1. 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.
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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.
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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. These could help health workers identify poor adherence in the absence of viral load testing and target patients for interventions to prevent virological failure. Further studies are needed to
verify whether the markers of adherence remain
the same beyond 6 months on ART and as the
proportion of patients on TDF-based regimens
increases.
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