Incidence and Predictors of Loss to Follow-Up Among ART Patients on Follow-Up at Public Health Facilities in Southwest Ethiopia. A Time-to-Event Analysis

Abstract

Background

Loss to follow-up (LTFU) remains a major challenge to successful antiretroviral therapy (ART), contributing to increased morbidity, mortality, and drug resistance, particularly in Sub-Saharan Africa. Evidence on the timing and predictors of LTFU in rural Ethiopia is limited. This study aimed to determine the time to LTFU and its predictors among adults on ART in selected health facilities of Ilu Aba Bor Zone, Southwest Ethiopia.

Methods

A retrospective cohort study was conducted among 372 adults living with human immune virus (HIV) on ART at 4 public health facilities from October 2018 to November 2022. Data were extracted from standardized ART forms and patient charts. Kaplan–Meier survival analysis estimated survival probabilities, and Cox proportional hazards regression identified predictors of LTFU, reporting adjusted hazard ratios with 95% confidence intervals (CI). Proportional hazards assumptions were checked using Schoenfeld residuals and log–log plots.

Results

Over 6993 person-months of follow-up (median: 19 months), 70 participants (18.8%) were LTFU, mostly within the first year (34.3% in the first 6 months; 32.9% in the second 6 months). Independent predictors included absence of a registered phone number, World Health Organization clinical stage III/IV, noninitiation of cotrimoxazole preventive therapy, nondisclosure of HIV status, and poor or fair adherence.

Conclusion

LTFU was common during early antiretroviral. Strengthening patient tracing, promoting disclosure, initiating preventive therapy, and improving adherence support may enhance retention and contribute to achieving human Immune deficiency virus epidemic control targets.

Plain Language Summary Title

Incidence and predictors of loss to follow-up among people living with HIV on antiretroviral therapy at public health facilities in southwest Ethiopia a time-to-event analysis

Plain Language Summary

Background:

Despite the transformative impact of antiretroviral therapy (ART) in managing human immune virus (HIV) as a chronic condition, loss to follow-up (LTFU) remains a critical barrier to treatment success, particularly in Sub-Saharan Africa. LTFU contributes to increased morbidity, mortality, and drug resistance, yet data on its timing and predictors in rural Ethiopia are scarce.

Objective:

This study aimed to determine the time to LTFU and its predictors among adults on ART in selected health facilities of Ilu Aba Bor Zone, southwest Ethiopia.

Methods:

A retrospective cohort study was conducted among 372 adult ART patients from 4 public health facilities (October 2018–November 2022). Data were extracted from standardized ART forms and patient charts. Kaplan–Meier survival analysis and Cox proportional hazards regression were used to estimate survival probabilities and identify predictors, respectively. Proportional hazards assumptions were checked using Schoenfeld residuals and log–log plots. Cox proportional hazards regression was applied to identify predictors, with adjusted hazard ratios and 95% confidence intervals (CI) reported. Statistical significance was set at P < .05.

Results:

During 6993 person-months of follow-up (median: 19 months), 70 patients (18.8%) were LTFU, yielding an incidence rate of 10.01 per 1000 person-months (95% CI [7.9-12.65]). Most losses occurred within the first year after ART initiation. Independent predictors of LTFU included absence of a phone number, advanced World Health Organization stage (III/IV), noninitiation of cotrimoxazole preventive therapy, nondisclosure of HIV status, and poor or fair adherence.

Conclusion:

LTFU was common, particularly during the early treatment phase, strengthening patient tracing through updates

Keywords

antiretroviral therapy Ethiopia HIV/AIDS loss to follow-up predictors survival analysis

Introduction

The advent of antiretroviral therapy (ART) has transformed human immune virus (HIV)/acquired immune deficiency syndrome (AIDS) from a fatal disease into a manageable chronic condition. However, loss to follow-up (LTFU) continues to undermine treatment outcomes, particularly in resource-limited settings.¹ In 2023, an estimated 39.9 million people globally were living with HIV, with the majority receiving ART.² Despite programmatic successes, LTFU remains a persistent challenge, especially in Sub-Saharan Africa, where reported rates range from 13.45% to 57.4%.^3–9

