SPUR 2022: Impacts of Ambient Air pollution on Pre-Term Birth and Associated Healthcare Costs

NOTE: This project is funded by a grant from the National Institute of Environmental Health Sciences (PIs: Sara Grineski and Tim Collins). In addition to being part of SPUR, it is also part of the HAPPIEST program. Applicants must be University of Utah students who identify in one or more of the following ways (defined by the National Institutes of Health): Blacks or African Americans, Hispanics or Latinxs, American Indians or Alaska Natives, Native Hawaiians, and other Pacific Islanders. Two students will be selected to work on this project together.

Background

Exposure to ambient and indoor air pollution, particularly PM2.5, has been associated with multiple adverse birth outcomes including pre-term birth (PTB), defined as birth with a gestational age of less than 37 weeks, (Ghosh,et al., 2021; Stieb et al., 2012) and these effects may depend on the timing of exposure during gestation (Chen et al., 2021). Personal, home and community factors may change the relationship between ambient PM2.5 exposure and the risk of pre-term birth (Lu, et al., 2021; do Nascimento et al., 2022). However, it is difficult to disentangle the household and parental factors from intrinsic genetic factors that affect fetal growth. Further, impacting policy often requires arguments based on cost and the cost of pre-tem birth is significant (Waitzman, Jalali, & Grosse, 2021). While better estimates of the associations of exposure specific risks and timing is critical, so is information on the costs resulting from the adverse health effects caused by exposure to air pollution. The Specific Aims for this project are:

  1. Estimate the association between trimester-specific exposure to PM2.5 and near-road exposures and the risk of PTB in Utah.
  2. Estimate the healthcare-related costs associated with the excess number of PTB in Utah.

Daily air quality predictions at a 1km2 resolution will be linked to pregnant women throughout pregnancy and used to assess the relationship between trimester specific exposures and the risk of PTB.

Student Role

We will design this research experience so that each student has the opportunity to gain skills and execute a variety of research methods, primarily analyses of air quality and health outcome data using GIS and statistical software, under the supervision and guidance of both the Graduate and Faculty mentors. The students will gain skills and experience using GIS, analyzing air quality data, assessing the relationship between exposure and pre-term birth using an ecological study design, and creating publication-quality graphs and tables. Students with GIS knowledge will learn more advanced techniques while those with little or no experience will gain beginning skills. This project will include the following:

  1. Examine and map predicted air quality levels using the Harvard Ensemble model results.
  2. Estimate near-road exposures using major roads and traffic density estimates.
  3. Generate PM2.5 exposure estimates by linking air quality measures to each study subjects’ residential location.
  4. Identify and review current literature on this topic.
  5. Conduct an ecologic epidemiologic study linking the prevalence of pre-term birth with air quality exposures during various time periods during pregnancy.
  6. Summarize the methods and results in tables and figures; produce a scientific poster for the OUR Summer Symposium.

Student Learning Outcomes & Benefits

By the end of this research experience each student will be able to a) conduct a literature search and retrieve published manuscripts, b) examine and visualize using graphs and maps air quality data, c) develop exposure measures using direct estimates as well as using proximity models from point and line sources, d) calculate the association between pre-term birth prevalence and exposure to air pollutants, and e) create a publication-quality research poster. GIS and analysis skills will help students in the advanced classes and give them useful skills to help them with their research and in obtaining employment as a data analyst.

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James VanDerslice
Professor

Family & Preventative Medicine
School of Medicine

In mentoring learners I believe that a positive attitude is paramount and all feedback must be constructive. Everyone comes with different skills, knowledge and motivation. I believe that in a learning environment the program should be adapted to meet the learner’s current level of experience, knowledge and skill. To me, each experience should be viewed in terms of what was learned. At times we learned because we found a great result. At other times we learned because a given technique didn’t work. Finally the most important advances in learning, and in becoming a scholar, lies in the relationships formed, through the experience of working as a team, having common goals, working through problems with good colleagues.

To make this process work I hold weekly team meetings, and a separate weekly meeting with the graduate student mentor. At the team meeting we review the objectives, progress and products and provide feedback on each aspect of the work. We problems solve and set goals for the next week.