Efficacy of stand-alone digital mental health applications for anxiety and depression: A meta-analysis of randomized controlled trials.

BACKGROUND: Anxiety and depressive disorders affect 20% of the population, cause functional impairment, and represent a leading cause of disability. Although evidence-based treatments exist, the shortage of trained clinicians and high demand for mental health services have resulted in limited access to evidence-based care. Digital mental health applications (DMHA) present innovative, scalable, and sustainable solutions to address disparities in mental health care. METHODS: The present study used meta-analytic techniques to evaluate the therapeutic effect of DMHAs in randomized controlled trials (RCTs) for individuals experiencing anxiety and/or depressive symptoms. Search terms were selected based on concepts related to digital mental health applications, mental health/wellness, intervention type, trial design, and anxiety and/or depression symptoms/diagnosis outcomes to capture all potentially eligible results. Potential demographic, DMHA, and trial design characteristics were examined as moderators of therapeutic effects. RESULTS: Random effects meta-analyses found that stand-alone DMHAs produced a modest reduction in anxiety (g = 0.31) and depressive (g = 0.35) symptom severity. Several moderators influenced the therapeutic effects of DMHAs for anxiety and/or depressive symptoms including treatment duration, participant inclusion criteria, and outcome measures. LIMITATIONS: Minimal information was available on DMHA usability and participant engagement with DMHAs within RCTs. CONCLUSIONS: While DMHAs have the potential to be scalable and sustainable solutions to improve access and availability of evidence-based mental healthcare, moderator analyses highlight the considerations for implementation of DMHAs in practice. Further research is needed to understand factors that influence therapeutic effects of DMHAs and investigate strategies to optimize its implementation and overcome the extant research-to-practice gap.

Post-acute sequelae of SARS-CoV-2 with clinical condition definitions and comparison in a matched cohort.

Disease characterization of Post-Acute Sequelae of SARS-CoV-2 (PASC) does not account for pre-existing conditions and time course of incidence. We utilized longitudinal data and matching to a COVID PCR-negative population to discriminate PASC conditions over time within our patient population during 2020. Clinical Classification Software was used to identify PASC condition groupings. Conditions were specified acute and persistent (occurring 0-30 days post COVID PCR and persisted 30-120 days post-test) or late (occurring initially 30-120 days post-test). We matched 3:1 COVID PCR-negative COVIDPCR-positive by age, sex, testing month and service area, controlling for pre-existing conditions up to four years prior; 28,118 PCR-positive to 70,293 PCR-negative patients resulted. We estimated PASC risk from the matched cohort. Risk of any PASC condition was 12% greater for PCR-positive patients in the late period with a significantly higher risk of anosmia, cardiac dysrhythmia, diabetes, genitourinary disorders, malaise, and nonspecific chest pain. Our findings contribute to a more refined PASC definition which can enhance clinical care.

Saliva-based SARS-CoV-2 serology using at-home collection kits returned via mail.

Serology provides tools for epidemiologic studies, and may have a role in vaccine prioritization and selection. Automated serologic testing of saliva, especially specimens that are self-collected at home and sent to a laboratory via the mail without refrigeration, could be a highly-scalable strategy for population-wide testing. In this prospective study, non-vaccinated patients were recruited after PCR testing to self-collect saliva and return their specimens via mail. Longitudinal specimens were analyzed in order to monitor seroconversion in the weeks after a diagnostic PCR test for SARS-CoV-2. Diverse users self-collected saliva and returned specimens via mail in compliance with shipping regulations. At our pre-established threshold (0.963 AU/mL), salivary IgG reactivity to full-length spike protein achieved 95.8% sensitivity and 92.4% specificity at 2-4 weeks after diagnostic testing, which is comparable to the typical sensitivity and specificity achieved for serum testing. Reactivity to N antigen also was detected with 92.6% sensitivity and 90.7% specificity at 4-8 weeks after diagnostic testing. Moreover, serologic testing for endemic coronaviruses performed in multiplex with SARS-CoV-2 antigens has the potential to identify samples that may require retesting due to effects of pre-analytical factors. The easy-to-use saliva collection kit, coupled with thresholds for positivity and methods of flagging samples for retest, provides a framework for large-scale serosurveillance of SARS-CoV-2.

Analysis of Severe Illness After Postvaccination COVID-19 Breakthrough Among Adults With and Without HIV in the US.

Importance: Understanding the severity of postvaccination SARS-CoV-2 (ie, COVID-19) breakthrough illness among people with HIV (PWH) can inform vaccine guidelines and risk-reduction recommendations. Objective: To estimate the rate and risk of severe breakthrough illness among vaccinated PWH and people without HIV (PWoH) who experience a breakthrough infection. Design, Setting, and Participants: In this cohort study, the Corona-Infectious-Virus Epidemiology Team (CIVET-II) collaboration included adults (aged ≥18 years) with HIV who were receiving care and were fully vaccinated by June 30, 2021, along with PWoH matched according to date fully vaccinated, age group, race, ethnicity, and sex from 4 US integrated health systems and academic centers. Those with postvaccination COVID-19 breakthrough before December 31, 2021, were eligible. Exposures: HIV infection. Main Outcomes and Measures: The main outcome was severe COVID-19 breakthrough illness, defined as hospitalization within 28 days after a breakthrough SARS-CoV-2 infection with a primary or secondary COVID-19 discharge diagnosis. Discrete time proportional hazards models estimated adjusted hazard ratios (aHRs) and 95% CIs of severe breakthrough illness within 28 days of breakthrough COVID-19 by HIV status adjusting for demographic variables, COVID-19 vaccine type, and clinical factors. The proportion of patients who received mechanical ventilation or died was compared by HIV status. Results: Among 3649 patients with breakthrough COVID-19 (1241 PWH and 2408 PWoH), most were aged 55 years or older (2182 patients [59.8%]) and male (3244 patients [88.9%]). The cumulative incidence of severe illness in the first 28 days was low and comparable between PWoH and PWH (7.3% vs 6.7%; risk difference, -0.67%; 95% CI, -2.58% to 1.23%). The risk of severe breakthrough illness was 59% higher in PWH with CD4 cell counts less than 350 cells/µL compared with PWoH (aHR, 1.59; 95% CI, 0.99 to 2.46; P = .049). In multivariable analyses among PWH, being female, older, having a cancer diagnosis, and lower CD4 cell count were associated with increased risk of severe breakthrough illness, whereas previous COVID-19 was associated with reduced risk. Among 249 hospitalized patients, 24 (9.6%) were mechanically ventilated and 20 (8.0%) died, with no difference by HIV status. Conclusions and Relevance: In this cohort study, the risk of severe COVID-19 breakthrough illness within 28 days of a breakthrough infection was low among vaccinated PWH and PWoH. PWH with moderate or severe immune suppression had a higher risk of severe breakthrough infection and should be included in groups prioritized for additional vaccine doses and risk-reduction strategies.