Hypertension is a marker of the micro-epidemiologic transition in ageing HIV populations in Kenya, Uganda and Tanzania (AFRICOS, 2013–2023)

Hypertension is a marker of the micro-epidemiologic transition in ageing HIV populations in Kenya, Uganda and Tanzania (AFRICOS, 2013–2023) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Hypertension is a marker of the micro-epidemiologic transition in ageing HIV populations in Kenya, Uganda and Tanzania (AFRICOS, 2013–2023) Denis Mayambala, Wandera Stephen Ojjambo, Charles Lwanga, Dan Haydon, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8863155/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Apr, 2026 Read the published version in BMC Public Health → Version 1 posted 10 You are reading this latest preprint version Abstract Background Although mortality among people living with HIV (PLHIV) in sub-Saharan Africa has decreased markedly with the scale-up of antiretroviral therapy (ART), the demographic consequences of this success remain underexamined. Methods Using ten years of longitudinal data from the African Cohort Study (AFRICOS; 2013–2023) in Kenya, Tanzania, and Uganda, we estimated the association of hypertension with all-cause mortality among adults aged 40 years and older. We combined descriptive decremental life-table analysis with discrete-time logistic regression, applying both lagged and exponentially weighted moving-average (EWMA) exposure models to capture cumulative risk. Results At baseline, 18.6% of participants were hypertensive; 60.1% experienced hypertension during follow-up, and all-cause mortality was 6.4%. Life-table estimates showed cumulative excess mortality (Δqₓ) among hypertensive participants increasing from 0.4% in the first interval to 4.4% by year nine. Excess mortality was larger among adults aged ≥ 50 years (Δqₓ ≈ 5.4% vs. 4.4% at ages 40–49), men (Δqₓ ≈ 8.0% vs. 2.8% in women), underweight participants (Δqₓ > 20% in early intervals), and those with high viral load (Δqₓ ≈ 7.0%). In adjusted discrete-time models controlling for age, sex, body mass index, viral load, and country, hypertension was associated with higher mortality under a standard lag specification (aOR = 2.04; 95% CI 1.10–3.80) and under EWMA exposure definitions (aOR = 3.25; 95% CI 1.26–8.40 at α = 0.3; aOR = 2.51; 95% CI 1.16–5.44 at α = 0.7). Mortality odds were higher among participants aged ≥ 60 years (aOR = 2.23–2.40; 95% CIs 0.85–6.16) and those with high viral load (aOR = 2.36–2.44; 95% CIs 1.28–4.51), while overweight and obese participants had substantially lower odds of death (aOR = 0.29; 95% CI 0.10–0.85 and aOR = 0.14; 95% CI 0.04–0.56). Conclusions These findings highlight a demographic transformation of the HIV epidemic in East Africa, where mortality among PLHIV increasingly reflects a growing influence of chronic diseases in addition to infection control. Hypertension has become a key driver of excess mortality and a demographic indicator of the region’s compressed health transition. HIV hypertension mortality life-course East Africa demography AFRICOS Figures Figure 1 Background Over the past two decades, lower- and middle-income countries (LMICs) have undergone a profound epidemiological and demographic transition, marked by a shift away from communicable, maternal, neonatal, and nutritional causes toward non-communicable diseases (NCDs) as the dominant sources of mortality and disability. NCDs now account for approximately 71% of global deaths, including nearly 18 million cardiovascular deaths annually ( 1 ). sub-Saharan Africa (SSA) is experiencing a rapid rise in NCD-related disability and mortality ( 2 ) driven by urbanisation, dietary change, and declining physical activity ( 3 ). Within this broader transition, HIV remains endemic in SSA, and it currently comprises about two-thirds of the worldwide population of people living with HIV (PLHIV) ( 4 , 5 ). The large-scale expansion of antiretroviral therapy (ART) has fundamentally reshaped mortality patterns among PLHIV. Cohorts once characterised by high levels of premature adult mortality are now surviving into older ages, producing a marked upward shift in the age schedule of HIV mortality ( 6 , 7 ). In Uganda’s Rakai cohort, partial life expectancy before age 50 increased by more than a decade within a single treatment generation ( 8 ). As a result, morbidity and mortality among PLHIV increasingly reflect the same epidemiological forces driving population-level NCD transitions, rather than HIV-specific opportunistic disease. As a consequence, PLHIV's present-day population structure is demographically distinct: it is larger, older, and increasingly characterised by the co-occurrence of chronic conditions. Globally, the proportion of PLHIV aged 50 years and older nearly doubled between 2000 and 2016, with almost 80% residing in LMICs and SSA accounting for the largest absolute numbers ( 9 ). Although PLHIV remain a numerical minority within older adult populations, modelling studies consistently show that they are disproportionately represented among individuals experiencing NCD incidence and multimorbidity, particularly combinations involving HIV, hypertension, and diabetes in high-prevalence settings ( 10 , 11 ) ). Those patterns are consequential in SSA, which continues to host nearly two-thirds of the global PLHIV population ( 4 ). Longitudinal and simulation-based analyses further suggest that, even as survival improves, PLHIV accumulate chronic conditions earlier and spend fewer years free of multimorbidity, positioning this population as a critical lens through which to examine multi-disease accumulation under conditions of constrained health system capacity ( 5 , 10 ). The main problem of the existence of NCDs is the emergence of multimorbidity ( 12 ), especially the combination of HIV, hypertension, and diabetes. Meta-analyses estimate hypertension in roughly one-in-five PLHIV in SSA, with disproportionate clustering among older adults, men, and those with higher CD4 counts on long-term ART ( 4 , 10 ). The rise of hypertension is a quiet but consequential demographic force. Its prevalence now rivals that of any major infectious comorbidity among PLHIV, ranging from 18 to 27 per cent in East Africa ( 4 , 13 ). Hypertension manifests earlier in the life course, often beginning in the forties (40s) and reflecting etiologies that extend beyond pharmacologic side effects to include prolonged immune activation, nutritional transition, and psychosocial stress ( 14 ). As AIDS-related mortality declines, cardiovascular and hypertensive causes increasingly occupy the mortality schedule of treated HIV populations ( 15 , 16 ). This shift exemplifies what Kuate Defo described as Africa’s dual burden of disease: the coexistence of infectious and chronic degeneration within the same cohorts ( 17 ). This paper introduces the concept of a micro-epidemiologic transition, which is a shift from infection-dominated to chronic, circulatory mortality occurring within ART-treated HIV cohorts rather than across whole societies. The central question is structural rather than purely clinical: to what extent do chronic vascular processes now account for mortality among ageing PLHIV, and how do they reshape cohort survival patterns? Conventional HIV mortality models continue to prioritise AIDS-specific causes of death, thereby understating the contribution of chronic comorbidities to all-cause mortality. Although life-table decomposition and decrement analysis offer robust tools for quantifying cause-specific contributions to survival change ( 18 ). These methods have rarely been applied to African HIV cohorts now entering older adulthood. This gap limits our ability to measure the population-level consequences of the health transition already underway. Theoretically, this transition can be understood through the lenses of cumulative disadvantage and the life course ( 19 , 20 ). Early HIV infection, delayed treatment initiation, and chronic psychosocial stress accumulate physiological wear, which McEwen termed allostatic load, manifesting as hypertension later in life ( 21 ). Thus, hypertension becomes not only a clinical risk factor but as a demographic signal of inequality embedded across decades of exposure ( 22 ). Empirical evidence underscores the stakes of this transition, where uncontrolled hypertension has been shown to double all-cause mortality among PLHIV in South Africa ( 16 ), while cardiovascular deaths are rising within Tanzanian ART programmes ( 15 ). Yet these findings remain expressed in clinical effect measures, such as odds ratios or hazard ratios, without translation into demographic quantities such as excess mortality, survival probabilities, or years of life lost. This paper reframes hypertension as a demographic determinant of survival in ageing HIV populations and contributes a new conceptual framework for understanding the ART-era mortality shift as a micro-epidemiologic transition within treated cohorts. This is demonstrated through longitudinal cohort data from the African Cohort Study (AFRICOS, 2013–2023), showing how selective survival and chronic disease accumulation jointly reshape mortality structures among PLHIV aged 40 and older in Kenya, Uganda, and Tanzania. Data and Methods We used longitudinal data from the African Cohort Study (AFRICOS), a multi-country cohort following adults receiving antiretroviral therapy at sites in Kenya, Uganda, and Tanzania since 2013. The study design linked clinical records with demographic and behavioural data collected every six months, providing repeated observations of treatment history, biomarkers, non-communicable disease indicators, and vital status. For this analysis, we restricted the sample to people living with HIV aged 40 years and older who contributed at least two follow-up visits between 2013 and 2023. After excluding cases with missing blood-pressure or mortality information, the analytic file contained 1,169 individuals at baseline year, increased to a total of 1,360 participants at the end of the tenth-year visit, contributing 12,462 person-periods across the follow-up period. The outcome was all-cause mortality observed during follow-up. Deaths were confirmed through clinic and community tracing systems and coded at the next scheduled visit. Time was measured in half-year intervals corresponding to the AFRICOS visit schedule. Hypertension, the principal exposure, is defined as systolic blood pressure ≥ 140 mm Hg, diastolic ≥ 90 mm Hg, or reported antihypertensive use at a given visit. To reflect cumulative exposure over the life course, we construct a lagged exponentially weighted moving average (EWMA) of hypertension status. This measure assigned greater weight to recent observations while retaining information on prior exposure, translating the concept of risk accumulation from life-course theory into an empirical variable. Participants who remained normotensive across all visits formed the reference group. Covariates captured demographic and clinical factors known to shape survival: age, sex, body mass index (BMI), viral load, and country of follow-up. BMI was calculated as weight (kg) divided by height (m) using measured height and weight. Categories were defined as: underweight (< 18.5), normal weight (18.5–24.9), overweight (25.0–29.9), and obese (≥ 30). Viral load was categorised using standard WHO clinical thresholds as: 1 = suppressed (< 50 copies/mL), 2 = low-level viremia (50–999 copies/mL), and 3 = high (≥ 1000 copies/mL). Age, BMI, and viral load were treated as time-varying. Missing covariate values were rare and handled by carrying the last observation forward. The analysis proceeded in two stages. First, we construct decrement life tables to estimate interval probabilities of death \(\:\left(qx\right)\) separately for hypertensive and non-hypertensive individuals. The difference \(\:\varDelta\:{q}_{\text{x}}={{q}_{\text{x}}}^{HTN}-{{q}_{\text{x}}}^{non-HTN}\) ​ represents the excess probability of dying associated with hypertension. Life tables summarised the cumulative, unadjusted mortality differences over time, capturing both direct and compositionally mediated effects. This provided a population-level description of mortality differentials and their evolution across follow-up, following the logic of cause-deleted life-table analysis (Beltrán-Sánchez et al. 2008). Second, we fitted lagged discrete-time logistic-regression models of the probability of death in each interval as a function of prior hypertension exposure and covariates: $$\:\text{log}\left(\frac{\left(P\right({Death}_{it}=1)}{1-P\left({Death}_{it}=1\right)}\right)={\beta\:}_{0}++{\beta\:}_{1}{EMA}_{{HTN}_{i\left(t-1\right)}}+{\beta\:}_{2}{X}_{i\left(t-1\right)}+\gamma\:t$$ where \(\:{EMA}_{{HTN}_{i\left(t-1\right)}}\) represented the lagged exponential moving average of hypertension status for individual i at time \(\:t-1\) , \(\:{X}_{i\left(t-1\right)}\) is a vector of lagged covariates including country, age category, sex, BMI category, and viral load category, \(\:\:{\beta\:}_{0}\) ​ is the intercept, \(\:\beta\:2\) ​ is the vector of coefficients for confounders, and \(\:\gamma\:t\) represented the visit-specific fixed effects, accounting for unobserved heterogeneity across time points. Standard errors were clustered at the individual level to account for repeated measures. Estimated coefficients were presented as odds ratios and converted to adjusted probabilities to facilitate interpretation. This isolated the independent effect of each covariate after controlling for correlated characteristics. Attenuation of some covariates in the regression indicates that their life-table differentials were partly driven by shared pathways or selective survival rather than independent effects. In this sense, the life-table analysis and regression results are complementary: together, they document both the demographic accumulation of risk and the net, adjusted contributions of key exposures. Results The analysis that follows traces how the survival gains of the ART era are being re-inscribed in new forms of chronic risk, allowing the demographic contours of this micro-epidemiologic transition to be observed directly. Descriptive characteristics Table 1 summarises the demographic and clinical composition of the analytic cohort (n = 1,169), representing a maturing population of PLHIV transitioning into mid- and later life. Participants