Differentiated Services Delivery Model Uptake and Outcomes in Uganda: Gaps, Facilitators, and Barriers - A Mixed Methods Study 2019–2021

Differentiated Services Delivery Model Uptake and Outcomes in Uganda: Gaps, Facilitators, and Barriers - A Mixed Methods Study 2019–2021 | 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 Differentiated Services Delivery Model Uptake and Outcomes in Uganda: Gaps, Facilitators, and Barriers - A Mixed Methods Study 2019–2021 Fred Collins Semitala, Shardrack Wanyina, Nelson Kalema, Rodgers Katwesigye, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6963111/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Differentiated service delivery (DSD) models have emerged as innovative approaches to optimize HIV service delivery, particularly in bridging gaps across the HIV test–treat–care cascade. We assessed the performance of DSD models in Uganda, focusing on enrollment, retention in care, viral load (VL) testing, and suppression, while identifying key challenges and facilitators of effective implementation. Methods We conducted a retrospective review of records for closed cohorts of people living with HIV (PLHIV) active in care from 2019--2021 across health facilities supported by the Makerere University Joint AIDS Program (MJAP) and Mildmay Uganda. We determined the proportion of PLHIV who were active in care, enrolled in a DSD model, completed a VL test, and achieved suppression. Additionally, we conducted key informant interviews and focus group discussions to explore stakeholder perspectives on implementation challenges and facilitators. Results Among the 1,141 PLHIV, 530 (2019), 432 (2020), and 393 (2021) were active in care. DSD model enrollment increased from 48% in 2019 to 90% in 2021. The fast-track drug refill (FTDR) model had the highest uptake, increasing from 31% to 72%, whereas the facility-based individual management (FBIM) model declined from 55% to 10%. Viral load testing coverage improved from 73% to 85%, with suppression rates rising from 86% to 96% over the study period. Qualitative data revealed key facilitators, including reduced patient costs, improved provider–patient engagement, and partner support. Barriers included stigma, medical stockouts, patient relocation, and limited service integration in community-based models. Discussion This mixed-methods study analyzed three years of differentiated service delivery (DSD) for Ugandan PLHIV, tracking enrollment, viral load testing, and suppression and interviewing stakeholders. Enrollment and suppression improved the most in facility-based fast-track drug refill, facilitated by training, mentorship, and partner support. Stockouts, stigma, and unstable patients address hindered progress. Strengthening community models and integrating comorbidity care remain key. Conclusion The uptake and quality of DSD models improved over time, with a strong preference for the FTDR model. However, addressing persistent barriers, especially stigma, service integration, and resource availability, is essential to achieve sustained scale-up and improved HIV care outcomes in Uganda. Differentiated service delivery model HIV Uganda Viral load suppression Mixed methods Figures Figure 1 Figure 2 Introduction Early and sustained antiretroviral therapy (ART) is critical for reducing HIV-related morbidity and mortality[ 1 ]. To simplify and adapt HIV services to the unique needs of people living with HIV (PLHIV), the Ministry of Health (MoH) in Uganda adopted Differentiated Service Delivery (DSD) models of care, recommended by the World Health Organization (WHO), in 2016[ 2 ], [ 3 ]. However, there are limited data on the uptake and quality of care for PLHIV in DSD models at public ART facilities in Uganda. It is essential to address policy implementation gaps, as countries scale up these models to optimize HIV prevention and care, ultimately improving patient outcomes. DSD models are classified into “more” and “less” intensive models on the basis of how stable or needful of care PLHIV are; unstable PLHIV (newly diagnosed initiating ART, opportunistic infections, unsuppressed viral load, and/or comorbidities) enroll in more intensive models (MIMs) of care, such as facility-based groups (FBGs) and facility-based individual management (FBIM) [ 2 ]. In contrast, stable PLHIV enroll in less intensive models (LIMs), such as fast track drug refill (FTDR), community drug distribution point (CDDP), and community client-led ART delivery (CCLAD) models. National programs must utilize routine program data and engage stakeholders in understanding, addressing challenges, and informing the scale-up and integration of all-care within the DSD model[ 4 ]. The enrollment of PLHIV in LIMs and MIMs in Uganda remains suboptimal[ 5 ]. By 2020, only 25% of the sampled health facilities had implemented DSD[ 6 ], [ 7 ]. In contrast to guidelines, PLHIV enrollment in DSD models was driven by preference or availability rather than the LIM or MIM criteria. Annual trends in care quality (DSD enrollment, retention, satisfaction, viral load testing, and suppression) are underexplored. Prior evaluations in Uganda have shown limited improvement in patient outcomes[ 8 ]. Within this context, it is important to understand the trends and drivers of the use of DSD models, quality of care, and factors influencing DSD implementation via the use of routinely collected national DSD program data complemented by stakeholder insights. Using national DSD program data from East and Central Uganda, we assessed DSD uptake, viral load testing, and suppression in DSD models of care. Additionally, we sought stakeholders’ insights into challenges and opportunities to understand and improve the implementation of the DSD models of care. Methods Study design, setting, and participants We conducted an explanatory sequential mixed-methods study in 12 health facilities run by the Makerere University Joint AIDS Program (MJAP, Eastern Uganda) and Mildmay Uganda (Central Uganda) in January and February 2023. First, we conducted a retrospective review of records for PLHIV registered in care from 2019–2021. We assessed DSD model uptake, viral load, and suppression. We then conducted focus group discussions (FGDs) with PLHIV, in-depth interviews (IDIs) with healthcare providers and managers, and key informant interviews (KIIs) with policy makers and implementing partners to explore their insights into the challenges and enablers of the implementation of DSD models of care. Participating health facilities were purposively selected on the basis of the provision of DSD models of care, availability of clinical outcome data, and performance in achieving MoH enrollment targets for LIMs over the preceding 12 months. Facilities were classified as "high enrollment" if they met their targets for a given LIM and as "low enrollment" if they did not. We selected two high-enrollment and two low-enrollment health facilities for each of the three LIMs, for a total of 12 facilities. The selected facilities represented a mix across multiple levels of care, from regional referral hospitals to health center IIIs, and an equal proportion of urban and rural facilities (Table 1 ). All PLHIV records registered in ART care during the study period were included. Qualitatively, participants for FGDs and interviews were purposively sampled: PLHIV by their DSD model enrollment, healthcare providers and managers by their role in model implementation, and partners and policymakers by regional oversight roles. The participants provided written informed consent following in-person recruitment. FGDs and IDIs continued until thematic saturation; KIIs depended on informant availability[ 9 ], [ 10 ]. In total, we conducted 13 FGDs with 50 PLHIV, 24 IDIs with healthcare providers, 10 IDIs with healthcare managers (facility heads), three KIIs with two community systems program managers from Mildmay Uganda and MJAP, and one program officer from the AIDS Control Program at the MoH. We followed the Consolidated Criteria for Reporting Qualitative Research (COREQ) when writing this manuscript[ 11 ]. Data collection Quantitative We electronically extracted patient record data from the electronic ART registry for all PLHIV registered in ART care by the end of June of each study year (2019–2021) on the following parameters: age, sex, DSD model of enrollment (CCLAD, CDDP, FBG, FBIM, and FTDR), year of registration for ART care, access to a viral load test, viral load counts, ART regimen, TB status, TB treatment history, WHO staging, and health facility. We conducted interviews and FGDs from January–February 2023 via guides developed from quantitative findings and pretested with non-study participants. Two trained research assistants, unfamiliar with the participants, conducted all the sessions in person. The interviews were in English, whereas the FGDs with PLHIV were in Luganda or Lusoga to increase engagement. Sessions explored DSD model enrollment, viral load testing, and suppression. Each interview lasted 45–60 minutes; the FGDs lasted 70–80 minutes. We obtained written informed consent, built rapport, and recorded all sessions. The transcripts were anonymized and analyzed in NVivo v14.23.0. Thematic saturation was reached when no new insights emerged. Analysis All the quantitative data were analyzed via STATA v14 (Stata Corp., College Station, TX, USA). We used descriptive statistics to determine the proportion of PLHIV registered in care who were active in care and the proportion enrolled in DSD models of care by the end of June of each study year (2019–2021). We also determined the proportions of those in active care and enrolled in a DSD model for whom viral load testing was completed and whose viral suppression was achieved. Qualitative Four members of the research team (FCS, SW, RK, and KG) conducted the data analysis via a thematic analysis approach, which was deemed appropriate for exploring participant perspectives, identifying both commonalities and differences, and uncovering unanticipated insights. We adopted an inductive approach, beginning with open coding to allow themes to emerge organically from the data. Initially, two analysts (RK and SW) independently read three similar transcripts to familiarize themselves with the content, noting potential codes and themes. The broader team (FCS, RK, SW, and KG) then convened to compare and discuss the initial codes. Any discrepancies in coding were deliberated and resolved through consensus. Through this process, we developed a coding framework that was systematically applied to the remaining transcripts. Emerging themes were continuously reviewed and refined through iterative team discussions. We categorized these themes as either facilitators or barriers to the performance of differentiated service delivery (DSD). Themes that enhanced DSD performance were classified as