Do digital innovations for HIV and sexually transmitted infections work? Results from a systematic review (1996-2017).

OBJECTIVE: Digital innovations with internet/mobile phones offer a potential cost-saving solution for overburdened health systems with high service delivery costs to improve efficiency of HIV/STI (sexually transmitted infections) control initiatives. However, their overall evidence has not yet been appraised. We evaluated the feasibility and impact of all digital innovations for all HIV/STIs. DESIGN: Systematic review. SETTING/PARTICIPANTS: All settings/all participants. INTERVENTION: We classified digital innovations into (1) mobile health-based (mHealth: SMS (short message service)/phone calls), (2) internet-based mobile and/or electronic health (mHealth/eHealth: social media, avatar-guided computer programs, websites, mobile applications, streamed soap opera videos) and (3) combined innovations (included both SMS/phone calls and internet-based mHealth/eHealth). PRIMARY AND SECONDARY OUTCOME MEASURES: Feasibility, acceptability, impact. METHODS: We searched databases MEDLINE via PubMed, Embase, Cochrane CENTRAL and Web of Science, abstracted data, explored heterogeneity, performed a random effects subgroup analysis. RESULTS: We reviewed 99 studies, 63 (64%) were from America/Europe, 36 (36%) from Africa/Asia; 79% (79/99) were clinical trials; 84% (83/99) evaluated impact. Of innovations, mHealth based: 70% (69/99); internet based: 21% (21/99); combined: 9% (9/99).All digital innovations were highly accepted (26/31; 84%), and feasible (20/31; 65%). Regarding impacted measures, mHealth-based innovations (SMS) significantly improved antiretroviral therapy (ART) adherence (pooled OR=2.15(95%CI: 1.18 to 3.91)) and clinic attendance rates (pooled OR=1.76(95%CI: 1.28, 2.42)); internet-based innovations improved clinic attendance (6/6), ART adherence (4/4), self-care (1/1), while reducing risk (5/5); combined innovations increased clinic attendance, ART adherence, partner notifications and self-care. Confounding (68%) and selection bias (66%) were observed in observational studies and attrition bias in 31% of clinical trials. CONCLUSION: Digital innovations were acceptable, feasible and generated impact. A trend towards the use of internet-based and combined (internet and mobile) innovations was noted. Large scale-up studies of high quality, with new integrated impact metrics, and cost-effectiveness are needed. Findings will appeal to all stakeholders in the HIV/STI global initiatives space

Evaluation of a Rapid Point of Care Test for Detecting Acute and Established HIV Infection, and Examining the Role of Study Quality on Diagnostic Accuracy: A Bayesian Meta-Analysis.

INTRODUCTION:Fourth generation (Ag/Ab combination) point of care HIV tests like the FDA-approved Determine HIV1/2 Ag/Ab Combo test offer the promise of timely detection of acute HIV infection, relevant in the context of HIV control. However, a synthesis of their performance has not yet been done. In this meta-analysis we not only assessed device performance but also evaluated the role of study quality on diagnostic accuracy. METHODS:Two independent reviewers searched seven databases, including conferences and bibliographies, and independently extracted data from 17 studies. Study quality was assessed with QUADAS-2. Data on sensitivity and specificity (overall, antigen, and antibody) were pooled using a Bayesian hierarchical random effects meta-analysis model. Subgroups were analyzed by blood samples (serum/plasma vs. whole blood) and study designs (case-control vs. cross-sectional). RESULTS:The overall specificity of the Determine Combo test was 99.1%, 95% credible interval (CrI) [97.3-99.8]. The overall pooled sensitivity for the device was at 88.5%, 95% [80.1-93.4]. When the components of the test were analyzed separately, the pooled specificities were 99.7%, 95% CrI [96.8-100] and 99.6%, 95% CrI [99.0-99.8], for the antigen and antibody components, respectively. Pooled sensitivity of the antibody component was 97.3%, 95% CrI [60.7-99.9], and pooled sensitivity for the antigen component was found to be 12.3%, 95% (CrI) [1.1-44.2]. No significant differences were found between subgroups by blood sample or study design. However, it was noted that many studies restricted their study sample to p24 antigen or RNA positive specimens, which may have led to underestimation of overall test performance. Detection bias, selection (spectrum) bias, incorporation bias, and verification bias impaired study quality. CONCLUSIONS:Although the specificity of all test components was high, antigenic sensitivity will merit from an improvement. Besides the accuracy of the device itself, study quality, also impacts the performance of the test. These factors must be kept in mind in future evaluations of an improved device, relevant for global scale up and implementation

PRISMA flow diagram of study selection.

<p>The flow diagram can be broken down by stage, beginning at identification of records, screening titles and abstract, screening full-text for eligibility, and study inclusion.</p

Results from Bayesian hierarchical meta-analysis.

<p>Point estimates given are posterior medians, with 95% credible intervals (CrI).</p

Forest plots showing performance of the Determine Combo test in individual studies.

<p>The first forest plot shows the studies which evaluated the overall performance of the test, the second plot shows studies which evaluated the antibody component, and the third plot shows studies which evaluated the antigen component. The blue squares represent the estimate for sensitivity or specificity from each study, and the horizontal lines represent the 95% confidence intervals. It should be noted that missing data (for TP, FP, FN, TN) was treated as having a value of zero by RevMan software; this did not affect the sensitivity and specificity estimates, however please refer to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0149592#pone.0149592.s004" target="_blank">S2 Table</a> for raw cell values extracted from each study.</p

Graphical representation of quality assessment using QUADAS-2.

<p>Each domain of QUADAS-2 is represented by a horizontal bar, divided into the proportion of studies which scored low, high, or unclear risk of bias.</p

Costs of major depression covered / not covered in British Columbia, Canada

Abstract Background Major depressive disorder (MDD) is one of the world’s leading causes of disability. Our purpose was to characterize the total costs of MDD and evaluate the degree to which the British Columbia provincial health system meets its objective to protect people from the financial impact of illness. Methods We performed a population-based cohort study of adults newly diagnosed with MDD between 2015 and 2020 and followed their health system costs over two years. The expenditure proportion of MDD-related, patient paid costs relative to non-subsistence income was estimated, incidences of financial hardship were identified and the slope index of inequality (SII) between the highest and lowest income groups compared across regions. Results There were 250,855 individuals diagnosed with MDD in British Columbia over the observation period. Costs to the health system totalled >$1.5 billion (2020 CDN), averaging$138/week for the first 12 weeks following a new diagnosis and $65/week to week 52 and$55/week for weeks 53–104 unless MDD was refractory to treatment ($125/week between week 12–52 and$101/week over weeks 53–104). The proportion of MDD-attributable costs not covered by the health system was 2-15x greater than costs covered by the health system, exceeding $700/week for patients with severe MDD or MDD that was refractory to treatment. Population members in lower-income groups and urban homeowners had disadvantages in the distribution of financial protection received by the health system (SII reached − 8.47 and 15.25, respectively); however, financial hardship and inequities were mitigated province-wide if MDD went into remission (SII − 0.07 to 0.6). Conclusions MDD-attributable costs to health systems and patients are highest in the first 12 weeks after a new diagnosis. During this time, lower income groups and homeowners in urban areas run the risk of financial hardship