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. Author manuscript; available in PMC: 2026 Jan 23.
Published before final editing as: AIDS. 2025 Dec 30:10.1097/QAD.0000000000004431. doi: 10.1097/QAD.0000000000004431

One-year mortality among adults with advanced HIV in sub-Saharan Africa: a systematic review and meta-analysis

Thomas C Scheier 1,#, Keisha De Gouveia 2,#, Mark E Engel 3,4, Ameer S-J Hohlfeld 4, Alex Cen 5, Anne Berhe 6, Sabrina Fan 5, Jeffery Li 5, Shakeap Elliott 5, Nathan Ford 7,8, Graeme Meintjes 9,10, Dominik Mertz 1,11,12, John Eikelboom 1,12, Sean Wasserman 10,13,14,✉
PMCID: PMC7618665  EMSID: EMS212067  PMID: 41467712

Summary

Background

In sub-Saharan Africa (SSA), people with HIV continue to present with advanced HIV disease (AHD), putting them at high risk of life-threatening opportunistic diseases. We aimed to estimate mortality among this population.

Methods

We conducted a systematic review and meta-analysis of studies reporting one-year mortality among adults living with HIV and presenting to care with CD4 counts ≤200 cells/mm3 in SSA. MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials were searched for studies (comprising >500 participants) published between January 1, 2016, and March 21, 2025. Screening and data extraction were done in duplicate. Pooled mortality proportions across CD4 count and time strata were calculated using a generalised linear mixed model. Risk of bias was assessed using a modified Newcastle-Ottawa scale. The protocol is registered with PROSPERO, CRD42023451498.

Results

Thirty-six studies with 313,362 participants were included. The weighted median age was 35 years, 64% were female, and 98.9% were antiretroviral therapy-naive. One-year mortality was 12% (95% CI 8 – 16) among people with CD4 count ≤200 cells/mm3 and increased with lower CD4 counts (≤100 cells/mm3, 15% (95% CI 11 – 19); ≤50 cells/mm3, 20% (95% CI 12 – 31)). Most deaths occurred within the first three months after AHD presentation. Heterogeneity was substantial. Risk of bias was high in 18 (50%) of 36 included studies.

Discussion

There is high one-year mortality among people presenting with AHD in SSA. It is a priority to identify AHD with CD4 testing, improve retention in care, and evaluate additional interventions to reduce mortality in this population.

Keywords: HIV, Acquired Immunodeficiency Syndrome, mortality, Africa South of the Sahara, CD4-Positive T-Lymphocytes

Introduction

The global decline in HIV-related mortality has plateaued despite expanded access to antiretroviral therapy (ART) and implementation of evidence-based prevention of opportunistic infections. In 2023, approximately 630,000 people died from AIDS-related illnesses, with eastern and southern Africa disproportionately affected[1]. Advanced HIV disease (AHD), defined in adults as a CD4 count ≤200 cells/mm3 or the presence of a WHO stage 3 or 4 clinical event, is the major contributor to this public health concern[2–6].

Recent household surveys found that approximately 10% of people living with HIV in sub-Saharan Africa have AHD, translating to an estimated 1.88 million individuals (uncertainty interval 1.58–2.20) affected by AHD in the region[7]. This persistent burden of advanced disease is driven by late presentation and, increasingly, by disengagement from care. Illustrating this, approximately two-thirds of people starting ART in the Western Cape province of South Africa are treatment experienced and in several cohort studies AHD is present in up to 30% of people initiating or re-initiating ART[3–6, 8–11]. Due to profound immunocompromise, individuals with AHD are at high risk of developing life-threatening opportunistic infections and other HIV-related illnesses, which are the leading reported causes of hospitalisation and death in this population[12].

