Asthma and Air Pollution- A Decision Support for Health Administration

Transcription

1 Asthma and Air Pollution- A Decision Support for Health Administration Swatantra Kethireddy PhD Student Environmental Science PhD program Jackson State University, Jackson MS

2 Asthma is a common chronic disorder characterized by the periods of reversible airflow obstruction known as asthma attacks (CDC, 2012). It has become a burden on Health and Economy of US population. 1 in every 14 Americans are currently diagnosed with Asthma, costs the nation $20 billion every year (CDC, Asthma facts). Children and elderly are sensitive groups.

3 Hypothesis: Health care access could be disproportionately distributed among asthma populace. Patients are under served or over served? Spatiotemporal signatures of respiratory health problem could be a consequence of the prevailing air-pollution.

4 Study region and quick facts United States of America Mississippi Population (2010 census): 2,967,297 July 2013 estimate: 2,991,207 Individuals below poverty level: 22.3% Median household income: $38,882 Urban centers: Jackson, Gulfport, Southaven, Hattiesburg, Biloxi, Meridian, Tupelo, Olive Branch, and Greenville.

5 Objectives What is the disease per capita? Identify underserved/ over served patients Identify the disease clusters / hot and cold spots Relation between air pollution and asthma? Identify suitable areas for satellite clinics

6 Approach Spatial data analytical technologies in ArcGIS Continued

7 Asthma hospitalization data Hospital network and patient bed data Geocoded to zipcode boundaries Geocoded to street lines Data integration and analysis Quantitative choropleth mapping of Diseased population Spatiotemporal model of air pollution Environmental risk factors PM 2.5 and Ozone(O 3 ) Deterministic/Geostatistical modeling Spatiotemporal model of disease Extract multi values Establish linear regression Identify disease hotspots, find the under served and over served population

8 Disease scenario over the years Asthma related health data source: MSDH (Mississippi State Department of Health)

9

10

11 Disease rate

12 Predicted pollution scenario

13

14 Asthma related hospitalization trend Count Monthly total Month

15 Asthma related hospitalization trend Count Monthly Average Month

16 Under served patients Jackson Urban area: ZipCode areas in Hinds, Rankin, and Madison counties 2492 hospital beds Vs 4867 patients Patients underserved: 48.79% Gulfport-Biloxi Urban area: ZipCode areas in Hancock, Harrison, and Jackson counties 1344 hospital beds Vs 2696 patients Patients underserved: 50.1%

17 Under served patients Area Jackson Urban Patients 2011 Beds 2011 Under/over served patients New satellite clinic(s) % Potential area Gulfport- Biloxi % Potential area Hattiesburg % Meridian % Tupelo % -----

18 Hot and cold spots, patients 2011

19 Hot and Cold Spots, Disease rate 2011

20 Regression analysis, data from 2007 to 2011 Asthma rate Vs PM 2.5 annual arithmetic mean values Air Pollutant data source: US EPA (Environmental Protection Agency), data were extracted from IDW(Inverse Distance weighing) predicted surface and regressed against asthma rate

21 Asthma rate Vs Annual fourth max ozone values Air pollutant data source: US EPA (Environmental Protection Agency), data were extracted from IDW(Inverse Distance weighing) predicted surface and regressed against asthma rate

22 Asthma rate Vs Poverty rate Poverty data source: US Census Bureau

23 Regression analysis result of annual data

24 Regression analysis of Daily data from 2007 to 2011 Asthma daily count Vs PM 2.5 daily average

25 Asthma daily count Vs O 3 daily max 8-hour

26 Regression analysis result of daily data

27 Average asthma count Monthly trend of Ozone and Asthma Mar_07 Apr_07 May_07 Jun_07 Jul_07 Aug_07 Sep_07 Oct_07 Mar_08 Apr_08 May_08 Jun_08 Jul_08 Aug_08 Sep_08 Oct_08 Mar_09 Apr_09 May_09 Jun_09 Jul_09 Aug_09 Sep_09 Oct_09 Mar_10 Apr_10 May_10 Jun_10 Jul_10 Aug_10 Sep_10 Oct_10 Mar_11 Apr_11 May_11 Jun_11 Jul_11 Aug_11 Sep_11 Oct_11 Month_Year Average of Asthma_count Average of O3_ppmv O3 daily max in ppm The overall correlation coefficient ( r) is (negligible). In recent years, the trend appeared to be synchronized from the mid of From then, r has increased to (moderate positive correlation).

28 Observations and Conclusions Statistically significant hot spots were observed in urban areas of Jackson and Gulfport. Emergency room visit and wait time data must be analyzed to quantify the underserved patients. A strong season specific hospitalization trend has been observed for the analyzed years. Season specific pollution must be geographically visualized to better understand the relation.

29 Observations and Conclusions Central and south central Mississippi appeared to be the victim of disease, and the highest rate of asthma (4-5.5%) is observed in prairie, Monroe county for the three consecutive analyzed years. Further investigation may reveal causation factors if there any. Regression analysis hasn t revealed a better clue of disease association with annual means of Ozone and particulates. However, poverty factor might be playing a critical role in the disease prevalence.

30 Observations and Conclusions Time series analysis indicated that there is a time dependent moderate correlation between the two variables in recent years. Statistical relationship could be an association, may not necessarily be a casual relationship.

31 Acknowledgements Dr. Lei Zhang, (Mississippi State Department of Health) Mississippi State Asthma Control program Dr. Paul B. Tchounwou (Jackson State University) Environmental Science PhD program Dissertation committee: Dr. Paul B. Tchounwou Dr. Jeffrey C. Luvall (NASA MSFC) Dr. Mohmmad Z. Al-Hamdan (USRA MSFC) Dr. Francis Tuluri (Jackson State University) Dr. Hafiz A. Ahmad (Jackson State University)

32 Thank you Questions or Comments? Vicksburg national military park Vicksburg, MS