Grantee Research Project Results
2018 Progress Report: Southeast Wisconsin Interdisciplinary Study of Childrens Health, Ecological Exposures and Social Environment
EPA Grant Number: R839278Title: Southeast Wisconsin Interdisciplinary Study of Childrens Health, Ecological Exposures and Social Environment
Investigators: Magzamen, Sheryl , Dilworth-Bart, Janean E , Wilson, Ander , Carter, Ellison , Jathar, Shantanu
Current Investigators: Magzamen, Sheryl , Carter, Ellison , Jathar, Shantanu , Wilson, Ander , Dilworth-Bart, Janean E
Institution: Colorado State University , University of Wisconsin - Madison
EPA Project Officer: Hahn, Intaek
Project Period: January 1, 2018 through December 31, 2020 (Extended to December 31, 2022)
Project Period Covered by this Report: January 1, 2018 through December 31,2018
Project Amount: $600,000
RFA: Using a Total Environment Framework (Built, Natural, Social Environments) to Assess Life-long Health Effects of Chemical Exposures (2017) RFA Text | Recipients Lists
Research Category: Human Health
Objective:
Objectives of the Research
Objective 1: Develop community- and
individual-level profiles for social, physical, and chemical
environments and determine the relative associations of these exposure
profiles with respiratory, neurodevelopmental, and injury-related
outcomes in preschool children in Southeast Wisconsin.
Objective
2: Evaluate the role of community-level social and physical
environmental profiles on modification of the effect of chemical
exposures on children's respiratory and neurodevelopmental-related
outcomes.
Objective 3: Evaluate the role of residential mobility
on respiratory, neurodevelopmental, and physical health in preschool
children in Southeast Wisconsin.
Progress Summary:
Accessibility:The researcherswill use deep/transfer learning on "UC Merced Land Use" dataset, includes aerial optical images, with low-level characteristics for evaluating the land use mix for every CBG. The source of UC Merced dataset is from large optical images (RGB color space) of the US Geological Survey, taken over various regions of the United States. The dataset is classified into 21 land use classes, 100 for each class. The class representatives of the UC-Merced dataset are (a) agricultural; (b) airplane; (c) baseball diamond; (d) beach; (e) buildings; (f) chaparral; (g) dense residential; (h) forest; (i) freeway; (j) golf course; (k) harbor; (l) intersection; (m) medium residential; (n) mobile home park; (o) overpass; (p) parking lot; (q) river; (r) runway; (s) sparse residential; (t) storage tanks; (u) tennis court. The studymotive is to predict/extract the above 21 classes from satellite images obtained or downloaded from google static map for every CBG in Milwaukee and Racine counties. The study will develop a scoring index for every CBG based on the outcome /percentage class prediction of our satellite images fed into the transfer learning applied. The researcherswill be using Inception IV architectural network to process the built environment features in our transfer learning approach.
Neighborhood density:The studywill use the google places of interest (POI) API to download POI data for every CBG in the two counties. The researcherswill carefully select a comprehensive list of all the places that we believe can influence an individual's behavior and physical activity frequency. These places of interest will be extracted in every CBG in the two counties by using a nearby search google places of interest (POI) API. The research will sum the number of places extracted from the API dataset for each CBG and then create a scoring index for evaluating the neighborhood density.
Individual Housing variables: The housing variables or characteristics have been selected from the tax parcel data for all the residential homes in the city of Milwaukee. The researcherswere able to estimate the number of residential homes with or without the selected housing variables in each CBG. Some of the variables are homes with/without an air conditioner, fireplace, median housing age, parking garage, housing types (single family home, multi-family homes or manufactured/mobile homes), etc. The studyplans to reproduce the same method for all the other cities in Milwaukee and Racine counties. The studyisstill in the process of securing the tax parcel data for the whole county of Milwaukee and Racine. A scoring approach similar to the ones used in (1) & (2) above will be used for allotting scores for each CBG.
Walkability score:The researchershave produced R-code that can be combined with an API key to retrieve walkability score for every longitude and latitude queried into www.walkabilityscore.com. The website has been used/verified as a reliable website for different scientific/academic research purposes.
Distance from residential homes to nearby major roads: The studyused ArcGIS to estimate the distance from every residential home listed in the tax parcel data for the city of Milwaukee to their nearby major roads. The researchfocuses mainly on the three main feature class A, B and C roads as classified under the U.S. Census Feature Class Code (Maybe it might be a useful dataset with the chemical community model from CMAQ).