facilitators, whereas those that hindered performance were classified as barriers. To illustrate key findings, we extracted representative quotations that captured participants' views in their own words. Ethics This study was approved by The AIDS Support Organization, Research Ethics Committee, Kampala, Uganda (TASO-2022-144), and the Uganda National Council for Science and Technology, Kampala, Uganda (HS2497ES). We also obtained administrative clearance from the Ministry of Health and the respective District Health Offices. Results Sample characteristics Among the 1,141 PLHIV in the ART registry, 530, 432 and 393 were active in ART care at the end of June 2019, 2020 and 2021, respectively. Among these PLHIV, 252/530 (48%), 281/432 (65%), and 354/393 (90%) PLHIV were included in a DSD model for 2019, 2020, and 2021, respectively (Figure 1). Overall, the population characteristics were comparable across the years 2019--2021. The majority of PLHIV enrolled in the DSD model were young (aged 25--40 years), 61% female, with no symptoms of TB, and at WHO stage I. Notable increases in the use of DTG-based regimens and client preferences for the FTDR model of care in 2021 were observed. (Table 3). Table 3. Characteristics of PLHIV in DSD model in June 2019, 2020, 2021 Characteristics 2019 ( n = 252) 2020 ( n = 280)* 2021 ( n = 351)* Female 150/252 (59.5%) 170/280 (60.7%) 215/351 (61.3%) Age years, median (IQR)* 31 (25-39) 32 (25-40) 32 (25-40) TB status* No signs 243/247 (98%) 259/278 (93.2%) 325/349 (93%) On TB treatment 2 (1%) 2 1 TB Presumed 2 (1%) 2 2 Completed TB Rx 0 2 0 No TB data documented 4 13 21 WHO status* 1 210/243 (86.4%) 244/279 (87.5%) 320/350 (91.4%) 2 29 (12%) 13 11 3 3 (1.2%) 2 1 4 1 (0.4%) 1 0 No WHO status documented 7 19 18 Base ART regimen EFV-based 239 (95%) 212/277 (77%) 12/350 (0.3%) DTG-based 5 (2%) 55/277 (19.9%) 315/350 (90%) Others 8 (3%) 10/277 (3.7%) 23/350 (6.6%) DSD model CCLAD 0/252 5/1 9/354 CDDP 1 (0.4%) 5 (1.8%) 8 (2.3%) FBG 36 (14%) 51 (11%) 48 (13.6%) FBIM 138 (55%) 67 (23.8%) 37 (10.5%) FTDR 77 (30.6%) 153 (54.4%) 258 (72.9%) * *Missing data General comment on Table – certainly it would have been more helpful if data were less missing, to include a column of non-DSD patients but seeing the level of missing data in this table alone and not being able to tell if it meant missing or just unfilled discouraged any ambition to. The fast-track-drug-fill (FTDR) model had the highest uptake, being 31% in 2019 and 65% in 2021, whereas the facility-based-individual-management model (FBIM) was the least preferred, dropping from 55% to 9%, and the uptake in the other models [Facility-based groups (FBG), Community Drug Distribution Points (CDDP) and Community Client Led Distribution (CCLAD)] remained low and unchanged. Notably, the uptake of the DSD model improved each subsequent year. Completion of viral load testing and viral load suppression In 2019, 2020 and 2021, viral load testing was completed for 388/530 (73%), 359/432 (83%) and 336/393 (85%) PLHIV active in care, respectively, and viral load suppression was achieved for 333/388 (86%), 322/359 (90%) and 323/336 (96%) PLHIV, respectively. (Figure 2). Facilitators of improved DSD performance Reduced costs for patients Patients reported that community-based DSD models greatly reduce costs by minimizing the need for frequent visits to health facilities, leading to savings in terms of transportation and meals. This also allowed them to dedicate more time to work and personal responsibilities, as highlighted by a focus group participant about the CCLAD model. “This arrangement is so good for us workers since we have limited time to fetch medicine; waiting in long queues is difficult. Our cluster is small, one of us picks all our medicine and calls us to meet sometimes at Bugembe playgrounds or the park, and everyone gets their medicine.” (FGD_PLHIV_Katikamu HCIII (rural)) Increased engagement between patients Some patients also reported that participation in community-based models such as Community Drug Distribution Points (CDDPs) encouraged increased interaction and peer-to-peer support, allowing them to share experiences and ways around common challenges. These groups also led to the formation of savings groups, fostering financial cooperation, and being a source of mutual support. “Thus far, we have saving groups out of those CDDPs, and we are doing well. Then, also, some clients form groups, others even have WhatsApp groups.” (IDI_HCM_Jinja (urban) Increased engagement between patients and healthcare providers in community models Some healthcare providers reported that DSD models improved interactions between patients and healthcare providers, particularly through community-based meetings where patients would share their progress and challenges directly with health workers. This increased interaction has enhanced the quality of care and strengthened relationships between the two groups. One health manager noted: “Because these people do not have to travel to the facility, especially in regard to CDDP, we find them in the community at a chosen meeting place… you interact with them, update them on what is new, what services we have at the hospital, so you have good engagements with them.” (IDI_HCM_Kiwoko(rural)) Support from HIV program implementing partners Some policymakers at the Uganda Ministry of Health reported that support from national and international partners also played a significant role in the successful implementation of the DSD models, as acknowledged by one key informant: “Once you have something in the guidelines, it is policy. It has to be implemented. The fact that the different funders were interested, and we had full support from donors, the IPs, the government, CBOs, NGOs—once you have all the different stakeholders embracing it, it usually moves.” (KII_PM_MoH) Training, capacity building, and mentorship Healthcare providers also highlighted that training and capacity building equipped them, group leaders, and patients with the necessary skills and knowledge to implement and benefit from DSD models. Continuous mentorship and supervision provided by implementing partners further reinforced these efforts, ensuring effective service delivery. As one healthcare provider from Bugiri noted, “The preparation was enough for us to start because we had training and got the knowledge on how to go about it; we had the tools required for data collection.” (IDI_HCP_Bugiri (rural) Availability of resources Healthcare providers reported that the availability of essential resources such as drugs, registers, and financial support has been crucial for the effective implementation of DSD models, ensuring smooth service delivery and consistent communication with patients. A healthcare provider from Busia mentioned the following: “We facilitate them with transport and safari day allowances; as a project, we ensure that the drugs are there and all the tools they need to use in these CDDPs. Then, we also provide airtime, which is used to make reminder calls.” (KII_IP_MoH) Reduced congestion and workload at health facilities Healthcare managers also observed that the community-based models significantly reduced congestion at health facilities, leading to a lighter workload for healthcare providers. This reduction in patient numbers allowed for more manageable workloads and improved the quality of care provided. A healthcare manager from Nakasongola observed the following: “DSD has greatly impacted our life here because you will not find the patient volumes we used to have… it has saved us a lot of time and it has created quality time for the patients we see.” (IDI_HCM_Nakasongola (rural)) Potential Barriers Stigma Both patients and healthcare providers identified stigma as a major barrier to the success of DSD models, particularly the CDDP and CCLAD models, where medication is distributed in community settings. Patients reported discomfort with gathering in public due to fear of being identified as HIV positive, leading some to miss appointments or switch models in hopes of reducing stigma. This has also been discussed in a study by Walusaga et al [12]. “One of the big problems is stigma, especially at the community level. We still have people who join the CCLAD groups, but the moment they find there someone they know, they jump out” (KII_PM_MoH). Patient changes in location Healthcare providers reported that frequent patient relocations, driven by factors such as family separation, divorce, and employment, present major challenges to delivering DSD models, particularly the CCLAD model. This model depends on the commitment and initiative of the CCLAD group leader to track and notify health facilities, which varies and is not consistently reliable across all groups. These relocations may lead to unaddressed clinical issues. “Patients keep changing residences and by the time the facilitator determines sometimes this person has deteriorated so much that you get confused on the next course of action” (IDI_HCP_Bugono_HCIV (rural)). Similarly, healthcare providers reported that frequent relocations also contribute to patient nonadherence to treatment and the potential for developing undetected health issues. “There are people who have become too reluctant again because someone knows they get medicine for six months and even if she doesn’t go, others will go, that person can disappear for two years without showing up” (IDI_HCP_Bugiri (rural)). Unreported and undetected clinical challenges Healthcare providers reported facing challenges in detecting and following up with patients on ART (in the LIMS) to identify clinical issues, including concerns about pregnancies that go unreported. They also expressed worries about patients being assigned to inappropriate care models. “Some of our patients are starting to have high viral loads because we are giving them treatment, and we are not monitoring them closely” (IDI_HCM_Jinja (urban)). Medicine stockouts Healthcare providers highlighted frequent drug stock-out as another key challenge that affected the delivery of a sufficient supply of medications for patients in some models. Some of the healthcare providers below explain how stock-outs affect the delivery of services under the CCLAD model. “We are having drug stockouts even right now, so you may find someone is in the CCLAD, but the drugs are not there” (IDI_HCP_Masafu (urban)). Transport challenges for healthcare providers The lack of reliable transportation was reported as a major challenge in implementing DSD models, especially in the CDDP model, where healthcare providers struggle to transport equipment and medicines to remote areas owing to inadequate transport options and poor road conditions. “Transportation of equipment is a challenge since we move with it all on boda boda (motorcycle), and now we are getting to a rainy season. Sometimes you find on a