The status of geo-environmental health in Mississippi: Application of spatiotemporal statistics to improve health and air quality

Swatantra R. Kethireddy; Grace A. Adegoye; Paul B. Tchounwou; Francis Tuluri; H. Anwar Ahmad; John H. Young; Lei Zhang; Swatantra R. Kethireddy; Grace A. Adegoye; Paul B. Tchounwou; Francis Tuluri; H. Anwar Ahmad; John H. Young; Lei Zhang

doi:10.3934/environsci.2018.4.273

AIMS Environmental Science

2018, Volume 5, Issue 4: 273-293. doi: 10.3934/environsci.2018.4.273

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Research article

The status of geo-environmental health in Mississippi: Application of spatiotemporal statistics to improve health and air quality

1.
Department of Natural Sciences and Environmental Health, Mississippi Valley State University, 14000 Highway 82 W, Itta Bena, MS 38941, USA
2.
College of Science, Engineering and Technology, Jackson State University, 1400 John R Lynch St., Jackson, MS 39217, USA
3.
Office of Health Data and Research, Mississippi State Department of Health, 570 East Woodrow Wilson Avenue, Jackson, MS 39215, USA

Received: 15 March 2018 Accepted: 04 September 2018 Published: 12 September 2018

Data enabled research with a spatial perspective may help to combat human diseases in an informed and cost-effective manner. Understanding the changing patterns of environmental degradation is essential to help in determining the health outcomes such as asthma of a community. In this research, Mississippi asthma-related prevalence data for 2003–2011 were analyzed using spatial statistical techniques in Geographic Information Systems. Geocoding by ZIP code, choropleth mapping, and hotspot analysis techniques were applied to map the spatial data. Disease rates were calculated for every ZIP code region from 2009 to 2011. The highest rates (4–5.5%) were found in Prairie in Monroe County for three consecutive years. Statistically significant hotspots were observed in urban regions of Jackson and Gulf port with steady increase near urban Jackson and the area between Jackson and meridian metropolis. For 2009–2011, spatial signatures of urban risk factors were found in dense population areas, which was confirmed from regression analysis of asthma patients with population data (linear increase of R2 = 0.648, as it reaches a population size of 3,5000 per ZIP code and the relationship decreased to 59% as the population size increased above 3,5000 to a maximum of 4,7000 per ZIP code). The observed correlation coefficient (r) between monthly mean O3 and asthma prevalence was moderately positive during 2009–2011 (r = 0.57). The regression model also indicated that 2011 annual PM2.5 has a statistically significant influence on the aggravation of the asthma cases (adjusted R-squared 0.93) and the 2011 PM2.5 depended on asthma per capita and poverty rate as well. The present study indicates that Jackson urban area and coastal Mississippi are to be observed for disease prevalence in future. The current results and GIS disease maps may be used by federal and state health authorities to identify at-risk populations and health advisory.
- environmental health,
- GIS,
- spatial analysis,
- asthma,
- air quality,
- regression analysis,
- spatiotemporal statistics,
- kriging,
- geocoding
Citation: Swatantra R. Kethireddy, Grace A. Adegoye, Paul B. Tchounwou, Francis Tuluri, H. Anwar Ahmad, John H. Young, Lei Zhang. The status of geo-environmental health in Mississippi: Application of spatiotemporal statistics to improve health and air quality[J]. AIMS Environmental Science, 2018, 5(4): 273-293. doi: 10.3934/environsci.2018.4.273

Related Papers:

Abstract

Data enabled research with a spatial perspective may help to combat human diseases in an informed and cost-effective manner. Understanding the changing patterns of environmental degradation is essential to help in determining the health outcomes such as asthma of a community. In this research, Mississippi asthma-related prevalence data for 2003–2011 were analyzed using spatial statistical techniques in Geographic Information Systems. Geocoding by ZIP code, choropleth mapping, and hotspot analysis techniques were applied to map the spatial data. Disease rates were calculated for every ZIP code region from 2009 to 2011. The highest rates (4–5.5%) were found in Prairie in Monroe County for three consecutive years. Statistically significant hotspots were observed in urban regions of Jackson and Gulf port with steady increase near urban Jackson and the area between Jackson and meridian metropolis. For 2009–2011, spatial signatures of urban risk factors were found in dense population areas, which was confirmed from regression analysis of asthma patients with population data (linear increase of R2 = 0.648, as it reaches a population size of 3,5000 per ZIP code and the relationship decreased to 59% as the population size increased above 3,5000 to a maximum of 4,7000 per ZIP code). The observed correlation coefficient (r) between monthly mean O3 and asthma prevalence was moderately positive during 2009–2011 (r = 0.57). The regression model also indicated that 2011 annual PM2.5 has a statistically significant influence on the aggravation of the asthma cases (adjusted R-squared 0.93) and the 2011 PM2.5 depended on asthma per capita and poverty rate as well. The present study indicates that Jackson urban area and coastal Mississippi are to be observed for disease prevalence in future. The current results and GIS disease maps may be used by federal and state health authorities to identify at-risk populations and health advisory.

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