Factors associated with mortality among people with advanced HIV disease in rural Uganda: a retrospective study

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This study investigated factors associated with mortality among PLHIV with AHD in rural North-Central Uganda from January 2018 to December 2021. Methods : We conducted a retrospective review of electronic medical records from 18 health facilities, and obtained data on patient demographics and clinical characteristics including baseline CD4 count, baseline ART regimen, current ART regimen, ART adherence, body mass index (BMI), tuberculosis (TB) status, TB preventive therapy (TPT) use, WHO clinical stage and viral load status. AHD was defined as CD4 cell count <200 cells/mm 3 . A Cox proportional hazard model was fitted to identify factors associated with mortality among individuals with AHD. Factors were summarized by adjusted hazard ratios (aHRs) with their 95% confidence intervals (CIs) and considered statistically significant at 5%. Results : 1,161 PLHIV with AHD records were analyzed, contributing 1,565.56 person-years (pya). Of these, 84 (7.2%) deaths were reported, equivalent to a mortality rate of 5.37 deaths per 100 pya (95% CI: 4.33–6.64). Factors significantly associated with mortality included age ≥50 years (aHR=4.16, 95% CI: 1.77–9.77,), never having had a viral load test (aHR=16.23, 95% CI: 7.44–35.39), viral load non-suppression (≥1000 copies/ml) (aHR=9.05, 95% CI: 3.37–24.29,), baseline CD4 count ≤50 (aHR=1.91, 95% CI: 1.08–3.39,), never having taken TB prophylaxis (aHR=3.51, 95% CI: 1.83–6.74) and WHO stage 3 or 4 (aHR=1.91, 95% CI: 1.12–3.27). Conclusion : Key predictors of mortality among patients with AHD were older age, absence of tuberculosis preventive therapy, CD4 ≤50 cells/mm³, viral load non-suppression, and WHO clinical stages 3–4. Interventions targeting early identification of AHD, routine viral load monitoring, ART optimization and adherence support, and universal TB preventive therapy—alongside close patient follow-up—are essential to reduce mortality and improve outcomes, contributing to HIV epidemic control by 2030. Mortality HIV advanced HIV disease Uganda viral load tuberculosis adherence Background Acquired immunodeficiency syndrome (AIDS)-related deaths have decreased globally, however, the Human immunodeficiency virus (HIV) pandemic still claimed approximately 630,000 lives in 2022(1–3). Sub-Saharan Africa, which has 67% of all the people living with HIV (PLHIV), had over 60.3% AIDS-related deaths in 2022(1). East and Southern Africa reported 41.3% of deaths(1), of which Uganda had 2.7%(4). In sub-Saharan Africa, among the 20%-25% of PLHIV starting ART with severe immunosuppression (CD4 < 100cells/mm 3 ), the mortality rate was approximately 10%(5,6), with high early mortality often reported in the first six months of Antiretroviral Therapy (ART) initiation for PLHIV with advanced HIV disease (AHD)(7,8). The World Health Organization (WHO) defines AHD as a CD4 count below 200 cells/mm³ or WHO stage 3 or 4 in adults and adolescents. All children under five years old are classified as having AHD(9). Several risk factors are known to predispose PLHIV to high mortality, including low CD4 cell count, high viral load levels, recent fever, low body mass index (BMI), clinical depression, WHO clinical stage 3 and 4, higher hemoglobin levels, non-optimized ART regimen, and opportunistic infections(10,11). However, while these studies have primarily focused on the general population of PLHIV, they have not sufficiently addressed factors associated with mortality among PLHIV with AHD. With the release of WHO’s 2016 test and treat policy(12), many PLHIV are enrolled on ART early in the disease course, with the benefits of reducing mortality rates(13). Nevertheless, between September 2022 and March 2023, among HIV patients aged 15 to 24 years at a major hospital in Sierra Leone, the prevalence of AHD was 51.5% for outpatients and 39.3% for inpatients(14). Additionally, a systematic meta-analysis from South Africa found that the pooled prevalence of AHD was 43.45% among ART-naive patients and 58.6% among ART-experienced patients(15). In 2018, Uganda implemented an updated HIV care package, specifically targeting individuals with AHD to reduce morbidity and mortality within this sub-population. The guidelines recommended that the components of the care package for PLHIV with AHD include interventions for screening, prophylaxis, and treatment for opportunistic conditions, rapid ART initiation, and enhanced adherence support. This initiative was supported by the United States (U.S) President's Emergency Plan for AIDS Relief (PEPFAR) and implemented through the U.S. Centers for Disease Control and Prevention (CDC)(16). This study aimed to identify factors associated with mortality among PLHIV with AHD in the North-Central region of Uganda from January 2018 to December 2021. Understanding factors associated with mortality is important in informing mitigation strategies for reducing mortality among PLHIV with AHD. Methods Study design and setting This study involved a retrospective review of HIV programmatic data from 18 rural public health facilities across 3 districts (Luweero, Kyankwanzi, and Kiboga) within the Mubende Region in North-Central Uganda. The selection criteria for these facilities included i) the availability of comprehensive electronic medical record (EMR) systems and ii) the quality of recorded data. (i.e., completeness and accuracy of HIV diagnosis, ART initiation date, CD4 status, ART status, and mortality outcome data, verified using PEPFAR’s Data Quality Assessment (DQA) framework) (17). Hospitals and Health Centre IVs (HCIVs) were included due to their referral nature, while Health Centre IIIs (HCIIIs) were chosen based on the availability and completeness of their EMR data. HCIII facilities each cater to a sub-county with around 20,000 residents, managing community health workers and lower-level facilities. In contrast, HCIV/District Hospitals serve a county of roughly 100,000 people, offering comprehensive services including inpatient care with distinct wards for men, women, and children; an emergency surgery operating theatre; and a blood transfusion service(18). Study population This study included only people aged ≥ 5 years, diagnosed with AHD, defined as those with a baseline CD4 count ≤ 200 at ART initiation, between January 2018 and December 2021, with follow-up until December 2021. People with missing data on key study variables (facility name, age, baseline CD4 count, baseline and current ART regimen, ART adherence, date of death) were excluded. In this study, we included only newly diagnosed PLHIV who were initiating ART with a documented baseline CD4 count, allowing us to apply the WHO immunological definition of AHD (CD4 < 200 cells/mm³). We prioritized CD4 measurements over WHO clinical staging (Stages 3 or 4) to enhance consistency and objectivity, since clinical staging can be subjective and is known to vary with healthcare provider expertise and diagnostic capacity in resource-limited settings such as Uganda (19). Moreover, a systematic review and meta-analysis of sub-Saharan African cohorts found considerable heterogeneity in the performance of WHO Stage 3/4 criteria—sensitivity ranged from 20 % to 83 % and specificity from 63 % to 100 %—underscoring the limitations of relying on clinical signs for identifying advanced disease (20). Data collection This study utilized secondary data abstracted from EMRs at the 18 participating facilities. A data abstraction tool was developed based on the Consolidated Guidelines for Prevention and Treatment of HIV in Uganda(16). The tool was piloted on electronic data from the Mityana and Kassanda districts, refined to capture study-specific data, and then used to extract information from the EMR. Study variables The outcome variable for this study was all-cause mortality. The independent variables included both demographics and clinical patient characteristics. The demographic variables were age at ART initiation, sex, and health facility level. The clinical characteristics considered were baseline CD4, baseline WHO clinical stage, baseline BMI, baseline ART regimen, ART regimen at the end of the follow-up period, TB preventive therapy (TPT) use, and most recent viral load status (suppressed: ≤1000 copies/mL, non-suppressed: ≥ 1000 copies/mL, no viral load record)(21). People were categorized as using TPT (completed or currently taking TPT) or non-users. Statistical analysis Data were analyzed using the STATA 14.0 analysis software (College Station, Texas, U.S.A.). For descriptive statistics, categorical variables were summarized using proportions and frequencies. Means and standard deviations (SD) were reported for normally distributed continuous variables; for non-normally distributed continuous variables, medians and interquartile ranges (IQR) were reported. To determine the factors associated with mortality among persons with AHD, Cox proportional hazards models were fitted considering time to death as the event of interest. Individuals who were reported as lost to follow-up or transferred out by the study endline (December 2021) or followed up to the endline were right censored during the analysis. As AHD was screened at ART initiation, each patient's person-time of observation was estimated starting from the date of initiating ART and continued until the earliest occurrence of either death or censoring. Hazard ratios (HRs) and their 95% confidence intervals (CIs) were used to estimate the effect of predictor variables on the outcome of interest. The mortality rate was estimated as the number of deaths reported among PLHIV divided by the total person-time at risk and presented as deaths per 100 person-years. The association of each variable with mortality was independently assessed using bivariate level Cox proportional hazards (PH) models. Variables with a p-value of <0.2 in the bivariate analysis were considered for the multivariable analysis. Variables that remained statistically significant at a p-value of <0.05 were considered as factors associated with mortality. Results Characteristics of study participants A total of 14,089 ART-naïve people were diagnosed with HIV between January 2018 and December 2021. Among them, 4,993 (35.4 %) were tested for CD4 count, and among these, 1,238 (24.8 %) were identified with CD4 ≤ 200 cells/mm 3. A total of 1,161 (93.8%) PLHIV with AHD with data on key variables were included in the analysis. The median age was 35 years (IQR: 29-48); the majority (611, 52.6%) were female. The median baseline CD4 cell count was 94 cells/mm 3 (IQR: 42-150), with 47.6% (n=553) of individuals having CD4 counts ranging between 101-200 cells/mm3. WHO stages 1, T1, or T2 were observed in 90.0% of the participants. Additionally, 52.9% of individuals had a BMI between 18.6-24.9 kg/m². A large proportion of the participants (513, 44.2%) were diagnosed in hospital facilities. By the end of the follow-up, most PLHIV with AHD (1,011, 87.1%) were on a Dolutegravir (DTG)-based regimen, while a smaller proportion (21, 1.8%) were on a PI-based regimen. Additionally, most (663, 57.1%) people had a record of having completed a course of TPT. Regarding TB status at the most recent follow-up period, 1,021 (87.9%) showed no symptoms of TB ( Table 1). Table 1. Characteristics of persons living with Advanced HIV Disease in North-Central Uganda, 2018-2021 Characteristic Frequency (%) Number of PLHIV with CD4 4,993 (35.4%) Total number of PLHIV with AHD 1161 (93.8%) Sex • Female 611 (52.6%) • Male 550 (47.4%) Age Group (years) • 5–19 28 (2.4%) • 20–29 267 (23.0%) • 30–49 692 (59.6%) • ≥50 174 (15.0%) Median age (IQR) 35 (29–48) Facility Level • Health Center III 462 (39.8%) • Health Center IV 186 (16.0%) • Hospital 513 (44.2%) Baseline CD4 cell count (cells/mm³) • 101–200 553 (47.6%) • 51–100 266 (22.9%) • ≤50 342 (29.5%) Documented WHO Stage • Stage 1, T1, T2 1,045 (90.0%) • Stage 3, 4, T3 116 (10.1%) Baseline BMI (kg/m²)* • ≤18.5 135 (11.8%) • 18.6–24.9 593 (51.9%) • 25.0–29.9 278 (24.3%) • ≥30.0 136 (11.9%) • Missing 19 (1.6%) Baseline ART Regimen • DTG‑based 745 (64.2%) • NNRTI‑based 404 (34.8%) • PI‑based 12 (1.0%) Endline ART Regimen • DTG‑based 1,011 (87.1%) • NNRTI‑based 129 (11.1%) • PI‑based 21 (1.8%) Most recent