PM25 and Asthma_Spiker_Final

Running head: PM2.5 AND ASTHMA IN KENT COUNTY, MI 1
Fine Particulate Matter Concentration and Adult Asthma Prevalence
in Kent County, MI from 2005-2012
Brenton L. Spiker
Grand Valley State University

PM2.5 AND ASTHMA IN KENT COUNTY, MI 2
Abstract
Asthma morbidity is associated with exposure to fine particulate matter concentration. To our
knowledge, there has not been a study that has assessed the association between PM2.5 and same-
year asthma prevalence, or prior-year PM2.5 concentration and subsequent year asthma
prevalence, in Kent County, MI. Using health information collected during BRFSS SMART
from 2005-2012, and air monitor data from AirData from 2004-2012, we conducted a cross-
sectional study to determine if there was an association between asthma prevalence and annual
PM2.5 concentration in Kent County. After adjusting for meteorological, health, and
demographic confounders, we identified a 35.0% increase in the prevalence of asthma for a
10µg/m3 increase in same-year PM2.5 concentration, although the findings were not statistically
significant (PR = 1.35, 95% CI [0.96, 1.90], p = 0.085). Findings were limited by the use of
secondary data, and missing data for some potential confounders including COPD, and
race/ethnicity, but the suggestive association identified highlights the importance of low ambient
PM2.5 concentration with adult asthma prevalence in Kent County, MI. Advocacy groups and
policymakers may benefit from these findings, to ensure low ambient PM2.5 concentrations are
maintained in Kent County, MI. Analysis of seasonal, climate, and meteorological changes,
additional health, and demographic confounders, as well as the geospatial distribution of PM2.5, is
recommended for future research.
Keywords: PM2.5, adult asthma prevalence, Kent County, Michigan

Fine Particulate Matter Concentration and Adult Asthma Prevalence
in Kent County, MI from 2005-2012
Asthma is a respiratory disease that affects both children and adults, across the globe.
According to the 2013 National Health Interview Survey, 16.5 million adults, and 6.1 million
children in the United States (U.S.) suffered from asthma. This equates to approximately 7.0%
and 8.3% of the U.S. population, respectively (Centers for Disease Control and Prevention
[CDC], 2015a). The prevalence of asthma shifts from males to females from adolescence into
adulthood. Under 18 years, approximately 9.3% of boys and 7.3% of girls have been diagnosed
with asthma, whereas in adults (18+ years), approximately 5.2% of men, compared to 8.6% of
women, have asthma (CDC, 2015a). Furthermore, asthma disproportionately affects black
Americans, compared to whites, Hispanics, and other race/ethnicities, and is highest for low-
income persons under 100% of the federal poverty level (CDC, 2015a).
Asthma attacks occur when a person with asthma is exposed to various “triggers,”
including dust mites, cockroaches, tobacco smoke, pets, and environmental exposures such as air
pollution, although individuals respond differently (CDC, 2015c). Research has identified that
the cause of asthma is multifactorial, and there is evidence that air pollution plays a part in both
the formation of asthma, and morbidity or exacerbation of asthma symptoms, however there are
many components of air pollution (Burra, Moineddin, Agha, & Glazier, 2009; Canova et al.,
2012; Delfino et al., 2014; Laurent et al., 2008; Lemke et al., 2014; Slaughter, Lumley, Sheppard,
Koenig, & Shapiro, 2013; Young et al., 2014).
One component of air pollution, particulate matter (PM), is a category composed of
numerous different particles and liquid droplets. These subcomponents of PM include organic
chemicals, acids, metals, and dust, among others (EPA, 2015a). PM is further classified by the

