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, mi2

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.


References

Bui, D., Burgess, J., Matheson, M., Erbas, B., Perret, J., Morrison, S., . . . Dharmage, S. (2013).

Ambient wood smoke, traffic pollution and adult asthma prevalence and severity.

Respirology, 2013(February). http://doi.org/10.1111/resp.12108

Canova, C., Dunster, C., Kelly, F. J., Minelli, C., Shah, P. L., Caneja, C., . . . Burney, P. (2012).

PM10-induced hospital admissions for asthma and chronic obstructive pulmonary

disease. Epidemiology, 23(4), 607–615. http://doi.org/10.1097/EDE.0b013e3182572563

Centers for Disease Control and Prevention. (2015a). Asthma: Most recent asthma data.

Retrieved from http://www.cdc.gov/asthma/most_recent_data.htm

Centers for Disease Control and Prevention. (2015b). Behavioral risk factor surveillance system.

Retrieved from http://www.cdc.gov/brfss/

Centers for Disease Control and Prevention. (2015c). Learn how to control asthma. Retrieved

from http://www.cdc.gov/asthma/faqs.htm

Delamater, P. L., Finley, A. O., & Banerjee, S. (2012). An analysis of asthma hospitalizations, air

pollution, and weather conditions in Los Angeles County, California. Science of the Total

Environment, 425, 110–118. http://doi.org/10.1016/j.scitotenv.2012.02.015

Delfino, R. J., Wu, J., Tjoa, T., Gullesserian, S. K., Nickerson, B., & Gillen, D. L. (2014).

Asthma morbidity and ambient air pollution. Epidemiology, 25(1), 48–57.

http://doi.org/10.1097/EDE.0000000000000016

Girardot, S. P., Ryan, P. B., Smith, S. M., Davis, W. T., Hamilton, C. B., Obenour, R. A., …

Reed, G. D. (2006). Ozone and PM2.5 exposure and acute pulmonary health effects: A

study of hikers in the Great Smoky Mountains national park. Environmental Health

Perspectives, 114(7), 1044–1052. http://doi.org/10.1289/ehp.8637


Jacquemin, B., Kauffmann, F., Pin, I., Le Moual, N., Bousquet, J., Gormand, F., . . . Siroux, V.

(2012). Air pollution and asthma control in the Epidemiological study on the Genetics

and Environment of Asthma. Journal of Epidemiology & Community Health, 66(9), 796–

802. http://doi.org/10.1136/jech.2010.130229

Johnston, F. H., Webby, R. J., Pilotto, L. S., Bailie, R. S., Parry, D. L., & Halpin, S. J. (2006).

Vegetation fires, particulate air pollution and asthma: A panel study in the Australian

monsoon tropics. International Journal of Environmental Health Research, 16(6), 391–

404. http://doi.org/10.1080/09603120601093642

Keet, C. A., McCormack, M. C., Pollack, C. E., Peng, R. D., McGowan, E., & Matsui, E. C.

(2015). Neighborhood poverty, urban residence, race/ethnicity, and asthma: Rethinking the

inner-city asthma epidemic. Journal of Allergy and Clinical Immunology, 135(3), 655–662.

http://doi.org/10.1016/j.jaci.2014.11.022

Kim, J., Kim, H., & Kweon, J. (2015). Hourly differences in air pollution on the risk of asthma

exacerbation. Environmental Pollution, 203, 15–21.

http://doi.org/10.1016/j.envpol.2015.03.040

Kim, S. Y., Peel, J. L., Hannigan, M. P., Dutton, S. J., Sheppard, L., Clark, M. L., & Vedal, S.

(2012). The temporal lag structure of short-term associations of fine particulate matter

chemical constituents and cardiovascular and respiratory hospitalizations. Environmental

Health Perspectives, 120(8), 1094–1099. http://doi.org/10.1289/ehp.1104721

Laurent, O., Pedrono, G., Segala, C., Filleul, L., Havard, S., Deguen, S., . . . Bard, D. (2008). Air

pollution, asthma attacks, and socioeconomic deprivation: A small-area case-crossover

study. American Journal of Epidemiology, 168(1), 58–65.

http://doi.org/10.1093/aje/kwn087


Lemke, L. D., Lamerato, L. E., Xu, X., Booza, J. C., Reiners, J. J., Raymond III, D. M., . . .