rainy day we fail to get to the field” (IDI_HCP_Bugono (rural)). Discussion Our study assessed the uptake and quality (VL testing and suppression) of DSD models of care offered to PLHIV at public HIV care facilities. We also explored stakeholders’ perceptions of challenges and opportunities present in the implementation of DSD models of care in Uganda. Our results show that over a three-year period, enrollment in DSD models of care generally improved and was amplified the most for LIMs. Additionally, the proportion of PLHIV who completed viral load testing and achieved suppression improved over time. Notably, the majority of PLHIV showed an increasing preference for facility-based care, particularly the fast-track-drug refill (FTDR) model. The adoption of differentiated service delivery (DSD) care models was facilitated by training, capacity building, mentorship, resource availability, and support from HIV-implementing partners. The less intensive models enhanced stakeholder engagement, strengthened patient–provider interactions, and reduced costs for patients, as well as congestion and workload for healthcare providers. However, challenges such as medical stockouts, patient stigma, and frequent changes in patient addresses hinder their effectiveness. A review of 57 studies across Africa highlighted diverse factors influencing the implementation and scale-up of differentiated service delivery (DSD) models for HIV treatment[ 13 ]. Consistent with our findings, key facilitators included reduced patient visit costs, decreased staff workloads and less strain on health facilities, whereas major barriers included stigma and drug stock-outs. Moreover, depending on context, system factors such as leadership and governance facilitate or hinder DSD care implementation to the extent that they influence provider compliance or resource availability[ 14 ]. At the patient level, another study conducted by Zakumumpa et al . in Uganda identified similar patient-specific challenges to DSD model implementation identified in our study—positing that enrollment in DSD was influenced by anticipated or experienced community-related stigma in community-based models, a fear of detachment from health facilities and preferences on the basis of patients’ socioeconomic status[ 7 ]. Within the facility-based model of care, our study confirms reports of the FTDR being the most preferred model of care, mostly attributable to the anticipated convenience and speed of accessing ART, which, in combination with the multiple-month dispensing of ART, has the potential to improve care outcomes[ 15 ]. In our study, providers cited additional quality-of-care concerns over patients’ unmet care needs outside of ART provision in the LIMs, i.e., community-based models, compared with MIMs, facility-based models where patients receive more attention and care for comorbidities. This is of major concern, especially because the survival of PLHIV has improved with ART, resulting in a heightened risk of non-communicable disease comorbidities requiring concurrent care. It is clear that the DSD program still needs to address patient and health provider challenges related to inadequate resources for community models, stigma, and the ability to reach and offer integrated care for all comorbidities to improve uptake. Similarly, the service package for community-based models should be expanded beyond ART refills to incorporate family planning, TB screening, TB medicine refills for the continuation phase of treatment, and non-communicable disease (NCD) screening and refills, among other essential services that are provided in facility-based models. Using viral load testing and suppression as indicators of the quality of care offered in DSD models at public HIV care facilities, our findings revealed a progressive increase in the proportion of PLHIV tested and a decrease in the viral load as more PLHIV enrolled in DSD care each subsequent year, the highest being in the FTDR model. This may have been due to fidelity to DSD model guideline implementation, which limited enrollment to virally suppressed PLHIV. This, in turn, reflects an overall improvement in PLHIV care within the standard of care, ensuring that only those meeting the criteria are transitioned into DSD care. (9, 12). However, persistent gaps in viral load testing coverage remain a concern, as they create uncertainty regarding patients' treatment needs, level of care and eligibility for DSDs. Our study revealed 85% viral load coverage among PLHIV in the DSD care model, which was comparable to the 87% reported by Esther L. et al .[ 16 ]. Limitations Using MoH routinely collected program data, we found significant missing data, and as such, we may have biased estimates of DSD care uptake or viral load suppression. We did not restrict the analysis to facilities with more reliable data, as this would have inadvertently introduced selection bias. Nonetheless, the data used were representative of real-world settings and highlight the need for increased health system strengthening to improve data quality for use, as per WHO recommendations[ 4 ]. We defined quality of care as limited to viral load testing and suppression, as we lacked data to assess patient quality of life, care for comorbidities or reasons for patient switching between DSD models. As HIV programs scale-up DSD models, these models will increasingly be important to consider to inform the improvement of PLHIV care outcomes. Generalizability Our study findings are generalizable only to public ART care programs of similar settings receiving comparable levels of funding support. Conclusion The differentiated service delivery DSD model of care, particularly fast-track drug refill, has quickly become the preferred model of care for PLHIV and has shown promise for improving patient outcomes. However, continued support in the form of funding, above site supervision, non-communicable disease care integration, addressing stigma, and improving data quality for monitoring and evaluation are critical for achieving the MoH LIM and MIM targets. Declarations Ethics approval and consent to participate Ethical approval for this study was obtained from the AIDS Support Organization Research and Ethics Committee (TASO-2022-144) and the Uganda National Council for Science and Technology (HS2497ES). Administrative clearance was also obtained from the Ministry of Health and the relevant district health offices. This research was conducted in full accordance with the principles of the World Medical Association Declaration of Helsinki (1964) and its subsequent amendments. Consent for publication Not applicable. This manuscript does not contain any individual person’s data, images, or personal details that would require consent for publication. All qualitative data are presented in aggregate form or as anonymized quotations, and no identifiable information has been included. Data availability statement The datasets generated and/or analyzed during the current study contain sensitive personal health information and are subject to Uganda National Council for Science and Technology (UNCST) regulations as well as Makerere University School of Medicine Research & Ethics Committee (SOMREC) approvals. Quantitative data – A fully de identified version of the quantitative dataset, together with the data dictionary, will be made available from the corresponding author upon reasonable request. Access will be granted to researchers who provide a methodologically sound proposal, sign a data-use agreement, and obtain clearance from both SOMREC and UNCST. Qualitative materials – Because audio recordings and verbatim transcripts can inadvertently reveal participant identity, the full files will not be shared publicly. However, de identified excerpted quotes supporting the study’s conclusions can be obtained from the corresponding author after the same ethical clearance process described above. Statistical code – The STATA do files and NVivo codebook used for data cleaning, analysis, and visualization are available on request directly from the corresponding author. Researchers requesting access will be asked to state the intended use, agree not to attempt re identification, and destroy the data after the approved project is complete. Competing Interests The authors declare no competing interests. Funding This study received support from the Africa Resource Centre (ARC) with administrative support from the Makerere University Joint AIDS Program (MJAP) and the Uganda Ministry of Health AIDS Control Program plus the district health offices of the areas of intervention. Author Contributions FCS, CK and KG conceptualized the study. SW, NK, RK, and GO IA led the data collection and analysis. FCS, NK, IA, CK, CD, NV, CVR, ENM, HB, GO, and KG contributed to the study design and interpretation. SW, NK, RK, FCS, KG and HB contributed to manuscript drafting and revision. All the authors reviewed and approved the final manuscript. Acknowledgments We are deeply grateful to all the people living with HIV who graciously shared their time and experiences—your participation is the cornerstone of this study. We also thank the management and frontline teams at every participating health facility for their unstinting cooperation and hospitality during data collection. This work was made possible through financial support from the Africa Resource Centre (ARC) and the administrative backing of the Makerere University Joint AIDS Program (MJAP) and the AIDS Control Program, Uganda Ministry of Health . We further acknowledge the District Health Offices in our study districts for their facilitation and oversight. Our appreciation goes to the research assistants, data clerks, and transcribers whose diligence ensured high-quality quantitative and qualitative data and to the NVivo and STATA user-support communities for timely technical guidance. Finally, we thank our academic and implementing-partner colleagues for their constructive feedback throughout concept development, fieldwork, and manuscript preparation. References WHO. World Health Organization. The Global Health Observatory – HIV/AIDS data. 2023. Available from: https://www.who.int/data/gho/data/themes/hiv-aids. WHO, 2023. WHO. World Health Organization. Consolidated guidelines on the use of antiretroviral drugs for treating and preventing HIV infection. WHO, 2016. [Online]. Available: https://www.ncbi.nlm.nih.gov/books/NBK374294/ WHO. World Health Organization. Updated recommendations on service delivery for the treatment and care of people living with HIV. Geneva. 2021. [Online]. Available: https://www.ncbi.nlm.nih.gov/books/NBK570385/toc/?report=printable WHO. World Health Organization. Consolidated guidelines on person-centered HIV strategic information. Geneva: WHO; 2023. WHO, 2023. MOH UGANDA. Ministry of Health Uganda. Differentiated Service Delivery Model Uganda. 