Most of the current knowledge on AHD-associated mortality comes from individual clinical trials or single-centre cohort studies. While valuable, these sources provide limited insights around outcomes from broader programmatic settings across sub-Saharan Africa, where variable access to diagnostics, prophylaxis, and treatment may affect HIV outcomes. To better quantify the impact of AHD - defined in this study as people with CD4 counts ≤200 cells/mm3 - and support evidence-based policy, resource allocation, and research prioritisation, population-level mortality estimates are needed. We conducted a systematic review and meta-analysis to estimate prevalence of one-year mortality among adults with CD4 counts ≤200 cells/mm3 in sub-Saharan Africa irrespective of ART status.

Methods

Search strategy and selection criteria

For this systematic review and meta-analysis, we searched three electronic databases, including MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials (CENTRAL) for studies reporting mortality in people with AHD published between January 1, 2016, and July 14, 2023. This time frame was chosen to capture mortality outcomes in the era of universal test and treat for HIV, following its global adoption in 2016. The search was updated on March 21, 2025. Search terms included combinations of ‘HIV’, ‘mortality’, ‘sub-Saharan Africa’ and ‘adults’ and the search was restricted to articles in English or French. Conference abstracts, letters to the editor, guidelines, case reports pre-prints, and case series were not included.

Studies were eligible if they included at least 500 adults (aged 18 years or older) from sub-Saharan Africa living with HIV and reported mortality at any timepoint within one year of identifying a CD4 count of ≤200 cells/mm3. For studies that reported outcomes on individuals aged <18 or with CD4 counts >200 cells/mm3, we required that at least 90% were adults or at least 90% had CD4 counts ≤200 cells/mm3. If a study did not report the number of participants in each eligibility category, the mean and standard deviation (SD) were used to estimate if 90% of individuals were adults or had CD4 counts ≤200 cells/mm3. If only the median was reported, the mean was assumed to be equal to the median and SD was calculated from the interquartile range (IQR)[13]. Where multiple publications reported on the same cohort of participants, we included the main report or the publication contributing the largest sample size. References were imported into Covidence (Melbourne, Australia). Screening for eligibility (TCS, KDG, AC, AB, SF, JL, SE) and data extraction were conducted independently in duplicate (TS and KDG), with disagreements resolved through discussion, or where necessary, by a third investigator (JWE).

Data analysis

Summary-level data were extracted directly into a spreadsheet and included: author, year of publication, study period, baseline characteristics of study population (age, sex, CD4 count, ART status), trial setting (country, multicentre/single centre, hospital/clinic), number of participants with CD4 counts ≤200 cells/mm3, ≤100 cells/mm3 and ≤50 cells/mm3, number of deaths, timing of deaths, and whether the study was conducted in a specific disease context (e.g., tuberculosis). Countries were grouped into regions as per the United Nations Statistics Division[14].

The primary outcome was mortality at 1 year after study enrolment. If a study reported CD4 counts within non-standard thresholds (e.g. ≤150 cells/mm3), the population was classified under the next higher standard category (e.g. ≤200 cells/mm3). Where no cut off was reported, data were analysed in the stratum estimated to include 90% of CD4 counts by using either mean and SD or median and IQR as described above. Baseline characteristics were extracted from the overall study population if not specified for individual CD4 strata. Mortality was extracted for different time points (1 month, 3 months, 6 months, 1 year). If the number of deaths was not reported, mortality was estimated from other sources (e.g., hazard ratios). The IPDfromKM package for R software version 4.4.0. was used to extract mortality data from Kaplan-Meier curves[15, 16].

Prespecified subgroup analyses included enrolment period (before vs. after 2016), study design (randomised controlled trial vs. non-randomised studies), geographic region (Western, Central, Eastern and Southern Africa) and care setting (clinic vs. hospital). Studies enrolling participants both before and after 2016 were categorised based on the period during which the majority of enrollment occurred. Additional pre-planned subgroup analyses included receipt of AHD management components (e.g., co-trimoxazole) and current ART status (naïve vs. experienced), but these were not performed because of limited data.

