Participatory Methods for Climate Change and Mental Health Research: Photovoice in Nepal

Background: The relationship between mental health and climate change are poorly understood. Participatory methods represent ethical, feasible, and culturally-appropriate approaches to engage community members for mental health promotion in the context of climate change. Aim: Photovoice, a community-based participatory research methodology uses images as a tool to deconstruct problems by posing meaningful questions in a community to find actionable solutions. This community-enhancing technique was used to elicit experiences of climate change among women in rural Nepal and the association of climate change with mental health. Subjects and methods: Mixed-methods, including in-depth interviews and self-report questionnaires, were used to evaluate the experience of 10 women participating in photovoice. Quantitative tools included Nepali versions of Beck Depression Inventory (BDI) and Beck Anxiety Inventory (BAI) and a resilience scale. Results: In qualitative interviews after photovoice, women reported climate change adaptation and behavior change strategies including environmental knowledge-sharing, group mobilization, and increased hygiene practices. Women also reported beneficial effects for mental health. The mean BDI score prior to photovoice was 23.20 (SD=9.00) and two weeks after completion of photovoice, the mean BDI score was 7.40 (SD=7.93), paired t-test = 8.02, p<.001, n=10. Conclusion: Photovoice, as a participatory method, has potential to inform resources, adaptive strategies and potential interventions to for climate change and mental health.

A Framework to Support the Sharing and Reuse of Computable Phenotype Definitions Across Health Care Delivery and Clinical Research Applications.

INTRODUCTION: The ability to reproducibly identify clinically equivalent patient populations is critical to the vision of learning health care systems that implement and evaluate evidence-based treatments. The use of common or semantically equivalent phenotype definitions across research and health care use cases will support this aim. Currently, there is no single consolidated repository for computable phenotype definitions, making it difficult to find all definitions that already exist, and also hindering the sharing of definitions between user groups. METHOD: Drawing from our experience in an academic medical center that supports a number of multisite research projects and quality improvement studies, we articulate a framework that will support the sharing of phenotype definitions across research and health care use cases, and highlight gaps and areas that need attention and collaborative solutions. FRAMEWORK: An infrastructure for re-using computable phenotype definitions and sharing experience across health care delivery and clinical research applications includes: access to a collection of existing phenotype definitions, information to evaluate their appropriateness for particular applications, a knowledge base of implementation guidance, supporting tools that are user-friendly and intuitive, and a willingness to use them. NEXT STEPS: We encourage prospective researchers and health administrators to re-use existing EHR-based condition definitions where appropriate and share their results with others to support a national culture of learning health care. There are a number of federally funded resources to support these activities, and research sponsors should encourage their use.