The study was conducted in public health facilities in the Ilu Aba Bor Zone, a predominantly rural area where populations are engaged in subsistence farming and trade. These facilities provide free ART services as part of the national HIV program but face challenges common to resource-limited settings, including high patient volumes and limited staffing. In Ethiopia, LTFU estimates range from 8.95% to 21.3%, reflecting ongoing gaps in patient retention across regions.^7,10–13 LTFU contributes to adverse clinical and public health outcomes, including treatment failure, opportunistic infections (OIs), drug resistance, and increased morbidity and mortality.^12,14 Interruptions in care accelerate disease progression through cluster of differentiation 4 (CD4) decline and viral load rebound, while also increasing the risk of transmitting drug-resistant HIV strains.^15–17

Multiple factors have been associated with LTFU. Evidence from various settings identifies sociodemographic factors (eg, illiteracy, marital status, rural residence, unemployment, male gender), clinical factors (eg, low hemoglobin, advanced World Health Organization [WHO] stage, low CD4 count, OIs, tuberculosis coinfection, regimen changes, poor functional status), and behavioral factors (eg, nondisclosure, alcohol use, smoking, poor adherence) as significant predictors.^4,15,17–19

To address these challenges, Sub-Saharan African programs have implemented retention strategies such as directly observed therapy with additional support, community-based adherence models, and targeted care for high-risk people living with HIV/AIDS (PLWHA).²⁰ Patient tracing approaches—including phone calls, home visits, and their combination—have shown varying effectiveness. For example, in southern South Africa, 33% of LTFU PLWHA were traced by phone, 48% through home visits, and 19% via combined methods.⁴ In Ethiopia, a study at Pawi General Hospital found that of traced PLWHA, 77.1% were alive; among them, 41.2% continued ART at the same facility, 20.8% had transferred to another clinic, and 38% had discontinued treatment.¹⁸

Ethiopia aims to achieve the “third 95” target that 95% of people on ART have viral load suppression by 2030 as part of efforts to end the AIDS epidemic. Achieving this goal requires addressing LTFU through timely identification of at-risk PLWHA. Although several studies have examined factors contributing to defaulting from care, there is limited evidence on the time to LTFU after ART initiation, particularly considering behavioral predictors. This study aimed to estimate the time to LTFU and identify its predictors, including behavioral factors, among adult PLWHA receiving ART in selected public health facilities in the Ilu Aba Bor Zone, Southwest Ethiopia.

Methods

Study Design and Setting

A facility-based retrospective cohort study was conducted in selected health facilities of Ilu Aba Bor Zone Oromia, south western Ethiopia.

Population

The study population consisted of selected records from adult PLWHA enrolled in ART at health facilities in Ilu Aba Bor Zone between October 1, 2018 and November 1, 2022. Records were included if they detailed the date of ART initiation and were excluded if this date was missing.

Sample Size Determination and Sampling Technique

Sample Size Determination

The sample size was calculated for a Cox proportional hazard model using STATA version 14, considering key predictors from previous literature.²¹ Accounting for a 10% adjustment for potential withdrawals, a total sample size of 372 participants was required. The variables considered included WHO clinical stage, cotrimoxazole preventive therapy (CPT) use, and baseline CD4 count <200 cells/mm³. CPT was selected for final sample size estimation as it had the larger sample size while requiring an adequate number of events. The probability of event was derived from prior literature and used in the calculation.²¹ Sample size determination was performed, assuming a 95% confidence interval, 80% power, 5% significance level, and a 10% adjustment for potential withdrawals, resulting in a total sample size of 372 participants. The medical records of adult ART PLWHA receiving ART patient's receiving ART from 4 Health facilities were compiled to create a sampling frame, and participants were subsequently selected through a computer-generated random sampling technique. To report this study, we followed the guidelines in the Strengthening the Reporting of Observational Studies in Epidemiology Statement (Supplemental Material S1).

Sampling Procedure

From 12 health facilities providing ART services, 4 health facilities were selected by simple random sampling (Lottery Method). The estimated sample size was proportionally allocated to each selected Health facilities according to their total population, and the data were collected using systematic random sampling. The sampling interval (k) for each facility was calculated by dividing the total number of ART charts (N) by the allocated sample size (n). The first chart was then selected using a random start between 1 and k.

Study Variables

The study variables included time to LTFU from ART services as the main outcome. The variables considered were sociodemographic factors such as age, sex, marital status, educational status, religion, residence, occupational status, and distance from home to health facility, ethnicity, and registered phone number. Clinical and laboratory characteristics included last functional status, baseline WHO staging, the most recent viral load, baseline body mass index, baseline regimen, and regimen changes. Behavioral factors, including ART drug adherence and disclosure status, as well as past medical history such as history of OIs, active tuberculosis, CPT, and tuberculosis preventive therapy (TPT), were also examined.

