Impact of Covid -19 on caller characteristics and quit rates: An experience from regional tobacco Quitline from India.

BACKGROUND: The tobacco epidemic is a major health concern amplified by Covid-19. We aimed to study differences in caller profiles to the regional tobacco quitline services of South India during the Covid-19 pandemic in comparison with the pre-pandemic. METHOD: Using a descriptive cross-sectional research design, we examined registered caller profiles to the quitline between March and July 2019 (Pre-Pandemic N=7845) and the same months in 2020 (Covid-19 pandemic phase N=6447) phases. RESULTS: The proportion of registered callers with an expressed intent to quit tobacco increased by 1.73 times during pandemic (16.7% versus 9.6%). Health concerns were cited as the major reason (93.25%) to quit tobacco in 2020 as compared to 2019 (88.02%). Cough (28.50%) and psychological difficulties (14.20%) were reported significantly more by RCs in 2020. Self-reported quit rates were significantly higher among RCs in 2020 as compared to 2019 on the quit day (2019 - 47.37% & 2020 - 77.54%, P=0.001), at one week (2019 - 25.17% and 2020 - 56.06%, P=0.001) as was one-month continuous abstinence (2019 - 11.88% and 2020 - 39.60% P=0.001). CONCLUSION: The pandemic resulted in a greater intent to quit among registered callers to the quitline. However, awareness about the quitline services as well as other tobacco cessation services needs to be expanded to reach more tobacco users. IMPLICATION: Pandemics offer an opportunity to change health risk behaviours. During the Covid-19 pandemic, callers to the tobacco quitline were more motivated to quit tobacco and attributed it to concerns about the health risks from tobacco use, particularly during the pandemic. Quit rates also increased significantly during the pandemic as compared to before. These gains in encouraging tobacco cessation need to be maintained beyond the pandemic by strengthening existing quitlines and other supports for tobacco cessation.

Impact of Technology Driven Mental Health Task-shifting for Accredited Social Health Activists (ASHAs): Results from a Randomised Controlled Trial of Two Methods of Training.

Mental health task shifting is a potential way to address the burgeoning treatment gap for mental illness. Easily available and accessible digital technology can be utilised to continuously engage grassroot level health workers (for example, Accredited Social Health Activists (ASHAs). However, the impact of such a strategy is not yet systematically evaluated. In this randomised controlled trial, longitudinal hybrid training of ASHAs [1 day in-person classroom training and seven online sessions (ECHO model), aimed to screen and refer to commonly prevalent mental health issues in communities] was compared with traditional one-day in-person classroom training. ASHAs (n = 75) from six Primary Health Centres in Ramanagara district, Karnataka, India were randomized into study (SG-ASHAs) and control (CG-ASHAs) groups. After excluding drop-outs, 26 ASHAs in each group were included in the final analysis of the scores on their Knowledge, attitude, and practices (KAP) in mental health. Two house-to-house surveys were conducted by both groups to identify and refer possible cases. The number of screen positives (potential persons with mental illnesses) and the KAP scores formed the outcome measures. Online sessions for SG-ASHAs were completed over 18 months, the COVID-19 pandemic being the main disruptor. SG-ASHAs identified significantly higher number of persons with potential alcohol use disorders [n = 873 (83%); p ≤ 0.001] and common mental disorders [n = 96(4%); p = 0.018], while CG-ASHAs identified significantly higher number of those with potential severe mental disorders [n = 61(61.61%); p ≤ 0.001]. As regards KAP, after controlling for baseline scores, the time effect in RMANOVA favoured SG-ASHAs. Mean total KAP score increased from 16.76 to18.57 (p < 0·01) in SG-ASHAs and from 18.65 to 18.84 (p = 0.76) in CG-ASHAs. However, the Time-group interaction effect did not favour either (F = 0.105; p = 0.748). Compared to traditional training, mentoring ASHAs for extended periods is more impactful. Easily accessible digital technology makes the latter feasible. Scaling up such initiatives carry the potential to considerably improve treatment access for those in need.

Relapse prediction in schizophrenia with smartphone digital phenotyping during COVID-19: a prospective, three-site, two-country, longitudinal study.

Smartphone technology provides us with a more convenient and less intrusive method of detecting changes in behavior and symptoms that typically precede schizophrenia relapse. To take advantage of the aforementioned, this study examines the feasibility of predicting schizophrenia relapse by identifying statistically significant anomalies in patient data gathered through mindLAMP, an open-source smartphone app. Participants, recruited in Boston, MA in the United States, and Bangalore and Bhopal in India, were invited to use mindLAMP for up to a year. The passive data (geolocation, accelerometer, and screen state), active data (surveys), and data quality metrics collected by the app were then retroactively fed into a relapse prediction model that utilizes anomaly detection. Overall, anomalies were 2.12 times more frequent in the month preceding a relapse and 2.78 times more frequent in the month preceding and following a relapse compared to intervals without relapses. The anomaly detection model incorporating passive data proved a better predictor of relapse than a naive model utilizing only survey data. These results demonstrate that relapse prediction models utilizing patient data gathered by a smartphone app can warn the clinician and patient of a potential schizophrenia relapse.

