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Objectives: Mortality in patients starting antiretroviral therapy (ART) is higher in Malawi and Zambia than in South Africa. We examined whether different monitoring of ART (viral load [VL] in South Africa and CD4 count in Malawi and Zambia) could explain this mortality difference. Design:: Mathematical modelling study based on data from ART programmes. Methods: We used a stochastic simulation model to study the effect of VL monitoring on mortality over 5 years. In baseline scenario A all parameters were identical between strategies except for more timely and complete detection of treatment failure with VL monitoring. Additional scenarios introduced delays in switching to second-line ART (scenario B) or higher virologic failure rates (due to worse adherence) when monitoring was based on CD4 counts only (scenario C). Results are presented as relative risks (RR) with 95% prediction intervals and percent of observed mortality difference explained. Results: RRs comparing VL with CD4 cell count monitoring were 0.94 (0.74–1.03) in scenario A, 0.94 (0.77–1.02) with delayed switching (scenario B) and 0.80 (0.44–1.07) when assuming a 3-times higher rate of failure (scenario C). The observed mortality at 3 years was 10.9% in Malawi and Zambia and 8.6% in South Africa (absolute difference 2.3%). The percentage of the mortality difference explained by VL monitoring ranged from 4% (scenario A) to 32% (scenarios B and C combined, assuming a 3-times higher failure rate). Eleven percent was explained by non-HIV related mortality. Conclusions: VL monitoring reduces mortality moderately when assuming improved adherence and decreased failure rates. [ABSTRACT FROM AUTHOR]

Objectives: We examined the effect of switching to second-line antiretroviral therapy (ART) on mortality in patients who experienced immunological failure in ART programmes without access to routine viral load monitoring in sub-Saharan Africa., Design and Setting: Collaborative analysis of two ART programmes in Lusaka, Zambia and Lilongwe, Malawi., Methods: We included all adult patients experiencing immunological failure based on WHO criteria. We used Cox proportional hazards models weighted by the inverse probability of switching to compare mortality between patients who switched and patients who did not; and between patients who switched immediately and patients who switched later. Results are expressed as hazard ratios with 95% credible intervals (95% CI)., Results: Among 2411 patients with immunological failure 324 patients (13.4%) switched to second-line ART during 3932 person-years of follow-up. The median CD4 cell count at start of ART and failure was lower in patients who switched compared to patients who did not: 80 versus 155 cells/μl (P < 0.001) and 77 versus 146 cells/μl (P < 0.001), respectively. Adjusting for baseline and time-dependent confounders, mortality was lower among patients who switched compared to patients remaining on failing first-line ART: hazard ratio 0.25 (95% CI 0.09-0.72). Mortality was also lower among patients who remained on failing first-line ART for shorter periods: hazard ratio 0.70 (95% CI 0.44-1.09) per 6 months shorter exposure., Conclusion: In ART programmes switching patients to second-line regimens based on WHO immunological failure criteria appears to reduce mortality, with the greatest benefit in patients switching immediately after immunological failure is diagnosed.

Objectives: To compare outcomes of antiretroviral therapy (ART) in South Africa, where viral load monitoring is routine, with those in Malawi and Zambia, where monitoring is based on CD4 cell counts., Methods: We included 18,706 adult patients starting ART in South Africa and 80,937 patients in Zambia or Malawi. We examined CD4 responses in models for repeated measures and the probability of switching to second-line regimens, mortality and loss to follow-up in multistate models, measuring time from 6 months., Results: In South Africa, 9.8% [95% confidence interval (CI) 9.1-10.5] had switched at 3 years, 1.3% (95% CI 0.9-1.6) remained on failing first-line regimens, 9.2% (95% CI 8.5-9.8) were lost to follow-up and 4.3% (95% CI 3.9-4.8) had died. In Malawi and Zambia, more patients were on a failing first-line regimen [3.7% (95% CI 3.6-3.9], fewer patients had switched [2.1% (95% CI 2.0-2.3)] and more patients were lost to follow-up [15.3% (95% CI 15.0-15.6)] or had died [6.3% (95% CI 6.0-6.5)]. Median CD4 cell counts were lower in South Africa at the start of ART (93 vs. 132 cells/μl; P < 0.001) but higher after 3 years (425 vs. 383 cells/μl; P < 0.001). The hazard ratio comparing South Africa with Malawi and Zambia after adjusting for age, sex, first-line regimen and CD4 cell count was 0.58 (0.50-0.66) for death and 0.53 (0.48-0.58) for loss to follow-up., Conclusion: Over 3 years of ART mortality was lower in South Africa than in Malawi or Zambia. The more favourable outcome in South Africa might be explained by viral load monitoring leading to earlier detection of treatment failure, adherence counselling and timelier switching to second-line ART.

Background: Evaluation of antiretroviral treatment (ART) programmes in sub-Saharan Africa is difficult because many patients are lost to follow-up. Outcomes in these patients are generally unknown but studies tracing patients have shown mortality to be high. We adjusted programme-level mortality in the first year of antiretroviral treatment (ART) for excess mortality in patients lost to follow-up., Methods and Findings: Treatment-naïve patients starting combination ART in five programmes in Côte d'Ivoire, Kenya, Malawi and South Africa were eligible. Patients whose last visit was at least nine months before the closure of the database were considered lost to follow-up. We filled missing survival times in these patients by multiple imputation, using estimates of mortality from studies that traced patients lost to follow-up. Data were analyzed using Weibull models, adjusting for age, sex, ART regimen, CD4 cell count, clinical stage and treatment programme. A total of 15,915 HIV-infected patients (median CD4 cell count 110 cells/µL, median age 35 years, 68% female) were included; 1,001 (6.3%) were known to have died and 1,285 (14.3%) were lost to follow-up in the first year of ART. Crude estimates of mortality at one year ranged from 5.7% (95% CI 4.9-6.5%) to 10.9% (9.6-12.4%) across the five programmes. Estimated mortality hazard ratios comparing patients lost to follow-up with those remaining in care ranged from 6 to 23. Adjusted estimates based on these hazard ratios ranged from 10.2% (8.9-11.6%) to 16.9% (15.0-19.1%), with relative increases in mortality ranging from 27% to 73% across programmes., Conclusions: Naïve survival analysis ignoring excess mortality in patients lost to follow-up may greatly underestimate overall mortality, and bias ART programme evaluations. Adjusted mortality estimates can be obtained based on excess mortality rates in patients lost to follow-up.