E, Cluver L, Gulaid L, Mark D, Bains A. Transition pathways out of pediatric care and associated HIV outcomes for adolescents living with HIV in South Africa. JAIDS Journal of Acquired Immune Deficiency Syndromes. 2019;82(2):166-74.\u003c/li\u003e\n\u003cli\u003eSaberi P, Johnson MO. Moving toward a novel and comprehensive behavioral composite of engagement in HIV care. AIDS care. 2015;27(5):660-4.\u003c/li\u003e\n\u003cli\u003eTepper V, Zaner S, Ryscavage P. HIV healthcare transition outcomes among youth in North America and Europe: a review. Journal of the International AIDS Society. 2017;20:21490.\u003c/li\u003e\n\u003cli\u003eAgwu AL, Lee L, Fleishman JA, Voss C, Yehia BR, Althoff KN, et al. Aging and loss to follow-up among youth living with human immunodeficiency virus in the HIV Research Network. Journal of Adolescent Health. 2015;56(3):345-51.\u003c/li\u003e\n\u003cli\u003eJerene D, Abebe W, Taye K, Ruff A, Hallstrom I. Adolescents living with HIV are at higher risk of death and loss to follow up from care: Analysis of cohort data from eight health facilities in Ethiopia. PloS one. 2019;14(10):e0223655.\u003c/li\u003e\n\u003cli\u003eTesha ED, Kishimba R, Njau P, Revocutus B, Mmbaga E. Predictors of loss to follow up from antiretroviral therapy among adolescents with HIV/AIDS in Tanzania. PLoS One. 2022;17(7):e0268825.\u003c/li\u003e\n\u003cli\u003eKris EE, Uchenna NS, Selvaggio MP, Babatunde O L-A. Predictors of Lost to Follow Up (LTFU) among HIV Positive Patients Enrolled in 70 PEPFAR Supported Treatment Facilities in Edo, Bayelsa and Lagos States, Nigeria. Global Journal of Health Science. 2022;14(11).\u003c/li\u003e\n\u003cli\u003eMekonnen N, Abdulkadir M, Shumetie E, Baraki AG, Yenit MK. Incidence and predictors of loss to follow-up among HIV infected adults after initiation of first line anti-retroviral therapy at University of Gondar comprehensive specialized Hospital Northwest Ethiopia, 2018: retrospective follow up study. BMC Research Notes. 2019;12:1-7.\u003c/li\u003e\n\u003cli\u003eEFMoH. National Comprehensive HIV Prevention, Care and Treatment Training for Healthcare Providers In: Health-Ethiopia Mo, editor. Addis Ababa2022. p. 1-585.\u003c/li\u003e\n\u003cli\u003eOrganization WH. HIV and adolescents: guidance for HIV testing and counselling and care for adolescents living with HIV: recommendations for a public health approach and considerations for policy-makers and managers: World Health Organization; 2013.\u003c/li\u003e\n\u003cli\u003eAtnafu GT, Moges NA, Wubie M, Gedif G. Incidence and predictors of viral load suppression after enhanced adherence counseling among HIV-positive adults in West Gojjam Zone, Amhara Region, Ethiopia. Infection and drug resistance. 2022:261-74.\u003c/li\u003e\n\u003cli\u003eFMOH. National consolidated guidelines for comprehensive HIV prevention, care and treatment. Addis Ababa: Fmoh. 2018:1-238.\u003c/li\u003e\n\u003cli\u003eKranzer K, Bradley J, Musaazi J, Nyathi M, Gunguwo H, Ndebele W, et al. Loss to follow‐up among children and adolescents growing up with HIV infection: age really matters. African Journal of Reproduction and Gynaecological Endoscopy. 2017;20(1).\u003c/li\u003e\n\u003cli\u003eTeeraananchai S, Puthanakit T, Kerr SJ, Chaivooth S, Kiertiburanakul S, Chokephaibulkit K, et al. Attrition and treatment outcomes among adolescents and youths living with HIV in the Thai National AIDS Program. Journal of virus eradication. 2019;5(1):33-40.\u003c/li\u003e\n\u003cli\u003eMburu C, Njuguna I, Neary J, Mugo C, Moraa H, Beima-Sofie K, et al. Mortality and Loss to Follow-Up Among Adolescents and Young Adults Attending HIV Care Programs in Kenya. AIDS Patient Care and STDs. 2023;37(7):323-31.\u003c/li\u003e\n\u003cli\u003eOjwang\u0026rsquo; V, Penner J, Blat C, Agot K, Bukusi E, Cohen C. Loss to follow-up among youth accessing outpatient HIV care and treatment services in Kisumu, Kenya. AIDS care. 2016;28(4):500-7.\u003c/li\u003e\n\u003cli\u003eWeigel R, Estill J, Egger M, Harries AD, Makombe S, Tweya H, et al. Mortality and loss to follow-up in the first year of ART: Malawi national ART programme. Aids. 2012;26(3):365-73.\u003c/li\u003e\n\u003cli\u003eMberi MN, Kuonza LR, Dube NM, Nattey C, Manda S, Summers R. Determinants of loss to follow-up in patients on antiretroviral treatment, South Africa, 2004-2012: a cohort study. BMC Health Serv Res. 2015;15:259.\u003c/li\u003e\n\u003cli\u003eMegerso A, Garoma S, Eticha T, Workineh T, Daba S, Habtamu Z, et al. Predictors of loss to follow-up in antiretroviral treatment for adult patients in the Oromia region, Ethiopia. HIV/AIDS - Research and Palliative Care. 2016.