Urban HEART @: Technical Report/User Guide

Centre for Research in Inner City Health Toronto, ON

March 2014

i

Centre for Research in Inner City Health (2014). Urban HEART @Toronto: Technical Report/User Guide. March 2014.

To learn more about Urban HEART @Toronto, contact: [email protected] http://www.torontohealthprofiles.ca/urbanheartattoronto.php

ii TABLE OF CONTENTS

TABLE OF CONTENTS ...... iii LIST OF TABLES ...... v LIST OF FIGURES ...... vii

1. Introduction: Using Urban HEART @Toronto ...... 1

2. General Overview: Indicator Selection and Testing ...... 3 Indicator Testing and Validation ...... 3 Quality Variations ...... 5 Challenges ...... 5 Conclusions ...... 5

3. General Overview of Benchmarks and Targets Selection ...... 10

4. Statistical Methods Used to Create Indices ...... 20

5. Description of Indicators ...... 26

5.1. Economic Opportunity ...... 26 Indicator 1: Unemployment Rate ...... 26 Indicator 2: Low Income ...... 32 Indicator 3: Social Assistance ...... 38

5.2. Social and Human Development ...... 43 Indicator 4: High School Graduation ...... 44 Indicator 5: Marginalization ...... 47 Indicator 6: Post- ...... 51

5.3. Governance and Civic Engagement ...... 61 Indicator 7: Voter Participation ...... 62

5.4. Physical Environment and Infrastructure ...... 69 Indicator 8: Access to Meeting and Gathering Places ...... 69 Indicator 9: Walkability ...... 73 Indicator 10: Access to Healthier Food Stores ...... 78 Indicator 11: Green Space ...... 82

5.5. Population Health...... 90 Indicator 12: Premature Mortality Rate ...... 91 Indicator 13: Mental Health ...... 96 Indicator 14: Potentially Preventable Hospitalizations/Ambulatory Case Sensitive Conditions Hospitalization Rate ...... 102 Indicator 15: Diabetes Prevalence ...... 107

iii 6. Accountability and Acknowledgements ...... 116

References ...... 118

Appendix A: The National Household Survey (NHS) ...... 120

Appendix B: Correlation Analyses ...... 123

Appendix C: Urban HEART @Toronto master matrix ...... 128

iv LIST OF TABLES

Table 1. Indicator testing and validation guidelines ...... 3 Table 2. Indicator definition and data sources ...... 5 Table 3. Advantages, disadvantages, and examples of specific cut-off points ...... 12 Table 4. Comparison cities used to determine selected benchmarks and target cut-off points ...... 18 Table 5. List of external comparator health regions and comparator cities ...... 18 Table 6. Comparison between ‘sum of colour categories’ method and ‘score range transformation’ method across all domains ...... 23 Table 7. Economic opportunity indices ...... 24 Table 8. External comparators for unemployment rate ...... 29 Table 9. Potential cut-off measures for unemployment to determine ‘red’ and ‘green’ neighbourhoods ...... 30 Table 10. Unemployment rate by neighbourhood income quintile, 2011 ...... 31 Table 11. Correlation analysis for unemployment rate ...... 31 Table 12. Comparison between two neighbourhood census tracts before and after modifications...... 34 Table 13. Correlation matrix for income ...... 34 Table 14. Potential cut-off measures for low income to determine ‘red’ and ‘green’ neighbourhoods ...... 35 Table 15. Low income by neighbourhood income quintile, 2010 ...... 36 Table 16. External comparators for low income ...... 37 Table 17. Potential cut-off measures for social assistance to determine ‘red’ and ‘green’ neighbourhoods ...... 40 Table 18. Social assistance by neighbourhood income quintile, 2012 ...... 41 Table 19. Social and human development indices ...... 43 Table 20. List of indicators used for high school completion rates ...... 46 Table 21. Potential cut-off measures for the ON-MARG index to determine ‘red’ and ‘green’ neighbourhoods ...... 49 Table 22. ON-MARG index by neighbourhood income quintile, 2006 ...... 50 Table 23. Correlations between NHS, TSBS, and census education variables (N=140).. 53 Table 24. Potential cut-off measures for post-secondary completion rates to determine ‘red’ and ‘green’ neighbourhoods ...... 54 Table 25. Post-secondary completion by neighbourhood income quintile ...... 55 Table 26. Comparison of external comparators of post-secondary completion rates, 15 comparison Canadian cities ...... 56 Table 27. Social and human development summary table: High school graduation, marginalization, and post-secondary completion ...... 57 Table 28. Governance and civic engagement: Municipal voting ...... 61 Table 29. Potential cut-off measures for voting participation to determine ‘red’ and ‘green’ neighbourhoods ...... 63 Table 30. Voter participation by neighbourhood income quintile, 2010 ...... 64 Table 31. Comparison of external comparators of voter participation rates, 10 comparison Canadian cities ...... 65

