# Presentation: 505521

## Public Health Air Surveillance Evaluation (PHASE)

What  is  it  ?
Where  has  it  been?
Where  is it  going?

***What  is  it  ?***
***Where  has  it  been?***
***Where  is it  going?***

- AIR QUALITY CONFERENCE
- February 6,  2006
- San Antonio, Texas
- Fred Dimmick
- (for the PHASE development team)
- Office of Research and Development
- Environmental Protection Agency

## Public Health Departments

- EPA

- CDC

- Maine, New York, and Wisconsin

- - Multi-Disciplinary Partnership -

## Overview of Presentation 

- Environmental Public Health Tracking
- Preliminary Air Quality- Health Activities
- Air quality data
- Public health tracking data
- Assessment activities
- Thoughts on next steps

## Slide 4

## Slide 5

## Public Health Air Surveillance Evaluation

***P******ublic****** ******H******ealth****** ******A******ir****** ******S******urveillance****** ******E******valuation***

**Tracking**

**Air Quality**

## Sources of Air Quality Data

- Ambient Air Monitoring
**AQS: PM2.5 and Ozone for 2001**
- Air Quality Modeling
**CMAQ: PM2.5, Ozone and Met for 2001**
- Spatial Predictions:  Combined Data
**EPA/National Exposure Research Lab**

## Ambient Air Monitoring

- “True” measure of air quality
- Spatial gaps - rural areas have few sites
- Temporal quality varies – hourly to weekly
- Emerging with routinely available information (hourly - daily)

## Air Quality Modeling

- Estimate of air quality
- High spatial and temporal resolution
- 36 km horizontal grid or less, 1-hr time steps
-  *but*  potential for higher measurement error relative to monitoring data
- Air Quality Forecasting
- Emerging source of routine data

## Spatial Prediction Using Monitor and Model Data

**Spatial Prediction Using Monitor and Model Data**

## Key Remanding Uncertainty

- _Ambient vs Personal Exposure_
- Methods estimate ambient concentrations, but . . .
- People experience health impacts from the air they breathe (i.e., their personal exposure)
- How do the outputs from the various air quality characterization methods relate to personal exposure?

- Personal Monitor

## Quick Comparisons of Air Quality Methods

**Notes:**
Unable to do quantitative evaluations a this time

## Preliminary Analysis

- Analysis & display by NYDOH

**(PM2.5)**

**Notes:**
This plot compares the CMAQ model to some monitor data in the NYC area.  In this case, the CMAQ estimate was based on 11 monitors in a 36 km grid cell.  Here I plotted the data for 5 of the monitors.  This plot demonstrates that the CMAQ model tends to be higher than the monitor data in New York City, although the comparison is better in other parts of the state.  Emissions inventory is wrong.

## Preliminary Analysis

- Analysis & display by NYDOH

**(PM2.5)**

**Notes:**
This plot shows the Hierarchical Bayesian model data overlayed on the same monitor data as in the previous slide.  The HB model follows the same trend as the CMAQ model, but stays closer to the monitored values.

## Environmental Public Health Tracking and Association with Air Quality

- CDC (Vickie Boothe), New York Department of Health (Tom Talbot and Valerie Haley), State of Maine Bureau of Health (Chris Paulu and Andy Smith), Wisconsin Division of Public Health (Mark Werner and Marni Bekkedal), John Hopkins University and Tom Bateson, Consultant

## PHASE Analysis Plan: Linking Air Quality & Health Data

- Link health data to the PM and ozone air quality data for each EPA air quality characterization method.
- Determine the strength of association of adverse health outcomes for each method (in particular compared to existing detailed studies).
- Show how the strength of association varies between each method:
**Evaluate ease of use of the data**
**Temporal Resolution**
**Spatial Coverage**
**Observe differences between geographic scales**

## Measured Strength of Association With “Case-Crossover” Analysis

- “Was a health event triggered by something that happened just before?”
- Each case serves as his/her own control
- Compare air pollution right before someone had a heart attack, to the air pollution on days when that person did not have a heart attack

*Slide Courtesy of NYSDOH*

**Notes:**
The case-control study is a method to answer the question, “Was this event triggered by something that happened just before?”  This study design was introduced in 1991, so it’s relatively new as far a statistics goes.  It has recently been used in several studies of air pollution and health as an alternative to traditional time-series studies.

The advantage of this study design is that each case serves as his or her own control.  In this way, we control for time-invariant confounders, such as a smoking-status, race, and gender, by design.

For example, What you do is make a list of all the people who had heart attacks, and for each person on the list, you compare the air...

Given that the person had symptoms at time 1 but not time 2 or time 3, what is the probability that it occurred at time 1 but not time 2 or 3.

## Example: PM2.5 & Heart Attack

*Slide Courtesy of NYSDOH*

**Heart attack**
**40 ug/m****3**

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***        ******October 2005***

**Heart attack**
**40 ug/m****3**

**Notes:**
Let’s say that someone had a heart attack yesterday.  You could compare the PM concentration on that day...

## Comparing Lag 0 (current day)

**No heart attack**
**30 ug/m****3**

**No heart attack**
**10 ug/m****3**

**No heart attack**
**20 ug/m****3**

*Slide Courtesy of NYSDOH*

**Heart attack**
**40 ug/m****3**

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***        ******October 2005***

**Heart attack**
**40 ug/m****3**

**Notes:**
...to the concentrations on the three previous Mondays when the person did not have a heart attack.  BY making the comparison on other mondays, you are controlling for the fact that Mondays are generally a more stressful day than other days of the week, and by comparing the case to himself, you are controlling for personal factors that may be also be related to heart attacks, like age, SES, smoking status.
On this slide, we study the effect of the current day average particulate matter concentration...

## Comparing Lag 1 (previous day)

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**Lag 1**
**30 ug/m****3**** **
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**33 ug/m****3**
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**15 ug/m****3**
**                                      ****2**

**1**

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***        ******October 2005***

**Heart attack**

*Slide Courtesy of NYSDOH*

**Notes:**
We could also study the effect of the previous day’s PM concentration, often referred to as lag 1, ... or other lags within a few days of the case.  

## Results: Highlights

- Assessments have been completed in ME, NY and WI.
- Core applications
- Special studies
- Preliminary results are consistent with existing literature.
- Effective collaboration among state and federal programs important for quality product.
- Challenges:
- Addressing geo-coding of health data
- Developing code for “case crossover” model

## Where are we going ?

_**Phase 1 - Development and Evaluation**_
- Can we produced better air quality maps by combining data sets (Ambient Monitoring, Modeling, Satellite Data)?
- If so, do these maps improve currently available EPHT capabilities (proximity, interpolation)?

_**Phase 2 - Operability**_
- How do State partners acquire the improved air quality information?
- - Generate the maps themselves
- - Maps supplied to them

- State partners to use currently available approaches

- Phase 1
- Successful?

- YES

- NO

## Next Steps 

- Completing intercomparisons and preparing publications for review.
- Developing procedures to creating combined air quality data routinely.
- Constructing “case-cross” model for use by public health agencies.
- Additional dialogue and implementation of air- related EPHT.

## Disclaimer

*Although this work was reviewed by EPA and approved for publication, it may not necessarily reflect official Agency policy*