Digital phenotyping correlates of mobile cognitive measures in schizophrenia: A multisite global mental health feasibility trial.

Traditional cognitive assessments in schizophrenia are time-consuming and necessitate specialized training, making routine evaluation challenging. To overcome these limitations, this study investigates the feasibility and advantages of utilizing smartphone-based assessments to capture both cognitive functioning and digital phenotyping data and compare these results to gold standard measures. We conducted a secondary analysis of data from 76 individuals with schizophrenia, who were recruited across three sites (one in Boston, two in India) was conducted. The open-source mindLAMP smartphone app captured digital phenotyping data and Trails A/B assessments of attention / memory for up to 12 months. The smartphone-cognitive tasks exhibited potential for normal distribution and these scores showed small but significant correlations with the results from the Brief Assessment of Cognition in Schizophrenia, especially the digital span and symbol coding tasks (r2 = 0.21). A small but significant correlation (r2 = 0.29) between smartphone-derived cognitive scores and health-related behaviors such as sleep duration patterns was observed. Smartphone-based cognitive assessments show promise as cross-cultural tools that can capture relevant data on momentary states among individuals with schizophrenia. Cognitive results related to sleep suggest functional applications to digital phenotyping data, and the potential of this multimodal data approach in research.

Erratum: Scaling up the task-sharing of psychological therapies: A formative study of the PEERS smartphone application for supervision and quality assurance in rural India - CORRIGENDUM.

[This corrects the article DOI: 10.1017/gmh.2024.11.].

Scaling up the task-sharing of psychological therapies: A formative study of the PEERS smartphone application for supervision and quality assurance in rural India.

Measurement-based peer supervision is one strategy to assure the quality of psychological treatments delivered by non-mental health specialist providers. In this formative study, we aimed to 1) describe the development and 2) examine the acceptability and feasibility of PEERS (Promoting Effective mental healthcare through peER Supervision)-a novel smartphone app that aims to facilitate registering and scheduling patients, collecting patient outcomes, rating therapy quality and assessing supervision quality-among frontline treatment providers delivering behavioral activation treatment for depression. The PEERS prototype was developed and tested in 2021, and version 1 was launched in 2022. To date, 215 treatment providers (98% female; ages 30-35) in Madhya Pradesh and Goa, India, have been trained to use PEERS and 65.58% have completed the supplemental, virtual PEERS course. Focus group discussions with 98 providers were examined according to four themes-training and education, app effectiveness, user experience and adherence and data privacy and safety. This yielded commonly endorsed facilitators (e.g., collaborative learning through group supervision, the convenience of consolidated patient data), barriers (e.g., difficulties with new technologies) and suggested changes (e.g., esthetic improvements, suicide risk assessment prompt). The PEERS app has the potential to scale measurement-based peer supervision to facilitate quality assurance of psychological treatments across contexts.

ESSENCE: An Implementation Research Program to Scale Up Depression Care in Rural Communities.

BACKGROUND: Task sharing may involve training nonspecialist health workers (NSHWs) to deliver brief mental health interventions. This approach is promising for reducing the global mental health treatment gap. However, capacity is limited for training large cadres of frontline workers in low- and middle-income countries, hindering uptake of these interventions at scale. METHODS: The ESSENCE (enabling translation of science to service to enhance depression care) project in Madhya Pradesh, India, aims to address these challenges through two sequential randomized controlled trials. First, a training trial will evaluate the effectiveness and cost-effectiveness of digital training, compared with conventional face-to-face training, in achieving clinical competency of NSHWs in delivering an intervention for depression. This initial trial will be followed by an implementation trial aimed at evaluating the effectiveness of a remote enhanced implementation support, compared with routine implementation support, in addressing barriers to delivery of depression care in primary care facilities. RESULTS: This project involved developing and pilot testing a scalable smartphone-based program for training NSHWs to deliver a brief psychological intervention for depression screening. This initial research guided a randomized trial of a digital training approach with NSHWs to evaluate the effectiveness of this approach. This trial will be followed by a cluster-randomized trial to evaluate the effectiveness of remote implementation support in ensuring efficient delivery of depression care in primary care facilities. NEXT STEPS: Findings from these trials may inform sustainable training and implementation support models to integrate depression care into primary care for scale-up in resource-constrained settings.

