_{2}and CO on Cardiovascular Hospitalizations and Emergency Department Visits: Effect Size Modeling of Study Findings

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This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).

Although particulate matter (PM), nitrogen dioxide (NO_{2}) and carbon monoxide (CO) typically exist as part of a complex air pollution mixture, the evidence linking these pollutants to health effects is evaluated separately in the scientific and policy reviews of the National Ambient Air Quality Standards (NAAQS). The objective of this analysis was to use meta-regression methods to model effect estimates for several individual yet correlated NAAQS pollutants in an effort to identify factors that explain differences in the effect sizes across studies and across pollutants. We expected that our consideration of the evidence for several correlated pollutants in parallel could lead to insights regarding exposure to the pollutant mixture. We focused on studies of hospital admissions for congestive heart failure (CHF) and ischemic heart disease (IHD), which have played an important role in the evaluation of the scientific evidence communicated in the PM, NO_{2}, and CO Integrated Science Assessments (ISAs). Of the studies evaluated, 11 CHF studies and 21 IHD studies met our inclusion requirements. The size of the risk estimates was explained by factors related to the pollution mixture, study methods, and monitoring network characteristics. Our findings suggest that additional analyses focusing on understanding differences in effect sizes across geographic areas with different pollution mixtures and monitor network designs may improve our understanding of the independent and combined effects of correlated pollutants.

_{2}

Recent reports outline strategies for evaluating the health effects of air pollutants using a multipollutant framework [

Although the science needed to inform our understanding of the health effects of air pollution mixtures is not adequately developed at present [_{2} with cardiovascular hospitalizations reported across a range studies. We applied meta-regression methods to understand the differences in the size of the observed effect estimates across studies. We hypothesized that several factors including those related to characteristics of the study design, pollution mixture and monitoring network would explain some of heterogeneity observed.

A large number of studies of the effect of air pollution on hospitalizations or emergency department (ED) visits for cardiovascular diseases were conducted since the early 1990's. Studies of ischemic heart disease (IHD) and Congestive Heart Failure (CHF) were the object of this analysis because evidence on these specific health outcomes formed the basis for EPA's conclusions regarding the short-term health effects attributed to CO and PM in the most recent Integrated Science Assessments (ISAs) conducted by EPA as part of the NAAQS review process [_{2} on cardiovascular diseases was more limited overall, associations of IHD and CHF with short term NO_{2} exposure were consistently reported in hospitalization studies. Potential confounding by co-pollutants added uncertainty in determining whether causal association exists between CVD hospitalizations and each of these correlated individual pollutant exposures. We expected that our consideration of the evidence for these correlated pollutants, simultaneously and in parallel, would improve our understanding of the air pollution mixture and/or help to characterize what is not known about the health effects of the mixture.

Studies included in this analysis were selected from among those identified for inclusion in the ISAs. The methods employed for study selection and literature review are described in detail in Chapter 1 of these documents (e.g., [_{2.5}, PM_{10}, NO_{2} or CO).

An additional set of criteria were then applied to select one effect estimate for each pollutant from each study for inclusion in the meta-regression analysis. We selected effect estimates based on the following criteria: (1) if estimates for IHD and Myocardial Infarction (MI) were presented the MI estimate was selected; (2) if effect estimates for gases and PM_{10} were available, but an effect estimate for PM_{2.5} was not presented, the PM_{10} estimate was selected; (3) if estimates for multiple age groups were presented, the estimate for older adults was selected; (4) if multiple averaging times were presented, the 24-h average was selected; (5) if multiple lags were presented, the multi-day average lag was included, and if only single day lags were presented, lag 0 was chosen first, then lag 1, otherwise the “most significant” lag if presented. No co-pollutant adjusted estimates were included in meta-regression analysis.

