HIV Viral Load Suppression and Associated Factors Among PLHIV with Non-Communicable Diseases in Uganda’s Teso Region: A Cross-sectional Retrospective Study

doi:10.21203/rs.3.rs-8240242/v1

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Research Article

HIV Viral Load Suppression and Associated Factors Among PLHIV with Non-Communicable Diseases in Uganda’s Teso Region: A Cross-sectional Retrospective Study

https://doi.org/10.21203/rs.3.rs-8240242/v1

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Background:

As Uganda scales up integrated chronic care, understanding viral load suppression (VLS) among people living with HIV (PLHIV) with non-communicable diseases (NCDs) is critical for optimizing service delivery. This study thus determined VLS among the PLHIV with NCDs and associated factors.

Methods:

We conducted a cross-sectional analysis using routine program data from 10 districts in Eastern Uganda. Categorical variables reported as frequencies and percentages, while continuous as mean and standard deviation. Robust Poisson regression estimated crude and adjusted prevalence ratios (aPRs) for VLS, controlling for NCD status, age, sex, and facility level using 95% confidence interval (CI) and p < 0.05 for statistical significance. A forest plot visualized effect sizes and confidence intervals. STATA Corp version 15 was used for the analysis.

Results:

Among 8,306 PLHIV, 62.4% were female, with a mean (Standard Deviation) age of 46.7 years (13.8). Overall VLS was 94.7%. NCD comorbidity was 8.45%, predominantly hypertension (7%). Crude analysis showed higher VLS among PLHIV with NCDs (PR = 1.023; p = 0.002), but adjusted estimates attenuated (aPR = 1.015; 95% CI: 0.999–1.031; p = 0.065). Older age groups (30–39, 40–49, 50+) had significantly higher VLS than those under 30 ( aPR = 1.063; 95% CI: 1.034–1.091; p < 0.001), (aPR = 1.073; 95% CI: 1.044–1.102; p < 0.001), (aPR = 1.071; 95% CI: 1.043–1.100; p < 0.001) respectively. Males had lower VLS than females (aPR = 0.978, 95% CI [0.968, 0.989] p < 0.001). Clients at Health Centre III had reduced VLS (aPR = 0.979 95%CI [0.961, 0.997]; p = 0.023), while Health Centre IV and hospitals showed no significant difference when compared with those in the special clinic.

Conclusion:

VLS among PLHIV with NCDs was comparable to those without, suggesting integrated care may mitigate disparities. However, age, sex, and facility level remain key determinants. Targeted interventions are needed for younger clients, men, and health centre IIIs to sustain viral suppression.

People living with HIV (PLHIV)

HIV

viral load suppression

non-communicable diseases

Uganda has made significant strides in the fight against HIV/AIDS, with national viral load suppression (VLS) rates reaching approximately 97% among adults on antiretroviral therapy (ART) as of 2024 [1]. However, regional disparities persist, particularly in underserved areas like the Teso Region, where health system constraints, comorbidities, and socioeconomic factors may undermine sustained viral suppression [2]. The intersection of HIV and non-communicable diseases (NCDs) such as hypertension, diabetes mellitus, alcohol & substance misuse and mental illnesses, poses a growing challenge to Uganda’s public health system [3–6], complicating care pathways and potentially affecting ART adherence and retention in care.

Recent evidence highlights that PLHIV with NCDs experience higher rates of missed appointments, fragmented care, and medication fatigue, which can compromise VLS outcomes [6–8]. Uganda’s Ministry of Health has responded by promoting integrated service delivery models, including differentiated service delivery (DSD) and multi-month dispensing (MMD), to streamline care for PLHIV with comorbidities [9]. Yet, evidence on the effectiveness of these models in rural regions like Teso remains limited.

The Teso Region, like many settings in Uganda, faces unique challenges: limited health workforce capacity, inconsistent NCD screening, and low uptake of integrated HIV/NCD services. These gaps are compounded by patient-level factors such as stigma, low health literacy, and economic vulnerability [10]. While national data suggest promising trends, localized studies are essential to uncover context-specific barriers and facilitators to VLS among PLHIV with NCDs. Moreover, few settings often provide integrated models that are context responsive and the impact of integration of HIV/NCD services remain scanty [11, 12].

