Since 2014, my team has been tracking the changing landscapes of hospital-based obstetric services in urban and rural America, using American Hospital Association Annual Surveys validated by Centers for Medicare and Medicaid Services Provider of Services Files. After identifying nationwide hospital-based obstetric units each year, we continuously calculate the one-way trip driving distances in miles from each residential ZCTA centroid to the nearest hospital-based obstetric unit’s location per hospital longitude and latitude using the MapQuest application. There are many potential routes driving between each residential ZCTA centroid to the nearest hospital-based obstetric unit. My team has documented driving distances in miles, driving time in minutes, and transit using public transportation in minutes. The most updated data as of December 2022 was based on the U.S. hospital obstetric supply on November 2020. 

Funding sources: USC Office of Vice President for Research & Federal Office of Rural Health Policy (U1C45498)

The Rural and Minority Health Research Center (RMHRC)'s mission is to illuminate and address the health and social inequities experienced by rural and minoritized populations to promote the health of all through policy-relevant research and advocacy. Currently, I am serving as a Deputy Director at the RMHRC. We conduct methodologically rigorous and policy-relevant research to provide a clear picture of health status, health care needs, and health services utilization among rural and minoritized populations; Investigate policies aimed at improving health and reducing barriers to care among rural and minoritized populations, especially persons experiencing poverty; and Provide expert advice to national, state, and local governments and constituency groups to empower policy development and advocacy. Our work can be found here: https://www.ruralhealthresearch.org/centers/southcarolina/publications.

Funding source: Federal Office of Rural Health Policy (U1C45498)

As part of the NIH COVID-19 Social, Behavioral, and Economic Consortium , my team is conducting a longitudinal, real-world data study, using recurring national electronic health records (EHR) data [National COVID Cohort Collaborative (N3C)] and integrated statewide population-based data in South Carolina and Florida which complement the overrepresentation of urban populations in N3C. With multiple innovative approaches using common data modeling, multi-level imputation for missingness, and Bayesian statistics simulation methods, this study aims to: 1) investigate the impact of the COVID-19 pandemic on maternal care access, quality, and maternal and birth outcomes by maternal race/ethnicity and rural/urban residence; 2) examine whether perinatal telehealth uptake mitigates the pandemic's effects on disparities in maternal care access, quality, and outcomes by maternal race/ethnicity and rural/urban residence; and 3) assess how state-level telehealth policies (relaxation for originating sites, reimbursements for store-and- forward services, remote patient monitoring, and provider expansion) – relate to perinatal telehealth uptake by race-ethnicity and rural/urban residence. We will also 4) develop a stochastic simulation model to predict long-term changes in maternal care access, quality, outcomes, and expenditures of maternity care, with and without each respective state telehealth policy. Our overarching goal is to advance the understanding of the three-way intersections among the COVID-19 pandemic, state telehealth policy, and perinatal telehealth uptake on health disparities facing vulnerable maternal populations – rural and racial/ethnic minority women. This study will be carried out over 2022-2017. 

Funding Source: NIH/NICHD (1U01HD110062)

This study used a socio-ecological framework and employed a concurrent triangulation, mixed methods study design to achieve three specific aims: 1) to examine the impacts of the COVID-19 pandemic on racial/ethnic disparities in MMM; 2) to examine and explore how the key features of social contexts have contributed to the widening of racial/ethnic disparities in MMM during the pandemic (Aim 2a) and identify distinct mediating pathways through maternity care and mental health (Aim 2b); and 3) to examine the role of social contextual factors and identify protective factors for racial/ethnic disparities in pregnancy-related, long-standing morbidities using machine learning algorithms. My role is to lead Aim 2a - assess how the key features of social contexts have contributed to the widening of racial/ethnic disparities in MMM during the pandemic. Our results highlight the need to consider historical structural racism in a racially, socioeconomically, and geographically diverse population of pregnant women. This project will be completed by May 2023. 

Funding source: NIH/NIAID (R01AI127203-05S2)

This study aims to 1) use natural language processing, text mining and machine learning techniques to extract just-in-time polysubstance use disorder (PSUD) data among pregnant women on Twitter, and then explore their exposure and treatment, communication patterns, risky health perceptions, sentiment, and maternal and fetal health outcomes associated with polysubstance use in pregnancy; 2) analyze electronic health records to identify clinical, sociodemographic and geographic factors to accessibility to PSUD treatment; and 3) explore individual- and community-level stressors for adverse birth outcomes associated with prenatal PSUD through expert consultation and content analysis.  These results will serve to inform clinicians, public health officials, and policy makers on polysubstance use prevention and intervention programs and policy changes among this vulnerable group.

Funding Source: USC Big Data Health Sciences Center Pilot Project 2022-2023