Speaker
Description
The San Francisco Bay Area, known for its concentration of well-educated individuals in high-paying jobs, ranks among the wealthiest cities in the United States. However, the post-COVID-19 landscape has posed significant challenges, including a notable population decline, widening economic disparities, rising crime rates, pervasive drug use and homelessness, and an increase in foreclosed and vacant properties. These deteriorations/disorder may be linked to declining health of vulnerable populations in different stages of their lifecourse. This study employs syndemic theory as a conceptual framework to investigate synergisms between social, political, environmental and health service conditions and the clustering/escalation of comorbidities and multimorbidities (communicable, non-communicable, accidents and injuries, and mental health outcomes) in the San Francisco Bay Area. A conceptual framework will be presented that includes the driving forces behind emigration and how these same forces -e.g., changes in social, political, environmental and health service conditions contribute to emerging/escalation of comorbidities and multimorbidities clustered in population groups left behind and/or those who have newly entered. Deep learning techniques will be used to advance knowledge about the process of syndemic development (syndemogenesis) and trends in the synergistic effects on comorbidities and multimorbidities from pre- to post-COVID-19 pandemic. The findings from this research are intended to inform appropriate social and public health/health care policies and interventions to improve the health of vulnerable population groups in the San Francisco Bay Area.
Key words: Syndemic theory, health inequalities, urban decline, social determinants, San Francisco Bay Area