This study employs a cross-sectional design to examine associations between NCD status and viral load suppression (VLS) rates among PLHIV in the Teso sub-region of Uganda. Quantitative data will be analysed to identify patterns and correlations that may inform understanding of service utilization and treatment outcomes. Guided by Dubin’s theory-building framework [13], the analysis conceptualized the interplay between health system factors and patient-level outcomes, offering insights into how structural and service delivery variables influence VLS within the regional context.

By focusing on Teso, this study aimed to generate actionable insights for policymakers, donors, and implementing partners seeking to strengthen HIV/NCD integration and improve VLS outcomes. Findings will inform targeted interventions, capacity-building efforts, and strategic planning aligned with Uganda’s national HIV response and global 95-95-95 targets.

Study Design and Setting

This was a cross-sectional retrospective study conducted across 10 districts in Uganda’s Teso sub-region region, leveraging routine programmatic data from HIV care and treatment facilities. The study aimed to assess factors associated with viral load suppression (VLS) among people living with HIV (PLHIV), with a particular focus on the influence of non-communicable disease (NCD) comorbidity. The participating districts include Soroti city, Soroti district, Kalaki, Kaberamaido, Serere, Kumi, Bukedea, Ngora, Kapelebyong and Katakwi. There were three hospitals, seven health center IVs, seven HC IIIs and one HIV Special Clinic (Centre of Excellence). Teso region is located about 330kms northeast of Uganda’s capital city, Kampala with an estimated population of 2.6 million people including 52,000 people living with HIV [1, 14]. Recent programmatic data indicates that the region has achieved the UNAIDS first and second 95 but remains disproportionately behind regarding the third. In line with the national guidelines [9], the region started active integration of NCD services into routine HIV care in October 2023. These services include screening for and management of common NCDs such as HTN, DM, depression, anxiety, alcohol and substance abuse during routine clinic visits for PLHIV at least 15 years of age.

Study Population

The study included PLHIV aged 20 years and above who had documented viral load results rom April 1st, 2025, to 30th June 2025. Eligible participants had complete clinical records, including NCD diagnosis status, demographic information, and ART history. Individuals with missing key covariates or incomplete viral load documentation were excluded from the analysis.

Measurements

Blood pressure (BP) was measured routinely during clinic visits using digital or manual sphygmomanometers. Hypertension was diagnosed basing on national standards: BP of at least 140/90 mmHg. On the other hand, blood sugar was measured using routine fasting blood sugar (FBS) tests. FBS > 7.0 was used to diagnose diabetes mellitus (DM). For depression and anxiety, self reporting questionnaire (SRQ-20) was used to screen PLHIV for the disorders. The tool consists of 20 questions, patients answer yes if they experienced the symptom for the past 30 days or no. Each question attracts 1 point and a score of 6 points or a report of suicidal ideations implied depression/anxiety [9]. Similarly, PLHIV were screened for the alcohol use disorder using the Audit-C tool that helps to identify and quantify the levels of use, based on drinking frequency [15].

Data Sources and Variables

Data were extracted from electronic medical records (EMRs), ART registers, and district-level DHIS2 repositories triangulation with the national VL dashboard [16]. Variables included:

Demographics: Age, gender, district of residence
Clinical: NCD diagnosis (hypertension, diabetes mellitus, depression & anxiety and alcohol use disorder), viral load, and ART duration.
Programmatic: Facility type (Special HIV Clinic, health center III, health centre IV & Hospitals)

Viral load suppression was defined as VL < 1000 copies/mL, consistent with national guidelines. Age was categorized into four groups: below 30 years, 30–39, 40–49, and ≥ 50 years.

Statistical Analysis

Descriptive Analysis and Bivariate Analysis

Data was downloaded initially into Microsoft Excel for cleaning and preparations before exporting into STATA Corp, version 15 for the final analysis. Frequencies and proportions were used to summarize categorical variables, while means and standard deviation described continuous variables. Initial comparisons between groups were conducted using chi-square tests, at 95% confidence intervals (95%CI). Variables with p-value < 0.2 were then considered for multivariable analysis.

