Markers of poor adherence among HIV-positive adults on antiretroviral therapy at Themba Lethu Clinic Nnambalirwa Maria1, Denise Evans2, Lynne McNamara3, Peter Nyasulu4, 1School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa, 2Health Economics and Epidemiology Research Office, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa, 3Clinical HIV Research Unit, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa, 4School of Health Sciences, Monash University, Johannesburg, South Africa, BACKGROUND The prevalence of the Human Immunodeficiency Virus (HIV) in South Africa was 17.8% among 15 to 49 year olds in 2010 [1]. Antiretroviral therapy (ART) has thus played a crucial role in mitigating the impact of the HIV epidemic. One of the challenges of ART provision is ensuring adherence to taking the medication. To date there has been no clear consensus on the ideal way to measure adherence in resource limited settings (RLS). Viral load is perhaps the best and most reliable indicator of poor adherence but is expensive and not easily accessible or available in many RLS [2 – 4]. Surrogate markers such as mean cell volume (MCV), CD4 cell count, self-reported adherence and missed visits have been shown to be useful to measure adherence but their reliability remains unclear [4 – 6]. We aimed to identify routinely collected markers that could be used to assess poor adherence to ART. Image METHODS RESULTS Study design Retrospective analysis. Eligibility criteria All HIV- positive ART-naïve adults (≥ 18 years) initiating standard first-line ART at the Themba Lethu Clinic in Johannesburg, South Africa between 1st April 2004 and 1st January 2012. Patients should have been on ART for at least 6 months. Study procedure Ethics approval was obtained from the University of the Witwatersrand (HREC M120858). Data were obtained from TherapyEdge-HIV™, an electronic patient medical record database, at Themba Lethu Clinic. The data were de-identified and exported into STATA Release 12.1 for analysis. Outcome variable Proportion of patients with poor adherence defined as a viral load ≥ 400 copies/ml at 6 months on ART (150 to 300 days after ART initiation). Data management Variables of interest included: Demographic characteristics: age, gender, education level and professional status. Clinical characteristics: the last self-reported adherence, change in MCV calculated from baseline to 6 months, change in CD4 count calculated from baseline to 6 months (≥ or < the expected increase of 50 cells/mm3 at 6 months) [7] and missed visits (defined as a scheduled appointment that had been missed by ≥ 7 days but not by more than 3 months) and WHO stage at baseline. Other characteristics were change in regimen, TB, pregnancy and adverse events or side effects during the first 6 months on ART. Statistical analysis Patient demographics and clinical characteristics of the groups were summarized. We investigated the association between each potential factor and poor adherence estimating incidence rate ratios (IRRs) and 95% confidence intervals (CIs) using poisson regression models with robust error variance. The IRR was used to approximate the relative risk (RR) of poor adherence. The diagnostic accuracy of each identified marker of adherence was also tested using sensitivity, specificity, positive predictive values (PPVs) and negative predictive values (NPVs). Table 1: Summary of patient demographics and clinical characteristics, by group Characteristics All eligible patients All eligible patients on AZT-based regimens Total N = 7160 Viral load < 400 copies/ml N = 5806 ≥ 400 copies/ml N = 1354 N = 177 N = 143 ≥ 400 copies/ml N = 34 Gender Females Males 4523 (63.2%) 2637 (36.8%) 3716 (64%) 2090 (36%) 807 (59.6%) 547 (40.4%) 97 (54.8%) 80 (45.2%) 82 (57.3%) 61 (42.7%) 15 (44.1%) 19 (55.9%) Age in years Median Interquartile range (IQR) / Mean ± standard deviation 36.7 (31.4 – 43.4) 36.8 (31.6 – 43.5) 36.3 (30.7 – 42.9) 41.2 ± 9.1 41.8 ± 8.9 38.4 ± 9.6 Baseline CD4 count in cells/mm3 Median (IQR) 101 (39 – 175) 104 (40 – 176) 90 (34 – 170) 156 (74 – 244) 154 (74 – 243) 169 (61 – 249) WHO stage at baseline I II III IV Missing 2161 (30.2%) 1135 (15.9%) 1744 (24.4%) 716 (10%) 1404 (19.5%) 1826 (31.5%) 920 (15.8%) 1409 (24.3%) 572 (9.9%) 1079 (18.5%) 335 (24.7%) 215 (15.9%) 144 (10.6%) 325 (24.1%) 75 (42.4%) 15 (8.5%) 25 (14.1%) 11 (6.2%) 51 (28.8%) 13 (9.1%) 19 (13.3%) 11 (7.7%) 39 (27.2%) 14 (41.2%) 2 (5.9%) 6 (17.6%) 0 (0%) 12 (35.3%) MCV at baseline in fL 88.4 (84 – 92.4) 88.4 (83.9 – 92.3) 88.8 (84.3 – 92.6) 91.4 (86.9 – 101.9) 91.9 (87.4 – 102.2) 89.5 (82.9 – 97.4) Change in CD4 count at 6 months in cells/mm3 129 (69 – 205) 135 (76 – 208) 97 (33 – 180) 95.5 (45 – 140) 102 (54 – 145) 52 ( -9 – 61) Change in MCV at 6 months in fL 12.5 (5.8 – 18.5) 13.6 (6.8 – 19) 6.8 (2.6 – 11.8) 15.1 (4.2 – 20.7) 16.1 (5.9 – 22) 7.4 (3.2 – 16.9) Pregnancy: Pregnancy during first 6 months on ART: Never At baseline During follow-up Missing (males) 4294 (60.0%) 82 (1.1%) 147 (2.1%) 3534 (60.9%) 67 (1.2%) 115 (1.9%) 2090 (36.0%) 760 (56.1%) 15 (1.1%) 32 (2.4%) 92 (52.0%) 5 (2.8%) 80 (55.9%) 2 (1.4%) 3 (8.8%) Table 2: Markers of poor adherence using poisson regression models with robust error variance Variable All eligible patients All eligible patients on AZT-based regimens Univariate Analyses Multivariate Analysis Crude IRR (95% CI) P > |z| Adjusted IRR Gender Females Males 1 1.16 (1.05 – 1.28) - 0.002 1.09 (0.92 – 1.28) 0.320 1.54 (0.83 – 2.83) 0.168 Age ≤ median age (≤ 36.7 years) > median age (> 36.7 years) 0.94 (0.85 – 1.04) 0.213 0.94 (0.80 – 1.11) 0.463 0.51 (0.28 – 0.93) 0.028 1.18 (0.31 – 4.53) 0.808 Baseline CD4 count in cells/mm3 > 200 101 – 200 51 – 100 ≤ 50 0.96 (0.81 – 1.13) 1.09 (0.92 – 1.31) 1.19 (1.01 – 1.40) 0.594 0.318 0.034 1.05 (0.80 – 1.38) 1.08 (0.80 – 1.47) 1.34 (1.02 – 1.76) 0.716 0.612 0.035 0.74 (0.30 – 1.79) 0.77 (0.28 – 2.14) 0.88 (0.35 – 2.24) 0.498 0.614 0.794 0.82 (0.07 – 9.24) 3.11 (0.47 – 20.67) 2.10 (0.12 – 37.31) 0.870 0.240 WHO stage at baseline I II III IV 1.22 (1.05 – 1.43) 1.24 (1.08 – 1.42) 1.30 (1.09 – 1.55) 0.012 0.004 1.16 (0.90 – 1.48) 1.27 (1.04 – 1.55) 1.44 (1.12 – 1.84) 0.248 0.021 0.71 (0.18 – 2.84) 1.29 (0.55 – 3.00) -‡ 0.653 0.560 MCV at baseline < 80fL 80 – 100fL > 100fl 1.23 (1.01 – 1.51) 1.36 (0.99 – 1.87) 0.042 0.059 1.33 (1.01 – 1.75) 0.98 (0.62 – 1.55) 0.044 0.937 0.61 (0.24 – 1.58) 0.43 (0.13 – 1.36) 0.314 0.151 Change in CD4 count at 6 months in cells/mm3 ≥ expected < expected 1.85 (1.64 – 2.09) 0.000 4.21 (1.68 – 10.57) 7.66 (0.98 – 59.91) 0.052 Change in MCV at 6 months ≥ 14.5 fL < 14.5 fL 3.42 (2.83 – 4.14) 2.80 ( 0.95 – 8.28) 0.062 Change in CD4 count stratified by change in MCV at 6 months -Change in CD4 count ≥ expected and change in MCV ≥ 14.5fL: -Change in CD4 count ≥ expected and change in MCV < 14.5fL: -Change in CD4 count < expected and change in MCV ≥ 14.5fL: -Change in CD4 count < expected and change in MCV < 14.5fL: 2.90 (2.29 – 3.66) 1.02 (0.64 – 1.63) 6.10 (4.80 – 7.76) 0.925 3.11 (2.41 – 4.02) 1.23 (0.76 – 2.00) 6.98 (5.35 – 9.09) 0.394 1.91 (0.34 – 10.80) 3.36 (0.53 – 21.40) 6.48 (1.47 – 28.50) 0.462 0.199 0.013 Pregnancy: Pregnancy during first 6 months on ART: Never At baseline During follow-up 1.03 (0.65 – 1.64) 1.23 (0.90 – 1.68) 0.889 0.195 4.6 (1.88 – 11.24) 0.001 9.11 (2.17 – 38.25) 0.003 ‡Cell has no observations. Image Sensitivity, specificity, PPV and NPV For patients on d4T or AZT-based regimens, the sensitivity and specificity of the change in MCV at 6 months were 70.2% and 61.4% respectively. For patients on TDF-based regimens, the sensitivity and specificity of the change in MCV at 6 months were 97.4% and 3.1% respectively. The sensitivity, specificity, PPV and NPV of the change in CD4 count stratified by change in MCV at 6 months as a predictor of poor adherence were 86.5%, 37.3%, 18.8% and 94.3% respectively. The sensitivity, specificity, PPV and NPV of pregnancy during the first 6 months on ART as a predictor of poor adherence for patients on AZT-based regimens were 20%, 97.6% 60% and 87% respectively. CONCLUSIONS The markers of poor adherence to ART are change in CD4 count stratified by change in MCV at 6 months and pregnancy during the first 6 months on ART for patients on AZT-based regimens. 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