Predictors of HIV interruption in treatment among people living with HIV in 6 regions in Ghana: a retrospective cohort study

Predictors of HIV interruption in treatment among people living with HIV in 6 regions in Ghana: a retrospective cohort study | 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 HIV interruption in treatment among people living with HIV in 6 regions in Ghana: a retrospective cohort study Williams Kwarah, Jasmin Kwarah, Yakubu Alhassan, Frances Baaba da-Costa Vroom, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7871647/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Jan, 2026 Read the published version in BMC Public Health → Version 1 posted 10 You are reading this latest preprint version Abstract Background Achieving the UNAIDS 95-95-95 targets is challenged by suboptimal retention in antiretroviral therapy (ART) programmes, particularly in resource-limited settings. In Ghana, where ART coverage remains low, HIV interruption in treatment (IIT) poses a significant barrier to sustained viral suppression and HIV control. This study aimed to quantify the magnitude and identify predictors of HIV interruption in treatment across six administrative regions in Ghana. Methods We conducted a retrospective cohort analysis of ART program data in Ghana across 6 regions from January 2019 to December 2023. A total of 33,613 participants contributed 227,319 follow-up visits. The primary outcome was interruption in treatment (IIT), defined as a missed clinic visit more than 28 days after the scheduled appointment. We used the Andersen–Gill model for recurrent event analysis to assess the predictors of IIT. Results We found a high rate of IIT, with about 68.3% of participants experiencing at least one treatment interruption, especially early in their care journey within the first three to six months. The risk of IIT was found to cluster in specific regions and clinics, with newer or less-experienced sites showing significantly higher rates of interruption. Patients on NNRTI-based regimens were identified to have high hazard for IIT. Conversely, factors like providing a phone number, receiving care at primary care facilities or at faith-based facilities were found to be protective against IIT. Conclusions To improve retention, Ghana's HIV programmes must intensify efforts, with a focus on targeting high-risk sites and sub-groups, optimising ART regimens by transitioning to dolutegravir-based alternatives, and leveraging multi-month dispensing and differentiated service delivery (DSD) models. Addressing these determinants is crucial for sustaining lifelong ART and advancing Ghana toward its national and global HIV control goals. HIV Interruption in treatment ART adherence Ghana retrospective cohort recurrent events risk factors Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Human immunodeficiency virus (HIV) persists as a global public health threat. In 2024, about 40.8 million people were living with HIV (PLHIV) worldwide and roughly 1.3 million new infections occurred, despite significant treatment scale-up [ 1 ]. Global AIDS-related mortality has declined substantially (by 70% since 2004) due to antiretroviral therapy (ART) [ 2 ], yet western and central Africa still harbours a disproportionately high share of the epidemic (approximately 5.2 million PLHIV in 2024) [ 3 , 4 ]. Ghana’s epidemic is firmly established, with recent estimates reporting roughly 330,000 people living with HIV, 15,000 new infections, and 13,000 AIDS-related deaths in 2024 [ 5 ]. Progress toward the UNAIDS 95-95-95 targets is limited, with only about 68% of PLHIV aware of their status, 47% of those diagnosed receiving ART, and 42% of those on ART virally suppressed [ 5 , 6 ]. HIV interruption in treatment (IIT) is a major barrier to achieving sustained viral suppression. Such interruptions can undermine both individual and programmatic success by increasing viral rebound, drug resistance, and adverse clinical outcomes. Globally, retention on ART is suboptimal, with only about 46% to 85% clients remaining on ART after two years of initiation [ 7 ]. In sub-Saharan Africa, financial and structural constraints are major barriers. For example, a Cameroonian study found that transportation costs (47.5%) and stigma were the leading predictors of interruption [ 8 ]. Similarly, in South Africa, financial hardship, long travel distances and forgetfulness were cited as key barriers to adherence [ 9 ]. In Nigeria’s national cohort, Ikpe and colleagues identified sex, ART anchor drug class, unsuppressed viral load or CD4 count, WHO stage, and sociodemographic (e.g. education, marital status, and urban vs. rural residence) as significant predictors [ 10 ]. In Ghana, evidence on interruptions is limited, but adherence studies highlight important clues, with a recent meta-analysis found only approximately 70% ART adherence overall and younger patients especially at risk [ 11 ]. Ghanaian cohorts report that comorbid illness and drug side-effects undermine adherence, whereas family support and regular clinic follow-up promote it [ 12 ]. Despite these insights, few studies have simultaneously quantified IIT and examined facility factors, patient factors (age, sex, education), system factors (access, clinic support), and regimen factors (pill burden, ART regimen class, tolerability) in Ghana. The aim of this study was to quantify the magnitude of HIV interruption in treatment and identify key predictors of interruption in treatment among PLHIV in Ghana, using data from the national HIV electronic tracker (eTracker) system between 2019 and 2023. Specifically, the study sought to estimate the incidence and recurrence of IIT, examine demographic, clinical, and facility-level predictors associated with both first and subsequent interruptions, and explore regional and health system variations that influence patient retention. Methods Study design and participants We conducted a retrospective cohort study using routinely collected data from the National HIV eTracker, an instance on the District Health Information Management System (DHIMS2) platform in Ghana. We extracted data on individuals enrolled in ART services between January 2019 and December 2023 across all health facilities (i.e., medical centres, hospitals, health centres, clinics, and Community-based Health Planning and Services (CHPS) compounds) in six administrative regions (i.e., Ahafo, Bono, Upper East, Volta, Western, and Western North). The HIV eTracker captures standardised patient-level transactional data from public, private, faith-based, and quasi-government facilities. This data included health facility details, demographic information of participants and clinical information. The study sites were purposively selected based on regions PEPFAR supported versus non-regions PEPFAR supported that had high ART coverages. Figure 1 shows the location of the study sites across the country. We included PLHIVs who initiated ART within the study period and had at least one follow-up visit recorded in the HIV eTracker. We also included individuals if they had complete ART initiation dates and follow-up data. Children and adults were both eligible, with the time origin (baseline) defined as the date of ART initiation. Participants were followed until the end of December 2023. Multiple episodes of IIT were documented. We excluded participants who died or were transferred out of health facilities outside the study sites from the analysis. Participants who never interrupted treatment were censored at the end of the follow-up period. Outcome definition The primary outcome was time-to-HIV interruption in treatment (IIT), defined as a missed clinic visit more than 28 days after the scheduled appointment date, in accordance with the national HIV/AIDS & STI Control Program (NACP) definition and World Health Organization (WHO) guidelines [ 13 ]. We defined a participant’s IIT status as 1 if treatment was interrupted, as per the definition, and 0 otherwise. Covariates Independent variables included demographic (age, sex, education, marital status, occupation, region of residence, mobile phone access, physical address documented, and national health insurance coverage), clinical (HIV type, ART regimen classification, ART initiation time, Tuberculosis (TB) screening, viral load testing and suppression), and facility-level characteristics (facility type, facility ownership, number of ART staff, settings (rural/urban). Number of years ART site/clinic operated). The covariates were treated as fixed or time-varying based on their nature and data availability. Data processing and management De-identified datasets (i.e. Registration, Initial Assessment, Follow-up, and Viral load) were received from the NACP. Further, data on ART clinic characteristics was collected through the regional HIV coordinators in Microsoft Excel sheet. All these five datasets were processed, cleaned and merged into one master dataset. Participants who died or were transferred out to other facilities outside the study areas were excluded from the final analysis. Using the unique identification numbers created from the raw dataset, clients who transferred to other facilities within the study regions were noted and included in the analysis such that the continuum of care for these participants were appropriately tracked. Duplicates in each dataset were first excluded before merging into a final dataset. Complete case analysis and deletion was conducted to address missing data, assuming data were missing completely at random [ 14 ]. The outcome measure, HIV interruption in treatment, was generated using data on number of ARV pills dispensed, date of current follow-up, and the 28-day threshold per the standard definition of IIT. Statistical analysis We summarised participant characteristics using frequency (percentage) for categorical variables and means (standard deviation - ±SD) for continuous variables. For the primary analysis, we used the Andersen-Gill (AG) model to determine the predictors of IIT. The AG model is a recurrent events survival model that extends the Cox proportional hazards model to analyse multiple events per subject, such as repeated instances of IIT [ 15 ]. The AG model assumes that the instantaneous risk to experience an event at time t since study entry remains the same irrespective of whether previous events occurred or not, implying the recurrent events are independent. If this assumption is fulfilled, the all-cause hazard can be estimated by using the event times of every observed event. Thus, a single patient contributes more than one piece of information depending on the number of individually observed events. The Andersen-Gill model therefore aims at estimating the same quantity as the common Cox model given by the all-cause hazard ratio θ AllCause . However, the estimation is based on more information as an individual who has experienced an event remains under risk for further events. This implies that the corresponding partial likelihood is based on a higher number of events and on a modified risk set as shown in Eq. 1. \(\:{R}^{AG}\left(t\right)≔\{l,\:l=1,\dots\:.n\::\:\exists\:j\:\in\:\left\{1,\dots\:..,\:kl\right\}\:with\:Tlj\:\ge\:\:\text{t}\}\:\) Eq. 1. were Tlj are the distinct event times for individual l , l = 1,. . . , n , and for the j th occurring event j = 1,. . . , kl , with kl being the individual-specific number of distinct observed event times, where kl ≤ k , l = 1,. . . , n , is assumed meaning that the maximal number of events which are taken into account is given by k . If the assumption of independent recurrent event times is not fulfilled, the Anderson-Gill model might still be applied but no longer estimates the all-cause hazard ratio. Instead, the resulting treatment effect estimator is given as a hazard ratio combining direct and indirect effects [ 16 ]. The mixed effect resulting from the Anderson-Gill model will be denoted as θ MixAG . This treatment effect cannot easily be parametrized and might therefore be considered as difficult for interpretation. We used the Schoenfeld residuals plot to assess the proportional hazards assumptions. The adjusted hazard ratios (HRs), along with their 95% confidence intervals (CIs) and p-values, were reported. Sensitivity Analysis To ensure the robustness of our findings from the AG model, we conducted sensitivity analyses using the mixed-effects Cox model, Prentice-Williams-Peterson (PWP) models, and marginal models, with each model approaching the analysis of multiple events differently and providing unique insights and a more comprehensive view of the data. The mixed-effects Cox model, also known as the frailty model, is a robust approach that accounts for the non-independence of events within the same individual by incorporating a random effect, or "frailty," for each subject. The Prentice-Williams-Peterson (PWP) models are a family of recurrent event models that focus on the order of the events. Unlike the AG model, which treats all events equally, PWP models distinguish between different event occurrences. The PWP Conditional Probability Model stratifies the risk of IIT by the event order. This allows for a separate baseline hazard function for each event number, which is useful if the risk changes significantly after the first event. The PWP Gap-Time model focuses on the time between events, or the "gap time" and analyses the duration between consecutive events. This approach is particularly useful when the time it takes to experience the next event is of primary interest. The marginal models, such as the Wei-Lin-Weisfeld (WLW) model, offer an alternative perspective by treating each recurrent event as a separate observation. These models do not specify the exact correlation structure between events but use robust standard errors to account for the non-independence within subjects and provides population-averaged hazard ratio estimates, which are interpreted as the change in the average risk of an event across the entire population, rather than for a specific individual. The performance of all models was compared using the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and the concordance index. A p-value less than 0.05 was considered statistically significant. All analyses were performed using R (version 4.3.0; R Foundation). Results A total of 245 health facilities across the 6 administrative regions (23 from Ahafo, 43 from Bono, 27 from Upper East, 57 from Volta, 72 from Western North regions) were included in this study. Of the 245 health facilities, 131 (53.5%) were CHPS or health centers, 93 (38.0%) were hospitals, 11 (4.5%) were clinics, 7 (2.9%) were polyclinics and 3 (1.2%) were medical centers. Majority 191 (78.0%) of the health facilities were publicly owned, 34 (13.9%) were faith-based organizations, 16 (6.5%) were private facilities, and 4 (1.6%) were quasi-government facilities. Between January 2019 and December 2023, a total of 56,040 participants made 371,661 follow-up visits across all the health facilities. Of the 371,661 visits, 144,342 records were excluded from the final analysis because of missing values, negative values recorded, and no first visit records available. A final dataset of 33,613 participants contributing a total of 227,319 follow-up visits was used in the analysis. Figure 2 provides details of the participants’ flow. Of the 33,613 participants, 4,408 (13.1%) participants had 2 successful visits, 3,710 (11.0%) had 3 visits, 3,171 (9.4%) had 4 visits, 2,890 (8.6%) had 5 follow-ups visits and the remaining 19,434 (57.8%) had at least 6 follow-up visits. Details of the number of follow-up visits are provided as supplementary information. Demographic characteristics The mean age of the participants was 35.6 (± 14) years, ranging from 0 years to 89 years, with 24,070 (71.6%) females and 9,543 (28.4%) males. More than half, 21,829 (64.9%), had at least secondary education, 5,772 (17.2%) had primary education, while 6,360 (18.9%) had no education. Unemployment or no paid work was prevalent among 11,784 (35.1%), while 21,829 (64.9%) were employed or engaged in paid work. Most 17,900 (53.6%) of participants were married or cohabiting, 5,326 (15.8%) were divorced/separated/widowed, while 10,387 (30.9%) of participants were single. A small proportion (2.1%) had ever smoked, while 75.0% provided mobile phone numbers at initiation. More than a quarter (28.4%) of participants were in the Western region, 23.0% in the Bono region, and 20.3% in the Volta region. More than half of the participants were in rural areas (55.4). Averagely, the ART clinics have been providing ART services for 12.4 (± 6.4) years. HIV-I was the most common type among 97.2% participants, with 2.1% having both HIV-I & HIV-II types. The majority (58.6%) were seeking HIV services from integrated ART clinics, with the remaining 41.4% receiving services from standalone care. The majority 18,174 (56.5%) of participants were initiated on ART within the same day of diagnosis, 7,205 (22.4%) initiated within 1 to 7 days of diagnosis, and the remaining 6,771 (21.1%) were initiated