Assessing the Impact of Neighborhood Trees and Pre-Term Birth in Philadelphia, PA
Kelli is a 1st-year Epidemiology PhD student at the University of Pennsylvania, with an interest in social epidemiology. She has a BS in Biological Sciences from Georgia State University, and an MPH from Morehouse School of Medicine. Her research experience includes working with minority, underserved, vulnerable, and/or rural populations in Georgia. She has worked in the multidisciplinary field of developing and implementing research projects and programs in the health fields of lupus, cancer, and various epidemic rate chronic diseases such as diabetes, hypertension, and cardiovascular disease across the state of Georgia.
Her main research interest is in chronic diseases and how social determinants of health impact underserved and minority populations. Her career goal is to address social health disparities in morbidity and mortality rates of chronic diseases in the United States; specifically, with studying comorbidities related to autoimmune diseases in underserved populations with independent research in academia. She hopes to cultivate spaces and opportunities to diversify the representation at every level in science, so that researchers reflect their research population.
Adverse maternal and fetal outcomes in the United States continue to climb, with one important outcome being preterm birth. We explore the effect of tree coverage within neighborhoods of Philadelphia and risk of pre-term birth. Preterm birth is defined as a baby being born before 37 weeks gestation. We created a Directed Acyclic Graph (DAG) for both individual and neighborhood factors important in our exposure (trees) and outcome (pre-term birth) processes. We focused on neighborhood-level analysis with data obtained from OpenTreeMap, OpenData Philly, and Penn Medicine’s Electronic Health Record. Trees were mapped per person per Census tract, which was then linked to Penn’s preterm birth data based on the proportion of preterm deliveries, and separately linked to public lead exposure data. We constructed a spatial regression model to explore the correlations using the R software language. Results showed that neighborhood-level risk factors that were significant in increasing the risk of pre-term birth were the proportion of the number of individuals per neighborhood identifying as Black (aOR=1.03, p-value<0.001), highest educational attainment of high school (aOR=1.06, p-value=0.001) and marginally significant were housing violations (aOR=0.99, p-value=0.09), and violent crime (aOR=1, p-value=0.09). While the number of trees was not significant in the adjusted model, we did find a correlation between lead(pb) exposure, the proportion of preterm birth and both the number of trees and number of trees per person. Future work should include tree species, assess the individual level contributors, and include neighborhood deprivation rather than individual neighborhood characteristic variables.
Keywordsmaternal outcomes, fetal outcomes, preterm birth, trees, lead
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