Conceived and designed the experiments: MC JF WAB. Performed the experiments: AH EE PE II SA. Analyzed the data: MC MO RB PM. Contributed reagents/materials/analysis tools: MC AH EE PE II SA ME UG EI AA PD JF WAB. Wrote the paper: MC MO ME MAE AA JF WAB. Supervised patient care: AH EE PE II SA. Coordinated the ACTION Project: PD.
The authors have declared that no competing interests exist.
Substantial resources and patient commitment are required to successfully scale-up antiretroviral therapy (ART) and provide appropriate HIV management in resource-limited settings. We used pharmacy refill records to evaluate risk factors for loss to follow-up (LTFU) and non-adherence to ART in a large treatment cohort in Nigeria.
We reviewed clinic records of adult patients initiating ART between March 2005 and July 2006 at five health facilities. Patients were classified as LTFU if they did not return >60 days from their expected visit. Pharmacy refill rates were calculated and used to assess non-adherence. We identified risk factors associated with LTFU and non-adherence using Cox and Generalized Estimating Equation (GEE) regressions, respectively. Of 5,760 patients initiating ART, 26% were LTFU. Female gender (p<0.001), post-secondary education (p = 0.03), and initiating treatment with zidovudine-containing (p = 0.004) or tenofovir-containing (p = 0.05) regimens were associated with decreased risk of LTFU, while patients with only primary education (p = 0.02) and those with baseline CD4 counts (cell/ml3) >350 and <100 were at a higher risk of LTFU compared to patients with baseline CD4 counts of 100–200. The adjusted GEE analysis showed that patients aged <35 years (p = 0.005), who traveled for >2 hours to the clinic (p = 0.03), had total ART duration of >6 months (p<0.001), and CD4 counts >200 at ART initiation were at a higher risk of non-adherence. Patients who disclosed their HIV status to spouse/family (p = 0.01) and were treated with tenofovir-containing regimens (p≤0.001) were more likely to be adherent.
These findings formed the basis for implementing multiple pre-treatment visit preparation that promote disclosure and active community outreaching to support retention and adherence. Expansion of treatment access points of care to communities to diminish travel time may have a positive impact on adherence.
Nigeria is home to the second highest number of people living with HIV in the world and the highest in West Africa, with an estimated 2.6 million infected at the end of 2007
Although substantial rates of LTFU have been shown in ART programs in resource limited settings
Several studies in Africa have shown a high level of medication adherence
Lack of adherence to antiretroviral medications and attrition from health services contribute to poorer health outcomes and waste limited resources. Identifying patient characteristics that are associated with these outcomes could be used for making evidence-informed decisions to improve patient care and programmatic outcomes. In this paper, we describe risk factors for LTFU and non-adherence to antiretrovirals in a large antiretroviral therapy program in Nigeria.
In 2005, the AIDS Care and Treatment in Nigeria (ACTION) project was created as a joint initiative between the Institute of Human Virology of the University of Maryland School of Medicine, the Institute of Human Virology Nigeria, the Nigerian Federal Ministry of Health, and local partner treatment facilities. The goals of ACTION are to implement a multidisciplinary program of primary HIV prevention, and HIV care and support that employs an evidence-based approach using robust strategic information systems to monitor performance and evaluate quality in order to continually improve program sustainability.
