Predictors of Treatment Outcome Under Multivariate and Univariate Models
Multivariate tests were modeled to assess independent associations between variables among the 514 patients in the 3 HAART arms. Age, sex, regimen, CPS, adherence, and HIV disclosure were fitted into the stepwise model as predictors, whereas CD4 counts, HAART duration, and VL were fitted as outcome variables. CPS, HAART regimen, and adherence, but not HIV disclosure, age, or sex, were independently and strongly associated with outcomes (Table 3). Specifically, CPS and adherence influenced VL (P ≤ 0.001), whereas regimen was associated with treatment duration. Both CPS and adherence interacted significantly to affect virologic outcome (P < 0.029). CD4 T-cell counts had no relationship with the predictors at all levels.
Model estimated marginal means showed that VL declined significantly with increasing participation in CPS at all levels of adherence (Fig. 1B). Patients actively in CPS had nearly 20 times lower VL (1.87 log10 copies) than those not in CPS (3.13 log10 copies) or those who participated only occasionally (P < 0.001). In an univariate analysis, good adherence was separately associated with lower VL (1.93 log10 HIV-1 copies) than fair (2.51 log10) or poor (3.54 log10, P < 0.001) adherence whether compared for all patients (data not shown) or for only patients failing treatment (Fig. 1C).
Associations of Peer Support Activity, Adherence, and HIV Disclosure
We sought to understand how adherence and CPS might associate to influence treatment outcomes. The cross-tabulation Table 4 shows adherence by proportions of patients in each regimen, HIV disclosure, and CPS groups. Of the 238 patients active in CPS (CPS++), 82.8% had good adherence, as compared, respectively, with 39% and 28.7% who only partly (CPS+) or never (CPS−) participated in CPS. Higher proportions of patients reported poor adherence within CPS− than those in CPS+ or CPS++ arms (χ2P < 0.001). Prevalence of poor adherence was lowest in the TDF arm compared with D4T or AZT arms, but good adherence was comparable across groups. Adherence was not associated with age group (χ2P = 0.771). The reasons for adherence behavior were significantly correlated with adherence outcome (χ2P < 0.001, SDC table 6, http://links.lww.com/QAI/A649). Most (69.9%) patients with poor adherence cited “bad” feeling, hopelessness, or inconvenience as reason for adherence behavior. Most (74.2%) patients with good adherence believed that the drugs would prolong their lives and make them healthy. Eight of 514 patients were children aged 5–17 years, and 5 had good adherence. Longevity and health were the main reason for adherence behavior in 6 of 8 patients, but these should be interpreted within veracity limits of guardian disclosure. In a Cox proportional hazard analysis using duration of HAART as time variable and VF as status variable, the risk of treatment failure was low for CPS++ participants but increased comparably in all patients as treatment duration increased (Fig. 1D). The 3 CPS and 3 adherence categories were used to develop a compliance matrix (see Methods). Univariate analysis conducted to compare VL among the various levels of the matrix showed that a combination of CPS++ and good or fair adherence also produced the lowest VL (Fig. 1E, P < 0.001). The same was true for patients in the CPS+ group who had good adherence.
Some (81.5%) of the patients had disclosed their HIV status, including all who were in CPS++. More patients had good adherence compared with those with poor or fair adherence (P = 0.012), although the numbers were comparable across HIV disclosure statuses. Disclosure was associated with peer support (χ2P < 0.001), but this association was biased by the exclusive disclosure in the CPS++ group. The association disappeared when CPS++ patients were excluded from the analysis. VL was comparable for patients disclosing (mean 2.37) and those not disclosing (mean 2.58) their HIV status (analyses of variance, P = 0.55). Pearson χ2 tests showed no association in treatment response between patients disclosing and those not disclosing status (P = 0.41). Taken together, disclosure had no effect on outcome and appeared unrelated to peer support in this setting.
Virologic Response to Second-line HAART
Switching regimen is an important decision when patients fail first-line treatment. Of the initial 546 patients, only 17.4% switched regimen after a median treatment time of 32 months. Duration before regimen switch was 35, 30.5, 36, and 14 months, respectively, for patients in the AZT, D4T, TDF, or assorted arms and 57, 24, 30.5, or 35 months for those aged 12–17, 18–35, 36–45, or above 45 years. Most (76%) patients switching treatment were in the D4T+ regimen arm, whereas only 9.5% and 13.7% switched, respectively, from primary TDF and AZT arms. VL did not differ significantly between age groups (data not shown) or between regimen categories of patients switching ART. Patients who switched were not more likely to have failed respective first-line regimens before the switch than those not switching. Of the patients switching regimen, 36.2% still failed second-line treatment (Table 2). Considering VL before the time of switch, no difference was observed between patients who switched (3.311 log10 HIV copies) and those not switching regimen (P = 0.969, SDC table 7, http://links.lww.com/QAI/A649). Using a Cox proportional hazards analysis, patients in active peer support were more likely to remain longer on treatment before switching regimen and had lower risk of failure than their counterparts in CPS+ or CPS− (Fig. 1F). Together, these data suggest that patients are switched to second-line without proper guidelines, whereas the deserving ones are not promptly linked to care.