Risk of bias was assessed independently by two authors (TS and KDG) using a modified Newcastle-Ottawa scale. Data from randomised trials were treated as observational, pooling participants irrespective of allocated treatment. Disagreements were resolved by discussion, or if unresolved, by a third reviewer. Risk of bias was classified as low (8-9 points), moderate (6-7 points) or high (<5 points). We assessed the certainty of evidence for all outcomes using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology[17]. The following domains were assessed: precision, consistency, risk of bias, and directness[18–21]. We presented the evidence in a summaries of findings table[22].

Data pooling and statistical analyses were performed using the metanalysis for single proportion function and the generalised linear mixed model (GLMM) in the meta package in R software version 4.4.0.[16, 23]. Summary estimates are presented as proportions with 95% confidence intervals (CI) calculated using Clopper-Pearson method. Heterogeneity was measured using I2. Tests for subgroup differences were performed using the Q-statistic, comparing pooled mortality estimates across subgroups. Post hoc leave-one-out sensitivity analysis was conducted to assess single-study influence using the random-effects GLMM for proportions (meta package).

We followed the PRISMA checklist for reporting this systematic review[24]. The protocol was registered on PROSPERO (CRD42023451498).

Role of the funding source

There was no funding source for this study.

Results

Our search identified 11,276 publications, of which 4,371 were screened after removing duplicates. Following full-text review of 717 studies, 36 met the eligibility criteria (Figure 1)[25–60]. The most common reasons for exclusion were sample size <500 or the absence of mortality data within one year of identifying a CD4 count ≤200 cells/mm3. Included studies were conducted in eastern (n=15), southern (n=13), or western Africa (n=1) or across multiple regions (n=7). Study sizes ranged from 541 to 186,863 individuals with CD4 counts ≤200 cells/mm3. Ten studies were randomised controlled trials. Two studies enrolled participants exclusively after 2016. Mortality was most frequently reported at the one-year timepoint (23 studies). Mortality outcomes for individuals with CD4 counts of ≤200 cells/mm3, ≤100 cells/mm3, and ≤50 cells/mm3 were reported in 24, 21, and 9 studies, respectively (Table 1).

Figure 1. PRISMA flowchart.

Figure 1

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Table 1. Characteristics of included studies.

