Improving the representativeness of the tribal behavioral risk factor surveillance system through data integration.

BACKGROUND: Previous literature showed significant health disparities between Native American population and other populations such as Non-Hispanic White. Most existing studies for Native American Health were based on non-probability samples which suffer with selection bias. In this paper, we are the first to evaluate the effectiveness of data integration methods, including calibration and sequential mass imputation, to improve the representativeness of the Tribal Behavioral Risk Factor Surveillance System (TBRFSS) in terms of reducing the biases of the raw estimates. METHODS: We evaluated the benefits of our proposed data integration methods, including calibration and sequential mass imputation, by using the 2019 TBRFSS and the 2018 and 2019 Behavioral Risk Factor Surveillance System (BRFSS). We combined the data from the 2018 and 2019 BRFSS by composite weighting. Demographic variables and general health variables were used as predictors for data integration. The following health-related variables were used for evaluation in terms of biases: Smoking status, Arthritis status, Cardiovascular Disease status, Chronic Obstructive Pulmonary Disease status, Asthma status, Cancer status, Stroke status, Diabetes status, and Health Coverage status. RESULTS: For most health-related variables, data integration methods showed smaller biases compared with unadjusted TBRFSS estimates. After calibration, the demographic and general health variables benchmarked with those for the BRFSS. CONCLUSION: Data integration procedures, including calibration and sequential mass imputation methods, hold promise for improving the representativeness of the TBRFSS.

Development of a Resource Guide to Support the Engagement of Mental Health Providers and Patients With Digital Health Tools: Multimethod Study.

BACKGROUND: As mental illness continues to affect 1 in 5 individuals, and the need for support has increased during the COVID-19 pandemic, the promise of digital mental health tools remains largely unrealized due to a lack of uptake by patients and providers. Currently, most efforts on supporting the uptake of digital mental health tools remain fragmented across organizations and geography. There is a critical need to synthesize these efforts in order to provide a coordinated strategy of supporting the adoption of digital mental health tools. OBJECTIVE: The specific aim of this project is to develop a web-based resource document to support the engagement of mental health providers and patients in the use of digital mental health tools. METHODS: The web-based resource was developed using a multimethod approach. A grey literature review was conducted in 2019 to identify relevant toolkits that are available in the public domain. This was supplemented with an environmental scan where individuals with expertise in the development, acquisition, implementation, and evaluation of digital mental health tools were invited to contribute additional tools or documents not identified in the grey literature search. An engagement workshop was held with stakeholders to explore how the resource document should be developed and delivered. These findings were collectively used to develop the final iteration of the resource document. RESULTS: Based on a gray literature review and environmental scan with 27 experts, 25 resources were identified and included in the resource guide. These resources were developed for patients and providers by organizations from 5 countries. An engagement workshop was held with 14 stakeholders, and barriers related to cultural sensitivity, sustainability, and accessibility of the toolkit were identified. The final iteration of the resource document was developed by the research team using findings from the gray literature review, environmental scan, and engagement workshop. The contents of the 45-page resource guide are directed at mental health care providers, administrators, and patients (inclusive of families and caregivers). CONCLUSIONS: The use of a multimethod approach led to the development of a resource guide that builds on existing evidence on digital mental health tools and was co-designed with stakeholders and end-users. The resource guide is now publicly available online for free and is being promoted through digital health and mental health websites. Future work should explore how this document can be integrated into clinical care delivery and pathways.

Health-related quality of life of hospitalized COVID-19 survivors: An initial exploration in Nanning city, China.

Understanding the health-related quality of life (HrQoL) of hospitalized COVID-19 survivors is an emerging global challenge arising from the current pandemic. A qualitative study of the experiences of sixteen hospitalized COVID-19 survivors from Nanning City, China, was conducted using semi-structured telephone interviews in May 2020. These first-hand accounts were critically and empirically analysed to identify emerging health and social issues, and provide potential solutions to improve survivors' quality of life. This in-depth, qualitative study of HrQoL for hospitalized COVID-19 survivors provides the first empirical evidence and conceptual framework with eight dimensions (physical symptoms, anxiety, trauma, economic loss, place-based identity, self-stigma, health self-interventions, and changing lifestyle) for understanding the physiological, psychological, socio-economic and health behavioral aspects of their daily lives. We argue that local and global governments should provide integrated healthcare, social and digital infrastructure to support this vulnerable group. More comparative and multi-disciplinary studies in this area are needed to generate academic standards of assessing health-related quality of life and produce good practice guidelines for promoting urban resilience in response to public health disasters.