2024. 2024. [Online]. Available: https://dsduganda.com/ Zakumumpa H, Rujumba J, Kwiringira J, Katureebe C, Spicer N. Understanding implementation barriers in the national scale-up of differentiated ART delivery in Uganda. BMC Health Serv Res. Mar. 2020;222. 10.1186/s12913-020-5069-y . Zakumumpa H, Makobu K, Ntawiha W, Maniple E. A mixed-methods evaluation of the uptake of novel differentiated ART delivery models in a national sample of health facilities in Uganda. PLoS ONE. Jul. 2021;16(7):e0254214. 10.1371/journal.pone.0254214 . Baleeta K, et al. Factors that influence the satisfaction of people living with HIV with differentiated antiretroviral therapy delivery models in East Central Uganda: a cross-sectional study. BMC Health Serv Res. Feb. 2023;23(1):127. 10.1186/s12913-023-09114-2 . Hennink M, Kaiser BN. Sample sizes for saturation in qualitative research: A systematic review of empirical tests. Soc Sci Med. Jan. 2022;292:114523. 10.1016/j.socscimed.2021.114523 . Hennink MM, Kaiser BN, Marconi VC. Code Saturation Versus Meaning Saturation: How Many Interviews Are Enough? Qual. Health Res. , vol. 27, no. 4, pp. 591–608, Mar. 2017, 10.1177/1049732316665344 Consolidated criteria for reporting qualitative research (COREQ). a 32-item checklist for interviews and focus groups | EQUATOR Network. Accessed: May 14, 2025. [Online]. Available: https://www.equator-network.org/reporting-guidelines/coreq/ Walusaga HAG et al. Oct., Perceptions and factors associated with the uptake of the community client-led antiretroviral therapy delivery model (CCLAD) at a large urban clinic in Uganda: a mixed methods study, BMC Health Serv. Res. , vol. 23, no. 1, p. 1165, 2023, 10.1186/s12913-023-10182-7 Belay YA, Yitayal M, Atnafu A, Taye FA. Barriers and facilitators to the implementation and scale up of differentiated service delivery models for HIV treatment in Africa: a scoping review, BMC Health Serv. Res. , vol. 22, no. 1, p. 1431, Nov. 2022, 10.1186/s12913-022-08825-2 Long L, et al. Retention in care and viral suppression in differentiated service delivery models for HIV treatment delivery in sub-Saharan Africa: a rapid systematic review. J Int AIDS Soc. Nov. 2020;23(11):e25640. 10.1002/jia2.25640 . Machumu N, Frumence G, Anaeli A. Facilitators and barriers to optimum uptake of multimonth dispensing of antiretroviral treatment in Morogoro, Tanzania: a qualitative study, BMJ Open , vol. 14, no. 6, p. e080434, Jun. 2024, 10.1136/bmjopen-2023-080434 Nkolo EKK, et al. Clients in Uganda accessing preferred differentiated antiretroviral therapy models achieve higher viral suppression and are less likely to miss appointments: a cross-sectional analysis. J Int AIDS Soc. Jul. 2023;26:e26122. 10.1002/jia2.26122 . no. Suppl 1. Tables Table 1: Description of the sampled health facilities Selected health facilities Geographical setting Ownership 1 Mubende Regional Referral Hospital Urban Government/Public 2 Jinja Regional Referral Hospital Urban Government/Public 3 Kiwoko Hospital Rural NGO/PNFP 4 Bugiri Hospital Urban Government/Public 5 Masafu Hospital Rural Government/Public 6 Nakasongola HC IV Urban Government/Public 7 Nabishwera HC IV Rural Government/Public 8 Walukuba HC IV Urban Government/Public 9 Bugono HC IV Rural Government/Public 10 Lumino HC III Rural Government/Public 11 Katikamu HC III Rural NGO/PNFP 12 Kakooge HC III Urban Government/Public Table 2: Categories and number of interviews and focus group discussion respondents Category Number of interviews/(respondents ) Number of FGDs with PLHIV 13 (125) Number of KIIs with policy makers and IPs 3 Number of IDIs with heath care providers 24 Number of IDIs with health care managers 10 TOTAL 50 (162) Additional Declarations No competing interests reported. Supplementary Files TableofContents.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 14 Aug, 2025 Reviewers agreed at journal 07 Aug, 2025 Reviewers invited by journal 05 Aug, 2025 Editor assigned by journal 30 Jul, 2025 Editor invited by journal 14 Jul, 2025 Submission checks completed at journal 12 Jul, 2025 First submitted to journal 12 Jul, 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6963111","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":495857617,"identity":"14983f3f-42b4-424c-bab0-ef9a8819bbc8","order_by":0,"name":"Fred Collins Semitala","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIiWNgGAWjYBAC9gYgkcDAIANiGYCFeAho4TkA0pIAVAdkkaCFAaRFIgEmREgL/+GjGx7+sOPhn/k6oeDnDoZo/p4DjB9+MBy2a8ClRSIt7UZCQjKPxO3cDYa9ZxhyZ5xtYJbsYTicjEuLvQSPGVALMw8DUIsBbxtDbsN5BgZpBqAW3A47/w2opZ5H/ubZDYZ/gVrmn2dg/o1XC0MOG1DLYR6DG7wbjEG2bDjbwAayxQ6nFok0oMPSjvMYnsndYCx7RiJ345mDbZY9BukJuB12+NnNHzbVcnLHz24zfLvDJnfemeTDN35UWNvj0oIM2AwYGySANGMDAzBWExuI0ML8gBFJGVG2jIJRMApGwYgAAIHGWjA9xm8kAAAAAElFTkSuQmCC","orcid":"","institution":"Makerere University Joint AIDS Program","correspondingAuthor":true,"prefix":"","firstName":"Fred","middleName":"Collins","lastName":"Semitala","suffix":""},{"id":495857618,"identity":"06937dcc-cfd8-4472-8b23-5b8c1d623b5d","order_by":1,"name":"Shardrack Wanyina","email":"","orcid":"","institution":"Makerere University Joint AIDS 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Health","correspondingAuthor":false,"prefix":"","firstName":"Eleanor","middleName":"","lastName":"Namusoke-Magongo","suffix":""},{"id":495857625,"identity":"b42a03f8-048b-4cc1-b020-8797e8204f46","order_by":8,"name":"Hudson Balidawa","email":"","orcid":"","institution":"Ministry of Health","correspondingAuthor":false,"prefix":"","firstName":"Hudson","middleName":"","lastName":"Balidawa","suffix":""},{"id":495857626,"identity":"ce7e4a7e-7be1-4902-af9b-7be4b6c314c0","order_by":9,"name":"Carla Riet","email":"","orcid":"","institution":"KU Leuven","correspondingAuthor":false,"prefix":"","firstName":"Carla","middleName":"","lastName":"Riet","suffix":""},{"id":495857627,"identity":"fe414a17-dba7-474d-b6f9-a7feaf7cc4b2","order_by":10,"name":"Nico Vandaele","email":"","orcid":"","institution":"KU 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08:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6963111/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6963111/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88645649,"identity":"d75d5a80-e1ca-42e0-bfdb-a9591854dbf1","added_by":"auto","created_at":"2025-08-08 16:28:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":64316,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram showing persons living with HIV active in care by DSD model, year\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6963111/v1/048158e60cf9e876c515abd1.png"},{"id":88644277,"identity":"2a77390b-a88e-4780-a669-4b26424ca49a","added_by":"auto","created_at":"2025-08-08 16:20:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":57582,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram showing the completion of viral load testing and suppression by model and year\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6963111/v1/596675109a4ab7bc41118a74.png"},{"id":88646902,"identity":"7787c396-d359-4d06-ad89-bc4908c5084d","added_by":"auto","created_at":"2025-08-08 16:36:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1175576,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6963111/v1/7261e0ff-1f44-44ea-9379-b48513c1529f.pdf"},{"id":88644282,"identity":"037acb1d-3d33-4d9a-8f7c-c52fb52c9300","added_by":"auto","created_at":"2025-08-08 16:20:05","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":5592,"visible":true,"origin":"","legend":"","description":"","filename":"TableofContents.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6963111/v1/9066c9a7e3707b0ff40120c9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eDifferentiated Services Delivery Model Uptake and Outcomes in Uganda: Gaps, Facilitators, and Barriers - A Mixed Methods Study 2019–2021\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEarly and sustained antiretroviral therapy (ART) is critical for reducing HIV-related morbidity and mortality[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. To simplify and adapt HIV services to the unique needs of people living with HIV (PLHIV), the Ministry of Health (MoH) in Uganda adopted Differentiated Service Delivery (DSD) models of care, recommended by the World Health Organization (WHO), in 2016[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, there are limited data on the uptake and quality of care for PLHIV in DSD models at public ART facilities in Uganda. It is essential to address policy implementation gaps, as countries scale up these models to optimize HIV prevention and care, ultimately improving patient outcomes.\u003c/p\u003e\u003cp\u003eDSD models are classified into “more” and “less” intensive models on the basis of how stable or needful of care PLHIV are; unstable PLHIV (newly diagnosed initiating ART, opportunistic infections, unsuppressed viral load, and/or comorbidities) enroll in more intensive models (MIMs) of care, such as facility-based groups (FBGs) and facility-based individual management (FBIM) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In contrast, stable PLHIV enroll in less intensive models (LIMs), such as fast track drug refill (FTDR), community drug distribution point (CDDP), and community client-led ART delivery (CCLAD) models.\u003c/p\u003e\u003cp\u003eNational programs must utilize routine program data and engage stakeholders in understanding, addressing challenges, and informing the scale-up and integration of all-care within the DSD model[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The enrollment of PLHIV in LIMs and MIMs in Uganda remains suboptimal[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. By 2020, only 25% of the sampled health facilities had implemented DSD[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In contrast to guidelines, PLHIV enrollment in DSD models was driven by preference or availability rather than the LIM or MIM criteria. Annual trends in care quality (DSD enrollment, retention, satisfaction, viral load testing, and suppression) are underexplored. Prior evaluations in Uganda have shown limited improvement in patient outcomes[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Within this context, it is important to understand the trends and drivers of the use of DSD models, quality of care, and factors influencing DSD implementation via the use of routinely collected national DSD program data complemented by stakeholder insights.\u003c/p\u003e\u003cp\u003eUsing national DSD program data from East and Central Uganda, we assessed DSD uptake, viral load testing, and suppression in DSD models of care. Additionally, we sought stakeholders’ insights into challenges and opportunities to understand and improve the implementation of the DSD models of care.