COVID-19 Vaccine decision-making: trust among the transgender and disability communities in India.

BACKGROUND: Historical marginalisation and ongoing trust deficits in health and government systems shape present-day vaccine perceptions among marginalised communities. This paper sought to understand the role of trust in decision-making about COVID-19 vaccine uptake in the transgender and disability communities in India. METHODS: Using a participatory approach we interviewed 24 community representatives, identifying themselves as transgender individuals or as persons with disability, and 21 key informants such as vaccine programme managers, vaccine providers, and community advocates. We undertook an inductive thematic analysis of the data using a socio-ecological model. RESULTS: Fear of side effects in relation to specific needs of the two communities and mistrust of systems involved in vaccination shaped four different pathways for vaccine decision-making. Mistrust of systems was influenced by past negative experiences with the health system, creating contexts in which information and misinformation are shared and interpreted. Participants negotiated their doubts about safety and mistrust of systems by interacting with different sources of influence showing patterns of decision-making that are dynamic, context-dependent, and intersectional. CONCLUSION: These findings will help in determining the content, strategies and approaches to equitable vaccine communication for these two communities. The two communities ought to be included in vaccine trials. Vaccine information must respond to the specific needs of these two communities which could be enabled by collaboration and engagement with community members and influencers. Finally, long-term investment towards the needs of marginalised communities is vital to dismantle cycles of marginalisation and distrust and in turn improve vaccine acceptance and uptake.

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.

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.

Assessing costs of developing a digital program for training community health workers to deliver treatment for depression: A case study in rural India.

Digital technology has emerged as a promising approach for training and building capacity of community health workers in low-income and middle-income countries (LMICs). Little is known about the cost of developing digital training programs in LMICs, which hinders the adoption, implementation, and scaling up of the programs in routine primary care settings. This study assessed the costs of developing a digital program for training community health workers to deliver a psychological treatment for depression in a rural district of Madhya Pradesh, India. We developed survey instruments to document required resources in development, including involved personnel (their roles, responsibilities, time spent, and salaries or payments), information technologies (e.g., smartphones, software programs), and infrastructure-related costs (e.g., vehicle, office space, utilities). Costs were estimated from an accounting perspective. Over a 10-month developmental period, the total costs were 208,814 USD, with the largest portion on human resources (61%, with 14% on management and supervision), followed by information technologies (33%), and infrastructure-related costs (6%). These findings could inform policymakers in LMICs on costs of developing online-training programs, which will be especially useful during the COVID-19 pandemic.

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.

Smartphone Health Assessment for Relapse Prevention (SHARP): a digital solution toward global mental health.

BACKGROUND: Predicting and preventing relapse presents a crucial opportunity and first step to improve outcomes and reduce the care gap for persons living with schizophrenia. Using commercially available smartphones and smartwatches, technology now affords opportunities to capture real-time and longitudinal profiles of patients' symptoms, cognition, physiology and social patterns. This novel data makes it possible to explore relationships between behaviours, physiology and symptoms, which may yield personalised relapse signals. AIMS: Smartphone Health Assessment for Relapse Prevention (SHARP), an international mental health research study supported by the Wellcome Trust, will inform the development of a scalable and sharable digital health solution to monitor personal risk of relapse. The resulting technology will be studied toward predicting and preventing relapse among individuals diagnosed with serious mental illness. METHOD: SHARP is a two-phase study with research sites in Boston, Massachusetts, and Bangalore and Bhopal, India. During phase 1, focus groups will be conducted at each study site to collect feedback on the design and features available on mindLAMP, a digital health platform. Individuals with serious mental illness will use mindLAMP for the duration of a year during phase 2. RESULTS: The results of the research outlined in this protocol will guide the development of technology and digital tools to help address pervasive challenges in global mental health. CONCLUSIONS: The digital tools developed as a result of this study, and participants' experiences using them, may offer insight into opportunities to expand digital mental health resources and optimize their utilisation around the world.

Impact of community-based health insurance and economic status on utilization of healthcare services: A household-level cross-sectional survey from rural central India.