were predominantly aged 40–49 years (56.7%), female (48.8%), and resident in Kenya (59.7%), with a median follow-up of about seven years. Hypertension was present in nearly one-fifth of participants at baseline (18.6%) and affected more than half of the cohort (60%) across the ten-year observation period. Overall mortality during follow-up was 6.4%, consistent with a cohort in which infectious mortality has largely receded but not disappeared. Age and compositional differentials reveal clear population structuring. The prevalence of hypertension increased from 13.6% among those aged 40–49 years to 32.3% among those 60 years and older (p < 0.001), while mortality followed a similar gradient from 5.7% to 12.1% (p = 0.04). Men and women displayed near-equal hypertension prevalence (18–19%), yet men’s mortality remained higher (7.9% vs. 4.9%, p = 0.04), reflecting the durable sex-gap in adult survival observed across African populations and its persistence within the HIV-positive subpopulation. Nutritional status showed an inverted demographic pattern. Hypertension prevalence increased steadily with body mass from 44% among the underweight to 82% among the obese (p < 0.001). In contrast, mortality declined across the same gradient, from 17.6% among the underweight to 2.5% among the obese (p < 0.001). Viral suppression status further stratified survival outcomes. Although overall suppression was high (77%), mortality differed sharply by viral load, with significantly lower mortality among suppressed individuals (4.3%) compared with 15.9% among those with high viremia (p < 0.001). Hypertension was more common among the suppressed (62%) than the unsuppressed (50%, p = 0.006), reflecting compositional ageing within the cohort. Geographic variations observed mirror structural differences in health-system performance. Kenya, contributing the largest share of participants, exhibited intermediate hypertension prevalence (17%) and mortality (6%). Tanzania showed the highest hypertension prevalence (32%) but lowest mortality (4%), whereas Uganda showed the reverse pattern, with lower hypertension (10%) but higher mortality (9%). Table 1 Characteristics of the study population at baseline and distribution of hypertension and mortality (AFRICOS 2013–2023) Variable n (%) Hypertensive n (row%) p -value Dead n (row%) p -value Total 1 169 (100) 217 (18.6) — 75 (6.4) — Sex Male 599 (51.2) 107 (17.9) 0.526 47 (7.9) 0.041 * Female 570 (48.8) 110 (19.3) 28 (4.9) Age group (years) 40–49 663 (56.7) 98 (13.6) < 0.001 *** 37 (5.6) 0.046 * 50–59 347 (29.7) 90 (25.9) 22 (6.3) 60 + 99 (8.5) 31 (32.3) 12 (12.1) BMI category Underweight 148 (12.7) 65 (43.9) < 0.001 *** 26 (17.6) < 0.001 *** Normal weight 631 (54.0) 340 (53.9) 38 (6.0) Overweight 228 (19.5) 165 (72.4) 7 (3.1) Obese 162 (13.8) 132 (81.5) 4 (2.5) Viral load status Suppressed 900 (77.0) 557 (61.9) 0.006 ** 39 (4.3) < 0.001 *** Low-level viremia 87 (7.4) 55 (63.2) 7 (8.1) High viral load 182 (15.6) 90 (49.5) 29 (15.9) Country of residence Kenya 698 (59.7) 118 (16.9) < 0.001 *** 44 (6.3) 0.086 Tanzania 256 (21.9) 82 (32.0) 11 (4.3) Uganda 215 (18.4) 21 (9.8) 20 (9.3) Ever hypertensive (during follow-up) 702 (60.1) — — — Mortality rate (all participants) — — 75 (6.4) — Median follow-up years (IQR) — — 7.2 (3.6) — Life-table analysis of excess mortality Decrement life tables were constructed to examine mortality differentials between hypertensive and non-hypertensive participants across eleven annual intervals (2013–2023). Excess mortality (Δqₓ) for hypertensives increased steadily over time, from approximately 0.4% in the first interval to 4.4% by the ninth, after which it plateaued (Table 2 ). The temporal progression of Δqₓ shows a gradual widening of mortality differences between exposure groups during follow-up. Age-specific estimates revealed clear gradients. Among participants aged 40–49 years, Δqₓ increased from near zero in the early intervals to 4–5% by the end of observation. For those aged 50–59 years, the increase was more pronounced, exceeding 5% by year nine. Participants aged 60 years and older showed positive excess mortality in early intervals (≈ 2%), though estimates became unstable in later intervals due to smaller denominators. These trends indicate that the relative disadvantage associated with hypertension increases with age within the observed range. Sex differences followed a consistent pattern across intervals. Excess mortality among men was positive in all intervals, reaching about 8% by year nine, while among women, the difference remained modest (≈ 3%). The persistent positive Δqₓ for men points to higher mortality at each stage of follow-up. Clear gradients were also observed by body mass index. Underweight participants recorded large positive excesses throughout the period, exceeding 20% in early intervals. Normal-weight and overweight groups exhibited moderate positive values (3–7%), while the obese group maintained near-zero excess. The direction of these gradients remained stable over time, suggesting that differences in nutritional status are strongly associated with mortality outcomes within this cohort. Viral-load categories also showed systematic contrasts. Participants with high viral load recorded consistently positive Δqₓ values of about 7%, whereas suppressed individuals accumulated smaller excesses (≈ 6% by year nine). Estimates for those with low-level viremia fluctuated around zero due to small numbers. Across groups, mortality differences persisted through follow-up, with no indication of convergence. Country-specific life tables revealed heterogeneity in the magnitude and timing of excess mortality. In Kenya, Δqₓ remained near zero through mid-follow-up, before turning slightly positive (≈ 3%) in later intervals. In Tanzania, values were positive and stable around 5% across intervals, while Uganda recorded the highest and most sustained excess mortality, averaging 7–8% throughout the follow-up. These differences correspond to the relative timing and coverage of integrated chronic-disease care across the sites. By allostatic load, mortality differentials were modest for the low-load group during early intervals (≈ 3%) but widened mid-follow-up (≈ 8%). For the high-load group, estimates were negative or near zero early on but increased to about 12% by the final interval, reflecting delayed divergence between physiological stress categories. Across subgroups, the trajectory of Δqₓ consistently widened through the observation period before stabilising, suggesting that survival differences accumulate gradually over successive intervals. Table 2 Cumulative excess mortality (Δqₓ) by demographic and clinical subgroup, AFRICOS 2013–2023 Variable 1 2 3 4 5 6 7 8 9 10 11 Hypertension Status 0.0040 0.0140 0.0099 0.0058 0.0020 0.0168 0.0178 0.0349 0.0442 0.0322 0.0322 Age Groups Age 40–49 -0.0020 0.0270 0.0180 0.0087 0.0068 0.0068 -0.0029 0.0191 0.0445 0.0445 0.0445 Age 50–59 0.0112 0.0030 0.0117 0.0208 0.0163 0.0164 0.0326 0.0539 0.0539 0.0539 0.0539 Age 60+ 0.0182 0.0017 -0.0319 -0.0659 0.0156 0.0156 0.0156 -0.0171 -0.0171 NA NA Sex Male 0.0178 0.0275 0.0160 0.0179 0.0105 0.0312 0.0400 0.0779 0.0623 0.0393 NA Female -0.0086 0.0022 0.0057 -0.0042 -0.0042 0.0055 -0.0010 -0.0010 0.0281 0.0281 0.0281 BMI Underweight 0.1212 0.1716 0.2040 0.1950 0.2872 0.2774 0.2774 NA NA NA NA Normal -0.0048 -0.0062 -0.0116 -0.0107 − .0146 0.0080 0.0139 0.0314 0.0189 0.0006 0.0006 Overweight 0.0017 0.0115 0.0146 0.0066 0.0066 0.0066 0.0066 0.0066 0.0698 0.0698 0.0698 Obese -0.0161 0.0151 0.0151 0.0151 0.0151 0.0151 0.0151 0.0151 0.0151 NA NA Viral Load Suppressed 0.0105 0.0162 0.0182 0.0164 0.0144 0.0298 0.0346 0.0567 0.0669 0.0495 0.0495 Low-Level Viremia 0.0000 -0.0326 -0.0674 -0.0910 -0.1033 -0.1033 -0.1215 -0.1215 NA NA NA High 0.0096 0.0629 0.0548 0.0507 0.0463 0.0759 0.0702 0.0702 0.0702 0.0702 0.0702 Country Kenya -0.0037 -0.0077 -0.0063 -0.0066 -0.0125 -0.0087 − .0049 0.0198 0.0308 0.0308 NA Tanzania 0.0275 0.0671 0.0534 0.0534 0.0534 0.0534 0.0534 0.0534 0.0534 0.0534 NA Uganda 0.0007 -0.0050 0.1021 0.0840 0.0840 0.0840 0.0762 0.0762 0.0762 -0.0107 -0.0107 Allostatic load low 0.0282 0.0456 0.0351 0.0511 0.0479 0.0868 0.0808 0.078 -0.0569 -0.0707 -0.0707 high -0.0261 -0.0384 -0.0369 -0.0461 -0.0518 -0.0532 -0.0510 -0.038 -0.0144 -0.0144 0.1160 Multivariable results Figure 1 presents the odds ratio (log scale) for all-cause mortality associated with hypertension across successive follow-up visits, estimated from adjusted discrete-time logistic regression models. Shaded bands indicate 95% confidence intervals. Both models reveal elevated mortality risk during the early observation period (visits 2–7; OR ≈ 13–18), followed by progressive attenuation to below OR = 3 by visit 16. The close alignment of the two curves demonstrates the robustness of the results to alternative smoothing parameters: α = 0.3 captures long-term cumulative exposure, while α = 0.7 emphasizes short-term variation. Discrete-time logistic regression models (Table 3 ) quantified mortality differentials associated with hypertension after accounting for demographic and clinical composition. In the unadjusted model using the exponentially weighted moving average (EWMA α = 0.3), hypertensive participants had more than twice the odds of death compared with those without hypertension (OR = 2.15; 95% CI 0.86–5.33). Adjustment for covariates strengthened this association, with the odds of death among hypertensives approximately three times higher (AOR = 3.25; 95% CI 1.26–8.40). The adjusted model using the standard lag specification produced a slightly smaller estimate (AOR = 2.04; 95% CI 1.10–3.80), while the alternative EWMA α = 0.7 model yielded a coefficient of similar magnitude (AOR = 2.51; 95% CI 1.16–5.44). Age maintained a consistent positive gradient with mortality. Relative to participants aged 40–49 years, the odds of death were about twice as high among those aged 60 years or older (AOR ≈ 2.3; 95% CI 0.9–6.0). Sex differentials remained in the expected direction, with females exhibiting lower odds of death than males (AOR ≈ 0.63; 95% CI 0.31–1.26). Body mass index showed a clear inverse pattern: mortality declined steadily with increasing BMI. Overweight and obese participants experienced substantially lower odds of death (AOR = 0.29 and 0.14, respectively), while underweight participants represented the highest-risk group. Gradients by viral load were pronounced. Compared with virally suppressed participants, those with high viral load had more than double the odds of death (AOR ≈ 2.4; 95% CI 1.3–4.5). Mortality differences across countries were consistent with those observed in the life-table estimates: odds were lowest in Tanzania (AOR ≈ 0.19) and highest in Uganda (AOR ≈ 1.24) relative to Kenya. Inclusion of the allostatic-load variable modestly reduced the hypertension coefficient but did not alter its significance. Table 3 Discrete-time logistic regression of all-cause mortality among PLHIV (Unadjusted and adjusted models, AFRICOS 2013–2023) Predictor Unadjusted EWMA α = 0.3 OR (95% CI) Adjusted Standard Lag aOR (95% CI) Adjusted EWMA α = 0.3 aOR (95% CI) Adjusted EWMA α = 0.7 aOR (95% CI) Hypertension exposure 2.15 (0.86–5.33) 2.04 (1.10–3.80) ** 3.25 (1.26–8.40) ** 2.51 (1.16–5.44) ** Country (ref = Kenya) Tanzania — 0.21(0.08–0.55) 0.19 (0.07–0.52) 0.20 (0.08–0.54) Uganda — 1.21 (0.52–2.82) 1.27 (0.54–2.96) 1.24 (0.53–2.89) Age (ref = 40–49 y) 50–59 y — 1.16 (0.59–2.27) 1.10 (0.56–2.16) 1.13 (0.57–2.23) 60 + y — 2.40 (0.94–6.16) 2.23 (0.85–5.83) 2.32 (0.89–6.01) Sex (ref = female) 0.53 (0.24–1.17) 0.63 (0.31–1.25) 0.63 (0.31–1.26) BMI (ref = underweight) Normal BMI — 0.63 (0.31–1.31) 0.63 (0.30–1.30) 0.63 (0.31–1.31) Overweight — 0.31 (0.11–0.90) 0.29 (0.10–0.85) 0.30 (0.10–0.88) Obese — 0.15(0.04–0.59) 0.14 (0.04–0.53) 0.14 (0.04–0.56) Viral load (ref = suppressed) Low viremia — 2.41 (1.16–4.99) ** 2.43 (1.16–5.09) ** 2.42 (1.16–5.04) ** High viral load — 2.36 (1.28–4.33) ** 2.44 (1.32–4.51) ** 2.39 (1.30–4.41) ** Observations (person-periods) 12 462 12 462 12 462 12 462 Notes: aOR adjusted Odds Ratios; EWMA= Exponentially weighted moving averages; α = smoothing parameter; Adj = adjusted; OR = unadjusted Odds Ratios. All models include time-fixed effects for the follow-up wave results presented as ORs (95% Confidence Intervals). Discussion The objective of this study was to examine how hypertension shapes all-cause mortality trajectories among people living with HIV (PLHIV) aged 40 years and above in Kenya, Uganda, and Tanzania. Life-table analyses address cumulative mortality experience at the population level and revealed widening excess mortality among hypertensive participants over follow-up, particularly among older adults, men, and underweight individuals. These patterns capture the accumulation of unadjusted mortality differences as cohorts age under sustained ART. By contrast, discrete-time regression models estimate conditional mortality risks after accounting for correlated demographic and clinical characteristics. In these adjusted models, hypertension remained strongly associated with mortality, while many other covariates exhibited smaller or non-significant effects. This attenuation reflects the distinction between unconditional, cumulative differences (life tables) and conditional, composition-adjusted effects (regression): covariates that appear salient in life tables often share pathways or exposure histories, so adjustment reduces their independent contribution. Together, the two approaches demonstrate that hypertension is an independent mortality risk embedded within a survival-selected population, while other apparent differentials largely reflect selective survival and compositional change. Hypertension prevalence partly indexes successful survival through the HIV care cascade, a demonstration that chronic disease burden is now influencing mortality outcomes, marking a demographic shift toward chronic-condition-dominated survival. The findings may be affirming that the expansion of antiretroviral therapy (ART) in sub-Saharan Africa has fundamentally altered survival patterns among people living with HIV (PLHIV), creating cohorts that are increasingly older and exposed to chronic disease risks ( 10 , 23 – 25 ) such as hypertension and diabetes. Participants who maintain long-term suppression survive into older ages, where hypertensive