after 7 days of diagnosis. More than half (54.6%) of the participants had their first viral load done after ART initiation. Additional information is provided in Table 1 . Table 1 Descriptive characteristics of study participants by sex Overall Females Males Characteristics N = 33,613 n = 24,070 n = 9.543 Age (in years) 35.6 [± 14.2] 35.1 [± 13.4] 36.7 [± 15.8] Age group (in years) <15 1,994 (5.9) 990 (4.1) 1,004 (10.5) 15–19 1,332 (4.0) 1,053 (4.4) 279 (2.9) 20–24 3,247 (9.7) 2,697 (11.2) 550 (5.8) 25–34 9,959 (29.6) 7,931 (32.9) 2,028 (21.3) 35+ 17,081 (50.8) 11,399 (47.4) 5,682 (59.5) Highest education None 6,360 (18.9) 5,003 (20.8) 1,357 (14.2) Primary 5,772 (17.2) 4,276 (17.8) 1,496 (15.7) Secondary School and beyond 21,829 (64.9) 14,791 (61.4) 6,690 (70.1) Occupation Unemployed or not engaged in paid work 11,784 (35.1) 7,562 (31.4) 4,222 (44.2) Employed or engaged in paid work 21,829 (64.9) 16,508 (68.6) 5,321 (55.8) Marital status Single 10,387 (30.9) 7,448 (30.9) 2,939 (30.8) Divorced/Separated/Widowed 5,326 (15.8) 4,324 (18.0) 1,002 (10.5) Married /Cohabiting 17,900 (53.3) 12,298 (51.1) 5,602 (58.7) Ever smoked No 32,920 (97.9) 23,577 (98.0) 9,343 (97.9) Yes 693 (2.1) 493 (2.0) 200 (2.1) Provided mobile phone number at initiation No 7,690 (22.9) 5,514 (22.9) 2,176 (22.8) Yes 25,923 (77.1) 18,556 (77.1) 7,367 (77.2) Area of residence Rural 18,630 (55.4) 13,652 (56.7) 4,978 (52.2) Urban 14,983 (44.6) 10,418 (43.3) 4,565 (47.8) Region of residence Ahafo 3,038 (9.0) 2,224 (9.2) 814 (8.5) Bono 7,732 (23.0) 5,495 (22.8) 2,237 (23.4) Upper East 3,066 (9.1) 2,163 (9.0) 903 (9.5) Volta 6,826 (20.3) 4,859 (20.2) 1,967 (20.6) Western 9,535 (28.4) 6,800 (28.3) 2,735 (28.7) Western North 3,416 (10.2) 2,529 (10.5) 887 (9.3) Years ART clinic operated 12.4 [± 6.4] 12.2 [± 6.3] 12.8 [± 6.5] HIV type HIV I 32,662 (97.2) 23,402 (97.2) 9,260 (97.0) HIV II 252 (0.7) 176 (0.7) 76 (0.8) HIV I and II 699 (2.1) 492 (2.0) 207 (2.2) ART clinic status Integrated 19,700 (58.6) 14,117 (58.6) 5,583 (58.5) Standalone 13,913 (41.4) 9,953 (41.4) 3,960 (41.5) Type of client Adult 31,619 (94.1) 23,080 (95.9) 8,539 (89.5) Pediatric 1,994 (5.9) 990 (4.1) 1,004 (10.5) Time to ART initiation Same-day 18,174 (56.5) 12,984 (56.4) 5,190 (56.9) 1–7 days 7,205 (22.4) 5,143 (22.3) 2,062 (22.6) More than 7 days 6,771 (21.1) 4,895 (21.3) 1,876 (20.6) First Viral load timing After ART Initiation 18,638 (55.4) 13,282 (55.2) 5,356 (56.1) Not done 14,975 (44.6) 10,788 (44.8) 4,187 (43.9) Have NHIS number No 20,976 (62.4) 14,381 (59.7) 6,595 (69.1) Yes 12,637 (37.6) 9,689 (40.3) 2,948 (30.9) Provided physical address at initiation No 4,103 (12.2) 2,973 (12.4) 1,130 (11.8) Yes 29,510 (87.8) 21,097 (87.6) 8,413 (88.2) Facility type CHPS/Health Centre 7,219 (21.5) 5,446 (22.6) 1,773 (18.6) Polyclinic 450 (1.3) 317 (1.3) 133 (1.4) Clinic 310 (0.9) 245 (1.0) 65 (0.7) Hospital 25,575 (76.1) 18,015 (74.8) 7,560 (79.2) Medical Centre 59 (0.2) 47 (0.2) 12 (0.1) Facility ownership Public 24,196 (72.0) 17,444 (72.5) 6,752 (70.8) Private 1,175 (3.5) 832 (3.5) 343 (3.6) FBO 8,134 (24.2) 5,722 (23.8) 2,412 (25.3) Quasi-Government 108 (0.3) 72 (0.3) 36 (0.4) Number of staffs at ART clinics 7.6 [± 3.8] 7.5 [± 3.8] 7.6 [± 3.8] HIV Treatment Interruption Among the 33,613 participants, a total of 22,956 (68.3%, 95% CI: 67.8–68.8) interrupted treatment at least once during the follow-up time while participants who never interrupted treated were 10,657 (31.7%, 95% CI: 31.2–32.2). Within the first 3 months of follow-up, 1,752 (21.8%) participants interrupted treatment. This increased to 3,281 (40.9%) participants in 6 months before dropping to 1,975 (24.6%) by 12 months. A total of 1,018 (12.7%) participants interrupted treatment after 12 months of follow-up. The rate of interruption in treatment decreased as the number of follow-up visits increased as shown in Fig. 3 . The highest treatment interruption occurred at the first 2 visits (24%), reducing to 23% on follow-up visits 3 to 5 and then to 22% on follow-up visits 6 and 7. Interruption rate decreased gradually to 8% on follow-up visits 27 and 28 and then increased to 14% on follow-up visit 29 before falling back to 9% on follow-up visits 30. No treatment interruption was observed at follow-up visits 31 to 34. Figure 4 illustrates the cumulative risk curves of interruption in treatment among participants, stratified by the order of interruption event following antiretroviral therapy (ART) initiation. The first interruption (1st IIT) had the highest initial number participants at risk (n = 33,613) and exhibited a steady rise in cumulative risk, reaching over 85% by approximately 1,000 days. Subsequent interruptions, such as the 2nd IIT (n = 13,113) and 3rd IIT (n = 7,123), followed a similar but steeper trajectory, indicating a higher risk of repeated interruptions among those previously interrupted. From the 4th to the 12th IIT, although the number at risk declined substantially, the cumulative risk increased more rapidly, with nearly all individuals experiencing another interruption within a shorter time span. Predictors of HIV treatment interruption Covariates that were found to be significantly associated with IIT were included in the multivariate Andersen Gill model to determine the predictors. The supplementary file provides details of the bivariate analysis. The AG model is particularly suited for this analysis as it accounts for multiple events per individual and provides an estimate of the overall effect of covariates on the hazard of recurrent events, such as a patient interrupting treatment more than once. The results are presented as adjusted hazard ratios (aHR), which indicate the change in the instantaneous risk of IIT for a one-unit increase in the predictor, while controlling for all other variables in the model. Independent variables that were found to be significantly associated with interruption in treatment were fitted into the multivariable AG model. Several statistically significant predictors of HIV treatment interruption in the overall study population were observed as provided in Table 2 . Geographic region was a significant factor, with patients in the Bono region having a 10% higher adjusted hazard of IIT (aHR = 1.10, 95% CI: 1.066–1.139, P < 0.001) and those in the Upper East region having an 8% higher hazard (aHR = 1.08, 95% CI: 1.036–1.124, P < 0.001) compared to the Western region. Conversely, the Ahafo region was associated with a 6% lower hazard (aHR = 0.94, 95% CI: 0.903–0.983, P = 0.006). Healthcare facility characteristics were also critical. Patients receiving care at a CHPS/Health Centre had a 13% lower hazard of IIT (aHR = 0.87, 95% CI: 0.847–0.902, P < 0.001) compared to those at a hospital. Similarly, attendance at a Faith-Based Organization (FBO) owned facility was associated with an 8% lower hazard (aHR = 0.92, 95% CI: 0.896–0.943, P < 0.001) compared to a public facility. An important finding was the inverse relationship between the number of years an ART clinic has been operational and the hazard of IIT (aHR = 0.99, 95% CI: 0.991–0.995, P < 0.001), suggesting that more established clinics have a lower risk of treatment interruption. Patient-level factors also played a role. Providing a phone number (aHR = 0.95, 95% CI: 0.931–0.974, P < 0.001) and a higher follow-up visit number (aHR = 0.95, 95% CI: 0.946–0.953, P < 0.001) were both associated with a 5% decreased hazard of IIT. The specific antiretroviral therapy (ART) regimen was also a significant predictor. The use of a regimen containing a Nucleoside/tide Reverse Transcriptase Inhibitor (NtRTI), a Nucleoside Reverse Transcriptase Inhibitor (NRTI), and a Non-Nucleoside Reverse Transcriptase Inhibitor (NNRTI) was associated with a 33% higher adjusted hazard of IIT (aHR = 1.33, 95% CI: 1.164–1.529, P < 0.001) compared to the NtRTI + NRTI + PI combination. This drug combination is a common first-line treatment regimen, but some NNRTIs have a lower barrier to resistance and can cause neuropsychiatric side effects, which may affect adherence. Gender-specific sub-analyses revealed distinct patterns. Regional Differences: Regional disparities were more pronounced. For males, the Bono (aHR = 1.09, 95% CI: 1.027–1.163, P = 0.005) and Upper East (aHR = 1.09, 95% CI: 1.006–1.172, P = 0.034) regions were associated with a higher hazard of IIT, whereas the Western North region was associated with a 8% lower hazard (aHR = 0.92, 95% CI: 0.858–0.987, P = 0.020). For females, the Bono (aHR = 1.11, 95% CI: 1.064–1.150, P < 0.001) and Upper East (aHR = 1.08, 95% CI: 1.026–1.130, P = 0.003) regions were also associated with a higher hazard, while the Ahafo region had a 6% lower hazard (aHR = 0.94, 95% CI: 0.890–0.985, P = 0.011). Females receiving care in an urban setting had a 3% lower hazard of IIT (aHR = 0.97, 95% CI: 0.941–0.997, P = 0.029) compared to those in rural settings, a finding not observed in males. Patients of both genders benefited from care at a CHPS/Health Centre (males: aHR = 0.88, 95% CI: 0.831–0.940, P < 0.001; females: aHR = 0.87, 95% CI: 0.838–0.902, P < 0.001) and an FBO-owned facility (males: aHR = 0.91, 95% CI: 0.870–0.956, P < 0.001; females: aHR = 0.92, 95% CI: 0.896–0.951, P < 0.001). For males, being treated at a Clinic was associated with a 21% lower hazard of IIT (aHR = 0.79, 95% CI: 0.641–0.977, P = 0.029), while for females, treatment at a Polyclinic was associated with a 12% lower hazard (aHR = 0.88, 95% CI: 0.794–0.980, P = 0.020), both compared to hospitals. The 15–19 years age group for females showed a 5% lower hazard of IIT (aHR = 0.95, 95% CI: 0.896–0.999, P = 0.048) compared to the 35 + age group. Consistent with the overall model, providing a phone number and having a higher follow-up visit number were both associated with a lower hazard of IIT for both genders. The use of the NtRTI + NRTI + NNRTI regimen was a significant predictor of IIT for both males (aHR = 1.39, 95% CI: 1.085–1.781, P = 0.009) and females (aHR = 1.31, 95% CI: 1.114–1.543, P = 0.001). Table 2 Predictors of HIV interruption in treatment among PLHIVs Both Male and Female Males Females Predictor aHR 95% CI P-Value aHR 95% CI P-Value aHR 95% CI P-Value Region Western 1.00 [Reference] Ahafo 0.94 0.903–0.983 0.006 0.95 0.879–1.033 0.241 0.94 0.890–0.985 0.011 Bono 1.10 1.066–1.139 < 0.001 1.09 1.027–1.163 0.005 1.11 1.064–1.150 < 0.001 Upper East 1.08 1.036–1.124 0.000 1.09 1.006–1.172 0.034 1.08 1.026–1.130 0.003 Volta 0.98 0.946–1.011 0.185 0.97 0.910–1.032 0.333 0.98 0.942–1.019 0.305 Western North 0.97 0.935–1.003 0.078 0.92 0.858–0.987 0.020 0.99 0.946–1.028 0.505 Setting Rural 1.00 [Reference] Urban 0.98 0.954–1.001 0.064 1.00 0.951–1.041 0.834 0.97 0.941–0.997 0.029 Facility Type Hospital 1.00 [Reference] CHPS/Health Centre 0.87 0.847–0.902 < 0.001 0.88 0.831–0.940 < 0.001 0.87 0.838–0.902 < 0.001 Polyclinic 0.96 0.876–1.042 0.298 1.15 0.995–1.226 0.059 0.88 0.794–0.980 0.020 Clinic 0.94 0.847–1.033 0.185 0.79 0.641–0.977 0.029 0.98 0.875–1.092 0.692 Medical Center 0.95 0.748–1.206 0.672 1.00 0.622–1.612 0.994 0.94 0.716–1.229 0.644 Facility Ownership Public 1.00 [Reference] Private 0.97 0.911–1.025 0.254 0.97 0.875–1.070 0.523 0.97 0.901–1.040 0.376 FBO 0.92 0.896–0.943 < 0.001 0.91 0.870–0.956 < 0.001 0.92 0.896–0.951 < 0.001 Quasi-Government 0.90 0.727–1.114 0.333 0.82 0.564–1.179 0.2778 0.94 0.733–1.206 0.628 Years ART clinic operated 0.99 0.991–0.995 < 0.001 0.99 0.990–0.997 < 0.001 0.99 0.989–0.995 < 0.001 ART Clinic Status Integrated 1.00 [Reference] Standalone 1.01 0.983–1.043 0.392 1.03 0.975–1.093 0.272 1.01 0.971–1.041 0.774 Age group < 15 years 0.98 0.938–1.021 0.320 0.97 0.910–1.033 0.342 0.99 0.931–1.045 0.633 15–19 years 0.96 0.913–1.006 0.084 1.01 0.917–1.123 0.779 0.95 0.896–0.999 0.048 20–24 years 0.99 0.959–1.024 0.573 0.97 0.898–1.046 0.422 0.99 0.958–1.031 0.755 25–34 years 1.01 0.983–1.028 0.649 1.00 0.956–1.047 0.977 1.01 0.982–1.034 0.577 35+ 1.00 [Reference] Occupation Engaged in paid work 1.00 [Reference] Not engaged in paid work 0.99 0.972–1.014 0.518 1.01 0.969–1.046 0.743 0.99 0.961–1.012 0.285 Provided phone number No 1.00 [Reference] Yes 0.95 0.931–0.974 < 0.001 0.96 0.916–0.996 0.033 0.95 0.926–0.978 < 0.001 Time to ART initiation Same day 1.00 [Reference] 1–7 days 1.00 0.974–1.022 0.856 1.02 0.972–1.061 0.501 0.99 0.963–1.019 0.504 More than 7 days 0.98 0.958–1.005 0.119 0.98 0.933–1.021 0.293 0.98 0.956–1.011 0.239 TB screening at initiation No 1.00 [Reference] Yes 1.01 0.961–1.051 0.819 1.03 0.949–1.117 0.482 1.00 0.945–1.052 0.916 TB screening at follow-up No 1.00 [Reference] Yes 0.98 0.932–1.028 0.396 0.96 0.867–1.052 0.352 0.99 0.935–1.047 0.716 Use of family planning method No 1.00 [Reference] Yes 0.99 0.952–1.030 0.638 1.02 0.945–1.095 0.650 0.98 0.936–1.027 0.408 ART combination classification NtRTI + NRTI + PI 1.00 [Reference] NtRTI + NRTI + INSTI 1.06 0.927–1.215 0.387 1.16 0.910–1.486 0.227 1.02 0.869–1.200 0.802 NtRTI + NRTI + NNRTI 1.33 1.164–1.529 < 0.001 1.39 1.085–1.781 0.009 1.31 1.114–1.543 0.001 Number of ARV pills dispensed 1.00 0.997–0.997 < 0.001 1.00 0.996–0.998 < 0.001 1.00 0.997–0.997 < 0.001 Multi-month dispensing Not on MMD 0.92 0.705–1.197 0.528 0.72 0.473–1.099 0.128 1.03 0.738–1.433 0.869 On 3–5 months MMD 0.88 0.682–1.146 0.352 0.68 0.449–1.022 0.063 1.00 0.721–1.383 0.994 6 + months MMD 1.00 [Reference] Follow-up visit number 0.95 0.946–0.953 < 0.001 0.95 0.940–0.954 < 0.001 0.95 0.946–0.955 < 0.001 Sensitivity Analysis To ensure the robustness of the findings from the primary Andersen-Gill Cox-Model, a series of sensitivity analyses were conducted using alternative recurrent event models as shown in Table 3 . The performance of the primary model, which yielded a Concordance Index of 0.59 and an AUROC of 0.55, was compared to other models using several metrics. The Andersen-Gill Counting Process Model and the Andersen-Gill Counting Process with Clustering model both showed identical performance metrics to the primary model, with an AIC of 967326, a BIC of 967626, a log-likelihood of -483629, a Concordance Index of 0.59, and an AUROC of 0.55. The Marginal Model (Wei–Lin–Weisfeld model) with robust standard errors also demonstrated identical results, confirming the stability of the primary model's estimates when accounting for within-subject correlation. However, other models designed to handle different assumptions about recurrent events revealed variations in model fit and predictive accuracy. The Frailty Model with time difference showed a significantly higher Concordance Index of 0.83 but a lower AUROC of 0.51, indicating strong within-subject clustering but a weaker overall predictive ability. In contrast, the Prentice-Williams-Peterson (PWP) Conditional Probability model had a lower Concordance Index of 0.54 but a higher AUROC of 0.67, while the PWP Gap Time model demonstrated the highest predictive power with a Concordance Index of 0.86 and an AUROC of 0.72. These findings suggest that a model accounting for the time gap between recurrent events may provide a better fit for the data. Nonetheless, the consistency of the Andersen-Gill Cox-Model with the Counting Process and Marginal models provides confidence in the core findings, despite the superior performance metrics of the PWP Gap Time model in this specific context. Table 3 Sensitivity analysis Model AIC BIC Loglikelihood Concordance Index AUROC Andersen-Gill Cox-Model 967482 967782 -483707 0.59 (0.583–0.589) 0.55 Andersen-Gill Cox-Model Counting Process Model 967326 967626 -483629 0.59 (0.584, 0.589) 0.55 Andersen-Gill Cox-Model Counting Process with Clustering 967326 967626 -483629 0.59 (0.584, 0.589) 0.55 Frailty Model with time difference 991295 992145 -495551 0.83 (0.831, 0.836) 0.51 Prentice-Williams-Peterson Conditional Probability Model 809299 809598 -404615 0.54 (0.541, 0.546) 0.67 Prentice-Williams-Peterson (PWP) Gap Time model 914302 914602 -457117 0.86 (0.856, 0.860) 0.72 Marginal Model (Wei–Lin–Weisfeld model) with robust SE clustered 967326 967626 -483629 0.59 (0.584, 0.589) 0.55 Discussion We assessed the magnitude and predictors of IIT among PLHIVs in the selected regions, utilizing a large retrospective cohort from the national HIV eTracker system. Overall, 68.3% of study participants experienced at least one IIT while 31.7% stayed in treatment during the study period. About 21.8% of participants interrupted treatment by 3 months, 40.9% by 6 months before declining. Geographic region was strongly associated with IIT. Relative to the Western region, participants in Bono and Upper East had 10% and 8% higher hazards of IIT, respectively, while those in Ahafo had a 6% lower hazard. Facility factors such as participants receiving care at CHPS/health centers or faith-based organizations (FBOs) had significantly lower IIT risk (13% and 8% lower, respectively) than hospital. Participant-level factors also mattered as providing a mobile phone number and having more prior follow-up visits each reduced IIT hazard by approximately 5%. Participants on regimens containing an NNRTI (NtRTI + NRTI + NNRTI) had 33% higher IIT hazard than those on