STUDY CHARACTERISTICS CD4 count (cells/mm3)
Author Country Cohort/Study name Design Setting Study period Age, median Female (%) ≤200 ≤100 Median CD4 count (IQR) ART naïve* (%) Specific population
Amour (2022)[25] Tanzania Observational Multicenter:
clinics		 01/2015 – 12/2019 22 87 541 n.r. n.r.
Auld (2020)[26]# Botswana XPRES Randomized controlled trial Multicenter: hospitals and healthcare facilities 08/2012 – 03/2014 I: 35,
II: 34,
III: 34		 64 7158 3204 I: 184 (100-241), II: 246 (148-310), III: 241 (132-321) n.r Tuberculosis
Bassett (2017)[27] South Africa Sizanani Observational Multicenter: outpatients 08/2010 – 01/2013 33 49 848 192 (72-346) 100
Blanc (2020)[28]# Uganda,
Ivory Coast, Cambodia, Vietnam		 STATIS Randomized controlled trial multicenter: ambulatory 09/2014 – 05/2017 35 42 583 I: 28 (12-56),
II: 32 (13-55)		 100 Tuberculosis
Butler (2018)[29]# South Africa Observational Single center 01/2004 – 10/2011 I: 33
II:54		 63 6417 n.r. 100
Chaisson (2019)[30] Uganda Observational Multicenter:
clinics		 07/2013 – 12/2016 33 56 711 181 (82-278) 100
Chimbetete (2020)[31] Zimbabwe Observational Single center:
clinic		 02/2004 – 12/2017 33 62 1341 190 (89-314) 100
Cornell (2017)[32]# South Africa IeDEA-SA Observational Multicentre:
clinics and hospitals		 2004-2015 I: 38,
II: 33		 67 43662 148 (71-227) 100
Drain (2021)[33] South Africa Observational single center:
clinic		 09/2013 – 02/2019 33 43 908 107 (52-153) n.r. Cryptococcal disease
Faini (2019)[34] Tanzania KIULARCO Observational Single center:
clinic		 10/2013 – 07/2015 38 56 <150: 560 61 (26-103) 100 Cryptococcal disease
Fekade (2017)[35] Ethiopia Observational Multicenter: hospitals 01/2009 – 07/2013 33 61 639 330 144 (78-205) 100
Grant (2020)[36] South Africa TB Fast Track Randomized controlled trial Multicenter: primary health care centers 12/2012 – 12/2014 37 56 <150: 3022 72 (35-112) n.r. Tuberculosis
Gupta-Wright (2018)[37] Malawi,
South Africa		 STAMP Randomized controlled trial Multicenter: inpatients 10/2015 – 09/2017 40 57 748 227 (79-436) 8 Tuberculosis
Hakim (2017)[38] Uganda, Zimbabwe, Malawi,
Kenya		 REALITY Randomized controlled trial Multicenter 06/2013 – 04/2015 36 47 1805 37 (16-63) 100
Hirasen (2018)[39] South Africa Observational Single center; clinics 09/2011-08/2012 and 09/2013-08/2014 38 59 1513 844 171 (70-273) 100
Honge (2016)[40] Guinea-Bissau Bissau HIV Cohort Observational Single center:
ART clinic		 06/2005 – 12/2014 36 67 1811 206 (89 – 381) n.r.
Hurt (2021)[41] Botswana Observational Multicenter:
clinics and hospital		 01/2014 – 01/2016 37 50 1645 54 (25-78) 55 Cryptococcal disease
Inzaule (2022)[42] Kenya,
South Africa, Zambia, Zimbabwe, Uganda		 PASER-M Observational Multicenter 2007 - 2015 37 58 1972 135 (63-205) n.r.
Jarvis (2022)[43]# Botswana, Malawi,
South Africa, Uganda		 AMBITION-cm Randomized controlled trial Multicenter: hospitals 01/2018 – 02/2021 37 40 814 I: 26 (9-56),
II: 28 (11-59)		 36 Cryptococcal disease
Kimaro (2019)[44] Tanzania, Zambia REMSTART Randomized controlled trial Clinic 02/2012 – 09/2013 n.r. n.r. 1999 1431 n.r. n.r. Cryptococcal disease
Kiragga (2016)[45]# Uganda ORCAS Observational Multicenter:
clinics		 07/2011 – 12/2011 and 07/2012 – 12/2014 32 55 851 I: 34 (12-63),
II: 42 (17-71)		 100
Lafort (2018)[46] Mozambique Observational Multicenter: facilities 01/2013 – 06/2014 n.r. 72 3869 n.r. n.r.
Longley (2016)[47] South Africa Observational Multicenter:
clinics		 05/2011 – 04/2014 36 53 645 56 (28-78) 100 Cryptococcal disease
Makadzange (2021)[48] Zimbabwe Observational Multicenter: outpatient facilities 04/2015 – 06/2016 37 44 1320 31 (14-55) 90 Cryptococcal disease
Mody (2020)[49] Zambia Observational Multicenter:
clinics		 04/2014-07/2015 34 58 9234 268 (134-430) 100
Moyo (2016)[50]# South Africa Observational Multicenter:
clinic		 07/2007 – 12/2012 37 59 8263 4050 I: 159
II: 113		 100
Nacarapa (2021)[51] Mozambique Observational Single center:
HIV clinic		 01/2002 – 12/2019 35 59 8375 I: 192 (n.r.) 95
Peter (2016)[52] South Africa, Tanzania, Zambia, Zimbabwe Randomized controlled trial Multicenter: hospitals 01/2013 – 10/2014 37 51 1725 1272 84 (26 – 208) n.r. Tuberculosis
Sossen (2020)[53]# South Africa SILVAMP TB LAM Observational Multicenter:
district hospitals		 06/2012 – 10/2013 and 01/2014-10/2016 36 55 746 I: 150 (56-311),
II: 63 (24-131)		 40 Tuberculosis
Ssempijja (2020)[54] Uganda MHCHC Observational Single center 2006 - 2016 34 69 1893 n.r. n.r.
Stadelman (2021)[55]# Uganda,
South Africa		 ASTRO CM and others Includes randomized controlled trial and observational data Multicenter I: 11/2010 – 04/2012,
II: 05/2012 – 06/2013,
III: 08/2013 – 08/2014		 I: 36,
II: 32		 41 977 I: 14 (6, 44),
II: 24 (8, 61)		 n.r. Cryptococcal disease
Steytler (2017)[56]# South Africa Phidisa-II Randomized controlled trial Multicenter:
sites		 02/2004 – 12/2007 I: 36,
II: 35,
III: 36,
IV: 35,
V: 36,
VI: 41		 32 1771 I: 99 (40-156),
II: 119 (51-175), III: 85 (28-141), IV: 104 (43–153), V: 110 (54-160), VI: 120 (38-156)		 n.r.
Sudfeld (2020)[57] Tanzania ToV-4 Randomized controlled trial Multicenter 02/2014 – 02/2017 I: 39,
II: 39		 68 1711 n.r. n.r.
Teasdale (2018)[58] Ethiopia, Kenya, Mozambique, Tanzania Observational Multicenter:
health facilities		 01/2005 – 12/2014 35 64 186863 95075 164 (78-255) 100
Tenforde (2019)[59] Botswana Botswana national meningitis survey Observational Multicenter; hospitals 01/2004 – 12/2015 37 50 1018 630 139 (63-271) n.r.
Worodria (2018)[60] Uganda MIND-IHOP Observational Single center 04/2011 – 09/2015 34 48 854 81 (21-226) n.r.
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n.r. = not reported