Viral Load Status • No viral load recorded 449 (38.8%) • Non‑suppressed 41 (3.5%) • Suppressed 671 (57.8%) Documented TB Preventive Therapy • TPT – started 99 (8.5%) • TPT – completed 663 (57.1%) • Non‑TPT use 399 (34.4%) Survival Status (Outcome) • Active 752 (64.9%) • Died 84 (7.2%) • LTFU 123 (10.7%) • Transferred out 202 (17.4%) Note: T1, T2, T3: patient is on ART treatment, EFV Efavirenz, DTG: Dolutegravir, PI: protease inhibitor, NVP, Nevirapine, TPT, TB preventive therapy, TB: Tuberculosis, WHO, World Health Organization: BMI, body mass index; NNRTI, Non-nucleotide reverse transcriptase inhibitor, ART, Antiretroviral therapy; LFTU, loss to follow-up, *only (98.4%, 1142) PLHIV with AHD had baseline BMI information Mortality rate among people with AHD In this study, a total of 1,161 PLHIV with AHD were followed for a median of 13.6 months (IQR 5.0 – 25.8) contributing 1,566 person-years (pya) of follow-up. Eighty-four (7.2%) deaths occurred throughout the study period, the overall mortality rate among individuals was 5.37 per 100 person-years (pya). Mortality rates varied significantly across different characteristics. Older individuals (≥ 50 years) had a higher mortality rate (7.92 per 100 pya) compared to younger age groups. Males exhibited a higher mortality rate (7.02 per 100 pya) than females (3.93 per 100 pya). Advanced WHO clinical stages (Stages 3 and 4) were associated with a markedly increased mortality rate of 34.29 per 100 pya, in contrast to earlier stages (Stage 1, T1, and T2), which had a rate of 3.89 per 100 pya. Mortality rates also varied by baseline ART regimen, with DTG-based regimens showing a rate of 7.37 per 100 pya and NNRTI-based regimens showing a lower rate of 3.26 per 100 pya. Lower baseline CD4 counts were associated with higher mortality rates, with counts below 50 showing a rate of 9.23 per 100 pya and counts between 51-100 showing a rate of 6.11 per 100 pya, compared to counts between 101-200, which had a rate of 2.92 per 100 pya. A lower mortality was observed among individuals with viral load suppression (0.78 per 100 pya) compared to non-suppressed individuals (12.85 per 100 pya). Additionally, individuals who completed TPT had a lower mortality rate of 1.47 per 100 pya compared to those who did not. BMI and facility level also impacted mortality rates, with underweight individuals having a higher mortality rate of 11.32 per 100 pya compared to those with normal or higher BMI. These findings underscore the complex interplay of clinical, demographic, and treatment-related factors in determining mortality among this population (Table 2). Table 2 Distribution of mortality rates of persons living with Advanced HIV Disease in North-Central Uganda by participant characteristics, 2018-2021 Characteristic Number of deaths Mortality rate per 100 pya (95%CI) Overall 84 5.37 [4.33, 6.64] Age group (years) ≥ 50 21 7.92 [5.16, 12.14] 30 - 49 52 5.58 [4.25, 7.32] 20 - 29 9 2.74 [1.43, 5.26] 5 - 19 2 5.08 [1.27, 20.30] Sex Female 33 3.93 [2.80, 5.53] Male 51 7.02 [5.34, 9.24] Documented WHO stage Stage 1, T1, T2 58 3.89 [3.01, 5.04] Stage 3,4, T3 26 34.29 [23.35, 50.36] Baseline ART DTG-based 55 7.37 [5.66, 9.60] NNRTI - based 26 3.26 [2.22, 4.79] PI -based 3 13.91 [4.49, 43.14] Baseline CD4 Count ( cells/mm³) 101 - 200 23 2.92 [1.94, 4.39] 51-100 21 6.11 [3.98, 9.37] ≤ 50 40 9.23 [6.77, 12.58] Documented TB Preventive Therapy. TPT – started 4 4.40 (1.65 to 11.71) TPT - Completed 18 1.47 (0.92 to 2.33) Non-TPT use 62 25.13 [19.60, 32.24] Current ART Regimen DTG - based 59 4.10 [3.18, 5.29] NNRTI - based 21 24.06 [15.69, 36.90] PI-based 4 10.16 [3.81, 27.07] Most recent Viral Load Status Suppressed 10 0.78 [0.42, 1.45] Non-suppressed 9 12.85 [6.68, 24.69] No viral load recorded 65 30.36 [23.81, 38.72] Baseline BMI a (kg/m²) ≤ 18.5 19 11.32 [7.22, 17.75] 18.6 - 24.9 28 3.35 [2.32, 4.86] 25.0 - 29.9 18 4.80 [3.03, 7.62] ≥ 30.0 14 7.75 [4.59, 13.09] Facility level Hospital 35 4.80 [3.45, 6.69] Health Center IV 17 7.14 [4.44, 11.48] Health Center III 32 5.35 [3.78, 7.56] Note: T1, T2, T3: patient is on ART treatment, EFV Efavirenz, DTG: Dolutegravir, PI: protease inhibitor, NVP, Nevirapine, TPT, TB preventive therapy, TB: Tuberculosis, WHO, World Health Organization: BMI, body mass index; NNRTI, Non-nucleotide reverse transcriptase inhibitor, ART, Antiretroviral therapy; LFTU, loss to follow-up BMI a - Note: Only 1142 PLHIV with AHD had baseline BMI information, including 79 patients who died. Factors associated with mortality among PLHIV with AHD Factors associated with mortality in PLHIV with AHD are presented in Table 3 . Individuals aged ≥50 years had a significantly higher mortality risk compared to those aged 20-29 years (aHR=4.16, 95% CI: 1.77–9.77, p=0.001). People with a baseline CD4 count ≤50 cells/mm³ also faced an elevated mortality risk (aHR=1.91, 95% CI: 1.08–3.39, p=0.027). Non-use of TPT was strongly associated with increased mortality (aHR=3.51, 95% CI: 1.83–6.74, p<0.001). Individuals at WHO Stage 3 or 4 had a higher mortality risk compared to those at lower stages (aHR=1.91, 95% CI: 1.12–3.27, p=0.018). In terms of viral load, non-suppressed individuals had a markedly higher mortality risk (aHR=9.05, 95% CI: 3.37–24.29, p<0.001), and those with no viral load testing had the highest risk (aHR=16.23, 95% CI: 7.44–35.39, p<0.001). Table 3: Unadjusted and adjusted effects of participant characteristics on mortality rate among persons living with Advanced HIV Disease Characteristics Crude HR (95% CI) P - value Adjusted HR (aHR) (95% CI) P – value Age Group (Years) 5-19 2.05 (0.44 – 9.48) 0.360 2.62 (0.53 – 12.91) 0.236 20 - 29 1 30 – 49 2.10 (1.03 – 41.26) 0.040* 1.75 (0.83 – 3.71) 0.142 ≥ 50 3.11 (1.43 – 6.80) 0.004* 4.16 (1.77 – 9.77) 0.001* Sex Female 1 Male 1.73 (1.11 - 2.68) 0.014* 1.21 (0.75 – 1.96) 0.435 Documented WHO Stage Stage 1, T1, T2 1 Stage 3,4, T3 6.77 (4.22 - 10.86) <0.001* 1.91 (1.12 – 3.27) 0.018* Baseline CD4 Count (cells/mm³) 101 - 200 1 51-100 2.00 (1.10 - 3.61) 0.022* 1.71 (0.91 – 3.21) 0.094 ≤ 50 3.03 (1.81 - 5.06) <0.001* 1.91 (1.08 – 3.39) 0.027* Documented TB Preventive Therapy. TPT - Completed 1 TPT – Started 2.64 [0.88, 7.88] 0.082 2.11 (0.68 – 6.49) 0.194 Non-TPT Use 14.43 [8.33, 25.00] <0.001* 3.51 (1.83 – 6.74) <0.001* Current ART Regimen DTG-Based 1 NNRTI-Based 4.66 (2.82 - 7.71) 0.051* 1.60 (0.56 – 4.61) 0.384 PI-Based 2.75 (1.00 - 7.59) <0.001* 1.07 (0.59 – 1.95) 0.819 Most recent Viral Load Status Suppressed 1 Non-Suppressed 16.40 (6.66 - 40.39) <0.001* 9.05 (3.37 – 24.29) <0.001* No Viral Load 34.17 (17.08- 68.37) <0.001* 16.23 (7.44 – 35.39) <0.001* Baseline BMI (kg/m²) 18.6 - 24.9 1 ≤ 18.5 3.26 (1.82 - 5.83) <0.001* 1.49 (0.80 – 2.79) 0.210 25.0 - 29.9 1.41 (0.78 - 2.55) 0.256 1.38 (0.74 – 2.58) 0.304 ≥30.0 2.27 (1.19 - 4.31) 0.012* 1.26 (0.63 – 2.52) 0.505 Facility Level Hospital 1 Health Center IV 1.39 (0.78 – 2.49) 0.262 Health Center III 1.05 (0.65 – 1.70) 0.837 Note: T1, T2, T3: patient is on ART treatment, EFV Efavirenz, DTG: Dolutegravir, PI: protease inhibitor, NVP, Nevirapine, TPT, TB preventive therapy, TB: Tuberculosis, WHO, World Health Organization: BMI, body mass index; NNRTI, Non-nucleotide reverse transcriptase inhibitor, ART, Antiretroviral therapy; LFTU, loss to follow-up Notes : The proportional-hazard assumption test based on the Global test derived from Schoenfeld residuals was insignificant (df = 16, ch2 = 17.72, p= 0.3403); * denotes a significant P-value. Discussion Our findings reveal that mortality among people living with HIV (PLHIV) with advanced HIV disease (AHD) in rural North-Central Uganda was significantly associated with several factors: age ≥50 years (aHR=4.16), never having undergone viral load testing (aHR=16.23), viral load non-suppression (≥1000 copies/mL; aHR=9.05), baseline CD4 count ≤50 cells/µL (aHR=1.91), never receiving TB prophylaxis (aHR=3.51), and presenting with WHO stage 3 or 4 disease (aHR=1.91). This study is one of the few that specifically investigates mortality determinants among PLHIV with AHD rather than among all PLHIV, marking a significant contribution to our understanding of outcomes in this high-risk group. The study showed that there were 84 deaths over 1,566 pya, with an overall mortality rate of 5.37 deaths per 100 pya. Our study showed a low mortality rate in PLHIV with AHD compared to other surveys done in Tanzania and China which showed mortality rates of 16 per 100 pya(22), and 163.1 per 100 pya (23) respectively. The low mortality rate found in this population may be attributed to the concerted efforts of HIV programs aimed at achieving the UNAIDS 95-95-95 treatment targets(24) such as improved uptake of the Test and Treat policy, improved retention rate on ART, and having a treatment supporter. The studies conducted in Tanzania (2013–2019) and China (2005–2020) spanned two periods: before and after implementing the Test and Treat policy. Both studies showed that the prevalence of AHD was significantly lower following the national adoption of the 2016 WHO Test and Treat guidelines. Specifically, Tanzania reported a prevalence of 66.8% before the policy, which declined after implementing the guidelines. Data from China also demonstrated a positive impact on HIV care and treatment outcomes after adopting the Treat All policy(25). Furthermore, the prevalence of AHD in both countries was notably higher than that observed in Uganda, where prevalence rates range from 15% to 30%(16). In contrast, Tanzania and China reported AHD prevalence ranging from 35% to 61% and 34%, respectively. In addition to being conducted during the era of the Test and Treat policy, this study benefited from the widespread adoption of well-optimized and simplified DTG-based regimens, which have positively impacted HIV viral suppression. This is evident in the high rates of viral suppression observed in Uganda (26). Although in this study, almost 40% of the patients did not have a VL test result. The North-Central rural districts are among the regions benefiting from PEPFAR-funded HIV program interventions, which have supported Uganda in developing and implementing HIV guidelines in alignment with WHO recommendations. For example, the 2016 Uganda Consolidated HIV Guidelines did not include any provisions for addressing AHD. By the time the 2020 Uganda HIV Guidelines were introduced, AHD had become a key focus in the effort to reduce the HIV/AIDS burden. The 2022 Uganda HIV/AIDS Consolidated Guidelines further emphasized the implementation of comprehensive care for PLHIV with AHD, aligning to achieve the 95-95-95 target by 2030 (16). Furthermore, the majority of people had documented WHO clinical stages 1 and 2, which could have partially contributed to the low mortality rates, and this is evidenced by the fact that those identified with stages 3 and 4 had a higher risk of mortality(22). The predominance of Stage 1 cases may reflect the achievements of the Test and Treat strategy, even in rural areas of Uganda where this study was conducted, enabling early HIV diagnosis and ART initiation before the onset of clinical symptoms. Limited access to diagnostics for detecting Stage 3 or 4 conditions may also have contributed to under-classification of disease severity, as reported in other studies where individuals with CD4 counts <100 cells/mm³ were still categorized as Stage 1 or 2 (27). The identified predictors of mortality provide insights into the mechanisms underlying mortality risk in individuals with AHD. In this study, younger persons had a lower mortality risk than older persons, possibly due to stronger immune responses and overall better health in younger populations(28). Aging comes with comorbidities and deterioration of several physiological functions related to inflammation and systemic aging (29). These changes may contribute to the likely immune incompetence seen among elderly persons with AHD and a possible increase in mortality risk in the aging population. Further, the impact of different ART regimens on mortality risk underscores the importance of ART optimization. Maximizing ART efficacy through DTG-based regimens, as guided by current recommendations, is crucial while recognizing that PI-based regimens may still be necessary for specific clinical scenarios. Rapid ART initiation, age-specific ART optimization, and index case testing are fundamental aspects