Operational Definitions

❖ LTFU: People living with HIV (PLWH) who have not been seen at the ART clinic for at least 90 days or 3 months after the last missed appointment, but not transferred out from the facility to another facility or dead.^11,13

❖ Time to LTFU: is the time interval between the dates of ART initiation and the last missed appointment.^11,13

❖ Transfer-out: PLWH for whom a formal transfer letter has been issued to continue ART care and treatment at another facility.

❖ Events: refers to PLWH who were LTFU form treatments.

❖ Censored: refers to those who died during their follow-up, transferred to other health facilities, and remain on treatment at the end of the study period.

❖ Survival time: refers to the time from clients enrolled to treatments to the occurrence of events or censoring.

❖ Death: when health care professionals documented as death in the ART care and treatment follow up sheet.

❖ ART adherence: is defined as the percentage of ART drug dosage calculated from a monthly total dose, and it was classified as good (if missed < 2 doses), fair (if missed 2-4 doses), or poor (if missed ≥ 5 doses).²²

Data Collection Instrument and Procedure

Data were extracted using a checklist based on ART entry follow-up forms and intake forms. The follow-up forms included baseline variables such as weight, height, functional status, WHO stage, ART initiation date, drug adherence history, and viral load status. The intake forms had 2 parts: Part A covered sociodemographic characteristics, disclosure status, and caregiver availability, while Part B focused on past medical history. Data collectors and supervisors received a 1-day orientation on proper questionnaire completion and confidentiality. Supervisors monitored data collection daily to ensure quality. The data was collected by trained BSc nurses from ART clinics using patient charts and forms. To ensure data quality, the tools were pretested on 5% of the sample at Hurmu health center, and adjustments were made based on feedback.

Data Processing and Analysis

After checking the data for completeness and consistency, it was entered into EPI version 4.6 and then exported to STATA version 17 for cleaning, coding and analysis. Descriptive statistics was carried out and summarized using tables and graphs. Recategorization of categorical variables and categorization of continuous variables was done to be suitable for analysis. The outcome of each participant was dichotomized into LTFU or censored. Incidence density rate was determined by using person time of follow-up as a denominator for the entire cohort and for groups classified based on sociodemographic, clinical and behavioral characteristics. Month was used as time scale to calculate median survival time. Kaplan–Meier survival curve was used for analysis of probabilities of LTFU. Log rank test was used to compare survival curves between the different categories of the explanatory variables. Schoenfeld residuals test for the individual covariates, log–log plot and global tests was used to assess proportional hazard assumption. The Log-likelihood (LL) value was considered to select the best fit model. Finally, Cox Snell residual graph was used to assess overall model adequacy of proportional hazard model. Model fitness for cox proportional hazard assumption was checked by using both statistical and graphical methods. All variables with P-value < .25 of bivariate analysis were considered as candidate for multivariable analysis. Hazard ratio (CHR and adjusted hazard ratio [AHR]) and 95% confidence interval (CI) was calculated by selected model. Variables which had a P-value less than .05 by multivariable were considered statistically significant predictors of loss to follow.

Results

Sociodemographic Characteristics

A total of 372 PLWH were included in the analysis. The median age was 30 years (range: 15-75), and 70 (18.8%) were LTFU over a median follow-up time of 19 months. The incidence density rate was 10.01 per 1000 person-months (95% CI [7.9-12.65]). The sociodemographic, clinical, and behavioral characteristics of the cohort, along with bivariate analysis results for candidate variables (P < .25), are presented in Table 1. Variables such as sex, marital status, and residence were not associated with LTFU in the bivariate analysis and are not shown (Table 1).

Table 1.

Sociodemographic and Clinical Characteristics of Patients by LTFU Status at Selected Health Facilities of Ilu Aba Boor Zone, 2023 (n = 372).

