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Big Data for Health Policy, November 6, 2014 Linking population-based data to study effects of the built environment on health Gillian Booth MD MSc Associate Professor of Medicine, and Health Policy, Management and Evaluation, University of

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Linking population-based data to study effects of the built environment on health

Gillian Booth MD MSc Associate Professor of Medicine, and Health Policy, Management and Evaluation, University of Toronto

Big Data for Health Policy, November 6, 2014

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Trends in urban design car-oriented communities

Compact Communities Urban sprawl

Vs.

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Greater reliance on automobiles

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Are walkable neighborhoods experiencing a slower rise in

besity and diabetes?

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Overview of methods

Neighborhood level data Provincial health records

All residents aged 30-64 living in study area (community-dwelling)

Postal code of residence Walkability

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Study area

Includes 15 municipalities, with a combined population > 7 million people
Represents more than one-fifth of the Canadian population

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Neighborhood exposures

Walkability Index:

Population density
Residential density
Street connectivity
Walkable destinations

Glazier, Creatore, Weyman, Fazli, Matheson, Gozdyra, Moineddin, Shriqui VK, Booth GL. PLoS one 2014

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Results

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10 20 30 40 50 60 70 2001 2002 2003 2004 2005 2006 2007 2008 2009

Percent (%) Fiscal Year

Q1 (least walkable) Q2 Q3 Q4 Q5

Walkability Quintiles (Q)

Age-/sex-adjusted prevalence of overweight or

besity* by walkability quintile (Q)

+13% in least walkable areas

9% in most walkable areas

Data Source: Canadian Community Health Survey *adjusted for age, sex and based on ethnic-specific BMI thresholds; aged 30-64

N~50,000 participants across 5 CCHS cycles

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Data Source: Ontario Diabetes Database, Registered Persons Database *adjusted for age, sex, income, ethnicity; aged 30-64

Adjusted diabetes incidence* by walkability quintile (Q)

Number per 1,000 Fiscal Year

6 6.5 7 7.5 8 8.5 9 9.5 10 2001 2002 2003 2004 2005 2006 2007 2008 2009 Q1 (least walkable) Q2 Q3 Q4 Q5 (most walkable)

+ 6% in least walkable areas

7% in most walkable areas

N~3 million diabetes- free residents/year

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Mode of transportation:* Number of car trips per person per day by walkability quintile (Q)

Number of trips per capita Year Data Source: Transportation Tomorrow Survey *mode of transportation to work or school; aged 30-64

0.00 0.50 1.00 1.50 2.00 2.50 3.00 2001 2006 2011 Q1 Q2 Q3 Q4 Q5

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Mode of transportation:* Number of public transit trips per person per day by walkability quintile (Q)

Number of trips per capita Year Data Source: Transportation Tomorrow Survey *mode of transportation to work or school; aged 30-64

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 2001 2006 2011 Q1 Q2 Q3 Q4 Q5

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Mode of transportation:* Number of walking/cycling trips per person per day by walkability quintile (Q)

Number of trips per capita Year Data Source: Transportation Tomorrow Survey *mode of transportation to work or school; aged 30-64

0.00 0.05 0.10 0.15 0.20 0.25 0.30 2001 2006 2011 Q1 Q2 Q3 Q4 Q5

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Other lifestyle characteristics* by walkability quintile (Q)

0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 2003 2004 2005 2006 2007 2008 2009

Proportion

Year

Inadequate fruit and vegetable intake

Data Source: Canadian Community Health Survey *adjusted for age and sex; aged 30-64

0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 2003 2004 2005 2006 2007 2008 2009

Proportion Year

‘Inactive’ leisure time

Q1 Q2 Q3 Q4 Q5

Walkability Quintile

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Are individuals living in more walkable areas at lower risk of developing diabetes?

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Creation of study cohort

Lowest walkability quintile = 491,610 Highest walkability quintile = 466,957 March 31, 2012 for the development of diabetes (Ontario Diabetes Database) using postal code on April 1, 2002

Excluding

prior diagnosis of diabetes
living in long-term care facilities or
ther institutions

Provincial health databases

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Inverse Probability Treatment Weighting (IPTW) to create balanced groups

weights were assigned to each individual

based on their propensity score

5 region-specific Cox P.H. models
Toronto
Greater Toronto Area
Ottawa
Hamilton
London
Random effects to generate summary HRs
propensity scores reflecting the likelihood of

living in the highest vs. lowest walkability area

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Baseline characteristics after IPTW weights

Characteristic Low walkability High walkability

Standardized differences

Mean age 48.4 48.6 0.009 % Males 48.5 48.6 0.003 % Recent immigrants 7.1 6.3 0.03 % South Asian 5.4 5.6 0.01 % Other visible minority 20.1 19.2 0.03 % Highest deprivation quintile 15.7 12.6 0.09 Mean no. primary care visits/year 4.66 4.64 0.003 % Unstable chronic disease 24.8 24.7 0.001 % Myocardial infarction 0.7 0.7 0.001 Standardized difference < 0.1 = well balanced groups

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Based on weights from IPTW; * includes age, sex, income, % visible minority, % South Asians baseline comorbidity, hypertension, cardiovascular disease (MI, stroke)

Diabetes incidence in highest vs. lowest walkability quintile among individuals age 30-64 yrs

Favours high walkability

Hazard Ratio Toronto Greater Toronto Area Ottawa Hamilton London Overall

Summary HR 0.85

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Diabetes incidence in highest vs. lowest walkability quintile, all analyses, aged 30-64

Recent immigrants Long-term residents Low income High income

Overall Summary HR 0.85

Adjusted Hazard Ratio

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Conclusions

High neighborhood walkability appears to be

protective for the development of diabetes in young and middle-aged urban populations

Changes in zoning, urban planning, and design that

promote walking and other forms of active transportation may help to curb the ongoing rise in

besity and diabetes.
Further research is needed to understand the full

impact that such interventions will have.

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