of PEPFAR policy, with ART being the most critical intervention to prevent AHD-related mortality. Our findings underscore the potential benefit of rapid initiation or re-initiation of ART using regimens that effectively reduce viral load and exhibit minimal side effects, particularly those based on integrase inhibitors, in possibly reducing advanced HIV disease and enhancing health outcomes among PLHIV (30,31). Although these regimens may increase the risk of immune reconstitution inflammatory syndrome (IRIS), the evidence on its severity and outcomes is mixed. Nevertheless, due to their effectiveness, integrase inhibitor-based regimens are being widely adopted in PEPFAR programs (32). Our findings underscore that achieving viral load suppression is crucial for lowering mortality risk, as individuals with unsuppressed and those with no viral load test exhibited higher mortality hazards. Suboptimal adherence to HIV treatment can have far-reaching consequences, including treatment failure and compromised viral suppression(33), as well as increasing the risk of resistance to the prescribed treatment regimen(34). Most of the deaths in our study occurred within the first months of ART initiation, highlighting a critical limitation in Uganda’s current viral load testing algorithm, which recommends testing at 6 months post-ART initiation. This poses significant risks for people with AHD, who are particularly vulnerable to early mortality. The HIV.gov Clinical Guidelines (2023) (35) recommend more frequent viral load monitoring, such as at 1–3 months, for high-risk patients like those with AHD, to identify treatment failure or non-adherence and enable timely interventions. In tandem, Ford et al. (2018) found that mortality in AHD patients often occurs very soon after ART initiation, reinforcing the urgency of earlier monitoring(19). Uganda has made significant strides toward the UN 95-95-95 targets, with 92% of PLHIV knowing their status, 84% receiving sustained ART, and 79% achieving viral suppression as of 2023 (36). These figures indicate that Uganda’s healthcare system is performing strongly in identifying PLHIV and linking them to care. However, gaps remain in ensuring universal ART coverage and viral suppression—key factors in preventing PLHIV from progressing to AHD or dying from AIDS-related complications. Progress toward these targets is particularly critical for vulnerable populations, where viral suppression rates lag the national average. In Uganda, where testing infrastructure may be constrained, adhering to a 6-month testing schedule for all patients may miss critical windows for intervention, potentially contributing to preventable deaths. Adjusting national guidelines to prioritize earlier VL testing for AHD patients could significantly improve outcomes and optimize HIV care. This aligns with several other studies that highlight effective adherence to ART as essential for achieving viral suppression, preventing disease progression, and reducing mortality.(33,37–40). Non-use of TPT was significantly associated with increased mortality risk in this study population (aHR=3.51), highlighting its potential importance in improving outcomes among PLHIV with AHD. A mortality rate of 25.13 per 100 person-years was noted among PLHIV who had no documented use of TPT. This observation aligns with similar studies in Ethiopia, reporting high mortality rates among PLHIV people who were not receiving isoniazid prophylactic therapy(41). The delay in initiating TPT, as recommended by Uganda’s HIV guidelines, to allow time for the potential unmasking of TB (16), may contribute to episodes of opportunistic infections and adverse reactions that may result in the death of affected persons(42,43). This study highlights the potential impact of TPT as part of a comprehensive HIV care package to lower the risk of TB and death, particularly for individuals with AHD. Given the heightened mortality risk in individuals with AHD due to compromised immunity, addressing barriers to TPT utilization could improve health outcomes among PLHIV. While our findings provide valuable insights into mortality predictors among PLHIV with AHD in the rural North-Central region of Uganda, they may be relevant in other contexts, such as similar rural settings across sub-Saharan Africa. Strengths of the Study The strengths of our study included the large sample size, multi-center design, the long follow-up period, and the use of a rigorous statistical analytical approach, enabling generalizability of the study findings. The study also benefits from a robust evaluation of diverse clinical predictors of mortality in individuals with AHD, providing valuable insights into the understanding of the mechanisms underlying AHD. Another key strength is that this study specifically examines mortality rates among PLHIV with AHD, rather than all PLHIV, offering a focused insight into this high-risk group. Study limitations The study had several limitations, including its retrospective design and reliance on secondary data, which may have introduced information bias due to the exclusion of records with incomplete key variables. Additionally, all-cause mortality was determined without identifying specific causes of death, potentially leading to an overestimation of HIV-related mortality. At the same time, the lack of follow-up for missing records could have resulted in underestimation. The inclusion criteria, based solely on baseline CD4 counts, may have missed PLHIV with AHD who presented with WHO stage 3 or 4 clinical features but lacked a CD4 count at ART initiation, possibly underestimating the true burden of AHD and affecting the observed association between CD4 and mortality, as individuals without baseline measurements might have had similar or worse outcomes but were not captured. Furthermore, children under five years were excluded despite their high mortality rates, though they are presumed to have AHD. A significant proportion of patients were diagnosed at hospitals, suggesting they may have been sicker at presentation. Yet, the absence of a markedly higher rate implies other underlying factors may have influenced outcomes. Moreover, antiretroviral therapy adherence data were not properly captured and thus had to be excluded from the analysis, which may have introduced residual confounding into our hazard estimates. Finally, the study period coincided with the COVID-19 pandemic, but its impact on AHD diagnosis and mortality could not be assessed due to data limitations, highlighting an important area for future research. Conclusion Key risk factors for mortality among persons with AHD in this study included older age, non-use of TPT, a CD4 ≤ 50 cells/mm 3 viral load non-suppression, and WHO clinical stages 3 and 4. Despite the presence of these risk factors, the overall mortality rate in this population was relatively low. The identified predictors present opportunities for interventions to reduce mortality and continued close monitoring of patients to improve survival among people with AHD. Future studies may consider exploring causes of mortality among people with AHD through post-mortem studies and using non-invasive methods such as verbal mortality audits. In addition, investigating psychosocial, socioeconomic, and facility-related factors associated with mortality in PLHIV with AHD may provide useful insights into strategies to further reduce deaths in this vulnerable population. Abbreviations AHD - Advanced HIV Disease, AIDS - Acquired Immunodeficiency Syndrome, ART - Antiretroviral Therapy, BMI - Body Mass Index, CDC - Centers for Disease Control and Prevention, CD4 - Cluster of Differentiation 4, DTG - Dolutegravir, EMR - Electronic Medical Records, ESA - Eastern & Southern Africa, HC - Health Centre, HIV - Human Immunodeficiency Virus, HR - Hazard Ratio, IRIS - Immune Reconstitution Inflammatory Syndrome, NNRTI - Non-Nucleoside Reverse Transcriptase Inhibitor, PEPFAR - President's Emergency Plan for AIDS Relief, PI - Protease Inhibitor, PLHIV - People Living with HIV, SD - Standard Deviation, SSA - Sub-Saharan Africa, TB - Tuberculosis, TPT - TB Preventive Therapy, and WHO - World Health Organization. Declarations Ethics approval and consent to participate This activity was reviewed and approved by Mildmay Uganda Institutional Review Board (No. 0804-2018), as well as the U.S Centers for Disease Control and Prevention (U.S CDC) under 45 C.F.R. part 46.101(c); 21 C.F.R. part 56. A waiver of consent was granted to utilize secondary data, because data were sourced from publicly accessible Electronic Medical Records (EMR) platform.. Consent for publication Not applicable Competing interests The authors declare that they have no competing interests. Funding This publication has been supported by the President's Emergency Plan for AIDS Relief (PEPFAR) through the U.S. Centers for Disease Control and Prevention (CDC) under the terms of grant number 1NU2GGH002046-01–00. The findings and conclusions in this manuscript are those of the authors and do not represent the official position of the funding agencies. Authors' contributions BK and JKN conceived the study. BK, JKN, IK, CB, CN, GW, JBB, JN, GN, JK, JNK, CS, RM, AGF and BM participated in study design and data collection. BK, IK, CNL, RN and AGF participated in data analysis. All authors participated in the writing and review of the manuscript. All authors read and approved the final manuscript. Availability of data and material The clinical data sets used and /or analyzed during this study are available from the corresponding author upon request. Acknowledgements We are grateful to all the patients whose data was used in the study. We thank Dr. Richard Muhindo for the insightful advice during the manuscript's writing and review. References The Global HIV/AIDS Epidemic [Internet]. KFF. 2023 [cited 2024 Jan 12]. Available from: https://www.kff.org/global-health-policy/fact-sheet/the-global-hiv-aids-epidemic/ Global HIV & AIDS statistics — Fact sheet [Internet]. [cited 2024 Jan 13]. Available from: https://www.unaids.org/en/resources/fact-sheet HIV [Internet]. [cited 2024 Aug 31]. 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Eduardo E, Lamb MR, Kandula S, Howard A, Mugisha V, Kimanga D, et al. Characteristics and Outcomes among Older HIV-Positive Adults Enrolled in HIV Programs in Four Sub-Saharan African Countries. Thorne C, editor. PLoS ONE. 2014 Jul 30;9(7):e103864. Chauvin M, Sauce D. Mechanisms of immune aging in HIV. Clin Sci. 2022 Jan 14;136(1):61–80. World Health Organization. Guidelines for managing advanced HIV disease and rapid initiation of antiretroviral therapy, July 2017 [Internet]. Geneva: World Health Organization; 2017 [cited 2025 Apr 19]. 56 p. Available from: https://iris.who.int/handle/10665/255884 Initiation of Antiretroviral Therapy | NIH [Internet]. 2019 [cited 2025 Apr 19]. Available from: https://clinicalinfo.hiv.gov/en/guidelines/hiv-clinical-guidelines-adult-and-adolescent-arv/initiation-antiretroviral-therapy Boyd AT, Oboho I, Paulin H, Ali H, Godfrey C, Date A, et al. Addressing advanced HIV disease and mortality in global HIV programming. AIDS Res Ther. 2020 Jul 10;17(1):40. Wakooko P, Gavamukulya Y, Wandabwa JN. Viral load Suppression and Associated Factors among HIV Patients on Antiretroviral Treatment in Bulambuli District, Eastern Uganda: A Retrospective Cohort Study. Infect Dis. 2020;13:1178633720970632. Press N, Tyndall MW, Wood E, Hogg RS, Montaner JSG. Virologic and Immunologic Response, Clinical Progression, and Highly Active Antiretroviral Therapy Adherence: JAIDS J Acquir Immune Defic Syndr. 2002 Dec;31:S112–7. Laboratory Testing: Plasma HIV-1 RNA (Viral Load) and CD4 Count Monitoring | NIH [Internet]. 2022 [cited 2025 Apr 22]. Available from: https://clinicalinfo.hiv.gov/en/guidelines/hiv-clinical-guidelines-adult-and-adolescent-arv/plasma-hiv-1-rna-cd4-monitoring Uganda | UNAIDS [Internet]. 