Multivariate Analysis

Crude prevalence ratios (PRs) were estimated using Poisson regression with robust standard errors to assess the unadjusted association between each predictor and viral load suppression. Adjusted PRs were derived using multivariate Poisson regression, controlling for age group, gender, district, and NCD status, with the level of significance set at p < 0.05 using 95% CI. Model diagnostics included checks for multicollinearity and goodness-of-fit [17]. The prevalence ratio (PR) was selected over the odds ratio (OR) as the measure of association, given its appropriateness in cross-sectional analyses where the outcome of interest has a prevalence exceeding 10%. In such contexts, the OR can substantially overestimate the strength of associations, potentially leading to misinterpretation of effect sizes and limiting the applicability of findings in public health decision-making [18].

Adjusted prevalence ratios and their 95% confidence intervals were visualized using a forest plot generated in Stata with the coefplot command. The plot included a vertical reference line at PR = 1.0 to aid interpretation. This graphical representation provided a vivid summary of the adjusted associations, highlighting both statistically significant and borderline effects. The study was reported using a critical appraisal tool for cross sectional studies (AXIS) [19].

Ethical Considerations

Ethical approvalwas obtained from Mbale Regional Referral Hospital Research Ethics Committee, registration number MRRH-2025-558. Data were anonymized and analysed in aggregate form. A waiver approval for the need for obtaining informed consent was received.

Table one: Demographic and clinical characteristics of study participants included in the study

Descriptive Characteristics of the Study Population

Participant Characteristics

From table one, the study included 8,306 individuals enrolled across multiple health facilities in Eastern Uganda for the analysis. Viral load suppression was notably high, with 94.7% of clients achieving suppression, suggesting strong adherence and effective ART delivery across the region.

Most clients were served at Health Centre IVs and General Hospitals, reflecting the central role of mid- and high-level facilities in HIV service provision. The relatively lower representation at Health Centre IIIs may reflect lower numbers of enrolled PLHIV in care compared to higher volume sites.

The age distribution skewed older, with over 70% of participants aged 40 years and above. This mature cohort likely reflects long-term ART retention and may contribute to the high suppression rates observed. Conversely, younger clients (< 30 years) were underrepresented, which may warrant targeted engagement strategies given their known vulnerability to poor adherence.

Women constituted nearly two-thirds of the sample, consistent with national HIV epidemiology and care-seeking trends. While this reflects successful female engagement, the lower male representation underscores persistent gender disparities in access and retention.

Duration on ART was predominantly ≥ 10 years, highlighting a stable treatment population. This long-term engagement may explain the favourable viral suppression outcomes, though it also raises considerations for aging-related comorbidities.

About 8.5% of clients had a documented NCD diagnosis, with hypertension being the most common. The low prevalence of diabetes, mental health conditions, and substance use may reflect underdiagnosis resulting potentially from low reporting.

Geographically, Soroti City accounted for nearly a quarter of the sample, followed by Serere, Katakwi, and Kumi. Districts like Kalaki, Bukedea, and Soroti District were minimally represented, which may limit statistical power for localized comparisons but also signal service delivery or data capture disparities.

Bivariate Analysis of Factors Associated with Viral Load Suppression.

Table two: Bivariate analysis of viral load suppression (VLS) status by district, facility level, age group, gender, ART duration, and non-communicable disease (NCD) diagnosis among study participants in the Teso sub-region (N = 8,306).

Bivariate comparisons in table two revealed statistically significant differences in viral load suppression across several demographic and clinical variables.

District-level variation was notable (p = 0.001). Kalaki district exhibited the lowest suppression rate (78.3%), substantially below the overall average, suggesting localized challenges in adherence or service delivery. Kapelebyong and Soroti District also showed lower suppression rates (91.7% and 92.9%, respectively), while Bukedea and Serere had the highest rates (> 96%), indicating potential best practices or stronger system performance in those areas.

Facility level was significantly associated with VLS (p = 0.025). Clients served at Health Centre IIIs had lower suppression rates (93.1%) compared to those at General Hospitals and Special HIV Clinics (> 95%), possibly reflecting differences in clinical capacity, staffing, or follow-up systems.