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy design, setting, and participants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe conducted an explanatory sequential mixed-methods study in 12 health facilities run by the Makerere University Joint AIDS Program (MJAP, Eastern Uganda) and Mildmay Uganda (Central Uganda) in January and February 2023. First, we conducted a retrospective review of records for PLHIV registered in care from 2019–2021. We assessed DSD model uptake, viral load, and suppression. We then conducted focus group discussions (FGDs) with PLHIV, in-depth interviews (IDIs) with healthcare providers and managers, and key informant interviews (KIIs) with policy makers and implementing partners to explore their insights into the challenges and enablers of the implementation of DSD models of care. Participating health facilities were purposively selected on the basis of the provision of DSD models of care, availability of clinical outcome data, and performance in achieving MoH enrollment targets for LIMs over the preceding 12 months. Facilities were classified as \"high enrollment\" if they met their targets for a given LIM and as \"low enrollment\" if they did not. We selected two high-enrollment and two low-enrollment health facilities for each of the three LIMs, for a total of 12 facilities. The selected facilities represented a mix across multiple levels of care, from regional referral hospitals to health center IIIs, and an equal proportion of urban and rural facilities (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003cp\u003eAll PLHIV records registered in ART care during the study period were included. Qualitatively, participants for FGDs and interviews were purposively sampled: PLHIV by their DSD model enrollment, healthcare providers and managers by their role in model implementation, and partners and policymakers by regional oversight roles. The participants provided written informed consent following in-person recruitment. FGDs and IDIs continued until thematic saturation; KIIs depended on informant availability[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn total, we conducted 13 FGDs with 50 PLHIV, 24 IDIs with healthcare providers, 10 IDIs with healthcare managers (facility heads), three KIIs with two community systems program managers from Mildmay Uganda and MJAP, and one program officer from the AIDS Control Program at the MoH.\u003c/p\u003e\u003cp\u003eWe followed the Consolidated Criteria for Reporting Qualitative Research (COREQ) when writing this manuscript[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eData collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eQuantitative\u003c/p\u003e\u003cp\u003eWe electronically extracted patient record data from the electronic ART registry for all PLHIV registered in ART care by the end of June of each study year (2019–2021) on the following parameters: age, sex, DSD model of enrollment (CCLAD, CDDP, FBG, FBIM, and FTDR), year of registration for ART care, access to a viral load test, viral load counts, ART regimen, TB status, TB treatment history, WHO staging, and health facility.\u003c/p\u003e\u003cp\u003eWe conducted interviews and FGDs from January–February 2023 via guides developed from quantitative findings and pretested with non-study participants. Two trained research assistants, unfamiliar with the participants, conducted all the sessions in person. The interviews were in English, whereas the FGDs with PLHIV were in Luganda or Lusoga to increase engagement. Sessions explored DSD model enrollment, viral load testing, and suppression. Each interview lasted 45–60 minutes; the FGDs lasted 70–80 minutes. We obtained written informed consent, built rapport, and recorded all sessions. The transcripts were anonymized and analyzed in NVivo v14.23.0. Thematic saturation was reached when no new insights emerged.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnalysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll the quantitative data were analyzed via STATA v14 (Stata Corp., College Station, TX, USA). We used descriptive statistics to determine the proportion of PLHIV registered in care who were active in care and the proportion enrolled in DSD models of care by the end of June of each study year (2019–2021). We also determined the proportions of those in active care and enrolled in a DSD model for whom viral load testing was completed and whose viral suppression was achieved.\u003c/p\u003e\u003cp\u003eQualitative\u003c/p\u003e\u003cp\u003eFour members of the research team (FCS, SW, RK, and KG) conducted the data analysis via a thematic analysis approach, which was deemed appropriate for exploring participant perspectives, identifying both commonalities and differences, and uncovering unanticipated insights. We adopted an inductive approach, beginning with open coding to allow themes to emerge organically from the data.\u003c/p\u003e\u003cp\u003eInitially, two analysts (RK and SW) independently read three similar transcripts to familiarize themselves with the content, noting potential codes and themes. The broader team (FCS, RK, SW, and KG) then convened to compare and discuss the initial codes. Any discrepancies in coding were deliberated and resolved through consensus. Through this process, we developed a coding framework that was systematically applied to the remaining transcripts.\u003c/p\u003e\u003cp\u003eEmerging themes were continuously reviewed and refined through iterative team discussions. We categorized these themes as either facilitators or barriers to the performance of differentiated service delivery (DSD). Themes that enhanced DSD performance were classified as facilitators, whereas those that hindered performance were classified as barriers. To illustrate key findings, we extracted representative quotations that captured participants' views in their own words.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEthics\u003c/b\u003e\u003c/p\u003e\u003cp\u003e This study was approved by The AIDS Support Organization, Research Ethics Committee, Kampala, Uganda (TASO-2022-144), and the Uganda National Council for Science and Technology, Kampala, Uganda (HS2497ES). We also obtained administrative clearance from the Ministry of Health and the respective District Health Offices.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eSample characteristics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 1,141 PLHIV in the ART registry, 530, 432 and 393 were active in ART care at the end of June 2019, 2020 and 2021, respectively. Among these PLHIV, 252/530 (48%), 281/432 (65%), and 354/393 (90%) PLHIV were included in a DSD model for 2019, 2020, and 2021, respectively (Figure 1). Overall, the population characteristics were comparable across the years 2019--2021. The majority of PLHIV enrolled in the DSD model were young (aged 25--40 years), 61% female, with no symptoms of TB, and at WHO stage I. Notable increases in the use of DTG-based regimens and client preferences for the FTDR model of care in 2021 were observed. (Table 3).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"585\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 585px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eCharacteristics of PLHIV in DSD model in June 2019, 2020, 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003en\u003c/em\u003e = 252)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003en\u003c/em\u003e = 280)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003en\u003c/em\u003e = 351)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e150/252 (59.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e170/280 (60.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e215/351 (61.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eAge years, median (IQR)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e31 (25-39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e32 (25-40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e32 (25-40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eTB status*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;No signs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e243/247 (98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e259/278 (93.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e325/349 (93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;On TB treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;TB Presumed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eCompleted TB Rx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eNo TB data documented\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eWHO status*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e210/243 (86.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e244/279 (87.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e320/350 (91.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e29 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e3 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eNo WHO status documented\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eBase ART regimen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; EFV-based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e239 (95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e212/277 (77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e12/350 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; DTG-based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e5 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e55/277 (19.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e315/350 (90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e8 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e10/277 (3.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e23/350 (6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eDSD model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eCCLAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0/252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e5/1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e9/354\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eCDDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e5 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e8 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eFBG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e36 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e51 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e48 (13.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eFBIM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e138 (55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e67 (23.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e37 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eFTDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e77 (30.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e153 (54.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e258 (72.