v Table 32 summarizes the municipal voting domain by colour codes...... 66 Table 33. Potential cut-off measures for voting participation to determine ‘red’ and ‘green’ neighbourhoods ...... 71 Table 34. Access to meeting and gathering places by neighbourhood income quintile, 2011...... 72 Table 35. External comparators for Walk Score ...... 75 Table 36. Potential cut-off measures for Walk Scores to determine ‘red’ and ‘green’ neighbourhoods ...... 76 Table 37. Walk Score by neighbourhood income quintile, 2012 ...... 77 Table 38. Potential cut-off measures for access to healthier food stores to determine ‘red’ and ‘green’ neighbourhoods ...... 80 Table 39. Access to healthier food stores by neighbourhood income quintile, 2013 ...... 81 Table 40. Potential cut-off measures for green space to determine ‘red’ and ‘green’ neighbourhoods ...... 84 Table 41. Green space by neighbourhood income quintiles, 2011 ...... 85 Table 42. Summary of indicators for physical environment and infrastructure domain .. 86 Table 43. Population health indices ...... 90 Table 44. Potential cut-off measures for premature mortality to determine ‘red’ and ‘green’ neighbourhoods ...... 93 Table 45. Premature mortality rates by neighbourhood income quintile, 2005-09 ...... 94 Table 46. External comparator health regions and cities for premature mortality rate .... 95 Table 47. Potential cut-off measures for self-rated mental health to determine ‘red’ and ‘green’ neighbourhoods ...... 99 Table 48. Self-rated mental health rate by neighbourhood income quintile, 2005-11 ... 100 Table 49. External comparator health regions and cities for self-rated mental health ... 101 Table 50. Potential cut-off measures for potentially avoidable hospitalizations to determine ‘red’ and ‘green’ neighbourhoods ...... 104 Table 51. ACSC hospitalizations by neighbourhood income quintile, 2009-11 ...... 105 Table 52. External comparator health regions and cities for ACSC hospitalizations ..... 106 Table 53. Potential cut-off measures for diabetes prevalence to determine ‘red’ and ‘green’ neighbourhoods ...... 109 Table 54. Diabetes rates by neighbourhood income quintile, 2011 ...... 110 Table 55. Population health domain summary ...... 111 Table 56. Urban HEART @Toronto Contributors ...... 116

vi LIST OF FIGURES

Figure 1. Values for benchmarks and targets ...... 11 Figure 2. Histogram of neighbourhood unemployment rates (total population aged 15 and over), 2011 ...... 27 Figure 3. Unemployment rate by neighbourhood income quintile, 2011 ...... 31 Figure 4. Histogram of neighbourhood low income measures (all families and non-family persons by number of persons), 2010 ...... 32 Figure 5. Low income by neighbourhood income quintile, 2010 ...... 37 Figure 6. Histogram of neighbourhood social assistance rates ( Works recipients, and ODSP recipients participating in OW employment programs, and non-OW members receiving special assistance for medical items), 2012 ...... 39 Figure 7. Social assistance by neighbourhood income quintile, 2012 ...... 41 Figure 8. Histogram of neighbourhood Ontario Marginalization Indexes, 2006 ...... 48 Figure 9. ON-MARG index by neighbourhood income quintile, 2006 ...... 50 Figure 10. Histogram of neighbourhood completion of post-secondary education rates, 2011...... 52 Figure 11. Histogram of neighbourhood municipal voting, 2010 ...... 62 Figure 12. Voter participation by neighbourhood income quintile, 2010 ...... 65 Figure 13. Histogram of neighbourhood community places for meeting (places of worship and the parks and recreation centres), 2011 ...... 69 Figure 14. Calculating access to meeting and gathering places ...... 70 Figure 15. Access to meeting and gathering places by neighbourhood income quintile, 2011...... 72 Figure 16. Histogram of neighbourhood Walk Scores (walkability index), 2012 ...... 74 Figure 17. Walk Score by neighbourhood income quintile, 2012 ...... 77 Figure 18. Neighbourhood access to healthier food stores, 2013 ...... 78 Figure 19. Calculating access to healthier food stores ...... 79 Figure 20. Access to healthier food stores by neighbourhood income quintile, 2013 ...... 81 Figure 21. Histogram of neighbourhood green space (shapefile of parks and green space), 2011...... 82 Figure 22. Calculating green space ...... 83 Figure 23. Green space by neighbourhood income quintiles, 2011 ...... 85 Figure 24. Histogram of neighbourhood premature mortality rates (number of deaths age 0–74 (Both sexes) age 0–74), 2005-09 ...... 92 Figure 25. Premature mortality rates by neighbourhood income quintile, 2005-09 ...... 94 Figure 26. Histogram of neighbourhood self-rated mental health (proportion of residents in the neighbourhood (over age 12) who said they have very good or excellent mental health), 2005–11 ...... 97 Figure 27. Self-rated mental health rate by neighbourhood income quintile, 2005-11 .. 100 Figure 28. Histogram of neighbourhood potentially avoidable hospitalizations (age and sex standardized rate of hospitalizations age 0–74, 2009-11 ...... 103 Figure 29. ACSC hospitalizations by neighbourhood income quintile, 2009-11 ...... 105

vii Figure 30. Histogram of neighbourhood diabetes prevalence (number of persons age 20+ with at least one hospitalization or two physician visits in two years with a diagnosis of diabetes), 2011 ...... 108 Figure 31. Diabetes rates by neighbourhood income quintile, 2011 ...... 111

viii 1. Introduction: Using Urban HEART @Toronto

The Urban Health Equity Assessment and Response Tool (Urban HEART) is a place-based tool for measuring equity. It was also developed to assist with processes of priority setting and planning. This tool was originally designed by the World Health Organization (WHO) in 2010 in response to the WHO Commission on Social Determinants of Health Report (2008) which argued that there is a need to measure social inequities. The tool was initially intended to be used to assess inequity and plan collaborative responses for cities in Low and Middle Income Countries.