Variables	Categories	LTFUn (%)	Not LTFUn (%)
Sex	Female	49 (70.0)	206 (68.2)
Age (years)	15–24	36 (51.4)	66 (21.9)
Marital status	Single	19 (27.1)	62 (20.5)
Residence	Rural	10 (14.3)	54 (17.9)
Educational status	Primary	47 (67.1)	150 (49.7)
Occupational status	FSW	24 (34.3)	34 (11.3)
Distance from facility	≥5 km	19 (27.1)	116 (38.4)
Registered phone	No	47 (67.1)	65 (21.5)
History of OI	Yes	20 (28.6)	81 (26.8)
History of active TB	Yes	5 (7.1)	27 (8.9)
TPT initiation	No	20 (28.6)	54 (17.9)
CPT initiation	No	51 (72.9)	169 (56.0)
Disclosure status	Not disclosed	49 (70.0)	99 (32.8)
Care giver availability	No	33 (47.1)	99 (32.8)
ART adherence	Poor	16 (22.9)	36 (11.9)
WHO clinical stage	Stage IV	10 (14.3)	29 (9.6)
Baseline regimen	1e (TDF+3TC/TC+EFV)	8 (11.4)	41 (13.6)
Nutritional status	Underweight	6 (8.6)	56 (18.5)
Viral load status	Not done	60 (85.7)	71 (23.5)
Functional status	Bedridden	7 (10.0)	12 (4.0)

Abbreviations: LTFU, lost to follow-up; ART, antiretroviral therapy; TB, tuberculosis; TPT, tuberculosis preventive therapy; CPT, cotrimoxazole preventive therapy; WHO, World Health Organization; FSW, female sex workers; OI, opportunistic infectionTDF, tenofovir disoproxil fumarate, 3TC/TC: lamivudine, EFV, Efavirenz.

Note: Only categories with significant distribution are shown.

Log Rank Estimate of the Variables

In determining the log rank estimate of LTFU among variables, all independent variables were analyzed. Among socio demographic variables age, occupation, educational status, and registered phone numbers had P-value less than .05. Among clinical characteristics, WHO clinical staging, last viral load, last functional status, initiation of TPT and CPT as a prophylaxis were variables which had P-value less than .05. Among behavioral factors disclosure status, availability of caregiver and adherence status were variables which had P-value less than .05 (Table 2).

Table 2.

The Log Rank Estimate of Variables Among PLWH Attending ART in Selected Health Facilities of Ilu Aba Boor Zone, 2023.

Variables	Log Rank Estimate of the Variables
Variables	X2	P-Value
Age	429.9	.000
Sex	0.00	.9556
Marital status	2.79	.4243
Residence	0.32	.5705
Educational status	9.30	.0255
Occupation	16.56	.0054
Region	1.11	.8929
Distance from health facility	2.032	.1516
Registered phone number	21.15	.0000
WHO clinical staging	18.59	.0005
Baseline regimen	1.31	.7278
BMI	4.03	.133
Regimen change	1.02	.3123
Recent viral load	160.97	.0000
Last functional status	33.36	.0000
History of opportunistic infection	2.13	.1444
Active TB	0.31	.5752
Initiation of TPT	13.44	.0002
Initiation of CPT	8.15	.0043
Disclosure status	33.13	.0000
Availability of care giver	27.60	.0000
Adherence	153.80	.0000

Abbreviations: TB, tuberculosis; TPT, tuberculosis preventive therapy; CPT, cotrimoxazole preventive therapy; WHO, World Health Organization; BMI, body mass index.

Incidence and Survival Outcomes of ART PLWHA

The study participants were followed for a total of 6993 person-months, with a median follow-up of 19 months. During this period, 70 HIV-infected PLWHA were LTFU from ART services. Nearly one-third of the LTFU cases, 24 (34.3%), occurred within the first 6 months of ART initiation, while 23 (32.9%) occurred during the second 6 months. The incidence density rate of LTFU was 10.01 per 1000 person-months (95% CI [7.9, 12.65]). The proportional hazards assumption was tested using Schoenfeld residuals, and the global test yielded a P-value of .1198, indicating that the assumption was not violated (P > .05). The Cox–Snell residual plot further confirmed that the estimated hazard function followed the 45° reference line closely, showing that the Cox regression model adequately fit the data.

A Kaplan–Meier survival analysis revealed that PLWHA who did not initiate CPT had a higher probability of LTFU compared to those who received CPT. At the end of follow-up, 243 (65.33%) participants remained on ART, while 70 (18.8%) were LTFU, 38 (10.2%) were transferred out, and 21 (5.7%) had died (Figure 1).

Figure 1.

Time to Loss to Follow-Up Among PLWH Attending ART in Ilu Aba Bor Zone, Ethiopia (2023). (A) Overall Kaplan–Meier Survival Estimate for the Entire Cohort. (B) Kaplan–Meier Survival Estimates Stratified by Initiation of Cotrimoxazole Preventive Therapy (CPT). (C) Final Outcome Status of the Study Cohort at the End of the Follow-Up Period.

