Jordan D. Silverman. Neighbourhood Marginalization as a Risk Factor for Pedestrian & Cyclist Collisions in Toronto Intersections

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1 Neighbourhood Marginalization as a Risk Factor for Pedestrian & Cyclist Collisions in Toronto Intersections Jordan D. Silverman Dr. Michael Cusimano, Dr. Michael Hutchison

2 Outline Objectives Background & Motivation Methodology Results Interpretation & Significance Questions

3 Objectives 1) To identify socio-demographic factors associated with pedestrian & cyclist collisions in Toronto intersections 2) To demonstrate methodology for use of ON-Marg Index and GIS-based approach to study road collisions

4 Background Canada among OECD Countries 150/ yr / million vehicles 90 / yr / million population In 2006: 2889 fatalities injuries

5 Background Vulnerable road users in Toronto November 1 October 20 October 9 September 18 October 16 October 30

6 Background Risk factors for urban pedestrian & cyclist collisions Road factors residential, speed limits > 50 km/h, no pedestrian signals Driver factors alcohol, driving record Injured person factors unsupervised, male, Aboriginal, children, low education, poor English skills, low income & home values Intersections Complex, high collision potential 50% of urban collisions Toronto High cycling rates leads Canada in cycling infrastructure

7 Methods 1) Identify intersections & collect collision data 2) Calculate ON-Marg for each intersection a. Identify Dissemination Areas (DAs) b. Calculate weighted ON-Marg scores 3) Evaluate association between collision frequency (1) and ON-Marg Index (2b)

8 Methods 1 Identify intersections & collect collision data 114 intersections in Toronto Collisions from TTDC database ( ) 114 Intersections Studied Cyclist collisions ( ) Pedestrian & cyclist collisions within 20m of intersections

9 Methods 1) Identify intersections & collect collision data 2) Calculate ON-Marg for each intersection a. Identify Dissemination Areas (DAs) b. Calculate weighted ON-Marg scores 3) Evaluate association between collision frequency (1) and ON-Marg Index (2b)

10 Methods 2a Identify Dissemination Areas Dissemination Area (DA): Smallest geographical area for which census data are available

11 Methods 2b Identify Dissemination Areas ArcMap GIS: Find latitude & longitude of each intersection Draw 250m buffer Determine DAs that buffer overlaps & area of overlap DA 1 DA 2 250m DA 3

12 Methods 1) Identify intersections & collect collision data 2) Calculate ON-Marg for each intersection a. Identify Dissemination Areas (DAs) b. Calculate weighted ON-Marg scores 3) Evaluate association between collision frequency (1) and ON-Marg Index (2b)

13 Methods 2b Calculate weighted ON-Marg scores What is the? To understand trends in: marginalization, health outcomes Based on 2006 Canadian census Scoring DAs: mean = 0, SD = 1, = more marginalized Four dimensions of marginalization

14 Methods 2b Calculate weighted ON-Marg scores Residential Instability (RI)» Population fluctuation over time Material Deprivation (MD)» Socio-economic status Dependency (DP)» Reliance on work force Ethnic Concentration (EC)» Immigrant populations

15 Methods 2b Calculate weighted ON-Marg scores For each intersection, 4 ON-Marg scores were calculated (weighted average based on buffer area overlapping DAs) I x = where: 1:y (ON-Marg DAy *A DAx,y ) 1:y (A DAx,y ) 250m x = 1 : 114 y = 1: # DAs overlapping buffer I x = RI/MD/DP/EC for intersection x ON-Marg DAy = RI/MD/DP/EC of each DA A DAxy = Area of DA y -buffer x overlap

16 Methods 2b Calculate weighted ON-Marg scores I x = 1:y (ON-Marg DAy *A DAx,y ) 1:y (A DAx,y ) MD DA1 = 0.5 MD DA2 = m Example: MD for Intersection 7 (0.5*100, *50, *50,000) MD 7 = (100, , ,000) Intersection 7 MD DA3 = 1.5 = A DA7,1 = m 2 MD DA7,1 = 0.5 MD DA7,2 = 1.0 MD DA7,3 = 1.5 A DA7,2 = m 2 A DA7,3 = m 2

17 Methods 1) Identify intersections & collect collision data 2) Calculate ON-Marg for each intersection a. Identify Dissemination Areas (DAs) b. Calculate weighted ON-Marg scores 3) Evaluate association between collision frequency (1) and ON-Marg Index (2b)

18 Methods 3 ON-Marg vs collision frequency Ordered logistic regression Intersection risk quartiles α collision frequency: Risk Quartile Pedestrian Collisions Cyclist Collisions Total Collisions 1 (low) (high) Regression Output: Odds Ratio (OR), 95% CI likelihood of an intersection to be in higher risk quartile based on unit increases in ON-Marg index