2025 [cited 2025 Apr 22]. Available from: https://www.unaids.org/en/regionscountries/countries/uganda Nyaboke R, Ramadhani HO, Lascko T, Awuor P, Kirui E, Koech E, et al. Factors associated with adherence and viral suppression among patients on second-line antiretroviral therapy in an urban HIV program in Kenya. SAGE Open Med. 2023 Jan;11:205031212311623. Mwangi A, Van Wyk B. Factors Associated with Viral Suppression Among Adolescents on Antiretroviral Therapy in Homa Bay County, Kenya: A Retrospective Cross-Sectional Study. HIVAIDS - Res Palliat Care. 2021 Dec;Volume 13:1111–8. Maina EK, Mureithi H, Adan AA, Muriuki J, Lwembe RM, Bukusi EA. Incidences and factors associated with viral suppression or rebound among HIV patients on combination antiretroviral therapy from three counties in Kenya. Int J Infect Dis. 2020 Aug;97:151–8. De Olalla PG, Knobel H, Carmona A, Guelar A, López-Colomés JL, Caylà JA. Impact of Adherence and Highly Active Antiretroviral Therapy on Survival in HIV-Infected Patients: JAIDS J Acquir Immune Defic Syndr. 2002 May;30(1):105–10. Misgina KH, Weldu MG, Gebremariam TH, Weledehaweria NB, Alema HB, Gebregiorgis YS, et al. Predictors of mortality among adult people living with HIV/AIDS on antiretroviral therapy at Suhul Hospital, Tigrai, Northern Ethiopia: a retrospective follow-up study. J Health Popul Nutr. 2019 Dec;38(1):37. Sime T, Oljira L, Diriba A, Firdisa G, Gezimu W. Effect of active tuberculosis on the survival of HIV-infected adult patients who initiated antiretroviral therapy at public hospitals of Eastern Ethiopia: A retrospective cohort study. Samaranayaka A, editor. PLOS ONE. 2022 Oct 31;17(10):e0277021. Birhanu A, Dingeta T, Tolera M. Predictors of Mortality Among Adult HIV-Infected Patients Taking Antiretroviral Therapy (ART) in Harari Hospitals, Ethiopia. HIVAIDS - Res Palliat Care. 2021 Jul 2;13:727–36. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6328943","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":447161691,"identity":"e024a90e-77af-4aa3-ad84-fd648c8a066b","order_by":0,"name":"Bwogi Kabali","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYBACCQYeBoYHBgeATMbGBwwMB4jUkgDR0mxAghaISjYJorRItp89+CGh4E5iP//itmqemjty/AzMDx/dwKNFmicvWSLB4FnizBkP227zHHtmLNnAZmycg0eLHEOOAVDL4dwNNw4CtbAdTtxwgIdNGq8W/jfGP2Bainn+EaFFWiLHDGLL+cY2Zt42IrRIzniXZgH0S/3MGYzNknP7DhtLNhPwi8T53MM3Pvy5Y8zPf/zhhzffDsvxszc/fIxPC5LmBAYmHhCDmSjlIMB/gIHxB9GqR8EoGAWjYCQBAHocVnN67wcyAAAAAElFTkSuQmCC","orcid":"","institution":"Mildmay Uganda","correspondingAuthor":true,"prefix":"","firstName":"Bwogi","middleName":"","lastName":"Kabali","suffix":""},{"id":447161695,"identity":"8fa87b97-ea05-480b-9579-e83ffc619756","order_by":1,"name":"Catherine Nassozi Lwanira","email":"","orcid":"","institution":"Clarke International University","correspondingAuthor":false,"prefix":"","firstName":"Catherine","middleName":"Nassozi","lastName":"Lwanira","suffix":""},{"id":447161697,"identity":"c0804fdc-bc67-4e1e-b71d-8ab7223ee11a","order_by":2,"name":"Ivan Kasamba","email":"","orcid":"","institution":"Mildmay Uganda","correspondingAuthor":false,"prefix":"","firstName":"Ivan","middleName":"","lastName":"Kasamba","suffix":""},{"id":447161698,"identity":"d219cee1-1040-4e76-b6dc-fc176e9ec8bd","order_by":3,"name":"Joseph Baruch Baluku","email":"","orcid":"","institution":"Mildmay Uganda","correspondingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"Baruch","lastName":"Baluku","suffix":""},{"id":447161700,"identity":"dc45da06-3480-4b2b-ad39-d12fdcd16826","order_by":4,"name":"Justine K. 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Sub-Saharan Africa, which has 67% of all the people living with HIV (PLHIV), had over 60.3% AIDS-related deaths in 2022(1). East and Southern Africa reported 41.3% of deaths(1), of which Uganda had 2.7%(4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn sub-Saharan Africa, among the 20%-25% of PLHIV starting ART with severe immunosuppression (CD4 \u0026lt; 100cells/mm\u003csup\u003e3\u003c/sup\u003e), the mortality rate was approximately 10%(5,6), with high early mortality often reported in the first six months of Antiretroviral Therapy (ART) initiation for PLHIV with advanced HIV disease (AHD)(7,8). The World Health Organization (WHO) defines AHD as a CD4 count below 200 cells/mm\u0026sup3; or WHO stage 3 or 4 in adults and adolescents. All children under five years old are classified as having AHD(9).\u003c/p\u003e\n\u003cp\u003eSeveral risk factors are known to predispose PLHIV to high mortality, including low CD4 cell count, high viral load levels, recent fever, low body mass index (BMI), clinical depression, WHO clinical stage 3 and 4, higher hemoglobin levels, non-optimized ART regimen, and opportunistic infections(10,11). However, while these studies have primarily focused on the general population of PLHIV, they have not sufficiently addressed factors associated with mortality among PLHIV with AHD.\u0026nbsp;With the release of WHO\u0026rsquo;s 2016 test and treat policy(12), many PLHIV are enrolled on ART early in the disease course, with the benefits of reducing mortality rates(13). Nevertheless, between September 2022 and March 2023, among HIV patients aged 15 to 24 years at a major hospital in Sierra Leone, the prevalence of AHD was 51.5% for outpatients and 39.3% for inpatients(14). Additionally, a systematic meta-analysis from South Africa found that the pooled prevalence of AHD was 43.45% among ART-naive patients and 58.6% among ART-experienced patients(15).\u003c/p\u003e\n\u003cp\u003eIn 2018, Uganda implemented an updated HIV care package, specifically targeting individuals with AHD to reduce morbidity and mortality within this sub-population. The guidelines recommended that the components of the care package for PLHIV with AHD include interventions for screening, prophylaxis, and treatment for opportunistic conditions, rapid ART initiation, and enhanced adherence support. This initiative was supported by the United States (U.S) President\u0026apos;s Emergency Plan for AIDS Relief (PEPFAR) and implemented through the U.S. Centers for Disease Control and Prevention (CDC)(16). This study aimed to identify factors associated with mortality among PLHIV with AHD in the North-Central region of Uganda from January 2018 to December 2021. Understanding factors associated with mortality is important in informing mitigation strategies for reducing mortality among PLHIV with AHD.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and setting \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study involved a retrospective review of HIV programmatic data from 18 rural public health facilities across 3 districts (Luweero, Kyankwanzi, and Kiboga) within the Mubende Region in North-Central Uganda. \u0026nbsp;The selection criteria for these facilities included i) the availability of comprehensive electronic medical record (EMR) systems and ii) the quality of recorded data. (i.e., completeness and accuracy of HIV diagnosis, ART initiation date, CD4 status, ART status, and mortality outcome data, verified using PEPFAR\u0026rsquo;s Data Quality Assessment (DQA) framework) (17). Hospitals and Health Centre IVs (HCIVs) were included due to their referral nature, while Health Centre IIIs (HCIIIs) were chosen based on the availability and completeness of their EMR data. \u0026nbsp;HCIII facilities each cater to a sub-county with around 20,000 residents, managing community health workers and lower-level facilities. In contrast, HCIV/District Hospitals serve a county of roughly 100,000 people, offering comprehensive services including inpatient care with distinct wards for men, women, and children; an emergency surgery operating theatre; and a blood transfusion service(18).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study included only people aged \u0026ge; 5 years, diagnosed with AHD, defined as those with a baseline CD4 count \u0026le; 200 at ART initiation, between January 2018 and December 2021, with follow-up until December 2021. People with missing data on key study variables (facility name, age, baseline CD4 count, baseline and current ART regimen, ART adherence, date of death) were excluded. In this study, we included only newly diagnosed PLHIV who were initiating ART with a documented baseline CD4 count, allowing us to apply the WHO immunological definition of AHD (CD4\u0026nbsp;\u0026lt;\u0026nbsp;200\u0026nbsp;cells/mm\u0026sup3;). We prioritized CD4 measurements over WHO clinical staging (Stages\u0026nbsp;3 or\u0026nbsp;4) to enhance consistency and objectivity, since clinical staging can be subjective and is known to vary with healthcare provider expertise and diagnostic capacity in resource-limited settings such as Uganda (19). Moreover, a systematic review and meta-analysis of sub-Saharan African cohorts found considerable heterogeneity in the performance of WHO Stage\u0026nbsp;3/4 criteria\u0026mdash;sensitivity ranged from 20\u0026nbsp;% to 83\u0026nbsp;% and specificity from 63\u0026nbsp;% to 100\u0026nbsp;%\u0026mdash;underscoring the limitations of relying on clinical signs for identifying advanced disease (20).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eData collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized secondary data abstracted from EMRs at the 18 participating facilities.\u003c/p\u003e\n\u003cp\u003eA data abstraction tool was developed based on the Consolidated Guidelines for Prevention and Treatment of HIV in Uganda(16). The tool was piloted on electronic data from the Mityana and Kassanda districts, refined to capture study-specific data, and then used to extract information from the EMR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe outcome variable for this study was all-cause mortality. \u0026nbsp;The independent variables included both demographics and clinical patient characteristics. The demographic variables were age at ART initiation, sex, and health facility level. The clinical characteristics considered were baseline CD4, baseline WHO clinical stage, baseline BMI, baseline ART regimen, ART regimen at the end of the follow-up period, TB preventive therapy (TPT) use, and most recent viral load status (suppressed: \u0026le;1000 copies/mL, non-suppressed: \u0026ge; 1000 copies/mL, no viral load record)(21). \u0026nbsp;People were categorized as using TPT (completed or currently taking TPT) or non-users.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were analyzed using the STATA 14.0 analysis software (College Station, Texas, U.S.A.). For descriptive statistics, categorical variables were summarized using proportions and frequencies. Means and standard deviations (SD) were reported for normally distributed continuous variables; for non-normally distributed continuous variables, medians and interquartile ranges (IQR) were reported.\u003c/p\u003e\n\u003cp\u003eTo determine the factors associated with mortality among persons with AHD, Cox proportional hazards models were fitted considering time to death as the event of interest. Individuals who were reported as lost to follow-up or transferred out by the study endline (December 2021) or followed up to the endline were right censored during the analysis. As AHD was screened at ART initiation, each patient\u0026apos;s person-time of observation was estimated starting from the date of initiating ART and continued until the earliest occurrence of either death or censoring. Hazard ratios (HRs) and their 95% confidence intervals (CIs) were used to estimate the effect of predictor variables on the outcome of interest. The mortality rate was estimated as the number of deaths reported among PLHIV divided by the total person-time at risk and presented as deaths per 100 person-years. The association of each variable with mortality was independently assessed using bivariate level Cox proportional hazards (PH) models. Variables with a p-value of \u0026lt;0.2 in the bivariate analysis were considered for the multivariable analysis. Variables that remained statistically significant at a p-value of \u0026lt;0.05 were considered as factors associated with mortality.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCharacteristics of study participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 14,089 ART-na\u0026iuml;ve people were diagnosed with HIV between January 2018 and December 2021. Among them, 4,993 (35.4 %) were tested for CD4 count, and among these, 1,238 (24.8 %) were identified with CD4 \u0026le; 200 cells/mm\u003csup\u003e3.