aerodynamic diameter of the particle or droplet. Particles less than 10µm in aerodynamic
diameter are classified as coarse particulate matter (PM10), and particles less than 2.5µm in
diameter are classified as fine particulate matter (PM2.5) (EPA, 2015a).
The larger diameter particles, PM10, have been associated with increased asthma
prevalence, increased frequency of inhaler use, and increased emergency department visits for
asthma-related symptoms (Canova et al., 2012; Jacquemin et al, 2012; J. Kim, Kim, & Kweon,
2015; Laurent et al., 2008; Lemke et al., 2014; Qiu et al., 2012; Slaughter et al., 2003).
However, results across studies are inconsistent. Lemke et al. (2014) studied PM10 on the border
of Detroit, Michigan and Windsor, Ontario and found an association with PM10 and asthma in
Ontario, but not in Detroit. Although, both PM10 and PM2.5 are considered inhalable particles,
the smaller diameter of PM2.5 can travel deeper into the lungs, and even cross into the
bloodstream, which may have a profound effect on asthma development and morbidity (EPA,
2015a).
Due to its smaller diameter and ability to cross the pleural layers into the bloodstream,
PM2.5 potentially poses an even greater risk to individuals than PM10 and necessitates continued
research regarding potential health outcomes. Fine particles are four times smaller than PM10,
and less than thirty times smaller than the diameter of a human hair (EPA, 2015b). A common
source of PM2.5 is from fossil fuel combustion, which includes gasoline and diesel automobiles,
industrial factories, and energy generating facilities. Regardless of the source, many studies have
identified associations with fine particle concentration and asthma morbidity (Delfino et al.,
2014; Slaughter et al., 2003). Vegetation fires, traffic emissions, and other point sources have
been associated with asthma exacerbation, increased asthma-related hospitalizations, and
increased oral steroid use (Bui et al., 2013; Delamater, Finley, & Banerjee, 2012; Johnston et al.,

2006; Li & Lin, 2014; Malig, Green, Basu, & Broadwin, 2013; Meng et al., 2010). Furthermore,
PM2.5 has been found to increase the risk of asthma development, as well as increase the risk for
wheezing, a notable symptom of asthma (Young et al., 2014).
Despite strong connections to asthma morbidity in many studies, much like PM10 studies,
PM2.5 is not consistently identified as a component of air pollution that is associated with asthma
morbidity (Girardot et al., 2006; Nachman and Parker, 2012). For example, Malig et al. (2013)
found an increase in emergency department admissions with a 10µg/m3 increase in PM2.5 but did
not identify an association between the same increase in PM2.5 and the exacerbation of asthma.
Finally, there has been some discussion and identification of a lag-effect of PM2.5 exposure and
subsequent asthma morbidity, and the lag periods are typically assessed and analyzed from one
to fourteen days prior to exacerbation of asthma or markers for asthma morbidity (Kim et al.,
2012; Slaughter et al., 2003).
The most recent data estimated the Kent County adult asthma prevalence to be around
14.9% in 2012 (CDC, 2015b). This is higher than the asthma prevalence reported for both the
state of Michigan and the United States, reported as 11.5% and 7.0%, respectively (CDC,
2015a). To our knowledge, there has not been a study that has assessed the association between
PM2.5 and same-year asthma prevalence, or prior-year PM2.5 concentration and subsequent year
asthma prevalence, in Kent County, MI. To address these gaps, and to provide this community
and environmental advocacy groups with information regarding these potential associations, we
conducted a study to determine if there was an association between asthma prevalence and
annual PM2.5 concentration in Kent County. We hypothesized that an increase in annual PM2.5
average concentration would be associated with an increase in adult asthma prevalence, for both
same-year and lag-year comparisons.