Predictors of LTFU

In this study, variables including registered phone number status, distance from the health facility, WHO clinical staging, last viral load status, last functional status, initiation of TPT and CPT, history of OI, disclosure status, availability of a caregiver, and adherence status were considered candidates for the multivariable Cox proportional hazards model. These variables had a P-value < .25 in the bivariate analysis.

After adjustment, several factors were independently associated with the risk of LTFU among adults on ART. PLWHA without a registered phone number had a threefold increased risk of LTFU compared to those with a registered phone number (AHR: 3.14; 95% CI [1.81, 7.15]). PLWHA in WHO clinical stages III and IV had almost 4 times higher risk of LTFU compared to those in stages I and II (AHR: 3.93; 95% CI [1.41, 10.20]). Not initiating CPT prophylaxis was associated with a threefold increased risk of LTFU compared to those who started CPT (AHR: 3.13; 95% CI [1.11, 9.31]). Additionally, PLWHA who did not disclose their HIV status to family members were more than 3 times as likely to be LTFU compared to those who disclosed their status (AHR: 3.61; 95% CI [1.61, 8.12]). Poor and fair adherence to ART drugs was associated with a more than fourfold higher risk of LTFU compared to good adherence (AHR: 4.54; 95% CI [1.70, 8.60]) (Table 3).

Table 3.

Bivariate and Multivariable Cox Proportional Hazards Analysis of Predictors of Loss to Follow-Up Among Adults on ART at Selected Health Faculties of Ilu Aba Boor, 2023.

Variable	Category	CHR [95% CI]	AHR [95% CI]
Distance from HF	< 5 km	1:00	1:00
	> 5 km	0.68 [0.40, 1.15]	1.51 [0.62, 3.82]
WHO stage	I	1:00	1:00
	II	0.32 [0.10, 1.04]	0.39 [0.10, 1.57]
	III and IV	2.36 [1.37, 4.06]	3.93 [1.41, 10.20]*
Functional status	Working	1:00	1:00
	Ambulatory and bedridden	2.26 [1.34-3.79]	0.90 [0.40, 2.00]
OI	Yes	1:00	1:00
	No	0.72 [0.40, 1.15]	0.83 [0.27, 2.52]
TPT	Yes	1:00	1:00
	No	2.91 [1.59, 5.21]	0.95 [0.42, 2.22]
CPT	Yes	1:00	1:00
	No	2.30 [1.27, 4.15]	3.13 [1.11, 9.31]*
Disclosure status	Yes	1:00	1:00
	No	4.75 [2.84, 7.95]	3.61 [1.61, 8.12]*
Availability of care giver	Yes	1:00	1:00
	No	1.62 [1.01, 2.59]	0.70 [0.30, 1.60]
Medication adherence	Good	1:00	1:00
	Poor and fair	6.22 [4.70, 8.15]	4.54 [1.70, 8.60]*
Registered phone	Yes	1:00	1:00
	No	5.60 [3.36, 8.17]	3.14 [1.81, 7.15]*

Abbreviations: OI, opportunistic infection; TPT, tuberculosis preventive therapy; CPT, cotrimoxazole preventive therapy; WHO, World Health Organization; AHR: adjusted hazard ratio; CHR: crude hazard ratio; CI: confidence interval; HF, health facility.

Discussion

Retention in ART programs remains a critical determinant of successful HIV management, yet LTFU continues to compromise treatment efficacy across diverse settings. In this study, conducted at Public health facilities in Ilu Aba Bor Zone, the attrition was most pronounced within the first year after ART initiation, with an incidence density of 10.01 per 1000 person-months. Although this figure is higher than estimates from public hospitals in southern Ethiopia,²³ the North Shoa Zone, and Kigali, Rwanda,²⁴ North Shoa Zone, Ethiopia,²¹ and Rwanda it is lower than findings from Gondar and Jigjiga town, Ethiopia.^13,17 The variations may reflect temporal differences, changes in HIV care strategies, and sociodemographic variations among study populations. More recent studies might capture improvements due to enhanced programmatic interventions and retention efforts.

Advanced HIV disease at ART initiation, operationalized as WHO clinical stages III and IV, was associated with an approximately threefold greater likelihood of LTFU. This pattern has been observed in Malawi,⁸ Tanzania,²⁵ and Debre Markos Hospital,²⁶ suggesting a consistent risk profile for PLWHA with severe immunosuppression. Higher morbidity and mortality, compounded by physical limitations in attending clinic visits, likely explain part of this association. Early identification and closer follow-up of these PLWHA may help reduce attrition.

