Kethireddy Swatantra R, Adegoye Grace A, Tchounwou Paul B, Tuluri Francis, Ahmad H Anwar, Young John H, Zhang Lei
Department of Natural Sciences and Environmental Health, Mississippi Valley State University, 14000 Highway 82 W, Itta Bena, MS 38941, USA.
College of Science, Engineering and Technology, Jackson State University, 1400 John R Lynch St., Jackson, MS 39217, USA.
AIMS Environ Sci. 2018;5(4):273-293. doi: 10.3934/environsci.2018.4.273. Epub 2018 Sep 12.
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 R = 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 () between monthly mean O and asthma prevalence was moderately positive during 2009-2011 ( = 0.57). The regression model also indicated that 2011 annual PM has a statistically significant influence on the aggravation of the asthma cases (adjusted R-squared 0.93) and the 2011 PM 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.
从空间角度进行的数据驱动型研究可能有助于以明智且具成本效益的方式对抗人类疾病。了解环境退化的变化模式对于确定社区的健康结果（如哮喘）至关重要。在本研究中，利用地理信息系统中的空间统计技术对2003 - 2011年密西西比州与哮喘相关的患病率数据进行了分析。通过邮政编码进行地理编码、绘制分级统计图以及热点分析技术被用于绘制空间数据。计算了2009年至2011年每个邮政编码区域的发病率。连续三年在门罗县的普雷里发现了最高发病率（4 - 5.5%）。在杰克逊和格尔夫波特的城市地区观察到具有统计学意义的热点，在杰克逊市区附近以及杰克逊和子午线大都市之间的区域稳步增加。对于2009 - 2011年，在人口密集地区发现了城市风险因素的空间特征，这通过对哮喘患者与人口数据的回归分析得到证实（当每个邮政编码区域的人口规模达到35000时，R的线性增加为0.648，而当人口规模增加到每个邮政编码区域35000以上至最大47000时，这种关系下降到59%）。在2009 - 2011年期间，月平均臭氧（O）与哮喘患病率之间观察到的相关系数（）为中度正相关（ = 0.57）。回归模型还表明，2011年的年度颗粒物（PM）对哮喘病例的加重具有统计学意义的影响（调整后的R平方为0.93），并且2011年的PM还取决于人均哮喘和贫困率。本研究表明，未来应关注杰克逊市区和密西西比州沿海地区的疾病患病率。联邦和州卫生当局可利用当前结果和地理信息系统疾病地图来识别高危人群并提供健康咨询。