This is a retrospective analysis of existing data and was determined to be exempt from oversight after review by the National Health Research Ethics Committee of Nigeria and the University of Maryland Baltimore Institutional Review Board. Patients were included in the analysis if they were non-pregnant treatment-naive adults initiating first-line ART between March 2005 and July 2006 at five tertiary hospitals in Nigeria. Among the first in Nigeria to provide large-scale ART access through support from PEPFAR, the local partner treatment facilities included were the University of Abuja Teaching Hospital (UATH), the University of Benin Teaching Hospital (UBTH), the Aminu Kano Teaching Hospital (AKTH), the Nnamdi Azikwe University Teaching Hospital (NAUTH), and the University of Calabar Teaching Hospital (UCTH). All WHO clinical stage 4 patients, stage 3 patients with a CD4 cell count <350 cells/ul, and stage 1 and stage 2 patients with a CD4 cell count <200 ul were eligible for ART in accordance with the Nigerian National Guidelines
At clinic enrollment, patients underwent a history and physical examination and were interviewed by staff in order to complete an intake record which included demographic data. During this period, adherence counseling was routinely provided to patients, but a regimented intensive HIV treatment preparation program was not offered as part of routine clinical care. Six first-line treatment regimens were prescribed: (stavudine [d4T] + lamivudine [3TC] + nevirapine [NVP]/efavirenz[EFV]), (zidovudine [ZDV] + 3TC + NVP/EFV), and (tenofovir [TDF] + 3TC + NVP/EFV). Although physicians were trained to prescribe ZDV as a first choice, with TDF used if anemia was present and utilize EFV with concomitant anti-tuberculosis treatment with exception for child-bearing age women, choice of regimen was at the discretion of the physician. Patients were scheduled for a follow-up visit one month after ART was first dispensed and then every two or three months at the discretion of the physician. A sufficient supply of ART was dispensed to last until the next scheduled visit. At each follow-up visit, patients underwent a history and physical and any side effects reported were recorded by the physician on a clinical encounter form. A pharmacy order form, which includes the date ART was dispensed, the formulations dispensed, and the quantity of each formulation dispensed, was completed by the physician at each visit and presented to the pharmacist for ART dispensing.
Form data were entered in real-time into the CAREWare health management information system. Originally developed for the US domestic HIV treatment and care program under the Ryan White CARE Act, the system has been adapted by the US Department of Health and Human Services Health Resources and Service Administration for resource-limited setting through PEPFAR and was deployed by the ACTION project.
For the LTFU analysis, time-to-event analytical method was used. Patients were considered LTFU when they did not return to the clinic more than 60 days from their last expected visit. The risk of LTFU was determined using Cox proportional hazard regression with ART initiation as time zero. The proportional hazards assumption was assessed with log-log plots and regression of the Schoenfeld residuals
For the non-adherence to ARV analysis, pharmacy dispensing records over the duration of ARV therapy for each patient were reviewed. Each patient was required to have been receiving ART for a minimum of 90 days to be included in the analysis. Days of medication dispensed between visits was calculated. A pharmacy refill adherence rate (Rx) was then calculated (days of medication dispensed/days between visits multiplied by 100). For each visit interval when the Rx is less than 95% (i.e. an episode of Rx<95%), a binary outcome of 1 was designated (i.e. a value of 0 denoted a visit where Rx ≥95%). Given the longitudinal nature of the outcome, the risk factor analysis for Rx<95% within the first 12 months of initiating ART were modeled as the odds of having Rx<95% at any given visit using Generalized Estimating Equation
In both of these primary regression analyses of LTFU and non-adherence to ARV, potential covariates were first examined in unadjusted models. Factors associated at the p<0.05 level with the outcome and known risk factors for LTFU and non-adherence regardless of their level of significance were then evaluated in multivariate models. To control for confounding, variables that altered any significant relative hazards or odds ratios by ≥20% were retained. To account for missing data that may have not occurred at random, multiple imputations for missing data were performed using Markov chain Monte Carlo method (SAS Proc MI). The analyses of the complete-data obtained through the imputation did not change the results; therefore, only the primary regression results are shown.
In order to model the effect of time since ART initiation as a continuous risk factor on the outcome of Rx<95%, we fitted a cubic polynomial model using the GEE method to obtain the predicted values of the proportion of individuals with the outcome from the associated β coefficients. Similar to the procedure used in the multivariate analyses above, the model with the smallest QIC value was chosen as the best model of time since ART initiation.
Statistical analyses were performed using STATA 10.0 (College Station, TX) for the LTFU analysis and SAS 9.2 (Cary, NC) for the GEE analysis.