In order to compare risk estimates for different pollutants within studies we standardized the effect estimates by their interquartile range (IQR) and plotted the estimates with their 95% confidence intervals (CI's) (e.g., ^{3} for PM, 0.5 ppm for 24-h average CO, 1 ppm for 1-h average CO and 25 ppb for NO_{2} for 24-h average concentrations). The metareg procedure in STATA was used to model effect sizes and determine whether differences in the size of the single pollutant effect estimates across studies was related to study characteristics. Both fixed and random effects models were considered to analyze these data. Results from the random-effects meta-regression are presented because heterogeneity between study estimates was detected. Study level summary data were modeled and each effect estimate was assigned a weight based on the inverse variance of the log Relative Risk (RR). Random effects meta-regression assumes that the log RR's are approximately normally distributed. Tests to identify publication bias were conducted using STATA metabias, which performs the Begg and Mazumdar [

The regression coefficient obtained from a meta-regression analysis represents the change in the log RR per incremental increase the air pollutant. The study characteristics examined included: study location (_{10} _{2.5}; multiday average lag

The univariate relationship between the log RR and several continuous variables including, mean concentrations, correlations between pollutants and monitor density, was also determined and plotted. Monitor density was defined as the log of the number of monitors divided by the area of the study as reported by the investigators or as estimated independently using Geographic Information Systems (GIS) methods. The size of the circle on the plots of these univariate relationships is proportional to the inverse of the variance of the log RR. The lines represent the inverse variance weighted regression equations. Larger circles indicate larger studies that are given more weight in the meta-regression analysis.

Eleven studies of CHF met our inclusion criteria (_{2}) nor do they consistently present quantitative results for the pollutants examined. Three studies present results from two-pollutant models and these results are not consistent regarding which pollutant remains robust after adjustment. Twenty-one studies of IHD/MI were included in the analysis (

Results of the meta-regression analysis are presented in _{10} _{2.5}) did not explain the differences in the size of estimates for PM across the CHF studies included. For the group of IHD/MI studies included in the analyses larger log RR's were observed depending on the PM size fraction (_{10} _{2.5}) and independent confirmation of diagnosis (

The univariate relationships between the log RR for PM, NO_{2} and CO and several continuous variables related to the pollutant mixture and monitoring network (_{2} exposure increases with PM_{10} concentration (_{2} and CHF hospitalization showing the largest increase with monitor density (_{10} and the correlations of PM_{10} with CO and NO_{2} for the IHD studies are in

EPA currently plans to develop a multipollutant science assessment whereby the health effects of exposures to multiple pollutants may be systematically evaluated [_{2} in such a multipollutant context.

Not all studies included in this analysis reported results for all pollutants evaluated and publication bias was detected for associations with each of the pollutants. Studies also varied in their methods and analytical approach. It would have been ideal to evaluate effect modification by relevant parameters of the mixture and design network including the long-term average concentration of the co-pollutant, correlations between pollutants and monitor density, while holding the effect of study design and method constant. However, there were too few studies to include multiple variables in the meta-regression models. Consequently we did not evaluate potential predictors of effect size related to the pollutant mixtures while controlling for study design variables. Further, we did not account for the variable methods of temperature adjustment our analysis because too many strategies were employed across the studies (e.g., stratification, matching, and inclusion of nonlinear or linear terms for temperature).

Despite these limitations, effect size modeling confirmed that some variation in the log RR between studies was due to study design, choice of lag and other methodological differences across studies. We observed that the log RR for NO_{2} associations increased with PM_{10} concentration and the log RR for PM associations increased with the correlation of PM_{10} and NO_{2}. We also found that the size of the risk estimate for CVD hospitalizations increased with increasing monitor density. Enhanced spatial coverage of the monitors is expected to reduce the potential for exposure error and consequently reduce attenuation of the risk estimates. A similar result suggesting increased effect estimates for CVD hospitalizations was observed by Bell et al. [_{2}, respectively. Sarnat _{2} (but not PM_{2.5}) when a rural monitor located 60 km away was used for the analysis. This finding may be due to the somewhat greater variability for CO and NO_{2} relative to PM_{2.5}. This is also supported by a simulation study showing a smaller impact of measurement error on effect estimates for pollutants with less spatial heterogeneity [

To date, only a few multicity studies of hospital admissions and ED visits for IHD/MI or CHF have included analyses of multiple pollutants, in contrast to the single-city analysis results described here. Bell _{2.5}, NO_{2} and elemental carbon (EC). In a separate report, these authors found a significant effect for PM_{2.5} in a single-pollutant model [_{2.5} components and size fractions, attempting to distinguish their independent effects through the use of multipollutant models [_{10} estimates across the cities examined could be explained by the PM_{10}-NO_{2} correlation. The PM_{10}-NO_{2} correlation explained a much smaller proportion (

The integration of findings across scientific disciplines in the ISA supported the independent effects of PM and CO exposure on IHD and CHF [_{2} exposure and cardiovascular hospitalizations were also consistently observed [_{2} effect estimates are higher when PM_{10} concentrations are also elevated may suggest a joint effect of these pollutants, or that PM is an indicator for poor air quality in general. However, our analyses were limited because findings for various pollutants, lags and averaging times examined were not reported consistently across studies, nor were the mean levels of co-pollutants and correlations between pollutants. Efforts by investigators to report results for the complete set of associations examined would maximize the utility of single city studies of air pollution for these types of meta-regression analyses.