Urban HEART @Toronto, hereafter referred to as Urban HEART, is a locally-based adaptation of the WHO’s tool. Urban HEART includes five different measurable domains that specifically relate to the well-being of Toronto’s neighbourhoods. These domains represent different policy areas that need to addressed together to reduce neighbourhood health inequities. This report provides an overview of the methods and data used to alter the original tool for use in Toronto.

The indicators for the local data set for Toronto neighbourhoods were selected through an electronic Delphi consultation process. This is a research technique for building consensus among individuals who are experts in a variety of areas. The Delphi process included over 80 participants and over 40 organizations. Using pre-defined selection criteria for inclusion, this process generated a set of required and strongly recommended indicators across five domains according to a set of criteria. Required indicators were defined as those that best capture inequalities across Toronto. We recommend that these indicators be used to conduct subsequent Urban HEART assessments in Toronto. Strongly recommended indicators include those that can augment the information reflected by the required indicators for each domain. In future Urban HEART assessments, these indicators can be selected by informed stakeholders.

Following our Delphi process, and once subjected to testing and validation, the final set of indicators was submitted to the Urban HEART Steering Committee for approval. This committee consisted of a variety of researchers, social services professionals, representatives from funding agencies, and policy makers.

Urban HEART uses a matrix to organize data in simple visual formats. This enables stakeholders to compare places, identify inequities, determine actionable responses, and monitor changes over time. This matrix uses red, yellow, and green coloured squares to identify the level of accomplishment of neighbourhoods in regards to health determinants and outcomes. Neighbourhoods which experience lower levels of success in relation to others are coloured red, those who experience high levels of success are coloured green, and those in-between are coloured yellow. Levels of accomplishment are measured across five domains. Each domain includes a variety of indicators which were used to assess neighbourhood experiences of equity.

The indicators included in Urban HEART are measures that can be used to demonstrate change in equity over time, allowing stakeholders to measure the success of plans designed to increase the health and wellbeing of Toronto’s neighbourhoods. However, these indicators are March 28, 2014, ver.1, 1

approximations for the complex realities that are reflected in each domain. Triangulation, defined in this study as using different indicators from different data sets, allowed us to better capture these complex realities. Additionally, using multiple data sources minimized the risk that any single indicator or single data set with quality issues would unfairly determine the categorization of a neighbourhood as being inside or outside the cluster identified as those most in need of support. A detailed master Excel worksheet which includes the calculations used in our construction of our matrices can be sent to interested parties upon request.

In the absence of measurable goals for most of the indicators selected for Urban HEART, multiple statistical techniques and data sources were used to categorize Toronto’s 140 neighbourhoods according to their scores on each of the 15 indicators. The five domains used for Urban HEART are: 1) economic opportunity, 2) social and human development, 3) governance and civic engagement, 4) physical environment and infrastructure, and 5) population health. In this report we present each domain and the indicators used as measures within each domain. Through this presentation, we discuss the data sources and methods used to reliably measure equity across the city of Toronto.

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2. General Overview: Indicator Selection and Testing

This section describes the guidelines used to select and test the 15 indicators used for Urban HEART. Part of the efficiency of Urban HEART is that the indicators are based on data that are currently available and the tool can be repopulated with future data to assist in monitoring changes in equity over time.

Indicator Testing and Validation Members of the research team developed a set of guidelines, which are listed in Table 1, that were used to test each of the 15 selected indicators for representativeness, variability, quality, integrity, reliability, and validity. Preliminary results regarding the testing of indicators were then shared with the Steering Committee for input. The final set of indicators was submitted to the Steering Committee for approval after they were tested and validated.

Table 1. Indicator testing and validation guidelines Criteria Actions Representativeness Identify that a minimum number of observations per geographical unit is achieved in order to report (according to data standards and Dothe data we have guidelines). adequately describe Identify that the data do not exceed maximum number of non- each of the reportable areas. Do not use an indicator at a geographic level if neighbourhoods in the data cannot be reported for 20% or more or the geographic Toronto? areas. Assess, consider and report the limitations of the representativeness of datasets where possible (the non-response rates, variable response rate, missing populations and representativeness of survey data). Combine years of data or cycles of surveys to include the sample size to improve the ability of the datasets to more fully represent the population in neighbourhoods. Variability Calculate basic statistics for each indicator (include minimum and maximum). Is there a sufficient Prepare histograms and graphing of the distribution of rates. variability of values Calculate standard deviation and z-scores that describe the type of for a variable to be a distribution, the number of neighbourhoods within one 1 standard good indicator of deviation above or below the mean, and the number of inequality/difference neighbourhoods that are outliers (i.e. have rates greater than 2 among the standard deviations from the mean). neighbourhoods? Calculate population percentiles, quintilesof neighbourhoods, rate ratios and confidence intervals/statistical significance where relevant. Quality and Validity Consider: the quality; limitations and scope of the indicator; and state data limitations; percent missing; and representativeness of Does the indicator sample. March 28, 2014, ver.1, 3