processes become common. Because life-table analyses summarise cumulative mortality experience over time, they are particularly well suited to capturing this shift, situating PLHIV within a micro-epidemiologic transition in which mortality is no longer dominated solely by opportunistic infections but increasingly by slow-acting, chronic conditions such as hypertension. The substantially higher cumulative mortality associated with hypertension reframes hypertension from a purely clinical comorbidity to a demographic force structuring survival among long-term ART users. While previous studies have highlighted the importance of expanding health systems beyond infectious mortality management through integrated HIV–NCD care and longitudinal monitoring of ageing cohorts ( 10 , 23 – 26 ), our findings show that without accounting for cumulative chronic disease exposure, population-level mortality patterns among PLHIV remain only partially explained. Hypertension increasingly indexes successful survival through the HIV care cascade, whereby those who remain virologically suppressed and engaged in care live long enough to accumulate chronic conditions that then shape their mortality trajectories. This interpretation follows directly from the life-table perspective, which reveals how survivorship redistributes risk within ageing cohorts over time. The excess-mortality life-table profiles, together with the pronounced early-follow-up mortality peaks observed in the regression models, indicate steeper mortality gradients among hypertensive PLHIV during the initial years of observation. This pattern is consistent with accelerated survival loss among individuals carrying higher cardiometabolic burden as ART shifts HIV survival schedules upward, relocating mortality from young adulthood into midlife and older ages ( 7 , 8 ). The subsequent attenuation of mortality differentials reflects selective survival: individuals who survive the early, high-risk period are disproportionately older, female, and virologically stable. As frailer individuals are removed earlier, risk becomes increasingly concentrated among long-term survivors, reshaping the cohort’s metabolic and mortality profile. In this context, hypertension emerges not as an anomaly, but as a predictable feature of ART-enabled longevity within a survival-selected population. A distinctive finding is the temporal inversion of mortality risk associated with hypertension. Although hypertension was linked to elevated mortality early in follow-up, this excess risk attenuated and, among older survivors, reversed. This pattern reflects classic survivorship and selective attrition processes in ageing cohorts, whereby early deaths disproportionately remove the frailest individuals, leaving a more robust subset at older ages ( 4 , 27 ). In this context, hypertension functions less as an immediate mortality trigger at later ages and more as an indicator of entry into a later life-course stage among PLHIV, in which cumulative, slow-acting processes dominate risk. The life-table perspective is critical here, as it captures how mortality differentials evolve across successive intervals rather than at a single point in time. The epidemiologic transition traditionally describes population-level shifts from infectious to chronic disease mortality over extended historical periods. Our findings suggest that a similar transition is occurring within a much shorter time frame and within a defined subpopulation, driven primarily by biomedical intervention rather than socioeconomic development. While Omran’s framework was formulated at the population level ( 28 ), growing evidence from Uganda, Tanzania, and South Africa indicates that cohort-level transitions are emerging among ageing PLHIV, characterised by rising multimorbidity and chronic disease burden ( 6 , 24 , 29 ). By combining life-table and regression approaches, our analysis quantifies how selective survival under sustained ART reshapes mortality structure within a single treatment generation. Mortality patterns by age-group, sex, nutritional status, viral suppression, and national context further reinforce this interpretation. In line with Preston’s description of steepening mortality curves during transitions between epidemiologic regimes ( 30 ), we observed a midlife spike in excess mortality between ages 50 and 59, followed by attenuation at older ages. This pattern is consistent with recent cohort analyses showing that frailty and comorbidities, particularly hypertension, are highly prevalent and strongly associated with increased mortality risk in midlife, but their impact diminishes among older survivors due to selective attrition and survivorship effects ( 31 – 33 ). Survivors in their 60s and beyond represent a highly selected cohort, capable of managing both HIV and the metabolic demands of long-term therapy ( 34 – 36 ). The emergence of hypertension alongside age-specific reversals in excess mortality suggests that the treated HIV population is entering a mortality regime increasingly shaped by cumulative physiological burden rather than infectious causes. This interpretation is consistent with evidence that ageing PLHIV experience rising multimorbidity, chronic disease vulnerability, and functional decline ( 29 ), and with studies linking HIV infection and long-term ART exposure to accelerated cardiometabolic risk through immune activation and endothelial dysfunction ( 37 ). The compositional gradients observed in our analysis reinforce this mechanism. Higher mortality among men reflects later presentation and poorer retention in care ( 13 , 38 ), yielding a survivor population increasingly composed of older, virologically stable women. Similarly, the inverse BMI–mortality gradient marked excess mortality among underweight participants and minimal risk among overweight and obese individuals indicates a survival process that favours metabolic resilience. This is consistent with evidence that improved nutritional status is associated with better survival and that cardiometabolic risk accumulates over time in ageing PLHIV populations ( 39 ). Together, these patterns show how selective survival reshapes cohort composition, concentrating chronic disease burden among long-term survivors. Hypertension’s higher prevalence among virally suppressed individuals further underscores its role as a marker of longevity rather than treatment failure, findings consistent with ( 32 , 33 ). Country-level differences in excess mortality and hypertension prevalence likely reflect variation in ART scale-up, NCD screening, and chronic-care integration across health systems in the region, as reported in comparative studies across the region ( 38 ). Collectively, these demographic and institutional processes support the view that chronic disease among PLHIV is not simply a clinical comorbidity, but evidence of a cohort that has survived into a new mortality regime. These patterns reinforce the need for integrated care models, as recent trials demonstrate that combining HIV and NCD management can maintain high viral suppression while improving chronic disease outcomes ( 40 ). Rather than occurring uniformly, the epidemiologic transition in HIV appears staggered, with hypertension serving as a sentinel marker of survivorship and health-system adaptation in the ART era. Recognising and quantifying this transition is essential for recalibrating surveillance, resource allocation, and policy as treated HIV populations age and the boundaries between infectious and chronic disease increasingly blur. Strengths and Limitations Our application of decrement life tables extended traditional demographic tools by disaggregating survival differences between hypertensive and non-hypertensive PLHIV across subgroups defined by BMI, viral load, and country. The addition of interval-specific excess mortality enhanced their interpretive value for population-level burden estimation. The use of lagged discrete-time logistic regression provided a pragmatic approach to incorporating time-varying exposures, thereby accommodating the sequencing of hypertension and mortality events. Combined with exponentially weighted moving averages (EWMA), this allowed us to approximate cumulative exposure and align with life course principles of accumulation. At the same time, several limitations must be acknowledged. The AFRICOS dataset captures survival and hypertension histories within a defined clinical population, but several dimensions essential to temporal inference remain incomplete. Precise dates of HIV diagnosis, ART initiation, and hypertension onset were not consistently available, limiting temporal sequencing of exposure and outcome. As a result, the study could not distinguish whether hypertension preceded or followed ART initiation, nor fully capture the cumulative duration of treated infection, which are both likely to influence mortality. Mortality recording was also limited to primary causes, potentially understating the contribution of chronic diseases to observed deaths. These gaps suggest that the excess mortality estimates presented here should be interpreted as conservative lower bounds. Analytically, the mechanisms of selective survival and frailty redistribution are inferred rather than directly modelled. Future studies should incorporate formal frailty or decomposition frameworks to quantify how compositional change contributes to the observed attenuation of hazards over time and to test the hypothesised micro-epidemiologic transition more explicitly. Linking clinical data to verbal autopsy or vital-registration systems would strengthen cause-of-death attribution and allow direct decomposition of mortality into infectious and chronic components. Incorporating repeated biomarker measurement and extending life-table analysis to additional chronic conditions such as diabetes, renal dysfunction, and dyslipidemia would further clarify how physiological ageing reshapes mortality within treated HIV populations. Conclusion The mortality experience of people living with HIV in East Africa has entered a new demographic phase in which hypertension plays a central role. As antiretroviral therapy extends survival into older ages, mortality is increasingly shaped by chronic vulnerabilities of longevity rather than infectious causes alone. Hypertension emerges as both a driver of excess mortality and a marker of selective survival, reflecting the accumulation of chronic disease risk among long-term ART users. The AFRICOS cohort reveals a redistribution of frailty toward older, heavier, and predominantly female survivors, signalling structural ageing of the treated HIV population. These findings demonstrate a micro-epidemiologic transition within HIV cohorts and underscore the need to integrate hypertension and chronic disease management into long-term HIV care. Abbreviations AFRICOS – African Cohort Study aOR – Adjusted Odds Ratio ART – Antiretroviral Therapy BMI – Body Mass Index CI – Confidence Interval CRVS – Civil Registration and Vital Statistics Δqₓ – Difference in probability of death (excess mortality) EWMA – Exponentially Weighted Moving Average HIV – Human Immunodeficiency Virus NCDs – Non-Communicable Diseases OR – Odds Ratio PLHIV – People Living with HIV REC – Research Ethics Committee Declarations Ethics approval and consent to participate The African Cohort Study (AFRICOS) received ethical approval from multiple institutional and national review boards in participating countries. In Uganda, approval was granted by the Makerere University School of Public Health Research Ethics Committee (MakSPH REC; MUSPH #173) and the Uganda National Council for Science and Technology (UNCST; HS 1175). In Kenya, approvals were obtained from the Kenya Medical Research Institute Scientific and Ethics Review Unit (KEMRI SERU; SSC 2371 and SSC 2396) and the Tenwek Hospital Institutional Review Committee (2020-0007). In Tanzania, the study was approved by the Mbeya Medical Research and Ethics Committee (MMREC; SZEC-2439/R.C/V.1/76) and the National Institute for Medical Research National Health Research Ethics Committee (NIMR NatHREC; NIMR/HQ/R.8b/Vol.I/1128). The study protocol was also reviewed and approved by the Walter Reed Army Institute of Research. All procedures were performed in accordance with the ethical standards of the institutional and national research committees and with the 1964 Declaration of Helsinki and its later amendments. All participants provided written informed consent at enrolment. The present analyses were conducted using de-identified data in accordance with the AFRICOS data-sharing agreement. Competing interests The authors declare that they have no competing interests. Funding This study is funded by the Science for Africa Foundation to the Developing Excellence in Leadership, Training and Science in Africa (DELTAS Africa) programme [Afrique One-ASPIRE, Del-15-008 and Afrique One-REACH, Del-22-011] with support from Wellcome Trust and the UK Foreign, Commonwealth & Development Office and is part of the EDCPT2 programme supported by the European Union. For purposes of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. The funders have no role in the study. Authorship contribution statement Conceptualisation, all authors; methodology, all authors; validation, W.S.O., C.L., D.H, B.B., F.S.N., S.G.M., and K.H; formal analysis, D.M.; investigation, D.M.; resources, B.B., K.H., and D.H.; data curation, D.M.; writing original draft preparation, D.M.; writing review and editing, all authors; visualisation, D.M., and W.S.O.; supervision, W.S.O., C.L., D.H, B.B., F.S.N. S.G.M and K.H; project administration, D.M. and F.S.N.; funding acquisition, B.B. and S.G.M. All authors have read and agreed to the published version of the manuscript. Acknowledgements I acknowledge the MHRP-AFRICOS study coordination for their support in facilitating data access, particularly thankful to Emma Duff for her responsiveness and technical assistance during the data request process. I also extend my appreciation to all the Principal Investigators of the AFRICOS study project for their contributions and data stewardship. References World Health Organisation. 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Handbook of child psychology. 2007;1. McEwen BS. Stress and Adaptation: Allostasis and Allostatic Load. Allostasis, homeostasis, and the costs of physiological adaptation. 