Extension for Community Healthcare Outcomes Based Telementoring of Physicians for Dementia-Effectiveness in India.

Objective: The study aimed to evaluate the effectiveness of a program based on the telementoring model [Extension for Community Healthcare Outcomes (ECHO)] for primary care physicians in diagnosing and treating patients with dementia. Method: The ECHO model was adapted through 12 live sessions of 2 h every 2 weeks consisting of a didactic presentation by the expert, peer-led new case discussions, and follow-up discussions. In addition, there were 10 h of self-paced e-learning and interim assignments. The impact was examined by noting participation, session ratings, monthly clinical reports, and comparing knowledge and competency scores before and after the course. Results: Among the 63 participants, 39.7% attended at least 80% of the sessions; completing the program successfully. The ratings for all sessions ranged from "good" to "excellent." The paired sample t-test revealed a statistically significant improvement (p < 0.001) in self-rated skills and confidence in diagnosing and treating dementia with an effect size of 1.25 and 1.37, respectively. No change in the knowledge score was observed throughout the course. A considerable increase in dementia-related clinical practice was observed during four monthly summary of clinical cases. Due to the limited data of monthly reports during the COVID pandemic, no statistical analysis was attempted. Conclusion: The ECHO model appears to have a positive immediate impact on the clinical ability of primary care physicians to diagnose and treat dementia. Its direct impact on patient health and at the community level should be aimed at in future studies.

Cross cultural and global uses of a digital mental health app: results of focus groups with clinicians, patients and family members in India and the United States.

BACKGROUND: Despite significant advancements in healthcare technology, digital health solutions - especially those for serious mental illnesses - continue to fall short of their potential across both clinical practice and efficacy. The utility and impact of medicine, including digital medicine, hinges on relationships, trust, and engagement, particularly in the field of mental health. This paper details results from Phase 1 of a two-part study that seeks to engage people with schizophrenia, their family members, and clinicians in co-designing a digital mental health platform for use across different cultures and contexts in the United States and India. METHODS: Each site interviewed a mix of clinicians, patients, and their family members in focus groups (n = 20) of two to six participants. Open-ended questions and discussions inquired about their own smartphone use and, after a demonstration of the mindLAMP platform, specific feedback on the app's utility, design, and functionality. RESULTS: Our results based on thematic analysis indicate three common themes: increased use and interest in technology during coronavirus disease 2019 (COVID-19), concerns over how data are used and shared, and a desire for concurrent human interaction to support app engagement. CONCLUSION: People with schizophrenia, their family members, and clinicians are open to integrating technology into treatment to better understand their condition and help inform treatment. However, app engagement is dependent on technology that is complementary - not substitutive - of therapeutic care from a clinician.

Acceptance of e-consult for Substance Use Disorders during the COVID 19 pandemic: A study from India.

OBJECTIVES: The COVID 19 pandemic has created challenges in providing timely care for patients and families with Substance Use disorders (SUDs). With the difficulties in face-to-face consultations because of social distancing measures, telepsychiatry services can be beneficial. The study proposes implementing an e-consult for SUD management and measuring its acceptability among the health care providers (HCPs) in India. METHODS: The mobile-based e-consult for SUD, connecting HCPs with addiction specialists, was implemented during the COVID lockdown period in India from 25 March to 31 May (71 days). A total of 153 HCPs, i.e., doctors, nurses, counselors, consulted for 110 cases of SUD. Sixty-eight provided feedback by filling the survey form derived from the Service User Technology Acceptability Questionnaire (SUTAQ). RESULT: More than 60% of HCPs reported overall high satisfaction. More than 98% providers reported high acceptability concerning"access to specialist care," "trusted to work appropriately", "saving time," "would like torecommend to others," easier to get touch with a specialist." The doctors reported significantly high acceptability about "access to specialist care," "satisfied with recommendations,"recommend to others" compared to other HCPs. CONCLUSION: During COVID 19 pandemic lockdown in India, e-consult was an acceptable tool in managing SUDs. The majority of HCPs could discuss their cases with addiction experts. There is a need to expand this further in other mental health conditions.

Complicated Alcohol Withdrawal-An Unintended Consequence of COVID-19 Lockdown.

AIM: To assess the impact of COVID-19-related lockdown in India on alcohol-dependent persons. METHOD: We examined the change in the incidence of severe alcohol withdrawal syndrome presenting to hospitals in the city of Bangalore. RESULTS: A changepoint analysis of the time series data (between 01.01.20 to 11.04.20) showed an increase in the average number of cases from 4 to 8 per day (likelihood ratio test: χ2 = 72, df = 2, P < 0.001). CONCLUSION: An unintended consequence of the lockdown was serious illness in some patients with alcohol use disorders.