*

either as reported by the authors or as people without previous ART exposure

#

Roman numerals reflect baseline characteristics of different study arms or periods

Outcomes were reported for 313,362 unique participants, including 228,296 with a CD4 count ≤200 cells/mm3, 126,910 with a CD4 count ≤100 cells/mm3, and 59,189 with a CD4 count ≤50 cells/mm3. The weighted median age was 35 years (range of medians: 22 to 52 years) and 200,552 (64%) were female. Where reported, the weighted proportion of ART-naïve participants was 98.9% (range: 8% - 100%).

One-year mortality after identification of a CD4 count ≤200 cells/mm3 was reported for 276,542 individuals across 18 studies. The pooled mortality estimate was 12% (95% CI 8 – 16%) (Figure 2).

Figure 2. One-year mortality for CD4 ≤200 cells/mm3.

Figure 2

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Blue squares show point estimates (vertical black line) and horizontal black line represents 95% CI. Red diamond represents pooled estimate including 95% CI. Dotted red line shows the overall pooled estimate.

For one-year mortality among individuals with CD4 ≤200 cells/mm3, evidence from non-randomised studies (270,368 participants, 14 studies) suggested a pooled mortality of 11% (95% CI 7–17%), but the certainty of evidence was rated low, mainly due to considerable heterogeneity (I2 ≥99.7%), moderate–high risk of bias, and indirectness. Evidence from randomised controlled trials (6,174 participants, 4 studies) suggested a mortality of 13% (95% CI 10–16%), with certainty rated very low because of high heterogeneity (I2 ≥94.8%) and indirectness, despite generally lower risk of bias. For individuals with CD4 ≤100 cells/mm3, pooled one-year mortality was 15% (95% CI 11–19%) based on 120,603 participants from 16 studies, with certainty of evidence rated low due to heterogeneity, risk of bias, and indirectness. Among those with CD4 ≤50 cells/mm3, mortality was higher at 20% (95% CI 12–31%) (57,293 participants, 8 studies), but the certainty of evidence was very low due to very high heterogeneity, small number of studies, and imprecision. (Figure 3, Table 2).