measure what is Consider: the impact of converting data from smaller geographic intended to and does areas and rolling this up to the neighbourhood level; the loss of it follow standards specificity or averaging out of differences that may be more of practice for data evident at the Dissemination Area (DA) or Census Tract (CT) integrity and levelto ensure that the roll up to neighborhood level did not mask reporting? or eliminate otherwise lesser scores. Ensure the use of appropriate methods for creating indicators at the neighbourhood level where required and where none exist. These are based on best research practices or validated approaches, and the availability of complete and current datasets (e.g. population weighted-access to places or resources, and so forththat ensure that measures do not include geographic areas where residents do not reside). Compare results with other published rates of the same indicator and other methods, and data sources that measure the same element. As well, ensure that the indicator is calculated consistently with the method used by other organizations that report/use it for comparison (e.g. standard age and sex adjustment methods used by other indicator producing organizations at the national, provincial level, etc.). Ensure data integrity through data transformation, manipulation, calculation and reporting (e.g. build formula checks into worksheets, manual computations, confirm by rerunning programs, confirm by independent analysis/checks by a second person. When using maps, examine map layers for face validity). If the indicator is new/original or produced from a new dataset, triangulate the results with indicators form other data sources or with different measures and/or conduct correlation analysis with other available indicators that measure the same or similar concept. Compare with similar/same indicators from other time periods and ensure variations are explainable and acceptableand identify potential errors in the dataset or method of transformation. Use spatially-weighted population datafor indicators where a city total (aggregated data for all neighbourhoods) is not available, as opposed to using the averages of neighbourhood rates. Ensure that we follow the guidelines and requirements set forth by the original owners of the data. For example, when reporting Canadian Community Health Survey (CCHS) data, Statistics requires using an extensive bootstrapping procedure according to the number of cycles combined to report 95% confidence intervals; not reporting results where the numerator is less than 10; not reporting rates where the co-efficient of variation is >33%; and reporting, with caution, those rates where the coefficient variation is between 16.6% and 33%.

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Consider: the quality; limitations and scope of the indicator; and state data limitations; percent missing; and representativeness of sample.

Quality Variations Through the testing and validation process it became clear that the quality of the indicators varied. Although some of the indicators were based on validated, standardized measures used in professional practice, resource allocation and performance measurement, other indicators were more problematic. For example, some indicators were based on survey data that were not representative at the neighbourhood level and other indicators were based on small numbers that may lack stability, such as a measurement of graduation rates for a single cohort of students in a single year. See Appendix A for details of the limitations of two primary sources of data: the 2011 National Household Survey (NHS) and the Canadian Community Health Survey (CCHS).

Challenges There were a number of challenges related to data availability and quality that had to be addressed. Many of the challenges we faced are common occurrences when relying on secondary datasets. Some of these included the use of different timelines, methods for data collection, and analysis procedures across the various datasets used. The inability to make revisions to existing datasets which could have improved the quality and usefulness of the information was also problematic. Delays in the production of data (e.g. legal agreements for production of local indicators, delays in releases of datasets) also had an effect. Finally, the 2011 NHS, a traditional source of socio-demographic neighborhood-level information, was limited in its ability to provide high quality information about some of our specific indicators.

In some situations, we faced a lack of available data altogether. Because we were unable to find datasets that could be used to measure some of the required indicators, we were forced to use stand-in or approximation variables to measure inequities using those indicators. In doing so, we relied on a variety of literature and theoretical understandings that assert that certain measures are appropriate approximations for others. Some datasets did not include information about very small or marginalized populations, or when we tried to find similar datasets from other cities that we could use to compare to Toronto to help set the benchmarks and targets, we were unable to find ones that matched our local indicators. Additionally, the cancelation of the long-form census means that income data that were readily available in the past were challenging to obtain.

Conclusions Because indicators are, by definition, an approximation of the concept being measured, there are always limitations. Table 2 provides a description of the testing, correlations, and other checks done for each indicator as part of the process of determining the final set of required and strongly recommended indicators. Despite limitations, the indicators prepared for Urban HEART provide a picture of the disparities experienced across Toronto’s neighbourhoods.

Table 2. Indicator definition and data sources Do- Indicators Numerator and data Denominator and source main source March 28, 2014, ver.1, 5

Unemployment Population age 15 and Total population aged 15 and over in rate over that is unemployed: the labour force: 2011 National 2011 National Household Household Survey (NHS); Statistics (Required) Survey (NHS); Statistics Canada, June 26, 2013. Canada, June 26, 2013. Low income All low income families All families and non-family persons by measure and non- family persons number of persons: T1-Family File; (Required) by number of persons: Table F-18; Statistics Canada; Income T1-Family File; Table F- Statistics Division, 2010; Annual 18, Statistics Canada, Estimates for Census Families and Income Statistics Individuals, 13C0016, used with Division, 2010, Annual modifications to two census tracts (CT Estimates for Census 35 and CT 299). Families and Individuals, 13C0016, used with modifications to two census tracts (CT 35 and

CT 299). Social assistance Ontario Works (OW) Total population 2001 Census, all age (Strongly recipients (cases and groups.

Economic opportunity recommended) dependents), and Ontario Disability Support Program ODSP recipients participating in OW employment programs, and non-OW members receiving special assistance for medical items: 2012 Toronto Employment and Social Services, Data Mart, 2012.

High school Number with Grade All age 20–24 in 2006 Census, All graduation 12/ Grade 9 cohort from 2005–2006, (Required) certificate (or 30 credits TDSB, all Grade 12 students in 2010, accumulated) from four TDSB; and all Grade 9 students in Used four datasets (2006 Census 2003–2004 TDSB. indicators age 20–24), TDSB Grade 9 Cohort graduating in 2011; and Grade 12 class graduating in 2011; TCDS Grade 9 cohort graduating in 2009). Social and human development March 28, 2014, ver.1, 6

Marginalization A combined measure of A combined measure of 18 census (Required) 18 census variables variables representing: residential representing: residential instability, ethnic concentration, instability, ethnic dependency and material deprivation. concentration, The data for these measures was derived dependency and material from the 2006 Census. deprivation. The data for these measures was derived from the 2006 Census.

