Research

Decisions Modeling of Universal Adolescent Depression Screening in Primary Care

My dissertation—which focuses on measuring the cost-effectiveness of universal mental health screening for adolescent depression—would likely have policy implications for setting screening practices for pediatricians and other family physicians, who are in a strong position to advocate for youth’s mental health.

One-half of Americans are diagnosed with a mental illness at least once, with most symptoms presenting by age 14-year-old. Mental health is an understudied field, and even more so when it comes to programs tailored to multicultural adolescent populations. A cost-effectiveness analysis could be useful for comparing the benefits and harms of multiple options for screening and treating depression.

Family-Centered Care of Telemedicine Use for Child Health Services

My work during my T32 Primary Care Research Post-Doctoral Fellowship continues to grow in the way of patient-centeredness, language justice, and data equity to support an increasingly diverse US population. I am expanding my qualitative and quantitative skills through the application of an innovative approach called decisions mental modeling to investigate how parents make decisions around using primary telehealth services for treating common respiratory illnesses among children. We are now developing a discrete choice experiment that will examine parental preferences and thresholds for making tradeoffs around the abovementioned topic. As telemedicine use has proliferated during the post-COVID pandemic era, there is a strong potential for telehealth and digital health technologies to overcome structural barriers typically presented in health care services for the benefit of underserved populations.

Data Disaggregation for Data Justice and Health Equity

I am a scholar-advocate for language and data equity, with a focus on the need for critical approaches to collecting and using disaggregated data in marginalized communities, including by race or ethnicity, gender or sexuality, ability (disability), language preferences, and more. By using data disaggregated by Asian ethnic groups, we published a manuscript reporting that employment status as a protective factor of good health was stronger in whites, compared to Asians especially in Chinese, Hmong, Koreans, Japanese, and Filipinos (Journal of Racial and Ethnic Health Disparities). In two different papers, we found the COVID-19 pandemic has contributed to a rise in anti-Asian sentiment and discriminatory incidents and has illustrated the major gaps in data available to disentangle the health and social concerns facing Asian communities in America (Frontiers in Public Health, Current Epidemiology Reports). Our research demonstrates that aggregating diverse ethnic groups into one Asian race category masks important distribution of disparities across these populations, making it challenging to deliver effective health care and interventions.