Figure 3. One-year mortality for (A) CD4 ≤100 cells/mm3 and (B) CD4 ≤50 cells/mm3.

Figure 3

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Blue squares show point estimates (vertical black line) and horizontal black line represents 95% CI. Red diamond represents pooled estimate including 95% CI. Dotted red line shows the overall pooled estimate.

Table 2. Summary of findings.

Outcome Population No. of participants
(studies)		 Pooled prevalence
(95% CI)		 Certainty of the
evidence (GRADE)		 Comments
One-year mortality (CD4 ≤200
cells/mm3;) non-RCT 		Adults with HIV/AIDS, CD4 ≤200 cells/mm3;  270,368 (14 studies) 11% (7–17%) ●●◯◯ Low123 Considerable heterogeneity (I2; ≥99.7%). Majority of studies moderate–high risk of bias.
One-year mortality (CD4 ≤200
cells/mm3;) RCTs 		Adults with HIV/AIDS, CD4 ≤200 cells/mm3; 6,174 (4 studies) 13% (10–16%) ●◯◯◯ Very low2 3 Considerable heterogeneity (I2; ≥94.8%). Not downgraded for risk of bias as the majority of the studies low – moderate risk of bias.
One-year mortality (CD4
≤100 cells/mm3;) 		Adults with HIV/AIDS, CD4 ≤100 cells/mm3; 120,603 (16 studies) 15% (11–19%) ●●◯◯ Low123 High heterogeneity. Over half of deaths occurred in first 3 months.
One-year mortality (CD4
≤50 cells/mm3;) 		Adults with HIV/AIDS, CD4 ≤50 cells/mm3 57,293 (8 studies) 20% (12–31%) ●◯◯◯ Very low1234 Very high heterogeneity, small number of studies, imprecise estimate.
Mortality within first 3
months (all strata) 		Adults with HIV/AIDS, CD4 ≤200 cells/mm3 ~60,000 (subset: 5 studies) >10% across all CD4 strata ●◯◯◯ Very low1234 Limited number of studies, high risk of bias, very high heterogeneity.
Regional subgroup:
Eastern Africa 		Adults with HIV/AIDS, CD4 ≤200 cells/mm3; 214,776 (9 studies) 9% (6–13%) ●●◯◯ Low12 Similar estimates across eastern and southern Africa.
Regional subgroup:
Southern Africa 		Adults with HIV/AIDS, CD4 ≤200 cells/mm3; 57,983 (7 studies) 11% (8–16%) ●●◯◯ Low12 Consistent with eastern Africa; single study in western Africa found higher mortality (20%).
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1

Risk of bias: Only 1/18 studies on primary outcome had low risk of bias; majority moderate–high (downgraded one level).

2

Inconsistency: Extremely high heterogeneity (I2 ≥99%) across all analyses, unexplained by subgroup analyses (downgraded one level).

3

Indirectness: Some studies differed in ART status, AHD management, or setting; reporting insufficient for subgroup stratification (downgraded one level).

4

Imprecision: Wide confidence intervals, especially in CD4 ≤50 group and early mortality estimates, leading to uncertainty about the true prevalence (downgraded one level).

Across all CD4 strata, early mortality within the first 3 months exceeded 10% (>60,000 participants, 5 studies). The certainty of evidence was very low, reflecting sparse data, high risk of bias, and very high heterogeneity (Figure 4, Table 2).

Figure 4. Mortality at discrete timepoints in the first year stratified by CD4 count.

Figure 4

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Bars indicate pooled estimate and lines represent 95% CI.

In the sub-set of studies that reported mortality at all timepoints (1, 3, 6 and 12 months), a similar early concentration of deaths was observed (Figure 5), with more than half of all deaths occurring within the first 3 months.