2004:65. Ferraro KF, Shippee TP. Aging and cumulative inequality: How does inequality get under the skin? The Gerontologist. 2009;49(3):333-43. Murphy L, Bulstra CA, Figi JT, Fladger A, Atun R. Integration of healthcare services for HIV and non-communicable diseases in sub-Saharan Africa: protocol for a scoping review of randomised controlled trials. BMJ Open. 2025;15(2):e091183. Modjadji P. Communicable and non-communicable diseases coexisting in South Africa. The Lancet Global Health. 2021;9(7):e889-e90. Noel NB, Banwat ME, Okoro LN, Bulus NG, Nkala CA, Anyamene EL, et al. Predictors of Non-Communicable Disease Risk Factors Among People Living with HIV and HIV-Negative Patients in a Nigerian Tertiary Hospital. HIV AIDS (Auckl). 2025;17:87-103. Mfinanga SG. Access to comprehensive services for HIV and non-communicable diseases in sub-Saharan Africa. Lancet Glob Health. 2023;11(9):e1317-e8. Roomaney RA, van Wyk B, Cois A, Pillay-van Wyk V. Multimorbidity Patterns in a National HIV Survey of South African Youth and Adults. Front Public Health. 2022;10:862993. Omran A-R. The epidemiologic transition: a theory of the epidemiology of population change/Abdel R. Omran. 2001. Siedner MJ. Aging, Health, and Quality of Life for Older People Living With HIV in Sub-Saharan Africa: A Review and Proposed Conceptual Framework. J Aging Health. 2019;31(1):109-38. Preston SH. The changing relation between mortality and level of economic development. Popul Stud (Camb). 1975;29:231-48. Piggott DA, Bandeen-Roche K, Mehta SH, Brown TT, Yang H, Walston JD, et al. Frailty transitions, inflammation, and mortality among persons aging with HIV infection and injection drug use. Aids. 2020;34(8):1217-25. Jones R, Enogela EM, Ruderman SA, Kitahata MM, Moore R, Jacobson JM, et al. Cardiometabolic disease among frailty phenotype clusters in adults aging with HIV. J Frailty Aging. 2025;14(2):100011. Yu B, Ren N, Xiao T, Chen J, Pi Z, He L, et al. Central Role of Hypertension in HIV Comorbidity Networks: A Population-Based Study of Age and Sex-Specific Patterns in Southwest China. J Am Heart Assoc. 2025;14(10):e040634. Trickey A, May MT, Vehreschild J, Obel N, Gill MJ, Crane H, et al. Cause-Specific Mortality in HIV-Positive Patients Who Survived Ten Years after Starting Antiretroviral Therapy. PLoS One. 2016;11(8):e0160460. Trickey A, McGinnis K, Gill MJ, Abgrall S, Berenguer J, Wyen C, et al. Longitudinal trends in causes of death among adults with HIV on antiretroviral therapy in Europe and North America from 1996 to 2020: a collaboration of cohort studies. Lancet HIV. 2024;11(3):e176-e85. Trickey A, Sabin CA, Burkholder G, Crane H, d'Arminio Monforte A, Egger M, et al. Life expectancy after 2015 of adults with HIV on long-term antiretroviral therapy in Europe and North America: a collaborative analysis of cohort studies. Lancet HIV. 2023;10(5):e295-e307. Masenga SK, Elijovich F, Koethe JR, Hamooya BM, Heimburger DC, Munsaka SM, et al. Hypertension and Metabolic Syndrome in Persons with HIV. Curr Hypertens Rep. 2020;22(10):78. Bigna JJ, Ndoadoumgue AL, Nansseu JR, Tochie JN, Nyaga UF, Nkeck JR, et al. Global burden of hypertension among people living with HIV in the era of increased life expectancy: a systematic review and meta-analysis. Journal of Hypertension. 2020;38(9):1659-68. Denu MK, Revoori R, Buadu MAE, Oladele O, Berko KP. Hypertension among persons living with HIV/AIDS and its association with HIV-related health factors. AIDS Research and Therapy. 2024;21(1):5. Kivuyo S, Birungi J, Okebe J, Wang D, Ramaiya K, Ainan S, et al. Integrated management of HIV, diabetes, and hypertension in sub-Saharan Africa (INTE-AFRICA): a pragmatic cluster-randomised, controlled trial. The Lancet. 2023;402(10409):1241-50. 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-8863155","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":594399926,"identity":"9068bf7d-439a-410d-a262-70bd8d5f377b","order_by":0,"name":"Denis Mayambala","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYDACCQZmIMkMQowPGBgOkKaF2YBELQwMbBJEadGd3fzY4OMeazlzdvZn1Tw1d+T4GZgfPrqBR4vZnWPGiTOepRtbNvOY3eY59sxYsoHN2DgHn5YbCcaHeQ4cTtxwmIftNg8bkHGAh00av5b0z4f/HDhcv+Ew+7Ninn9EackxTmY4cDjB4DCDGTNvGzFa7pwpNuw5kG4IdJix5Ny+w8aSzYT8crt9s8SPA9byBuePP/zw5tthOX725oeP8WlBAUw8IJKZWOUgwPiDFNWjYBSMglEwYgAAfPZQMg9WdxMAAAAASUVORK5CYII=","orcid":"","institution":"Makerere University","correspondingAuthor":true,"prefix":"","firstName":"Denis","middleName":"","lastName":"Mayambala","suffix":""},{"id":594399927,"identity":"07acd492-6772-471c-98d4-855c86724c3f","order_by":1,"name":"Wandera Stephen Ojjambo","email":"","orcid":"","institution":"Makerere University","correspondingAuthor":false,"prefix":"","firstName":"Wandera","middleName":"Stephen","lastName":"Ojjambo","suffix":""},{"id":594399928,"identity":"d1eca14a-9a5b-431f-8a4d-fddcc0837bde","order_by":2,"name":"Charles Lwanga","email":"","orcid":"","institution":"Makerere University","correspondingAuthor":false,"prefix":"","firstName":"Charles","middleName":"","lastName":"Lwanga","suffix":""},{"id":594399929,"identity":"066a2013-d819-48d9-96af-1779c328e174","order_by":3,"name":"Dan Haydon","email":"","orcid":"","institution":"University of Glasgow","correspondingAuthor":false,"prefix":"","firstName":"Dan","middleName":"","lastName":"Haydon","suffix":""},{"id":594399930,"identity":"b84c98cf-ee5e-458f-880e-bb97253c4962","order_by":4,"name":"Bassirou Bonfoh","email":"","orcid":"","institution":"Centre Suisse de Recherches Scientifiques en Côte d’Ivoire, Abidjan BP 1303","correspondingAuthor":false,"prefix":"","firstName":"Bassirou","middleName":"","lastName":"Bonfoh","suffix":""},{"id":594399931,"identity":"91bce2fa-0e65-40b0-8e9f-2fd0ec346200","order_by":5,"name":"Sayoki G. Mfinanga","email":"","orcid":"","institution":"National Institute for Medical Research, Muhimbili Medical Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Sayoki","middleName":"G.","lastName":"Mfinanga","suffix":""},{"id":594399932,"identity":"234767da-1059-489e-9c69-3853aa1c2b25","order_by":6,"name":"Hannah Kibuuka","email":"","orcid":"","institution":"Makerere University Walter Reed Program (MUWRP)","correspondingAuthor":false,"prefix":"","firstName":"Hannah","middleName":"","lastName":"Kibuuka","suffix":""},{"id":594399933,"identity":"824795aa-60b2-4812-9cbd-f9a4faed57fe","order_by":7,"name":"Francis Sena Nuvey","email":"","orcid":"","institution":"Friedrich-Loeffler-Institut","correspondingAuthor":false,"prefix":"","firstName":"Francis","middleName":"Sena","lastName":"Nuvey","suffix":""}],"badges":[],"createdAt":"2026-02-12 14:26:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8863155/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8863155/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-026-27241-3","type":"published","date":"2026-04-02T15:58:14+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":103301927,"identity":"d6acce8b-5d83-4f03-ae2e-effcb4b19d8f","added_by":"auto","created_at":"2026-02-24 08:21:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":137470,"visible":true,"origin":"","legend":"\u003cp\u003eAdjusted temporal pattern of mortality among hypertensive participants under alternative exposure-weighting schemes (EMA α = 0.3 and α = 0.7).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8863155/v1/6670f0bf64502433f1587d65.png"},{"id":106343980,"identity":"0a949fcc-e1d8-431d-a13d-e8ab5a8d160b","added_by":"auto","created_at":"2026-04-07 16:11:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1060673,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8863155/v1/68097a40-d014-4055-9db7-d9b4992bb8ce.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hypertension is a marker of the micro-epidemiologic transition in ageing HIV populations in Kenya, Uganda and Tanzania (AFRICOS, 2013–2023)","fulltext":[{"header":"Background","content":"\u003cp\u003eOver the past two decades, lower- and middle-income countries (LMICs) have undergone a profound epidemiological and demographic transition, marked by a shift away from communicable, maternal, neonatal, and nutritional causes toward non-communicable diseases (NCDs) as the dominant sources of mortality and disability. NCDs now account for approximately 71% of global deaths, including nearly 18\u0026nbsp;million cardiovascular deaths annually (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). sub-Saharan Africa (SSA) is experiencing a rapid rise in NCD-related disability and mortality (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) driven by urbanisation, dietary change, and declining physical activity (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWithin this broader transition, HIV remains endemic in SSA, and it currently comprises about two-thirds of the worldwide population of people living with HIV (PLHIV) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The large-scale expansion of antiretroviral therapy (ART) has fundamentally reshaped mortality patterns among PLHIV. Cohorts once characterised by high levels of premature adult mortality are now surviving into older ages, producing a marked upward shift in the age schedule of HIV mortality (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). In Uganda\u0026rsquo;s Rakai cohort, partial life expectancy before age 50 increased by more than a decade within a single treatment generation (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). As a result, morbidity and mortality among PLHIV increasingly reflect the same epidemiological forces driving population-level NCD transitions, rather than HIV-specific opportunistic disease.\u003c/p\u003e \u003cp\u003eAs a consequence, PLHIV's present-day population structure is demographically distinct: it is larger, older, and increasingly characterised by the co-occurrence of chronic conditions. Globally, the proportion of PLHIV aged 50 years and older nearly doubled between 2000 and 2016, with almost 80% residing in LMICs and SSA accounting for the largest absolute numbers (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Although PLHIV remain a numerical minority within older adult populations, modelling studies consistently show that they are disproportionately represented among individuals experiencing NCD incidence and multimorbidity, particularly combinations involving HIV, hypertension, and diabetes in high-prevalence settings (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) ).\u003c/p\u003e \u003cp\u003eThose patterns are consequential in SSA, which continues to host nearly two-thirds of the global PLHIV population (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Longitudinal and simulation-based analyses further suggest that, even as survival improves, PLHIV accumulate chronic conditions earlier and spend fewer years free of multimorbidity, positioning this population as a critical lens through which to examine multi-disease accumulation under conditions of constrained health system capacity (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The main problem of the existence of NCDs is the emergence of multimorbidity (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), especially the combination of HIV, hypertension, and diabetes. Meta-analyses estimate hypertension in roughly one-in-five PLHIV in SSA, with disproportionate clustering among older adults, men, and those with higher CD4 counts on long-term ART (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe rise of hypertension is a quiet but consequential demographic force. Its prevalence now rivals that of any major infectious comorbidity among PLHIV, ranging from 18 to 27 per cent in East Africa (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Hypertension manifests earlier in the life course, often beginning in the forties (40s) and reflecting etiologies that extend beyond pharmacologic side effects to include prolonged immune activation, nutritional transition, and psychosocial stress (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). As AIDS-related mortality declines, cardiovascular and hypertensive causes increasingly occupy the mortality schedule of treated HIV populations (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). This shift exemplifies what Kuate Defo described as Africa\u0026rsquo;s dual burden of disease: the coexistence of infectious and chronic degeneration within the same cohorts (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis paper introduces the concept of a micro-epidemiologic transition, which is a shift from infection-dominated to chronic, circulatory mortality occurring within ART-treated HIV cohorts rather than across whole societies. The central question is structural rather than purely clinical: \u003cem\u003eto what extent do chronic vascular processes now account for mortality among ageing PLHIV, and how do they reshape cohort survival patterns?\u003c/em\u003e Conventional HIV mortality models continue to prioritise AIDS-specific causes of death, thereby understating the contribution of chronic comorbidities to all-cause mortality. Although life-table decomposition and decrement analysis offer robust tools for quantifying cause-specific contributions to survival change (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). These methods have rarely been applied to African HIV cohorts now entering older adulthood. This gap limits our ability to measure the population-level consequences of the health transition already underway.\u003c/p\u003e \u003cp\u003eTheoretically, this transition can be understood through the lenses of cumulative disadvantage and the life course (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Early HIV infection, delayed treatment initiation, and chronic psychosocial stress accumulate physiological wear, which McEwen termed allostatic load, manifesting as hypertension later in life (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Thus, hypertension becomes not only a clinical risk factor but as a demographic signal of inequality embedded across decades of exposure (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Empirical evidence underscores the stakes of this transition, where uncontrolled hypertension has been shown to double all-cause mortality among PLHIV in South Africa (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), while cardiovascular deaths are rising within Tanzanian ART programmes (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Yet these findings remain expressed in clinical effect measures, such as odds ratios or hazard ratios, without translation into demographic quantities such as excess mortality, survival probabilities, or years of life lost.