Transcript Assessing the Impact of Community Policy on Physical Activity and Health with Health Impact Analysis Candace Rutt, Ph.D. Division of Nutrition and Physical Activity National Canter.

Assessing the Impact of
Community Policy on Physical
Activity and Health with Health
Impact Analysis
Candace Rutt, Ph.D.
Division of Nutrition and Physical Activity
National Canter for Chronic Disease Prevention and Health
Promotion
Centers for Disease Control and Prevention
What is Smart Growth?
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Greater density
Greater land-use mix
Greater connectivity
Range of housing opportunities
Strong “sense of place”
Pedestrian friendly
Numerous transportation choices
Preserve exisiting greenspace
How Does the Built Environment
Impact Health?
• Physical activity (recreation and
transportation)
• Social Capital
• Air quality
• Water quality
• Mental health
TRB/IOM
• Changing the built environment to make it
more activity conducive is desirable “even
in the absence of the goal of increasing
physical activity because of their positive
social effects on neighborhood safety,
sense of community, and quality of life”
Health Impact Assessment (HIA)
A combination of procedures, methods, and
tools by which a policy, program, or project
may be judged as to its potential effects on
the health of a population, and the
distribution of those effects within the
population (Gothenburg consensus statement, 1999)
Health Impact Assessment
 Tool to objectively evaluate a project/policy
before it is implemented
– Provide recommendations to increase positive and
minimize negative health outcomes
 Encompasses a variety of methods and tools
– Qualitative and quantitative
– Community input and/or expert opinion
 Has been performed extensively in Europe,
Canada and other countries
– Regulatory and voluntary basis
Potential Contributions of HIA
 Bring potential health impacts to the
attention of policy-makers, particularly when
they are not already recognized or are
otherwise unexpected
 Highlight differential effects on population
sub-groups
Using HIA for
Projects vs. Policies
 Projects: Physical developments (highway, rail
line, park, trail, housing complex, etc)
– Affect smaller population
– More detailed plans
– Easier to define target population, stakeholders,
and perform impact estimation
 Policies: Set of rules and regulations that
govern activities and budget expenditures
(zoning, farm subsidies, living wage law, etc.)
– Affect larger population
– Greater impact on public health
– Health impacts may be harder to quantify
HIA Level of Complexity
 Qualitative – describe direction but not magnitude
of predicted results
– Easy to predict; hard to use in cost/benefit models
– Example: Build a sidewalk and people will walk more
 Quantitative – describe direction and magnitude
of predicted results
– Difficult to obtain data; useful for cost/benefit models
– Hypothetical example: Build a sidewalk and 300
people who live within 200 yards of location will walk
an average of 15 extra minutes per day
Voluntary vs. Regulatory
 Voluntary (a tool used by a health officer to
inform a planning commission)
– Simpler, less expensive, less litigious
– Less likely to be used if not required
– More politically acceptable
 Regulatory (modeled on a required
environmental impact statement)
– More complex, more expensive, more litigious
– More likely to be used if required
– Less politically acceptable
Community Involvement in
Conducting an HIA
 Increases community buy-in to project
 Helps identify social issues as well as
health issues
 Commonly used in HIAs in Europe
 May add substantially to time and
resources needed to conduct HIA
HIA efforts outside the U.S.
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Extensive work for nearly a decade
Increasing interest
Usually focused on local projects
Often linked to EIA or focused on
facilitating community participation
HIA in the U.S.