PI-containing regimens (NtRTI + NRTI + PI). In the sex-stratified models, both male and female subgroups showed higher IIT risk in Bono and Upper East and lower risk in Western North or Ahafo (depending on sex). Both genders benefited from CHPS/health centers and FBO care while being treated at a clinic (for males) or polyclinic (for females) conferred lower IIT risk compared to hospitals. Female adolescents (15–19) had a slightly lower IIT hazard than older adults. The protective effects of having a phone number and additional visits applied to both sexes, and the regimen effect (NNRTI use) remained significant in each group. Our sensitivity analyses using alternative recurrent-event models yielded consistent effect estimates. Our findings extend prior knowledge on ART retention in sub-Saharan Africa. An IIT incidence of 21.7% within the first 3 months and 40.9% within 6 months of treatment suggests poorer retention than typically reported in the region. A systematic review in Nigeria found a pooled 12-month retention of approximately 72% [ 17 ]. Similarly, an urban Ghanaian district study found nearly half of patients lost by 90 days [ 18 ], indicating similarly rapid early attrition. The temporal pattern, with most interruptions occurring within the first six months of therapy, and then decreasing is consistent with prior studies [ 19 , 20 ]. This pattern indicates a major obstacle in achieving optimal HIV care outcomes and aligns with global concerns regarding ART adherence and retention [ 21 – 25 ]. This pronounced early drop-off underscores the urgency of addressing factors unique to Ghana. Many of our predictors mirror barriers such as patient level, structural and socioeconomic which has also been documented in other studies. In Cameroon, transportation costs and stigma were identified as the leading causes of ART interruption [ 26 ] which is consistent with our findings that region-level differences (e.g. Bono and Upper East) may have majority rural populations with access challenges. The lower IIT risk at CHPS/Health centers and FBO facilities aligns with evidence that decentralization and community-based care improve continuity. More importantly, community clinics often reduce travel burden and may offer more personalized support. This is consistent with broader literature noting that long travel distances and costs are key adherence barriers in South Africa and other settings [ 27 ] while closer community ties and more personalized care often provided at primary healthcare levels foster better patient-provider relationships and adherence [ 28 – 30 ]. Our finding that the longer an ART clinic operated, the more beneficial it was in reducing IIT suggest stronger ties between clients and service providers, better clinic efficiency and client experience, experienced staff and better infrastructure [ 31 ]. The association of NNRTI regimens with higher IIT is consistent with concerns about tolerability and resistance. Prior research in Ghana [ 32 ] and other locations has linked NNRTI-based regimens with greater neuropsychiatric side effects, which could contribute to poor adherence and discontinuation [ 33 – 35 ]. Our finding reinforces global shifts toward dolutegravir-based regimens with higher efficacy and tolerability which Ghana is currently moving most PLHIVs onto. Notably a study in Nigeria found age and sex not significant predictors of HIV treatment interruption [ 36 ], similar to our overall findings. However, we observed some gender-specific nuances like female urban residents had marginally lower IIT risk, and female youth were somewhat less likely to drop out of treatment These modest protective effects for younger and urban females partially echo qualitative insights that younger clients may adhere better with support [ 37 ] though other reports often highlight youth as at-risk [ 38 , 39 ]. Our study confirms the importance of structural and programmatic determinants of retention observed in other contexts, while adding new insights on clinic characteristics, age, and client contactability. Together, the evidence underscores that improving ART retention requires multifaceted strategies in addressing structural, geography, service delivery, and client engagement. The strengths of our study include its large, programmatic data source and rigorous analysis. By using the national HIV eTracker across six regions, we captured a diverse mix of public, private, and faith-based clinics serving 33,613 PLHIVs (227,319 follow-up visits) which is a significantly large cohort to analyse recurrent interruptions in ART. Our analysis accounted for multiple interruptions per participant, a major strength over single-event loss to follow-up analyses. The consistency of results across multiple sensitivity models enhances confidence in our findings. The standardized 28-day IIT definition and adjustment for a wide array of covariates (demographic, clinical, and facility-level) further strengthen the validity of our inferences. However, there are important limitations. The retrospective cohort design, while a strength for its cost-effectiveness and efficiency, comes with inherent limitations [ 40 ]. It relies on data collected for routine clinical care rather than for research, which can lead to incomplete or missing information on important confounders like socioeconomic status, travel distance, or reasons for interruption, which may bias the results [ 41 ]. For example, a study in the United States of America found a strong relationship between viral load suppression and retention [ 42 ], a factor we could not explore fully in our analysis due to data limitations. About 15.8% of follow-up records were excluded due to missing or inconsistent values, and our complete-case approach (assuming data were missing completely at random) may bias results if missingness was systematic due to weaker record-keeping at understaffed clinics. Relatedly, key participant factors such as socioeconomic status, stigma, transportation time were not captured, raising the possibility of residual confounding. Our purposive selection of the study sites may introduce selection bias which limits generalizability as IIT patterns may differ in other regions. These caveats notwithstanding, the study provides comprehensive new evidence on ART retention in Ghana. Our results have policy and programmatic implications for Ghana and similar settings. Efforts to improve retention should be prioritized in the high-risk sub-groups identified by expanding differentiated service delivery (DSD) approaches, including establishing community pharmacies to mitigate access barriers. The success of CHPS and faith-based clinics in reducing IIT suggests scaling up decentralized, community-based models of care. Consistent with evidence from Nigeria, expanding multi-month dispensing especially to clients living in rural settings could greatly enhance continuity. Routine use of mobile technology (SMS or phone reminders) and peer support can leverage the protective effect of contactability seen in our study. Clinics with less experience may benefit from mentorship and quality improvement initiatives, given the lower IIT risk in longer-established sites. Clinically, patients on NNRTI-based regimens should be closely monitored and supported, and wider transition to dolutegravir-based regimens may reduce side-effect-driven interruptions. Conclusions We identified low retention on ART and multiple actionable risk factors for treatment interruption. These findings emphasise that Ghana’s ART programmes must intensify retention efforts by supporting peripheral clinics, enhance DSD approaches and follow-up visits, and optimize ART regimens if the country is to reach the 95-95-95 targets by 2030. Policymakers and managers must address the structural and service-level determinants highlighted as key to sustaining lifelong ART and improving outcomes for people living with HIV. Abbreviations Abbreviation Meaning HIV Human Immunodeficiency Virus PLHIV people lived with HIV ART Antiretroviral therapy AIDS Acquired Immune Deficiency Syndrome UNAIDS Joint United Nations Programme on HIV/AIDS DHIMS2 District Health Information Management System, version 2 CHPS Community-based Health Planning and Services IIT interruption in treatment WHO World Health Organization SD Standard Deviation AG Anderson-Gill PWP Prentice-Williams-Peterson HR Hazard ratio AIC Akaike Information Criterion BIC Bayesian Information Criterion JHS/MSLC Junior High school / Middle School Leaving Certificate CI confidence interval NHIS National Health Insurance Scheme TB Tuberculosis aHR Adjusted Hazard Ratio NtRTI Nucleotide Reverse Transcriptase Inhibitor NRTI Nucleoside Reverse Transcriptase Inhibitor (for pediatrics) NNRTI Non-Nucleoside Reverse Transcriptase Inhibitor PI Protease inhibitors NACP National HIV/AIDS & STI Control Program DSD Differentiated Service Delivery IIT Interruption in Treatment TI Treatment Interruption Declarations Ethics approval and consent to participate The study protocol was reviewed and approved by the Ghana Health Service Ethical Review Committee with approval number GHS-ERC:003/08/24. Adequate permissions were received from the NACP prior to the extraction of the datasets from the HIV eTracker. This secondary data analysis used de-identified records, and informed consent was waived. All ethical principles were followed in this study. Consent to participate is not applicable. Consent for publication Not applicable Availability of data and materials The datasets analysed are not publicly available due sensitive nature but are available from the corresponding author on reasonable request. Competing interests All authors declare that they have no competing interests. Funding Not applicable Authors' contributions WK conceived the research topic, led data management, analysis, and wrote the first draft of the manuscript. JK proof-read and reviewed the manuscript. All authors read and approved the final manuscript. YA reviewed the R code used for the analysis, FBV, DD, and SB contributed to the methods, analysis, and reporting and reviewed the manuscript. 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Supplementary Files BivariateAnalysis.pdf Cite Share Download PDF Status: Published Journal Publication published 10 Jan, 2026 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 11 Dec, 2025 Reviews received at journal 30 Nov, 2025 Reviewers agreed at journal 10 Nov, 2025 Reviews received at journal 07 Nov, 2025 Reviewers agreed at journal 07 Nov, 2025 Reviewers invited by journal 29 Oct, 2025 Editor invited by journal 18 Oct, 2025 Editor assigned by journal 16 Oct, 2025 Submission checks completed at journal 16 Oct, 2025 First submitted to journal 15 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7871647","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":541915826,"identity":"40bae468-9bf7-463b-a7fd-6bcc0de1d248","order_by":0,"name":"Williams 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1","display":"","copyAsset":false,"role":"figure","size":62673,"visible":true,"origin":"","legend":"\u003cp\u003eStudy sites\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871647/v1/a95b79e1e9877934a127d73d.jpeg"},{"id":95655580,"identity":"924e43ff-6f12-4f1e-a464-b1d5c9504afe","added_by":"auto","created_at":"2025-11-11 16:16:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":42582,"visible":true,"origin":"","legend":"\u003cp\u003eCONSORT diagram on the flow of study participants\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7871647/v1/3fee603b8d017ed621c8307e.png"},{"id":95654643,"identity":"70565419-dad9-4985-b24a-02be7bf55c2b","added_by":"auto","created_at":"2025-11-11 16:12:39","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":282950,"visible":true,"origin":"","legend":"\u003cp\u003eTreatment interruptions pattern at follow-up points among PLHIV\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871647/v1/4488d5770461a110172e5536.jpeg"},{"id":95565815,"identity":"b0c924db-a2ad-4497-9a39-1adf1165b9c5","added_by":"auto","created_at":"2025-11-10 16:17:40","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":331888,"visible":true,"origin":"","legend":"\u003cp\u003eRisk of IIT pattern from ART initiation by order of event occurrence\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7871647/v1/68da9aab71cf13571b61cd44.jpeg"},{"id":100069099,"identity":"cfc37cb6-1c01-45d5-a184-a3aacd9d99c0","added_by":"auto","created_at":"2026-01-12 16:09:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2576500,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7871647/v1/c51d1164-c14f-4316-acea-dba31a72012b.pdf"},{"id":95565807,"identity":"4f484df4-efd4-4c60-bb8d-fbdf6bd78e7e","added_by":"auto","created_at":"2025-11-10 16:17:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":8692,"visible":true,"origin":"","legend":"","description":"","filename":"BivariateAnalysis.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7871647/v1/bc9db84ca79eebc0b567f108.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictors of HIV interruption in treatment among people living with HIV in 6 regions in Ghana: a retrospective cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHuman immunodeficiency virus (HIV) persists as a global public health threat. In 2024, about 40.8\u0026nbsp;million people were living with HIV (PLHIV) worldwide and roughly 1.3\u0026nbsp;million new infections occurred, despite significant treatment scale-up [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Global AIDS-related mortality has declined substantially (by 70% since 2004) due to antiretroviral therapy (ART) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], yet western and central Africa still harbours a disproportionately high share of the epidemic (approximately 5.2\u0026nbsp;million PLHIV in 2024) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Ghana\u0026rsquo;s epidemic is firmly established, with recent estimates reporting roughly 330,000 people living with HIV, 15,000 new infections, and 13,000 AIDS-related deaths in 2024 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Progress toward the UNAIDS 95-95-95 targets is limited, with only about 68% of PLHIV aware of their status, 47% of those diagnosed receiving ART, and 42% of those on ART virally suppressed [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHIV interruption in treatment (IIT) is a major barrier to achieving sustained viral suppression. Such interruptions can undermine both individual and programmatic success by increasing viral rebound, drug resistance, and adverse clinical outcomes. Globally, retention on ART is suboptimal, with only about 46% to 85% clients remaining on ART after two years of initiation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In sub-Saharan Africa, financial and structural constraints are major barriers. For example, a Cameroonian study found that transportation costs (47.5%) and stigma were the leading predictors of interruption [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Similarly, in South Africa, financial hardship, long travel distances and forgetfulness were cited as key barriers to adherence [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In Nigeria\u0026rsquo;s national cohort, Ikpe and colleagues identified sex, ART anchor drug class, unsuppressed viral load or CD4 count, WHO stage, and sociodemographic (e.g. education, marital status, and urban vs. rural residence) as significant predictors [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In Ghana, evidence on interruptions is limited, but adherence studies highlight important clues, with a recent meta-analysis found only approximately 70% ART adherence overall and younger patients especially at risk [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Ghanaian cohorts report that comorbid illness and drug side-effects undermine adherence, whereas family support and regular clinic follow-up promote it [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Despite these insights, few studies have simultaneously quantified IIT and examined facility factors, patient factors (age, sex, education), system factors (access, clinic support), and regimen factors (pill burden, ART regimen class, tolerability) in Ghana.\u003c/p\u003e\u003cp\u003eThe aim of this study was to quantify the magnitude of HIV interruption in treatment and identify key predictors of interruption in treatment among PLHIV in Ghana, using data from the national HIV electronic tracker (eTracker) system between 2019 and 2023. Specifically, the study sought to estimate the incidence and recurrence of IIT, examine demographic, clinical, and facility-level predictors associated with both first and subsequent interruptions, and explore regional and health system variations that influence patient retention.