\u003c/sup\u003e A total of 1,161 (93.8%) PLHIV with AHD with data on key variables were included in the analysis. The median age was 35 years (IQR: 29-48); the majority (611, 52.6%) were female. The median baseline CD4 cell count was 94 cells/mm\u003csup\u003e3\u003c/sup\u003e (IQR: 42-150), with 47.6% (n=553) of individuals having CD4 counts ranging between 101-200 cells/mm3. WHO stages 1, T1, or T2 were observed in 90.0% of the participants. Additionally, 52.9% of individuals had a BMI between 18.6-24.9 kg/m\u0026sup2;. A large proportion of the participants (513, 44.2%) were diagnosed in hospital facilities. By the end of the follow-up, most PLHIV with AHD (1,011, 87.1%) were on a Dolutegravir (DTG)-based regimen, while a smaller proportion (21, 1.8%) were on a PI-based regimen. Additionally, most (663, 57.1%) people had a record of having completed a course of TPT. Regarding TB status at the most recent follow-up period, 1,021 (87.9%) showed no symptoms of TB (\u003cstrong\u003eTable 1).\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Characteristics of persons living with Advanced HIV Disease in North-Central Uganda, 2018-2021\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of PLHIV with CD4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4,993 (35.4%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal number of PLHIV with AHD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1161 (93.8%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; Female\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e611 (52.6%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; Male\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e550 (47.4%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Group (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; 5\u0026ndash;19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e28 (2.4%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; 20\u0026ndash;29\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e267 (23.0%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; 30\u0026ndash;49\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e692 (59.6%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; \u0026ge;50\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e174 (15.0%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian age (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e35 (29\u0026ndash;48)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFacility Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; Health Center\u0026nbsp;III\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e462 (39.8%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; Health Center\u0026nbsp;IV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e186 (16.0%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; Hospital\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e513 (44.2%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline CD4 cell count (cells/mm\u0026sup3;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; 101\u0026ndash;200\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e553 (47.6%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; 51\u0026ndash;100\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e266 (22.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; \u0026le;50\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e342 (29.5%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDocumented WHO Stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; Stage\u0026nbsp;1,\u0026nbsp;T1,\u0026nbsp;T2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1,045 (90.0%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; Stage\u0026nbsp;3,\u0026nbsp;4,\u0026nbsp;T3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e116 (10.1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline BMI (kg/m\u0026sup2;)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; \u0026le;18.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e135 (11.8%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; 18.6\u0026ndash;24.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e593 (51.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; 25.0\u0026ndash;29.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e278 (24.3%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; \u0026ge;30.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e136 (11.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; Missing\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e19 (1.6%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline ART Regimen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; DTG‑based\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e745 (64.2%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; NNRTI‑based\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e404 (34.8%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; PI‑based\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12 (1.0%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEndline ART Regimen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; DTG‑based\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1,011 (87.1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; NNRTI‑based\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e129 (11.1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; PI‑based\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e21 (1.8%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMost recent Viral Load Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; No viral load recorded\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e449 (38.8%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; Non‑suppressed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e41 (3.5%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; Suppressed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e671 (57.8%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDocumented TB Preventive Therapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; TPT\u0026nbsp;\u0026ndash; started\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e99 (8.5%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; TPT\u0026nbsp;\u0026ndash; completed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e663 (57.1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; Non‑TPT use\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e399 (34.4%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurvival Status (Outcome)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; Active\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e752 (64.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; Died\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e84 (7.2%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; LTFU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e123 (10.7%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 420px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026bull; Transferred out\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e202 (17.4%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: T1, T2, T3: patient is on ART treatment, EFV Efavirenz, DTG: Dolutegravir, PI: protease inhibitor, NVP, Nevirapine, TPT, TB preventive therapy, TB: Tuberculosis, WHO, World Health Organization: BMI, body mass index; NNRTI, Non-nucleotide reverse transcriptase inhibitor, ART, Antiretroviral therapy; LFTU, loss to follow-up, *only (98.4%, 1142) PLHIV with AHD had baseline BMI information\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMortality rate among people with AHD\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, a total of 1,161 PLHIV with AHD were followed for a median of 13.6 months (IQR 5.0 \u0026ndash; 25.8) contributing 1,566 person-years (pya) of follow-up. Eighty-four (7.2%) deaths occurred throughout the study period, the overall mortality rate among individuals was 5.37 per 100 person-years (pya). Mortality rates varied significantly across different characteristics. Older individuals (\u0026ge; 50 years) had a higher mortality rate (7.92 per 100 pya) compared to younger age groups. Males exhibited a higher mortality rate (7.02 per 100 pya) than females (3.93 per 100 pya). Advanced WHO clinical stages (Stages 3 and 4) were associated with a markedly increased mortality rate of 34.29 per 100 pya, in contrast to earlier stages (Stage 1, T1, and T2), which had a rate of 3.89 per 100 pya. Mortality rates also varied by baseline ART regimen, with DTG-based regimens showing a rate of 7.37 per 100 pya and NNRTI-based regimens showing a lower rate of 3.26 per 100 pya. Lower baseline CD4 counts were associated with higher mortality rates, with counts below 50 showing a rate of 9.23 per 100 pya and counts between 51-100 showing a rate of 6.11 per 100 pya, compared to counts between 101-200, which had a rate of 2.92 per 100 pya.\u003c/p\u003e\n\u003cp\u003eA lower mortality was observed among individuals with viral load suppression (0.78 per 100 pya) compared to non-suppressed individuals (12.85 per 100 pya). \u0026nbsp;Additionally, individuals who completed TPT had a lower mortality rate of 1.47 per 100 pya compared to those who did not. BMI and facility level also impacted mortality rates, with underweight individuals having a higher mortality rate of 11.32 per 100 pya compared to those with normal or higher BMI. These findings underscore the complex interplay of clinical, demographic, and treatment-related factors in determining mortality among this population \u003cstrong\u003e(Table 2).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Distribution of mortality rates of persons living with Advanced HIV Disease in North-Central Uganda by participant characteristics, 2018-2021\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"628\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eof deaths\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMortality rate per 100 pya (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.37 [4.33, 6.64]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026ge; 50\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e7.92 [5.16, 12.14]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e30 - 49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e5.58 [4.25, 7.32]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e20 - 29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e2.74 [1.43, 5.26]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e5 - 19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e5.08 [1.27, 20.30]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e3.93 [2.80, 5.53]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e7.02 [5.34, 9.24]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDocumented WHO stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eStage 1, T1, T2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e3.89 [3.01, 5.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eStage 3,4, T3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e34.29 [23.35, 50.36]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline ART\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eDTG-based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e7.37 [5.66, 9.60]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eNNRTI - based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e3.26 [2.22, 4.79]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003ePI -based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e13.91 [4.49, 43.14]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline CD4 Count \u0026nbsp;(\u003c/strong\u003ecells/mm\u0026sup3;)\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e101 - 200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e2.92 [1.94, 4.39]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e51-100 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e6.11 [3.98, 9.37]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u0026le; 50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e9.23 [6.77, 12.58]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDocumented\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTB Preventive Therapy.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 229px;\"\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: 282px;\"\u003e\n \u003cp\u003eTPT \u0026ndash; started\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e4.40 (1.65 to 11.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eTPT - Completed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e1.47 (0.92 to 2.