2023 Undergraduate Research Symposium: Taral Patel and Sophie Habashy
Asthma negatively affects the quality of life of those who suffer from it, with the acknowledgment that the African American/Black community is disproportionately afflicted by the disease. Research suggests that increased atmospheric particulate matter among other pollutants is correlated with higher rates of reported asthma symptoms and hospitalizations.
The present study describes the methodology of cross-referencing Environmental Protection Agency (EPA) reported daily particulate matter 2.5 (PM2.5) with participant laboratory visits with the goal of allowing researchers to assess the effect of this pollutant with concurrent reports of psychosocial and asthma measures. Part of this data comes from the Asthma in the Lives of Families Today study which investigated psychosocial predictors of asthma-related health among African American children from the Detroit area between 2011-2018. Determining PM2.5 levels on lab visit days was conducted in multiple steps. Firstly, participants' zip codes were used to determine their geographical census block (i.e., GEOID). Next, GEOIDs were matched to the most closely located EPA PM2.5 sites of the twelve stationed in Wayne County.
After the EPA site was matched to the participant, the PM2.5 measurement on the day of each participant's ALOFT visit was identified. This methodological approach provided a clear air pollution reference on the date of lab visits, allowing ALOFT researchers to investigate its specific and subsequent impact on child asthma health.
Wayne County PM2.5 Levels on Lab Visit Days Among Youth With Asthma: A Methodological Approach
1. Wayne County PM2.5 Levels on Lab Visit Days
among Youth with Asthma:
A Methodological Approach
Taral Patel1*, Sophie Habashy1*
Jacqueline Rodriguez-Stanley1, MA
Samuele Zilioli1,2, PhD
*Denotes equally contributing first authors
1Department of Psychology,
2Department of Family Medicine and Public Health Sciences, Wayne State University
2. Asthma negatively affects the quality of life of those who suffer from it, with the
acknowledgement that the African American/Black community is
disproportionately afflicted by the disease. Research suggests that increased
atmospheric particulate matter among other pollutants is correlated with higher
rates of reported asthma symptoms and hospitalizations. The present study
describes the methodology of cross-referencing Environmental Protection Agency
(EPA) reported daily particulate matter 2.5 (PM2.5) with participant laboratory
visits with the goal of allowing researchers to assess the effect of this pollutant
with concurrent reports of psychosocial and asthma measures. Part of this data
come from the Asthma in the Lives of Families today study which investigated
psychosocial predictors of asthma-related health among African American children
from the Detroit area between 2011-2018. Determining PM2.5 levels on lab visit
days was conducted in multiple steps. Firstly, participants’ zip codes were used to
determine their geographical census block (i.e., GEOID). Next, GEOIDs were
matched to the most closely located EPA PM2.5 sites of the twelve stationed in
Wayne County. After the EPA site was matched to the participant, the PM2.5
measurement on the day of each participant’s ALOFT visit was identified. This
methodological approach provided a clear air pollution reference on the date of lab
visits, allowing ALOFT researchers to investigate its specific and subsequent
impact on child asthma health.
Abstract
3. Background
▪ Air pollutants such as particulate matter 10 (PM10), particulate matter 2.5, (PM2.5) and ozone
can exacerbate asthma symptoms (Ostro et. al., 2001; Huang et al., 2021)
▪ Harsher weather conditions, greater precipitation, higher pollen, and changes
in vegetation attributed to seasonal peaks and harsher climates, are associated with greater
asthma aggression (D'Amato & Cecci, 2008)
▪ Detroit has comparatively higher rates of PM2.5 relative to expected amounts in other urban
areas (Milando et al., 2016).
▪ Understanding environmental contaminants is especially important for the African American
population who are disproportionally affected by asthma-related morbidity (Huang et al.,
2021)
▪ This study focuses on PM2.5 recorded from the Environmental Protection Agency (EPA)
monitoring sites in Wayne County (EPA, 2018).
▪ Connecting one's physical environment with their psychosocial processes are important to
predict asthma symptomology.
Introduction
4. Research Aim
▪ Connect government pollutant data with the date youth with asthma participated in a
research study.
Introduction
Supplemental Hypotheses
▪ There will be higher PM2.5 in peak seasons
compared to when seasons are not in peak
▪ Higher PM2.5 will be associated with greater
asthma exacerbation measured by lower
peak flow values and higher self-reported
quantity and severity of asthma symptoms
5. Participants & Procedure
Demographic Distribution
• N = 144; 45.8% female
• M = 12.72, σ = 1.66, age range 10 - 17
• 93.1% Black/African American
ALOFT Study Overview (2011-2018)
• Multi-wave study on 297 families that examined
psychosocial and biological factors on asthma
• Consisted of lab 1 visit, 4-day daily assessment
period, and a final at-home visit 1 week later
Methodology
6. Measures
Peak Flow
• Measures lung capacity and function operationalized as peak expiratory flow rate (PEFR)
• Measured 3 times before bed each night.