\u003c/p\u003e \u003cp\u003eThis paper reframes hypertension as a demographic determinant of survival in ageing HIV populations and contributes a new conceptual framework for understanding the ART-era mortality shift as a micro-epidemiologic transition within treated cohorts. This is demonstrated through longitudinal cohort data from the African Cohort Study (AFRICOS, 2013\u0026ndash;2023), showing how selective survival and chronic disease accumulation jointly reshape mortality structures among PLHIV aged 40 and older in Kenya, Uganda, and Tanzania.\u003c/p\u003e"},{"header":"Data and Methods","content":"\u003cp\u003eWe used longitudinal data from the African Cohort Study (AFRICOS), a multi-country cohort following adults receiving antiretroviral therapy at sites in Kenya, Uganda, and Tanzania since 2013. The study design linked clinical records with demographic and behavioural data collected every six months, providing repeated observations of treatment history, biomarkers, non-communicable disease indicators, and vital status. For this analysis, we restricted the sample to people living with HIV aged 40 years and older who contributed at least two follow-up visits between 2013 and 2023. After excluding cases with missing blood-pressure or mortality information, the analytic file contained 1,169 individuals at baseline year, increased to a total of 1,360 participants at the end of the tenth-year visit, contributing 12,462 person-periods across the follow-up period.\u003c/p\u003e \u003cp\u003eThe outcome was all-cause mortality observed during follow-up. Deaths were confirmed through clinic and community tracing systems and coded at the next scheduled visit. Time was measured in half-year intervals corresponding to the AFRICOS visit schedule. Hypertension, the principal exposure, is defined as systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mm Hg, diastolic\u0026thinsp;\u0026ge;\u0026thinsp;90 mm Hg, or reported antihypertensive use at a given visit. To reflect cumulative exposure over the life course, we construct a lagged exponentially weighted moving average (EWMA) of hypertension status. This measure assigned greater weight to recent observations while retaining information on prior exposure, translating the concept of risk accumulation from life-course theory into an empirical variable. Participants who remained normotensive across all visits formed the reference group.\u003c/p\u003e \u003cp\u003eCovariates captured demographic and clinical factors known to shape survival: age, sex, body mass index (BMI), viral load, and country of follow-up. BMI was calculated as weight (kg) divided by height (m) using measured height and weight. Categories were defined as: underweight (\u0026lt;\u0026thinsp;18.5), normal weight (18.5\u0026ndash;24.9), overweight (25.0\u0026ndash;29.9), and obese (\u0026ge;\u0026thinsp;30). Viral load was categorised using standard WHO clinical thresholds as: 1\u0026thinsp;=\u0026thinsp;suppressed (\u0026lt;\u0026thinsp;50 copies/mL), 2\u0026thinsp;=\u0026thinsp;low-level viremia (50\u0026ndash;999 copies/mL), and 3\u0026thinsp;=\u0026thinsp;high (\u0026ge;\u0026thinsp;1000 copies/mL). Age, BMI, and viral load were treated as time-varying. Missing covariate values were rare and handled by carrying the last observation forward. The analysis proceeded in two stages. First, we construct decrement life tables to estimate interval probabilities of death \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(qx\\right)\\)\u003c/span\u003e\u003c/span\u003e separately for hypertensive and non-hypertensive individuals. The difference \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:{q}_{\\text{x}}={{q}_{\\text{x}}}^{HTN}-{{q}_{\\text{x}}}^{non-HTN}\\)\u003c/span\u003e\u003c/span\u003e​ represents the excess probability of dying associated with hypertension. Life tables summarised the cumulative, unadjusted mortality differences over time, capturing both direct and compositionally mediated effects. This provided a population-level description of mortality differentials and their evolution across follow-up, following the logic of cause-deleted life-table analysis (Beltr\u0026aacute;n-S\u0026aacute;nchez et al. 2008).\u003c/p\u003e \u003cp\u003eSecond, we fitted lagged discrete-time logistic-regression models of the probability of death in each interval as a function of prior hypertension exposure and covariates:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{log}\\left(\\frac{\\left(P\\right({Death}_{it}=1)}{1-P\\left({Death}_{it}=1\\right)}\\right)={\\beta\\:}_{0}++{\\beta\\:}_{1}{EMA}_{{HTN}_{i\\left(t-1\\right)}}+{\\beta\\:}_{2}{X}_{i\\left(t-1\\right)}+\\gamma\\:t$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{EMA}_{{HTN}_{i\\left(t-1\\right)}}\\)\u003c/span\u003e\u003c/span\u003erepresented the lagged exponential moving average of hypertension status for individual i at time \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t-1\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i\\left(t-1\\right)}\\)\u003c/span\u003e\u003c/span\u003e is a vector of lagged covariates including country, age category, sex, BMI category, and viral load category,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{\\beta\\:}_{0}\\)\u003c/span\u003e\u003c/span\u003e​ is the intercept, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:2\\)\u003c/span\u003e\u003c/span\u003e​ is the vector of coefficients for confounders, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\gamma\\:t\\)\u003c/span\u003e\u003c/span\u003e represented the visit-specific fixed effects, accounting for unobserved heterogeneity across time points. Standard errors were clustered at the individual level to account for repeated measures. Estimated coefficients were presented as odds ratios and converted to adjusted probabilities to facilitate interpretation. This isolated the independent effect of each covariate after controlling for correlated characteristics. Attenuation of some covariates in the regression indicates that their life-table differentials were partly driven by shared pathways or selective survival rather than independent effects. In this sense, the life-table analysis and regression results are complementary: together, they document both the demographic accumulation of risk and the net, adjusted contributions of key exposures.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe analysis that follows traces how the survival gains of the ART era are being re-inscribed in new forms of chronic risk, allowing the demographic contours of this micro-epidemiologic transition to be observed directly.\u003c/p\u003e\n\u003ch3\u003eDescriptive characteristics\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarises the demographic and clinical composition of the analytic cohort (n\u0026thinsp;=\u0026thinsp;1,169), representing a maturing population of PLHIV transitioning into mid- and later life. Participants were predominantly aged 40\u0026ndash;49 years (56.7%), female (48.8%), and resident in Kenya (59.7%), with a median follow-up of about seven years. Hypertension was present in nearly one-fifth of participants at baseline (18.6%) and affected more than half of the cohort (60%) across the ten-year observation period. Overall mortality during follow-up was 6.4%, consistent with a cohort in which infectious mortality has largely receded but not disappeared.\u003c/p\u003e \u003cp\u003eAge and compositional differentials reveal clear population structuring. The prevalence of hypertension increased from 13.6% among those aged 40\u0026ndash;49 years to 32.3% among those 60 years and older (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while mortality followed a similar gradient from 5.7% to 12.1% (p\u0026thinsp;=\u0026thinsp;0.04). Men and women displayed near-equal hypertension prevalence (18\u0026ndash;19%), yet men\u0026rsquo;s mortality remained higher (7.9% vs. 4.9%, p\u0026thinsp;=\u0026thinsp;0.04), reflecting the durable sex-gap in adult survival observed across African populations and its persistence within the HIV-positive subpopulation.\u003c/p\u003e \u003cp\u003eNutritional status showed an inverted demographic pattern. Hypertension prevalence increased steadily with body mass from 44% among the underweight to 82% among the obese (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, mortality declined across the same gradient, from 17.6% among the underweight to 2.5% among the obese (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eViral suppression status further stratified survival outcomes. Although overall suppression was high (77%), mortality differed sharply by viral load, with significantly lower mortality among suppressed individuals (4.3%) compared with 15.9% among those with high viremia (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Hypertension was more common among the suppressed (62%) than the unsuppressed (50%, p\u0026thinsp;=\u0026thinsp;0.006), reflecting compositional ageing within the cohort.\u003c/p\u003e \u003cp\u003eGeographic variations observed mirror structural differences in health-system performance. Kenya, contributing the largest share of participants, exhibited intermediate hypertension prevalence (17%) and mortality (6%). Tanzania showed the highest hypertension prevalence (32%) but lowest mortality (4%), whereas Uganda showed the reverse pattern, with lower hypertension (10%) but higher mortality (9%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the study population at baseline and distribution of hypertension and mortality (AFRICOS 2013\u0026ndash;2023)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypertensive\u003c/p\u003e \u003cp\u003en (row%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003cp\u003en (row%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 169 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e217 (18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e599 (51.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107 (17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47 (7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.041 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e570 (48.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110 (19.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e663 (56.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98 (13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.046 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e347 (29.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60 +\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e148 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26 (17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e631 (54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e340 (53.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e228 (19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e165 (72.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132 (81.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eViral load status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuppressed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e900 (77.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e557 (61.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006 **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-level viremia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (63.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh viral load\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e182 (15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (49.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29 (15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry of residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKenya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e698 (59.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTanzania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e256 (21.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82 (32.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUganda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e215 (18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEver hypertensive (during follow-up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e702 (60.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMortality rate (all participants)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMedian follow-up years (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.2 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eLife-table analysis of excess mortality\u003c/h3\u003e\n\u003cp\u003eDecrement life tables were constructed to examine mortality differentials between hypertensive and non-hypertensive participants across eleven annual intervals (2013\u0026ndash;2023). Excess mortality (Δqₓ) for hypertensives increased steadily over time, from approximately 0.4% in the first interval to 4.4% by the ninth, after which it plateaued (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The temporal progression of Δqₓ shows a gradual widening of mortality differences between exposure groups during follow-up.\u003c/p\u003e \u003cp\u003eAge-specific estimates revealed clear gradients. Among participants aged 40\u0026ndash;49 years, Δqₓ increased from near zero in the early intervals to 4\u0026ndash;5% by the end of observation. For those aged 50\u0026ndash;59 years, the increase was more pronounced, exceeding 5% by year nine. Participants aged 60 years and older showed positive excess mortality in early intervals (\u0026asymp;\u0026thinsp;2%), though estimates became unstable in later intervals due to smaller denominators. These trends indicate that the relative disadvantage associated with hypertension increases with age within the observed range.\u003c/p\u003e \u003cp\u003eSex differences followed a consistent pattern across intervals. Excess mortality among men was positive in all intervals, reaching about 8% by year nine, while among women, the difference remained modest (\u0026asymp;\u0026thinsp;3%). The persistent positive Δqₓ for men points to higher mortality at each stage of follow-up.