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eNon-TPT use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e25.13 [19.60, 32.24]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurrent ART Regimen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eDTG - based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e4.10 [3.18, 5.29]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eNNRTI - based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e24.06 [15.69, 36.90]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003ePI-based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e10.16 [3.81, 27.07]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMost recent Viral Load Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eSuppressed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e0.78 [0.42, 1.45]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eNon-suppressed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e12.85 [6.68, 24.69]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eNo viral load recorded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e30.36 [23.81, 38.72]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline BMI\u003csup\u003ea\u0026nbsp;\u003c/sup\u003e(kg/m\u0026sup2;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u0026le; 18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e11.32 [7.22, 17.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e18.6 - 24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e3.35 [2.32, 4.86]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e25.0 - 29.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e4.80 [3.03, 7.62]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u0026ge; 30.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e7.75 [4.59, 13.09]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFacility level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eHospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e4.80 [3.45, 6.69]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eHealth Center IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e7.14 [4.44, 11.48]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eHealth Center III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e5.35 [3.78, 7.56]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: T1, T2, T3: patient is on ART treatment, EFV Efavirenz, DTG: Dolutegravir, PI: protease inhibitor, NVP, Nevirapine, TPT, TB preventive therapy, TB: Tuberculosis, WHO, World Health Organization: BMI, body mass index; NNRTI, Non-nucleotide reverse transcriptase inhibitor, ART, Antiretroviral therapy; LFTU, loss to follow-up BMI\u003csup\u003ea\u003c/sup\u003e - Note: Only 1142 PLHIV with AHD had baseline BMI information, including 79 patients who died.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFactors associated with mortality among PLHIV with AHD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFactors associated with mortality in PLHIV with AHD are presented in \u003cstrong\u003eTable 3\u003c/strong\u003e. Individuals aged \u0026ge;50 years had a significantly higher mortality risk compared to those aged 20-29 years (aHR=4.16, 95% CI: 1.77\u0026ndash;9.77, p=0.001). People with a baseline CD4 count \u0026le;50 cells/mm\u0026sup3; also faced an elevated mortality risk (aHR=1.91, 95% CI: 1.08\u0026ndash;3.39, p=0.027). Non-use of TPT was strongly associated with increased mortality (aHR=3.51, 95% CI: 1.83\u0026ndash;6.74, p\u0026lt;0.001). Individuals at WHO Stage 3 or 4 had a higher mortality risk compared to those at lower stages (aHR=1.91, 95% CI: 1.12\u0026ndash;3.27, p=0.018). In terms of viral load, non-suppressed individuals had a markedly higher mortality risk (aHR=9.05, 95% CI: 3.37\u0026ndash;24.29, p\u0026lt;0.001), and those with no viral load testing had the highest risk (aHR=16.23, 95% CI: 7.44\u0026ndash;35.39, p\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Unadjusted and adjusted effects of participant characteristics on mortality rate among persons living with Advanced HIV Disease\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"658\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Crude HR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP - value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted HR (aHR) (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP \u0026ndash; value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Group (Years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003e5-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e2.05 (0.44 \u0026ndash; 9.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e2.62 (0.53 \u0026ndash; 12.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003e20 - 29\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003e30 \u0026ndash; 49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e2.10 (1.03 \u0026ndash; 41.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.040*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e1.75 (0.83 \u0026ndash; 3.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026ge; 50\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e3.11 (1.43 \u0026ndash; 6.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.004*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e4.16 (1.77 \u0026ndash; 9.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1.73 (1.11 - 2.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.014*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e1.21 (0.75 \u0026ndash; 1.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.435\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDocumented WHO Stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eStage 1, T1, T2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eStage 3,4, T3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e6.77 (4.22 - 10.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.001* \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e1.91 (1.12 \u0026ndash; 3.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.018*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline CD4 Count (cells/mm\u0026sup3;) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003e101 - 200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003e51-100 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e2.00 (1.10 - 3.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.022*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e1.71 (0.91 \u0026ndash; 3.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026le; 50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e3.03 (1.81 - 5.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e1.91 (1.08 \u0026ndash; 3.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.027*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDocumented TB Preventive Therapy.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eTPT - Completed\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eTPT \u0026ndash; Started\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e2.64 [0.88, 7.88]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e2.11 (0.68 \u0026ndash; 6.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eNon-TPT Use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e14.43 [8.33, 25.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e3.51 (1.83 \u0026ndash; 6.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurrent ART Regimen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eDTG-Based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eNNRTI-Based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e4.66 (2.82 - 7.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.051*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e1.60 (0.56 \u0026ndash; 4.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.384\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003ePI-Based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e2.75 (1.00 - 7.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e1.07 (0.59 \u0026ndash; 1.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMost recent Viral Load Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eSuppressed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eNon-Suppressed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e16.40 (6.66 - 40.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e9.05 (3.37 \u0026ndash; 24.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eNo Viral Load\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e34.17 (17.08- 68.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e16.23 (7.44 \u0026ndash; 35.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline BMI (kg/m\u0026sup2;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003e18.6 - 24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026le; 18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e3.26 (1.82 - 5.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e1.49 (0.80 \u0026ndash; 2.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003e25.0 - 29.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1.41 (0.78 - 2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e1.38 (0.74 \u0026ndash; 2.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026ge;30.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e2.27 (1.19 - 4.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.012*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e1.26 (0.63 \u0026ndash; 2.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.505\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFacility Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eHospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eHealth Center IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1.39 (0.78 \u0026ndash; 2.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 215px;\"\u003e\n \u003cp\u003eHealth Center III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1.05 (0.65 \u0026ndash; 1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\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\u003eNote: T1, T2, T3: patient is on ART treatment, EFV Efavirenz, DTG: Dolutegravir, PI: protease inhibitor, NVP, Nevirapine, TPT, TB preventive therapy, TB: Tuberculosis, WHO, World Health Organization: BMI, body mass index; NNRTI, Non-nucleotide reverse transcriptase inhibitor, ART, Antiretroviral therapy; LFTU, loss to follow-up\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes\u003c/strong\u003e: The proportional-hazard assumption test based on the Global test derived from Schoenfeld residuals was insignificant (df = 16, ch2 = 17.72, p= 0.3403); * denotes a significant P-value.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur findings reveal that mortality among people living with HIV (PLHIV) with advanced HIV disease (AHD) in rural North-Central Uganda was significantly associated with several factors: age \u0026ge;50 years (aHR=4.16), never having undergone viral load testing (aHR=16.23), viral load non-suppression (\u0026ge;1000 copies/mL; aHR=9.05), baseline CD4 count \u0026le;50 cells/\u0026micro;L (aHR=1.91), never receiving TB prophylaxis (aHR=3.51), and presenting with WHO stage 3 or 4 disease (aHR=1.91). This study is one of the few that specifically investigates mortality determinants among PLHIV with AHD rather than among all PLHIV, marking a significant contribution to our understanding of outcomes in this high-risk group. The study showed that there were 84 deaths over 1,566 pya, with an overall mortality rate of 5.37 deaths per 100 pya. Our study showed a low mortality rate in PLHIV with AHD compared to other surveys done in Tanzania and China which showed mortality rates of 16 per 100 pya(22), and 163.1 per 100 pya (23) respectively. The low mortality rate found in this population may be attributed to the concerted efforts of HIV programs aimed at achieving the UNAIDS 95-95-95 treatment targets(24) such as improved uptake of the Test and Treat policy, improved retention rate on ART, and having a treatment supporter. The studies conducted in Tanzania (2013\u0026ndash;2019) and China (2005\u0026ndash;2020) spanned two periods: before and after implementing the Test and Treat policy. Both studies showed that the prevalence of AHD was significantly lower following the national adoption of the 2016 WHO Test and Treat guidelines. Specifically, Tanzania reported a prevalence of 66.8% before the policy, which declined after implementing the guidelines. Data from China also demonstrated a positive impact on HIV care and treatment outcomes after