Asthma Trends in Mississippi Coastal Region with Air Pollutants and Meteorological Factors

Research Article

Austin J Allergy. 2017; 4(1): 1026.

Asthma Trends in Mississippi Coastal Region with Air Pollutants and Meteorological Factors

Tuluri F¹* and Gorai AK²

¹Department of Industrial Systems & Technology, Jackson State University, USA

²Department of Mining Engineering, National Institute of Technology, India

*Correspoing author: Tuluri F, Department of Industrial Systems & Technology, Jackson State University, USA

Received: November 28, 2016; Accepted: January 12, 2017; Published: January 19, 2017


An understanding of interplay between asthma, criteria air pollutants, and meteorological factors is essential to predicting human health and reducing the capital costs on controlling asthma prevalence. A systematic relationship between asthma and variables related to air quality and weather in a location will help mitigate or reduce impact of asthma over the people in it. The impact of industries, transportation, and weather on health is complex and needs continuous monitoring for controlling health disorders. The present study examines Poisson Regression Model of the daily data of asthma admissions, Particulate Matter (PM2.5), Ozone (O3), Nitrogen dioxide (NO2), temperature, and humidity in selective locations of Mississippi coastal region of Gulf of Mexico for the period 2003 to 2011. The study region consists of three locations, namely Gulfport, Pascagoula, and Waveland because of the extent of data availability. Overall, the results indicate a negative correlation of asthma with temperature and the effect was statistically significant (p < 0.05) in all the regions. The correlation of other variables was not consistent uniformly, and their influence on asthma was not statistically significant except in few cases.

Keywords: Poisson Regression Model; Pearson correlation; Asthma; Criteria Pollutants; time series


The inland regions of Gulf of Mexico are affected by industries, and transportation, in addition to extreme weather conditions caused by tropical storms. The increasing industrial activities such as oil and gas production and thermal power plants are drastically affecting the air quality and hence health over the heavily populated neighborhoods of US Gulf coast ranging from Houston to the Louisiana zone [1]. The air quality in these regions is also indirectly affected by the increasing transportation and construction associated with the rapid industrial growth over the US Gulf of Mexico. The weather patterns also play a prominent role in the dispersion of primary and secondary pollutants over the atmosphere to distant places from the sources [2,3].

A survey of literature does show a possibility of linkage of predominant cases of ill-health caused by air pollution in developing as well as developed countries [4-7]. Among other health disorders, asthma prevalence is also considered to be affected by air pollutants, and several research investigations have shown positive association between asthma and air pollution [8-13].

The present work investigates Poisson regression modeling to envisage the relationship between asthma with criteria air pollutants and meteorological parameters over three locations in the Mississippi Gulf coast, namely Gulfport, Pascagoula, and Waveland during the period 2003 to 2011. The locations are selected based on their industrial growth, proximity to the coast, and availability of data. The air pollutants considered are Particulate Matter of diameter less than 2.5 microns (PM2.5), Ozone (O3), and Nitrogen Oxide (NO2); while the meteorological parameters considered are temperature, and humidity.

Materials and Methods

Consider a random variable Yt, representing the time series count data Y1,Y2, …., YT and Xi representing the regression covariate variables, then Yt Poisson distributed over X with a mean value of μ is given by,

Yt~Poisson (μt)

The log linear regression model of Poisson distribution in terms of regression coefficient (β) is given by,

log(μt) = β01 X12X2+...

In the present situation, Yt is the daily counts of asthma admissions at time t and Xi the independent variable (for the air pollution and meteorological parameters); the parameter βi represents regression coefficient and is a measure of association between an independent variable, Xi (for the air pollution and meteorological parameters), and the risk of the outcome Y for the asthma admission. The log linear relationship is given by,

log(μt )=β01 O32 NO23 PM2.54 temperature+β5 Humidity

The regression coefficient βi also signifies forecasting proportional change in the value of Yt for a given a unit change in Xt,

Using SPSS statistical package, the Poisson Regression model is applied to the asthma data without time lag, and analyzed the associations by considering the causes variables individually. This was done to utilize the maximum number of valid datasets.

log(μt) = β01 O3

log(μt)= β0+ β1NO2

log(μt)= β0+ β1PM2.5

log(μt)= β0+ β4temperature+β5 Humidity

Study area

The locations considered for the study are Gulfport in Harrison county, Pascagoula in Jackson county, and Waveland in Hancock county. The three counties are adjacent to the Mississippi Gulf Coast and are strategically located in the Coast (See Figure 1 and Figure 2) [14,15]. The objective in choosing each of these locations in their respective county is because of the possibility of correlating the asthma prevalence of the local area to the corresponding air quality data, and meteorological data. The population of the Mississippi Gulf Coast appears to be growing and is projected to increase because of the demand for job employment in the casinos, aerospace and defense industries in addition to job opportunities in the oil & gas industries, power plants, and construction. Based on the US Census Bureau estimate, the population of the three cities to the corresponding county of Gulfport/Harrison, Pascagoula/Jackson, and Waveland/ Hancock are respectively 69,220/194,029, 22,429/140,298, and 6541/45,255 [16]. MS Department of Health Report reports adult asthma prevalence of 7.2% for the health district of these counties [17].