Methods
A serial cross-sectional study design was used to examine the association between annual
county-level air pollution and prevalence of asthma in Kent County, Michigan. Asthma was
assessed using self-reported data from the Behavioral Risk Factor Surveillance System (BRFSS)
Selected Metropolitan Area Risk Trends (SMART) for Kent County. BRFSS is an annual survey
conducted by the CDC (2015b) via telephone, collecting health-related information and
preventative service usage, currently in all 50 states. Participants who responded affirmatively to
the question: “(Ever told) you had asthma?” were considered to have prevalent asthma for this
analysis. Kent County specific SMART data was available for 2005 and 2007-2012. BRFSS
data from 2006 was unavailable for Kent County and was excluded from this analysis.
Annual PM2.5 concentration and ambient maximum temperature data were from retrieved
from AirData online database for years 2004-2012. AirData is a publicly accessible database for
air monitor data from the United States Environmental Protection Agency’s (EPA) Air Quality
System data mart (EPA, 2016). The air monitor for Kent County is located in Grand Rapids, MI.
Ambient air temperature and particulate matter data are collected daily, therefore, annual
averages were calculated for the analysis. These data sets are publicly available online, therefore,
consent is not required from the individuals to access, collect, and analyze the data, per the
Grand Valley State University Human Research Review Committee.
Study Population and Variables
All respondents were asked if they had ever been diagnosed or told they had asthma at
any time in their life. During the BRFSS survey, respondents were asked a number of other
questions, some of which were related to demographic and health factors which may confound
the association between ambient PM2.5 and asthma prevalence.

Asthma covariates. Additional data were collected for a number of potential asthma
prevalence confounders. Sex of the respondent was reported as male or female, and the
respondent’s age was reported in years. Respondents were asked if they had smoked at least 100
cigarettes in their entire life, and reported as “yes,” “no,” or “don’t know/not sure.” They were
asked if they had ever been told they had diabetes and reported as “yes,” “no,” “only during
pregnancy,” “don’t know/not sure,” or “refused.”
Demographic information was also collected to estimate socioeconomic status. These
variables included annual household income from all sources, highest grade or year of school
completed for each respondent, and current employment status. Annual household income was
reported into a series of stratified income brackets which were combined into three strata for this
analysis: low (<$25,000), middle ($25,000<$50,000) and high (≥$50,000). Education was
reported as the level of grade completed, which were combined into two strata for this analysis:
less than high school (highest completed education includes grade 11 or lower), or high school
graduate (completed grade 12, GED, or higher). Employment was reported in a series of
different responses, which were combined into two strata for this analysis: employed (employed
for wages or self-employed), and not employed (all other responses). Finally, respondents were
asked if they had any type of health care coverage, including government plans. Responses
included “yes,” “no,” “don’t know/not sure,” and “refused.”
Particulate matter concentration data. Daily particulate matter concentration (PM2.5),
and ambient maximum temperature were collected from AirData database for the Grand Rapids
air monitor. Fine particulate matter was collected daily and reported in micrograms per cubic
meter (µg/m3). The ambient maximum temperature was also recorded daily, and reported in
degrees Centigrade (oC).

Statistical Analysis
Variables for the overall sample of BRFSS SMART participants from 2005 to 2012 were
summarized and described using frequencies and percents for categorical variables. For
continuous variables, the mean and standard deviation were reported.
Poisson regression was used to develop models to identify whether a difference in the
prevalence ratio (PR) of asthma was associated annual PM2.5 concentration. First, a simple
model was run to identify if there was a significant change in the PR due to PM2.5, year, and
maximum temperature, and then adjusted models were constructed to include multiple additional
covariates. These covariates, as mentioned previously, were added to the Poisson regression to
analyze their potential impact on the PR of asthma in Kent County, MI. Interactions between
PM2.5 concentration and time (year) were also tested and interpreted for statistical significance.
If the interaction term was not statistically significant, it was removed from the model. The final,
adjusted model consisted of the pre-specified factors that may confound the association between
PM2.5 and asthma prevalence. The PR, 95% confidence intervals (95% CI), and p-values were
reported, and interpreted, for each model. All statistical analyses were performed utilizing a
significance threshold of α=0.05. Additionally, the models were analyzed using a one-year time
lag period (using the PM2.5 data from the year prior the BRFSS data), to identify if there was a
significant effect on the PR. All analyses were performed using SAS v9.4 (Cary, NC).
Results
From 2005 to 2012, there were 3,721 respondents in the Kent County, MI BRFSS
SMART. Of those respondents, all 3,721 responded to the asthma question, but nine participants
responded with “Don’t Know/Not Sure,” and their responses were omitted from the analyses. Of
these respondents, 501 reported having asthma (13.5%). Participants were 37.8% male with a