• Best nightly reading used for analyses.
• *M = 306.93 L/min, σ = 82.76 L/min; range = 115 -575 L/min
Asthma Symptom Frequency
• Number of symptoms experienced each night
• *M = 10.29, σ = 10.53; range = 0.00 - 45.00
Asthma Symptom Severity
• Self-reported severity/ intensity of wheezing, chest tightness, shortness of breath, coughing
• Scale: 0 (none), 4 (severe)
• *M = 1.33, σ = 0.39; range = 1.00 - 2.60
*Note: All values were calculated as an average across the 4-day at-home daily assessment period
Methodology
7. Measures (cont.)
Seasons
• Date of lab visit was used to code each
participant as visiting during season peak (1)
or non-peak (0)
• Peak Seasons
• Winter: Mid- January through February
• Summer: July through mid-August
• Spring: Mid-April through May
• Fall: October through mid-November
• Non-Peak Seasons
• Remaining months
Covariates
• Height, sex, age, race, medication use,
smoking
Methodology
8. Methodological Approach
1. EPA air pollutant monitoring site’s latitude & longitude
used to identify Wayne County zip-code
2. Cross-referenced zip-codes of participants and EPA
monitoring sites and assigned an EPA monitoring site to
each participant (see Wayne County PM2.5 Map)
3. PM2.5 values spreadsheet was downloaded from EPA for
each year of study (2011- 2028)
4. The PM2.5 value on the day of the participant's Lab 1
visit was recorded (Note: If this value was unavailable, the
value on the closest date before Lab 1 visit was used)
5. This methodological approach was repeated for all
participants
Results
9. Wayne County PM2.5 Map
Results
The resulting Wayne
County map depicts the
locations of the various EPA
PM2.5 monitoring sites used
to collect data from to
match with the participants
within the same or adjacent
zip-codes
10. Supplemental Results
Results
Table 1. Mean Difference of PM2.5 between Season’s Peaks
Table 2. PM2.5 predicting Asthma Outcomes
Although not statistically significant,
there is an evident increase in PM2.5
during peak seasons
PM2.5 was not associated with peak
flow values, asthma symptom
frequency, or asthma symptom severity
11. Discussion
Significance
• The constructed methodology of pin-pointing the residence of participants and
collecting data from the near monitoring site is a process that can be repeated for future
studies and methodologies
Limitations
• The PM2.5 values taken from publicly available EPA data had inconsistencies in
documentation
• Limited availability of PM10 & ozone data, preventing options to study other pollutants
• Small sample size contained within one county among one demographic
(Black/African American youth)
• Considering PM2.5 estimates over longer periods of time (i.e., 6 months) may be a
more accurate exposure composite
Future Directions
• Investigate the time in between air pollutant exposure and occurrence of asthma
symptom exacerbation
• Determine the psychosocial factors that may buffer air pollutants impact on asthma
symptoms
• Further investigation of association between peak seasons and asthma outcomes
12. Acknowledgements
We would like to extend our many thanks and acknowledgements to Jacqueline
Rodriguez-Stanley, MA and Dr. Samuele Zilioli, PhD for their time, guidance,
mentorship, and assistance throughout our research process and for their support
as we pieced this project together.
We would also like to thank the entire ALOFT team for supplying us with the
data used in this project.
We could not have accomplished this without the work of many dedicated
researchers that worked to collect this data.
Contacts
Taral Patel: taralpatel@wayne.edu
Sophie Habashy: sophiehabashy@wayne.edu
13. References
D'Amato, G., & Cecchi, L. (2008). Effects of climate change on environmental factors in respiratory allergic
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2222.2008.03033.x
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March 30, 2023, from https://www.epa.gov/outdoor-air-quality-data/download-daily-data
Environmental Protection Agency. (2022). Particulate Matter (PM) Basics. Environmental Protection Agency.
Retrieved April 6, 2023, from https://www.epa.gov/pm-pollution/particulate-matter-pm-basics
Huang, W., Schinasi, L. H., Kenyon, C. C., Moore, K., Melly, S., Hubbard, R. A., Zhao, Y., Diez Roux, A. V.,
Forrest, C. B., Maltenfort, M., & De Roos, A. J. (2021). Effects of ambient air pollution on childhood asthma
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https://doi.org/10.1016/j.envres.2021.110955
Milando, C., Huang, L., & Batterman, S. (2016). Trends in PM2.5 emissions, concentrations and apportionments in
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https://doi.org/10.1016/j.atmosenv.2016.01.012
Ostro, B., Lipsett, M., Mann, J., Braxton-Owens, H., & White, and M. (2001). Air pollution and exacerbation of
asthma in African-American children in Los Angeles. Epidemiology, 12(2), 200–208.
https://doi.org/10.1097/00001648-200103000-00012
Policy Map . (2023). Dig deeper. PolicyMap. Retrieved March 30, 2023, from
https://www.policymap.com/newmaps#/