adopting the Treat All policy(25). Furthermore, the prevalence of AHD in both countries was notably higher than that observed in Uganda, where prevalence rates range from 15% to 30%(16). In contrast, Tanzania and China reported AHD prevalence ranging from 35% to 61% and 34%, respectively. In addition to being conducted during the era of the Test and Treat policy, this study benefited from the widespread adoption of well-optimized and simplified DTG-based regimens, which have positively impacted HIV viral suppression. This is evident in the high rates of viral suppression observed in Uganda (26). \u0026nbsp;Although in this study, almost 40% of the patients did not have a VL test result. The North-Central rural districts are among the regions benefiting from PEPFAR-funded HIV program interventions, which have supported Uganda in developing and implementing HIV guidelines in alignment with WHO recommendations. \u0026nbsp;For example, the 2016 Uganda Consolidated HIV Guidelines did not include any provisions for addressing AHD. By the time the 2020 Uganda HIV Guidelines were introduced, AHD had become a key focus in the effort to reduce the HIV/AIDS burden. The 2022 Uganda HIV/AIDS Consolidated Guidelines further emphasized the implementation of comprehensive care for PLHIV with AHD, aligning to achieve the 95-95-95 target by 2030 (16). Furthermore, the majority of people had documented WHO clinical stages 1 and 2, which could have partially contributed to the low mortality rates, and this is evidenced by the fact that those identified with stages 3 and 4 had a higher risk of mortality(22). The predominance of Stage 1 cases may reflect the achievements of the Test and Treat strategy, even in rural areas of Uganda where this study was conducted, enabling early HIV diagnosis and ART initiation before the onset of clinical symptoms. Limited access to diagnostics for detecting Stage 3 or 4 conditions may also have contributed to under-classification of disease severity, as reported in other studies where individuals with CD4 counts \u0026lt;100 cells/mm\u0026sup3; were still categorized as Stage 1 or 2 (27).\u003c/p\u003e\n\u003cp\u003eThe identified predictors of mortality provide insights into the mechanisms underlying mortality risk in individuals with AHD. In this study, younger persons had a lower mortality risk than older persons, possibly due to stronger immune responses and overall better health in younger populations(28). Aging comes with comorbidities and deterioration of several physiological functions related to inflammation and systemic aging (29). These changes may contribute to the likely immune incompetence seen among elderly persons with AHD and a possible increase in mortality risk in the aging population. Further, the impact of different ART regimens on mortality risk underscores the importance of ART optimization. Maximizing ART efficacy through DTG-based regimens, as guided by current recommendations, is crucial while recognizing that PI-based regimens may still be necessary for specific clinical scenarios. Rapid ART initiation, age-specific ART optimization, and index case testing are fundamental aspects of PEPFAR policy, with ART being the most critical intervention to prevent AHD-related mortality. Our findings underscore the potential benefit of rapid initiation or re-initiation of ART using regimens that effectively reduce viral load and exhibit minimal side effects, particularly those based on integrase inhibitors, in possibly reducing advanced HIV disease and enhancing health outcomes among PLHIV (30,31). Although these regimens may increase the risk of immune reconstitution inflammatory syndrome (IRIS), the evidence on its severity and outcomes is mixed. Nevertheless, due to their effectiveness, integrase inhibitor-based regimens are being widely adopted in PEPFAR programs (32).\u003c/p\u003e\n\u003cp\u003eOur findings underscore that achieving viral load suppression is crucial for lowering mortality risk, as individuals with unsuppressed and those with no viral load test exhibited higher mortality hazards. Suboptimal adherence to HIV treatment can have far-reaching consequences, including treatment failure and compromised viral suppression(33), as well as increasing the risk of resistance to the prescribed treatment regimen(34). \u0026nbsp;Most of the deaths in our study occurred within the first months of ART initiation, highlighting a critical limitation in Uganda\u0026rsquo;s current viral load testing algorithm, which recommends testing at 6 months post-ART initiation. This poses significant risks for people with AHD, who are particularly vulnerable to early mortality. The HIV.gov Clinical Guidelines (2023)\u0026nbsp;(35)\u0026nbsp;recommend more frequent viral load monitoring, such as at 1\u0026ndash;3 months, for high-risk patients like those with AHD, to identify treatment failure or non-adherence and enable timely interventions. In tandem, Ford et al. (2018) found that mortality in AHD patients often occurs very soon after ART initiation, reinforcing the urgency of earlier monitoring(19). \u0026nbsp;Uganda has made significant strides toward the UN 95-95-95 targets, with 92% of PLHIV knowing their status, 84% receiving sustained ART, and 79% achieving viral suppression as of 2023\u0026nbsp;(36). These figures indicate that Uganda\u0026rsquo;s healthcare system is performing strongly in identifying PLHIV and linking them to care. However, gaps remain in ensuring universal ART coverage and viral suppression\u0026mdash;key factors in preventing PLHIV from progressing to AHD or dying from AIDS-related complications. Progress toward these targets is particularly critical for vulnerable populations, where viral suppression rates lag the national average. In Uganda, where testing infrastructure may be constrained, adhering to a 6-month testing schedule for all patients may miss critical windows for intervention, potentially contributing to preventable deaths. Adjusting national guidelines to prioritize earlier VL testing for AHD patients could significantly improve outcomes and optimize HIV care.\u003c/p\u003e\n\u003cp\u003eThis aligns with several other studies that highlight effective adherence to ART as essential for achieving viral suppression, preventing disease progression, and reducing mortality.(33,37\u0026ndash;40).\u003c/p\u003e\n\u003cp\u003eNon-use of TPT was significantly associated with increased mortality risk in this study population (aHR=3.51), highlighting its potential importance in improving outcomes among PLHIV with AHD. A mortality rate of 25.13 per 100 person-years\u0026nbsp;was noted among PLHIV who had no documented use of TPT. This observation aligns with similar studies in Ethiopia, reporting high mortality rates among PLHIV people who were not receiving isoniazid prophylactic therapy(41). The delay in initiating TPT, as recommended by Uganda\u0026rsquo;s HIV guidelines, to allow time for the potential unmasking of TB (16), may contribute to episodes of opportunistic infections and adverse reactions that may result in the death of affected persons(42,43). This study highlights the potential impact of TPT as part of a comprehensive HIV care package to lower the risk of TB and death, particularly for individuals with AHD. Given the heightened mortality risk in individuals with AHD due to compromised immunity, addressing barriers to TPT utilization could improve health outcomes among PLHIV.\u003c/p\u003e\n\u003cp\u003eWhile our findings provide valuable insights into mortality predictors among PLHIV with AHD in the rural North-Central region of Uganda, they may be relevant in other contexts, such as similar rural settings across sub-Saharan Africa.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths of the Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe strengths of our study included the large sample size, multi-center design, the long follow-up period, and the use of a rigorous statistical analytical approach, enabling generalizability of the study findings. The study also benefits from a robust evaluation of diverse clinical predictors of mortality in individuals with AHD, providing valuable insights into the understanding of the mechanisms underlying AHD. \u0026nbsp;Another key strength is that this study specifically examines mortality rates among PLHIV with AHD, rather than all PLHIV, offering a focused insight into this high-risk group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study had several limitations, including its retrospective design and reliance on secondary data, which may have introduced information bias due to the exclusion of records with incomplete key variables. Additionally, all-cause mortality was determined without identifying specific causes of death, potentially leading to an overestimation of HIV-related mortality. At the same time, the lack of follow-up for missing records could have resulted in underestimation. The inclusion criteria, based solely on baseline CD4 counts, may have missed PLHIV with AHD who presented with WHO stage 3 or 4 clinical features but lacked a CD4 count at ART initiation, possibly underestimating the true burden of AHD and affecting the observed association between CD4 and mortality, as individuals without baseline measurements might have had similar or worse outcomes but were not captured. Furthermore, children under five years were excluded despite their high mortality rates, though they are presumed to have AHD. A significant proportion of patients were diagnosed at hospitals, suggesting they may have been sicker at presentation. Yet, the absence of a markedly higher rate implies other underlying factors may have influenced outcomes. Moreover, antiretroviral therapy adherence data were not properly captured and thus had to be excluded from the analysis, which may have introduced residual confounding into our hazard estimates. Finally, the study period coincided with the COVID-19 pandemic, but its impact on AHD diagnosis and mortality could not be assessed due to data limitations, highlighting an important area for future research.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eKey risk factors for mortality among persons with AHD in this study included older age, non-use of TPT, a CD4 \u0026le; 50 cells/mm\u003csup\u003e3\u003c/sup\u003e viral load non-suppression, and WHO clinical stages 3 and 4. Despite the presence of these risk factors, the overall mortality rate in this population was relatively low. The identified predictors present opportunities for interventions to reduce mortality and continued close monitoring of patients to improve survival among people with AHD.\u003c/p\u003e\n\u003cp\u003eFuture studies may consider exploring causes of mortality among people with AHD through post-mortem studies and using non-invasive methods such as verbal mortality audits. In addition, investigating psychosocial, socioeconomic, and facility-related factors associated with mortality in PLHIV with AHD may provide useful insights into strategies to further reduce deaths in this vulnerable population.\u0026nbsp;\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAHD - Advanced HIV Disease, AIDS - Acquired Immunodeficiency Syndrome, ART - Antiretroviral Therapy, BMI - Body Mass Index, CDC - Centers for Disease Control and Prevention, CD4 - Cluster of Differentiation 4, DTG - Dolutegravir, EMR - Electronic Medical Records, ESA - Eastern \u0026amp; Southern Africa, HC - Health Centre, HIV - Human Immunodeficiency Virus, HR - Hazard Ratio, IRIS - Immune Reconstitution Inflammatory Syndrome, NNRTI - Non-Nucleoside Reverse Transcriptase Inhibitor, PEPFAR - President\u0026apos;s Emergency Plan for AIDS Relief, PI - Protease Inhibitor, PLHIV - People Living with HIV, SD - Standard Deviation, SSA - Sub-Saharan Africa, TB - Tuberculosis, TPT - TB Preventive Therapy, and WHO - World Health Organization.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis activity was reviewed and approved by Mildmay Uganda Institutional Review Board (No. 0804-2018), as well as the U.S Centers for Disease Control and Prevention (U.S CDC) under 45 C.F.R. part 46.101(c); 21 C.F.R. part 56. A waiver of consent was granted to utilize secondary data, because data were sourced from publicly accessible Electronic Medical Records (EMR) platform..