Patient and health-care worker experiences of an HIV viral load intervention using SMS: A qualitative study.

BackgroundMobile Health or mHealth interventions, including Short Message Service (SMS), can help increase access to care, enhance the efficiency of health service delivery and improve diagnosis and treatment for HIV. Text messaging, or SMS, allows for the low cost transmission of information, and has been used to send appointment reminders, information about HIV counselling and treatment, messages to encourage adherence and information on nutrition and side-effects. HIV Viral Load (VL) monitoring is recommended by the WHO and has been progressively adopted in many settings. In Zimbabwe, implementation of VL is routine and has been rolled out with support of Médecins Sans Frontières (MSF) since 2012. An SMS intervention to assist with the management of VL results was introduced in two rural districts of Zimbabwe. After completion of the HIV VL testing at the National Microbiology Reference Laboratory in Harare, results were sent to health facilities via SMS. Consenting patients were also sent an SMS informing them that their viral load results were ready for collection at their nearest health facilities. No actual VL results were sent to patients.MethodsA qualitative study was conducted in seven health-care facilities using in-depth interviews (n = 32) and focus group discussions (n = 5) to explore patient and health-care worker experiences of the SMS intervention. Purposive sampling was used to select participants to ensure that male and female patients, as well as those with differing VL results and who lived differing distances from the clinics were included. Data were transcribed, translated from Shona into English, coded and thematically analysed using NVivo software.ResultsThe VL SMS intervention was considered acceptable to patients and health-care workers despite some challenges in implementation. The intervention was perceived by health-care workers as improving adherence and well-being of patients as well as improving the management of VL results at health facilities. However, there were some concerns from participants about the intervention, including challenges in understanding the purpose and language of the messages and patients coming to their health facility unnecessarily. Health-care workers were more concerned than patients about unintentional HIV disclosure relating to the content of the messages or phone-sharing.ConclusionThis was an innovative intervention in Zimbabwe, in which SMS was used to send VL results to health-care facilities, and notifications of the availability of VL results to patients. Interventions such as this have the potential to reduce unnecessary clinic visits and ensure patients with high VL results receive timely support, but they need to be properly explained, alongside routine counselling, for patients to fully benefit. The findings of this study also have potential policy implications, as if implemented well, such an SMS intervention has the potential to help patients adopt a more active role in the self-management of their HIV disease, become more aware of the importance of adherence and VL monitoring and seek follow-up at clinics when results are high

Mortality among adults transferred and lost to follow-up from antiretroviral therapy programmes in South Africa : a multicenter cohort study

Little is known about outcomes after transfer out (TFO) and loss to follow-up (LTF) and how differential outcomes might bias mortality estimates, as analyses generally censor or exclude TFOs/LTF. Using data linked to the National Population Register, we explored mortality among TFO and LTF patients compared with patients who were retained and investigated how linkage impacted on mortality estimates.; A cohort analysis of routine data on adults with civil identification numbers starting antiretroviral therapy (ART) 2004-2009 in 4 large South African ART cohorts. The number, proportion, timing, and mortality of TFOs and LTF were reported. Mortality was compared using Kaplan-Meier curves, Cox's proportional hazards, and competing risks regression.; Before linkage, 1207 patients (6%) had died, 2624 (13%) were LTF, 1067 (5%) were TFO and 14,583 (75%) were retained. Compared with retained, mortality risk was 3 times higher among TFO patients [adjusted hazard ratio (aHR), 3.11; 95% confidence interval (CI): 2.42 to 3.99] and 20 times higher among LTF patients (aHR, 22.03; 95% CI: 20.05 to 24.21). Excluding early deaths after TFO or LTF, the risk was comparable among TFOs and retained (aHR, 0.75; 95% CI: 0.54 to 1.03) and higher among LTF (aHR, 2.85; 95% CI: 2.43 to 3.33). After linkage, corrected mortality was higher than site-reported mortality. Censoring did not, however, lead to substantial underestimation of mortality among TFOs.; Although TFO and LTF predicted mortality, the lower incidence of TFO and subsequent death compared with LTF meant that censoring TFOs did not bias mortality estimates. Future cohort analyses should explicitly consider proportions of TFO/LTF and mortality event rates

CD4 count slope and mortality in HIV-infected patients on antiretroviral therapy: multicohort analysis from South Africa