 To date only a handful have been
completed
 Voluntary basis
 Very few people currently trained to
complete HIAs
 However, there is a lot of interest in HIA
(APA, NACCHO, CDC, RWJF, FHWA,
ARC, CQGRD)
Relationship of HIA to Environmental
Impact Assessment
 EIA
– Regulatory
– Thousands conducted each year
– HIA components could logically fit within an
EIA
Learning from EIA
 But EIAs…
 Long, complex documents
 Process is time-consuming and expensive
 Often litigious process
 Tends to focus on projects, not policies
 Tends to stop short of considering health
outcomes
Steps in Conducting a
Health Impact Assessment
 Screening
– Identify projects or policies for which an HIA would be
useful
 Scoping
– Identify which health impacts should be included
 Risk assessment
– Identify how many and which people may be affected
– Assess how they may be affected
 Reporting of results to decision-makers
– Create report suitable in length and depth for audience
 Evaluation of impact on actual decision
process
Screening – When to do HIA
 In general, HIA is most useful
- For policy-decisions outside health sector
- When there are likely to be significant health
impacts that are not already being considered
- The HIA can be completed before key
decisions are made and stakeholders are
likely to use information
- There are sufficient data and resources
available
Scoping - Health Impacts to
Consider in an HIA
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Physical activity, obesity, CVD
Air quality, asthma, other respiratory diseases
Water quality, waterborne diseases
Food quality, food borne diseases, nutrition
Motor vehicle, pedestrian and other injuries
Accessibility for persons with disabilities
Noise
Mental health
Social capital
Social equity, environmental justice
Risk Assessment
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Logic frameworks
Assessing research evidence
Qualitative vs. quantitative outcomes
Calculate estimates of morbidity and
mortality
 Cost-effectiveness when feasible
Examine Feasibility of HIA is U.S.
 Received funding from RWJF to
complete two case studies of HIA
 Worked with UCLA to complete these
case studies
Screening – Initial List of HIAs
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General Walkability
Walk to School
Trails (recreation and transportation)
Active Commuting to Work
Worksite Interventions
Mass Transit
Zoning
Location Efficient Mortgage
Buford Highway
Beltine
Screening - Selection Criteria
 Specific enough to create quantitative
estimates
 Impact physical activity
 High quality data
 Not overly complicated
 Political interest
 Target at risk populations
 Foundation for other HIAs
 Generalizability
Screening - Selecting Case
Studies
 Walk-to-school HIA
 Natomas school district in Sacramento, CA
 Buford Highway HIA
 Highway redevelopment in Atlanta, GA
Screening - Site Selection
 Sacramento was selected because:
 Program already in-place, which facilitates
determination of project and population
parameters
 Program staff interested in cooperating with
research team
 Minimal seasonality
 Ethnically-mixed, modest income population
 24% of students currently walking to school
Diverse Student Population
in Natomas Schools
Elementary (K-5)
Middle School (6-8)
High School (9-12)
Male
Female
44.8%
24.7%
30.5%
51.8 %
48.2%
Non-Hispanic White
Black
Hispanic/Latino
Asian
Total
27.1%
24.7%
26.9%
12.5%
8,636
Streets around the target schools
Scoping - Create Logic Model
Policy
Education: safety
training
Engineering:
improve
pedestrian
facilities, traffic
calming
Enforcement:
increase police
presence,
crossing guards
Dedicated
resources:
walking school
busses
Proximal
Impacts
Intermediate
Impacts
Social norms
Air and noise
pollution
walkability
safety
Perceptions of
risk
Motor vehicle
use
Physical activity
(short-term)
Physical activity
(long-term)
Health
Outcomes
Asthma
Obesity
CVD risk
factors
Insulin
sensitivity
cancers
osteoporosis
Mental health
Injury
Risk Assessment
 More thorough literature review of all identified
proximal, intermediate, and health outcomes
 Determine which health impacts will be done
qualitatively versus quantitatively
 Physical activity
 BMI
 Gather data and perform quantitative analysis
Risk Assessment – Baseline Data
Enrollment in Natomas Unified schools
% of total enrollment in elementary
grades
6,000
64.5%
California Department of Education
enrollment statistics for Natomas Unified
School District 2003, k-8th grade
(http://data1.cde.ca.gov/dataquest/)
TABLE 1-1: SEX DISTRIBUTION FOR EACH SCHOOL LEVEL
(%)
Male
Female
total
%
n
%
n
%
n
Elementary
53.2%
2,060
46.8%
1,810
100.0%
3,870
Middle School
52.1%
1,110
47.9%
1,020
100.0%
2,130
Total
52.8%
3,170
47.2%
2,830
6,000
California Department of Education enrollment statistics for Natomas Unified School District 2003, k5th grade used for Elementary; 6-8th grade for Middle School (http://data1.cde.ca.gov/dataquest/)
Risk Assessment – Baseline Data
TABLE 1-2: PREVALENCE OF UNHEALTHY BODY COMPOSITION AND EXCESS BMI AT
BASELINE
Assumed fraction of "unhealthy body composition" due to excess BMI =
unhealthy body
composition
95.0%
Excess BMI
(based on fraction of unhealthy body
composition in the pop. due to
excess BMI)
%*
%
n
Elementary male
33.8%
32.1%
661
Elementary female
23.8%
22.6%
409
Middle school male
44.7%
42.47%
472
Middle school female
34.2%
32.5%
331
*California Dept of Ed "Fitnessgram" 2002-2003: 5th grade used for Elementary; 7th
grade for Middle School (http://data1.cde.ca.gov/dataquest)
Risk Assessment – Estimated Impact
TABLE 1-3: WALK-TO-SCHOOL PROGRAM CHARACTERISTICS
Default
Theoretical Max.
