Re-engagement in care of people living with HIV lost to follow-up after initiation of antiretroviral therapy in Mali: Who returns to care?

OBJECTIVES: We assessed cumulative incidence rates of and factors associated with re-engagement in HIV care for PLHIV lost to follow-up in Mali. METHODS: HIV-1-infected individuals lost to follow-up before 31/12/2013, ≥ 18 years old, who started ART from 2006 to 2012 at one of 16 care centres were considered. Loss to follow-up (LTFU) was defined as an interruption of ≥ 6 months during follow-up. The re-engagement in care in PLHIV lost to follow-up before 31/12/2013 was defined as having at least one clinical visit after LTFU. The cumulative incidence rates of re-engagement in care was estimated by Kaplan-Meier and its predictive factors were assessed using Cox models. Socio-demographic characteristics, clinical and immune status, period, region, centre expertise level, and distance from home at the start of ART plus a combined variable of duration of ART until LTFU and 12-month change in CD4 count were assessed. Multiple imputation was used to deal with missing data. RESULTS: We included 3,650 PLHIV lost to follow-up before December 2013, starting ART in nine outpatient clinics and seven hospitals (5+2 in Bamako and 4+5 in other regions): 35% male, median (IQR) age 35 (29-43), and duration of ART until LTFU 11 months (5-22). Among these PLHIV, 1,975 (54%) were definitively LTFU and 1,675 (46%) subsequently returned to care. The cumulative incidence rates of re-engagement in care rose from 39.0% at one year to 47.0% at three years after LTFU. Predictors of re-engagement in care were starting ART with WHO stage 1-2 and CD4 counts ≥ 200 cells/µL, being treated for ≥ 12 months with CD4 count gain ≥ 50 cells/µL, or being followed in Bamako. People followed at regional hospitals or outpatient clinics ≥ 5 km away, or being treated for ≥ 12 months with CD4 count gain < 50 cells/µL were less likely to return to care. CONCLUSIONS: Starting ART with a higher CD4 count, better gain in CD4 count, and being followed either in Bamako or close to home in the regions were associated with re-engagement in care.

Characterization of HIV-1 antiretroviral drug resistance after second-line treatment failure in Mali, a limited-resources setting.

OBJECTIVES: We describe the outcomes of second-line drug resistance profiles and predict the efficacy of drugs for third-line therapy in patients monitored without the benefit of plasma HIV-1 RNA viral load (VL) or resistance testing. METHODS: We recruited 106 HIV-1-infected patients after second-line treatment failure in Mali. VL was determined by the Abbott RealTime system and the resistance by the ViroSeq HIV-1 genotyping system. The resistance testing was interpreted using the latest version of the Stanford algorithm. RESULTS: Among the 106 patients, 93 had isolates successfully sequenced. The median age, VL and CD4 cells were respectively 35 years, 72 000 copies/mL and 146 cells/mm(3). Patients were exposed to a median of 4 years of treatment and to six antiretrovirals. We found 20% of wild-type viruses. Resistance to etravirine was noted in 38%, to lopinavir in 25% and to darunavir in 12%. The duration of prior nucleos(t)ide reverse transcriptase inhibitor exposure was associated with resistance to abacavir (P < 0.0001) and tenofovir (P = 0.0001), and duration of prior protease inhibitor treatment with resistance to lopinavir (P < 0.0001) and darunavir (P = 0.06). CONCLUSION: Long duration of therapy prior to failure was associated with high levels of resistance and is directly related to limited access to VL monitoring and delayed switches to second-line treatment, precluding efficacy of drugs for third-line therapy. This study underlines the need for governments and public health organizations to recommend the use of VL monitoring and also the availability of darunavir and raltegravir for third-line therapies in the context of limited-resource settings.