Post-secondary Age 25–64 with post- Total population aged 25–64 years by education secondary certificate, highest certificate, diploma or degree. (Recommended) diploma or degree Source: 2011 National Household Source: 2011 National Survey (NHS); Statistics Canada, June Household Survey 26, 2013. (NHS); Statistics Canada, June 26, 2013.

Municipal voter Number of eligible Denominator: Total eligible electors. participation electors who voted in the Source: Toronto Election and Registry (Required) 2010 municipal election. Services, obtained from Toronto Open Source: Toronto Election Data. and Registry Services, obtained from Toronto Governance and civic engagement Open Data. Community Addresses of places of DMTI Spatial CanMap Route Logistics places for worship and the parks Road network file, 2011 census meeting and recreation centres; population in dissemination blocks (Required) XLS data from Toronto (DB), aggregated up to the Open Data. neighbourhood level.

Walkability Walk Score® NA (Required) www.walkscore.com internally validated using the Toronto Utilitarian Walkability Index, 2012 (TUWI) from created by

Physical environment and infrastructure infrastructure and Physical environment Urban Design 4 Health Ltd.

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Access to Grocery stores, DMTI Spatial CanMap Route Logistics healthy food convenience stores, and Road network file, 2011 census choices fruit/farmers markets population in the dissemination blocks (Recommended) addresses, Dinesafe 2013 (DB), and aggregated up to the data. neighbourhood level.

Access to green Shapefile of parks and Centroids of dissemination blocks from space green space obtained 2011 Census. (Recommended) from Toronto Open Data.

Premature Number of deaths age 0– Denominator: Population age <75, mortality 74 (Both sexes) age 0– Statistics Canada, 2006 Census of (Required) 74. Ontario Mortality Canada. Standardized to the 1991 Data 2005–2009, Ontario population of Canada, Statistics Canada. Ministry of Health and Long-term Care, IntelliHEALTH ONTARIO.

Self-rated Proportion of residents in Respondents age 12 and over, CCHS mental health the neighbourhood (over 2005, 2009–2010, 2010–2011 and 2011 (Required) age 12) who said they combined. Individuals who did not have very good or respond to this question were removed. excellent mental health, Canadian Community Health Survey, 2005–2011. Dataset: Ontario Share Files, Statistics Canada. Canadian Community Health Survey, four waves, 2005–2011,

Population health Ontario Share Files.

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Potentially Age and sex standardized Denominator: Population, 2011 by avoidable rate of hospitalizations age groups. Age standardized to hospitalizations age 0–74 for specific the 1991 Canadian population. (Required) chronic conditions (diabetes, hypertension, angina, congestive heart failure, asthma, chronic obstructive lung diseases, grand mal seizures and other epileptic convulsions)/100,000, population age 0–74. Source: Discharge Abstract Database, Canadian Institute for Health Information (CIHI). Diabetes Number of persons aged All individuals age 20+ with a valid prevalence 20+ with at least one OHIP card, alive on April 1st, 2011 and (Recommended) hospitalization or two living in the City of Toronto derived physician visits in two from the Registered Persons Database years with a diagnosis of (RPDB) from MOHLTC. diabetes.

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3. General Overview of Benchmarks and Targets Selection

What is unique about the Urban HEART method is the use of a colour-coded “dashboard” to quickly identify neighbourhoods that are faring both well and less well according to a set of accepted benchmarks and potentially achievable targets. The choice of benchmarks and targets is important because these are the standards and goals against which the progress of Toronto and its neighbourhoods may be measured. The WHO’s guidelines recommend the selection of benchmarks and targets that are the most relevant, such as city averages rather than national averages, for the population and that have the greatest local resonance to enable meaningful comparisons (WHO, 2010). To complete this step, the WHO recommends constructing a matrix that identifies an internal benchmark for each indicator (i.e. national average, city average), a desired target for each indicator (i.e. pre-defined national targets), and the performance levels of highest income groups (WHO, 2010).

In adapting the WHO guidelines, we looked for existing local targets and benchmarks for our indicators that could be used to establish the cut-off levels for red and green. We wanted to identify cut-off values that were the most appropriate for each indicator and to provide the rationale for that selection. We have not tried to generate a single measure that could serve as the cut-off value for each indicator. In keeping with the WHO’s Urban HEART process, in constructing the matrix, we considered the intended use of the Urban HEART tool and sought both internal measures (those using data from Toronto) and external comparators (using data from cities other than Toronto) that could serve as cut-off values. We had to take the unique characteristics of Toronto into account when selecting external versus internal cut-off measures.

We found four pre-defined local, provincial or national goals or targets that could be used as Urban HEART indicators. These were: 1) the City of Toronto’s target unemployment rate of 6%; 2) the Ontario Government’s 25% reduction in poverty target; 3) the Ministry of Education’s target of an 85% graduation rate within five years for Grade 9 cohorts; and 4) the Walk Score® system target rates. We were unable to use the 6% target for unemployment in constructing the Urban HEART benchmark, as the available 2011 NHS data included too few neighbourhoods.

We reviewed a broad range of strategies for calculating the specific benchmark and target cut-off points. Multiple statistical measures were employed and external comparators were obtained that could be used to categorize Toronto’s 140 neighbourhoods according to their scores or rates on each of the 15 indicators. Ultimately, we used the following criteria to select benchmark and target cut-off points:

1. They need to be clear. The measures must be understandable and easy to interpret. 2. They need to be ‘achievable’ and ‘relevant’ to Toronto’s neighbourhoods. 3. They need to be equity-focused and have the ability to reveal inequities across neighbourhoods. 4. They need to detect ‘variability’ across Toronto; if over half of the neighborhoods are classified as ‘red’ or ‘green,’ the measurement is not particularly useful. 5. They need to be ‘analytically sound’, based on accepted and well-established theory and practice and be broadly supported as approaches for making comparisons.