Background: . Community-based health insurance (CBHI) is a health-financing mechanism based on voluntary membership, risk pooling, with a non-profit objective and relies on social capital as a driving force. It aims to improve equity in healthcare utilization in the community. We did this study to understand if CBHI schemes reach the poor, improve healthcare utilization and protect them from catastrophic health events. Methods: . Mahatma Gandhi Institute of Medical Sciences (MGIMS), Sevagram, located in Wardha district of Maharashtra, India, runs a variety of CBHI schemes in surrounding villages. Many households (HHs) have opted for these schemes. We conducted a cross-sectional survey of all HHs of 35 villages and collected information about sociodemographics, inpatient healthcare utilization (in previous 5 years), outpatient healthcare utilization (in previous 1 year) and insurance status of the HHs. We derived wealth index based on 33 sociodemographic variables and classified HHs in quintiles of wealth index. We compared the distribution of healthcare utilization variables by insurance status and wealth index and used logistic regression to evaluate if health insurance independently improves healthcare utilization, after adjusting for confounders. Results: . Of a total of 7261 HHs surveyed, 2210 (30.4%) were uninsured, 4153 (57.2%) were insured under MGIMS CBHI schemes, and 898 (12.4%) had family insurance either from MGIMS or other providers. Insured HHs had a higher wealth index compared to uninsured. Mean (SD) hospitalization episodes in an HH were 0.82 (1.75) among uninsured, 1.13 (1.56) in CBHI insured and 1.21 (1.55) in those with family insurance. Within each category, healthcare utilization was lower for poor HHs (lowest quintile of wealth index) and higher for affluent HHs (higher quintiles of wealth index). Among those who were hospitalized, catastrophic health events were less in CBHI insured (7.9%) compared to uninsured (12.3%). After adjusting for socioeconomic status and other confounders, our data suggest that participating in a CBHI scheme increased odds of utilization of inpatient services (OR 1.18; 95% CI 1.04-1.33) and protected from catastrophic health events (OR 0.52; 95% CI 0.43-0.64). Conclusion: . CBHI schemes improve healthcare utilization and protect against catastrophic health expenditure among those who get hospitalized. However, there also exists a socioeconomic gradient both in membership and in utilization of healthcare services favouring those who are more affluent.

Evaluating community engagement in global health research: the need for metrics.

BACKGROUND: Community engagement in research has gained momentum as an approach to improving research, to helping ensure that community concerns are taken into account, and to informing ethical decision-making when research is conducted in contexts of vulnerability. However, guidelines and scholarship regarding community engagement are arguably unsettled, making it difficult to implement and evaluate. DISCUSSION: We describe normative guidelines on community engagement that have been offered by national and international bodies in the context of HIV-related research, which set the stage for similar work in other health related research. Next, we review the scholarly literature regarding community engagement, outlining the diverse ethical goals ascribed to it. We then discuss practical guidelines that have been issued regarding community engagement. There is a lack of consensus regarding the ethical goals and approaches for community engagement, and an associated lack of indicators and metrics for evaluating success in achieving stated goals. To address these gaps we outline a framework for developing indicators for evaluating the contribution of community engagement to ethical goals in health research. There is a critical need to enhance efforts in evaluating community engagement to ensure that the work on the ground reflects the intentions expressed in the guidelines, and to investigate the contribution of specific community engagement practices for making research responsive to community needs and concerns. Evaluation mechanisms should be built into community engagement practices to guide best practices in community engagement and their replication across diverse health research settings.

Development of a research ethics knowledge and analytical skills assessment tool.

INTRODUCTION: The goal of this project was to develop and validate a new tool to evaluate learners' knowledge and skills related to research ethics. METHODS: A core set of 50 questions from existing computer-based online teaching modules were identified, refined and supplemented to create a set of 74 multiple-choice, true/false and short answer questions. The questions were pilot-tested and item discrimination was calculated for each question. Poorly performing items were eliminated or refined. Two comparable assessment tools were created. These assessment tools were administered as a pre-test and post-test to a cohort of 58 Indian junior health research investigators before and after exposure to a new course on research ethics. Half of the investigators were exposed to the course online, the other half in person. Item discrimination was calculated for each question and Cronbach's α for each assessment tool. A final version of the assessment tool that incorporated the best questions from the pre-/post-test phase was used to assess retention of research ethics knowledge and skills 3 months after course delivery. RESULTS: The final version of the REKASA includes 41 items and had a Cronbach's α of 0.837. CONCLUSION: The results illustrate, in one sample of learners, the successful, systematic development and use of a knowledge and skills assessment tool in research ethics capable of not only measuring basic knowledge in research ethics and oversight but also assessing learners' ability to apply ethics knowledge to the analytical task of reasoning through research ethics cases, without reliance on essay or discussion-based examination. These promising preliminary findings should be confirmed with additional groups of learners.