A performance comparison of machine learning models for stock market prediction with novel investment strategy.

Stock market forecasting is one of the most challenging problems in today's financial markets. According to the efficient market hypothesis, it is almost impossible to predict the stock market with 100% accuracy. However, Machine Learning (ML) methods can improve stock market predictions to some extent. In this paper, a novel strategy is proposed to improve the prediction efficiency of ML models for financial markets. Nine ML models are used to predict the direction of the stock market. First, these models are trained and validated using the traditional methodology on a historic data captured over a 1-day time frame. Then, the models are trained using the proposed methodology. Following the traditional methodology, Logistic Regression achieved the highest accuracy of 85.51% followed by XG Boost and Random Forest. With the proposed strategy, the Random Forest model achieved the highest accuracy of 91.27% followed by XG Boost, ADA Boost and ANN. In the later part of the paper, it is shown that only classification report is not sufficient to validate the performance of ML model for stock market prediction. A simulation model of the financial market is used in order to evaluate the risk, maximum draw down and returns associate with each ML model. The overall results demonstrated that the proposed strategy not only improves the stock market returns but also reduces the risks associated with each ML model.

Development of a Digital Program for Training Community Health Workers in the Detection and Referral of Schizophrenia in Rural India.

This study aimed to develop and assess the acceptability of a digital program for training community health workers (CHWs) in the detection and referral of patients with schizophrenia in community settings in rural India. An iterative design process was employed. First, evidence-based content from existing community programs for schizophrenia care was incorporated into the curriculum, and reviewed by experts to ensure clinical utility and fidelity of the adapted content. Second, CHWs provided feedback on the appropriateness of language, content, and an initial prototype of the digital training program to ensure relevance for the local context. Focus group discussions were then used to understand the acceptability of the digital training prototype and to inform modifications to the design and layout. Qualitative data was analysed using a rapid thematic analysis approach based on predetermined topics pertaining to acceptability of the training content and digital platform. Development of the initial prototype involved content review by 13 subject matter experts with clinical expertise or experience accessing and receiving mental health services, and engagement of 23 CHWs, of which 11 provided feedback for contextualization of the training content and 12 participated in focus group discussions on the acceptability of the prototype. Additionally, 2 service-users with lived experience of schizophrenia contributed to initial testing of the digital training prototype and offered feedback in a focus group discussion. During contextualization of the training content, key feedback pertained to simplifying the language and presentation of the content by removing technical terms and including interactive content and images to enhance interest and engagement with the digital training. During prototype testing, CHWs shared their familiarity with similar symptoms but were unaware of schizophrenia as a treatable illness. They shared that training can help them identify symptoms of schizophrenia and connect patients with specialized care. They were also able to understand misconceptions and discrimination towards people with schizophrenia, and how to address these challenges by supporting others and spreading awareness in their communities. Participants also appreciated the digital training, as it could save them time and could be incorporated within their routine work. This study shows the acceptability of leveraging digital technology for building capacity of CHWs to support early detection and referral of schizophrenia in community settings in rural India. These findings can inform the subsequent evaluation of this digital training program to determine its impact on enhancing the knowledge and skills of CHWs.

Applying Human-Centered Design in Global Mental Health to Improve Reach Among Underserved Populations in the United States and India.