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6. They need to be ‘comparable’. It must be possible to obtain comparison data that have been measured using the same methods as the Urban HEART indicator was. Likewise, when an external comparison is used, the characteristics of the city from which the data were gathered need to be comparable to Toronto’s urban density and population diversity.

Figure 1 provides a list of potential cut-off options for both red and green. Neighbourhoods that are coded as red have indicator values that are of concern, whereas neighbourhoods that are coded as green have indicator values that are high enough to be considered aspirational. Neighbourhoods are coded yellow if they fall in-between the red and green cutoffs.

Figure 1. Values for benchmarks and targets Cut-off values for ‘red’ (Benchmarks) Cut-off values for ‘green’ (Targets*) Internal options (Based on Toronto data) Internal options (Based on Toronto data) Percentile (The rate at the 20th percentile Rate of the wealthiest income quintiles of the population with the worst rates.) (20% of the population with the lowest poverty rates.) Rate worse than 1 standard deviation from Percentile (The rate at the 20th percentile the mean. of the population with the best rates) Rate ratio – Rate 1.2 times (20%) worse Rate better than 1 standard deviation from than city rate. the mean. Natural breaks/wide rate differences in the Rate ratio – Rate 1.2 times (20%) better ranking of rates from worst to best. than city rate. City of Toronto rate. Natural breaks/wide rate differences in the ranking of rates from worst to best. External options (Based on data from External options (Based on data from other cities) other cities) Rate at 20th percentile among comparison Rate at 20th percentile among comparison places ranked according to worst rates. places ranked according to best places. Policy target set by government (city council, provincial government or government department) or other targets in use. *The use of the term ‘target’ in this document is not meant to suggest that the City of Toronto should adopt these as targets for planning strategies to reduce inequities. Rather the ‘targets’ represent different options that emerged from the computational analysis and external references for the purpose of identifying differences among neighbourhoods that would be useful for populating the dashboard.

Table 3 describes each of the cut-off options that were calculated or obtained, as well advantages, disadvantages and examples of uses.

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Table 3. Advantages, disadvantages, and examples of specific cut-off points Cut-off measure Advantages Disadvantages Example of use 20th percentiles of Quintiles of the Same or similar Early Development the population population are values may end up Instruments (EDI) typically used in in different results, ‘readiness Rate worse than equity analysis categories while to learn” pre-school the rate at the 20th because this quite different use percentiles...to percentile of the includes a large values may end up create a three level population ranked enough population in the same class dashboard. For by the worst rates to capture (i.e. outliers and NHOODs results for cut-offs for meaningful highest rates are in see:http://www.mot ‘red’; and rates disparities that can the same quintile of hercraft.ca/assets/si ranked by best be addressed by rates that are more te/docs/resource- rates for cut-offs targeted place- or similar to adjacent library/research- for ‘green’, 20%. population-based rates at the cut-off data/YorkSouth- initiatives, but not points.). Weston_2010- so small as to only 11.pdf include outliers that may be atypical and have unique characteristics that may not be as amenable to place- or population-based strategies. Standard Standard deviations The SDs can fall For example rates deviations (SD) are routinely among rates that worse than one calculated and are similar in a standard deviation Rates<-1 SD or >1 reported in range so the SDs were used to SD from the mean descriptive may not serve as identify the “most representing the statistics. In useful cut-offs (i.e. challenged worst rates for cut- conjunction with the splits may fall neighbourhoods” in offs for ‘red’, and examination of the on a rate or the: Measuring best rates for cut- distribution of the between rates that Neighbourhood offs for ‘green’. rates, this describes are very similar. Vitality Report For some variables if the distribution is The identification (2005) prepared for +1SD represents normal (like a bell of outliers may not the Strong better than average curve) or skewed. be useful cut-offs Neighbourhoods while for other SDs and z-scores because the number Task Force indicators -1SD (the actual relative of places that are (http://www1.toront represents better distance from the outliers/most o.ca/wps/portal/con than average. In a mean) provide a different rates may tentonly?vgnextoid normal statistical measure be too few to be =1e68f40f9aae0410 distribution (i.e. of the distribution useful for the VgnVCM10000071 when the results of of rates and also intended uses.). d60f89RCRD&app the distribution of identify the rate InstanceName=defa March 28, 2014, ver.1, 12

rates by number of that are the outliers, ult).Percentiles, cases, takes the i.e. that are more standard deviations shape of a bell than two standard and natural breaks curve) the area in deviations from the were used together between -1 SD mean. to identify and +1 SD of the “moderately and mean includes severely approximately disadvantaged” 68% of the ethnoracial groups population. In this in Ornstein’s report document the on Ethnoracial mean used for the Inequality in SDs and z-scores Toronto 1976– is the mean of the 2006. neighbourhood rates which are all population-based rates (for the Geographic Information System (GIS) variables, population- weighted scores, for most health indicators they are age adjusted rates). The position for each neighbourhood rate in relation to the neighbourhood mean is noted by the z-score (i.e. (neighbourhood rate - mean)/standard deviation).