Lack of CPT was another independent risk factor for LTFU, consistent with evidence from Gondar (15). CPT's protective role is well-established, as it prevents OIs such as pneumocystis pneumonia, toxoplasmosis, and bacterial infections, which can compromise treatment continuity. However, findings from North Shoa²¹ indicated a lower risk of LTFU among those not on CPT, underscoring the need for further research to explore potential confounding influences. Regardless, timely CPT initiation remains an important component of comprehensive ART care.

Nondisclosure of HIV status increased the likelihood of LTFU by more than threefold, echoing findings from Jigjiga (10) where disclosure facilitates social support and reduces stigma-related disengagement. ART adherence was also a robust predictor: PLWHA with poor or fair adherence had over 4 times the risk of LTFU, consistent with studies from the Amhara region.¹³ Disclosure facilitates social support networks essential for adherence and coping, reducing stigma and isolation that often lead to disengagement from care. This underscores the importance of counseling interventions that encourage safe disclosure and strengthen social support to improve retention in ART programs.

Adherence to ART drugs was a robust predictor of retention. PLWHA with poor or fair adherence had over 4 times the risk of LTFU compared to those with good adherence. This is consistent with studies across the Amhara region,⁷ Oromia region,¹² Nigeria³ and Study in Goder.¹⁷ The observed LTFU may be attributed to several factors, including PLWHA feelings of hopelessness regarding their treatment, conflicts with religious beliefs, reliance on traditional healers, limited social support, fear of stigma and discrimination, adverse drug effects, and other socioeconomic challenges that interfere with medication adherence.

A pivotal finding of this study was the robust association between absence of a registered phone number and increased risk of LTFU; PLWHA lacking phone access were over 3 times more likely to disengage from care than their counterparts with phone connectivity. Similar findings have been documented in Pawi Hospital.^18,21 These findings support the routine updating of patient contact details as a low-cost, high-yield retention strategy. This study offers several strengths, including the inclusion of randomly selected health facilities across the Ilu Aba Bor Zone and a sufficient sample size to enhance the representativeness of findings. Importantly, most identified predictors are amenable to intervention within the current healthcare framework, thus providing pragmatic avenues for improving ART retention. However, certain limitations must be acknowledged. The study's reliance on secondary data sources introduces potential biases related to data completeness and accuracy, and the unavailability of variables such as socioeconomic status and psychosocial factors may have restricted a more nuanced understanding of retention dynamics. Future prospective studies incorporating these dimensions could deepen insights and inform tailored interventions.

Implications for Programmatic Practice

The modifiable nature of the key predictors identified here presents actionable targets for enhancing programs within the Ethiopian health system. Routine updating of patient contact information, intensified monitoring of PLWHA initiating ART at advanced disease stages, ensuring timely CPT administration, and strengthening adherence counseling coupled with disclosure support are feasible interventions that could substantially reduce LTFU. These strategies are congruent with Ethiopia's commitment to achieving the United Nation program on HIV/AIDS 95–95–95 goals, particularly the critical third “95” concerning viral suppression through continuous care engagement.

Conclusion

The risk of LTFU was highest in the first 6 months after ART initiation and declined after 18 months. A combination of structural, clinical, and behavioral factors contributes to ART attrition in this setting. Addressing these through feasible, targeted interventions could help reduce early disengagement, improve patient outcomes, and strengthen Ethiopia's progress toward its HIV control goals.

Supplemental Material

sj-docx-1-jia-10.1177_23259582261426232 - Supplemental material for Incidence and Predictors of Loss to Follow-Up Among ART Patients on Follow-Up at Public Health Facilities in Southwest Ethiopia. A Time-to-Event Analysis

Supplemental material, sj-docx-1-jia-10.1177_23259582261426232 for Incidence and Predictors of Loss to Follow-Up Among ART Patients on Follow-Up at Public Health Facilities in Southwest Ethiopia. A Time-to-Event Analysis by Oftana Daba, Dereje Tsegaye and Mohammed Reshad in Journal of the International Association of Providers of AIDS Care (JIAPAC)

Supplemental Material

sj-docx-2-jia-10.1177_23259582261426232 - Supplemental material for Incidence and Predictors of Loss to Follow-Up Among ART Patients on Follow-Up at Public Health Facilities in Southwest Ethiopia. A Time-to-Event Analysis

Supplemental material, sj-docx-2-jia-10.1177_23259582261426232 for Incidence and Predictors of Loss to Follow-Up Among ART Patients on Follow-Up at Public Health Facilities in Southwest Ethiopia. A Time-to-Event Analysis by Oftana Daba, Dereje Tsegaye and Mohammed Reshad in Journal of the International Association of Providers of AIDS Care (JIAPAC)

Footnotes

Acknowledgments

The authors express their gratitude to Mattu University, Ilu Aba Bor Zone Health department, health facilities, for their cooperation during data collection. Special thanks go to the data collectors and supervisors for their invaluable effort in ensuring the quality of the data.