Virologic Treatment Failure Based on Cross-sectional Single VL Test
Alternative cost-effective yet reliable VL monitoring strategies are necessary for prompt treatment decisions in settings constrained for resources. Limiting our analysis to 418 patients with complete VL1 and VL2 data, we assessed the effectiveness of a cross-sectional single VL (CSVL) strategy to reliably describe VF, applying only the VL2 for CSVL analysis. Patients who had VL2 of at least 1000 HIV-1 RNA copies but had not been treated for at least 6, 12, or 24 months were assigned UDVR status in the respective CSVL criteria. Using this assessment, 0.7% of the patients were UDVRs under the 6-month CSVL definition, whereas 7.4% and 19.6% were UDVRs under the 12- and 24-month definitions, respectively. Of the patients, 35.2%, 33%, and 27%, respectively, failed treatment under the alternate 6-, 12-, and 24-month CSVL strategies. Failure rates under the 6- and 12-month definition compared well with the 35.9% failure rate observed under the longitudinal definition (see Table S7, Supplemental Digital Content, http://links.lww.com/QAI/A649). Thus, a single VL test after 6 or 12 months of HAART defined treatment failure as efficiently as 2 sequential VLs under the standard longitudinal criteria.
We describe factors that influence adherence and VF and provide evidence to inform treatment decisions under conditions of limiting resources. Of the 514 patients in the 3 HAART arms, 35.9% failed first-line regimen by longitudinal strategy and 36% failed second-line after switching regimen. First-line HAART failure was highest among males and in the D4T arm. Industrialized countries have phased out D4T due to toxicities, and the WHO has recommended its discontinuation globally.3,24 Elsewhere, drug resistance was highest among Thai patients initiating D4T-containing regimen.25 In our study, 35% of all patients used D4T in first-line regimen and another 10.5% upon switching treatment. Adherence and peer support (CPS) influenced VL in a multivariate analysis, whereas HAART regimen was associated with length of treatment. Adherence is important to success of HAART,26–28 and in this study, the proportions of patients with poor adherence were highest in the D4T and AZT arms. Poor adherence was in turn associated with higher VL and increased VF. D4T recipients had stayed longer on treatment than patients on alternative regimens, mostly because D4T is comparably cheaper and readily available locally. We did not conduct toxicity assays, but speculate in concurrence with literature that toxicity may have influenced adherence and virologic outcome in the D4T arm.23,29 D4T should, therefore, be eliminated from the current treatment regimens.
A study of South African patients concluded that short-term viral suppression was achievable when adherence was at least 80%.30 Our study is uniquely focused on peer support mechanism, as opposed to non-peer counseling. Good and fair adherence was achieved in 55.6% and 21.6% of our patients, respectively. A combination of active CPS and good adherence resulted in lowest VL (see ACCESS matrix). Noncompliance is not uncommon in Kenya where cultural predisposition and family circumstances affect patients' attitudes toward HIV medication31 and CPS activity clearly enhanced their compliance. This study included a small number of children, and the accuracy of their adherence profiles was limited to the legitimacy of guardian disclosures. HIV status disclosure positively influences care uptake and is correlated with social support.32,33 Disclosure was associated with both adherence and CPS activity in this study, although CPS and adherence, but not disclosure, were independently associated with VL. Sampling bias may have influenced the association between CPS and disclosure, as all patients who were active in CPS also had disclosed status. A recent study did not find any association between treatment outcome and HIV disclosure among children.34 Thus, peer support influenced adherence independent of HIV disclosure in this study.
Patients who fail first-line HAART must promptly switch to second-line regimen to sustain viral suppression. A staggering 54% of ART switches occurred within 12 months of initiating HAART among predominantly urban Kenyans.35 Our study of mostly rural and suburban Kenyans showed that only 17% of the patients switched regimen 32 months after initiating treatment. Patients who switched were not more likely to have failed first-line regimen before the switch than those not switching, and both switchers and non-switchers had comparable VL before the time of switch. Thus, patients may be switched unnecessarily to secondary treatment, whereas others fail to gain timely access to critical treatment decisions.
The standard approach to treatment failure definition relies on sequential VL monitoring, which remains expensive in most developing countries.5,6,12,36,37 Less than 3% of all HAART-eligible patients initiate treatment, and VF diagnosis is delayed in most of those initiating HAART.12,25,38 We asked if a single VL measured in a cross-sectional context (CSVL) could effectively define VF and hasten treatment decision. Of all patients failing treatment under longitudinal criteria, 98% and 92% also failed under the 6- and 12-month CSVL criteria, respectively. Hence, when resources are limited, prompt and reliable treatment decisions can be made with just 1 VL taken between 6 and 12 months after HAART initiation. CD4 T-cell levels had no significant association with predictors, an observation that has also been reported elsewhere.39 Both clinical and immunologic criteria are less sensitive at predicting VF40; hence, overreliance on CD4 T-cell tests for ART decisions in low-income regions may be obscuring treatment failure.
CONCLUSIONS AND RECOMMENDATIONS
We have demonstrated a high rate of virologic treatment failure among Kenyan HAART patients and shown that peer support enhances adherence to improve treatment outcome. To mitigate failure, we recommend the government, through its various HIV/AIDS control agencies, to (1) institutionalize and support patient-focused peer support within provider facilities; (2) train, empower, employ, and deploy HIV-positive persons as (peer) councilors in community care facilities to facilitate linkage to care and adherence monitoring; (3) scale-up point-of-care VL testing with at least 1 test annually; (4) synchronize HIV care with the current WHO guidelines, including treatment sequencing, optimization, and initiation thresholds; and (5) improve overall counseling methodologies and instruments. These steps should be replicable in other low-income settings.
Dr. Matilu Mwau and Charity Hungu of KEMRI facilitated VL tests. Javan Okendo, Grace Akoth Ochieng, Meshack Ooko, Beatrice Oliech, Rita Ayodi, Paul O. Owuor, Hellen Aloo, Everlyne Githaiga, Zawadi Baya, and Lillian Maina interviewed and collected patient data.
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HIV; adherence; treatment failure; resource-limited; peer support
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