The study population included 5,760 patients; 2,385 (59%) were female and the median age at time of enrollment was 35 years (25th–75th percentile: 29–41 years) –
Characteristics | Number (%) | |
Female | 3375 (59) | |
Male | 2385 (41) | |
≤25 | 683 (12) | |
25–30 | 1262 (22) | |
31–35 | 1246 (22) | |
35–40 | 1071 (19) | |
41–45 | 850 (15) | |
46+ | 648 (11) | |
University of Abuja Teaching Hospital | 1829 (32) | |
University of Benin Teaching Hospital | 1140 (20) | |
Aminu Kano Teaching Hospital | 989 (17) | |
Nnmadi Azikwe University Teaching Hospital | 1404 (24) | |
University of Calabar Teaching Hospital | 398 (7) | |
Post-Secondary | 1239 (22) | |
Completed Secondary | 2049 (37) | |
Completed Primary | 1263 (23) | |
Quranic education | 260 (5) | |
None or some Primary | 531 (9) | |
Others | 163 (3) | |
(Missing) | 255 | |
Currently Married | 3134 (55) | |
Currently Not Married | 2596 (45) | |
(Missing) | 30 | |
Student or Unemployed | 1146 (22) | |
Other/Retired | 2224 (42) | |
Employed | 1942 (37) | |
(Missing) | 448 | |
≤1 Hour | 2862 (51) | |
>1–2 Hours | 1533 (27) | |
>2 Hours | 1196 (21) | |
(Missing) | 169 | |
Median (Interquartile Range) | 121(141) | |
>350 cells/mL3 | 251 (5) | |
201–350 cells/mL3 | 858 (19) | |
100–200 cells/mL3 | 1567 (34) | |
<100 cells/mL3 | 1954 (42) | |
(Missing) | 1130 | |
ZDV/3TC/NVP or EFV | 2669 (46) | |
TDF/3TC/NVP or EFV | 731 (13) | |
d4T/3TC/NVP or EFV | 2360 (41) | |
≤6 Months | 2529 (44) | |
>6 and <12 Months | 1826 (32) | |
12–18 Months | 1405 (24) |
During the follow-up period, 1494 (25.9%) patients became LTFU (
The characteristics associated with LTFU are shown in
Total | LTFU, n = 1494, (26%) | Unadjusted | Adjusted |
|||||
N |
n | % | HR (95% CI) | P-value | HR (95% CI) | P-value | ||
Female | 3375 | 773 | 23 | 0.75(0.67–0.82) | 0.70 (0.62–0.81) | |||
Male | 2385 | 721 | 30 | Ref | Ref | |||
≤25 | 683 | 167 | 24 | 1.01 (0.84–1.26) | 0.90 | 1.36 (1.03–1.79) | ||
25–30 | 1262 | 329 | 26 | 1.09 (0.90–1.32) | 0.37 | 1.31 (1.03–1.65) | ||
31–35 | 1246 | 333 | 27 | 1.11 (0.92–1.35) | 0.27 | 1.19 (0.94–1.50) | 0.15 | |
35–40 | 1071 | 303 | 28 | 1.18 (0.97–1.45) | 0.09 | 1.30 (1.03–1.64) | ||
41–45 | 850 | 209 | 25 | 1.03 (0.83–1.27) | 0.76 | 1.00 (0.78–1.29) | 0.97 | |
46+ | 648 | 153 | 24 | Ref | Ref | |||
Post-Secondary | 1239 | 271 | 22 | 0.80 (0.65–0.99) | 0.75 (0.58–0.96) | |||
Completed Secondary | 2049 | 564 | 28 | 1.02 (0.85–1.23) | 0.82 | 0.97 (0.78–1.21) | 0.67 | |
Completed Primary | 1263 | 362 | 29 | 1.07 (0.88–1.30) | 0.50 | 1.01 (0.80–1.27) | 0.93 | |
Quranic education | 260 | 75 | 29 | 1.02 (0.77–1.35) | 0.87 | 1.08 (0.74–1.56) | 0.70 | |
