ICUH 2016: Walkability study in Madrid (Spain)

1. Pedro Gullón1, Alba Cebrecos1,2, Xisca Sureda1, Usama Bilal1,3,
Julia Díez1, Francisco Escobar1,2, Manuel Franco1,3
1. Social and Cardiovascular Epidemiology Research Group, Universidad de Alcala, Madrid,
Spain
2. Department of Geology, Geography and Environment, Faculty of Biology, Chemistry and
Environmental Sciences, University of Alcalá, Alcalá de Henares, Madrid, Spain.
3. Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
Does walkability differ by area
sociodemographic profile? A
study of Madrid City

2. http://hhhproject.eu
JUSTIFICATION
PHYSICAL ACTIVITY
CVD,
Weight
DM II
Breast cancer
Colon cancer
Osteoporosis
Falls
Mental diseases
0.68 increase in life
expectancy* (if all
sedentarism is removed)
Source: blog “Bicicleta, ciudad y viajes”
http://bicicletasciudadesviajes.blogspot.com.es/2012/03/y-se-hizo-la-
luz.html
*Lee IM, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT. Effect of physical inactivity on major non-communicable diseases
worldwide: an analysis of burden of disease and life expectancy. Lancet. 2012 Jul 21;380(9838):219-29. PubMed PMID: 22818936.
Pubmed Central PMCID: PMC3645500. Epub 2012/07/24. eng.

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JUSTIFICATION
Physical activity population
approach: urban environment
Measuring urban environment:
“Subjective” methods
– Questionnaires (1)
– Interviews
“Objective” methods
– Official sources
(Geographical, Statistical)
– Systematic social
observation (2)
(1) Gullon P, Bilal U, Franco M. Physical activity environment measurement and same source bias. Gac Sanit. 2014 Jul-Aug;28(4):344-5.
(2) Gullón P, Badland HM, Alfayate S, Bilal U, Escobar F, Cebrecos A, Diez J, Franco M. Assessing Walking and Cycling Environments in
the Streets of Madrid: Comparing On-Field and Virtual Audits. J Urban Health. 2015 Oct;92(5):923-39

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OBJECTIVE
To study the association between area-
level sociodemographic characteristics
and walkability in the city of Madrid

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METHODS
2415 census sections
(~ US census block group)
Dependent variable:
• Walkability
Independient variables:
• % w/ Low education
• % Foreign-born
Source: Padron 2014 (municipal
registry)

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METHODS
𝑤𝑎𝑙𝑘𝑎𝑏𝑖𝑙𝑖𝑡𝑦 = 2 × 𝑐𝑜𝑛𝑛𝑒𝑐𝑡𝑖𝑣𝑖𝑡𝑦 + 𝐿𝑈𝑀 + (𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑑𝑒𝑛𝑠𝑖𝑡𝑦)
• Connectivity: Kernel density
• Land use mix (LUM)
𝐿𝑈𝑀 = −1(
𝑖=1
𝑛
𝑝𝑖 ×
ln(𝑝𝑖)
ln(𝑛)
)
• Population density (pop/km2)
Statistical analysis: ANOVA & mixed-effects model with
random intercept for district

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RESULTS

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RESULTS

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RESULTS
-2,00
-1,50
-1,00
-0,50
0,00
0,50
1,00
1 2 3 4 5
Walkability
Quintiles of % low education
Mean of walkability by quintiles of low education in
2415 census sections of Madrid

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RESULTS
Quartiles of % of low education
Q1 (High Ed.) Q2 Q3 (ref) Q4 Q5 (Low Ed.)
Mean p Mean P Mean Mean P Mean p
Population density 22.6 <0.001 34.3 0.20 37.3 37.4 0.60 33.8 0.01
Land use mix 0.78 <0.001 0.77 <0.001 0.73 0.71 0.01 0.72 0.19
Connectivity 0.015 <0.001 0.019 <0.001 0.021 0.023 <0.001 0.023<0.001
Walkability -1.53 <0,001 -0.12 0.01 0,32 0.78 0.01 0.53 0.22

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RESULTS

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RESULTS
-2,00
-1,50
-1,00
-0,50
0,00
0,50
1,00
1,50
2,00
2,50
1 2 3 4 5
Walkability
Quintiles of % migrants
Mean of walkability by quintiles of % migrants
living in the 2415 census sections of Madrid

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RESULTS
Quartiles of % of foreign-born
Q1 (High Ed.) Q2 Q3 (ref) Q4 Q5 (Low Ed.)
Mean p Mean P Mean Mean P Mean p
Population density 25.4 <0.001 27.6 <0.001 32.9 36.1 <0.001 44.7<0.001
Land use mix 0.77 0.05 0.77 0.06 0.75 0.72 <0.001 0.71<0.001
Connectivity 0.15 <0.001 0.17 <0.001 0.20 0.22 <0.001 0.26<0.001
Walkability -1.43 <0.001 -0.98 <0.001 -0.11 0.6 <0.001 1.91<0.001

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Walkability
% Low Ed
% Foreign-Born

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DISCUSSION AND CONCLUSSIONS
• The relationship between area-education
level and walkability shows complex
patterns.
• Areas with more foreign-born population
have a higher walkability index
• Urban policy to improve physical activity
and reduce health inequalities should take
into account specific sociodemographic
characteristics when designing more
walkable neighborhoods.

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Thank You!
Questions?