INTRODUCTION: Human-centered design (HCD) refers to a diverse suite of interactive processes that engage end users in the development of a desired outcome. We showcase how 2 global mental health research teams applied HCD to develop mobile health tools, each directed at reducing treatment gaps in underserved populations. CASE STUDY 1: Refugees face higher risks for mental health problems, yet these communities face structural and cultural barriers that reduce access to and use of services. To address these challenges, the Research Program on Children and Adversity at the Boston College School of Social Work, in partnership with resettled refugee communities in the northeastern United States, used codesign methodology to digitally adapt delivery of the Family Strengthening Intervention for Refugees-a program designed to improve mental health and family functioning among resettled families. We describe how codesign methods support the development of more feasible, acceptable, and sustainable interventions. CASE STUDY 2: Sangath, an NGO in India focused on mental health services research, in partnership with Harvard Medical School, designed and evaluated a digital training program for community health workers to deliver an evidence-based, brief psychological treatment for depression as part of primary care in Madhya Pradesh, India. We describe how HCD was applied to program development and discuss our approach to scaling up training and capacity-building to deliver evidence-based treatment for depression in primary care. IMPLICATIONS: HCD involves a variety of techniques that can be flexibly adapted to engage end users in the conceptualization, implementation, scale-up, and sustainment of global mental health interventions. Community solutions generated using HCD offer important benefits for key stakeholders. We encourage widespread adoption of HCD within global mental health policy, research, and practice, especially for addressing mental health disparities with underserved populations.

Stress Alleviation Methods for community-Based Health ActiVists (SAMBHAV): Development of a digital program for stress reduction for community health workers in rural India.

Background: Community health workers (CHWs) face high levels of stress (both professional and personal) and risk of burnout, highlighting the need for efforts to promote their mental health and well-being. This study seeks to develop a digital stress reduction program for CHWs in rural India. Methods: A stepwise design process was employed to adapt and digitize the evidence-based World Health Organization's Self-Help Plus (SH+) intervention for addressing psychological distress among CHWs in Madhya Pradesh, India. This involved participatory design workshops with CHWs to ensure that the digital stress reduction intervention would be relevant for their needs and the local culture and context. Small groups of CHWs reviewed the adapted program content, allowing the research team to make refinements such as simplifying language, tailoring content to the local setting, and ensuring that program materials are both interesting and relevant. Simultaneously, the research team digitized the content, leveraging a combination of video and graphical content, and uploaded it to the Sangath Learning Management System, a digital platform accessible on a smartphone app. Results: In total, 18 CHWs contributed to the adaptation of the SH+ content and digital intervention development. Participants commented on finding some terms difficult to follow and recommended simplifying the language and providing detailed explanations. Participants offered positive feedback on the adapted content, expressing that they found the examples covered in the material both relatable and relevant to their own personal experiences at home and in the workplace. By combining participants' insights and comments with feedback from content experts, it was possible to finalize a digital Hindi version of the SH+ intervention for CHWs in rural India. Conclusions: This study is timely given the continued detrimental impacts of the COVID-19 pandemic, and offers a promising and potentially scalable digital program to alleviate psychological distress among frontline health workers.

Schizophrenia Assessment, Referral and Awareness Training for Health Auxiliaries (SARATHA): Protocol for a Mixed-Methods Pilot Study in Rural India.

BACKGROUND: Workforce shortages pose major obstacles to the timely detection and treatment of schizophrenia, particularly in low-income and middle-income countries. The SARATHA (Schizophrenia Assessment, Referral, and Awareness Training for Health Auxiliaries) project involves the systematic development, iterative refinement, and pilot testing of a digital program for training community health workers in the early detection and referral of schizophrenia in primary care settings in rural India. METHODS: SARATHA is a three-phase study. Phase 1 involves consulting with experts and clinicians, and drawing from existing evidence to inform the development of a curriculum for training community health workers. Phase 2 consists of designing and digitizing the training content for delivery on a smartphone app. Design workshops and focus group discussions will be conducted to seek input from community health workers and service users living with schizophrenia to guide revisions and refinements to the program content. Lastly, Phase 3 entails piloting the training program with a target sample of 20 community health workers to assess feasibility and acceptability. Preliminary effectiveness will be explored, as measured by community health workers' changes in knowledge about schizophrenia and the program content after completing the training. DISCUSSION: If successful, this digital training program will offer a potentially scalable approach for building capacity of frontline community health workers towards reducing delays in early detection of schizophrenia in primary care settings in rural India. This study can inform efforts to improve treatment outcomes for persons living with schizophrenia in low-resource settings.

