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OBJECTIVE: To evaluate the clinical outcomes of a remote mental health program for managing anxiety and depression, primarily using asynchronous digital communication. METHODS: This retrospective cohort study examined U.S. adults seeking remote care for anxiety and depression from January 2021 to May 2022. The program involves clinician-led assessment, patient education, medication management, and ongoing monitoring, primarily via text. Anxiety and depression were measured using Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder (GAD-7) scores. Outcomes examined were changes in scores, 50% score improvement rate, and remission rate (score <5) at 1, 3, and 6 months. RESULTS: During the period evaluated, 11,844 program participants met the inclusion criteria. Most were female (n = 8328, 70.3%); their age ranged from 18-82 years (median 31 years). At baseline, median PHQ-9 and GAD-7 scores were 13 (IQR 9-17); 67% and 69% met score criteria for depression and anxiety, respectively. Most participants (80%) were prescribed a selective serotonin reuptake inhibitor (SSRI). By one month, average PHQ-9 and GAD-7 scores decreased significantly by 9.2 and 9.1 points (both p < .01). At 1-month follow-up, the 50% score improvement rate was 66% for PHQ-9 and 69% GAD-7 (p < .01). Scores continued to decrease with follow-up. At 3 months, over half achieved remission (percent [95% CI]: 52% [51-54] for anxiety, 53% [52-55] for depression). Similar improvement was observed at 6 months and in sensitivity analyses accounting for loss to follow-up. CONCLUSIONS: Use of a remote mental health program with digital tools was associated with significant clinical improvement in anxiety and depression. Challenges remain in maintaining patient engagement and ensuring appropriate care quality monitoring in digital mental health programs. Additional research comparing remote digital care to traditional in-person models is warranted. Studies should examine long-term outcomes, optimal care protocols, and the challenges to integrating these programs into existing healthcare systems and ensuring equitable access.
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In response to the outbreak of the coronavirus disease 2019 (Covid-19), governments worldwide have introduced multiple restriction policies, known as non-pharmaceutical interventions (NPIs). However, the relative impact of control measures and the long-term causal contribution of each NPI are still a topic of debate. We present a method to rigorously study the effectiveness of interventions on the rate of the time-varying reproduction number Rt and on human mobility, considered here as a proxy measure of policy adherence and social distancing. We frame our model using a causal inference approach to quantify the impact of five governmental interventions introduced until June 2020 to control the outbreak in 113 countries: confinement, school closure, mask wearing, cultural closure, and work restrictions. Our results indicate that mobility changes are more accurately predicted when compared to reproduction number. All NPIs, except for mask wearing, significantly affected human mobility trends. From these, schools and cultural closure mandates showed the largest effect on social distancing. We also found that closing schools, issuing face mask usage, and work-from-home mandates also caused a persistent reduction on Rt after their initiation, which was not observed with the other social distancing measures. Our results are robust and consistent across different model specifications and can shed more light on the impact of individual NPIs.