ORCID iD

Dereje Tsegaye

Ethical Considerations and Informed Consent

Ethical clearance was obtained from the Mattu University College of health science Research Ethical Review Committee (MaU-CHS-RERC) (Ref No. RCSIL/323/23). Permission letters were secured from the Ilu Aba Bor Zonal Health Department. The need of informed consent was waived by the Research Ethical Review Committee of Mattu University. Confidentiality was maintained throughout the study process. No person identifiers were used.

Authors’ Contributions

OD came up with the study's concept and discussed it with DT. OD, DT and MR designed the study protocol, took part in and supervised the study, and made substantial contributions to its execution. The manuscript was written by DT, who is also in charge of the content, and the work was then submitted for publication. The final version has been read by all authors and approved. DT accepts full responsibility for the finished work and/or the conduct of the study, had access to the data, and controlled the decision to publish.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

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

Supplemental Material

Supplemental material for this article is available online.

References

Harris

Rabkin

El-Sadr

. Achieving the Fourth 90: Healthy Aging for People Living with HIV. AIDS. Lippincott Williams and Wilkins;2018, Vol. 32:1563–1569.

UNAIDS. 2024. Global HIV and AIDS statistics - fact sheet .

Aliyu

Adelekan

Andrew

, et al.

Predictors of loss to follow-up in art experienced patients in Nigeria: a 13 year review (2004–2017).

AIDS Res Ther. 2019;16(1):30. doi:10.1186/s12981-019-0241-3

Arnesen

Moll

Shenoi

. Predictors of loss to follow-up among patients on ART at a rural hospital in KwaZulu-Natal, South Africa. PLOS ONE. 2017;12:e0177168. doi:10.1371/journal.pone.0177168

Kiwanuka

Waila

Muhindo Kahungu

Kitonsa

Kiwanuka

. Determinants of loss to follow-up among HIV positive patients receiving antiretroviral therapy in a test and treat setting: a retrospective cohort study in Masaka, Uganda. PloS one. 2020;15(4):e0217606. doi:10.1371/journal.pone.0217606

Mberi

Kuonza

Dube

Nattey

Manda

Summers

. Determinants of loss to follow-up in patients on antiretroviral treatment, South Africa, 2004–2012. BMC Health Serv Res. 2015;15(1):1–11. doi:10.1186/s12913-015-0912-2

Telayneh

Tesfa

Woyraw

, et al.

Time to lost to follow-up and its predictors among adult patients receiving antiretroviral therapy retrospective follow-up study Amhara Northwest Ethiopia.

Sci Rep. 2022;12(1):2916. doi:10.1038/s41598-022-07049-y

Tweya

Oboho

Gugsa

, et al.

Loss to follow-up before and after initiation of antiretroviral therapy in HIV facilities in Lilongwe, Malawi.

PloS one. 2018;13(1):e0188488. doi:10.1371/journal.pone.0188488

Wekesa

McLigeyo

Owuor

Mwangi

Nganga

Masamaro

. Factors associated with 36-month loss to follow-up and mortality outcomes among HIV-infected adults on antiretroviral therapy in central Kenya. BMC Public Health. 2020;20(1):328. doi:10.1186/s12889-020-8426-1

10.

Fentie

Kassa

Tiruneh

Muche

. Development and validation of a risk prediction model for lost to follow-up among adults on active antiretroviral therapy in Ethiopia: a retrospective follow-up study. BMC Infect Dis. 2022;22(1):1–13. doi:10.1186/s12879-022-07691-x

11.

Gebremichael

Gurara

Weldehawaryat

Mengesha

Berbada

. Predictors of loss to follow-up among HIV-infected adults after initiation of the first-line antiretroviral therapy at Arba Minch general hospital, southern Ethiopia: a 5-year retrospective cohort study. BioMed Res Int. 2021;2021:8659372. doi:10.1155/2021/8659372

12.