Smartphone digital phenotyping, surveys, and cognitive assessments for global mental health: Initial data and clinical correlations from an international first episode psychosis study.

Objective: To examine feasibility and acceptability of smartphone mental health app use for symptom, cognitive, and digital phenotyping monitoring among people with schizophrenia in India and the United States. Methods: Participants in Boston, USA and Bhopal and Bangalore, India used a smartphone app to monitor symptoms, play cognitive games, access relaxation and psychoeducation resources and for one month, with an initial clinical and cognitive assessment and a one-month follow-up clinical assessment. Engagement with the app was compared between study sites, by clinical symptom severity and by cognitive functioning. Digital phenotyping data collection was also compared between three sites. Results: By Kruskal-Wallis rank-sum test, we found no difference between app activities completed or digital phenotyping data collected across the three study sites. App use also did not correlate to clinical or cognitive assessment scores. When using the app for symptom monitoring, preliminary findings suggest app-based assessment correlate with standard cognitive and clinical assessments. Conclusions: Smartphone app for symptom monitoring and digital phenotyping for individuals with schizophrenia appears feasible and acceptable in a global context. Clinical utility of this app for real-time assessments is promising, but further research is necessary to determine the long-term efficacy and generalizability for serious mental illness.

A qualitative exploration of perceived needs and barriers of individuals with schizophrenia, caregivers and clinicians in using mental health applications in Madhya Pradesh, India.

Introduction: About 3.5 million people are living with schizophrenia in India, with most failing to receive minimally adequate care. Digital mental health applications could potentially decrease this treatment gap; however, these applications should be tailored to meet the needs and overcoming barriers of its end-users to ensure their adoption and sustained usage. Few studies in India have explored the perspectives of target stakeholders to understand how digital tools could be viable for supporting care. Therefore, this study explores the perceived needs and barriers of patients with schizophrenia, caregivers and clinicians in using digital mental health applications. Methods: Focus group discussions (FGDs) were conducted with patients having schizophrenia attending outpatient clinics at a government tertiary hospital, and their caregivers, and mental health clinicians in Bhopal, Madhya Pradesh, India. FGDs were audio-recorded and coded. Framework analysis was employed to guide the analysis, involving deductive and inductive generation of themes, data triangulation and comparison of perspectives between participant groups. Results: Six FGDs were conducted with individuals with schizophrenia (n â€‹= â€‹11), their caregivers (n â€‹= â€‹14), and mental health clinicians (n â€‹= â€‹19). Four a priori themes were established: a) Prior experiences with health applications; b) Content of a mental health application; c) Involvement of caregivers in mental health application usage and d) Supporting doctors' work through mental health applications. Additionally, two themes were generated inductively: a) Qualities of a mental health application and b) Data privacy and confidentiality. Conclusion: Exploration of stakeholder perspectives on the content, features, and uses of mental health applications is crucial to yield initial insights about the use of these digital programs in India. This study generated a multitude of suggestions on app functionality and components, which can guide ongoing efforts to develop and deliver digital mental health applications for patients living with schizophrenia in low-resource settings, with limited access to mental health services.

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.

Development and implementation of COVID-19 safety protocols for conducting a randomized trial in global mental health: Field report from Central India.