Rate ratios (RR) Rate ratios are a The rate ratio that The Toronto simple measure that could be considered Community Health Rate ratios are identifies the policy relevant or Profiles Partnership calculated by magnitude of actionable needs to uses a rate that dividing the difference, in be based on the is1.2 times higher March 28, 2014, ver.1, 13

neighbourhood relation to a dataset (the range or lower than the rate by the city standard such as the in rates) so using a city rate as the rate. The cut-offs city rate. specific level (20% default measure of are neighbourhood may not be relevant a difference rates 1.2 times Rate ratios are an for all indicators.). important enough higher or lower indication of a level to highlight (policy than the city rate of difference relevant). Rate (The lower than (policy relevance) ratios have also 1.2 times the city that may be more been used in rate is also important to take composite represented by a action on than rates indicators e.g. rate ratio of <0.8 more similar to the Equity Adjustment of the city rate.). city rate even Factors in needs though rates at adjusting the other levels may be population in statistically resource allocation significant. formulas public health funding formulas (e.g. Ontario, Nova Scotia). External External External Organizations such comparators comparators take comparisons may as Statistics into account not be as relevant to Canada, Toronto The 20th progress outside achievable Vital Signs, percentile rank of Toronto as most of reductions in local Toronto Board of comparison cities these domains are disparities as Trade (Scorecard (i.e. third lowest well recognized as measures of on Prosperity), or highest rates important to existing local Federation of according to healthy cities, and disparities. There Canadian ranking by worst actions to achieve is no other place in Municipalities, and to best or rate of them often include Canada that is truly the Ontario 15 cities or at the policies and comparable to Municipal 2.4th position in resources beyond Toronto’s Benchmarking the ranking or 12 local city and population Initiative (OMBI) health regions. community actions. composition and all select and The external cities The use of external the built conduct external were selected comparators help to environment. comparisons. because the improve the composition (at external validity in least 5% of the measuring population of inequalities. It recent immigrants helps by in the previous contextualizing five years) and Toronto density(at least neighbourhoods in March 28, 2014, ver.1, 14

1,000 relation to persons/km2) are neighbourhoods in in line with other Canadian Toronto. Health cities. indicators are reported according to Statistics Canada health regions so we chose those that included these cities. Rate of wealthiest The rate of the Not all indicators In health equity population highest income are best in highest analysis quintile quintile compared income areas so internationally to the lowest this measure cannot (including targets Neighbourhoods income quintile or be used as an set for reducing the are ranked from the rest of the aspirational target gap between the lowest income population is used for all indicators. highest and lowest (highest as an indicator of quintile) in Urban poverty/highest health inequities or HEART, in Canada rate of low the amount of (i.e. CIHI, Health income) to highest potentially Indicators Report income (lowest avoidable and 2012 and Toronto rate of low unfair problems due Public Health: income) and cut- to unjust social Unequal City off points are set arrangements. Report, 2009). that divide the ranked neighbourhoods into five population quintiles (each representing 20% of the total city population). We use this as the default ‘target’ measure for population health indicators and several other indicators as well. Natural breaks/ May minimize the Highly skewed The Jenks rate differences risk that the variables may result optimization recommended in a limited number algorithm is a March 28, 2014, ver.1, 15

The percent measure could be of neighbourhoods default method used difference in rates considered being assigned to in mapping that when ranked from arbitrary, i.e. if the the top or bottom optimizes groups so worst to best was rates near a cut-off categories. that there is a calculated for point were too Disadvantages minimum possible some indicators. similar to include standard deviation The extent of the categorize one explainabilty; the between values difference may neighbourhood as closeness of values within a value class need to be ‘red’ and one was in a range; and the (rate range) and a indicator-specific ‘yellow’, etc. multiple number of maximum possible but for the natural breaks standard deviation majority of Neighbourhoods versus the three between each data variables a rate with more similar colour-coding class. Each data difference greater values are system required by class is represented than 1.5 times the displayed as the Urban HEART. by a colour. average rate same colour difference was category and considered neighbourhoods important enough that vary greatly to highlight. from each other Because we are would be assigned only using this to different (rate differences categories or greater than the colours. average rate difference) as supplemental information to the core measures, we are only interested in the magnitude of the rate differences close to the main measures listed above.

Statistical Statistical When there are The production of significance significance testing multiple 95% confidence such as 95% comparisons the levels is required The 95% confidence chance that a for the reporting of confidence intervals around a difference occurs health indicators in intervals are rate are often by chance Ontario (See: calculated for all required before an increases. If the http://www.health.g of the health organization will denominators or ov.on.ca/english/pr March 28, 2014, ver.1, 16

indicators and a accept that a numerators are very oviders/pub/healtha column is included reported different is large, even small nalytics/health_tool noting if the rate is in fact a difference. differences can kit/health_toolkit.p H/L/NS. This emerge as df) and is the indicates that the statistically standard practice at chances are at significant. Statistics Canada least 19 in 20 that and the Canadian the rate is higher Institute for Health (H) or lower (L) Information (CIHI). than the City of Toronto rate (p<0.05). In the summary reports we only note if a rate is significantly higher (H) or lower (L) and those not significantly different (NS) are not marked with H, L or NS. Policy Targets or These are ideal Despite public and Some provincial other targetswhich measures because government targets were found, are validated or they are connected discussion of the more in other approved and used to a commitment to need to reduce countries with in other organized action to achieve health and social health disparity (and funded) them, and progress inequities there are reduction goals, projects. is supported and few targets for and globally monitored. eliminating these. (millennium development goals (MDGs).