mean age of 53.8 years (SD ± 17.8). Additionally, 45.8% of respondents had smoked at least
100 cigarettes in their lifetime, and 11.4% were ever told they had diabetes. Income was
reported as 25.6%, 29.3%, and 45.1%, in the low, middle, and high-income categories,
respectively. Furthermore, 94.3% of respondents reported they completed at least high school or
received their GED, 51.7% reported being employed, and 92.0% reported having any kind of
health care coverage (see Table 1). Only 1.6% of respondents reported their race/ethnicity, so
this variable was not included in the analyses.
Average PM2.5 concentration was recorded for each year, and there was an overall
decreasing trend over time. In 2005, the annual average was highest at 13.40µg/m3, and lowest
in 2011 at 9.47 µg/m3. Figure 1 shows the annual trends for both asthma prevalence and PM2.5
concentration for the study period, in Kent County. While PM2.5 appears to be decreasing, the
asthma prevalence data does not exhibit any notable trend, with a sharp peak in 2009. Similarly,
the ambient maximum temperature did not demonstrate a noticeable trend over the study period,
changing each year, with the highest annual ambient temperature recorded in 2012 at 18.42oC,
and the lowest in 2016 at 8.41oC (see Table 2).
Same-year analysis using Poisson regression did not find a significant association
between PM2.5 and asthma prevalence in a simple model without controlling for health and
demographic variables. This model only included average annual PM2.5 concentration, year, and
average annual maximum temperature. The simple model identified a 10µg/m3 increase in PM2.5
was associated with approximately 39% increase in the prevalence of asthma in Kent County, MI
(PR = 1.39, 95% CI [0.98, 1.95], p = 0.0627). When all variables were added, the association
between PM2.5 and asthma was attenuated, but was still not significant (PR = 1.35, 95% CI [0.97,
1.92], p = 0.077). Controlling for average annual maximum temperature, the age of the

respondent, diabetes, smoking, sex, annual household income, highest completed education,
employment status, and health care status resulted in a 35% increase in the prevalence of asthma
for each 10µg/m3 increase in PM2.5. The interaction between PM2.5 concentration and time was
not statistically significant (p = 0.227), and it was not included in the final model.
One-year lag analysis, using Poisson regression also failed to yield significant results.
The simple model identified a 10µg/m3 increase in PM2.5 was associated with a 12% lower odds
of asthma in Kent County, MI (PR = 0.87, 95% CI [0.73, 1.04], p = 0.122). The adjusted model,
controlling for average annual maximum temperature, age of respondent, diabetes, smoking, sex,
annual household income, highest completed education, employment status, and health care
status resulted in 12% lower odds of asthma for a 10µg/m3 increase in PM2.5 (PR = 0.88, 95% CI
[0.74, 1.05], p = 0.156) (see Table 3).
Discussion
We observed a suggestive, or borderline statistically significant association between
PM2.5 and same-year asthma prevalence after controlling for average annual maximum
temperature, the age of the respondent, diabetes, smoking, sex, annual household income, highest
completed education, employment status, and healthcare. Controlling for multiple variables did
improve the model fit considerably over the simpler model, but failed to be statistically
significant. All variables available for analysis were maintained in the models to reduce bias as
much as possible in this study.
We hypothesized that an increase in PM2.5 would be associated with an increase in the
adult asthma prevalence for Kent County, and we were correct for the same-year analysis,
despite not being significant. As of the most recent data, Kent County has a higher prevalence of
adult asthma, approximately 15%, than both the state of Michigan and the United States, 11.5%