Others | 163 | 27 | 17 | 0.61 (0.41–0.93) | 0.54 (0.33–0.89) | |||
None or some Primary | 531 | 143 | 27 | Ref | Ref | |||
Currently Married | 3134 | 819 | 26 | 1.01 (0.92–1.12) | 0.78 | 0.93 (0.82–1.06) | 0.30 | |
Currently Not Married | 2596 | 666 | 26 | Ref | Ref | |||
Student or Unemployed | 1146 | 269 | 23 | 0.90 (0.78–1.04) | 0.17 | 0.99 (0.82–1.20) | 0.97 | |
Other/Retired | 2224 | 614 | 28 | 1.04 (0.93–1.18) | 0.45 | 1.04 (0.90–1.20) | 0.53 | |
Employed | 1942 | 511 | 26 | Ref | Ref | |||
To Spouse or Family Members | 4058 | 1000 | 25 | 0.96 (0.82–1.12) | 0.57 | |||
To Others Only | 240 | 55 | 23 | 0.89 (0.66–1.20) | 0.45 | |||
To No One | 733 | 187 | 26 | Ref | ||||
>2 Hours | 1196 | 290 | 24 | 0.90 (0.78–1.02) | 0.11 | |||
>1–2 Hours | 1533 | 400 | 26 | 0.97(0.86–1.10) | 0.68 | |||
≤1 Hour | 2862 | 768 | 27 | Ref | ||||
>350 cells/mL3 | 251 | 78 | 31 | 1.47 (1.15–.1.89) | 1.62 (1.25–2.11) | |||
201–350 cells/mL3 | 858 | 187 | 22 | 1.04 (0.87–1.25) | 0.63 | 1.07 (0.89–1.30) | 0.47 | |
100–200 cells/mL3 | 1567 | 329 | 21 | Ref | Ref | |||
<100 cells/mL3 | 1954 | 576 | 30 | 1.41 (1.23–1.61) | 1.37 (1.18–1.57) | |||
ZDV+3TC+NVP or EFV | 2669 | 630 | 24 | 0.85 (0.76–0.95) | 0.76 (0.67–0.87) | |||
TDF+3TC+NVP or EFV | 731 | 196 | 27 | 0.93 (0.79–1.09) | 0.37 | 0.83 (0.69–1.00) | ||
d4T+3TC+NVP or EFV | 2360 | 668 | 28 | Ref | Ref |
Totals always do not sum to 5,760 due to missing data;
A total of 4,177 subjects included in the adjusted model; HR, hazard ratio; CI, confidence interval.
For the analysis of pharmacy refill rates, 4529 patients who returned for further visits beyond 90 days of ART initiation were included. Of these, 3362 (74.2%) had a pharmacy refill rate <95% at any time during follow-up. A total of 1747 (38.5%) had a summary pharmacy refill at the last visit <95%; of which 15.8% had Rx<50%, 33.6% with Rx of 50–79%, and 50.6% with Rx of 80–95%. In the bivariate analyses of those characteristics associated with pharmacy refill rate <95% (
Unadjusted | Adjusted |
|||||
N |
OR (95% CI) | P-value | OR (95% CI) | P-value | ||
Female | 2743 | 1.05 (0.98–1.12) | 0.15 | 0.99 (0.91–1.09) | 0.87 | |
Male | 1786 | Ref | Ref | |||
≤25 | 551 | 1.22 (1.07–1.38) | 1.18 (0.98–1.41) | 0.07 | ||
26–30 | 979 | 1.29 (1.15–1.44) | 1.28 (1.10–1.48) | |||
31–35 | 971 | 1.15 (1.02–1.28) | 1.16 (1.00–1.34) | |||
36–40 | 823 | 1.18 (1.04–1.32) | 1.12 (0.97–1.30) | 0.12 | ||
41–45 | 603 | 1.02 (0.89–1.16) | 0.80 | 1.03 (0.88–1.21) | 0.70 | |
≥46 | 602 | Ref | Ref | |||
Post-Secondary | 1011 | 1.05 (0.92–1.19) | 0.46 | 1.03 (0.88–1.21) | 0.73 | |
Completed Secondary | 1588 | 1.03 (0.91–1.16) | 0.69 | 1.02 (0.89–1.18) | 0.75 | |
