Tyra Dark

Research topics

Matched from this investigator's NIH project titles and abstracts.

• Anxiety Disorders
• Health Disparities in Mental Health

Active NIH awards

• Utilizing mixed methods to understand social determinants of successful disease management among populations with comorbid cardiometabolic syndrome and anxiety disorder

  5K01MD018415-03

  NIMHD · FY 2025 · $130K

  ABSTRACT Cardiometabolic and mental health conditions are highly prevalent and complicate management of each other when they co-occur, leading to greater healthcare costs. Such multi-comorbidity becomes even more challenging when it intersects with community health and other determinants of health culminating in amplified risk of adverse outcomes. Successful disease management that works at the intersection of the individual and the community must be implemented to mitigate disparities and optimal treatment must address aspects of both cardiometabolic and mental health disorders in a culturally relevant intervention. Evidence based interventions have not been demonstrated to be relevant for resource deficit populations. The community perspective is likely missing in the overall care management of comorbid conditions. Scientific study must include all populations both in the collection of data and also in the identification of community identified variables important in successful management of comorbid conditions. Subsequently, positioning these variables in community-generated conceptual models and then testable computer models will help to identify and highlight key points for development of culturally relevant intervention strategies. The candidate proposes a parallel, integrated dynamic process of understanding community perspectives, analyzing risk factors and determinants, and building and testing computer models that explain disparities and predict outcomes. Connecting the dots between epidemiologic research and computer modeling through systems science in a community based participatory framework will allow for the development of accurate replicable models of health disparities that are co-built by community voices and population scientists.

• Utilizing mixed methods to understand social determinants of successful disease management among populations with comorbid cardiometabolic syndrome and anxiety disorder

  5K01MD018415-02

  NIMHD · FY 2024 · $131K

  ABSTRACT Cardiometabolic and mental health conditions are highly prevalent and complicate management of each other when they co-occur, leading to greater healthcare costs. Such multi-comorbidity becomes even more challenging when it intersects with community health and other determinants of health culminating in amplified risk of adverse outcomes. Successful disease management that works at the intersection of the individual and the community must be implemented to mitigate disparities and optimal treatment must address aspects of both cardiometabolic and mental health disorders in a culturally relevant intervention. Evidence based interventions have not been demonstrated to be relevant for resource deficit populations. The community perspective is likely missing in the overall care management of comorbid conditions. Scientific study must include all populations both in the collection of data and also in the identification of community identified variables important in successful management of comorbid conditions. Subsequently, positioning these variables in community-generated conceptual models and then testable computer models will help to identify and highlight key points for development of culturally relevant intervention strategies. The candidate proposes a parallel, integrated dynamic process of understanding community perspectives, analyzing risk factors and determinants, and building and testing computer models that explain disparities and predict outcomes. Connecting the dots between epidemiologic research and computer modeling through systems science in a community based participatory framework will allow for the development of accurate replicable models of health disparities that are co-built by community voices and population scientists.

• Utilizing mixed methods to understand social determinants of successful disease management among populations with comorbid cardiometabolic syndrome and anxiety disorder

  1K01MD018415-01A1

  NIMHD · FY 2023 · $130K

  Cardiometabolic and mental health conditions are highly prevalent and complicate management of each other when they co-occur, leading to greater disability and healthcare costs. Such multi-comorbidity becomes even more challenging when it intersects with racial inequities and social determinants of health culminating in amplified risk of adverse outcomes. Successful disease management that works at the intersection of the individual and the community must be implemented to mitigate disparities and optimal treatment must address aspects of both cardiometabolic and mental health disorders in a culturally relevant intervention. Evidence based interventions have not been demonstrated to be culturally relevant for resource poor minority populations. The community perspective is likely missing in the overall care management of comorbid conditions. Scientific study must include minorities both in the collection of data and also in the identification of community identified variables important in successful management of comorbid conditions. Subsequently, positioning these variables in community-generated conceptual models and then testable computer models will help to identify and highlight key points for development of culturally relevant intervention strategies. The K01 candidate proposes a parallel, integrated dynamic process of understanding community perspectives, analyzing risk factors and determinants, and building and testing computer models that explain disparities and predict outcomes. Connecting the dots between epidemiologic research and computer modeling through systems science in a community based participatory framework will allow for the development of accurate replicable models of health disparities that are co-built by community voices and population scientists. .