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis publication has been supported by the President\u0026apos;s Emergency Plan for AIDS Relief (PEPFAR) through the U.S. Centers for Disease Control and Prevention (CDC) under the terms of grant number 1NU2GGH002046-01\u0026ndash;00. The findings and conclusions in this manuscript are those of the authors and do not represent the official position of the funding agencies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBK and JKN conceived the study. BK, JKN, IK, CB, CN, GW, JBB, JN, GN, JK, JNK, CS, RM, AGF and BM participated in study design and data collection. BK, IK, CNL, RN and AGF participated in data analysis. All authors participated in the writing and review of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe clinical data sets used and /or analyzed during this study are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to all the patients whose data was used in the study. We thank Dr. Richard Muhindo for the insightful advice during the manuscript\u0026apos;s writing and review.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eThe Global HIV/AIDS Epidemic [Internet]. KFF. 2023 [cited 2024 Jan 12]. Available from: https://www.kff.org/global-health-policy/fact-sheet/the-global-hiv-aids-epidemic/\u003c/li\u003e\n\u003cli\u003eGlobal HIV \u0026amp; AIDS statistics \u0026mdash; Fact sheet [Internet]. [cited 2024 Jan 13]. 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Clin Infect Dis Off Publ Infect Dis Soc Am. 2018 Apr 1;66(Suppl 2):S106-SS110.\u003c/li\u003e\n\u003cli\u003eMunthali C, Taegtmeyer M, Garner PG, Lalloo DG, Squire SB, Corbett EL, et al. Diagnostic accuracy of the WHO clinical staging system for defining eligibility for ART in sub-Saharan Africa: a systematic review and meta-analysis. J Int AIDS Soc. 2014 Jun 12;17(1):18932.\u003c/li\u003e\n\u003cli\u003eThe Role of HIV Viral Suppression in Reducing Transmission and Improving Individual Health: Policy Brief. 1st ed. Geneva: World Health Organization; 2022. 1 p.\u003c/li\u003e\n\u003cli\u003eSt\u0026ouml;ger L, Katende A, Mapesi H, Kalinjuma AV, Van Essen L, Klimkait T, et al. Persistent High Burden and Mortality Associated With Advanced HIV Disease in Rural Tanzania Despite Uptake of World Health Organization \u0026ldquo;Test and Treat\u0026rdquo; Guidelines. Open Forum Infect Dis. 2022 Dec 2;9(12):ofac611.\u003c/li\u003e\n\u003cli\u003eLi SS, Li K, Chen HH, Zhu QY, He JS, Feng Y, et al. Evaluation of factors associated with high advanced HIV disease and mortality in Southwestern China: a retrospective cohort study, 2005\u0026ndash;2020. Public Health. 2024 Feb 1;227:282\u0026ndash;90.\u003c/li\u003e\n\u003cli\u003eUnderstanding measures of progress towards the 95\u0026ndash;95\u0026ndash;95 HIV testing, treatment and viral suppression targets [Internet]. [cited 2024 Aug 31]. Available from: https://www.unaids.org/en/resources/documents/2024/progress-towards-95-95-95\u003c/li\u003e\n\u003cli\u003eWu X, Wu G, Ma P, Wang R, Li L, Sun Y, et al. Immediate and long-term outcomes after treat-all among people living with HIV in China: an interrupted time series analysis. Infect Dis Poverty. 2023 Aug 14;12(1):73.\u003c/li\u003e\n\u003cli\u003eUganda Viral Load Dashboard [Internet]. [cited 2024 Sep 21]. Available from: https://vldash.cphluganda.org/\u003c/li\u003e\n\u003cli\u003eHakim J, Musiime V, Szubert AJ, Mallewa J, Siika A, Agutu C, et al. Enhanced Prophylaxis plus Antiretroviral Therapy for Advanced HIV Infection in Africa. N Engl J Med. 2017 Jul 20;377(3):233\u0026ndash;45.\u003c/li\u003e\n\u003cli\u003eEduardo E, Lamb MR, Kandula S, Howard A, Mugisha V, Kimanga D, et al. Characteristics and Outcomes among Older HIV-Positive Adults Enrolled in HIV Programs in Four Sub-Saharan African Countries. Thorne C, editor. PLoS ONE. 2014 Jul 30;9(7):e103864.\u003c/li\u003e\n\u003cli\u003eChauvin M, Sauce D. Mechanisms of immune aging in HIV. Clin Sci. 2022 Jan 14;136(1):61\u0026ndash;80.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Guidelines for managing advanced HIV disease and rapid initiation of antiretroviral therapy, July 2017 [Internet]. Geneva: World Health Organization; 2017 [cited 2025 Apr 19]. 56 p. Available from: https://iris.who.int/handle/10665/255884\u003c/li\u003e\n\u003cli\u003eInitiation of Antiretroviral Therapy | NIH [Internet]. 2019 [cited 2025 Apr 19]. Available from: https://clinicalinfo.hiv.gov/en/guidelines/hiv-clinical-guidelines-adult-and-adolescent-arv/initiation-antiretroviral-therapy\u003c/li\u003e\n\u003cli\u003eBoyd AT, Oboho I, Paulin H, Ali H, Godfrey C, Date A, et al. Addressing advanced HIV disease and mortality in global HIV programming. AIDS Res Ther. 2020 Jul 10;17(1):40.\u003c/li\u003e\n\u003cli\u003eWakooko P, Gavamukulya Y, Wandabwa JN. Viral load Suppression and Associated Factors among HIV Patients on Antiretroviral Treatment in Bulambuli District, Eastern Uganda: A Retrospective Cohort Study. Infect Dis. 2020;13:1178633720970632.\u003c/li\u003e\n\u003cli\u003ePress N, Tyndall MW, Wood E, Hogg RS, Montaner JSG. Virologic and Immunologic Response, Clinical Progression, and Highly Active Antiretroviral Therapy Adherence: JAIDS J Acquir Immune Defic Syndr. 2002 Dec;31:S112\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eLaboratory Testing: Plasma HIV-1 RNA (Viral Load) and CD4 Count Monitoring | NIH [Internet]. 2022 [cited 2025 Apr 22]. Available from: https://clinicalinfo.hiv.gov/en/guidelines/hiv-clinical-guidelines-adult-and-adolescent-arv/plasma-hiv-1-rna-cd4-monitoring\u003c/li\u003e\n\u003cli\u003eUganda | UNAIDS [Internet]. 2025 [cited 2025 Apr 22]. Available from: https://www.unaids.org/en/regionscountries/countries/uganda\u003c/li\u003e\n\u003cli\u003eNyaboke R, Ramadhani HO, Lascko T, Awuor P, Kirui E, Koech E, et al. Factors associated with adherence and viral suppression among patients on second-line antiretroviral therapy in an urban HIV program in Kenya. SAGE Open Med. 2023 Jan;11:205031212311623.\u003c/li\u003e\n\u003cli\u003eMwangi A, Van Wyk B. Factors Associated with Viral Suppression Among Adolescents on Antiretroviral Therapy in Homa Bay County, Kenya: A Retrospective Cross-Sectional Study. HIVAIDS - Res Palliat Care. 2021 Dec;Volume 13:1111\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eMaina EK, Mureithi H, Adan AA, Muriuki J, Lwembe RM, Bukusi EA. Incidences and factors associated with viral suppression or rebound among HIV patients on combination antiretroviral therapy from three counties in Kenya. Int J Infect Dis. 2020 Aug;97:151\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eDe Olalla PG, Knobel H, Carmona A, Guelar A, L\u0026oacute;pez-Colom\u0026eacute;s JL, Cayl\u0026agrave; JA. Impact of Adherence and Highly Active Antiretroviral Therapy on Survival in HIV-Infected Patients: JAIDS J Acquir Immune Defic Syndr. 2002 May;30(1):105\u0026ndash;10.\u003c/li\u003e\n\u003cli\u003eMisgina KH, Weldu MG, Gebremariam TH, Weledehaweria NB, Alema HB, Gebregiorgis YS, et al. Predictors of mortality among adult people living with HIV/AIDS on antiretroviral therapy at Suhul Hospital, Tigrai, Northern Ethiopia: a retrospective follow-up study. J Health Popul Nutr. 2019 Dec;38(1):37.\u003c/li\u003e\n\u003cli\u003eSime T, Oljira L, Diriba A, Firdisa G, Gezimu W. Effect of active tuberculosis on the survival of HIV-infected adult patients who initiated antiretroviral therapy at public hospitals of Eastern Ethiopia: A retrospective cohort study. Samaranayaka A, editor. PLOS ONE. 2022 Oct 31;17(10):e0277021.\u003c/li\u003e\n\u003cli\u003eBirhanu A, Dingeta T, Tolera M. \u0026lt;p\u0026gt;Predictors of Mortality Among Adult HIV-Infected Patients Taking Antiretroviral Therapy (ART) in Harari Hospitals, Ethiopia\u0026lt;/p\u0026gt;. HIVAIDS - Res Palliat Care. 2021 Jul 2;13:727\u0026ndash;36.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mortality, HIV, advanced HIV disease, Uganda, viral load, tuberculosis, adherence","lastPublishedDoi":"10.21203/rs.3.rs-6328943/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6328943/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eDespite global efforts to improve HIV care, late identification and delayed antiretroviral therapy (ART) initiation continue to pose mortality risks among people living with HIV (PLHIV) with advanced HIV disease (AHD). This study investigated factors associated with mortality among PLHIV with AHD in rural North-Central Uganda from January 2018 to December 2021.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We conducted a retrospective review of electronic medical records from 18 health facilities, and obtained data on patient demographics and clinical characteristics including baseline CD4 count, baseline ART regimen, current ART regimen, ART adherence, body mass index (BMI), tuberculosis (TB) status, TB preventive therapy (TPT) use, WHO clinical stage and viral load status. AHD was defined as CD4 cell count \u0026lt;200 cells/mm\u003csup\u003e3\u003c/sup\u003e. A Cox proportional hazard model was fitted to identify factors associated with mortality among individuals with AHD. Factors were summarized by adjusted hazard ratios (aHRs) with their 95% confidence intervals (CIs) and considered statistically significant at 5%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: 1,161 PLHIV with AHD records were analyzed, contributing 1,565.56 person-years (pya). Of these, 84 (7.2%) deaths were reported, equivalent to a mortality rate of 5.37 deaths per 100 pya (95% CI: 4.33–6.64). Factors significantly associated with mortality included age \u0026nbsp;≥50 years (aHR=4.16, 95% CI: 1.77–9.77,), never having had a viral load test (aHR=16.23, 95% CI: 7.44–35.39), viral load non-suppression (≥1000 copies/ml) (aHR=9.05, 95% CI: 3.37–24.29,), baseline CD4 count ≤50 (aHR=1.91, 95% CI: 1.08–3.39,), never having taken TB prophylaxis (aHR=3.51, 95% CI: 1.83–6.74) and WHO stage 3 or 4 (aHR=1.91, 95% CI: 1.12–3.27).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Key predictors of mortality among patients with AHD were older age, absence of tuberculosis preventive therapy, CD4 ≤50\u0026nbsp;cells/mm³, viral load non-suppression, and WHO clinical stages\u0026nbsp;3–4. Interventions targeting early identification of AHD, routine viral load monitoring, ART optimization and adherence support, and universal TB preventive therapy—alongside close patient follow-up—are essential to reduce mortality and improve outcomes, contributing to HIV epidemic control by 2030.\u003c/p\u003e","manuscriptTitle":"Factors associated with mortality among people with advanced HIV disease in rural Uganda: a retrospective study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-24 04:08:35","doi":"10.21203/rs.3.rs-6328943/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-21T03:39:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-14T14:49:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-08T14:03:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253136007844103514319636777678686378416","date":"2025-05-01T14:54:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"339912477526885175535054284761736944790","date":"2025-04-28T14:40:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"47631610273081890417891719685857016697","date":"2025-04-28T14:35:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-26T15:13:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5115275166567491267342176359800010449","date":"2025-04-26T14:34:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-26T14:27:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-24T00:58:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2025-04-23T08:34:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dd7a9236-2778-4c8c-b029-1d6335f67252","owner":[],"postedDate":"April 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-04T16:44:36+00:00","versionOfRecord":{"articleIdentity":"rs-6328943","link":"https://doi.org/10.1186/s12879-025-11397-1","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2025-08-02 16:21:33","publishedOnDateReadable":"August 2nd, 2025"},"versionCreatedAt":"2025-04-24 04:08:35","video":"","vorDoi":"10.1186/s12879-025-11397-1","vorDoiUrl":"https://doi.org/10.1186/s12879-025-11397-1","workflowStages":[]},"version":"v1","identity":"rs-6328943","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6328943","identity":"rs-6328943","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}