Grantee Research Project Results
2008 Progress Report: Near Real Time Modeling of Weather, Air Pollution, and Health Outcome Indicators in New York City
EPA Grant Number: R833623Title: Near Real Time Modeling of Weather, Air Pollution, and Health Outcome Indicators in New York City
Investigators: Ito, Kazuhiko , Thurston, George D. , Nadas, Arthur , Matte, Thomas
Institution: New York University School of Medicine , New York City Department of Health and Mental Hygiene
EPA Project Officer: Hahn, Intaek
Project Period: December 1, 2007 through November 30, 2010 (Extended to November 30, 2011)
Project Period Covered by this Report: December 1, 2007 through November 30,2008
Project Amount: $494,552
RFA: Development of Environmental Health Outcome Indicators (2006) RFA Text | Recipients Lists
Research Category:
Objective:
The objective of this project is to develop models to predict acute respiratory morbidity (including asthma exacerbation) using near-real time weather, ambient air pollution, and respiratory emergency department (ED) visits in New York City (NYC). We will take advantage of a unique syndromic surveillance system that monitors ED visits daily by the New York City Department of Health and Mental Hygiene (NYCDOHMH). We will systematically characterize the sequence of events among weather conditions, air pollution buildup, and health effects indicators. Using sub-area analysis, we will also determine spatial and neighborhood/socio-economic factors that influence the prediction power and efficiency of the models. We will estimate model uncertainties by computing prediction errors of candidate models in a series of real-time validation tests. Overall, this proposed project will create a framework to model, in real time, acute health outcome indicators of environmental exposures in a large metropolitan area.
Progress Summary:
The highlights of our research findings from the first year:Impact of heat wave on mortality: We evaluated model performance for an exhaustive set of alternative weather models, using both parametric and non-parametric time-series Poisson models, to predict the heat wave effects on natural mortality. We initially chose to examine the impact of heat waves because the result will be directly useful in advising the New York City Office of Emergency Management (OEM) in determining the appropriate threshold of temperature index for heat wave advisory/warnings for the then coming summer (i.e., summer 2008). We examined various temperature indices (maximum temperature, heat index, apparent temperature, etc.) and various combinations of lags and averages of lags of these indices, adjusting for temporal trends, day-of-week, and holidays. We assessed the model fit through a variety of statistical diagnostics. We found that including individual lags 0 through 3 days of heat index (HI) best predicted impacts of heat waves on mortality in both parametric (which included linear, quadratic, and cubic terms) and non-parametric models. The fitted HI/mortality relationship showed a non-linear relationship, with a steeper slope above a 100 degrees HI. The impact of consecutive days of 95 degrees HI on mortality was approximately equivalent to a single day of 100 degrees HI. We met with the OEM and local National Weather Service office in mid-April 2008 to communicate these findings and subsequently DOHMH formally recommended that these agencies modify the threshold for heat advisories and activating a response. Thus, our research project has already contributed to the improvement of a public health program in New York City.
Within-city effect modifiers of short-term air pollution effects: We have conducted spatially stratified time-series analysis examining the relationship between air pollution and asthma ED syndromic data. Children’s (age 5 to 17 years old) asthma ED syndromic data were analyzed for the entire city, as well as at zip code level during the study period 2002-2006. We investigated the association between nitrogen dioxide (NO2), a marker of local combustion sources including traffic, and the asthma ED syndromic data. We first developed the city-wide Poisson GLM time-series model using the daily aggregate counts of asthma ED syndromic data for the entire city, adjusting for temporal trends and seasonal cycles, day-of-week, immediate and delayed non-linear temperature effects. We could run the Poisson model for 117 zip codes out of 183 zip codes where any counts were reported.
In the second-stage random effects meta-regression, the 117 estimated zip code level NO2 risk estimates were regressed on zip code level census data including median household income, percent college graduates, percent high-school graduates, percent Hispanics, percent non-Hispanic blacks, percent poverty, as well as zip code-level traffic density, estimated using the 2002 traffic estimates for New York City from the New York Metropolitan Transportation Council (NYMTC). The traffic density variable was a significant effect modifier of NO2 effect, and it was more significant than the census variables examined. This examination of the within-city variation of air pollution effects and the role of effect modifiers is one of our major goals of this project, and is a new and relevant contribution to this field. This line of research is also relevant to the EPA’s mission to identify at-risk populations of environmental pollution.
Future Activities:
In the first half of the year 2 of this project, we will focus on finalizing the candidate models to predict weather/air pollution effects on the asthma and respiratotry syndromic data and mortaltiy. We will prepare and submitt manuscripts describing the result we have yielded thus far.Journal Articles:
No journal articles submitted with this report: View all 7 publications for this projectSupplemental Keywords:
weather, air pollution, asthmaProgress 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.