\u003c/li\u003e\n\u003cli\u003eTeshale AB, Tsegaye AT, Wolde HF. Incidence and predictors of loss to follow up among adult HIV patients on antiretroviral therapy in University of Gondar Comprehensive Specialized Hospital: A competing risk regression modeling. PLoS One. 2020;15(1):e0227473.\u003c/li\u003e\n\u003cli\u003eOdafe S, Idoko O, Badru T, Aiyenigba B, Suzuki C, Khamofu H, et al. Patients\u0026rsquo; demographic and clinical characteristics and level of care associated with lost to follow‐up and mortality in adult patients on first‐line ART in Nigerian hospitals. African Journal of Reproduction and Gynaecological Endoscopy. 2012;15(2).\u003c/li\u003e\n\u003cli\u003eTran DA, Ngo AD, Shakeshaft A, Wilson DP, Doran C, Zhang L. Trends in and determinants of loss to follow up and early mortality in a rapid expansion of the antiretroviral treatment program in Vietnam: findings from 13 outpatient clinics. PloS one. 2013;8(9):e73181.\u003c/li\u003e\n\u003cli\u003eAgbaji O, Abah I, Falang K, Ebonyi A, Musa J, Ugoagwu P, et al. Treatment discontinuation in adult HIV-infected patients on first-line antiretroviral therapy in Nigeria. Current HIV research. 2015;13(3):184-92.\u003c/li\u003e\n\u003cli\u003eBantie B, Seid A, Kerebeh G, Alebel A, Dessie G. Loss to follow-up in \u0026ldquo;test and treat era\u0026rdquo; and its predictors among HIV-positive adults receiving ART in Northwest Ethiopia: Institution-based cohort study. Frontiers in Public Health. 2022;10:876430.\u003c/li\u003e\n\u003cli\u003eGuyo TG, Toma TM, Haftu D, Kote M, Merid F, Kulayta K, et al. Proportion of Attrition and Associated Factors Among Children Receiving Antiretroviral Therapy in Public Health Facilities, Southern Ethiopia. HIV/AIDS-Research and Palliative Care. 2023:491-502.\u003c/li\u003e\n\u003cli\u003eOrganization WH. Consolidated guidelines on HIV prevention, testing, treatment, service delivery and monitoring: recommendations for a public health approach: World Health Organization; 2021.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"youths, LTFU, loss to follow-up, transition, predictors, Ethiopia","lastPublishedDoi":"10.21203/rs.3.rs-6566568/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6566568/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe transition from pediatric to adult healthcare represents a crucial period for adolescents and youth living with HIV. When this healthcare transition is not properly managed, it can result in negative outcomes such as loss of follow-up, increased morbidity, and mortality. Therefore, it is essential to investigate the factors associated with loss of follow-up among youth living with HIV who have transitioned to adult care, especially given the limited evidence available in the study area.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA retrospective cohort study was conducted at Gambella General Hospital with 452 HIV-positive youths enrolled in HIV care between January 1, 2019, and December 30, 2022. Data were extracted from patient charts using the Kobo Toolbox. The Kaplan-Meier survival curve was used to estimate the survival time and Log-rank tests were used to compare the survival probabilities. Bivariable and multivariable Cox proportional hazard regression models were fitted to identify predictors of loss to follow-up among youth living with HIV who have transitioned to adult care. Adjusted Hazard Ratio with 95% confidence intervals was used to assess the strength of association and statistical significance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cohort followed for 1252.51 person-years of observation (PYO), exhibited an overall LTFU of 4.1 (95% CI: 3.1, 5.4) per 100 PYO. Predictors of LTFU included engaging in daily labor (AHR = 3.64; 95% CI: 1.84, 7.22), ambulatory/bedridden functional status (AHR = 2.51; 95% CI: 1.27, 4.95), suboptimal adherence to ART (AHR = 2.48; 95% CI: 1.30, 4.73), CD4 counts below 200 cells/mm3 (AHR = 3.59; 95% CI: 1.73, 7.43), and CD4 counts between 200–350 cells/mm3 (AHR = 2.85; 95% CI: 1.29, 6.32).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study underscores LTFU as a significant public health concern among youths who transitioned to adult care. Daily labor, ambulatory/bedridden status, suboptimal ART adherence, and low CD4 counts emerged as predictors of LTFU. Therefore, interventions such as message reminders, early tracing, and targeted health education are crucial, especially for youths with poor clinical profiles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enot applicable.\u003c/p\u003e","manuscriptTitle":"Predictors of loss to follow-up among youths living with HIV after transition from pediatric to adult care in Gambella, southwest Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-09 11:09:00","doi":"10.21203/rs.3.rs-6566568/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision 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