Effect estimates and 95% Confidence Intervals for Studies of Congestive Heart Failure (CHF) included in the analysis.

Effect estimates and 95% Confidence Intervals for Studies of Ischemic heart Disease (IHD) and Myocardial Infarction (MI) included in the analysis.

Univariate relationships between the log Relative Risk (RR) of hospitalization for (_{2} and, mean PM_{10} concentration.

Univariate relationships between the log relative risks (RR) of hospitalizations for congestive heart failure (CHF) and ischemic heart disease (IHD) and in association with exposure to PM (_{2} (

* Monitor density is the number of monitors reported divided by the study area in square kilometers (km). If study area was not reported we estimated it from information provided in the study using a Geographical Information System (GIS) approach.

Univariate relationship between the log relative risk (RR) of hospitalization for IHD in association with exposure to CO (_{2} (_{10}.

Results of the meta-regression models for studies of Congestive Heart Failure (CHF). Beta coefficients indicate the increase or decrease in the log RR that depends on the covariate.

_{2} ( | ||||
---|---|---|---|---|

Beta | −0.0355 | −0.0967 | −0.1117 | |

Standard Error | 0.0067 | 0.0697 | 0.0986 | |

<0.001 | 0.166 | 0.257 | ||

Sample Size | ||||

Beta | 0.0045 | 0.0360 | 0.1031 | |

Standard Error | 0.0091 | 0.0070 | 0.0933 | |

0.621 | <0.001 | 0.269 | ||

Sample Size | ||||

Beta | −0.0153 | 0.0262 | −0.0598 | |

Standard Error | 0.175 | 0.0073 | 0.1429 | |

0.381 | <0.001 | 0.676 | ||

Sample Size | ||||

_{10} |
Beta | −0.0163 | ||

Standard Error | 0.0141 | |||

0.244 | ||||

Sample Size | ||||

Beta | −0.0373 | −0.0852 | −0.0917 | |

Standard Error | 0065 | 0.0669 | 0.0964 | |

<0.001 | 0.203 | 0.341 | ||

Sample Size |

The meaning of the sign preceding the effect can be interpreted based on the following reference groups: USA and Canadian studies _{10} _{2.5}; Single day lag

Results of the meta-regression models for studies of Ischemic Heart Disease (IHD)

_{2}( | ||||
---|---|---|---|---|

Beta | −0.0031 | 0.0154 | 0.0243 | |

Standard Error | 0.0035 | 0.0033 | 0.0108 | |

0.382 | <0.001 | 0.025 | ||

Sample Size | ||||

Beta | 0.0192 | 0.0103 | 0.099 | |

Standard Error | 0.0077 | 0.0102 | 0.0271 | |

0.012 | 0.312 | <0.001 | ||

Sample Size | ||||

Beta | 0.0061 | 0.0070 | 0.0219 | |

Standard Error | 0.0036 | 0.0064 | 0.0244 | |

0.095 | 0.276 | 0.369 | ||

Sample Size | ||||

Beta | 0.0330 | −0.0052 | −0.0005 | |

Standard Error | 0.0144 | 0.0148 | 0.0856 | |

0.022 | 0.726 | 0.995 | ||

Sample Size | ||||

_{10} |
Beta | −0.018 | ||

Standard Error | 0.0059 | |||

0.002 | ||||

Sample Size | ||||

Beta | −0.0039 | −0.0087 | −0.187 | |

Standard Error | 0.0040 | 0.0065 | 0.0125 | |

0.338 | 0.181 | 0.134 | ||

Sample Size |

Including studies of Myocardial Infarction (MI);

The meaning of the sign preceding the effect can be interpreted based on the following reference groups: US and Canadian studies _{10} _{2.5}; Single day lag

The authors would like to acknowledge the skill and attention to detail of Kaylyn Siporin for the formatting and proofing of this manuscript. The views expressed in this article are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency.