An HIV Diagnostic Testing Algorithm Using the cobas HIV-1/HIV-2 Qualitative Assay for HIV Type Differentiation and Confirmation.

Human immunodeficiency virus types 1 and 2 (HIV-1 and HIV-2) diagnostic testing algorithms recommended by the Centers for Disease Control involve up to three tests and rely mostly on detection of viral antigen and host antibody responses. HIV-1 p24 antigen/HIV-1/HIV-2 antibody-reactive specimens are confirmed with an immunochromatographic HIV-1/HIV-2 antibody differentiation assay, and negative or indeterminate results from the differentiation assay are resolved by an HIV-1-specific nucleic acid amplification test (NAT). The performance of a proposed alternative algorithm using the cobas HIV-1/HIV-2 qualitative NAT as the differentiation assay was evaluated in subjects known to be infected with HIV-1 (n = 876) or HIV-2 (n = 139), at low (n = 6,017) or high (n = 1,020) risk of HIV-1 infection, or at high-risk for HIV-2 infection (n = 498) (study A). The performance of the cobas HIV-1/HIV-2 qualitative test was also evaluated by comparison to an HIV-1 or HIV-2 alternative NAT (study B). The HIV-1 and HIV-2 overall percent agreements (OPA) in study A ranged from 95% to 100% in all groups. The positive percent agreements (PPA) for HIV-1 and HIV-2 were 100% (876/876) and 99.4% (167/168), respectively, for known positive groups. The negative percent agreement in the HIV low-risk group was 100% for both HIV-1 and HIV-2. In study B, the HIV-1 and HIV-2 OPA ranged from 99% to 100% in all groups evaluated (n = 183 to 1,030), and the PPA for HIV-1 and HIV-2 were 100% and 99.5%, respectively, for known positive groups. The cobas HIV-1/HIV-2 qualitative assay can discriminate between HIV-1 and HIV-2 based on HIV RNA and can be included in an alternative diagnostic algorithm for HIV.

Implementation of an mHealth App to Promote Engagement During HIV Care and Viral Load Suppression in Johannesburg, South Africa (iThemba Life): Pilot Technical Feasibility and Acceptability Study.

BACKGROUND: South Africa has the largest HIV treatment program worldwide. Retention in care and medication adherence remain problematic necessitating innovative solutions for improving HIV care. The increasing availability and use of mobile technology can support positive clinical outcomes for persons living with HIV. iThemba Life is a mobile health app designed with input from South African health professionals and patients, promoting engagement with HIV care through access to medical results. OBJECTIVE: This study aimed to test the feasibility and acceptability of receiving HIV viral load (VL) results through the app and compare the time to HIV VL result return for study participants before and after app use. METHODS: Using convenience sampling, adults having routine VL phlebotomy were recruited from 2 Johannesburg health facilities. After signed consent, the app was downloaded on their Android smartphones, phlebotomy was performed, and the sample barcode was scanned through their phone to link the sample and app. Participants received a notification of the result availability and logged into the app to view results, their explanation and recommended action. RESULTS: Overall, 750 people were screened to enroll 500 participants. Of 750, 113 (15.1%) failed eligibility screening. 21.5% (137/637) had smartphone technical limitations preventing enrollment. Results were released to 92.2% (461/500) of participants' phones. App technical issues and laboratory operational issues limited the number of released results. Approximately 78.1% (360/461) results were viewed in the app. Median time from notification of availability to result viewed being 15.5 hours (0.6; range 0-150 days). Turnaround time from phlebotomy to the result being received was 6 (range 1-167) days for users versus 56 days (range 10-430 days; P<.001) before app use. Overall, 4% (20/500) of participants received unsuppressed results (VL>1000 copies/mL). Turnaround time for unsuppressed results was 7 days for participants versus 37.5 days before app use (P<.001). The difference before and after app use in the suppressed and unsuppressed users for time from sample collection to result delivery was statistically significant. Of 20 participants, 12 (60%) returned for a confirmatory VL during the study period. The time from an unsuppressed VL to a confirmatory VL was 106 days for app users versus 203 days before app use (P<.001). Overall, 52.4% (262/500) of participants completed an exit survey; 23.2% (58/250) reported challenges in viewing their VL results. Moreover, 58% (35/60) reported that they overcame challenges with technical assistance from others, and 97.3% (255/262) wanted to continue using the app for VL results. CONCLUSIONS: Using iThemba Life for VL results was well-received despite limited smartphone access for some participants. App users received results 10 times sooner than before the app and 5 times sooner if their VL >1000 copies/mL. This increased notification speed led to participants wanting to continue using iThemba Life.

A systematic review of vaccine preventable disease surveillance cost studies.

BACKGROUND: Planning and monitoring vaccine introduction and effectiveness relies on strong vaccine-preventable disease (VPD) surveillance. In low and middle-income countries (LMICs) especially, cost is a commonly reported barrier to VPD surveillance system maintenance and performance; however, it is rarely calculated or assessed. This review describes and compares studies on the availability of cost information for VPD surveillance systems in LMICs to facilitate the design of future cost studies of VPD surveillance. METHODS: PubMed, Web of Science, and EconLit were used to identify peer-reviewed articles and Google was searched for relevant grey literature. Studies selected described characteristics and results of VPD surveillance systems cost studies performed in LMICs. Studies were categorized according to the type of VPD surveillance system, study aim, the annual cost of the system, and per capita costs. RESULTS: Eleven studies were identified that assessed the cost of VPD surveillance systems. The studies assessed systems from six low-income countries, two low-middle-income countries, and three middle-income countries. The majority of the studies (n = 7) were conducted in sub-Saharan Africa and fifteen distinct VPD surveillance systems were assessed across the studies. Most studies aimed to estimate incremental costs of additional surveillance components and presented VPD surveillance system costs as mean annual costs per resource category, health structure level, and by VPD surveillance activity. Staff time/personnel cost represents the largest cost driver, ranging from 21% to 61% of total VPD surveillance system costs across nine studies identifying a cost driver. CONCLUSIONS: This review provides a starting point to guide LMICs to invest and advocate for more robust VPD surveillance systems. Critical gaps were identified including limited information on the cost of laboratory surveillance, challenges with costing shared resources, and missing data on capital costs. Appropriate guidance is needed to guide LMICs conducting studies on VPD surveillance system costs.