The COVID-19 pandemic impacted ongoing clinical trials globally resulting in the suspension, cancellation or transition to entirely remote implementation of studies. In India, the first countrywide lockdown was imposed in phases starting from March 2020 to June 2020, followed by a continued restriction on in-person activities including study procedures, which halted the ESSENCE (Enabling translation of Science to Service to ENhance Depression CarE) trial activities such as recruitment, consenting, baseline assessment, digital training orientation, face to face training and end-line assessment evaluation. This situation made it imperative to amend procedures in order to mitigate the risk and address safety requirements for participants and the research team. This paper summarizes the need, development and implementation of the protocols focused on risk reduction and safety enhancement with an objective to resume and continue the research activities while ensuring the safety of study participants and research staff. These protocols are comprised of guidelines and recommendations based on existing literature tailored according to different components in each arm of the trial such as guidelines for supervisors, travellers, training/recruitment venue safety procedures, individual safety procedures; and procedures to implement the study activities. These protocols can be adapted by researchers in other settings to conduct research trials during pandemics such as COVID-19.

Digital training for non-specialist health workers to deliver a brief psychological treatment for depression in India: Protocol for a three-arm randomized controlled trial.

BACKGROUND: Training non-specialist health workers (NSHWs) at scale is a major barrier to increasing the coverage of depression care in India. This trial will test the effectiveness of two forms of digital training compared to conventional face-to-face training in changing the competence of NSHWs to deliver a brief evidence-based psychological treatment for depression. METHODS: This protocol is for a three-arm, parallel group randomized controlled trial comparing three ways of training NSHWs to deliver the Healthy Activity Program (HAP), a brief manualized psychotherapy for depression, in primary care. The arms are: digital training (DGT); digital training combined with individualized coaching support (DGT+); and conventional face-to-face training (F2F). The target sample comprises N = 336 government contracted NSHWs in Madhya Pradesh, India. The primary outcome is change of competence to deliver HAP; secondary outcomes include cost-effectiveness of the training programs, change in participants' mental health knowledge, attitudes and behavior, and satisfaction with the training. Assessors blind to participant allocation status will collect outcomes pre- (baseline) and post- (endpoint) training to ascertain differences in outcomes between arms. Training program costs will be collected to calculate incremental costs of achieving one additional unit on the competency measure in the digital compared to face-to-face training programs. Health worker motivation, job satisfaction, and burnout will be collected as exploratory outcome variables. DISCUSSION: This trial will determine whether digital training is an effective, cost-effective, and scalable approach for building workforce capacity to deliver a brief evidence-based psychological treatment for depression in primary care in a low-resource setting. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04157816.

Digital Training for Non-Specialist Health Workers to Deliver a Brief Psychological Treatment for Depression in Primary Care in India: Findings from a Randomized Pilot Study.

Introduction: Task sharing holds promise for scaling up depression care in countries such as India, yet requires training large numbers of non-specialist health workers. This pilot trial evaluated the feasibility and acceptability of a digital program for training non-specialist health workers to deliver a brief psychological treatment for depression. Methods: Participants were non-specialist health workers recruited from primary care facilities in Sehore, a rural district in Madhya Pradesh, India. A three-arm randomized controlled trial design was used, comparing digital training alone (DGT) to digital training with remote support (DGT+), and conventional face-to-face training. The primary outcome was the feasibility and acceptability of digital training programs. Preliminary effectiveness was explored as changes in competency outcomes, assessed using a self-reported measure covering the specific knowledge and skills required to deliver the brief psychological treatment for depression. Outcomes were collected at pre-training and post-training. Results: Of 42 non-specialist health workers randomized to the training programs, 36 including 10 (72%) in face-to-face, 12 (86%) in DGT, and 14 (100%) in DGT+ arms started the training. Among these participants, 27 (64%) completed the training, with 8 (57%) in face-to-face, 8 (57%) in DGT, and 11 (79%) in DGT+. The addition of remote telephone support appeared to improve completion rates for DGT+ participants. The competency outcome improved across all groups, with no significant between-group differences. However, face-to-face and DGT+ participants showed greater improvement compared to DGT alone. There were numerous technical challenges with the digital training program such as poor connectivity, smartphone app not loading, and difficulty navigating the course content-issues that were further emphasized in follow-up focus group discussions with participants. Feedback and recommendations collected from participants informed further modifications and refinements to the training programs in preparation for a forthcoming large-scale effectiveness trial. Conclusions: This study adds to mounting efforts aimed at leveraging digital technology to increase the availability of evidence-based mental health services in primary care settings in low-resource settings.

