Unburden mental health in India: it's time to act now

Detection and disclosure of workplace mental health challenges: an exploratory study from India

Workplace mental health challenges have emerged as a significant concern post-pandemic. Despite this, the pervasive stigma surrounding mental illness leads to the concealment of symptoms and reluctance to seek professional help among employees. This study aims to explore the perception of different stakeholders towards the 'Detection and disclosure' of workplace mental health challenges in the Indian context. Fifteen semi-structured interviews were conducted with human resource professionals, counselors, and employees who had previously experienced mental health challenge(s). Thematic analysis was done to identify recurring themes and sub-themes. Three critical pathways were identified: minimizing the inhibitory factors, including lack of awareness, denial, low self-efficacy, stigma, and underestimating organizational capability; maximizing the encouraging factors, including psychological safety, perceived social support, and communicating success stories; and implementing supportive organizational practices, including generating awareness and literacy, build the organizational capability, strengthen the role of managers, leadership advocacy, policies, and processes. By fostering a culture of support and prioritizing employee well-being, organizations in India can create healthier and more resilient work environments, benefiting both individuals and the larger society.

Task sharing and stepped referral model for community mental health promotion in low- and middle-income countries (LMIC): insights from a feasibility study in India

BACKGROUND

This study is a low-cost community mental health task-sharing model driven by university students to strengthen the mental health workforce in poor resource settings. This article presents the feasibility of a stepped referral model using the community health workforce and university students. The primary feasibility objective is to detect and refer people with mental illness from the community using a task-sharing approach.

METHODS

We tested the model using a cross-sectional, one-phase door-knock survey in three geographically defined locations in Kerala, India, between May and July 2019. Students surveyed 549 residents above 18 years of age who consented to participate in the study to detect depressive symptoms and suicidality. The feasibility of the current model was evaluated based on four criteria: (a) identification and deployment of untapped human resources, (b) coordination of community health resources, (c) the acceptability of stepped referral pathways, and (d) identification of implementation challenges.

RESULTS

The mean age of the participants was 38.8, and more than 62% of the respondents were women. The results showed that 11.29%, 8.38%, and 4.91% of people reported mild, moderate, and severe levels of depression, respectively, and suicidal thoughts were found in 6.9% and suicidal ideation in 1.8%. The odds of depression were higher among females compared to males (OR: 1.64 (0.75-2.52), poor people (OR: 2.01 (1.14-2.88), and people with chronic illnesses (OR: 2.03 (1.24-2.81). The agreement of the findings with professional-administered research validated the strategy's efficiency. Twenty-seven patients with severe/extreme degrees of depression were sent for high-intensity interventions led by the mental health team, whereas 135 individuals with mild and above depression were referred for low-intensity interventions.

CONCLUSIONS

The newly recruited mental health workforce-driven screenings were acceptable and effective in detecting mental illness in the community population. We tested the care coordination systems and processes in creating referral pathways for the detected patients. Further, task-sharing stepped referral model will be tested in five panchayats (the lowest tier of local self-government) before replicating the model across India through Unnat Bharat Abhiyan (UBA) scheme.

Prevalence of intellectual disability in India: A meta-analysis

BACKGROUND

Burden due to intellectual disability (ID) is only third to the depressive disorders and anxiety disorders in India. This national burden significantly contributes to the global burden of ID and hence one has to think globally and act locally to reduce this burden. At its best the collective prevalence of ID is in the form of narrative reviews. There is an urgent need to document the summary prevalence of ID to enhance further policymaking, national programs and resource allocation.

AIM

To establish the summary prevalence of ID during the past 60 years in India.

METHODS

Two researchers independently and electronically searched PubMed, Scopus, and the Cochrane library from January 1961 to December 2020 using appropriate search terms. Two other investigators extracted the study design, setting, participant characteristics, and measures used to identify ID. Two other researchers appraised the quality of the studies using the Joanna Briggs Institute critical appraisal format for Prevalence Studies. Funnel plot and Egger’s regression test were used to ascertain the publication and small study effect on the prevalence. To evaluate the summary prevalence of ID, we used the random effects model with arcsine square-root transformation. Heterogeneity of I² ≥ 50% was considered substantial and we determined the heterogeneity with meta-regression. The analyses were performed using STATA (version 16).

RESULTS

Nineteen studies were included in the meta-analysis. There was publication bias; the trim-and-fill method was used to further ascertain bias. Concerns with control of confounders and the reliable measure of outcome were noted in the critical appraisal. The summary prevalence of ID was 2% [(95%CI: 2%, 3%); I² = 98%] and the adjusted summary prevalence was 1.4%. Meta-regression demonstrated that age of the participants was statistically significantly related to the prevalence; other factors did not influence the prevalence or heterogeneity.

CONCLUSION

The summary prevalence of ID in India was established to be 2% taking into consideration the individual prevalence studies over the last six decades. This knowledge should improve the existing disability and mental health policies, national programs and service delivery to reduce the national and global burden associated with ID.

i-MANN: A Web-Based System for Data Management of Mental Health Research in India

BACKGROUND

National Mental Health Program (NMHP) was launched by the government with an aim to improve mental health of the society through precise and focused interventions and policies. In order to provide reliable data and evidence for NMHP, there is a strong requirement of a comprehensive system for integrative collection, storage, and analysis of data generated by this program.

METHODS

Data collection tools, questionnaires, instruments, and scales provided by the National Coordinating Unit were digitized using the District Health Information Software 2 (DHIS2) framework (version 2.30). The rules for data validation and automated scoring were implemented as per the scales. The developed system (i-MANN, ICMR-Mental Health Assessment National Network) is based on modular architecture with role-based access to data input forms and dashboards.

RESULTS

The data are stored on a centralized server at ICMR. i-MANN captures data on basic and advanced demographic details followed by category specific forms from 15 multicentric ICMR-funded projects. Data collection module is divided into 12 categories containing 93 scales/instruments with built-in validation rules, scoring patterns, and indicators. As of August 2020, the system contains 17,690 records.

CONCLUSIONS

i-MANN is the first web-based, modular, robust, and extendable system for collection, integration, management, and analysis of data on mental health in India.