Megerso

Garoma

Eticha

, et al. Predictors of loss to follow-up in antiretroviral treatment for adult patients in the Oromia region, Ethiopia. HIV/AIDS (Auckland, N.Z.). 2016;8:83–92. doi:10.2147/HIV.S98137

13.

Seifu

Ali

Meresa

. Predictors of loss to follow up among adult clients attending antiretroviral treatment at Karamara General Hospital, Jigjiga Town, Eastern Ethiopia, 2015: a retrospective cohort study. BMC Infect Dis. 2018;18(1):280. doi:10.1186/s12879-018-3188-4

14.

Asiimwe

Kanyesigye

Bwana

Okello

Muyindike

. Predictors of dropout from care among HIV-infected patients initiating antiretroviral therapy at a public sector HIV treatment clinic in Sub-Saharan Africa. BMC Infect Dis. 2015;16:43. doi:10.1186/s12879-016-1392-7

15.

Gesesew

Ward

Hajito

Tolu Feyissa

Mohammadi

Mwanri

. Discontinuation from antiretroviral therapy: a continuing challenge among adults in HIV care in Ethiopia: a systematic review and meta-analysis. PloS one. 2017;12(1):e0169651. doi:10.1371/journal.pone.0169651

16.

De La Mata

Nguyen

, et al.

Loss to follow-up trends in HIV-positive patients receiving antiretroviral treatment in Asia from 2003 to 2013.

J Acquired Immune Deficiency Syndromes. 2017;74(5):555–562. doi:10.1097/QAI.0000000000001293

17.

Teshale

Tsegaye

Wolde

. Incidence and predictors of loss to follow up among adult HIV patients on antiretroviral therapy in university of Gondar comprehensive specialized hospital. PloS one. 2020;15(1):e0227473. doi:10.1371/journal.pone.0227473

18.

Assemie

Muchie

Ayele

. Incidence and predictors of loss to follow up among HIV-infected adults at Pawi general hospital, northwest Ethiopia: competing risk regression model. BMC Res Notes. 2018;11(1):287. doi:10.1186/s13104-018-3407-5

19.

Kebede

Mwanri

Ward

Gesesew

. Predictors of lost to follow up from antiretroviral therapy among adults in Sub-Saharan Africa: a systematic review and meta-analysis. Infect Dis Poverty. 2021;10(1):33. doi:10.1186/s40249-021-00822-7

20.

Penn

Azman

Horvath

, et al.

Supportive interventions to improve retention on ART in people with HIV in low- and middle-income countries: a systematic review.

PLoS ONE. 2018;13(12):1–16. doi:10.1371/journal.pone.0208814

21.

Shiferaw

Belete

Adela

Getnet

Aynalem

. Incidence and predictors of loss to follow-up among adult HIV-infected patients taking antiretroviral therapy at north Shewa zone public hospitals, northeast Ethiopia: a retrospective follow-up study. Afr Health Sci. 2022;22(2):12–26. doi:10.4314/ahs.v22i2.3

22.

Organization, World Health. Consolidated Guidelines on HIV Prevention, Testing, Treatment, Service Delivery and Monitoring: Recommendations for a Public Health Approach. World Health Organization; 2021: 1–594.

23.

Dessu

Mesele

Habte

Dawit

. Time until loss to follow-up, incidence, and predictors among adults taking ART at public hospitals in southern Ethiopia. HIV/AIDS (Auckland, N.Z.). 2021;13:205–215. doi:10.2147/HIV.S296226

24.

Ntabanganyimana

Rugema

Omolo

Nsekuye

Malamba

. Incidence and factors associated with being lost to follow-up among people living with HIV and receiving antiretroviral therapy in Nyarugenge the central business district of Kigali city, Rwanda. PLoS ONE. 2022;17(10 October):1–14. doi:10.1371/journal.pone.0275954

25.

Tesha

E-D

Kishimba

Njau

Revocutus

Mmbaga

. Predictors of loss to follow up from antiretroviral therapy among adolescents with HIV/AIDS in Tanzania. PloS one. 2022;17(7):e0268825. doi:10.1371/journal.pone.0268825

26.

Birhanu

Leshargie

Alebel

, et al.

Incidence and predictors of loss to follow-up among HIV-positive adults in northwest Ethiopia: a retrospective cohort study.

Trop Med Health. 2020;48:78. doi:10.1186/s41182-020-00266-z

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.02 MB

0.03 MB