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and participants\u003c/h2\u003e\u003cp\u003eWe conducted a retrospective cohort study using routinely collected data from the National HIV eTracker, an instance on the District Health Information Management System (DHIMS2) platform in Ghana. We extracted data on individuals enrolled in ART services between January 2019 and December 2023 across all health facilities (i.e., medical centres, hospitals, health centres, clinics, and Community-based Health Planning and Services (CHPS) compounds) in six administrative regions (i.e., Ahafo, Bono, Upper East, Volta, Western, and Western North). The HIV eTracker captures standardised patient-level transactional data from public, private, faith-based, and quasi-government facilities. This data included health facility details, demographic information of participants and clinical information. The study sites were purposively selected based on regions PEPFAR supported versus non-regions PEPFAR supported that had high ART coverages. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the location of the study sites across the country. We included PLHIVs who initiated ART within the study period and had at least one follow-up visit recorded in the HIV eTracker. We also included individuals if they had complete ART initiation dates and follow-up data. Children and adults were both eligible, with the time origin (baseline) defined as the date of ART initiation. Participants were followed until the end of December 2023. Multiple episodes of IIT were documented. We excluded participants who died or were transferred out of health facilities outside the study sites from the analysis. Participants who never interrupted treatment were censored at the end of the follow-up period.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eOutcome definition\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was time-to-HIV interruption in treatment (IIT), defined as a missed clinic visit more than 28 days after the scheduled appointment date, in accordance with the national HIV/AIDS \u0026amp; STI Control Program (NACP) definition and World Health Organization (WHO) guidelines [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. We defined a participant\u0026rsquo;s IIT status as 1 if treatment was interrupted, as per the definition, and 0 otherwise.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eIndependent variables included demographic (age, sex, education, marital status, occupation, region of residence, mobile phone access, physical address documented, and national health insurance coverage), clinical (HIV type, ART regimen classification, ART initiation time, Tuberculosis (TB) screening, viral load testing and suppression), and facility-level characteristics (facility type, facility ownership, number of ART staff, settings (rural/urban). Number of years ART site/clinic operated). The covariates were treated as fixed or time-varying based on their nature and data availability.\u003c/p\u003e\n\u003ch3\u003eData processing and management\u003c/h3\u003e\n\u003cp\u003eDe-identified datasets (i.e. Registration, Initial Assessment, Follow-up, and Viral load) were received from the NACP. Further, data on ART clinic characteristics was collected through the regional HIV coordinators in Microsoft Excel sheet. All these five datasets were processed, cleaned and merged into one master dataset. Participants who died or were transferred out to other facilities outside the study areas were excluded from the final analysis. Using the unique identification numbers created from the raw dataset, clients who transferred to other facilities within the study regions were noted and included in the analysis such that the continuum of care for these participants were appropriately tracked. Duplicates in each dataset were first excluded before merging into a final dataset. Complete case analysis and deletion was conducted to address missing data, assuming data were missing completely at random [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The outcome measure, HIV interruption in treatment, was generated using data on number of ARV pills dispensed, date of current follow-up, and the 28-day threshold per the standard definition of IIT.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eWe summarised participant characteristics using frequency (percentage) for categorical variables and means (standard deviation - \u0026plusmn;SD) for continuous variables. For the primary analysis, we used the Andersen-Gill (AG) model to determine the predictors of IIT. The AG model is a recurrent events survival model that extends the Cox proportional hazards model to analyse multiple events per subject, such as repeated instances of IIT [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe AG model assumes that the instantaneous risk to experience an event at time \u003cem\u003et\u003c/em\u003e since study entry remains the same irrespective of whether previous events occurred or not, implying the recurrent events are independent. If this assumption is fulfilled, the all-cause hazard can be estimated by using the event times of every observed event. Thus, a single patient contributes more than one piece of information depending on the number of individually observed events. The Andersen-Gill model therefore aims at estimating the same quantity as the common Cox model given by the all-cause hazard ratio \u003cem\u003eθ\u003c/em\u003e\u003csub\u003e\u003cem\u003eAllCause\u003c/em\u003e\u003c/sub\u003e. However, the estimation is based on more information as an individual who has experienced an event remains under risk for further events. This implies that the corresponding partial likelihood is based on a higher number of events and on a modified risk set as shown in Eq.\u0026nbsp;1.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}^{AG}\\left(t\\right)≔\\{l,\\:l=1,\\dots\\:.n\\::\\:\\exists\\:j\\:\\in\\:\\left\\{1,\\dots\\:..,\\:kl\\right\\}\\:with\\:Tlj\\:\\ge\\:\\:\\text{t}\\}\\:\\)\u003c/span\u003e\u003c/span\u003e Eq.\u0026nbsp;1.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewere \u003cem\u003eTlj\u003c/em\u003e are the distinct event times for individual \u003cem\u003el\u003c/em\u003e, \u003cem\u003el\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,. \u003cem\u003e. .\u003c/em\u003e, \u003cem\u003en\u003c/em\u003e, and for the \u003cem\u003ej\u003c/em\u003eth occurring event \u003cem\u003ej\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,. \u003cem\u003e. .\u003c/em\u003e, \u003cem\u003ekl\u003c/em\u003e, with \u003cem\u003ekl\u003c/em\u003e being the individual-specific number of distinct observed event times, where \u003cem\u003ekl\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;\u003cem\u003ek\u003c/em\u003e, \u003cem\u003el\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,. \u003cem\u003e. .\u003c/em\u003e, \u003cem\u003en\u003c/em\u003e, is assumed meaning that the maximal number of events which are taken into account is given by \u003cem\u003ek\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eIf the assumption of independent recurrent event times is not fulfilled, the Anderson-Gill model might still be applied but no longer estimates the all-cause hazard ratio. Instead, the resulting treatment effect estimator is given as a hazard ratio combining direct and indirect effects [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe mixed effect resulting from the Anderson-Gill model will be denoted as \u003cem\u003eθ\u003c/em\u003e\u003csub\u003e\u003cem\u003eMixAG\u003c/em\u003e\u003c/sub\u003e. This treatment effect cannot easily be parametrized and might therefore be considered as difficult for interpretation. We used the Schoenfeld residuals plot to assess the proportional hazards assumptions. The adjusted hazard ratios (HRs), along with their 95% confidence intervals (CIs) and p-values, were reported.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eSensitivity Analysis\u003c/h2\u003e\u003cp\u003eTo ensure the robustness of our findings from the AG model, we conducted sensitivity analyses using the mixed-effects Cox model, Prentice-Williams-Peterson (PWP) models, and marginal models, with each model approaching the analysis of multiple events differently and providing unique insights and a more comprehensive view of the data. The mixed-effects Cox model, also known as the frailty model, is a robust approach that accounts for the non-independence of events within the same individual by incorporating a random effect, or \"frailty,\" for each subject. The Prentice-Williams-Peterson (PWP) models are a family of recurrent event models that focus on the order of the events. Unlike the AG model, which treats all events equally, PWP models distinguish between different event occurrences. The PWP Conditional Probability Model stratifies the risk of IIT by the event order. This allows for a separate baseline hazard function for each event number, which is useful if the risk changes significantly after the first event. The PWP Gap-Time model focuses on the time between events, or the \"gap time\" and analyses the duration between consecutive events. This approach is particularly useful when the time it takes to experience the next event is of primary interest.\u003c/p\u003e\u003cp\u003eThe marginal models, such as the Wei-Lin-Weisfeld (WLW) model, offer an alternative perspective by treating each recurrent event as a separate observation. These models do not specify the exact correlation structure between events but use robust standard errors to account for the non-independence within subjects and provides population-averaged hazard ratio estimates, which are interpreted as the change in the average risk of an event across the entire population, rather than for a specific individual. The performance of all models was compared using the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and the concordance index. A p-value less than 0.05 was considered statistically significant. All analyses were performed using R (version 4.3.0; R Foundation).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 245 health facilities across the 6 administrative regions (23 from Ahafo, 43 from Bono, 27 from Upper East, 57 from Volta, 72 from Western North regions) were included in this study. Of the 245 health facilities, 131 (53.5%) were CHPS or health centers, 93 (38.0%) were hospitals, 11 (4.5%) were clinics, 7 (2.9%) were polyclinics and 3 (1.2%) were medical centers. Majority 191 (78.0%) of the health facilities were publicly owned, 34 (13.9%) were faith-based organizations, 16 (6.5%) were private facilities, and 4 (1.6%) were quasi-government facilities.\u003c/p\u003e\u003cp\u003eBetween January 2019 and December 2023, a total of 56,040 participants made 371,661 follow-up visits across all the health facilities. Of the 371,661 visits, 144,342 records were excluded from the final analysis because of missing values, negative values recorded, and no first visit records available. A final dataset of 33,613 participants contributing a total of 227,319 follow-up visits was used in the analysis. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides details of the participants\u0026rsquo; flow.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOf the 33,613 participants, 4,408 (13.1%) participants had 2 successful visits, 3,710 (11.0%) had 3 visits, 3,171 (9.4%) had 4 visits, 2,890 (8.6%) had 5 follow-ups visits and the remaining 19,434 (57.8%) had at least 6 follow-up visits. Details of the number of follow-up visits are provided as supplementary information.\u003c/p\u003e\n\u003ch3\u003eDemographic characteristics\u003c/h3\u003e\n\u003cp\u003eThe mean age of the participants was 35.6 (\u0026plusmn;\u0026thinsp;14) years, ranging from 0 years to 89 years, with 24,070 (71.6%) females and 9,543 (28.4%) males. More than half, 21,829 (64.9%), had at least secondary education, 5,772 (17.2%) had primary education, while 6,360 (18.9%) had no education. Unemployment or no paid work was prevalent among 11,784 (35.1%), while 21,829 (64.9%) were employed or engaged in paid work. Most 17,900 (53.6%) of participants were married or cohabiting, 5,326 (15.8%) were divorced/separated/widowed, while 10,387 (30.9%) of participants were single. A small proportion (2.1%) had ever smoked, while 75.0% provided mobile phone numbers at initiation. More than a quarter (28.4%) of participants were in the Western region, 23.0% in the Bono region, and 20.3% in the Volta region. More than half of the participants were in rural areas (55.4).\u003c/p\u003e\u003cp\u003eAveragely, the ART clinics have been providing ART services for 12.4 (\u0026plusmn;\u0026thinsp;6.4) years. HIV-I was the most common type among 97.2% participants, with 2.1% having both HIV-I \u0026amp; HIV-II types. The majority (58.6%) were seeking HIV services from integrated ART clinics, with the remaining 41.4% receiving services from standalone care. The majority 18,174 (56.5%) of participants were initiated on ART within the same day of diagnosis, 7,205 (22.4%) initiated within 1 to 7 days of diagnosis, and the remaining 6,771 (21.1%) were initiated after 7 days of diagnosis. More than half (54.6%) of the participants had their first viral load done after ART initiation. Additional information is provided in 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\u003eDescriptive characteristics of study participants by sex\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=\"left\" 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\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemales\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMales\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;33,613\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;24,070\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;9.543\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge (in years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35.6 [\u0026plusmn;\u0026thinsp;14.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.1 [\u0026plusmn;\u0026thinsp;13.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.7 [\u0026plusmn;\u0026thinsp;15.8]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge group (in years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,994 (5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e990 (4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,004 (10.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u0026ndash;19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,332 (4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,053 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e279 (2.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u0026ndash;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,247 (9.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,697 (11.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e550 (5.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9,959 (29.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7,931 (32.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,028 (21.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17,081 (50.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11,399 (47.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5,682 (59.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHighest education\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6,360 (18.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5,003 (20.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,357 (14.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,772 (17.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4,276 (17.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,496 (15.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary School and beyond\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21,829 (64.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14,791 (61.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6,690 (70.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnemployed or not engaged in paid work\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11,784 (35.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7,562 (31.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4,222 (44.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployed or engaged in paid work\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21,829 (64.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16,508 (68.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5,321 (55.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10,387 (30.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7,448 (30.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,939 (30.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDivorced/Separated/Widowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,326 (15.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4,324 (18.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,002 (10.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried /Cohabiting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17,900 (53.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12,298 (51.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5,602 (58.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEver smoked\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32,920 (97.