and 7.0%, respectively (CDC, 2015a). Noting the suggestive association between PM2.5 and
adult asthma identified in this study, it is important to ensure PM2.5 concentrations remain low in
the area, and potentially reduced further. These findings, along with additional research, may
provide evidence for strengthening air quality standards locally, or statewide.
To our knowledge, this is the first study to analyze PM2.5 and adult asthma prevalence in
Kent County. Strengths of this study include the sample and timeframe. This research was
conducted using a large, representative sample from BRFSS SMART for Kent County, MI, over
a time span of eight years, which provided over 3,700 participants with extensive demographic
and health data. Additionally, the Grand Rapids air monitor data was recorded daily, providing
us with accurate information for analysis. Due to the access to each of these datasets, we were
able to adjust for numerous health, demographic, and environmental confounders, in order to
reasonably reduce bias in the models presented.
There were some limitations to this study, which were due to missing data, and study
design. First, we cannot analyze time trends due to the cross-sectional nature of the data.
Behavioral Risk Factor Surveillance Survey data is collected throughout the year and reported
only after all health questionnaire surveys were completed. This limits the ability to identify
trends in asthma, and other diseases, as well as analysis of asthma prevalence with respect to
seasonal changes, or even daily changes, in particulate matter concentrations. Causation cannot
be implied from this study, because the data only offers respondent information on an annual
basis, and does not identify the incidence of asthma. Again, BRFSS data is reported annually,
and we are unable to identify when a person may have been surveyed, or first diagnosed with
certain diseases.

On the other hand, air monitor data from AirData is collected daily by monitors
throughout the United States, including the monitor in Grand Rapids, but in order to utilize the
air monitor data for this research, annual means of PM2.5 concentration and maximum ambient
temperature were calculated. This allowed us to analyze the association between annual
concentration of PM2.5 and the annually reported adult asthma proportion from BRFSS, but we
are not able to identify how asthma prevalence changes throughout the year. The monitor is also
centrally located in Grand Rapids and is the only monitor for Kent County, which has limitations
in and of itself. Air pollution dispersion from point sources, as well as proximity to highways
and high traffic areas, can vary greatly, causing certain homes, workplaces, and regions to be
affected by greater concentrations of air pollutants, including PM2.5 (Lemke et al., 2014;
Maantay, 2007). Using data from one monitor for all of Kent County, MI cannot capture the true
dispersion effect of air pollution.
Seasonal and meteorological changes have been associated with impacting asthma
morbidity, and both temperature and humidity can affect asthma outcomes (Delfino et al., 2014).
Although we unable to directly analyze the daily or seasonal change in temperature or other
meteorological factors in this cross-sectional study design, we tried to limit bias by including the
annual average maximum temperature. Relative humidity was not available through the AirData
dataset and was not incorporated in this analysis due to time restraints and access to data for this
project.
BRFSS SMART data is collected from a random sample of households in a smaller
region of a metropolitan area, in this case, Kent County, MI. After the annual survey is
completed, the responses are weighted in order to be representative of the entire population of
the metropolitan area. This is a strength of BRFSS data and is why this data is generalizable to

the entire surveyed metropolitan area, in this study, Kent County, MI. Unfortunately, the
weighting was not applied in this analysis. This may reduce the generalizability of the results,
but preliminary sensitivity tests revealed limited impact on outcomes.
Another limitation of this study included the inability to control for emphysema, chronic
bronchitis, or COPD throughout the entirety of the analysis. This data was only collected during
2011-2012 and was unavailable for 2005, 2007-2009. In order to increase the number of years of
data available for analysis, this variable was omitted. We attempted to perform a sensitivity
analysis to identify if this variable impacted the results, and but there was not enough variation in
the two years of data to provide coefficients in the model. We did include smoking (at least 100
cigarettes in lifetime), to help address this limitation and control for confounding.
Race and ethnicity were drastically underreported in the BRFSS SMART data and were
excluded from the analysis. From 2005-2012, only fifty-eight of 3,721, or 1.6% of all
respondents reported their preferred race or ethnicity. This could be due to either refusal to
answer the question or failure of the surveyor to ask the respondent. Ethnicity and race have
been addressed in previous research, and asthma is known to disproportionately affect black
Americans, compared to other race/ethnicities (Keet et al., 2015; Nachman & Parker, 2012).
Additionally, low-income people tend to reside near manufacturing, industry, and roadways,
including people of color, which may further confound the analysis (Maantay, 2007). The
inclusion of race may better address confounding, but we did not have a variable that could
directly address this issue. We did include household income, employment status, and health
care insurance access in the analysis, to control for the effect of low income, and other potential
associations with low socioeconomic status.