Input
Avg walk distance to school (mi)
0.6
N/A
0.6
Assumed walking speed (mi/hr)
1.8
N/A
1.8
3
5
3
Avg # days walked to school among those
who walk to school (days/week)
inputs below must be
>0 & ≤ max.
specified at left
% of total who walk to school at baseline:
Elementary
24%
90%
24%
Middle School
24%
90%
24%
inputs below must be
>0 & ≤ max.
specified at left
% increase in # walkers due to intervention:
Elementary
64%
317%
64%
Middle School
64%
317%
64%
Risk Assessment – Expected Outcomes on
Physical Activity
• 39% of students are expected to walk after
the intervention (64% increase)
• Avg. of 15 min/day additional walking
Risk Assessment – Expected
Outcomes for BMI
Change in average BMI due to
increase in pa
Change in hours * (change in
BMI/hrs. pa/day)
Number of
students in
sub-group
Participants
All students
Average all elem.
-.004
-.001
3870
Average all middle
-.015
-.002
2130
Average all males
-.009
-.001
3170
Average all females
-.020
-.003
2830
Average all
overweight
-.053
-.08
1873
Total
-.014
-.002
6000
* estimates from Berkey et al, 2003
Increase in Daily Hours of PA by Number
of Days Walked to School
Average Daily Hours of PA
0.60
0.45
0.30
0.15
0.00
1
2
3
4
5
# Days Walked to School
Number of days walked to school vs. average daily hours of physical activity among
participants; Assuming 24% baseline walking, 0.6 miles one-way & 64% increase in
walking due to intervention
Decrease in BMI
(overweight) by Days Walked to School
0.10
Average Decrease in BMI
0.09
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0.00
1
2
3
4
5
# Days Walked to School
Number of days walked to school vs. average decrease in BMI among obese participants;
Assuming 24% baseline walking, 0.6 miles one-way & 64% increase in walking due to
intervention
Increase in Daily Hours of PA by
Intervention Effect
Average Daily Hours of PA
0.25
0.20
0.15
0.10
0.05
0.00
10%
30%
50%
70%
Intervention effect
90%
317%
Traffic-related injury
 Walk-to-school programs can actually decrease
pedestrian injury rates:
 No injuries reported in first two years of Marin
County program
 Orange County program reported a decrease in
injury rates
 Estimating changes pedestrian injury rates not
feasible for small numbers/small areas
Air pollution:
Expected Impacts
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Walk-to-school programs may increase or
decrease exposure to air pollution depending on
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Current mode
 Exposure to several pollutants 50-400x times
higher inside diesel school buses than outside (Sabin,
Behrentz, Winer et al., 2003)
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Inhalation rates
Duration of trip
Traffic density along walking routes
Time and season
Marginal increase or decrease is probably small
relative to PA-related impacts
Assumptions for Kids Walk
 Assume a best-case scenario modeled after
Marin county (different demographics)
 Relationship between time spent walking and
BMI from Berkey et al (2003) apply to younger
children
 1 year time horizon for effects
 Average distance walked to school is 0.6 miles
(NHTS, 2001)
 Average walking speed is 1.8 miles/hour
Implications of Case Study
 Walk-to-school programs are important,
but only part of the solution of childhood
obesity
 HIA can either temper expectations,
provide justification for termination, or
provide strong support for
programs/policies
Key Challenges of HIA
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Uncertainties (data, models, policy)
Timeliness
Relevance to stakeholders and decision
makers
Summary
 HIA is a new and evolving science in the U.S.,
however it is a promising new approach to
quantify health impacts of a wide variety of
policies and projects
 HIA provides only one piece of information
(health) in complex decisions and stakeholders
may have different priorities
 HIA provides an outlet for health to be
appropriately factored into complex decisions