Assessing and Forecasting the Impact of Air
Q lit on H
Quality
Health
lth Outcomes
O t
for
f a Local
L
l
County Health Department
Jeroen van Meijgaard – UCLA School of Public Health
Academy Health
June 2010
WHAT IS HEALTH FORECASTING? A TOOL FOR HEALTH
IMPACT ASSESSMENT IN A DYNAMIC ENVIRONMENT
– …. Socio-economic and demographic
g p
information on population
p p
Research-based linkages between health
determinants and health outcomes
Projected future values of model
parameters
Inquiries:
Output:
• What will happen if nothing
changes?
• How do interventions stack up?
• What
Wh t iis th
the magnitude
it d off major
j
discrepancies in health
outcomes across ethnic and
geographic segments?
• Future population w/
demographic and socioeconomic characteristics and
expected
p
health outcomes
• Projections of impact of
intervention(s) on health
outcomes in a target
population
p
p
Effects of
interventions on key
y
health determinants
HEALTH IMPACT ASSESSMENT AND HEALTH
FORECASTING COMPARED
HIA
Examine
E
i iimpactt off a
particular policy or program
on exposures and
subsequent health outcomes
i static
in
t ti population
l ti
Policy and Program Alternatives
Behaviors and Exposures
Population Health Outcomes
Short-Medium
Term (2-5 Years)
Long Term
(10+ Years)
Health
Forecasting
Examine impact of exposures
on outcomes in dynamic
population (over time)
HOW HEALTH FORECASTING HAS BEEN USED TO MODEL
AIR QUALITY SCENARIOS IN PLACER COUNTY
• Placer County Department of Health and Human
Services teamed with UCLA HFT to examine impact of
poor air quality in Placer County
• Results intended for community awareness about air
quality and to increase community engagement in policy
and
a
d ad
advocacy
ocacy act
activities
t es
AIR QUALITY IN PLACER COUNTY
• Placer County
covers 3 air basins
– Sacramento
Valley, Mountain
Counties, and Lake
Tahoe
• Air quality data
obtained from the
C lif i Ai
California
Air
Resources Board
(CARB)
Simulation for Placer County Population
• Each simulated actor assigned a place of birth and location within
Placer Countyy
• Air quality indicators, PM 2.5 and Ozone, modeled for the simulated
p p
population.
• Baseline: what we expect to see if no change is set in place to modify
the pollutant levels (changes may be the result of interventions or
unintentional effects)
• Modeled scenarios: what we expect to see under various conditions
- Climate Change Scenario – Increase in ‘Summer type’ weather
- Wildfire Scenario – Increase in prevalence of years with more
wildfires
- Targeted Reduction Scenario – Gradual percentage reduction in
Ozone and PM 2
2.5
5 measures
IMPLEMENTATION OF SCENARIOS INTO THE MODEL
Before Scenario Applied
pp
(Reference)
(
)
After Scenario Applied
pp
2030 (Sac.Valley Air Basin)
Avg. Ozone (8hr)=43.49ppb
Avg. PM2.5 (24 hr)=12.17ug/mm3
2030 (Sac.Valley Air Basin)
Avg. Ozone (8hr)=46.56ppb
Avg. PM2.5 (24 hr)=12.08ug/mm3
Prob
Prob
Climate Scenario
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
A A
Avg
Acreage B
Burned
d = 39
39,000/yr
000/
E
Expected
t d Acreage
A
Burned
B
d = 50,000/yr
50 000/
Prob
Prob
Wildfire Scenario
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
20% reduction
AQ R
Reading
AQ R
Reading
Target Reduction
Ozone
PM2.5
20% reduction
Ozone
PM2.5
CHANGES IN PM2.5 AND OZONE UNDER THE
TARGETED REDUCTION SCENARIO
Number of days exceeding the EPA standard PM2.5 and
1 in
O
Ozone
i S
Sacramento V
Valley
ll Ai
Air B
Basin
i with
ihT
Targeted
d
Reduction, 2010-2030
1 EPA
Standard for PM 2.5 of 35 ug/m3, Ozone 75 ppb
Alternatively can model proposed
Ozone standards 60-70ppb
ASSESSING THE HEALTH IMPACTS FROM CHANGES
IN AIR QUALITY
• Environmental Benefits Mapping and Analysis Program
(BenMAP) derived functional forms based on the
published scientific literature to estimate the relationship
between PM2.5 and Ozone and a number of health
outcomes
• Functional forms (i.e. Relative Risks) were then applied
to the Model to forecast health outcomes in 2030 for the
different scenarios
ESTIMATED HEALTH IMPACTS IN 2030
GREATEST IMPACT FROM SCHOOL ABSENCES WITH
TARGETED REDUCTION OF OZONE AND PM2.5
• Number of days of school absence due to respiratory
illness is expected to decline by 33% in the Targeted
Reduction scenario compared to baseline
• This means, in 2030, there would be 73,500 fewer days
of school lost among children in Placer County
• When students are absent, schools lose funds. In 2030,
targeted
ta
geted reduction
educt o o
of PM2.5
5a
and
do
ozone
o e would
ou d sa
save
e tthe
e
school district about $2.5M annually that would have
otherwise been lost due to absences from respiratory
illness
WEB INTERFACE ENABLES STAKEHOLDERS TO USE
RESULTS FOR LOCAL POPULATIONS
The full model is maintained at UCLA by project team – users can request scenarios to
be simulated.