19 Results Collisions summary ( , 114 intersections) Risk Quartile Pedestrian Collisions Cyclist Collisions Total Collisions Total Median per intersection 5 (0-32) Range Eg. Cyclist Collisions across Toronto intersections

20 Results Logistic Regression Model ON-Marg Index Ped (OR, 95% CI) Cyclist ((OR, 95% CI) Total (OR, 95% CI) Residential Instability 1.84 ( ) ** 2.04 ( ) ** 2.16 ( ) ** Material Deprivation 1.19 ( ) 1.01 ( ) 1.18 ( ) Dependency 1.28 ( ) 0.89 ( ) 1.19 ( ) Ethnic Concentration 1.41 ( ) * 1.42 ( ) * 1.56 ( ) ** ** Statistically significant (p < 0.05) * Marginally significant (0.05 < p < 0.10) Summary: RI & EC are associated with pedestrian and cyclist collisions DP & MD are not significant in this model

21 Interpretation & Significance Why ethnic concentration? children, familiarity with road rules, cultural norms, signs tendency for poorer road safety infrastructure Why residential instability? apartments: walking, cycling, traffic volumes, lanes, transit stops, bike lanes, crosswalks Future work: Study physical infrastructure in vulnerable intersections Possible interventions to collisions: simple signage, separated bicycle tracks, maintained, well-marked sidewalks & cross-walks

22 Interpretation & Significance Can results be applied in different cities? ON-Marg Index 114 Intersections Ontario Average Chi-squared p-value Residential Instability 1.08 ± ± 0.95 p < Material Deprivation ± ± 0.89 p = 0.57 Dependency ± ± 0.94 p < Ethnic Concentration 1.22 ± ± 1.13 p < Limitations:» Probably large multicultural cities Inadequate flow data Relative infrequency of collisions underreporting short observation time not directly considering victim, road & driver characteristics

23 Methodology: Summary ON-Marg & GIS-based approach may be used to study collisions in urban settings Results: Residential instability (RI) & ethnic concentration (EC) are associated with collisions in Toronto Significance: Intersections in marginalized neighbourhoods can be targeted for strategies to reduce collision risk and prevent injury

24 Nov 7, 2011: Jenna Morrison

25 Acknowledgments Injury Prevention Research Office staff: Dr. Michael Cusimano Dr. Stanley Zhang Dr. Michael Hutchison Andreea Andrei Keenan Research Centre Dr. Flora Matheson Rosane Nisenbaum Map & Data Library, University of Toronto Gerald Romme Funding sources: CIHR, Ontario Neurotrauma Foundation

26 References 1. World Health Organization. WHO Statistical Information System: WHOSIS: WHO, Roudsari B, Kaufman R, Koepsell T. Turning at intersections and pedestrian injuries. Traffic Inj Prev. 2006;7(3): Wang Y, Nihan NL. Estimating the risk of collisions between bicycles and motor vehicles at signalized intersections. Accident Anal Prev. 2004;36(3): Desapriya E, Sones M, Ramanzin T, Weinstein S, Scime G, Pike I. Injury prevention in child death review: child pedestrian fatalities. Inj Prev. 2011;17(Suppl I):i4-i9. 5. Moudon AV, Lin L, Jiao J, Hurvitz P, Reeves P. The risk of pedestrian injury and fatality in collisions with motor vehicles, a social ecological study of state routes and city streets in King County, Washington. Accident Anal Prev. 2011;43(1): Oliver L, Kohen D. Neighbourhood income gradients in hospitalisations due to motor vehicle traffic incidents among Canadian children. Inj Prev. 2009;15(3): Chakravarthy B, Anderson CL, Ludlow J, Lotfipour S, Vaca FE. The relationship of pedestrian injuries to socioeconomic characteristics in a large Southern California County. Traffic Inj Prev. 2010;11(5): Matheson FI, Dunn JR, Smith KLW, Moineddin R, Glazier RH. Development of the Canadian Marginalization Index: A New Tool for the Study of Inequality. Can J Public Health. 2012;103(8):S Matheson FI et al. ON-Marg User Guide. Toronto, ON: Centre for Research on Inner City Health, Harris MA et al. Comparing the effects of infrastructure on bicycling injury at intersections and nonintersections using a case-crossover design. Inj Prev. 2013; 0: Doherty ST, Aultman-Hall L, Swaynos J. Commuter cyclist accident patterns in Toronto and Ottawa. J TRANSP ENG-ASCE. 2000;126(1):21-6.

27 Neighbourhood Marginalization as a Risk Factor for Pedestrian & Cyclist Collisions in Toronto Intersections Jordan D. Silverman Dr. Michael Cusimano, Dr. Michael Hutchison

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