Age group showed a strong association with suppression (p < 0.001). Clients under 30 years had the lowest suppression rate (89.6%), reinforcing known vulnerabilities in adherence among younger populations. In contrast, older age groups (≥ 30 years) consistently achieved suppression rates above 94%, suggesting greater stability and engagement in care.

Gender was also significantly associated with VLS (p = 0.001). Male clients had a lower suppression rate (93.6%) compared to females (95.4%), highlighting persistent gender disparities in treatment outcomes that may warrant targeted interventions.

NCD diagnosis was significantly associated with higher suppression (p = 0.013). Clients with documented NCDs had a suppression rate of 96.7%, compared to 94.5% among those without. This may reflect enhanced clinical monitoring or differentiated care models for clients with comorbidities.

No statistically significant differences were observed across ART duration categories (p = 0.305), suggesting that time on treatment alone may not predict suppression outcomes in this cohort.

Multivariate Analysis of Factors Associated with Viral Load Suppression

Table 3: Crude and adjusted prevalence ratios (PRs) for viral load suppression (VLS) among people living with HIV, stratified by NCD status, health facility level, sex, and age group. Reference categories are indicated.

Table 1

Demographic and clinical characteristics of study participants included in the study
Variable	N = 8306	Percentage (%)
Viral Load
Suppressed	7,867	94.71
Non-suppressed	439	5.29
Facility level
Special clinic	1989	23.95
Health Centre III	1274	15.34
Health Centre IV	2903	34.95
General Hospitals	2140	25.76
Age group
below 30	757	9.11
30–39	1582	19.05
40–49	2611	31.44
50+	3556	40.40
Gender
Female	5186	62.44
Male	3120	37.56
Duration on ART
Below 5 years	1269	15.23
5–9 years	1943	23.39
10–14 years	3609	43.45
15 + years	1485	17.88
NCD Status Diagnosis
Positive	702	8.50
Negative	7604	91.50
Diabetes Mellitus
Positive	64	0.77
Negative	8242	99.23
Hypertension
Positive	579	6.97
Negative	7727	93.03
Alcohol & substance abuse
Positive	67	0.82
Negative	8239	99.19
Depression &Anxiety
Positive	23	0.28
Negative	8283	99.72
Districts
Soroti City	1989	23.95
Soroti District	182	2.19
Kaberamaido	682	8.25
Kalaki	129	1.55
Bukedea	117	1.41
Kumi	1,326	15.96
Serere	1459	17.57
Ngora	287	3.46
Katakwi	1337	16.10
Kapelebyong	795	9.57

Table 2

Bivariate analysis of viral load suppression (VLS) status by district, facility level, age group, gender, ART duration, and non-communicable disease (NCD) diagnosis among study participants in the Teso sub-region (N = 8,306).
Variable	VLS		P-value
District	N = 439 Non-suppressed (%)	N = 7867 Suppressed (%)	*0.001
Soroti City	93 (4.68)	1896 (95.32)
Soroti District	13 (7.14)	169 (92.86)
Kaberamaido	39 (5.69)	646 (94.31)
Kalaki	28(21.71)	101 (78.29)
Bukedea	3 (2.56)	114 (97.44)
Kumi	59 (4.45)	1267 (95.55)
Serere	56 (3.84)	1403 (96.16)
Ngora	12 (4.18)	275 (95.82)
Katakwi	70 (5.29)	1267 (94.76)
Kapelebyong	66 (8.3)	729 (91.7)
Facility
Special HIV Clinic	93(4.68)	1896(95.32)	*0.025
Health Centre III	88(6.91)	1186(93.09)
Health Centre IV	156 (5.37)	2747(94.63)
General Hospital	102 (4.77)	2038 (95.23)
Age group
< 30 years	79(10.44)	678(89.56)	<*0.001
30–39 years	84(5.31)	1498(94.69)
40–49 years	119 (4.56)	2492(95.44)
50 + years	157 (4.68)	3199(95.32)
Gender
Female	240 (4.63)	4946 (95.37)	*0.001
Male	199(6.38)	2922 (93.62)	*0.001
ART Duration
Below 5 years	61 (4.81)	1208 (95.19)	0.305
5–9 years	112(5.72)	1831(94.24)
10–14 years	199 (5.51)	3410 (94.49)
15 + years	67 (4.51)	1418 (95.49)
NCDs diagnosis
Negative	416 (5.47)	7118 (94.53)	*0.013
Positive	23(3.28)	679 (96.72)	*0.013
*p < 0.2 (significant)

