Grantee Research Project Results
Final Report: Measuring the Impact of Particulate Matter Reductions by Environmental Health Outcome Indicators
EPA Grant Number: R833627Title: Measuring the Impact of Particulate Matter Reductions by Environmental Health Outcome Indicators
Investigators: Johnson, Jean , Yawn, Barbara , Pratt, Greg
Institution: Minnesota Department of Health , Olmsted Medical Center , Minnesota Pollution Control Agency
Current Institution: Minnesota Department of Health , Minnesota Pollution Control Agency , Olmsted Medical Center
EPA Project Officer: Hahn, Intaek
Project Period: January 1, 2007 through December 1, 2011 (Extended to May 31, 2012)
Project Amount: $488,650
RFA: Development of Environmental Health Outcome Indicators (2006) RFA Text | Recipients Lists
Research Category: Air Quality and Air Toxics , Airborne Particulate Matter Health Effects , Air Toxics , Particulate Matter
Objective:
A substantial body of scientific literature has associated airborne particulate matter exposures with health outcomes, most significantly with cardiovascular and respiratory disease mortality and morbidity in adults, and with asthma exacerbations in children. In Minnesota, several local and regional strategies have been implemented since 2005 in order to reduce air pollution. These local air pollution reduction initiatives have coincided with federal regulatory actions addressing air quality that were being implemented around the same period of time. This project utilizes existing data to develop and evaluate a set of outcome-based indicators for monitoring the impacts of these local, regional, and national particulate matter emission reduction strategies on population distributions of ambient PM exposures, and on population health in Minnesota communities.
Case-crossover and time series analytical methods were used to assess the short-term effect of ambient PM2.5 concentrations on the risk of hospitalizations, emergency department (ED) visits, and mortality. Analyses were conducted in two study locations: the 7-county Minneapolis-St. Paul (MSP) metropolitan area and Olmsted County, Minnesota. For each health endpoint, case- crossover and time series models were run for the entire study period length (2003-2009; 2005-2009 for ED visits), as well as for three sub-time periods that corresponded to the implementation timeline of the particulate matter reduction initiatives. These sub-time periods were: 2003-2005 (baseline), 2006-2007 (pre-implementation), 2008-2009 (implementation). Two types of indicators for the exposure-health association were developed in this project: (1) local concentration-response functions (odds ratios) that express the change in the risk in the health outcome of interest associated with unit increases in PM2.5, and (2) population attributable fractions that quantify the public health impact of PM2.5 within the study populations.
For Olmsted County, a supplemental health outcome indicator was developed using data on asthma encounters from the Rochester Epidemiology Project (REP), a medical records linkage database of all Olmsted County residents. The association between traffic exposure (as measured by traffic density as well as vehicle kilometers traveled within 250m and 500m buffers around geocoded addresses of patients) and asthma exacerbations among REP participants was explored.
Summary/Accomplishments (Outputs/Outcomes):
Findings for MSP metro region
For 2003-2009 analyses, respiratory hospitalization outcomes (total respiratory, chronic lower respiratory disease [CLRD], and asthma) within the MSP metro displayed the strongest associations with PM2.5 and displayed the most agreement between case-crossover and time series results, with odds ratios ranging from 1.032 to 1.043 per 10µg/m3 increase in the 3-day moving average (lag02) for PM2.5 (Table 1). There were no consistent associations across the methods between ambient PM2.5 levels and the risk of asthma ED visits, cardiovascular (CVD) hospitalizations, or the risk of all-cause or cardiopulmonary (CPD) mortality. Time series analyses yielded statistically significant associations for asthma ED visits, total CVD hospitalizations, and all-cause mortality; these results were not found with the case-crossover analyses.
When case-crossover and time series analyses were conducted by sub-time periods, statistically significant associations were found in both case-crossover and time series analyses in the 2003-2005 time period for total respiratory and CLRD hospitalizations. In later time periods, those associations were no longer statistically significant. However, time period-specific odds ratios for any given health outcome were not statistically different from each other. Further, there was considerably less comparability between case-crossover and time series results when analyses were conducted by individual sub-time period rather than over the entire 7-year study period. Due to lack of statistical significance and general lack of agreement across case-crossover and time series results, it was difficult to detect any trends in the association of PM2.5 with either the cardiovascular hospitalizations or the all-cause and CPD mortality outcomes.
Time period-specific population attributable fractions (PAFs) were calculated for the respiratory hospitalization outcomes that had statistically significant case-crossover risk estimates (Table 2). The odds ratios generated from the case-crossover analyses from the 7-year study period were used in these calculations. The PAFs showed change over time in the numbers of respiratory hospitalizations that were triggered by PM2.5. Results suggest that the proportions of total respiratory, CLRD, and asthma hospitalizations attributable to short-term PM2.5 exposures above a policy-relevant referent concentration of 5µg/m3 declined by approximately 3-4% after the 2003-2005 baseline period.