Neighborhood Chemical Environment Factors
A recent version of the Community Multiscale Air Quality (CMAQ, version 5.0.2) was run over a domain that covered the states of Colorado and Utah and parts of Wyoming, Idaho, New Mexico, and Arizona. The model was run at a horizontal resolution of 4 km with emissions and meteorological inputs for the year 2011. The particular domain was selected to familiarize the team, and particularly the graduate student who will be performing the air quality modeling simulations on this project, to the use and applicability of the CMAQ modeling system for thisproject. Activities included preparation of model inputs for base and nested grids, installation, setup, and simulations with CMAQ on a Linux server, and developing codes to post-process, visualize, and analyze CMAQ output. The researchersare currently in the process of evaluating model predictions of air pollutants (e.g., CO, NO2, O3, PM2.5, OC, EC/BC) against measurements made at long-term sites (e.g., AQS, CSN, IMPROVE) and during intensive field campaigns (e.g., ECHO). Having become familiar with the CMAQ modeling system, we are in the process of developing and acquiring emissions and meteorological inputs for the study domain relevant to this project, i.e., the Southeastern Wisconsin (Milwaukee and Racine Counties) region. These will be developed in collaboration with Zac Adelman and his team at Lake Michigan Air Directors Consortium. The inputs will be at a horizontal resolution of 4 km and 1.33 km with baseline emissions for the year 2016. Over the next year, we will focus our efforts to two tasks. First, we will develop high resolution (~1.33 km) predictions for a suite of air pollutants over the Milwaukee-Racine Counties and evaluate model predictions against measurements made at long-term monitoring sites. Second, we will work towards incorporating the TwO-Moment Aerosol Sectional (TOMAS) model of Adams and Seinfeld (2002) to simulate the evolution of the aerosol size distribution in CMAQ. The TOMAS model will replace the existing modal approach in CMAQ and provide key size-resolved information about PM2.5. This second task is being planned in collaboration with Dr. Benjamin Murphy at the US EPA.
Neighborhood Social Factors
Crime Data. In addition to data available from the US Census, the researchersdownloaded data from the National Incident-Based Reporting System (NIBRS) made available by the Inter-university Consortium for Political and Social Research (ICPSR) at the University of Michigan. NIBRS data are collected by the Federal Bureau of Investigation and reported through the Uniform Crime Reporting program. ICPSR takes the NIBRS data and creates a user-friendly dataset for researchers. The NIBRS data are incident-level data for crimes reported at the jurisdiction (local, state, and federal) level and include information on 22 offense categories. These data are available from 1991 to 2016. (Reference 1) An additional line of inquiry to understand the impacts of childhood lead exposure on later life outcomes was identified. The STELLAR data will be used to determine the extent to which disparities in childhood exposures to lead contribute to adult involvement in the Wisconsin Circuit Court system. The Wisconsin Circuit Court Access database provides court records search includes personal identifiers, including name and birthdate, and is accessible by the public. The STELLAR database will be linked to the court data by these personal identifiers, and an analysis of the relationship between blood lead levels and court involvement will be conducted.
Outcomes
- IRB Completed
- QAPP Completed
- Interinstitutional Authorization Agreement (IAA) between University of Wisconsin-Madison and Colorado State University near completion (final signatory authority expected May 2019).
- Two team hires completed: Postdoctoral Fellow Jeremy Auerbach and Graduate Research Assistant Charles He. Dr. Auerbach received his PhD in Geography at the University of Tennessee in June 2018. His background is in applied mathematics and network analysis for neighborhood development. Dr. Auerbach is jointly supervised by Drs. Magzamen and Wilson. Mr. He has an MS in Mechanical Engineering from Carnegie Melon University. As Dr. Jathar's doctoral advisee, he will be responsible for implementation of the CMAQ model for the study area.
- Oluwatobi Oke, a graduate student in Civil and Environmental Engineering, is completing his PhD with data from this project under the supervision of Dr. Carter.
- 1 Annual EPA STAR grantees conference attended
- Project Co-PI Ellison Carter was selected as part of the Harvard TH Chan School of Public Health JPB Environmental Health Fellows Program. The JPB Fellows are working across disciplines on research designed to address social and environmental health disparities that disproportionately impact vulnerable communities. These interdisciplinary collaborations among academia and Federal offices demonstrate a new dynamic and impactful way to implement science to complex social and environmental health challenges. (https://ehfellows.sph.harvard.edu/meet-the-fellows/)
Future Activities:
Future Research Activities:
1. Complete DUA with Wisconsin Department of Health Services for use of STELLAR Lead data, WIC data and Medicaid data.
- Link health data with chemical environment, built environment, and social exposure data.
- Travel to Milwaukee, WI to conduct Milwaukee Historical Neighborhood tour with David Reimer, former County executive committee.
- Redevelop timeline based on delays in completing DUA with WDHS.
- Resubmit all papers listed under Publications section.
- Develop improved methodological approaches for missing counterfactual population for lead and crime study.
Future Papers Planned
- Residential mobility among Medicaid children (led by S Magzamen)
- Use of machine learning algorithms to predict indoor air quality (led by O Oke and E Carter)
- Intentional and unintential Injury by neighborhood among Medicaid recipients (led by J Auerbach)
- Characterization of social, chemical and physical environments of Southeastern Wisconsin (led by Team)
- A multilevel approach to childhood respiratory health and environmental factors (led by S Magzamen).
References:
1. Inter-university Consortium for Political and Social Research [ICPSR]. Uniform Crime Reporting Program Data Series. https://www.icpsr.umich.edu/icpsrweb/ICPSR/series/57. Published 2018. Accessed July 23, 2018.
Journal Articles:
No journal articles submitted with this report: View all 8 publications for this projectSupplemental Keywords:
Medicaid, lead, children, injury, respiratory, neurocognitive, coarsened exact matching, machine learning, indoor air quality, residential mobility, WICProgress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.