Evaluation of a new set of indicators for mental health care implemented in Madhya Pradesh, India: a mixed methods study.

BACKGROUND: Mental health information systems are, in general, inadequate and unreliable in India. We have developed key mental health indicators for measuring mental health service coverage in primary care. This study aims to evaluate the use of these new indicators in seven health care facilities in Sehore District of Madhya Pradesh in India. METHODS: The study employed a mixed methods approach. We conducted: a qualitative study (n = 26) with health workers, Health Management Information Systems (HMIS) staff, project coordinators and supervisors; a review of case records (n = 61 at time 1 and n = 74 at time 2); and a structured questionnaire (n = 26) with health workers. The performance, user-friendliness, appropriateness, perceived utility and sustainability of the use of new mental health indicators was assessed. RESULTS: High levels of completion, and correctness of completion, of the new mental health indicators were found for the case records. The simplicity of the forms, as well as technical support from the project team, contributed to acceptability and feasibility of implementation. Perceived sustainability of the new forms was, however, affected by the overstretched primary care staff. Further work is needed to support the integration of mental health with routine HMIS. CONCLUSION: This study demonstrated that the implementation of key mental health service delivery indicators in Sehore District primary care facilities was feasible. Technical assistance was imperative in maintaining the performance of the indicators over the two studied time points. The integration of mental health indicators in routine health information systems, and political buy-in, are needed to monitor and sustain community mental health programmes in India.

Design and Development of a Digital Program for Training Non-Specialist Health Workers to Deliver an Evidence-Based Psychological Treatment for Depression in Primary Care in India.

BACKGROUND: Digital technologies hold promise for building capacity of non-specialist health workers towards scaling up depression care in low-resource settings. The purpose of this study was to describe the systematic approach to designing a digital program for training non-specialist health workers to deliver an evidence-based brief psychological treatment for depression, called the Healthy Activity Program, in primary care in rural India. METHODS: The design and development of the training program involved 5 steps: 1) develop program blueprint; 2) create instructional content; 3) digitize content for a smartphone app; 4) develop a platform for uploading and hosting the digital content; and 5) user-testing and refinements to ensure program functioning. This was followed by field-testing and focus group discussions with non-specialist health workers recruited from primary care facilities in Madhya Pradesh, India, to inform further modifications and improvements to the digital training program. RESULTS: Training program development occurred over 12-months, and the final digital training consisted of 16 modules with videos, role-plays, and digital content tailored to the local culture and context. Focus group discussions with 19 non-specialist health workers generated three key action items and modifications to the digital training in response to participant feedback: 1) addressing technical challenges by making the digital content available offline; 2) account for low digital literacy by including a comprehensive orientation session about navigating the smartphone app; and 3) addition of remote coaching to support participants in completing the training. CONCLUSIONS: This study illustrates a step-wise approach to combine evidence-based content with iterative feedback from stakeholders to develop a digital training program tailored to the context in a low-resource setting. Further research is needed to validate this approach and to evaluate the effectiveness of the final modified digital training program, while considering whether this approach can be adopted and replicated in other settings.

Acceptability and feasibility of digital technology for training community health workers to deliver brief psychological treatment for depression in rural India.

INTRODUCTION: Digital technology offers opportunities to train community health workers to deliver psychological treatments towards closing the gap in existing mental health services in low-resource settings. This study explored the acceptability and feasibility of using digital technology for training community health workers to deliver evidence-based brief psychological treatment for depression in rural India. METHODS: This study consisted of two sequential evaluations of digital training prototypes using focus group discussions to explore community health worker perspectives about the digital training platform and the program content. Through an iterative design process, feedback was collected about the first prototype to inform modifications to the second prototype. Qualitative data was analyzed using a framework analysis approach. RESULTS: Thirty-two community health workers participated in three separate focus group discussions. Five overarching themes related to acceptability and feasibility of digital training revealed that training on detection and treatment of depression was considered important by study participants for addressing 'stress' and 'tension' within their communities, while the digital platform was viewed as useful and convenient despite limited familiarity with using digital technology. Moreover, participants suggested simple language for the program and use of interactive content and images to increase interest and improve engagement. DISCUSSION: Digital technology appears acceptable and feasible for supporting training of community health workers to deliver evidence-based depression care in rural India. These findings can inform use of technology as a tool for developing the clinical skills of community health workers for treating depression in low-resource settings.