Figure 5. Pooled mortality by time since enrolment, stratified by CD4 count.

Figure 5

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Only studies reporting mortality at all specified timepoints were included. Number of studies: CD4 ≤ 200 cells/mm3: 5; CD4 ≤ 100 cells/mm3: 2; CD4 ≤ 50 cells/mm3: 1. Shaded areas represent 95% confidence intervals (not included for the CD4 < 50 stratum as this was based on a single study).

Leave-one-out sensitivity analyses yielded pooled mortality between 10 and 12% and did not alter overall conclusions.

Pooled mortality was higher in studies enrolling participants before 2016 (12% [95% CI 8–16], 16 studies, 276,542 participants) than in studies enrolling after 2016 (5% [95% CI 2–12], 2 studies, 1,449 participants) (p-value for subgroup differences = 0.055). Subgroup analysis by study design showed similar mortality estimates in non-randomised studies (11% [95% CI 7–17], 14 studies, 270,368 participants) and randomised trials (13% [95% CI 10–16], 4 studies, 6,174 participants) (p-value for subgroup differences = 0.67). Pooled mortality estimates were similar from studies in eastern (9 studies, 214,776 participants) and southern Africa (7 studies, 57,983 participants) with a mortality of 9% (95% CI 6–13) and 11% (95% CI 8–16) (p-value for subgroup differences = 0.17), respectively). Data from other regions could not be pooled due to a limited number of studies. The only study conducted exclusively in western Africa (1,811 participants) reported mortality of 20% (95% CI 18–22). In subgroups defined by care setting, pooled mortality was 11% (95% CI 8–16) in clinic-based cohorts (11 studies, 37,185 participants) and 27% (95% CI 7–63) in hospital-based cohorts (2 studies, 1,111 participants) without statistical evidence of heterogeneity (p-value for subgroup differences = 0.193). Subgroup analyses by AHD management components and ART status could not be conducted due to insufficient reporting across studies.

Risk of bias was low in 2 (6%), moderate in 16 (44%), and high in 18 (50%) of the 36 included studies. Among the 18 studies reporting on the primary outcome (mortality at one year among individuals with a CD4 count ≤200 cells/mm3), risk of bias was assessed as low in 1 study (6%), moderate in 11 studies (61%), and high in 6 studies (33%).

Discussion

Adults with advanced HIV disease, defined in our analysis as a CD4 count ≤200 cells/mm3, experience high early mortality with an estimated 12% dying within the first year of entry or re-entry to care, with mortality increasing as CD4 count declines. These findings highlight the persistent challenge of AHD in the region.

Most deaths occur within the first three months after presentation with a CD4 count ≤200 cells/mm3, reflecting vulnerability to rapid clinical deterioration, even in the context of ART initiation. This early mortality is likely driven by preventable opportunistic infections - particularly tuberculosis and severe bacterial infection - which remain leading causes of hospitalisation and death despite the availability of ART and WHO-recommended AHD care packages[2, 61–63]. Interventions such as antigen screening and preemptive therapy for cryptococcal disease and urine lipoarabinomannan testing for tuberculosis have shown promise in clinical trials, but their real-world impact is limited by implementation barriers, including diagnostic stockouts and attrition along the screening cascade[37, 64]. The REALITY trial demonstrated that a package of enhanced prophylaxis that included azithromycin reduced mortality among individuals with CD4 ≤100 cells/mm3[38]. However, azithromycin has not been adopted into guidelines due to uncertainty about its independent effect. The declining availability of CD4 testing, linked to a programmatic shift towards prioritising viral load monitoring under the universal test and treat strategy, has further reduced the ability to risk stratify patients and allow timely delivery of guideline-recommended management[65, 66]. These observations highlight the need to improve implementation of existing interventions and to develop new strategies to reduce mortality in this population.