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23,577 (98.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9,343 (97.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e693 (2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e493 (2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e200 (2.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eProvided mobile phone number at initiation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,690 (22.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5,514 (22.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,176 (22.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25,923 (77.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18,556 (77.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7,367 (77.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eArea of residence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18,630 (55.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13,652 (56.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4,978 (52.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14,983 (44.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10,418 (43.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4,565 (47.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRegion of residence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAhafo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,038 (9.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,224 (9.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e814 (8.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBono\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,732 (23.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5,495 (22.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,237 (23.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpper East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,066 (9.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,163 (9.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e903 (9.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVolta\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6,826 (20.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4,859 (20.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,967 (20.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWestern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9,535 (28.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6,800 (28.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,735 (28.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWestern North\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,416 (10.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,529 (10.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e887 (9.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYears ART clinic operated\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.4 [\u0026plusmn;\u0026thinsp;6.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.2 [\u0026plusmn;\u0026thinsp;6.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.8 [\u0026plusmn;\u0026thinsp;6.5]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHIV type\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHIV I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32,662 (97.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23,402 (97.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9,260 (97.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHIV II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e252 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e176 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76 (0.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHIV I and II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e699 (2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e492 (2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e207 (2.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eART clinic status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntegrated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19,700 (58.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14,117 (58.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5,583 (58.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStandalone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13,913 (41.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9,953 (41.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3,960 (41.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eType of client\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdult\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31,619 (94.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23,080 (95.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8,539 (89.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePediatric\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,994 (5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e990 (4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,004 (10.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTime to ART initiation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSame-day\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18,174 (56.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12,984 (56.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5,190 (56.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;7 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,205 (22.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5,143 (22.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,062 (22.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMore than 7 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6,771 (21.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4,895 (21.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,876 (20.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFirst Viral load timing\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAfter ART Initiation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18,638 (55.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13,282 (55.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5,356 (56.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot done\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14,975 (44.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10,788 (44.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4,187 (43.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHave NHIS number\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20,976 (62.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14,381 (59.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6,595 (69.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12,637 (37.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9,689 (40.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,948 (30.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eProvided physical address at initiation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,103 (12.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,973 (12.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,130 (11.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29,510 (87.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21,097 (87.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8,413 (88.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFacility type\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCHPS/Health Centre\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,219 (21.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5,446 (22.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,773 (18.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolyclinic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e450 (1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e317 (1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e133 (1.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e310 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e245 (1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65 (0.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25,575 (76.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18,015 (74.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7,560 (79.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedical Centre\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59 (0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47 (0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (0.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFacility ownership\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePublic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24,196 (72.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17,444 (72.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6,752 (70.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrivate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,175 (3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e832 (3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e343 (3.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFBO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8,134 (24.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5,722 (23.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,412 (25.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuasi-Government\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e108 (0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72 (0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36 (0.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNumber of staffs at ART clinics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.6 [\u0026plusmn;\u0026thinsp;3.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.5 [\u0026plusmn;\u0026thinsp;3.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.6 [\u0026plusmn;\u0026thinsp;3.8]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eHIV Treatment Interruption\u003c/h2\u003e\u003cp\u003eAmong the 33,613 participants, a total of 22,956 (68.3%, 95% CI: 67.8\u0026ndash;68.8) interrupted treatment at least once during the follow-up time while participants who never interrupted treated were 10,657 (31.7%, 95% CI: 31.2\u0026ndash;32.2). Within the first 3 months of follow-up, 1,752 (21.8%) participants interrupted treatment. This increased to 3,281 (40.9%) participants in 6 months before dropping to 1,975 (24.6%) by 12 months. A total of 1,018 (12.7%) participants interrupted treatment after 12 months of follow-up. The rate of interruption in treatment decreased as the number of follow-up visits increased as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The highest treatment interruption occurred at the first 2 visits (24%), reducing to 23% on follow-up visits 3 to 5 and then to 22% on follow-up visits 6 and 7. Interruption rate decreased gradually to 8% on follow-up visits 27 and 28 and then increased to 14% on follow-up visit 29 before falling back to 9% on follow-up visits 30. No treatment interruption was observed at follow-up visits 31 to 34.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the cumulative risk curves of interruption in treatment among participants, stratified by the order of interruption event following antiretroviral therapy (ART) initiation. The first interruption (1st IIT) had the highest initial number participants at risk (n\u0026thinsp;=\u0026thinsp;33,613) and exhibited a steady rise in cumulative risk, reaching over 85% by approximately 1,000 days. Subsequent interruptions, such as the 2nd IIT (n\u0026thinsp;=\u0026thinsp;13,113) and 3rd IIT (n\u0026thinsp;=\u0026thinsp;7,123), followed a similar but steeper trajectory, indicating a higher risk of repeated interruptions among those previously interrupted. From the 4th to the 12th IIT, although the number at risk declined substantially, the cumulative risk increased more rapidly, with nearly all individuals experiencing another interruption within a shorter time span.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003ePredictors of HIV treatment interruption\u003c/h2\u003e\u003cp\u003eCovariates that were found to be significantly associated with IIT were included in the multivariate Andersen Gill model to determine the predictors. The supplementary file provides details of the bivariate analysis. The AG model is particularly suited for this analysis as it accounts for multiple events per individual and provides an estimate of the overall effect of covariates on the hazard of recurrent events, such as a patient interrupting treatment more than once. The results are presented as adjusted hazard ratios (aHR), which indicate the change in the instantaneous risk of IIT for a one-unit increase in the predictor, while controlling for all other variables in the model. Independent variables that were found to be significantly associated with interruption in treatment were fitted into the multivariable AG model.\u003c/p\u003e\u003cp\u003eSeveral statistically significant predictors of HIV treatment interruption in the overall study population were observed as provided in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Geographic region was a significant factor, with patients in the Bono region having a 10% higher adjusted hazard of IIT (aHR\u0026thinsp;=\u0026thinsp;1.10, 95% CI: 1.066\u0026ndash;1.139, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and those in the Upper East region having an 8% higher hazard (aHR\u0026thinsp;=\u0026thinsp;1.08, 95% CI: 1.036\u0026ndash;1.124, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to the Western region. Conversely, the Ahafo region was associated with a 6% lower hazard (aHR\u0026thinsp;=\u0026thinsp;0.94, 95% CI: 0.903\u0026ndash;0.983, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006).\u003c/p\u003e\u003cp\u003eHealthcare facility characteristics were also critical. Patients receiving care at a CHPS/Health Centre had a 13% lower hazard of IIT (aHR\u0026thinsp;=\u0026thinsp;0.87, 95% CI: 0.847\u0026ndash;0.902, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to those at a hospital. Similarly, attendance at a Faith-Based Organization (FBO) owned facility was associated with an 8% lower hazard (aHR\u0026thinsp;=\u0026thinsp;0.92, 95% CI: 0.896\u0026ndash;0.943, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to a public facility. An important finding was the inverse relationship between the number of years an ART clinic has been operational and the hazard of IIT (aHR\u0026thinsp;=\u0026thinsp;0.99, 95% CI: 0.991\u0026ndash;0.995, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that more established clinics have a lower risk of treatment interruption.\u003c/p\u003e\u003cp\u003ePatient-level factors also played a role. Providing a phone number (aHR\u0026thinsp;=\u0026thinsp;0.95, 95% CI: 0.931\u0026ndash;0.974, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a higher follow-up visit number (aHR\u0026thinsp;=\u0026thinsp;0.95, 95% CI: 0.946\u0026ndash;0.953, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were both associated with a 5% decreased hazard of IIT. The specific antiretroviral therapy (ART) regimen was also a significant predictor. The use of a regimen containing a Nucleoside/tide Reverse Transcriptase Inhibitor (NtRTI), a Nucleoside Reverse Transcriptase Inhibitor (NRTI), and a Non-Nucleoside Reverse Transcriptase Inhibitor (NNRTI) was associated with a 33% higher adjusted hazard of IIT (aHR\u0026thinsp;=\u0026thinsp;1.33, 95% CI: 1.164\u0026ndash;1.529, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to the NtRTI\u0026thinsp;+\u0026thinsp;NRTI\u0026thinsp;+\u0026thinsp;PI combination. This drug combination is a common first-line treatment regimen, but some NNRTIs have a lower barrier to resistance and can cause neuropsychiatric side effects, which may affect adherence.\u003c/p\u003e\u003cp\u003eGender-specific sub-analyses revealed distinct patterns. Regional Differences: Regional disparities were more pronounced. For males, the Bono (aHR\u0026thinsp;=\u0026thinsp;1.09, 95% CI: 1.027\u0026ndash;1.163, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) and Upper East (aHR\u0026thinsp;=\u0026thinsp;1.09, 95% CI: 1.006\u0026ndash;1.172, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034) regions were associated with a higher hazard of IIT, whereas the Western North region was associated with a 8% lower hazard (aHR\u0026thinsp;=\u0026thinsp;0.92, 95% CI: 0.858\u0026ndash;0.987, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020). For females, the Bono (aHR\u0026thinsp;=\u0026thinsp;1.11, 95% CI: 1.064\u0026ndash;1.150, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Upper East (aHR\u0026thinsp;=\u0026thinsp;1.08, 95% CI: 1.026\u0026ndash;1.130, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) regions were also associated with a higher hazard, while the Ahafo region had a 6% lower hazard (aHR\u0026thinsp;=\u0026thinsp;0.94, 95% CI: 0.890\u0026ndash;0.985, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011).