Table 3

Crude and adjusted prevalence ratios (PRs) for viral load suppression (VLS) among people living with HIV, stratified by NCD status, health facility level, sex, and age group. Reference categories are indicated.
VLS	Crude PR	p-value	95% Conf.	Adjust PR	p-value	95% Conf.
NCD_Status
Negative	Ref
NCD Positive	1.023	0.002	1.008, 1.038	1.015	0.065	0.999, 1.031
Health Facility level
Special Clinic (COE)	Ref.
Health Center III	0.977	0.009	0.959, 0.994	0.979	*0.023	0.961, 0.997
Health Center IV	0.993	0.269	0.980, 1.006	0.996	0.543	0.982, 1.010
Hospital	0.999	0.891	0.986, 1.013	1.002	0.780	0.988, 1.016
SEX
Female	Ref.
Male	0.982	*0.001	0.971, 0.992	0.978	*<0.001**	0.968, 0.989
Age group
Below 30 Years	Ref.
30–39	1.057	<*0.001	1.029, 1.086	1.063	*<0.001**	1.034, 1.091
40–49	1.066	<*0.001	1.039, 1.093	1.073	*<0.001**	1.044, 1.102
50+	1.064	*<0.001**	1.038, 1.092	1.071	*<0.001**	1.043, 1.100
*p < 0.05 (significant)

Figure 2: Forest plot of adjusted prevalence ratios (aPRs) for viral load suppression among people living with HIV. Estimates were obtained using robust Poisson regression. Point estimates are shown as blue dots, with 95% confidence intervals represented by horizontal lines. The vertical red dashed line at aPR = 1 indicates the null value.

Factors Associated with Viral Load Suppression Among PLHIV with NCDs

Table 3 and Fig. 2 present the crude and adjusted prevalence ratios (PRs) for viral load suppression (VLS) among people living with HIV (PLHIV), with a focus on those with non-communicable diseases (NCDs). Estimates were derived using robust Poisson regression. The forest plot visually complements the adjusted model, displaying point estimates and 95% confidence intervals for each covariate, with a vertical reference line at aPR = 1 indicating the null effect.

NCD Status

In crude analysis, PLHIV with NCDs had significantly higher likelihood of VLS compared to those without (PR = 1.023; 95% CI: 1.008–1.038; p = 0.002). However, this association attenuated in the adjusted model (aPR = 1.015; 95% CI: 0.999–1.031; p = 0.065), suggesting potential confounding by demographic or facility-level factors.

Health Facility Level

Compared to Special Clinics, clients accessing care at Health Center III had significantly lower VLS both in crude (PR = 0.977; 95% CI: 0.959–0.994; p = 0.009) and adjusted models (aPR = 0.979; 95% CI: 0.961–0.997; p = 0.023). No significant differences were observed for HC IV or hospital settings, indicating that lower-level facilities may require targeted support to optimize viral suppression outcomes.

Sex

Male clients had consistently lower VLS compared to females in both crude (PR = 0.982; 95% CI: 0.971–0.992; p = 0.001) and adjusted models (aPR = 0.978; 95% CI: 0.968–0.989; p < 0.001), reinforcing gender disparities in treatment outcomes.

Age Group

Older age was positively associated with VLS. Compared to clients under 30 years, all older age groups showed significantly higher suppression rates:

30–39 years: aPR = 1.063; 95% CI: 1.034–1.091; p < 0.001
40–49 years: aPR = 1.073; 95% CI: 1.044–1.102; p < 0.001
50 + years: aPR = 1.071; 95% CI: 1.043–1.100; p < 0.001

This gradient suggests that older PLHIV may benefit from more stable engagement or differentiated care models.