\u003c/p\u003e \u003cp\u003eClear gradients were also observed by body mass index. Underweight participants recorded large positive excesses throughout the period, exceeding 20% in early intervals. Normal-weight and overweight groups exhibited moderate positive values (3\u0026ndash;7%), while the obese group maintained near-zero excess. The direction of these gradients remained stable over time, suggesting that differences in nutritional status are strongly associated with mortality outcomes within this cohort.\u003c/p\u003e \u003cp\u003eViral-load categories also showed systematic contrasts. Participants with high viral load recorded consistently positive Δqₓ values of about 7%, whereas suppressed individuals accumulated smaller excesses (\u0026asymp;\u0026thinsp;6% by year nine). Estimates for those with low-level viremia fluctuated around zero due to small numbers. Across groups, mortality differences persisted through follow-up, with no indication of convergence.\u003c/p\u003e \u003cp\u003eCountry-specific life tables revealed heterogeneity in the magnitude and timing of excess mortality. In Kenya, Δqₓ remained near zero through mid-follow-up, before turning slightly positive (\u0026asymp;\u0026thinsp;3%) in later intervals. In Tanzania, values were positive and stable around 5% across intervals, while Uganda recorded the highest and most sustained excess mortality, averaging 7\u0026ndash;8% throughout the follow-up. These differences correspond to the relative timing and coverage of integrated chronic-disease care across the sites.\u003c/p\u003e \u003cp\u003eBy allostatic load, mortality differentials were modest for the low-load group during early intervals (\u0026asymp;\u0026thinsp;3%) but widened mid-follow-up (\u0026asymp;\u0026thinsp;8%). For the high-load group, estimates were negative or near zero early on but increased to about 12% by the final interval, reflecting delayed divergence between physiological stress categories. Across subgroups, the trajectory of Δqₓ consistently widened through the observation period before stabilising, suggesting that survival differences accumulate gradually over successive intervals.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCumulative excess mortality (Δqₓ) by demographic and clinical subgroup, AFRICOS 2013\u0026ndash;2023\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.0322\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003eAge Groups\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.0445\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.0539\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 60+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.0171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.0281\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.2774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.0146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.0698\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003eViral Load\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuppressed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.0495\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-Level Viremia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.1033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.1033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.1215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.1215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.0702\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKenya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.0049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTanzania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUganda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.0107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.0107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003eAllostatic load\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.0569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.0707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.0707\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.0144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.0144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.1160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eMultivariable results\u003c/h3\u003e\n\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the odds ratio (log scale) for all-cause mortality associated with hypertension across successive follow-up visits, estimated from adjusted discrete-time logistic regression models. Shaded bands indicate 95% confidence intervals. Both models reveal elevated mortality risk during the early observation period (visits 2\u0026ndash;7; OR\u0026thinsp;\u0026asymp;\u0026thinsp;13\u0026ndash;18), followed by progressive attenuation to below OR\u0026thinsp;=\u0026thinsp;3 by visit 16. The close alignment of the two curves demonstrates the robustness of the results to alternative smoothing parameters: α\u0026thinsp;=\u0026thinsp;0.3 captures long-term cumulative exposure, while α\u0026thinsp;=\u0026thinsp;0.7 emphasizes short-term variation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDiscrete-time logistic regression models (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) quantified mortality differentials associated with hypertension after accounting for demographic and clinical composition. In the unadjusted model using the exponentially weighted moving average (EWMA α\u0026thinsp;=\u0026thinsp;0.3), hypertensive participants had more than twice the odds of death compared with those without hypertension (OR\u0026thinsp;=\u0026thinsp;2.15; 95% CI 0.86\u0026ndash;5.33). Adjustment for covariates strengthened this association, with the odds of death among hypertensives approximately three times higher (AOR\u0026thinsp;=\u0026thinsp;3.25; 95% CI 1.26\u0026ndash;8.40). The adjusted model using the standard lag specification produced a slightly smaller estimate (AOR\u0026thinsp;=\u0026thinsp;2.04; 95% CI 1.10\u0026ndash;3.80), while the alternative EWMA α\u0026thinsp;=\u0026thinsp;0.7 model yielded a coefficient of similar magnitude (AOR\u0026thinsp;=\u0026thinsp;2.51; 95% CI 1.16\u0026ndash;5.44).\u003c/p\u003e \u003cp\u003eAge maintained a consistent positive gradient with mortality. Relative to participants aged 40\u0026ndash;49 years, the odds of death were about twice as high among those aged 60 years or older (AOR\u0026thinsp;\u0026asymp;\u0026thinsp;2.3; 95% CI 0.9\u0026ndash;6.0). Sex differentials remained in the expected direction, with females exhibiting lower odds of death than males (AOR\u0026thinsp;\u0026asymp;\u0026thinsp;0.63; 95% CI 0.31\u0026ndash;1.26). Body mass index showed a clear inverse pattern: mortality declined steadily with increasing BMI. Overweight and obese participants experienced substantially lower odds of death (AOR\u0026thinsp;=\u0026thinsp;0.29 and 0.14, respectively), while underweight participants represented the highest-risk group.\u003c/p\u003e \u003cp\u003eGradients by viral load were pronounced. Compared with virally suppressed participants, those with high viral load had more than double the odds of death (AOR\u0026thinsp;\u0026asymp;\u0026thinsp;2.4; 95% CI 1.3\u0026ndash;4.5). Mortality differences across countries were consistent with those observed in the life-table estimates: odds were lowest in Tanzania (AOR\u0026thinsp;\u0026asymp;\u0026thinsp;0.19) and highest in Uganda (AOR\u0026thinsp;\u0026asymp;\u0026thinsp;1.24) relative to Kenya. Inclusion of the allostatic-load variable modestly reduced the hypertension coefficient but did not alter its significance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiscrete-time logistic regression of all-cause mortality among PLHIV (Unadjusted and adjusted models, AFRICOS 2013\u0026ndash;2023)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted EWMA α\u0026thinsp;=\u0026thinsp;0.3\u003c/p\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjusted Standard Lag\u003c/p\u003e \u003cp\u003eaOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted EWMA\u003c/p\u003e \u003cp\u003eα\u0026thinsp;=\u0026thinsp;0.3\u003c/p\u003e \u003cp\u003eaOR (95% CI)\u003c/p\u003e\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted EWMA\u003c/p\u003e \u003cp\u003eα\u0026thinsp;=\u0026thinsp;0.7\u003c/p\u003e \u003cp\u003eaOR (95% CI)\u003c/p\u003e\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension exposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.15 (0.86\u0026ndash;5.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.04 (1.10\u0026ndash;3.80) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.25 (1.26\u0026ndash;8.40) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.51 (1.16\u0026ndash;5.44) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eCountry (ref\u0026thinsp;=\u0026thinsp;Kenya)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTanzania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21(0.08\u0026ndash;0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19 (0.07\u0026ndash;0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.20 (0.08\u0026ndash;0.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUganda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21 (0.52\u0026ndash;2.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.27 (0.54\u0026ndash;2.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.24 (0.53\u0026ndash;2.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eAge (ref\u0026thinsp;=\u0026thinsp;40\u0026ndash;49 y)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;59 y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16 (0.59\u0026ndash;2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10 (0.56\u0026ndash;2.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.13 (0.57\u0026ndash;2.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026thinsp;+\u0026thinsp;y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.40 (0.94\u0026ndash;6.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.23 (0.85\u0026ndash;5.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.32 (0.89\u0026ndash;6.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSex (ref\u0026thinsp;=\u0026thinsp;female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.53 (0.24\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63 (0.31\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.63 (0.31\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eBMI (ref\u0026thinsp;=\u0026thinsp;underweight)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.63 (0.31\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63 (0.30\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.63 (0.31\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.31 (0.11\u0026ndash;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29 (0.10\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.30 (0.10\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15(0.04\u0026ndash;0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14 (0.04\u0026ndash;0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.14 (0.04\u0026ndash;0.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eViral load (ref\u0026thinsp;=\u0026thinsp;suppressed)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow viremia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.41 (1.16\u0026ndash;4.99) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.43 (1.16\u0026ndash;5.09) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.42 (1.16\u0026ndash;5.04) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh viral load\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.36 (1.28\u0026ndash;4.33) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.44 (1.32\u0026ndash;4.51) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.39 (1.30\u0026ndash;4.41) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations (person-periods)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 462\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: aOR adjusted Odds Ratios; EWMA= Exponentially weighted moving averages; α\u0026thinsp;=\u0026thinsp;smoothing parameter; Adj\u0026thinsp;=\u0026thinsp;adjusted; OR\u0026thinsp;=\u0026thinsp;unadjusted Odds Ratios. All models include time-fixed effects for the follow-up wave results presented as ORs (95% Confidence Intervals).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe objective of this study was to examine how hypertension shapes all-cause mortality trajectories among people living with HIV (PLHIV) aged 40 years and above in Kenya, Uganda, and Tanzania. Life-table analyses address cumulative mortality experience at the population level and revealed widening excess mortality among hypertensive participants over follow-up, particularly among older adults, men, and underweight individuals. These patterns capture the accumulation of unadjusted mortality differences as cohorts age under sustained ART. By contrast, discrete-time regression models estimate conditional mortality risks after accounting for correlated demographic and clinical characteristics. In these adjusted models, hypertension remained strongly associated with mortality, while many other covariates exhibited smaller or non-significant effects. This attenuation reflects the distinction between unconditional, cumulative differences (life tables) and conditional, composition-adjusted effects (regression): covariates that appear salient in life tables often share pathways or exposure histories, so adjustment reduces their independent contribution. Together, the two approaches demonstrate that hypertension is an independent mortality risk embedded within a survival-selected population, while other apparent differentials largely reflect selective survival and compositional change.\u003c/p\u003e \u003cp\u003eHypertension prevalence partly indexes successful survival through the HIV care cascade, a demonstration that chronic disease burden is now influencing mortality outcomes, marking a demographic shift toward chronic-condition-dominated survival. The findings may be affirming that the expansion of antiretroviral therapy (ART) in sub-Saharan Africa has fundamentally altered survival patterns among people living with HIV (PLHIV), creating cohorts that are increasingly older and exposed to chronic disease risks (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) such as hypertension and diabetes. Participants who maintain long-term suppression survive into older ages, where hypertensive processes become common. Because life-table analyses summarise cumulative mortality experience over time, they are particularly well suited to capturing this shift, situating PLHIV within a micro-epidemiologic transition in which mortality is no longer dominated solely by opportunistic infections but increasingly by slow-acting, chronic conditions such as hypertension.