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Missing data\u003c/p\u003e\n\u003cp\u003eGeneral comment on Table \u0026ndash; certainly it would have been more helpful if data were less missing, to include a column of non-DSD patients but seeing the level of missing data in this table alone and not being able to tell if it meant missing or just unfilled discouraged any ambition to.\u003c/p\u003e\n\u003cp\u003eThe fast-track-drug-fill (FTDR) model had the highest uptake, being 31% in 2019 and 65% in 2021, whereas the facility-based-individual-management model (FBIM) was the least preferred, dropping from 55% to 9%, and the uptake in the other models [Facility-based groups (FBG), Community Drug Distribution Points (CDDP) and Community Client Led Distribution (CCLAD)] remained low and unchanged. Notably, the uptake of the DSD model improved each subsequent year.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompletion of viral load testing and viral load suppression\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn 2019, 2020 and 2021, viral load testing was completed for 388/530 (73%), 359/432 (83%) and 336/393 (85%) PLHIV active in care, respectively, and viral load suppression was achieved for 333/388 (86%), 322/359 (90%) and 323/336 (96%) PLHIV, respectively. (Figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFacilitators of improved DSD performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReduced costs for patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients reported that community-based DSD models greatly reduce costs by minimizing the need for frequent visits to health facilities, leading to savings in terms of transportation and meals. This also allowed them to dedicate more time to work and personal responsibilities, as highlighted by a focus group participant about the CCLAD model.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;This arrangement is so good for us workers since we have limited time to fetch medicine; waiting in long queues is difficult. Our cluster is small, one of us picks all our medicine and calls us to meet sometimes at Bugembe playgrounds or the park, and everyone gets their medicine.\u0026rdquo; (FGD_PLHIV_Katikamu HCIII (rural))\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIncreased engagement between patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSome patients also reported that participation in community-based models such as Community Drug Distribution Points (CDDPs) encouraged increased interaction and peer-to-peer support, allowing them to share experiences and ways around common challenges. These groups also led to the formation of savings groups, fostering financial cooperation, and being a source of mutual support.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;Thus far, we have saving groups out of those CDDPs, and we are doing well. Then, also, some clients form groups, others even have WhatsApp groups.\u0026rdquo; (IDI_HCM_Jinja (urban)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIncreased engagement between patients and healthcare providers\u003c/strong\u003e in community models\u003c/p\u003e\n\u003cp\u003eSome healthcare providers reported that DSD models improved interactions between patients and healthcare providers, particularly through community-based meetings where patients would share their progress and challenges directly with health workers. This increased interaction has enhanced the quality of care and strengthened relationships between the two groups. One health manager noted:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;Because these people do not have to travel to the facility, especially in regard to CDDP, we find them in the community at a chosen meeting place\u0026hellip; you interact with them, update them on what is new, what services we have at the hospital, so you have good engagements with them.\u0026rdquo; (IDI_HCM_Kiwoko(rural))\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupport from HIV program implementing partners\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSome policymakers at the Uganda Ministry of Health reported that support from national and international partners also played a significant role in the successful implementation of the DSD models, as acknowledged by one key informant:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;Once you have something in the guidelines, it is policy. It has to be implemented. The fact that the different funders were interested, and we had full support from donors, the IPs, the government, CBOs, NGOs\u0026mdash;once you have all the different stakeholders embracing it, it usually moves.\u0026rdquo; (KII_PM_MoH)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTraining, capacity building, and mentorship\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHealthcare providers also highlighted that training and capacity building equipped them, group leaders, and patients with the necessary skills and knowledge to implement and benefit from DSD models. Continuous mentorship and supervision provided by implementing partners further reinforced these efforts, ensuring effective service delivery. As one healthcare provider from Bugiri noted,\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;The preparation was enough for us to start because we had training and got the knowledge on how to go about it; we had the tools required for data collection.\u0026rdquo;\u0026nbsp;\u003cbr\u003e\u0026nbsp;(IDI_HCP_Bugiri (rural)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of resources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHealthcare providers reported that the availability of essential resources such as drugs, registers, and financial support has been crucial for the effective implementation of DSD models, ensuring smooth service delivery and consistent communication with patients. A healthcare provider from Busia mentioned the following:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;We facilitate them with transport and safari day allowances; as a project, we ensure that the drugs are there and all the tools they need to use in these CDDPs. Then, we also provide airtime, which is used to make reminder calls.\u0026rdquo; (KII_IP_MoH)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReduced congestion and workload at health facilities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHealthcare managers also observed that the community-based models significantly reduced congestion at health facilities, leading to a lighter workload for healthcare providers. This reduction in patient numbers allowed for more manageable workloads and improved the quality of care provided. A healthcare manager from Nakasongola observed the following:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;DSD has greatly impacted our life here because you will not find the patient volumes we used to have\u0026hellip; it has saved us a lot of time and it has created quality time for the patients we see.\u0026rdquo; (IDI_HCM_Nakasongola (rural))\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePotential Barriers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStigma\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoth patients and healthcare providers identified stigma as a major barrier to the success of DSD models, particularly the CDDP and CCLAD models, where medication is distributed in community settings. Patients reported discomfort with gathering in public due to fear of being identified as HIV positive, leading some to miss appointments or switch models in hopes of reducing stigma. This has also been discussed in a study by Walusaga \u003cem\u003eet al\u003c/em\u003e[12].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u0026ldquo;One of the big problems is stigma, especially at the community level. We still have people who join the CCLAD groups, but the moment they find there someone they know, they jump out\u0026rdquo; (KII_PM_MoH).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient changes in location\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHealthcare providers reported that frequent patient relocations, driven by factors such as family separation, divorce, and employment, present major challenges to delivering DSD models, particularly the CCLAD model. This model depends on the commitment and initiative of the CCLAD group leader to track and notify health facilities, which varies and is not consistently reliable across all groups. These relocations may lead to unaddressed clinical issues.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u0026ldquo;Patients keep changing residences and by the time the facilitator determines sometimes this person has deteriorated so much that you get confused on the next course of action\u0026rdquo; (IDI_HCP_Bugono_HCIV (rural)).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSimilarly, healthcare providers reported that frequent relocations also contribute to patient nonadherence to treatment and the potential for developing undetected health issues.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u0026ldquo;There are people who have become too reluctant again because someone knows they get medicine for six months and even if she doesn\u0026rsquo;t go, others will go, that person can disappear for two years without showing up\u0026rdquo; (IDI_HCP_Bugiri (rural)).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnreported and undetected clinical challenges\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHealthcare providers reported facing challenges in detecting and following up with patients on ART (in the LIMS) to identify clinical issues, including concerns about pregnancies that go unreported. They also expressed worries about patients being assigned to inappropriate care models.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;Some of our patients are starting to have high viral loads because we are giving them treatment, and we are not monitoring them closely\u0026rdquo; (IDI_HCM_Jinja (urban)).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMedicine\u0026nbsp;\u003c/strong\u003estockouts\u003c/p\u003e\n\u003cp\u003eHealthcare providers highlighted frequent drug stock-out as another key challenge that affected the delivery of a sufficient supply of medications for patients in some models. Some of the healthcare providers below explain how stock-outs affect the delivery of services under the CCLAD model.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;We are having drug stockouts even right now, so you may find someone is in the CCLAD, but the drugs are not there\u0026rdquo; (IDI_HCP_Masafu (urban)).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTransport challenges for healthcare providers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe lack of reliable transportation was reported as a major challenge in implementing DSD models, especially in the CDDP model, where healthcare providers struggle to transport equipment and medicines to remote areas owing to inadequate transport options and poor road conditions.