Finally, it appears as though the one-year lag in exposure to PM2.5 is protective against
asthma prevalence, but this is most likely due to measurement error and loss of data when
shifting PM2.5 concentration for the lag analysis. Previous literature has identified a lag
association between exposure to PM2.5 and asthma morbidity, but most literature addresses much
shorter PM2.5 lag periods, usually between one to fourteen days (Kim et al., 2012; Slaughter et
al., 2003). The BRFSS health and demographic data is reported annually, so shorter lag periods
cannot be analyzed with this dataset. Additionally, when creating the lag analysis, some of the
PM2.5 annual concentration data was lost due to the shift. The annual average PM2.5
concentration decreased overall from 2005–2012, so the data that was no longer included in the
analysis was some of the highest PM2.5 concentration data, and may have impacted the
relationship between PM2.5 and asthma prevalence in the lag models.
Conclusion
This exploratory research was the first study conducted to identify potential associations
between adult asthma and fine particulate matter in Kent County, MI. We did identify a
suggestive association between PM2.5 and adult asthma prevalence in Kent County, MI, although
it was not statistically significant. Despite the limitations of this study, the results highlight the
importance of maintaining low ambient PM2.5 concentrations in Kent County, MI. Kent County
residents suffer from the burden of high adult asthma prevalence, compared to Michigan and the
rest of the United States (CDC, 2015a). Improved knowledge on the associations and influences
that impact adult asthma prevalence are important for targeting high-risk groups, and addressing
known exposures.
This research can provide supportive data for environmental and health advocacy groups
in the area, along with other Kent County policymakers and stakeholders. West Michigan

advocacy coalitions are interested in promoting enhanced clean air policy, as well as ensuring the
health and safety of West Michigan residents. This is an exceptional time to disseminate
research findings to these groups, as the Michigan Legislature is currently reevaluating its energy
generating facility decision-making process, and the United States Federal Government has
passed the Clean Power Plan. Fossil fuel combustion is one of the greatest contributors to air
pollution, and Michigan produces over half of its electricity via coal combustion facilities
(United States Energy Information Administration, 2015). This research may provide these
groups with additional information for their support of stricter guidelines in Michigan’s energy
resource decision-making process, ensuring cleaner air, reduced asthma prevalence, and an
overall healthier population.
The suggestive findings, despite the limitations, indicate the need for additional research
and insight on this topic. Future research is should address the limitations evident in this study,
to increase internal validity. This includes collection of race and ethnicity data, and other
potential confounding health variables, such as COPD status. Additionally, a research design
that allows for analysis of seasonal, climate, and meteorological changes, as well as the
geospatial distribution of air pollution throughout the county will increase the strength of any
associations identified, as well as allow high-risk populations and areas to be identified for
expedient action, if necessary.