\u003c/p\u003e\u003cp\u003eFemales receiving care in an urban setting had a 3% lower hazard of IIT (aHR\u0026thinsp;=\u0026thinsp;0.97, 95% CI: 0.941\u0026ndash;0.997, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029) compared to those in rural settings, a finding not observed in males. Patients of both genders benefited from care at a CHPS/Health Centre (males: aHR\u0026thinsp;=\u0026thinsp;0.88, 95% CI: 0.831\u0026ndash;0.940, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; females: aHR\u0026thinsp;=\u0026thinsp;0.87, 95% CI: 0.838\u0026ndash;0.902, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and an FBO-owned facility (males: aHR\u0026thinsp;=\u0026thinsp;0.91, 95% CI: 0.870\u0026ndash;0.956, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; females: aHR\u0026thinsp;=\u0026thinsp;0.92, 95% CI: 0.896\u0026ndash;0.951, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For males, being treated at a Clinic was associated with a 21% lower hazard of IIT (aHR\u0026thinsp;=\u0026thinsp;0.79, 95% CI: 0.641\u0026ndash;0.977, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029), while for females, treatment at a Polyclinic was associated with a 12% lower hazard (aHR\u0026thinsp;=\u0026thinsp;0.88, 95% CI: 0.794\u0026ndash;0.980, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020), both compared to hospitals. The 15\u0026ndash;19 years age group for females showed a 5% lower hazard of IIT (aHR\u0026thinsp;=\u0026thinsp;0.95, 95% CI: 0.896\u0026ndash;0.999, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048) compared to the 35\u0026thinsp;+\u0026thinsp;age group. Consistent with the overall model, providing a phone number and having a higher follow-up visit number were both associated with a lower hazard of IIT for both genders. The use of the NtRTI\u0026thinsp;+\u0026thinsp;NRTI\u0026thinsp;+\u0026thinsp;NNRTI regimen was a significant predictor of IIT for both males (aHR\u0026thinsp;=\u0026thinsp;1.39, 95% CI: 1.085\u0026ndash;1.781, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009) and females (aHR\u0026thinsp;=\u0026thinsp;1.31, 95% CI: 1.114\u0026ndash;1.543, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001).\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\u003ePredictors of HIV interruption in treatment among PLHIVs\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eBoth Male and Female\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eMales\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u003cp\u003eFemales\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eaHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eaHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eP-Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eaHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eP-Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWestern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.00 [Reference]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAhafo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.903\u0026ndash;0.983\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.879\u0026ndash;1.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.890\u0026ndash;0.985\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBono\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.066\u0026ndash;1.139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.027\u0026ndash;1.163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.064\u0026ndash;1.150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpper East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.036\u0026ndash;1.124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.006\u0026ndash;1.172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.026\u0026ndash;1.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVolta\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.946\u0026ndash;1.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.910\u0026ndash;1.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.942\u0026ndash;1.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.305\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWestern North\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.935\u0026ndash;1.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.858\u0026ndash;0.987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.946\u0026ndash;1.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSetting\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.00 [Reference]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.954\u0026ndash;1.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.951\u0026ndash;1.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.834\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.941\u0026ndash;0.997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFacility Type\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.00 [Reference]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCHPS/Health Centre\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.847\u0026ndash;0.902\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.831\u0026ndash;0.940\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.838\u0026ndash;0.902\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolyclinic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.876\u0026ndash;1.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.995\u0026ndash;1.226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.794\u0026ndash;0.980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.847\u0026ndash;1.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.641\u0026ndash;0.977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.875\u0026ndash;1.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.692\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedical Center\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.748\u0026ndash;1.206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.672\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.622\u0026ndash;1.612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.716\u0026ndash;1.229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.644\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFacility Ownership\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePublic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.00 [Reference]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrivate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.911\u0026ndash;1.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.875\u0026ndash;1.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.901\u0026ndash;1.040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.376\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFBO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.896\u0026ndash;0.943\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.870\u0026ndash;0.956\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.896\u0026ndash;0.951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuasi-Government\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.727\u0026ndash;1.114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.564\u0026ndash;1.179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.2778\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.733\u0026ndash;1.206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.628\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYears ART clinic operated\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.991\u0026ndash;0.995\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.990\u0026ndash;0.997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.989\u0026ndash;0.995\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eART Clinic Status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntegrated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.00 [Reference]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStandalone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.983\u0026ndash;1.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.975\u0026ndash;1.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.272\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.971\u0026ndash;1.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.774\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge group\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;15 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.938\u0026ndash;1.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.910\u0026ndash;1.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.931\u0026ndash;1.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.633\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u0026ndash;19 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.913\u0026ndash;1.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.917\u0026ndash;1.123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.779\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.896\u0026ndash;0.999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u0026ndash;24 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.959\u0026ndash;1.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.573\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.898\u0026ndash;1.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.958\u0026ndash;1.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.755\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;34 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.983\u0026ndash;1.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.649\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.956\u0026ndash;1.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.982\u0026ndash;1.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.577\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.00 [Reference]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEngaged in paid work\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.00 [Reference]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot engaged in paid work\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.972\u0026ndash;1.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.969\u0026ndash;1.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.961\u0026ndash;1.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eProvided phone number\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.00 [Reference]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.931\u0026ndash;0.974\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.916\u0026ndash;0.996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.926\u0026ndash;0.978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTime to ART initiation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSame day\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.00 [Reference]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;7 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.974\u0026ndash;1.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.972\u0026ndash;1.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.963\u0026ndash;1.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.504\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMore than 7 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.958\u0026ndash;1.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.933\u0026ndash;1.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.956\u0026ndash;1.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTB screening at initiation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.00 [Reference]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.961\u0026ndash;1.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.949\u0026ndash;1.117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.945\u0026ndash;1.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.916\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTB screening at follow-up\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.00 [Reference]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.932\u0026ndash;1.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.396\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.867\u0026ndash;1.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.935\u0026ndash;1.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.716\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUse of family planning method\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.00 [Reference]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.952\u0026ndash;1.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.638\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.945\u0026ndash;1.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.650\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.936\u0026ndash;1.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eART combination classification\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNtRTI\u0026thinsp;+\u0026thinsp;NRTI\u0026thinsp;+\u0026thinsp;PI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.00 [Reference]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNtRTI\u0026thinsp;+\u0026thinsp;NRTI\u0026thinsp;+\u0026thinsp;INSTI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.927\u0026ndash;1.215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.387\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.910\u0026ndash;1.486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.869\u0026ndash;1.200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.802\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNtRTI\u0026thinsp;+\u0026thinsp;NRTI\u0026thinsp;+\u0026thinsp;NNRTI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.164\u0026ndash;1.529\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.085\u0026ndash;1.781\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.114\u0026ndash;1.543\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNumber of ARV pills dispensed\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.997\u0026ndash;0.997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.996\u0026ndash;0.998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.997\u0026ndash;0.997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMulti-month dispensing\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot on MMD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.705\u0026ndash;1.197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.528\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.473\u0026ndash;1.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.738\u0026ndash;1.433\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.869\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOn 3\u0026ndash;5 months MMD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.682\u0026ndash;1.146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.449\u0026ndash;1.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.721\u0026ndash;1.383\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u0026thinsp;+\u0026thinsp;months MMD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.00 [Reference]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFollow-up visit number\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.946\u0026ndash;0.953\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.940\u0026ndash;0.954\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.946\u0026ndash;0.955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\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=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eSensitivity Analysis\u003c/h2\u003e\u003cp\u003eTo ensure the robustness of the findings from the primary Andersen-Gill Cox-Model, a series of sensitivity analyses were conducted using alternative recurrent event models as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The performance of the primary model, which yielded a Concordance Index of 0.59 and an AUROC of 0.55, was compared to other models using several metrics. The Andersen-Gill Counting Process Model and the Andersen-Gill Counting Process with Clustering model both showed identical performance metrics to the primary model, with an AIC of 967326, a BIC of 967626, a log-likelihood of -483629, a Concordance Index of 0.59, and an AUROC of 0.55. The Marginal Model (Wei\u0026ndash;Lin\u0026ndash;Weisfeld model) with robust standard errors also demonstrated identical results, confirming the stability of the primary model's estimates when accounting for within-subject correlation.\u003c/p\u003e\u003cp\u003eHowever, other models designed to handle different assumptions about recurrent events revealed variations in model fit and predictive accuracy. The Frailty Model with time difference showed a significantly higher Concordance Index of 0.83 but a lower AUROC of 0.51, indicating strong within-subject clustering but a weaker overall predictive ability. In contrast, the Prentice-Williams-Peterson (PWP) Conditional Probability model had a lower Concordance Index of 0.54 but a higher AUROC of 0.67, while the PWP Gap Time model demonstrated the highest predictive power with a Concordance Index of 0.86 and an AUROC of 0.72. These findings suggest that a model accounting for the time gap between recurrent events may provide a better fit for the data. Nonetheless, the consistency of the Andersen-Gill Cox-Model with the Counting Process and Marginal models provides confidence in the core findings, despite the superior performance metrics of the PWP Gap Time model in this specific context.