Delivering a complex mental health intervention in low-resource settings: lessons from the implementation of the PRIME mental healthcare plan in primary care in Sehore district, Madhya Pradesh, India.

BACKGROUND: The PRogramme for Improving Mental health care (PRIME) designed, implemented and evaluated a comprehensive mental healthcare plan (MHCP) for Sehore district, Madhya Pradesh, India.AimsTo provide quantitative measures of outputs related to implementation processes, describe the role of contextual factors that facilitated and impeded implementation processes, and discuss what has been learned from the MHCP implementation. METHOD: A convergent parallel mixed-methods design was used. The quantitative strand consisted of process data on mental health indicators whereas the qualitative strand consisted of in-depth interviews and focus group discussions with key stakeholders involved in PRIME implementation. RESULTS: The implementation of the MHCP in Sehore district in Madhya Pradesh, India, demonstrated that it is feasible to establish structures (for example Mann-Kaksha) and operationalise processes to integrate mental health services in a 'real-world' low-resource primary care setting. The key lessons can be summarised as: (a) clear 'process maps' of clinical interventions and implementation steps are helpful in monitoring/tracking the progress; (b) implementation support from an external team, in addition to training of service providers, is essential to provide clinical supervision and address the implementation barriers; (c) the enabling packages of the MHCP play a crucial role in strengthening the health system and improving the context/settings for implementation; and (d) engagement with key community stakeholders and incentives for community health workers are necessary to deliver services at the community-platform level. CONCLUSIONS: The PRIME implementation model could be used to scale-up mental health services across India and similar low-resource settings.Declaration of interestNone.

Community, facility and individual level impact of integrating mental health screening and treatment into the primary healthcare system in Sehore district, Madhya Pradesh, India.

INTRODUCTION: Programme for Improving Mental Health Care (PRIME) designed a comprehensive mental healthcare plan (MHCP) for Sehore district, Madhya Pradesh, India. The objective of this paper is to describe the findings of the district-level impact evaluation of the MHCP. METHODS: Repeat community-based CS were conducted to measure change in population-level contact coverage for depression and alcohol use disorders (AUD), repeat FDS were conducted to assess change in detection and initiation of treatment for depression and AUD, and the effect of treatment on patient outcomes was assessed using disorder-specific prospective cohort studies. RESULTS: PRIME MHCP did not have any impact on contact coverage/treatment seeking for depression (14.8% at the baseline and 10.5% at the follow-up) and AUD (7.7% at the baseline and 7.3% at the follow-up) and had a small impact on detection and initiation of treatment for depression and AUD (9.7% for depression and 17.8% for AUD compared with 0% for both at the baseline) in the health facilities. Patients with depression who received care as part of the MHCP had higher rates of response (52.2% in the treatment group vs 26.9% in the comparison/usual care group), early remission (70.2% in the treatment group vs 44.8% in the comparison/usual care group) and recovery (56.1% in the treatment group vs 28.5% in the comparison/usual care group), but there was no impact of treatment on their functioning. CONCLUSIONS: While dedicated human resources (eg, Case Managers) and dedicated space for mental health clinics (eg, Mann-Kaksha) strengthen the 'formal' healthcare platform, without substantial additional investments in staff, such as Community Health Workers/Accredited Social Health Activists to improve community level processes and provision of community-based continuing care to patients, we are unlikely to see major changes in coverage or clinical outcomes.