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Annals of Allergy, Asthma & Immunology, 91(4), 346–353. http://doi.org/10.1016/S1081-
1206(10)61681-X
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data from EPA’s air quality system data mart. Retrieved from
https://www3.epa.gov/airdata/
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http://www3.epa.gov/pm/
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from http://www3.epa.gov/pm/health.html
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Ambient air pollution exposure and incident adult asthma in a nationwide cohort of U.S.
women. American Journal of Respiratory and Critical Care Medicine, 190(8), 914–921.
http://doi.org/10.1164/rccm.201403-0525OC

Tables
Table 1
2005-2012 Kent County BRFSS SMART Respondent Characteristics
Characteristic No. (%)a
Age, yearsb 53.75 (17.75)
Ever Told Have Asthma
Yes 501 (13.5)
No 3,213 (86.5)
Respondent’s Sex
Male 1,405 (37.8)
Female 2,316 (62.2)
Smoked ≥100 Cigarettes in Lifetime
Yes 1,701 (45.8)
No 2,014 (54.2)
Ever Told Have Diabetes
Yes 424 (11.4)
No 3,293 (88.6)
Annual Household Incomec
Low 819 (25.6)
Middle 936 (29.3)
High 1,442 (45.1)
Education Status
≤ 11th Grade 212 (5.7)
≥12th Grade 3,507 (94.3)
Employment Statusd
Current 1,919 (51.7)
Not Employed 1,795 (48.3)
Have Health Insurancee
Yes 3,413 (92.0)
No 298 (8.0)
Note: Reported data was adapted for this analysis from Kent County BRFSS SMART.
a “Unsure/Don’t Know” and “Refused” responses were omitted.
bAge is reported as mean years (SD).
c BRFSS annual household income was combined into three strata: Low (<$25,000), Middle
($25,000<$50,000) and High (≥$50,000).
d Employment status was combined into two strata: Currently (employed for wage or self-
employed) and Not Employed (all others, except omitted values).
e Having health insurance included private health insurance, prepaid plans, and government
plans.

Table 2
BRFSS Asthma and PM2.5 data for Kent County, MI
Year Ever Told
Have
Asthma
Never Told
Have
Asthma
Proportion
with
Asthma
(%)a
Annual
Mean PM2.5
(µg/m³)
Average
Maximum
Temperature
(oC)
2004 .b .b - b 12.01 14.97
2005 92 586 13.57 13.40 14.86
2006 .b .b -b 12.84 8.41
2007 53 352 13.98 12.82 17.26
2008 50 422 10.57 10.61 14.96
2009 78 388 16.74 10.52 15.04
2010 57 388 12.78 9.65 16.88
2011 98 652 13.01 9.47 16.21
2012 73 450 13.88 9.65 18.42
Note: Asthma data is adapted from the Selected Metropolitan Area Risk Trends (SMART) data
for Kent County, from the Behavioral Risk Factor Surveillance Study (BRFSS), for the
associated years in the table. Particulate matter and maximum temperature data is adapted from
the AirData database, available for Grand Rapids, Michigan. Temperature is reported in degrees
Centigrade (oC).
a Proportions were calculated using all respondents, including responses of “Don’t Know/Not
Sure.”
b Kent County BRFSS SMART data unavailable for 2004 or 2006.

Table 3
Asthma Prevalence Ratio Models
Model PR 95% CI p-value
Unadjusteda 1.39 0.98, 1.95 0.0627
Adjustedb 1.35 0.96, 1.90 0.0850
Lag Unadjusteda 0.87 0.73, 1.04 0.122
Lag Adjustedb 0.88 0.74, 1.05 0.156
Note: Prevalence ratio (PR) is represented by a 10µg/m3 increase in PM2.5.
a Unadjusted models include average annual PM2.5, year, and average annual maximum
temperature.
b Adjusted models includes PM2.5, year, average annual maximum temperature, age, diabetes,
smokers, sex, household income, highest completed education, employment, and healthcare
status.

Figures
Figure 1. Annual adult asthma proportion and annual mean PM2.5 concentration for Kent
County, Michigan from 2005-2012. 2006 Kent County BRFSS SMART data not available.
0
2
4
6
8
10
12
14
16
0
2
4
6
8
10
12
14
16
18
2005 2006 2007 2008 2009 2010 2011 2012
PM2.5Concentration(µg/m3)
AsthmaProportion(%)
Year
Mean PM2.5 Adult Asthma

PM25 and Asthma_Spiker_Final

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