Findings for Olmsted County
Statistically significant associations were not found between PM2.5 and most of the health outcomes analyzed for Olmsted County, except for time series analyses for all-cause mortality at some exposure lags (not seen in case-crossover). Risk estimates were less precise (wider 95% confidence intervals) compared with those for the MSP metro, and varied across case-crossover and time series for most of the health outcomes. The lack of statistically significant associations in Olmsted County case-crossover and time series analyses could be a result of the much smaller population under study and possible exposure misclassification resulting from the presence of only one continuous PM2.5 monitor within the county.
A supplemental sub-analysis that utilized Olmsted County population-based medical encounter data and Olmsted County traffic data showed an effect of vehicular traffic on asthma exacerbations. In logistic regression and Poisson regression models that accounted for sex, age, and poverty, traffic exposure measures were statistically significantly associated with asthma exacerbations (Tables 3, 4). Poverty was also strongly associated with asthma exacerbations.
Conclusions:
Daily average concentrations of population exposure derived from continuous monitoring of PM2.5 provides a potentially useful indicator for tracking population exposure to ambient PM in a geographic area where multiple monitors can be averaged together, such as in major cities. In smaller communities with single monitors, such as Olmsted County, this indicator is less reliable and is vulnerable to monitor malfunction and equipment/method changes over time. Moreover, because there are many factors that influence PM concentrations, trends in ambient PM concentrations should be accompanied by trend data on chemical species, precursor gases, and pollutant emissions, in order to put the air quality of a given locale into context.
Because there are many external factors that contribute to health outcome trends, indicators of population health (i.e. measured trends in hospitalizations, ED visits, deaths) taken alone cannot be used as indicators of progress in the impact of air quality.
The degree of comparability of results from case-crossover analyses versus time series in this project was variable, with the most comparability occurring for respiratory hospitalization analyses using data over a 7-year period. There was less agreement between methods for analyses conducted over shorter time periods or for the other health outcomes.
Using local data to derive C-R functions and PAFs produces an economically and technically feasible measure of public health impact, especially in large populations for which robust measures can be derived and in areas with adequate pollutant monitoring. In many cases, local analyses may require pooling multiple years of data. Continued tracking of the PAF measure with more years of data is needed to confirm whether the trend that has been observed in reduced respiratory disease outcomes in this study for the MSP metro continues.
Due to the lack of statistical significance observed, it was difficult to detect similar time period trends in the association of PM2.5 with either cardiovascular hospitalization outcomes or all-cause and cause-specific mortality outcomes in the MSP metro. More study is needed to determine external factors or effect modifiers (e.g. declining heart disease trends, inequalities in health or access to quality care) that may explain this result.
For geographic areas with smaller populations such as Olmsted County, MN, alternate data sources and methods may need to be utilized in order to explore local air quality-health associations. For example, the effect of traffic on asthma exacerbations using alternate data sources that were linked at finer geographical resolutions was explored in a sub-analysis of this project.
Data inputs, assumptions, and methods must remain stable over time in order for C-R functions or PAFs to be used successfully as reportable indicators for the purpose of public health surveillance. Standardized methods must be set in order to assure that model inputs and assumptions (e.g. lag structure choice, referent pollutant concentration) are selected and sustained in a systematic and consistent manner.
The interpretation and communication of these indicators is important. For example, it is important to stress that the indicators developed in this project apply only to acute health effects from short-term exposures, and thus represent only a portion of the total impact of PM. As such, message testing with a broad audience of data users and policy makers is needed to ascertain the utility of these methods for informing sound public policy that ultimately benefits the environment and public health.
Journal Articles on this Report : 2 Displayed | Download in RIS Format
Other project views: | All 7 publications | 2 publications in selected types | All 2 journal articles |
---|
Type | Citation | ||
---|---|---|---|
|
Adgate JL, Mongin SJ, Pratt GC, Zhang J, Field MP, Ramachandran G, Sexton K. Relationships between personal, indoor, and outdoor exposures to trace elements in PM(2.5). Science of the Total Environment 2007;386(1-3):21-32. |
R833627 (Final) R827928 (Final) |
Exit Exit Exit |
|
Pratt GC, Parson, K, Shinoda N, Lindgren P, Dunlap S, Yawn B, Wollan P, Johnson J. Quantifying Traffic Exposure. Journal of Exposure Science and Environmental Epidemiology. 2014 24, 290-196. |
R833627 (Final) |
Exit |
Supplemental Keywords:
air quality index, EPA Region 5, Bayesian, decision-making, epidemiology, health effects, hospital admissions, Midwest, Minnesota, particulates, trafficRelevant Websites:
Measuring Health Impacts of Fine Particles in Air Exit
Minnesota Pollution Control Agency Exit
Progress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.
Project Research Results
- 2011 Progress Report
- 2010 Progress Report
- 2009 Progress Report
- 2008 Progress Report
- 2007 Progress Report
- Original Abstract
2 journal articles for this project