Early mortality in adults initiating antiretroviral therapy (ART) in low- and middle-income countries (LMIC): a systematic review and meta-analysis

Abstract

Background: We systematically reviewed observational studies of early mortality post-antiretroviral therapy (ART) initiation in low- and middle-income countries (LMIC) in Asia, Africa, and Central and South America, as defined by the World Bank, to summarize what is known.

Methods and findings: Studies published in English between January 1996 and December 2010 were searched in Medline and EMBASE. Three independent reviewers examined studies of mortality within one year post-ART. An article was included if the study was conducted in a LMIC, participants were initiating ART in a non-clinical trial setting and were ≥15 years. Fifty studies were included; 38 (76%) from sub-Saharan Africa (SSA), 5 (10%) from Asia, 2 (4%) from the Americas, and 5 (10%) were multi-regional. Median follow-up time and pre-ART CD4 cell count ranged from 3-55 months and 11-192 cells/mm(3), respectively. Loss-to-follow-up, reported in 40 (80%) studies, ranged from 0.3%-27%. Overall, SSA had the highest pooled 12-month mortality probability of 0.17 (95% CI 0.11-0.24) versus 0.11 (95% CI 0.10-0.13) for Asia, and 0.07 (95% CI 0.007-0.20) for the Americas. Of 14 (28%) studies reporting cause-specific mortality, tuberculosis (TB) (5%-44%), wasting (5%-53%), advanced HIV (20%-37%), and chronic diarrhea (10%-25%) were most common. Independent factors associated with early mortality in 30 (60%) studies included: low baseline CD4 cell count, male sex, advanced World Health Organization clinical stage, low body mass index, anemia, age greater than 40 years, and pre-ART quantitative HIV RNA.

Conclusions: Significant heterogeneity in outcomes and in methods of reporting outcomes exist among published studies evaluating mortality in the first year after ART initiation in LMIC. Early mortality rates are highest in SSA, and opportunistic illnesses such as TB and wasting syndrome are the most common reported causes of death. Strategies addressing modifiable risk factors associated with early death are urgently needed.

Figure 1. Study selection process and reasons for exclusion of studies.

Flow chart constructed using the PRISMA guidelines (Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med 6(6): e1000097. doi:10.1371/journal.pmed1000097).

Figure 2. Forest plot of estimates of mortality at 12 months by individual studies and pooled by region.

Pooled estimates are summary random effects estimates with 95% confidence intervals. Two studies were excluded as they did not provide appropriate data for pooling (Djomand and Yu [24]). The summary pooled estimate is 0.14 (95% CI 0.10–0.20). Test for heterogeneity by region was as follows : Sub-Saharan Africa Cochran Q = 7691, p-value<0.0001 and I² = 99.84% (95% CI 99.8%–99.8%)- suggesting there is an evidence of heterogeneity among studies; Asia Cochran Q = 1.67; p = 0.20– suggesting non-heterogeneous studies (I² cannot be estimated since only 2 studies); Americas Cochran Q = 51.54; p<0.0001 – suggesting there is evidence of heterogeneity (I² cannot be estimated since only 2 studies) and Multiregional: Cochran Q = 90.7; p<0.0001 and I² = 96.7% (95% CI 94.7%–98.7%) suggesting there is evidence of heterogeneity.

Figure 3. Sensitivity analyses for pooled regional estimates of mortality at 12 months by best case (all loss-to-follow-up assumed to have survived) and worst case (all lost-to-follow-up assumed to have died) scenarios.

Figure 4. Funnel plot.

The funnel plot assesses the hypothesis that the relationship between probability of death and study size, measured by standard error, is independent. This was tested using a Kendall's tau, which was estimated to be 0.4 with p-value = 0.0046, suggesting there is evidence of asymmetry. Although the presence of publication bias is a common explanation to an asymmetric funnel plot, data presented here are observational data without any intervention so the funnel plot asymmetry could also be due to heterogeneity in the data .