\u003c/p\u003e \u003cp\u003eThe substantially higher cumulative mortality associated with hypertension reframes hypertension from a purely clinical comorbidity to a demographic force structuring survival among long-term ART users. While previous studies have highlighted the importance of expanding health systems beyond infectious mortality management through integrated HIV\u0026ndash;NCD care and longitudinal monitoring of ageing cohorts (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), our findings show that without accounting for cumulative chronic disease exposure, population-level mortality patterns among PLHIV remain only partially explained. Hypertension increasingly indexes successful survival through the HIV care cascade, whereby those who remain virologically suppressed and engaged in care live long enough to accumulate chronic conditions that then shape their mortality trajectories. This interpretation follows directly from the life-table perspective, which reveals how survivorship redistributes risk within ageing cohorts over time.\u003c/p\u003e \u003cp\u003eThe excess-mortality life-table profiles, together with the pronounced early-follow-up mortality peaks observed in the regression models, indicate steeper mortality gradients among hypertensive PLHIV during the initial years of observation. This pattern is consistent with accelerated survival loss among individuals carrying higher cardiometabolic burden as ART shifts HIV survival schedules upward, relocating mortality from young adulthood into midlife and older ages (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The subsequent attenuation of mortality differentials reflects selective survival: individuals who survive the early, high-risk period are disproportionately older, female, and virologically stable. As frailer individuals are removed earlier, risk becomes increasingly concentrated among long-term survivors, reshaping the cohort\u0026rsquo;s metabolic and mortality profile. In this context, hypertension emerges not as an anomaly, but as a predictable feature of ART-enabled longevity within a survival-selected population.\u003c/p\u003e \u003cp\u003eA distinctive finding is the temporal inversion of mortality risk associated with hypertension. Although hypertension was linked to elevated mortality early in follow-up, this excess risk attenuated and, among older survivors, reversed. This pattern reflects classic survivorship and selective attrition processes in ageing cohorts, whereby early deaths disproportionately remove the frailest individuals, leaving a more robust subset at older ages (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). In this context, hypertension functions less as an immediate mortality trigger at later ages and more as an indicator of entry into a later life-course stage among PLHIV, in which cumulative, slow-acting processes dominate risk. The life-table perspective is critical here, as it captures how mortality differentials evolve across successive intervals rather than at a single point in time.\u003c/p\u003e \u003cp\u003eThe epidemiologic transition traditionally describes population-level shifts from infectious to chronic disease mortality over extended historical periods. Our findings suggest that a similar transition is occurring within a much shorter time frame and within a defined subpopulation, driven primarily by biomedical intervention rather than socioeconomic development. While Omran\u0026rsquo;s framework was formulated at the population level (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), growing evidence from Uganda, Tanzania, and South Africa indicates that cohort-level transitions are emerging among ageing PLHIV, characterised by rising multimorbidity and chronic disease burden (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). By combining life-table and regression approaches, our analysis quantifies how selective survival under sustained ART reshapes mortality structure within a single treatment generation.\u003c/p\u003e \u003cp\u003eMortality patterns by age-group, sex, nutritional status, viral suppression, and national context further reinforce this interpretation. In line with Preston\u0026rsquo;s description of steepening mortality curves during transitions between epidemiologic regimes (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), we observed a midlife spike in excess mortality between ages 50 and 59, followed by attenuation at older ages. This pattern is consistent with recent cohort analyses showing that frailty and comorbidities, particularly hypertension, are highly prevalent and strongly associated with increased mortality risk in midlife, but their impact diminishes among older survivors due to selective attrition and survivorship effects (\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Survivors in their 60s and beyond represent a highly selected cohort, capable of managing both HIV and the metabolic demands of long-term therapy (\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe emergence of hypertension alongside age-specific reversals in excess mortality suggests that the treated HIV population is entering a mortality regime increasingly shaped by cumulative physiological burden rather than infectious causes. This interpretation is consistent with evidence that ageing PLHIV experience rising multimorbidity, chronic disease vulnerability, and functional decline (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), and with studies linking HIV infection and long-term ART exposure to accelerated cardiometabolic risk through immune activation and endothelial dysfunction (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). The compositional gradients observed in our analysis reinforce this mechanism. Higher mortality among men reflects later presentation and poorer retention in care (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), yielding a survivor population increasingly composed of older, virologically stable women. Similarly, the inverse BMI\u0026ndash;mortality gradient marked excess mortality among underweight participants and minimal risk among overweight and obese individuals indicates a survival process that favours metabolic resilience. This is consistent with evidence that improved nutritional status is associated with better survival and that cardiometabolic risk accumulates over time in ageing PLHIV populations (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Together, these patterns show how selective survival reshapes cohort composition, concentrating chronic disease burden among long-term survivors.\u003c/p\u003e \u003cp\u003eHypertension\u0026rsquo;s higher prevalence among virally suppressed individuals further underscores its role as a marker of longevity rather than treatment failure, findings consistent with (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Country-level differences in excess mortality and hypertension prevalence likely reflect variation in ART scale-up, NCD screening, and chronic-care integration across health systems in the region, as reported in comparative studies across the region (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Collectively, these demographic and institutional processes support the view that chronic disease among PLHIV is not simply a clinical comorbidity, but evidence of a cohort that has survived into a new mortality regime. These patterns reinforce the need for integrated care models, as recent trials demonstrate that combining HIV and NCD management can maintain high viral suppression while improving chronic disease outcomes (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Rather than occurring uniformly, the epidemiologic transition in HIV appears staggered, with hypertension serving as a sentinel marker of survivorship and health-system adaptation in the ART era. Recognising and quantifying this transition is essential for recalibrating surveillance, resource allocation, and policy as treated HIV populations age and the boundaries between infectious and chronic disease increasingly blur.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eOur application of decrement life tables extended traditional demographic tools by disaggregating survival differences between hypertensive and non-hypertensive PLHIV across subgroups defined by BMI, viral load, and country. The addition of interval-specific excess mortality enhanced their interpretive value for population-level burden estimation. The use of lagged discrete-time logistic regression provided a pragmatic approach to incorporating time-varying exposures, thereby accommodating the sequencing of hypertension and mortality events. Combined with exponentially weighted moving averages (EWMA), this allowed us to approximate cumulative exposure and align with life course principles of accumulation.\u003c/p\u003e \u003cp\u003eAt the same time, several limitations must be acknowledged. The AFRICOS dataset captures survival and hypertension histories within a defined clinical population, but several dimensions essential to temporal inference remain incomplete. Precise dates of HIV diagnosis, ART initiation, and hypertension onset were not consistently available, limiting temporal sequencing of exposure and outcome. As a result, the study could not distinguish whether hypertension preceded or followed ART initiation, nor fully capture the cumulative duration of treated infection, which are both likely to influence mortality. Mortality recording was also limited to primary causes, potentially understating the contribution of chronic diseases to observed deaths. These gaps suggest that the excess mortality estimates presented here should be interpreted as conservative lower bounds.\u003c/p\u003e \u003cp\u003eAnalytically, the mechanisms of selective survival and frailty redistribution are inferred rather than directly modelled. Future studies should incorporate formal frailty or decomposition frameworks to quantify how compositional change contributes to the observed attenuation of hazards over time and to test the hypothesised micro-epidemiologic transition more explicitly. Linking clinical data to verbal autopsy or vital-registration systems would strengthen cause-of-death attribution and allow direct decomposition of mortality into infectious and chronic components. Incorporating repeated biomarker measurement and extending life-table analysis to additional chronic conditions such as diabetes, renal dysfunction, and dyslipidemia would further clarify how physiological ageing reshapes mortality within treated HIV populations.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe mortality experience of people living with HIV in East Africa has entered a new demographic phase in which hypertension plays a central role. As antiretroviral therapy extends survival into older ages, mortality is increasingly shaped by chronic vulnerabilities of longevity rather than infectious causes alone. Hypertension emerges as both a driver of excess mortality and a marker of selective survival, reflecting the accumulation of chronic disease risk among long-term ART users. The AFRICOS cohort reveals a redistribution of frailty toward older, heavier, and predominantly female survivors, signalling structural ageing of the treated HIV population. These findings demonstrate a micro-epidemiologic transition within HIV cohorts and underscore the need to integrate hypertension and chronic disease management into long-term HIV care.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAFRICOS \u0026ndash; African Cohort Study\u003c/p\u003e\n\u003cp\u003eaOR \u0026ndash; Adjusted Odds Ratio\u003c/p\u003e\n\u003cp\u003eART \u0026ndash; Antiretroviral Therapy\u003c/p\u003e\n\u003cp\u003eBMI \u0026ndash; Body Mass Index\u003c/p\u003e\n\u003cp\u003eCI \u0026ndash; Confidence Interval\u003c/p\u003e\n\u003cp\u003eCRVS \u0026ndash; Civil Registration and Vital Statistics\u003c/p\u003e\n\u003cp\u003e\u0026Delta;qₓ \u0026ndash; Difference in probability of death (excess mortality)\u003c/p\u003e\n\u003cp\u003eEWMA \u0026ndash; Exponentially Weighted Moving Average\u003c/p\u003e\n\u003cp\u003eHIV \u0026ndash; Human Immunodeficiency Virus\u003c/p\u003e\n\u003cp\u003eNCDs \u0026ndash; Non-Communicable Diseases\u003c/p\u003e\n\u003cp\u003eOR \u0026ndash; Odds Ratio\u003c/p\u003e\n\u003cp\u003ePLHIV \u0026ndash; People Living with HIV\u003c/p\u003e\n\u003cp\u003eREC \u0026ndash; Research Ethics Committee\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThe African Cohort Study (AFRICOS) received ethical approval from multiple institutional and national review boards in participating countries. In Uganda, approval was granted by the Makerere University School of Public Health Research Ethics Committee (MakSPH REC; MUSPH #173) and the Uganda National Council for Science and Technology (UNCST; HS 1175). In Kenya, approvals were obtained from the Kenya Medical Research Institute Scientific and Ethics Review Unit (KEMRI SERU; SSC 2371 and SSC 2396) and the Tenwek Hospital Institutional Review Committee (2020-0007). In Tanzania, the study was approved by the Mbeya Medical Research and Ethics Committee (MMREC; SZEC-2439/R.C/V.1/76) and the National Institute for Medical Research National Health Research Ethics Committee (NIMR NatHREC; NIMR/HQ/R.8b/Vol.I/1128). The study protocol was also reviewed and approved by the Walter Reed Army Institute of Research.\u0026nbsp;All procedures were performed in accordance with the ethical standards of the institutional and national research committees and with the 1964 Declaration of Helsinki and its later amendments. All participants provided written informed consent at enrolment. The present analyses were conducted using de-identified data in accordance with the AFRICOS data-sharing agreement.