Developing Analytic Forecasting Methodologies for Health Impact Assessment Rajiv Bhatia, MD, MPH San Francisco Department of Public Health

Developing Analytic Forecasting Methodologies for Health Impact Assessment

Rajiv Bhatia, MD, MPHSan Francisco Department of Public Health

ForecastingPage 2

Rajiv BhatiaAlaska Health Impact Assessment Training 2008

Presentation Overview

The Distinction Among Assessment of Existing Conditions vs. Monitoring vs. Forecasting

Three Examples:Existing Forecasting MethodNew Method Based on Existing ResearchNew Method Based on New Research

Implications for Alaska HIA

ForecastingPage 3


Example 1: Assessment and Mitigation of Roadway Air Pollution Impacts on Sensitive Uses

State and National air quality standards concern limited pollutants

Regional monitoring does not capture intra-urban variation in exposure

Regulations limit tailpipe emissions per mile but not vehicle intensity

Local agencies do not regulate air quality land use conflicts related to high volume roadways

ForecastingPage 4


Available Health Effects Assessment Methods For Air Quality Assessment

Dose response functions can associate area-level air quality exposures with health effects

Air quality dispersion models and other techniques can assess roadway related air quality exposure based on: Vehicle Flow, Speed Emissions Meteorology Relationship between Facilities and Sensitive

Receptors

ForecastingPage 5


Estimating Mortality Impacts From Exposure to PM2.5 based on CARB CR Functions

Mortality = R0 • [exp (-*∆PM2.5 -1) ] • P

R0 = Baseline Mortality Rate = Coefficient Derived from Relative Risk P = Affected Population

ForecastingPage 6


Spatial Extent of Vehicle PM2.5 All Vehicle Sources using CAL3QHCR—West Oakland, CA

ForecastingPage 7


Applications

Location of Sensitive Uses Transportation System Planning Indoor Air Quality Ventilation Standards

ForecastingPage 8


Example 2: Quantifying the Health Benefits of a Living Wage

Few analyses health benefits of labor policies Plausible relationship meditated through material needs

Consistent association among high quality epidemiologic studies on income and health looking at multiple health outcomes

ForecastingPage 9


Data Required For Impact Analysis

The baseline income of the population targeted by the living wage

The estimated income gains of workers benefiting from the new wage

A dose response function between income and health outcomes

ForecastingPage 10


Inclusion Criteria For Studies Providing Dose-Response Relationships

English language peer reviewed literature 1990-1998 Studies of income and mortality, hospitalizations, or

health status indicators Subjects representative of the U.S. general population Income measured at the household, family or individual

level Longitudinal design Statistical adjustment for age and gender year of income

ascertainment provided Income applied as a continuous variable

ForecastingPage 11


Estimated Health Effects Due To Living Wage Income Gains For Workers With A Current Family Income of $20,000

Study/Outcome Model Effect Measure Full Time Workers Part Time Workers

Backlund, 1996

Mortality-Male Proportional Hazards Hazard Ratio 0.94 (0.92-0.97) 0.97 (0.96-0.98)

Mortality-Female Proportional Hazards Hazard Ratio 0.96 (0.95-0.98) 0.98 (0.97-0.99)

Ettner, 1996

Health Status Ordered Probit Relative Risk 0.94 (0.93-0.96) 0.97 (0.96-0.98)

ADL Limitations Probit Relative Risk 0.96 (0.95-0.98) 0.98 (0.97-0.99)

Work Limitations Probit Relative Risk 0.94 (0.92-0.96) 0.97 (0.95-0.98)

CES—Depression Scale Two Part Elasticity -1.9% -1.1%

Number of Sick Days Two Part Elasticity -5.8% -3.2%

Alcohol Consumption Two Part Elasticity +2.4% +1.3%

Duncan, 1998

Completed Schooling OLS Regression Years of Schooling 0.25 (0.20-0.30) 0.15 (0.12-0.17)

H.S. Completion Logistic Regression Odds Ratio 1.34 (1.20-1.49) 1.18 (1.11-1.26)

Non-Marital Birth Proportional Hazards Hazard Ratio 0.78 (0.69-0.86) 0.86 (0.81-0.92)

ForecastingPage 12


Example 3: Area-level Model of Pedestrian Vehicle Collisions

Transportation Analyses in EIA provides little analysis of Pedestrian Safety Impacts:

Vehicle-pedestrian injuries and fatalities are preventable.

Key area-level environmental determinants of collisions include: Traffic volumes Traffic speed Pedestrian activity

ForecastingPage 13


An Environmental Approach:Evident area-level patterns – correlate with the freeway network, concentrations of streets with heavy arterial traffic, pedestrian activity centers (e.g., downtown, Golden Gate Park).

27 - 191

15 - 26

8 - 14

0 - 7

Number of Collisions

Highways/Freeways

Source: California Highway Patrol, Statewide Integrated Traffic Records System

0 3 61.5 Miles

Vehicle-pedestrian injury collisions: San Francisco, California census tracts (2001–2005)

ForecastingPage 14


Model development framework

How do transportation, land use, and population factors predict change in pedestrian injury collisions in San Francisco census tracts?