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;Transportation of equipment is a challenge since we move with it all on boda boda (motorcycle), and now we are getting to a rainy season. Sometimes you find on a rainy day we fail to get to the field\u0026rdquo; (IDI_HCP_Bugono (rural)).\u003c/em\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e Our study assessed the uptake and quality (VL testing and suppression) of DSD models of care offered to PLHIV at public HIV care facilities. We also explored stakeholders\u0026rsquo; perceptions of challenges and opportunities present in the implementation of DSD models of care in Uganda. Our results show that over a three-year period, enrollment in DSD models of care generally improved and was amplified the most for LIMs. Additionally, the proportion of PLHIV who completed viral load testing and achieved suppression improved over time. Notably, the majority of PLHIV showed an increasing preference for facility-based care, particularly the fast-track-drug refill (FTDR) model. The adoption of differentiated service delivery (DSD) care models was facilitated by training, capacity building, mentorship, resource availability, and support from HIV-implementing partners. The less intensive models enhanced stakeholder engagement, strengthened patient\u0026ndash;provider interactions, and reduced costs for patients, as well as congestion and workload for healthcare providers. However, challenges such as medical stockouts, patient stigma, and frequent changes in patient addresses hinder their effectiveness.\u003c/p\u003e\u003cp\u003eA review of 57 studies across Africa highlighted diverse factors influencing the implementation and scale-up of differentiated service delivery (DSD) models for HIV treatment[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Consistent with our findings, key facilitators included reduced patient visit costs, decreased staff workloads and less strain on health facilities, whereas major barriers included stigma and drug stock-outs. Moreover, depending on context, system factors such as leadership and governance facilitate or hinder DSD care implementation to the extent that they influence provider compliance or resource availability[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. At the patient level, another study conducted by Zakumumpa \u003cem\u003eet al\u003c/em\u003e. in Uganda identified similar patient-specific challenges to DSD model implementation identified in our study\u0026mdash;positing that enrollment in DSD was influenced by anticipated or experienced community-related stigma in community-based models, a fear of detachment from health facilities and preferences on the basis of patients\u0026rsquo; socioeconomic status[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Within the facility-based model of care, our study confirms reports of the FTDR being the most preferred model of care, mostly attributable to the anticipated convenience and speed of accessing ART, which, in combination with the multiple-month dispensing of ART, has the potential to improve care outcomes[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn our study, providers cited additional quality-of-care concerns over patients\u0026rsquo; unmet care needs outside of ART provision in the LIMs, i.e., community-based models, compared with MIMs, facility-based models where patients receive more attention and care for comorbidities. This is of major concern, especially because the survival of PLHIV has improved with ART, resulting in a heightened risk of non-communicable disease comorbidities requiring concurrent care. It is clear that the DSD program still needs to address patient and health provider challenges related to inadequate resources for community models, stigma, and the ability to reach and offer integrated care for all comorbidities to improve uptake. Similarly, the service package for community-based models should be expanded beyond ART refills to incorporate family planning, TB screening, TB medicine refills for the continuation phase of treatment, and non-communicable disease (NCD) screening and refills, among other essential services that are provided in facility-based models.\u003c/p\u003e\u003cp\u003e Using viral load testing and suppression as indicators of the quality of care offered in DSD models at public HIV care facilities, our findings revealed a progressive increase in the proportion of PLHIV tested and a decrease in the viral load as more PLHIV enrolled in DSD care each subsequent year, the highest being in the FTDR model. This may have been due to fidelity to DSD model guideline implementation, which limited enrollment to virally suppressed PLHIV. This, in turn, reflects an overall improvement in PLHIV care within the standard of care, ensuring that only those meeting the criteria are transitioned into DSD care. (9, 12). However, persistent gaps in viral load testing coverage remain a concern, as they create uncertainty regarding patients' treatment needs, level of care and eligibility for DSDs. Our study revealed 85% viral load coverage among PLHIV in the DSD care model, which was comparable to the 87% reported by Esther L. \u003cem\u003eet al\u003c/em\u003e.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUsing MoH routinely collected program data, we found significant missing data, and as such, we may have biased estimates of DSD care uptake or viral load suppression. We did not restrict the analysis to facilities with more reliable data, as this would have inadvertently introduced selection bias. Nonetheless, the data used were representative of real-world settings and highlight the need for increased health system strengthening to improve data quality for use, as per WHO recommendations[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe defined quality of care as limited to viral load testing and suppression, as we lacked data to assess patient quality of life, care for comorbidities or reasons for patient switching between DSD models. As HIV programs scale-up DSD models, these models will increasingly be important to consider to inform the improvement of PLHIV care outcomes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGeneralizability\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOur study findings are generalizable only to public ART care programs of similar settings receiving comparable levels of funding support.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe differentiated service delivery DSD model of care, particularly fast-track drug refill, has quickly become the preferred model of care for PLHIV and has shown promise for improving patient outcomes. However, continued support in the form of funding, above site supervision, non-communicable disease care integration, addressing stigma, and improving data quality for monitoring and evaluation are critical for achieving the MoH LIM and MIM targets.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Ethical approval for this study was obtained from the AIDS Support Organization Research and Ethics Committee (TASO-2022-144) and the Uganda National Council for Science and Technology (HS2497ES). Administrative clearance was also obtained from the Ministry of Health and the relevant district health offices. This research was conducted in full accordance with the principles of the World Medical Association Declaration of Helsinki (1964) and its subsequent amendments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This manuscript does not contain any individual person\u0026rsquo;s data, images, or personal details that would require consent for publication. All qualitative data are presented in aggregate form or as anonymized quotations, and no identifiable information has been included.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study contain sensitive personal health information and are subject to Uganda National Council for Science and Technology (UNCST) regulations as well as Makerere University School of Medicine Research \u0026amp; Ethics Committee (SOMREC) approvals.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eQuantitative data\u003c/strong\u003e \u0026ndash; A fully de identified version of the quantitative dataset, together with the data dictionary, will be made available from the corresponding author upon reasonable request. Access will be granted to researchers who provide a methodologically sound proposal, sign a data-use agreement, and obtain clearance from both SOMREC and UNCST.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eQualitative materials\u003c/strong\u003e \u0026ndash; Because audio recordings and verbatim transcripts can inadvertently reveal participant identity, the full files will not be shared publicly. However, de identified excerpted quotes supporting the study\u0026rsquo;s conclusions can be obtained from the corresponding author after the same ethical clearance process described above.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eStatistical code\u003c/strong\u003e \u0026ndash; The STATA do files and NVivo codebook used for data cleaning, analysis, and visualization are available on request directly from the corresponding author.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eResearchers requesting access will be asked to state the intended use, agree not to attempt re identification, and destroy the data after the approved project is complete.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003cbr\u003e\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received support from the Africa Resource Centre (ARC) with administrative support from the Makerere University Joint AIDS Program (MJAP) and the Uganda Ministry of Health AIDS Control Program plus the district health offices of the areas of intervention.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003cbr\u003e\u0026nbsp;\u003c/strong\u003eFCS, CK and KG conceptualized the study. SW, NK, RK, and GO IA led the data collection and analysis. FCS, NK, IA, CK, CD, NV, CVR, ENM, HB, GO, and KG contributed to the study design and interpretation. SW, NK, RK, FCS, KG and HB contributed to manuscript drafting and revision.\u003c/p\u003e\n\u003cp\u003eAll the authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are deeply grateful to all the people living with HIV who graciously shared their time and experiences\u0026mdash;your participation is the cornerstone of this study. We also thank the management and frontline teams at every participating health facility for their unstinting cooperation and hospitality during data collection.