Our analysis suggests a trend toward lower mortality in studies conducted after 2016, potentially reflecting advances in HIV care including broader access and more rapid initiation of ART thereby reducing loss to follow-up[1, 3, 67, 68]. However, the observation of lower mortality after 2016 is based on only two relatively small and heterogeneous studies, resulting in considerable uncertainty in the estimate of mortality, as also reflected by the wide confidence intervals (95%CI 0.02 - 0.12). The lack of contemporary data underscores the need for improved data collection and reporting to assess the effectiveness of current strategies. Geographic representation was skewed toward eastern and southern Africa, with limited data from western and central regions. This restricts generalisability and further highlights the need for expanded data collection. Notably, the only study from western Africa reported higher mortality, suggesting regional variation in outcomes. Comparing care settings, higher hospital mortality was driven by one cohort undergoing lumbar puncture for suspected meningitis. In leave-one-out sensitivity of the primary analysis, omitting this study had the largest impact, but did not change conclusions. It therefore seems likely that baseline acuity and opportunistic infection burden, rather than enrolment setting, explains the differences. High heterogeneity (I2 >99%) was observed across studies and resulted in downgrading of the certainty of evidence (GRADE). While expected in proportional meta-analyses, this likely reflects true clinical and programmatic variation, including differences in ART status, access to diagnostics, and implementation of AHD care[7, 13, 69–73].

Several limitations warrant consideration. First, our reliance on CD4 count to define AHD may exclude individuals with WHO stage 3 or 4 disease, who may have different outcomes. However, concordance between clinical disease stage and CD4 count is poor, and a CD4-based approach provides an objective and reproducible definition [74–76]. Second, loss to follow-up was substantial or unreported in many studies and there was heterogeneity in outcome ascertainment, potentially underestimating mortality[77]. Third, included cohorts were predominantly ART-naïve, likely over-representing first-time presenters and under-representing people re-engaging in care with AHD. This limits generalisability of our findings to the broader AHD population where a substantial proportion of people with AHD are treatment experienced[78, 79]. However, we cannot exclude the possibility that ART status at baseline was incorrect because of missing information. Fourth, some planned subgroup analyses (e.g. ART status, and receipt of AHD management package components) could not be conducted due to insufficient data. Fifth, our search strategy was limited to English and French language publications and excluded grey literature. Finally, we acknowledge that RCTs are not designed to estimate prevalence and often include selective populations, limiting generalisability to the wider public. This may introduce selection bias and reduce the applicability of findings for public health planning. However, a sub-group analysis showed similar mortality estimates for RCTs and non-RCTs.

In conclusion, mortality among individuals presenting with AHD in sub-Saharan Africa remains unacceptably high, particularly in the early months of care. National HIV programs must prioritise AHD by restoring CD4 testing capacity, ensuring consistent implementation of the WHO care package, and improving access to diagnostics and treatments for leading causes of death. Future research should focus on better identifying causes of death, evaluating new interventions, preventing disengagement from care, and include specific patient populations within AHD such as children or adolescents.

Acknowledgments

TS, GM, JE, and SW designed the study. TS, NF, DM, JE, and SW wrote the study protocol. TS and KDG did the statistical analyses. TCS, KDG, AC, AB, SF, JL, and SE screened the articles and extracted data from the articles included in the meta-analysis. TS and KDG supervised screening and data extraction. TS, KDG, and ME assessed the risk of bias and quality of studies. TS, KDG and ME accessed and verified the data. TS and KDG did the statistical analyses. AH and ME conducted the GRADE assessment. TS and KDG drafted the first version of the manuscript. SW, JWE, DM, NF, ME, AH and GM critically reviewed the manuscript. All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication. All authors approved the final manuscript.

We want to thank Jo-Anne Petropoulos for her support in developing the search strategy.

Footnotes

Declaration of interests

We declare no competing interests.

Data sharing

The data from this study will be made available to researchers upon request to the corresponding author.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data from this study will be made available to researchers upon request to the corresponding author.

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