In the United States, several pressing issues constantly surface in various news sources and political debates. One of these issues deals with public health; more specifically the short and long term effect of pollution on respiratory health of the residents. Ambient air pollutants from agricultural, industrial and vehicular sources are under constant observation by several state-wide and federal entities like the Environmental Protection Agency (EPA), National Institutes of Health (NIH), California Air Resources Board (CARB). Spikes in pollutant levels are known to cause increased prevalence of respiratory illness, specifically chronic obstructive pulmonary disease (COPD).
The goal of this analysis is to accurately model and describe the short and long-run dynamic relationship between common ambient pollutants and the prevalence of COPD in three major Southern California counties, Los Angeles, San Bernardino and Orange. Autoregressive distributed lag models (ARDL) are commonly seen in an econometric context, but provide an opportunity to perform ARDL bounds testing to determine if a cointegrating relationship is present between a combination of the pollutants and the number of COPD inpatient diagnoses. Finally, the analysis includes the construction of a restricted error correction model and a levels model, providing insights into the short and long-run effects of the pollutants on prevalence of respiratory illness.
If the hypothesis holds true, being able to capture the cointegrating relationship between some combination of pollutants and the number of monthly chronic respiratory illness diagnoses will yield useful analyses for modeling the expected month-to-moth percentage change in COPD related cases on a short-run basis, and changes in individual cases over the long-run. This may help hospitals react to drastic changes pollution climate such as natural disasters like fire or earthquakes. Additionally, healthcare researchers may use these results to model the long-term effect of these pollutants on the respiratory health of specific populations.
|Commitee:||Suaray, Kagba, Zhang, Wenlu|
|School:||California State University, Long Beach|
|Department:||Mathematics and Statistics|
|School Location:||United States -- California|
|Source:||MAI 82/2(E), Masters Abstracts International|
|Subjects:||Statistics, Epidemiology, Atmospheric sciences, Pathology|
|Keywords:||Autoregressive Distributed Lag Model, Cointegration, Error Correction Model, Ozone, Respiratory, Southern California|
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