BACKGROUND In many resource-limited settings monitoring of combination antiretroviral therapy (cART) is based on the current CD4 count, with limited access to HIV RNA tests or laboratory diagnostics. We examined whether the CD4 count slope over 6 months could provide additional prognostic information. METHODS We analyzed data from a large multicohort study in South Africa, where HIV RNA is routinely monitored. Adult HIV-positive patients initiating cART between 2003 and 2010 were included. Mortality was analyzed in Cox models; CD4 count slope by HIV RNA level was assessed using linear mixed models. RESULTS About 44,829 patients (median age: 35 years, 58% female, median CD4 count at cART initiation: 116 cells/mm) were followed up for a median of 1.9 years, with 3706 deaths. Mean CD4 count slopes per week ranged from 1.4 [95% confidence interval (CI): 1.2 to 1.6] cells per cubic millimeter when HIV RNA was 100,000 copies per milliliter. The association of CD4 slope with mortality depended on current CD4 count: the adjusted hazard ratio (aHRs) comparing a >25% increase over 6 months with a >25% decrease was 0.68 (95% CI: 0.58 to 0.79) at 350 with <100 cells per cubic millimeter, was 0.10 (95% CI: 0.05 to 0.20). CONCLUSIONS Absolute CD4 count remains a strong risk for mortality with a stable effect size over the first 4 years of cART. However, CD4 count slope and HIV RNA provide independently added to the model

Monitoring of antiretroviral therapy and mortality in HIV programmes in Malawi, South Africa and Zambia: mathematical modelling study

OBJECTIVES Mortality in patients starting antiretroviral therapy (ART) is higher in Malawi and Zambia than in South Africa. We examined whether different monitoring of ART (viral load [VL] in South Africa and CD4 count in Malawi and Zambia) could explain this mortality difference. DESIGN Mathematical modelling study based on data from ART programmes. METHODS We used a stochastic simulation model to study the effect of VL monitoring on mortality over 5 years. In baseline scenario A all parameters were identical between strategies except for more timely and complete detection of treatment failure with VL monitoring. Additional scenarios introduced delays in switching to second-line ART (scenario B) or higher virologic failure rates (due to worse adherence) when monitoring was based on CD4 counts only (scenario C). Results are presented as relative risks (RR) with 95% prediction intervals and percent of observed mortality difference explained. RESULTS RRs comparing VL with CD4 cell count monitoring were 0.94 (0.74-1.03) in scenario A, 0.94 (0.77-1.02) with delayed switching (scenario B) and 0.80 (0.44-1.07) when assuming a 3-times higher rate of failure (scenario C). The observed mortality at 3 years was 10.9% in Malawi and Zambia and 8.6% in South Africa (absolute difference 2.3%). The percentage of the mortality difference explained by VL monitoring ranged from 4% (scenario A) to 32% (scenarios B and C combined, assuming a 3-times higher failure rate). Eleven percent was explained by non-HIV related mortality. CONCLUSIONS VL monitoring reduces mortality moderately when assuming improved adherence and decreased failure rates

Comparison of all-cause mortality based on model predictions and observed data.

<p>Orange lines show Kaplan-Meier estimates from ART programmes in South Africa, Malawi and Zambia <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0057611#pone.0057611-Keiser4" target="_blank">[12]</a> and blue lines the model predictions. Solid lines represent routine viral load monitoring (South Africa) and broken lines CD4 cell monitoring (Malawi, Zambia).</p

Model parameters and data sources.

<p>Distributions of times to event were assumed to be exponential, Weibull or double Weibull, based on the cohort data. Cohort data are from the Khayelitsha and Gugulethu ART programmes in Cape Town, South Africa, unless otherwise specified.</p><p>CI, confidence interval; ART, antiretroviral therapy; HR, hazard ratio; ASSA, Actuarial Society of South Africa; LTFU, loss to follow-up; n/a, not applicable.</p>*<p>)Relative decrease in second-line efficacy per year spent on failing first-line ART.</p>**<p>)Age-specific mortality rates.</p>***<p>)Non-HIV related mortality estimated from the ASSA2008 model deducted from cohort data on all-cause mortality.</p>****<p>)Weighted sum of two Weibull distributions.</p>*****<p>)Data from Ministry of Health-Centre for Infectious Disease Research in Zambia.</p

All-cause mortality after five years on antiretroviral therapy (ART) – 1000 simulations of 1000 patients in cohorts with or without routine viral load monitoring.

<p>ART, antiretroviral therapy; VL, routine viral load monitoring.</p><p>A (baseline scenario): identical virologic failure rates in both monitoring strategies, switch to second-line ART immediately after confirmed failure. B (delayed switching): identical virologic failure rates in both monitoring strategies, switch to second-line ART after a realistic delay (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0057611#pone-0057611-t001" target="_blank"><u>Table 1</u></a> for parameters). C (higher virologic failure rates with CD4 monitoring): rate of virologic failure set to be 2 or 3 times higher with CD4 monitoring by adjusting the scale parameter of the Weibull distribution (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0057611#pone-0057611-t001" target="_blank"><u>Table 1</u></a>), switch to second-line ART immediately after confirmed failure.</p>*<p>Uncorrected mortality: mortality based on observed mortality from data.</p>**<p>Corrected mortality: mortality based on observed mortality, observed LTFU and estimated mortality among patients lost <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0057611#pone.0057611-Egger2" target="_blank">[22]</a>.</p>***<p>Ratios of uncorrected mortality, comparing VL with CD4 monitoring.</p