This study represents an example of investigating the associations between the joint exposure to ozone (O3) and particulate matter of sizes less than or equal to 2.5 micrometers in aerodynamic diameter (PM2.5) and cardiovascular disease (CVD) emergency room (ER) visits and chronic obstructive pulmonary disease (COPD) ER visits using multivariate geostatistics in Houston, Texas, from 2004 to 2009. Analyses showed lack of strong pair-wise association among the predictors of O3, PM2.5, wind speed, relative humidity, and temperature. Whereas CVD and COPD ER visits exhibited a strong positive correlation. Both outcomes drastically increased from 2006 possibly due to immigration from neighboring locations. Parametric testing showed that the data differed significantly between the years. Multivariate multiple regression results on the 2009 data showed that PM2.5, relative humidity, and temperature were significant to both CVD and COPD ER visits. Codispersion coefficients were constant which justified the assumption of intrinsic correlation. That is, our predictors had strong influence on the spatial variability of CVD and COPD ER visits. This multivariate geostatistics approach predicted an increase of 34% in CVD ER visits and 24% increase in COPD ER visits, which calls for more attention from policy makers. The use of multivariate geostatistics analyses enabled us to successfully detect the effects of risk factors on both outcomes.
Assessing causal inferences between exposure to air pollution and respiratory and cardiovascular diseases provides epidemiological evidence regarding the adverse health effects of air pollution. Most of relevant studies quantify the possible causality through association between the pollutants and the health outcomes due to exposure to pollutants either singularly or jointly. However, the effect of local air pollution versus neighboring levels is not fully explored yet [
This study capitalizes on previous studies which assess the effect of ozone and particulate matter on CVD and COPD ER visits in Harris County, Texas from 2004 to 2009 [
Daily ozone, PM2.5, wind speed, wind direction, temperature, and relative humidity were obtained from EPA’s AirData website [
Ozone (ppb) | PM2.5 (µg/m3) | COPD | CVD | Wind speed (KPH) | Relative Humidity (%) | Temperature (F) | |
---|---|---|---|---|---|---|---|
Mean | 115.25 | 11.83 | 49.20 | 51.78 | 4.50 | 71.28 | 70.44 |
Median | 25.25 | 10.82 | 48.00 | 50.00 | 4.28 | 72.20 | 72.60 |
Standard Dev. | 292.85 | 5.02 | 26.51 | 33.91 | 2.28 | 11.69 | 12.07 |
Kurtosis | 16.89 | 1.79 | −0.85 | −1.50 | −0.37 | 1.36 | −0.52 |
Skewness | 3.92 | 1.13 | 0.31 | 0.01 | 0.40 | −0.27 | −0.65 |
Minimum | 1.88 | 0.01 | 0.00 | 1.00 | 0.09 | 29.21 | 33.37 |
Maximum | 2,515.97 | 40.87 | 131.00 | 130.00 | 12.66 | 143.65 | 89.04 |
a greater than 900% increase (
The number of CVD and COPD ER visits was then summed up by date and zip code. Fisher’s F test to test the null hypothesis of non-changing variances of each outcome between the years, gave p-values less than the significance level of 0.05. The same result was obtained for t test. Hence we reject the null hypothesis of equal variances and means respectively. That is, neither CVD nor COPD ER cases for Houston during those years can be combined as if they were coming from one statistical distribution. Consequent analyses were conducted on the 2009 data. Multivariate multiple regression results showed that the model is statistically significant and that lagged ozone and PM2.5 concentrations were not statistically significant to both outcomes. Interestingly, only PM2.5, relative humidity and temperature were significant to both CVD and COPD ER visits (
Ozone | PM2.5 | COPD | CVD | Wind Speed | Relative Humidity | Temperature | |
---|---|---|---|---|---|---|---|
Ozone | 1.00 | ||||||
PM2.5 | 0.05 | 1.00 | |||||
COPD | 0.07 | −0.12 | 1.00 | ||||
CVD | 0.17 | −0.02 | 0.74 | 1.00 | |||
Wind Speed | −0.05 | −0.13 | 0.04 | 0.00 | 1.00 | ||
Relative Humidity | −0.04 | −0.03 | −0.03 | −0.08 | 0.06 | 1.00 | |
Temperature | 0.16 | 0.24 | −0.27 | 0.04 | −0.09 | 0.16 | 1.00 |
O3 (0d lag) | O3 (1d lag) | PM2.5 (0d day lag) | PM2.5 (1d lag) | Relative Humidity | Temperature |
---|---|---|---|---|---|
Dependent variable: COPD | |||||
√ | √ | √ | |||
Dependent variable: CVD | |||||
√ | √ | √ | |||
Dependent variables: CVD & COPD | |||||
√ | √ | √ |
of these three factors on the mechanisms of toxicity for respiratory and cardiovascular disease can be attributed to the oxidative stress leading to inflammation, which generates the physiological processes evolving as respiratory and/or cardiovascular symptoms like narrowing of airways, shortness of breath, wheezing, cough, and the ability of particles to penetrate the lung wall accumulating in the pulmonary interstitium between the lung and the bloodstream [
As for the spatial association between CVD and COPD ER visits across the different zip codes of Houston, the codispersion coefficients were constant with a variance of 0.001 (
The persistent increase in CVD and COPD ER visits (
This paper presents an approach to study the multivariate spatial nature between health outcomes and multipollutants using multivariate geostatistics. The example presented modeled the effect of ozone, PM2.5, relative humidity, wind speed, and temperature on both CVD and COPD ER visits at the same time. Now that we answered questions like the effect on both outcomes simultaneously, we can address more complex questions like the effect on more health outcomes that have been historically associated to each other. Further investigation is recommended in order to address potential issues like computational complexity and bias [
Faye Anderson, (2016) Application of Multivariate Geostatistics in Environmental Epidemiology: Case Study from Houston, Texas. Journal of Geoscience and Environment Protection,04,110-115. doi: 10.4236/gep.2016.44014