Completed Primary | 962 | 0.94 (0.82–1.07) | 0.34 | 0.98 (0.84–1.14) | 0.79 | |
Quranic | 205 | 1.40 (1.15–1.69) | 1.37 (1.01–1.84) | |||
None or Some Primary | 414 | Ref | Ref | |||
Currently Married | 2454 | 0.96 (0.90–1.02) | 0.20 | |||
Currently Not Married | 2052 | Ref | ||||
To Spouse or Family Members | 3228 | 0.85 (0.76–0.94) | 0.85 (0.75–0.97) | |||
To Others Only | 193 | 0.94 (0.78–1.12) | 0.49 | 0.92 (0.74–1.14) | 0.45 | |
To No One | 579 | Ref | Ref | |||
Student or Unemployed | 931 | 1.16 (1.06–1.27) | 1.10 (0.98–1.24) | 0.12 | ||
Other/Retired | 1705 | 0.99 (0.91–1.07) | 0.76 | 1.07 (0.98–1.18) | 0.14 | |
Employed | 1513 | Ref | Ref | |||
>2 Hours | 963 | 1.01 (0.93–1.10) | 0.80 | 1.11 (1.01–1.23) | ||
>1–2 Hours | 1202 | 0.97 (0.89–1.05) | 0.39 | 1.07 (0.97–1.17) | 0.19 | |
≤1 Hour | 2223 | Ref | Ref | |||
>350 Cells/mL3 | 187 | 1.38 (1.18–1.62) | 1.25 (1.05–1.49) | |||
>200 and ≤350 Cells/mL3 | 707 | 1.20 (1.08–1.32) | 1.18 (1.06–1.32) | |||
≥100 and ≤200 Cells/mL3 | 1293 | Ref | Ref | |||
<100 Cells/mL3 | 1464 | 1.00 (0.92–1.09) | 0.98 | 0.98 (0.89–1.08) | 0.63 | |
TDF+3TC+NVP or EFV | 671 | 0.61 (0.54–0.69) | 0.73 (0.62–0.84) | |||
ZDV+3TC+NVP or EFV | 2942 | 0.88 (0.83–0.94) | 0.96 (0.88–1.05) | 0.37 | ||
d4T+3TC+NVP or EFV | 916 | Ref | Ref | |||
>15 and ≤20 months | 515 | 1.54 (1.38–1.72) | 1.35 (1.15–1.58) | |||
>12 and ≤15 months | 880 | 1.33 (1.20–1.48) | 1.20 (1.05–1.37) | |||
>9 and ≤12 months | 800 | 1.30 (1.17–1.45) | 1.22 (1.07–1.39) | |||
>6 and ≤9 months | 1025 | 1.20 (1.08–1.34) | 1.20 (1.06–1.36) | |||
≥3 and ≤6 months | 1309 | Ref | Ref |
Totals do not always sum to 4,529 due to missing data;
A total of 3,136 subjects included in the adjusted model; OR, odds ratio; CI, confidence interval.
In the multivariate GEE regression which included 3,135 (70.4%) patients with complete data and adjusting for ART duration, decreased odds of Rx<95% remained associated with TDF-containing regimen (OR = 0.73) and disclosure of HIV status to spouse or family member (OR = 0.85). Compared to patients with CD4 cell counts between 100 and 200, patients with CD4 above 200 were at increased odds of Rx<95%; OR = 1.18 (95%CI: 1.06–1.32) for patients with CD4 cell counts between 200 and 350 and OR = 1.25 (95%CI: 1.05–1.49) for patients with CD4 cell counts greater than 350. There was no difference in Rx<95% between patients with CD4<100 and those with CD4 between 100 and 200 cells/mm3 (OR = 0.98, p = 0.66). Longer travel time to the clinic (>2 hours) was associated with increased odds of Rx<95% (OR = 1.11; 95%CI: 1.01–1.23). Increased odds of Rx<95% was also associated with increasing time on ART.