A user friendly interface that uses static model output to enable users to perform
analysis on a local communities or counties. Users may input community specific
d
demographic
hi information,
i f
ti
and
d th
the iinterface
t f
provides
id ttables
bl and
d graphics
hi b
based
d on
modeling results.
The website is a primary means of wide
distribution of tools, results, and analyses
• Baseline forecasts
• Technical documentation
• Simplified version of the model that can
be used by local health officers
officers, their
staffs and other stakeholders.
Extra Slides
HEALTH FORECASTING ENABLES INFORMED DECISION
MAKING
The model allows decision makers to answer questions at various levels
of detail:
How will mortality rates in the state of California (or
any county) change over time?
What is the incidence or prevalence of disease X in
different counties in California, and how is this
expected to change in the next 10 years?
How much of the differences in disease incidence rates and
other key health outcomes across ethnic and geographic
segments can be attributed to known factors?
10 years from now, what will be the effect of a
public health intervention Y on the health outcomes
for different ethnic and racial groups in Ventura
County and Los Angeles County?
USING HEALTH FORECASTING TO MODEL SCENARIOS
OF INTEREST
• “What-if” scenarios: situations that are not necessarily
policy-specific, but spell out alternative future scenarios
– e
e.g.
g how does climate change affect air quality in the future
(compared to baseline/no change)?
• Target
g scenarios: hypothetical
yp
action-related scenario
– e.g. how does a targeted reduction in PM2.5 and Ozone affect
future outcomes?
• Intervention scenarios: modeling specific policy
intervention
– e
e.g.
g how does a specific policy intervention targeted at improving
air quality affect outcomes?
AIR QUALITY SCENARIO
Targeted Percentage Reduction in Pollutants Scenario
• Rationale: PM2.5 and Ozone levels are correlated. By modeling a
percentage
t
reduction
d ti iin PM2
PM2.5
5 and
dO
Ozone simultaneously
i lt
l it iis
assumed that overall pollution levels are reduced, and both primary
and secondary air pollution is impacted. Depending on current
levels, a target reduction in levels can be obtained by changing the
percentage reduction.
• Implementation: Apply across
across-the-board
the board 20% decrease in PM 2
2.5
5
and Ozone measurements. First baseline levels of pollution are
generated which are subsequently reduced by a certain percentage.
The scenario is implemented gradually over time to reflect time of
implementation, with reductions starting in 2010, and reaching 20%
reduction by 2030.
AIR QUALITY SCENARIO
Climate Change Scenario
• Rationale: Numerous climate change models suggest lengthening
summers Summer is also when many of the highest PM2
summers.
PM2.5
5 and
ozone levels are observed
• Approach: Shift sampling of historical data to over-represent summer
months and extend the length of summer. Since the research does
not necessarily suggest that there will be a reciprocal reduction in the
frequency
q
y and duration of winter inversion layers
y
((which are
associated with high particulate levels), the under-represented
months should be Fall and Spring Months.
AIR QUALITY SCENARIO
Frequency of severe wildfire seasons
• Rationale: Wildfire and climate models suggest increasing
frequency and increasing amount of biomass burned in
wildfires in the Central and Northern California (biomass
burned incorporates both fire size and severity)
severity). Due to highyear-to-year variability the percentage increase over a short
period of time is difficult to increase but it might be on the
order of 5% annual increase in biomass burned.
• Approach: During the period 1999-2008, the years 1999,
2000 2001,
2000,
2001 2004 and 2008 were the 5 most severe fire
seasons in Placer and surrounding counties based on
acreage burned. Wildfires generally cause an increase in
levels of PM 2.5. and Ozone. This scenario reflects years with
substantially more acreage burned.
USER FRIENDLY INTERFACE – FORECAST OUTCOMES
FOR SPECIFIC POPULATIONS
USER FRIENDLY INTERFACE – COMPARE OUTCOMES
ACROSS DIMENSIONS