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThis study is funded by the Science for Africa Foundation to the Developing Excellence in Leadership, Training and Science in Africa (DELTAS Africa) programme [Afrique One-ASPIRE, Del-15-008 and Afrique One-REACH, Del-22-011] with support from Wellcome Trust and the UK Foreign, Commonwealth \u0026amp; Development Office and is part of the EDCPT2 programme supported by the European Union. For purposes of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. The funders have no role in the study.\u003c/p\u003e\n\u003ch2\u003eAuthorship contribution statement\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eConceptualisation, all authors; methodology, all authors; validation, W.S.O., C.L., D.H, B.B., F.S.N., S.G.M., and K.H; formal analysis, D.M.; investigation, D.M.; resources, B.B., K.H., and D.H.; data curation, D.M.; writing original draft preparation, D.M.; writing review and editing, all authors; visualisation, D.M., and W.S.O.; supervision, W.S.O., C.L., D.H, B.B., F.S.N. S.G.M and K.H; project administration, D.M. and F.S.N.; funding acquisition, B.B. and S.G.M. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eI acknowledge the MHRP-AFRICOS study coordination for their support in facilitating data access, particularly thankful to Emma Duff for her responsiveness and technical assistance during the data request process. I also extend my appreciation to all the Principal Investigators of the AFRICOS study project for their contributions and data stewardship. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organisation. Noncommunicable Diseases2024 08/01/2026 [cited 2026. Available from: https://www.afro.who.int/health-topics/noncommunicable-diseases.\u003c/li\u003e\n\u003cli\u003eBarry A, Impouma B, Wolfe CM, Campos A, Richards NC, Kalu A, et al. Non-communicable diseases in the WHO African region: analysis of risk factors, mortality, and responses based on WHO data. Sci Rep. 2025;15(1):12288.\u003c/li\u003e\n\u003cli\u003eOrganisation WH. COMMUNICABLE AND NON-COMMUNICABLE DISEASES IN AFRICA IN 2021/22. 2024.\u003c/li\u003e\n\u003cli\u003eChen A, Chan YK, Mocumbi AO, Ojji DB, Waite L, Beilby J, et al. Hypertension among people living with human immunodeficiency virus in sub-Saharan Africa: a systematic review and meta-analysis. Sci Rep. 2024;14(1):16858.\u003c/li\u003e\n\u003cli\u003ePatel P, Rose CE, Collins PY, Nuche-Berenguer B, Sahasrabuddhe VV, Peprah E, et al. Noncommunicable diseases among HIV-infected persons in low-income and middle-income countries: a systematic review and meta-analysis. Aids. 2018;32 Suppl 1(Suppl 1):S5-s20.\u003c/li\u003e\n\u003cli\u003eNegin J, B\u0026auml;rnighausen T, Lundgren JD, Mills EJ. Aging with HIV in Africa: the challenges of living longer. Aids. 2012;26 Suppl 1(0 1):S1-5.\u003c/li\u003e\n\u003cli\u003eReniers G, Slaymaker E, Nakiyingi-Miiro J, Nyamukapa C, Crampin AC, Herbst K, et al. Mortality trends in the era of antiretroviral therapy: evidence from the Network for Analysing Longitudinal Population-based HIV/AIDS data on Africa (ALPHA). Aids. 2014;28 Suppl 4(4):S533-42.\u003c/li\u003e\n\u003cli\u003eNabukalu D, Reniers G, Risher KA, Blom S, Slaymaker E, Kabudula C, et al. Population-level adult mortality following the expansion of antiretroviral therapy in Rakai, Uganda. Popul Stud (Camb). 2020;74(1):93-102.\u003c/li\u003e\n\u003cli\u003eAutenrieth CS, Beck EJ, Stelzle D, Mallouris C, Mahy M, Ghys P. Global and regional trends of people living with HIV aged 50 and over: Estimates and projections for 2000-2020. PLoS One. 2018;13(11):e0207005.\u003c/li\u003e\n\u003cli\u003eMoyo-Chilufya M, Maluleke K, Kgarosi K, Muyoyeta M, Hongoro C, Musekiwa A. The burden of non-communicable diseases among people living with HIV in Sub-Saharan Africa: a systematic review and meta-analysis. EClinicalMedicine. 2023;65:102255.\u003c/li\u003e\n\u003cli\u003eSmit M, Brinkman K, Geerlings S, Smit C, Thyagarajan K, Sighem A, et al. Future challenges for clinical care of an ageing population infected with HIV: a modelling study. Lancet Infect Dis. 2015;15(7):810-8.\u003c/li\u003e\n\u003cli\u003eKaluvu L, Asogwa OA, Marz\u0026agrave;-Florensa A, Kyobutungi C, Levitt NS, Boateng D, et al. Multimorbidity of communicable and non-communicable diseases in low- and middle-income countries: A systematic review. J Multimorb Comorb. 2022;12:26335565221112593.\u003c/li\u003e\n\u003cli\u003eTegegne KD, Adela GA, Kassie GA, Mengstie MA, Seid MA, Zemene MA, et al. Prevalence and factors associated with hypertension among peoples living with HIV in East Africa, a systematic review and meta-analysis. BMC Infect Dis. 2023;23(1):724.\u003c/li\u003e\n\u003cli\u003eMasenga SK, Hamooya BM, Nzala S, Kwenda G, Heimburger DC, Mutale W, et al. Patho-immune Mechanisms of Hypertension in HIV: a Systematic and Thematic Review. Curr Hypertens Rep. 2019;21(7):56.\u003c/li\u003e\n\u003cli\u003eMollel GJ, Moshi L, Hazem H, Eichenberger A, Kitau O, Mapesi H, et al. Causes of death and associated factors over a decade of follow-up in a cohort of people living with HIV in rural Tanzania. BMC Infect Dis. 2022;22(1):37.\u003c/li\u003e\n\u003cli\u003eChidumwa G, Mazibuko L, Olivier S, Rahman K, Gareta D, Aung TN, et al. HIV, hypertension and diabetes care and all-cause mortality in rural South Africa in the HIV antiretroviral therapy era: a longitudinal cohort study. BMJ Public Health. 2023;1(1).\u003c/li\u003e\n\u003cli\u003eKuate Defo B. Demographic, epidemiological, and health transitions: are they relevant to population health patterns in Africa? Glob Health Action. 2014;7:22443.\u003c/li\u003e\n\u003cli\u003eBeltr\u0026aacute;n-S\u0026aacute;nchez H, Preston SH, Canudas-Romo V. An integrated approach to cause-of-death analysis: cause-deleted life tables and decompositions of life expectancy. Demogr Res. 2008;19:1323.\u003c/li\u003e\n\u003cli\u003eDannefer D. 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Communicable and non-communicable diseases coexisting in South Africa. The Lancet Global Health. 2021;9(7):e889-e90.\u003c/li\u003e\n\u003cli\u003eNoel NB, Banwat ME, Okoro LN, Bulus NG, Nkala CA, Anyamene EL, et al. Predictors of Non-Communicable Disease Risk Factors Among People Living with HIV and HIV-Negative Patients in a Nigerian Tertiary Hospital. HIV AIDS (Auckl). 2025;17:87-103.\u003c/li\u003e\n\u003cli\u003eMfinanga SG. Access to comprehensive services for HIV and non-communicable diseases in sub-Saharan Africa. Lancet Glob Health. 2023;11(9):e1317-e8.\u003c/li\u003e\n\u003cli\u003eRoomaney RA, van Wyk B, Cois A, Pillay-van Wyk V. Multimorbidity Patterns in a National HIV Survey of South African Youth and Adults. Front Public Health. 2022;10:862993.\u003c/li\u003e\n\u003cli\u003eOmran A-R. The epidemiologic transition: a theory of the epidemiology of population change/Abdel R. Omran. 2001.\u003c/li\u003e\n\u003cli\u003eSiedner MJ. Aging, Health, and Quality of Life for Older People Living With HIV in Sub-Saharan Africa: A Review and Proposed Conceptual Framework. J Aging Health. 2019;31(1):109-38.\u003c/li\u003e\n\u003cli\u003ePreston SH. The changing relation between mortality and level of economic development. Popul Stud (Camb). 1975;29:231-48.\u003c/li\u003e\n\u003cli\u003ePiggott DA, Bandeen-Roche K, Mehta SH, Brown TT, Yang H, Walston JD, et al. Frailty transitions, inflammation, and mortality among persons aging with HIV infection and injection drug use. Aids. 2020;34(8):1217-25.\u003c/li\u003e\n\u003cli\u003eJones R, Enogela EM, Ruderman SA, Kitahata MM, Moore R, Jacobson JM, et al. Cardiometabolic disease among frailty phenotype clusters in adults aging with HIV. J Frailty Aging. 2025;14(2):100011.\u003c/li\u003e\n\u003cli\u003eYu B, Ren N, Xiao T, Chen J, Pi Z, He L, et al. Central Role of Hypertension in HIV Comorbidity Networks: A Population-Based Study of Age and Sex-Specific Patterns in Southwest China. J Am Heart Assoc. 2025;14(10):e040634.\u003c/li\u003e\n\u003cli\u003eTrickey A, May MT, Vehreschild J, Obel N, Gill MJ, Crane H, et al. Cause-Specific Mortality in HIV-Positive Patients Who Survived Ten Years after Starting Antiretroviral Therapy. PLoS One. 2016;11(8):e0160460.\u003c/li\u003e\n\u003cli\u003eTrickey A, McGinnis K, Gill MJ, Abgrall S, Berenguer J, Wyen C, et al. Longitudinal trends in causes of death among adults with HIV on antiretroviral therapy in Europe and North America from 1996 to 2020: a collaboration of cohort studies. Lancet HIV. 2024;11(3):e176-e85.\u003c/li\u003e\n\u003cli\u003eTrickey A, Sabin CA, Burkholder G, Crane H, d\u0026apos;Arminio Monforte A, Egger M, et al. Life expectancy after 2015 of adults with HIV on long-term antiretroviral therapy in Europe and North America: a collaborative analysis of cohort studies. Lancet HIV. 2023;10(5):e295-e307.\u003c/li\u003e\n\u003cli\u003eMasenga SK, Elijovich F, Koethe JR, Hamooya BM, Heimburger DC, Munsaka SM, et al. Hypertension and Metabolic Syndrome in Persons with HIV. Curr Hypertens Rep. 2020;22(10):78.\u003c/li\u003e\n\u003cli\u003eBigna JJ, Ndoadoumgue AL, Nansseu JR, Tochie JN, Nyaga UF, Nkeck JR, et al. Global burden of hypertension among people living with HIV in the era of increased life expectancy: a systematic review and meta-analysis. Journal of Hypertension. 2020;38(9):1659-68.\u003c/li\u003e\n\u003cli\u003eDenu MK, Revoori R, Buadu MAE, Oladele O, Berko KP. Hypertension among persons living with HIV/AIDS and its association with HIV-related health factors. AIDS Research and Therapy. 2024;21(1):5.\u003c/li\u003e\n\u003cli\u003eKivuyo S, Birungi J, Okebe J, Wang D, Ramaiya K, Ainan S, et al. Integrated management of HIV, diabetes, and hypertension in sub-Saharan Africa (INTE-AFRICA): a pragmatic cluster-randomised, controlled trial. The Lancet. 2023;402(10409):1241-50.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"HIV, hypertension, mortality, life-course, East Africa, demography, AFRICOS","lastPublishedDoi":"10.21203/rs.3.rs-8863155/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8863155/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAlthough mortality among people living with HIV (PLHIV) in sub-Saharan Africa has decreased markedly with the scale-up of antiretroviral therapy (ART), the demographic consequences of this success remain underexamined.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing ten years of longitudinal data from the African Cohort Study (AFRICOS; 2013\u0026ndash;2023) in Kenya, Tanzania, and Uganda, we estimated the association of hypertension with all-cause mortality among adults aged 40 years and older. We combined descriptive decremental life-table analysis with discrete-time logistic regression, applying both lagged and exponentially weighted moving-average (EWMA) exposure models to capture cumulative risk.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAt baseline, 18.6% of participants were hypertensive; 60.1% experienced hypertension during follow-up, and all-cause mortality was 6.4%. Life-table estimates showed cumulative excess mortality (Δqₓ) among hypertensive participants increasing from 0.4% in the first interval to 4.4% by year nine. Excess mortality was larger among adults aged\u0026thinsp;\u0026ge;\u0026thinsp;50 years (Δqₓ \u0026asymp; 5.4% vs. 4.4% at ages 40\u0026ndash;49), men (Δqₓ \u0026asymp; 8.0% vs. 2.8% in women), underweight participants (Δqₓ \u0026gt; 20% in early intervals), and those with high viral load (Δqₓ \u0026asymp; 7.0%). In adjusted discrete-time models controlling for age, sex, body mass index, viral load, and country, hypertension was associated with higher mortality under a standard lag specification (aOR\u0026thinsp;=\u0026thinsp;2.04; 95% CI 1.10\u0026ndash;3.80) and under EWMA exposure definitions (aOR\u0026thinsp;=\u0026thinsp;3.25; 95% CI 1.26\u0026ndash;8.40 at α\u0026thinsp;=\u0026thinsp;0.3; aOR\u0026thinsp;=\u0026thinsp;2.51; 95% CI 1.16\u0026ndash;5.44 at α\u0026thinsp;=\u0026thinsp;0.7). Mortality odds were higher among participants aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years (aOR\u0026thinsp;=\u0026thinsp;2.23\u0026ndash;2.40; 95% CIs 0.85\u0026ndash;6.16) and those with high viral load (aOR\u0026thinsp;=\u0026thinsp;2.36\u0026ndash;2.44; 95% CIs 1.28\u0026ndash;4.51), while overweight and obese participants had substantially lower odds of death (aOR\u0026thinsp;=\u0026thinsp;0.29; 95% CI 0.10\u0026ndash;0.85 and aOR\u0026thinsp;=\u0026thinsp;0.14; 95% CI 0.04\u0026ndash;0.56).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThese findings highlight a demographic transformation of the HIV epidemic in East Africa, where mortality among PLHIV increasingly reflects a growing influence of chronic diseases in addition to infection control. Hypertension has become a key driver of excess mortality and a demographic indicator of the region\u0026rsquo;s compressed health transition.\u003c/p\u003e","manuscriptTitle":"Hypertension is a marker of the micro-epidemiologic transition in ageing HIV populations in Kenya, Uganda and Tanzania (AFRICOS, 2013–2023)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-24 08:20:51","doi":"10.21203/rs.3.rs-8863155/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-27T04:27:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-24T15:39:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"153005604155140255823659691890354410803","date":"2026-02-23T10:03:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-18T14:59:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"102298232569817261210416626891577721043","date":"2026-02-17T13:10:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-17T12:22:28+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-16T09:10:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-14T00:55:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-14T00:54:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-02-12T14:13:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ef48583f-9c36-47ee-9b82-1148eeaacc80","owner":[],"postedDate":"February 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:07:02+00:00","versionOfRecord":{"articleIdentity":"rs-8863155","link":"https://doi.org/10.1186/s12889-026-27241-3","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2026-04-02 15:58:14","publishedOnDateReadable":"April 2nd, 2026"},"versionCreatedAt":"2026-02-24 08:20:51","video":"","vorDoi":"10.1186/s12889-026-27241-3","vorDoiUrl":"https://doi.org/10.1186/s12889-026-27241-3","workflowStages":[]},"version":"v1","identity":"rs-8863155","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8863155","identity":"rs-8863155","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}