Overall, the adjusted model highlights key disparities in viral suppression among PLHIV with NCDs. Male sex, younger age, and care at lower-level facilities were independently associated with reduced VLS. Although NCD status showed a positive trend, it did not reach statistical significance after adjustment. These findings underscore the importance of targeted interventions—particularly for younger males and clients at HC III facilities—to sustain viral suppression in the context of NCD comorbidity.

This study examined viral load suppression (VLS) among people living with HIV (PLHIV) in Uganda’s Teso sub-region, with a specific focus on those with non-communicable diseases (NCDs). Contrary to concerns that NCD comorbidity may compromise ART outcomes, our findings revealed no statistically significant association between NCD status and VLS. This aligns with emerging evidence suggesting that integrated chronic care platforms may buffer the potential negative impact of multimorbidity on HIV treatment outcomes [7, 20–22].

In our study, clients with NCDs had a slightly higher but non-significant likelihood of achieving VLS (PR = 1.01, 95% CI: 0.997–1.029, p = 0.108). This finding is consistent with a Ugandan and a South African cohort study where VLS rates among PLHIV with hypertension or diabetes were comparable to those without NCDs, provided that ART and chronic care services were co-located and synchronized [3, 23–25]. Similarly, studies from Uganda, south Africa and Kenya found that integrated NCD-HIV care models improved retention and adherence, contributing to stable viral suppression rates [26, 27]. On the contrary, a study from the United States (N = 7450, 6.7% prevalence of hypertension) found lower VLS among the PLHIV with hypertension [28]. In addition, a study from Uganda (N = 664) reported lower odds of VLS among PLHIV with medium alcohol use [15]. These discrepancies may underscore the importance of context: where integration is weak, NCDs may exacerbate treatment fatigue and reduce adherence.

Sex-based differences in VLS were also observed. Male clients had significantly lower VLS prevalence compared to females (PR = 0.98, p < 0.001), consistent with regional evidence that men are less likely to engage in routine HIV care and more likely to present late or interrupt treatment [29]. This gender gap has been documented across multiple sub-Saharan African settings and highlights the need for male-centred interventions, such as community-based ART delivery, peer-led adherence clubs, and flexible clinic hours [30].

Age was a strong predictor of VLS. Clients aged 30–39, 40–49, and 50 + years had significantly higher VLS rates compared to those under 30 years. This trend mirrors findings from Ghana and Nigeria, where older adults demonstrated better adherence and retention, likely due to greater health literacy, longer ART experience, and more stable life circumstances [31, 32]. In contrast, younger clients often face unique barriers such as stigma, mobility, and psychosocial instability that disrupt treatment continuity [33].

Facility-level variation revealed that clients attending Health Centre III, IV, and Hospitals had significantly lower VLS prevalence than those at the Special Clinic. This may reflect differences in client complexity and service delivery models. The AIDS Support Organization (TASO) clinics serve more than 60% of their clients through community-based models such as community drug delivery points and some of the oldest cohorts of PLHIV [34–36] and staff with more experience in providing HIV services given it is their core business. Indeed, Special Clinics often serve stable clients enrolled in differentiated care, while higher-level facilities manage more complex cases, including transfers-in and clients with advanced disease or comorbidities. These factors have been reported in Rwanda to contribute to VLS differently across sites [37]. Therefore, our findings suggest that client mix and care model design may be more influential than facility tier alone.

Overall, our findings suggest that in settings with integrated chronic care, PLHIV with NCDs are not disadvantaged in achieving VLS. However, disparities by age, sex, facility level, and geography persist and warrant targeted interventions.

Strengths and Limitations

Strengths

This study leverages a large, real-world dataset from ten districts in Teso, enhancing the generalizability of findings across diverse health system contexts. The use of a robust Poisson regression model allows for accurate estimation of adjusted prevalence ratios (aPRs), which are more interpretable for programmatic decision-making than odds ratios. By focusing on PLHIV with non-communicable diseases (NCDs), the analysis addresses a critical and underexplored intersection in HIV care, offering timely insights into the performance of integrated chronic care platforms. The inclusion of district-level and facility-level variables enables nuanced understanding of geographic and structural determinants of VLS.