\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\u003eSensitivity analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLoglikelihood\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eConcordance Index\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAUROC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAndersen-Gill Cox-Model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e967482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e967782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-483707\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.59 (0.583\u0026ndash;0.589)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAndersen-Gill Cox-Model Counting Process Model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e967326\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e967626\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-483629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.59 (0.584, 0.589)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAndersen-Gill Cox-Model Counting Process with Clustering\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e967326\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e967626\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-483629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.59 (0.584, 0.589)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrailty Model with time difference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e991295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e992145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-495551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.83 (0.831, 0.836)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrentice-Williams-Peterson Conditional Probability Model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e809299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e809598\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-404615\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.54 (0.541, 0.546)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrentice-Williams-Peterson (PWP) Gap Time model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e914302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e914602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-457117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.86 (0.856, 0.860)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarginal Model (Wei\u0026ndash;Lin\u0026ndash;Weisfeld model) with robust SE clustered\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e967326\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e967626\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-483629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.59 (0.584, 0.589)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.55\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"},{"header":"Discussion","content":"\u003cp\u003eWe assessed the magnitude and predictors of IIT among PLHIVs in the selected regions, utilizing a large retrospective cohort from the national HIV eTracker system. Overall, 68.3% of study participants experienced at least one IIT while 31.7% stayed in treatment during the study period. About 21.8% of participants interrupted treatment by 3 months, 40.9% by 6 months before declining. Geographic region was strongly associated with IIT. Relative to the Western region, participants in Bono and Upper East had 10% and 8% higher hazards of IIT, respectively, while those in Ahafo had a 6% lower hazard. Facility factors such as participants receiving care at CHPS/health centers or faith-based organizations (FBOs) had significantly lower IIT risk (13% and 8% lower, respectively) than hospital. Participant-level factors also mattered as providing a mobile phone number and having more prior follow-up visits each reduced IIT hazard by approximately 5%. Participants on regimens containing an NNRTI (NtRTI\u0026thinsp;+\u0026thinsp;NRTI\u0026thinsp;+\u0026thinsp;NNRTI) had 33% higher IIT hazard than those on PI-containing regimens (NtRTI\u0026thinsp;+\u0026thinsp;NRTI\u0026thinsp;+\u0026thinsp;PI). In the sex-stratified models, both male and female subgroups showed higher IIT risk in Bono and Upper East and lower risk in Western North or Ahafo (depending on sex). Both genders benefited from CHPS/health centers and FBO care while being treated at a clinic (for males) or polyclinic (for females) conferred lower IIT risk compared to hospitals. Female adolescents (15\u0026ndash;19) had a slightly lower IIT hazard than older adults. The protective effects of having a phone number and additional visits applied to both sexes, and the regimen effect (NNRTI use) remained significant in each group. Our sensitivity analyses using alternative recurrent-event models yielded consistent effect estimates.\u003c/p\u003e\u003cp\u003eOur findings extend prior knowledge on ART retention in sub-Saharan Africa. An IIT incidence of 21.7% within the first 3 months and 40.9% within 6 months of treatment suggests poorer retention than typically reported in the region. A systematic review in Nigeria found a pooled 12-month retention of approximately 72% [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Similarly, an urban Ghanaian district study found nearly half of patients lost by 90 days [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], indicating similarly rapid early attrition. The temporal pattern, with most interruptions occurring within the first six months of therapy, and then decreasing is consistent with prior studies [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This pattern indicates a major obstacle in achieving optimal HIV care outcomes and aligns with global concerns regarding ART adherence and retention [\u003cspan additionalcitationids=\"CR22 CR23 CR24\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This pronounced early drop-off underscores the urgency of addressing factors unique to Ghana. Many of our predictors mirror barriers such as patient level, structural and socioeconomic which has also been documented in other studies. In Cameroon, transportation costs and stigma were identified as the leading causes of ART interruption [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] which is consistent with our findings that region-level differences (e.g. Bono and Upper East) may have majority rural populations with access challenges. The lower IIT risk at CHPS/Health centers and FBO facilities aligns with evidence that decentralization and community-based care improve continuity. More importantly, community clinics often reduce travel burden and may offer more personalized support. This is consistent with broader literature noting that long travel distances and costs are key adherence barriers in South Africa and other settings [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] while closer community ties and more personalized care often provided at primary healthcare levels foster better patient-provider relationships and adherence [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Our finding that the longer an ART clinic operated, the more beneficial it was in reducing IIT suggest stronger ties between clients and service providers, better clinic efficiency and client experience, experienced staff and better infrastructure [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe association of NNRTI regimens with higher IIT is consistent with concerns about tolerability and resistance. Prior research in Ghana [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and other locations has linked NNRTI-based regimens with greater neuropsychiatric side effects, which could contribute to poor adherence and discontinuation [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Our finding reinforces global shifts toward dolutegravir-based regimens with higher efficacy and tolerability which Ghana is currently moving most PLHIVs onto.\u003c/p\u003e\u003cp\u003eNotably a study in Nigeria found age and sex not significant predictors of HIV treatment interruption [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], similar to our overall findings. However, we observed some gender-specific nuances like female urban residents had marginally lower IIT risk, and female youth were somewhat less likely to drop out of treatment These modest protective effects for younger and urban females partially echo qualitative insights that younger clients may adhere better with support [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] though other reports often highlight youth as at-risk [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Our study confirms the importance of structural and programmatic determinants of retention observed in other contexts, while adding new insights on clinic characteristics, age, and client contactability. Together, the evidence underscores that improving ART retention requires multifaceted strategies in addressing structural, geography, service delivery, and client engagement.\u003c/p\u003e\u003cp\u003eThe strengths of our study include its large, programmatic data source and rigorous analysis. By using the national HIV eTracker across six regions, we captured a diverse mix of public, private, and faith-based clinics serving 33,613 PLHIVs (227,319 follow-up visits) which is a significantly large cohort to analyse recurrent interruptions in ART. Our analysis accounted for multiple interruptions per participant, a major strength over single-event loss to follow-up analyses. The consistency of results across multiple sensitivity models enhances confidence in our findings. The standardized 28-day IIT definition and adjustment for a wide array of covariates (demographic, clinical, and facility-level) further strengthen the validity of our inferences.\u003c/p\u003e\u003cp\u003eHowever, there are important limitations. The retrospective cohort design, while a strength for its cost-effectiveness and efficiency, comes with inherent limitations [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. It relies on data collected for routine clinical care rather than for research, which can lead to incomplete or missing information on important confounders like socioeconomic status, travel distance, or reasons for interruption, which may bias the results [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. For example, a study in the United States of America found a strong relationship between viral load suppression and retention [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], a factor we could not explore fully in our analysis due to data limitations. About 15.8% of follow-up records were excluded due to missing or inconsistent values, and our complete-case approach (assuming data were missing completely at random) may bias results if missingness was systematic due to weaker record-keeping at understaffed clinics. Relatedly, key participant factors such as socioeconomic status, stigma, transportation time were not captured, raising the possibility of residual confounding. Our purposive selection of the study sites may introduce selection bias which limits generalizability as IIT patterns may differ in other regions. These caveats notwithstanding, the study provides comprehensive new evidence on ART retention in Ghana.\u003c/p\u003e\u003cp\u003eOur results have policy and programmatic implications for Ghana and similar settings. Efforts to improve retention should be prioritized in the high-risk sub-groups identified by expanding differentiated service delivery (DSD) approaches, including establishing community pharmacies to mitigate access barriers. The success of CHPS and faith-based clinics in reducing IIT suggests scaling up decentralized, community-based models of care. Consistent with evidence from Nigeria, expanding multi-month dispensing especially to clients living in rural settings could greatly enhance continuity. Routine use of mobile technology (SMS or phone reminders) and peer support can leverage the protective effect of contactability seen in our study. Clinics with less experience may benefit from mentorship and quality improvement initiatives, given the lower IIT risk in longer-established sites. Clinically, patients on NNRTI-based regimens should be closely monitored and supported, and wider transition to dolutegravir-based regimens may reduce side-effect-driven interruptions.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe identified low retention on ART and multiple actionable risk factors for treatment interruption. These findings emphasise that Ghana\u0026rsquo;s ART programmes must intensify retention efforts by supporting peripheral clinics, enhance DSD approaches and follow-up visits, and optimize ART regimens if the country is to reach the 95-95-95 targets by 2030. Policymakers and managers must address the structural and service-level determinants highlighted as key to sustaining lifelong ART and improving outcomes for people living with HIV.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"591\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 405px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeaning\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 186px;\"\u003e\n \u003cp\u003eHIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 405px;\"\u003e\n \u003cp\u003eHuman Immunodeficiency Virus\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 186px;\"\u003e\n \u003cp\u003ePLHIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 405px;\"\u003e\n \u003cp\u003epeople lived with HIV\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 186px;\"\u003e\n \u003cp\u003eART\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 405px;\"\u003e\n \u003cp\u003eAntiretroviral therapy\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 186px;\"\u003e\n \u003cp\u003eAIDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 405px;\"\u003e\n \u003cp\u003eAcquired Immune Deficiency Syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 186px;\"\u003e\n \u003cp\u003eUNAIDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 405px;\"\u003e\n \u003cp\u003eJoint United Nations Programme on HIV/AIDS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 186px;\"\u003e\n \u003cp\u003eDHIMS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 405px;\"\u003e\n \u003cp\u003eDistrict Health Information Management System, version 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 186px;\"\u003e\n \u003cp\u003eCHPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 405px;\"\u003e\n \u003cp\u003eCommunity-based Health Planning and Services\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 186px;\"\u003e\n \u003cp\u003eIIT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 405px;\"\u003e\n \u003cp\u003einterruption in treatment\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 186px;\"\u003e\n \u003cp\u003eWHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 405px;\"\u003e\n \u003cp\u003eWorld Health Organization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 186px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 405px;\"\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 186px;\"\u003e\n \u003cp\u003eAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 405px;\"\u003e\n \u003cp\u003eAnderson-Gill\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 186px;\"\u003e\n \u003cp\u003ePWP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 405px;\"\u003e\n \u003cp\u003ePrentice-Williams-Peterson\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 186px;\"\u003e\n \u003cp\u003eHR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 405px;\"\u003e\n \u003cp\u003eHazard ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 186px;\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 405px;\"\u003e\n \u003cp\u003eAkaike Information Criterion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 186px;\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 405px;\"\u003e\n \u003cp\u003eBayesian Information Criterion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 186px;\"\u003e\n \u003cp\u003eJHS/MSLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 405px;\"\u003e\n \u003cp\u003eJunior High school / Middle School Leaving Certificate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 186px;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 405px;\"\u003e\n \u003cp\u003econfidence interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 186px;\"\u003e\n \u003cp\u003eNHIS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 405px;\"\u003e\n \u003cp\u003eNational Health Insurance Scheme\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 186px;\"\u003e\n \u003cp\u003eTB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 405px;\"\u003e\n \u003cp\u003eTuberculosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 186px;\"\u003e\n \u003cp\u003eaHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 405px;\"\u003e\n \u003cp\u003eAdjusted Hazard Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 186px;\"\u003e\n \u003cp\u003eNtRTI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 405px;\"\u003e\n \u003cp\u003eNucleotide Reverse Transcriptase Inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 186px;\"\u003e\n \u003cp\u003eNRTI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 405px;\"\u003e\n \u003cp\u003eNucleoside Reverse Transcriptase Inhibitor (for pediatrics)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 186px;\"\u003e\n \u003cp\u003eNNRTI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 405px;\"\u003e\n \u003cp\u003eNon-Nucleoside Reverse Transcriptase Inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 186px;\"\u003e\n \u003cp\u003ePI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 405px;\"\u003e\n \u003cp\u003eProtease inhibitors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 186px;\"\u003e\n \u003cp\u003eNACP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 405px;\"\u003e\n \u003cp\u003eNational HIV/AIDS \u0026amp; STI Control Program\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 186px;\"\u003e\n \u003cp\u003eDSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 405px;\"\u003e\n \u003cp\u003eDifferentiated Service Delivery\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 186px;\"\u003e\n \u003cp\u003eIIT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 405px;\"\u003e\n \u003cp\u003eInterruption in Treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 186px;\"\u003e\n \u003cp\u003eTI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 405px;\"\u003e\n \u003cp\u003eTreatment Interruption\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe study protocol was reviewed and approved by the Ghana Health Service Ethical Review Committee with approval number GHS-ERC:003/08/24. Adequate permissions were received from the NACP prior to the extraction of the datasets from the HIV eTracker. This secondary data analysis used de-identified records, and informed consent was waived. All ethical principles were followed in this study. Consent to participate is not applicable.