Travel Behaviors:walking, public transit,

private vehicle use

Population Characteristics: number of residents and workers, socio-demographic characteristics

Built Environment: street and land use

characteristics

Vehicle-Pedestrian Collisions (Number):

pedestrian injuryand death

ForecastingPage 15


Vehicle-Pedestrian Injury Collision Model

o Publicly available data (SWITRS, U.S. Census, SF Planning,

SF MTA)

o Traffic Volume, Street Characteristics – SF DPH/UC Berkeley

o Continuous, census-tract level variables

o Multivariate, linear regression model – predicts the natural log of vehicle-pedestrian injury collisions:

ln(PIC) = b0 + ∑biXi

ForecastingPage 16


Vehicle-Pedestrian Injury Collision Final Model Variables

Traffic volume (+) Arterial streets (+) Neighborhood commercial zoning (+) Employees (+) Residents (+) Land area (-) Below poverty level (+) Age 65 and over (-)

ForecastingPage 17


Final Ordinary Least Squares Regression Model Vehicle-Pedestrian Injury Collisions: San Francisco, California, 2001-2005 (n=175 census tracts)

a Excludes grade-separated street segments inaccessible to pedestrians.

Census Tract-Level Variable Coefficient SE p-value95% CI, Lower

limit95% CI, Upper

limitTraffic volume

(n, natural log, aggregated average daily traffic counts )a 0.753 0.115 0.000 0.526 0.981

Arterial streets, without public transit (%, street length) 0.017 0.004 0.000 0.009 0.025Neighborhood commercial (%, land area) 0.029 0.007 0.000 0.016 0.042Residential-neighborhood commercial (%, land area) 0.021 0.006 0.000 0.009 0.032Land area (square miles) -0.704 0.195 0.000 -1.089 -0.319Employee population (n, natural log) 0.228 0.046 0.000 0.136 0.319Resident population (n) 0.00010 0.00003 0.000 0.00005 0.00015Living below the poverty level last year (%, resident population) 0.019 0.006 0.003 0.006 0.031Age 65 and older (%, resident population) -0.016 0.007 0.013 -0.029 -0.003

Constant -9.954 1.283 0.000 -12.488 -7.420Adjusted Pearson R2

0.7154

ForecastingPage 18


Comparing the Simple Bivariate Model to Our Multivariate Model Approach

Holding all covariates constant (as above), the model is equivalent to a power function with β=0.753.

0

10

20

30

40

50

60

70

80

0 10 20 30 40 50 60 70 80 90 100

% Increase in Traffic Volume

% In

crea

se in

Ped

estr

ian

Inju

ry C

olli

sio

ns

PF: β = 0.8

FM: β = 0.753

PF: β = 0.5

PF: β = 0.25

ForecastingPage 19


Vehicle-Pedestrian Injury Collision Model: Eastern Neighborhoods Plans EIR Analysis

Census Tract Characteristics

Traffic

Volumeb (CT sum)

Population (CT sum)

Traffic

Volumeb (% increase, CT)

Populationd

(% increase, CT)

San Francisco (N=176) 212,238 776,733 na na

Eastern SOMA (N=5)a 20,550 19,954 15%c 25%

Mission (N=13)a 20,307 60,202 15%c 8%

Show Place Square/Potrero

Hill (N=9)a 27,771 20,984 15%c 39%

Central Waterfront (N=3)a 8,682 6,397 15%c 58%

All Eastern Neighborhoods

(N=23)a 52,602 91,109 15%c 16%

Planning Area (N, Census Tracts)

Existing Conditions Estimated Changes

a Areas defined based on SF Planning boundaries, and census tracts used for the Eastern Neighborhoods Rezoning Socioeconomic Impacts analysis.

b Census Tract, Aggregate Traffic Volumes. c Based on the Air Quality Chapter, Eastern Neighborhoods Pre-draft Environmental Impact Report, 2007. d Population increases based on increased population and housing units projected in Rezoning Option B, detailed in the

draft Eastern Neighborhoods Rezoning and Community Plans, Environmental Setting and Impacts, April 2007.

ForecastingPage 20


Vehicle-Pedestrian Injury Collision Model: Eastern Neighborhoods Plans EIR Analysis

20%

21%

15%

24%

Predicted % change in pedestrian injury collisions based on estimated changes inresident population and traffic volume.

ForecastingPage 21


Vehicle-Pedestrian Injury Collision Model: Application

Land Use Development Transportation Facilities Planning and

Funding Congestion Pricing and other

Transportation Policy

ForecastingPage 22


A General Approach to Predicting Health Effects Using Epidemiological Research

1. Develop Clear Analytic Objectives2. Literature Review

A. Develop study criteria and data needsB. Identify SourcesC. Establish Adequacy of Existing ReviewsD. Evaluate studiesE. Formal summary and documentation of review

3. Make qualitative inferences on health effects 4. If appropriate and feasible, quantify effects

A. Select or generate a summary effect measureB. Estimate Baseline and Changes to “Exposure”C. Predict Health Impacts (PAR, Forecasting)

5. Qualify certainty of assessment & predictions

ForecastingPage 23


Developing Forecasting Methods for Alaskan HIA

Some environmental - health relationships may be generalizable from general population studies

HIA forecasting methods in Alaska probably requires new research environmental-health relationships

Data collection and monitoring will support long term research efforts

Documents

Developing Analytic Forecasting Methodologies for Health Impact Assessment Rajiv Bhatia, MD, MPH San Francisco Department of Public Health