Krouse, H. J. (2014). Geospatial relationships of air pollution and acute asthma events

across the Detroit–Windsor international border: Study design and preliminary results.

Journal of Exposure Science and Environmental Epidemiology, 24(4), 346–357.

http://doi.org/10.1038/jes.2013.78

Li, T., & Lin, G. (2014). Examining the role of location-specific associations between ambient

air pollutants and adult asthma in the United States. Health & Place, 25, 26–33.

http://doi.org/10.1016/j.healthplace.2013.10.007

Malig, B. J., Green, S., Basu, R., & Broadwin, R. (2013). Coarse particles and respiratory

emergency department visits in California. American Journal of Epidemiology, 178(1),

58–69. http://doi.org/10.1093/aje/kws451

Maantay, J. (2007). Asthma and air pollution in the Bronx: methodological and data

considerations in using GIS for environmental justice and health research. Health & Place,

13(1), 32–56. http://doi.org/10.1016/j.healthplace.2005.09.009

Meng, Y.-Y., Rull, R. P., Wilhelm, M., Lombardi, C., Balmes, J., & Ritz, B. (2010). Outdoor air

pollution and uncontrolled asthma in the San Joaquin Valley, California. Journal of

Epidemiology & Community Health, 64(2), 142–147.

http://doi.org/10.1136/jech.2009.083576

Nachman, K. E., & Parker, J. D. (2012). Exposures to fine particulate air pollution and

respiratory outcomes in adults using two national datasets: A cross-sectional study.

Environmental Health, 11(1), 25. http://doi.org/10.1186/1476-069X-11-25


Qiu, H., Yu, I. T. S., Tian, L., Wang, X., Tse, L. A., Tam, W., & Wong, T. W. (2012). Effects of

coarse particulate matter on emergency hospital admissions for respiratory diseases: A

time-series analysis in Hong Kong. Environmental Health Perspectives, 120(4), 572–576.

http://doi.org/10.1289/ehp.1104002

Slaughter, J. C., Lumley, T., Sheppard, L., Koenig, J. Q., & Shapiro, G. G. (2003). Effects of

ambient air pollution on symptom severity and medication use in children with asthma.

Annals of Allergy, Asthma & Immunology, 91(4), 346–353. http://doi.org/10.1016/S1081-

1206(10)61681-X

SAS/STAT [Computer Software]. (2015). Retrieved from http://www.sas.com/en_us/home.html

United States Environmental Protection Agency. (2016). Airdata: Access to monitored air quality

data from EPA’s air quality system data mart. Retrieved from

https://www3.epa.gov/airdata/

United States Environmental Protection Agency. (2015a). Particulate matter. Retrieved from

http://www3.epa.gov/pm/

United States Environmental Protection Agency. (2015b). Particulate matter: Health. Retrieved

from http://www3.epa.gov/pm/health.html

Young, M. T., Sandler, D. P., DeRoo, L. A., Vedal, S., Kaufman, J. D., & London, S. J. (2014).

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 SexMale 1,405 (37.8)Female 2,316 (62.2)

Smoked ≥100 Cigarettes in LifetimeYes 1,701 (45.8)No 2,014 (54.2)

Ever Told Have DiabetesYes 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)

AverageMaximum Temperature (oC)

2004 .b .b - b 12.01 14.972005 92 586 13.57 13.40 14.862006 .b .b -b 12.84 8.412007 53 352 13.98 12.82 17.262008 50 422 10.57 10.61 14.962009 78 388 16.74 10.52 15.042010 57 388 12.78 9.65 16.882011 98 652 13.01 9.47 16.212012 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

2005 2007 2008 2009 2010 2011 20120

2

4

6

8

10

12

14

16

18

0

2

4

6

8

10

12

14

16

Mean PM2.5 Adult Asthma

Year

Ast

hma

Prop

ortio

n (%

)

PM2.

5 C

once

ntra

tion

(µg/

m3)

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.

pm25 and asthma_spiker_final

Documents