In the regression model where time since ART initiation was modeled as a continuous risk factor, the best fitting model included a cubic polynomial model for time. Modeled probabilities of Rx<95% across each time-points and 95% confidence intervals for the final best-fitting model are shown in
In a large antiretroviral therapy program in Nigeria, approximately 1 in 4 patients were LTFU between March 2005 and July 2006; this is very similar to findings from other HIV treatment programs in sub-Saharan Africa
The finding that a higher proportion of women initiated on ART remained in follow-up care in this cohort mirrors findings from other treatment cohorts in Africa
Although the majority of the patients had a CD4 level within the range of requiring ART according to the National and WHO guidelines
Nearly half of patients LTFU never returned after receiving their first prescription of ARVs. This figure is much larger than previously reported across different ART programs in Africa
In the analysis of the impact of various first-line regimens, patients initiated on a stavudine-containing first-line regimen had higher risk of LTFU compared to other regimens containing either zidovudine or tenofovir. Stavudine has been shown to be associated with peripheral neuropathy
At the end of the analysis time, more than one third of the cohort had an overall Rx<95%. There was a concordance between the proportion of patients with at least one visit interval with Rx<95% (i.e. an episodic Rx<95%) and the proportion of patients with an overall summary Rx<95%, suggesting that once patients demonstrate poor adherence in the course of follow-up, the behavior is likely to continue and intensive adherence support should be initiated immediately for this group of patients. The vast majority of episodes of adherence <95% were simply due to the patient not returning to the health care facility for pharmacy refill in a timely matter. Periodic discontinuation (≥48 hours) of a non-nucleoside-based ART regimen is particularly concerning for the development of resistance due to the long half-life of these agents
Good adherence was associated with disclosure of HIV status to either spouse or family member and with a tenofovir-containing first-line regimen. The protective effect of disclosure on non-adherence is the first finding from a large treatment program in Nigeria and speaks to the importance of stigma as a barrier to effective life-long treatment adherence as also reported from South Africa
While our program did not collect information on religion, interestingly, patients with Q'uranic education were at increased risk of being non-adherent. Studies on understanding the cultural context of health care maintenance and adapting HIV treatment and care services to local customs are important for ensuring optimal clinical outcomes in various patient populations. This finding also underscores the importance of religious organizations and community leaders in further supporting people living with HIV/AIDS.
Patients who spent considerable time traveling to the treatment facilities were at increased risk of non-adherence. Although the number of treatment facilities in Nigeria continue to increase, patients may continue avoid accessing care from facilities within their communities because of stigma. As a result, scale up of treatment facilities must be coupled with support from the communities. Beyond the standard and simple public health approach to ART and based on the data presented herein, the ACTION project has initiated a treatment support structure with emphasis on multiple visit pre-treatment preparation, promotion of disclosure, and treatment companions. The ACTION project is also implementing different models of comprehensive care delivery such as task-shifting, expanding development of ART points of service at the local primary health center level, home-based care
Our findings should be interpreted in light of several caveats. First, the retrieved data were based on a clinical cohort rather than a “classical” structured cohort that resulted in a variable interval of follow-up time for patients. We accounted for these variations in the analysis using the GEE approach
In summary, although rapid scale-up of ART in Nigeria has been remarkable, issues such as patient retention and adherence are likely to remain critical factors in patient-level and program-level success. A variety of models of service delivery such as availability of ART services at the primary health center level, home-based care, and task-shifting are being piloted and evaluated to enhance treatment adherence support. Our findings formed the basis for implementing multiple pre-treatment visit preparation that promote disclosure and active community outreaching to support retention and adherence. Keeping patients on treatment should be considered as important a factor as boosting the numbers of patients initiation ART. Coupled with expansion of treatment access points of care to communities to diminish travel and to create an environment that reduces stigma may further have a positive impact on adherence.
We thank the President's Emergency Plan for AIDS Relief for providing HIV care, treatment, and prevention support to the people of Nigeria.