\u003c/p\u003e\n\u003cp\u003eThis work was made possible through financial support from the \u003cstrong\u003eAfrica Resource Centre (ARC)\u003c/strong\u003e and the administrative backing of the \u003cstrong\u003eMakerere University Joint AIDS Program (MJAP)\u003c/strong\u003e and the \u003cstrong\u003eAIDS Control Program, Uganda Ministry of Health\u003c/strong\u003e. We further acknowledge the District Health Offices in our study districts for their facilitation and oversight.\u003c/p\u003e\n\u003cp\u003eOur appreciation goes to the research assistants, data clerks, and transcribers whose diligence ensured high-quality quantitative and qualitative data and to the NVivo and STATA user-support communities for timely technical guidance. Finally, we thank our academic and implementing-partner colleagues for their constructive feedback throughout concept development, fieldwork, and manuscript preparation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWHO. World Health Organization. The Global Health Observatory \u0026ndash; HIV/AIDS data. 2023. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/data/gho/data/themes/hiv-aids.\u003c/span\u003e\u003cspan address=\"https://www.who.int/data/gho/data/themes/hiv-aids.\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e WHO, 2023.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWHO. World Health Organization. Consolidated guidelines on the use of antiretroviral drugs for treating and preventing HIV infection. WHO, 2016. [Online]. 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Jul. 2023;26:e26122. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jia2.26122\u003c/span\u003e\u003cspan address=\"10.1002/jia2.26122\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. no. Suppl 1.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1: Description of the sampled health facilities\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"582\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 247px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelected health facilities \u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeographical setting\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOwnership\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003e1 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eMubende Regional Referral Hospital \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eUrban \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eGovernment/Public \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003e2 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eJinja Regional Referral Hospital \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eUrban \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eGovernment/Public \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003e3 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eKiwoko Hospital \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eRural \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eNGO/PNFP \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003e4 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eBugiri Hospital \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eUrban \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eGovernment/Public \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003e5 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eMasafu Hospital \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eRural \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eGovernment/Public \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003e6 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eNakasongola HC IV \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eUrban \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eGovernment/Public \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003e7 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eNabishwera HC IV \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eRural \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eGovernment/Public \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003e8 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eWalukuba HC IV \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eUrban \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eGovernment/Public \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003e9 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eBugono HC IV \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eRural \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eGovernment/Public \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003e10 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eLumino HC III \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eRural \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eGovernment/Public \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003e11 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eKatikamu HC III \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eRural \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eNGO/PNFP \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003e12 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eKakooge HC III \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eUrban \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eGovernment/Public \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Categories and number of interviews and focus group discussion respondents\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"596\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory \u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of interviews/(respondents\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003e\n \u003cp\u003eNumber of FGDs with PLHIV\u003cstrong\u003e \u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 238px;\"\u003e\n \u003cp\u003e13\u0026nbsp;(125)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003e\n \u003cp\u003eNumber of KIIs with policy makers and IPs\u003cstrong\u003e \u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 238px;\"\u003e\n \u003cp\u003e3 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003e\n \u003cp\u003eNumber of IDIs with heath care providers\u003cstrong\u003e \u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 238px;\"\u003e\n \u003cp\u003e24 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003e\n \u003cp\u003eNumber of IDIs with health care managers \u003cstrong\u003e \u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 238px;\"\u003e\n \u003cp\u003e10 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTOTAL \u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e50\u003c/strong\u003e (162)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Differentiated service delivery model, HIV, Uganda, Viral load suppression, Mixed methods","lastPublishedDoi":"10.21203/rs.3.rs-6963111/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6963111/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferentiated service delivery (DSD) models have emerged as innovative approaches to optimize HIV service delivery, particularly in bridging gaps across the HIV test–treat–care cascade. We assessed the performance of DSD models in Uganda, focusing on enrollment, retention in care, viral load (VL) testing, and suppression, while identifying key challenges and facilitators of effective implementation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003cbr\u003e\nWe conducted a retrospective review of records for closed cohorts of people living with HIV (PLHIV) active in care from 2019--2021 across health facilities supported by the Makerere University Joint AIDS Program (MJAP) and Mildmay Uganda. We determined the proportion of PLHIV who were active in care, enrolled in a DSD model, completed a VL test, and achieved suppression. Additionally, we conducted key informant interviews and focus group discussions to explore stakeholder perspectives on implementation challenges and facilitators.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003cbr\u003e\nAmong the 1,141 PLHIV, 530 (2019), 432 (2020), and 393 (2021) were active in care. DSD model enrollment increased from 48% in 2019 to 90% in 2021. The fast-track drug refill (FTDR) model had the highest uptake, increasing from 31% to 72%, whereas the facility-based individual management (FBIM) model declined from 55% to 10%. Viral load testing coverage improved from 73% to 85%, with suppression rates rising from 86% to 96% over the study period. Qualitative data revealed key facilitators, including reduced patient costs, improved provider–patient engagement, and partner support. Barriers included stigma, medical stockouts, patient relocation, and limited service integration in community-based models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis mixed-methods study analyzed three years of differentiated service delivery (DSD) for Ugandan PLHIV, tracking enrollment, viral load testing, and suppression and interviewing stakeholders. Enrollment and suppression improved the most in facility-based fast-track drug refill, facilitated by training, mentorship, and partner support. Stockouts, stigma, and unstable patients address hindered progress. Strengthening community models and integrating comorbidity care remain key.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003cbr\u003e\nThe uptake and quality of DSD models improved over time, with a strong preference for the FTDR model. However, addressing persistent barriers, especially stigma, service integration, and resource availability, is essential to achieve sustained scale-up and improved HIV care outcomes in Uganda.\u003c/p\u003e","manuscriptTitle":"Differentiated Services Delivery Model Uptake and Outcomes in Uganda: Gaps, Facilitators, and Barriers - A Mixed Methods Study 2019–2021","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-08 16:20:00","doi":"10.21203/rs.3.rs-6963111/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"292742321815223387067910162018839485494","date":"2025-08-14T13:03:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"25224354405632334792513494842680129394","date":"2025-08-07T08:04:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-05T06:54:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-30T05:21:22+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-14T08:45:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-12T21:42:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2025-07-12T21:40:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"747f19dc-3d91-4ebf-8644-a99706fd3913","owner":[],"postedDate":"August 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-08-08T16:20:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-08 16:20:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6963111","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6963111","identity":"rs-6963111","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}