Limitations

As a cross-sectional analysis based on routine program data, causal inferences cannot be established. The reliance on facility-reported viral load data may introduce reporting bias or misclassification, especially in districts with weaker data systems. NCD status was captured as a binary variable without differentiation by type, severity, or duration, limiting granularity in assessing its impact on VLS. Additionally, unmeasured confounders such as socioeconomic status, stigma, or psychosocial support may influence outcomes but were not available in the dataset. Finally, the exclusion of clients with missing data may have introduced selection bias.

This study provides important insights into viral load suppression (VLS) among people living with HIV (PLHIV) with non-communicable diseases (NCDs) in Eastern Uganda. Contrary to concerns that NCD comorbidity may compromise HIV treatment outcomes, our findings show no statistically significant difference in VLS between clients with and without NCDs. This suggests that integrated chronic care platforms where HIV and NCD services are co-delivered may be effectively mitigating disparities in treatment outcomes.

However, disparities persist across other domains. Younger clients and men remain less likely to achieve VLS, underscoring the need for age- and gender-responsive interventions. Facility-level differences suggest that differentiated care models, such as those implemented in Special Clinics, may offer advantages in supporting stable clients.

Policy implications include the need to:

Strengthen NCD-HIV integration across all facility levels to sustain equitable VLS outcomes as the burden of multimorbidity grows.
Scale up male-friendly and youth-centred interventions, including community ART delivery, peer support, and digital adherence tools.
Expand differentiated service delivery (DSD) models to more facilities, ensuring that stable clients benefit from streamlined care while complex cases receive tailored support.

As Uganda advances toward epidemic control, sustaining high levels of VLS among all subpopulations, including those with NCDs will be critical. These findings support continued investment in integrated, person-centred care models that address both clinical and contextual determinants of HIV outcomes.

ART- Antiretroviral therapy

BP- Blood pressure

DM- Diabetes Mellitus

DSD- Differentiated Service Delivery

EMR-Electronic Medical Records (EMR)

FBS- Fasting Blood Sugar

HTN- Hypertension

NCD- Non-communicable Diseases

PLHIV- People living with HIV

SRQ-Self reported questionnaire

VLS- Viral load suppression

Funding Statement

This study had no specific funding

Author Contribution

BO, SMS, AIO, HS, BB and AM conceptualized the study and wrote the initial draftBO, SMS, AIO conducted the analysisTO, ARA, DJO reviewed the initial draft, making significant intellectual contributionsAll authors reviewed the final draft and approved for publication

Acknowledgement

We extend our sincere gratitude to all people living with HIV (PLHIV) whose data contributed to this analysis. In addition, we acknowledge the generous support of the U.S. President’s Emergency Plan for AIDS Relief (PEPFAR), implemented through the U.S. Centers for Disease Control and Prevention (CDC), for providing both logistical and technical assistance that enabled the integration of NCD/HIV services across the Teso region. We also recognize the dedication of facility-based teams whose commitment to service delivery made this data generation possible.

Data Availability

The study dataset has been attached as a supplementary material

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HIV Viral Load Suppression and Associated Factors Among PLHIV with Non-Communicable Diseases in Uganda’s Teso Region: A Cross-sectional Retrospective Study

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Abstract

Background:

Methods:

Results:

Conclusion:

Figures

Background

Methodology

Study Design and Setting

Study Population

Measurements

Data Sources and Variables

Statistical Analysis

Descriptive Analysis and Bivariate Analysis

Multivariate Analysis

Ethical Considerations

Results

Descriptive Characteristics of the Study Population

Participant Characteristics

Multivariate Analysis of Factors Associated with Viral Load Suppression

Factors Associated with Viral Load Suppression Among PLHIV with NCDs

NCD Status

Health Facility Level

Sex

Age Group

Discussion

Strengths and Limitations

Strengths

Limitations

Conclusion and Policy Implications

Abbreviations

Declarations

Funding Statement

Author Contribution

Acknowledgement

Data Availability

References

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