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe datasets analysed are not publicly available due sensitive nature but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eAll authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eWK conceived the research topic, led data management, analysis, and wrote the first draft of the manuscript. JK proof-read and reviewed the manuscript. All authors read and approved the final manuscript. YA reviewed the R code used for the analysis, FBV, DD, and SB contributed to the methods, analysis, and reporting and reviewed the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to the National HIV/AIDS \u0026amp; STI Control Program, Ghana Health Service, and the Program Manager Dr Stephen Ayisi Addo, for granting permission and access for the data to be used in this study. Also, our appreciation goes to Mr Ekow Wiah and Mr Philip Boakye for extracting the datasets from the HIV eTracker for this work.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGlobal HIV \u0026amp; AIDS statistics \u0026mdash; Fact sheet | UNAIDS. 2025. https://www.unaids.org/en/resources/fact-sheet. 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Coombs, Neha Singh, Shubhi Gupta, Aarushi Bhan, Babayemi O. Olakunde, and Echezona E. Ezeanolue. 2024. Retention in Care Among People Living with HIV in Nigeria: A Systematic Review and Meta-analysis. \u003cem\u003eJournal of Research in Health Sciences\u003c/em\u003e 24: e00618. https://doi.org/10.34172/jrhs.2024.153.\u003c/li\u003e\n\u003cli\u003eMensah, Benedicta Ayiedu, Henrietta E. Mensah-Brown, Frederica D. Partey, Christabel Addo, Gertrude Buah, Gifty A. Afudego, Daniel Okyere, et al. 2025. Identifying risk factors for loss to follow-up in adults living with HIV in a high-burden district in Ghana. \u003cem\u003eBMC Public Health\u003c/em\u003e 25: 1042. https://doi.org/10.1186/s12889-025-22254-w.\u003c/li\u003e\n\u003cli\u003eMa, Jing, Yan Jin, Kedi Jiao, Yao Wang, Lijie Gao, Xinrui Li, and Wei Ma. 2023. Antiretroviral treatment interruption and resumption within 16 weeks among HIV-positive adults in Jinan, China: a retrospective cohort study. \u003cem\u003eFrontiers in Public Health\u003c/em\u003e 11: 1137132. https://doi.org/10.3389/fpubh.2023.1137132.\u003c/li\u003e\n\u003cli\u003eEndebu, Tamrat, Girma Taye, and Wakgari Deressa. 2024. Rate and predictors of loss to follow-up in HIV care in a low-resource setting: analyzing critical risk periods. \u003cem\u003eBMC Infectious Diseases\u003c/em\u003e 24: 1176. https://doi.org/10.1186/s12879-024-10089-6.\u003c/li\u003e\n\u003cli\u003eBrinkhof, Martin W. G., Mar Pujades-Rodriguez, and Matthias Egger. 2009. Mortality of patients lost to follow-up in antiretroviral treatment programmes in resource-limited settings: systematic review and meta-analysis. \u003cem\u003ePloS One\u003c/em\u003e 4: e5790. https://doi.org/10.1371/journal.pone.0005790.\u003c/li\u003e\n\u003cli\u003eMann, Marita, Lameck Diero, Emmanuel Kemboi, Fidelis Mambo, Mary Rono, Wilfred Injera, Allison Delong, et al. 2013. Antiretroviral Treatment Interruptions Induced by the Kenyan Postelection Crisis Are Associated With Virological Failure. \u003cem\u003eJournal of acquired immune deficiency syndromes (1999)\u003c/em\u003e 64: 220\u0026ndash;224. https://doi.org/10.1097/QAI.0b013e31829ec485.\u003c/li\u003e\n\u003cli\u003eJiamsakul, Awachana, Stephen J. Kerr, Oon Tek Ng, Man Po Lee, Romanee Chaiwarith, Evy Yunihastuti, Kinh Van Nguyen, et al. 2016. Effects of unplanned treatment interruptions on HIV treatment failure\u0026ndash; results from TAHOD. \u003cem\u003eTropical medicine \u0026amp; international health : TM \u0026amp; IH\u003c/em\u003e 21: 662\u0026ndash;674. https://doi.org/10.1111/tmi.12690.\u003c/li\u003e\n\u003cli\u003ePennings, Pleuni S. 2013. HIV Drug Resistance: Problems and Perspectives. \u003cem\u003eInfectious Disease Reports\u003c/em\u003e 5: e5. https://doi.org/10.4081/idr.2013.s1.e5.\u003c/li\u003e\n\u003cli\u003ePEPFAR study shows the deadly impact of stopping children\u0026rsquo;s HIV treatment. 2025. \u003cem\u003eaidsmap.com\u003c/em\u003e. March 12.\u003c/li\u003e\n\u003cli\u003eNsoh, Marius, Katayi E Tshimwanga, Busi A Ngum, Avelina Mgasa, Moses O Otieno, Bokwena Moali, Nathanael Sirili, Ndeso S Atanga, and Gregory Edie Halle-Ekane. 2021. Predictors of antiretroviral therapy interruptions and factors influencing return to care at the Nkolndongo Health District, Cameroon. \u003cem\u003eAfrican Health Sciences\u003c/em\u003e 21: 29\u0026ndash;38. https://doi.org/10.4314/ahs.v21i1.6S.\u003c/li\u003e\n\u003cli\u003eMagura, Judie, Sibongile R. Nhari, and Thokozani I. Nzimakwe. 2025. Barriers to ART adherence in sub-Saharan Africa: a scoping review toward achieving UNAIDS 95-95-95 targets. \u003cem\u003eFrontiers in Public Health\u003c/em\u003e 13. Frontiers. https://doi.org/10.3389/fpubh.2025.1609743.\u003c/li\u003e\n\u003cli\u003eOwusu, Richmond, Emmanuel Bugyei Kwarteng, Serwaa Akoto Bawua, Desmond Dzidzornu Otoo, and Justice Nonvignon. 2024. Health-related quality of life of HIV patients with comorbidities of hypertension or diabetes in Ghana. \u003cem\u003eFrontiers in Public Health\u003c/em\u003e 12: 1383743. https://doi.org/10.3389/fpubh.2024.1383743.\u003c/li\u003e\n\u003cli\u003eShim, Mi-So, Inah Kim, and Goun Kim. 2025. Health problems and challenges due to aging among people living with HIV aged 50 and older: a qualitative study. \u003cem\u003eBMC Public Health\u003c/em\u003e 25: 2122. https://doi.org/10.1186/s12889-025-23252-8.\u003c/li\u003e\n\u003cli\u003eAbdulai, Marijanatu, Philip Teg-Nefaah Tabong, Harriet Affran Bonful, Adolphina Addo-Lartey, and Bismark Sarfo. 2025. Exploring quality of life among elderly persons living with HIV in Accra, Ghana. \u003cem\u003ePLOS One\u003c/em\u003e 20: e0324824. https://doi.org/10.1371/journal.pone.0324824.\u003c/li\u003e\n\u003cli\u003eMosadeghrad, Ali Mohammad. 2014. Factors influencing healthcare service quality. \u003cem\u003eInternational Journal of Health Policy and Management\u003c/em\u003e 3: 77\u0026ndash;89. https://doi.org/10.15171/ijhpm.2014.65.\u003c/li\u003e\n\u003cli\u003eSarfo, Fred S., Maame A. Sarfo, and David Chadwick. 2016. Incidence and risk factors for neuropsychiatric events among Ghanaian HIV patients on long-term non-nucleoside reverse transcriptase inhibitor-based therapy. \u003cem\u003eEneurologicalsci\u003c/em\u003e 3. Elsevier: 21\u0026ndash;25.\u003c/li\u003e\n\u003cli\u003eVanangamudi, Murugesan, Sonali Kurup, and Vigneshwaran Namasivayam. 2020. Non-nucleoside reverse transcriptase inhibitors (NNRTIs): A brief overview of clinically approved drugs and combination regimens. \u003cem\u003eCurrent opinion in pharmacology\u003c/em\u003e 54. Elsevier: 179\u0026ndash;187.\u003c/li\u003e\n\u003cli\u003eSimpson, Kit N., Kristin A. Hanson, Gale Harding, Seema Haider, Margaret Tawadrous, Alexandra Khachatryan, Chris L. Pashos, and Albert W. Wu. 2014. Review of the impact of NNRTI-based HIV treatment regimens on patient-reported disease burden. \u003cem\u003eAIDS Care\u003c/em\u003e 26. Informa UK Limited: 466\u0026ndash;475. https://doi.org/10.1080/09540121.2013.841825.\u003c/li\u003e\n\u003cli\u003eOn behalf of the HIV STAR Study Group, Torsak Bunupuradah, Ploenchan Chetchotisakd, Supunnee Jirajariyavej, Victor Valcour, Chureeratana Bowonwattanuwong, Warangkana Munsakul, et al. 2012. Neurocognitive impairment in patients randomized to second-line lopinavir/ritonavir-based antiretroviral therapy vs. lopinavir/ritonavir monotherapy. \u003cem\u003eJournal of NeuroVirology\u003c/em\u003e 18. Springer Science and Business Media LLC: 479\u0026ndash;487. https://doi.org/10.1007/s13365-012-0127-9.\u003c/li\u003e\n\u003cli\u003eTomescu, Silviu, Thomas Crompton, Jonathan Adebayo, Constance Wose Kinge, Francis Akpan, Marcus Rennick, Charles Chasela, Evans Ondura, Dauda Sulaiman Dauda, and Pedro T. Pisa. 2021. Factors associated with an interruption in treatment of people living with HIV in USAID-supported states in Nigeria: a retrospective study from 2000\u0026ndash;2020. \u003cem\u003eBMC Public Health\u003c/em\u003e 21: 2194. https://doi.org/10.1186/s12889-021-12264-9.\u003c/li\u003e\n\u003cli\u003eAbdulai, Martha Ali, Fraukje E. F. Mevissen, Veerle Marien, Robert A. C. Ruiter, Seth Owusu-Agyei, Kwaku Poku Asante, and Arjan E. R. Bos. 2023. A qualitative analysis of factors influencing the implementation of antiretroviral treatment adherence policy in Ghana: stakeholders perspective. \u003cem\u003eHealth Research Policy and Systems\u003c/em\u003e 21: 54. https://doi.org/10.1186/s12961-023-01010-9.\u003c/li\u003e\n\u003cli\u003eEbonyi, Augustine O., Emeka U. Ejeliogu, Sylvanus E. Okpe, David D. Shwe, Esther S. Yiltok, Martha O. Ochoga, and Stephen Oguche. 2015. Factors associated with antiretroviral treatment interruption in human immunodeficiency virus (HIV)-1-infected children attending the Jos University Teaching Hospital, Jos, Nigeria. \u003cem\u003eNigerian Medical Journal : Journal of the Nigeria Medical Association\u003c/em\u003e 56: 43\u0026ndash;47. https://doi.org/10.4103/0300-1652.149170.\u003c/li\u003e\n\u003cli\u003eMtisi, Expeditho L., Stella E. Mushy, Simon G. Mkawe, Antony Ndjovu, Eric Mboggo, Boniface S. Mlay, Frida Ngalesoni, and Aisa Muya. 2023. Risk factors for interruption in treatment among HIV-infected adolescence attending health care and treatment clinics in Tanzania. \u003cem\u003eAIDS Research and Therapy\u003c/em\u003e 20: 19. https://doi.org/10.1186/s12981-023-00512-4.\u003c/li\u003e\n\u003cli\u003eJournals, Innovative Research. 2025. Retrospective Cohort Study Design, Advantages, Limitations, and Applications. \u003cem\u003eInnovative Research Journals\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eDziadkowiec, Oliwier, Jeffery Durbin, Vignesh Jayaraman Muralidharan, Megan Novak, and Brendon Cornett. 2020. Improving the Quality and Design of Retrospective Clinical Outcome Studies that Utilize Electronic Health Records. \u003cem\u003eHCA healthcare journal of medicine\u003c/em\u003e 1: 131\u0026ndash;138. https://doi.org/10.36518/2689-0216.1094.\u003c/li\u003e\n\u003cli\u003eYehia, Baligh R., Benjamin French, John A. Fleishman, Joshua P. Metlay, Stephen A. Berry, P. Todd Korthuis, Allison L. Agwu, and Kelly A. Gebo. 2014. Retention in Care is More Strongly Associated with Viral Suppression in HIV-Infected Patients with Lower versus Higher CD4 Counts. \u003cem\u003eJournal of acquired immune deficiency syndromes (1999)\u003c/em\u003e 65: 333\u0026ndash;339. https://doi.org/10.1097/QAI.0000000000000023.\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-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"HIV, Interruption in treatment, ART adherence, Ghana, retrospective cohort, recurrent events, risk factors","lastPublishedDoi":"10.21203/rs.3.rs-7871647/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7871647/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eAchieving the UNAIDS 95-95-95 targets is challenged by suboptimal retention in antiretroviral therapy (ART) programmes, particularly in resource-limited settings. In Ghana, where ART coverage remains low, HIV interruption in treatment (IIT) poses a significant barrier to sustained viral suppression and HIV control. This study aimed to quantify the magnitude and identify predictors of HIV interruption in treatment across six administrative regions in Ghana.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a retrospective cohort analysis of ART program data in Ghana across 6 regions from January 2019 to December 2023. A total of 33,613 participants contributed 227,319 follow-up visits. The primary outcome was interruption in treatment (IIT), defined as a missed clinic visit more than 28 days after the scheduled appointment. We used the Andersen\u0026ndash;Gill model for recurrent event analysis to assess the predictors of IIT.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWe found a high rate of IIT, with about 68.3% of participants experiencing at least one treatment interruption, especially early in their care journey within the first three to six months. The risk of IIT was found to cluster in specific regions and clinics, with newer or less-experienced sites showing significantly higher rates of interruption. Patients on NNRTI-based regimens were identified to have high hazard for IIT. Conversely, factors like providing a phone number, receiving care at primary care facilities or at faith-based facilities were found to be protective against IIT.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eTo improve retention, Ghana's HIV programmes must intensify efforts, with a focus on targeting high-risk sites and sub-groups, optimising ART regimens by transitioning to dolutegravir-based alternatives, and leveraging multi-month dispensing and differentiated service delivery (DSD) models. Addressing these determinants is crucial for sustaining lifelong ART and advancing Ghana toward its national and global HIV control goals.\u003c/p\u003e","manuscriptTitle":"Predictors of HIV interruption in treatment among people living with HIV in 6 regions in Ghana: a retrospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-10 16:17:35","doi":"10.21203/rs.3.rs-7871647/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-11T09:41:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-30T17:56:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"333627156192188955512567009159824240256","date":"2025-11-10T09:28:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-07T08:58:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"319767676401619095432760947600714066851","date":"2025-11-07T08:11:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-29T11:08:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-18T09:05:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-16T23:34:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-16T23:34:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-10-15T20:58:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e8416cf1-e4b3-4ecd-ac9a-4f74d9c625e1","owner":[],"postedDate":"November 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T16:01:03+00:00","versionOfRecord":{"articleIdentity":"rs-7871647","link":"https://doi.org/10.1186/s12889-026-26243-5","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2026-01-10 15:57:24","publishedOnDateReadable":"January 10th, 2026"},"versionCreatedAt":"2025-11-10 16:17:35","video":"","vorDoi":"10.1186/s12889-026-26243-5","vorDoiUrl":"https://doi.org/10.1186/s12889-026-26243-5","workflowStages":[]},"version":"v1","identity":"rs-7871647","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7871647","identity":"rs-7871647","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}