Quintiles Simple counts of The splits for the Quintiles are a neighbourhoods counts can fall common default in Counts of (i.e. 28 between rates that mapping of neighbourhoods in neighbourhoods per are similar or equal neighbourhood quintiles (20%), quintile) are easier and thus may be rates in Toronto. quartiles (25%) or to create and more arbitrary. tertiles (33.3%). consistent number of places than the other measures March 28, 2014, ver.1, 17

above.

Selecting External Comparators as a Potential Cut-off Measure

Although it was not possible to use external comparators for cut-offs for all of the indicators, several external comparisons are relevant to understanding the depth and breadth of health inequities in Toronto beyond relative intra-city comparisons.

We identified external comparisons for Toronto by reviewing similar studies or approaches used in other urban centers in Canada. Fifteen comparison cities (based on census subdivisions or CSDs) were selected based on composition. In other words, urban centers were selected if their (percent of recent immigrants within five years was greater than 5%), and they had a population density greater than 1,000 per km2. CSDs that included rural areas were excluded. The cities used as external comparators are presented in Table 4.

Table 4. Comparison cities used to determine selected benchmarks and target cut-off points Census Profile: Census sub-divisions Population, Population Recent (CSDs) comparable to Toronto 2011 Census density per square immigrants kilometer (%) Toronto (3520) or (3520005) C00001 2,615,060 4,150 9.9 Montréal (2466023) V 00000 1,649,519 4,518 8.9 Calgary (4806016) CY 00001 1,096,833 1,329 6.3 Edmonton (4811061) CY 00000 812,201 1,187 5.8 Mississauga (3521005) CY 00001 713,443 2,440 8.8 Winnipeg (4611040) CY 00000 663,617 1,430 6.9 Vancouver (5915022) CY 00000 603,502 5,249 8.4 (3521010) CY 00000 523,911 1,967 8.4 Surrey (5915004) CY 00000 468,251 1,480 7.8 Markham (3519036) T 00000 301,709 1,419 7.5 Burnaby (5915025) CY 00000 223,218 2,464 7.2 Saskatoon (4711066) CY 00001 222,189 1,060 5.1 Richmond (5915015) CY 00000 190,473 1,474 6.9 Richmond Hill (3519038) T 00000 185,541 1,838 6.1 Coquitlam (5915034) CY 00000 126,456 1,034 6.7 We used Statistics Canada Health Regions, in lieu of municipalities, as external comparators for health indicators. Therefore, the comparison areas are the 12 Health Regions that include the 15 comparison cities. These are listed in Table 5.

Table 5. List of external comparator health regions andcomparator cities Health Regions Comparison Cities Saskatoon Health Region, SK Saskatoon Edmonton Zone, AB Edmonton ASSS de Montréal, QC Montréal Calgary Zone, AB Calgary March 28, 2014, ver.1, 18

City of Toronto Health Unit, ON Toronto Fraser South Health Service Delivery Area, BC Surrey Peel Regional Health Unit, ON Mississauga and Brampton Fraser North Health Service Delivery Area, BC Burnaby and Coquitlam Winnipeg Regional Health Authority, MA Winnipeg Vancouver Health Service Delivery Area, BC Vancouver Richmond Health Service Delivery Area, BC Richmond York Regional Health Unit, ON Markham and Richmond Hill

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4. Statistical Methods Used to Create Indices

The Urban HEART process produced a wealth of information that can be used to develop policy and plan initiatives to reduce health and social inequality across Toronto. After deciding on the indicators, benchmarks, and targets, the research team had to decide how to organize the data so that it would be both useful and easily understood by individuals and groups using the tool.

One approach to displaying the results is to show the number of indicators that are red, yellow or green for each neighbourhood, essentially creating a ranking system for neighbourhoods based on the highest number of reds (representing worst scores) and lowest number of greens (representing best scores). We would like to add a note of caution about using this as the only approach to looking at the results, as it may obscure some of the important variations between neighbourhoods. For additional information see Appendix C.

Although it may be tempting to rank neighbourhoods based on a single indicator of interest, we want to stress that Urban HEART is intended to be used as a set of indicators across multiple domains. Data triangulation provides a more complete description of the subject the domain intends to capture because it captures multiple dimensions of that domain. It also minimizes the risk that any single indicator or single dataset will unfairly determine the categorization of a neighbourhood.

We present two approaches that can be used to create an index that displays the neighborhoods in an order based on a final tally of red, yellow, and green scores. The first and most simple approach is summing the number of red, yellow, and green scores for each domain (Method One). The second approach involves a more complex method of calculating domain scores using a standardizing and averaging process (Method Two). Finally, we also compared the neighbourhoods in the top quintile of neighbourhoods using both methods.

Although an individual indicator measures the same outcome across all neighbourhoods, the meaning of that measurement may differ between neighbourhoods based on the larger socio- demographic characteristics of those areas. For example, in many high-income communities, walkability scores or other indicators of strong environmental and physical infrastructure may appear poor compared to some of the lower-income communities located in the urban center of Toronto. However, the impact that poor physical infrastructure has on low-income communities is likely to be more negative than it would be in wealthier neighbourhoods, where residents have access to resources (such as automobiles and gyms) that can partially counteract these infrastructural weaknesses. Moreover, depending on how the Urban HEART tool is being used, different indicators or domains may be more important to consider than others. Finally, an additional concern was the desire to be able to capture the range of scores within the red, yellow, and green categories. For example, a neighbourhood that scores just below the cut-off point for the indicators within a specific domain clearly has different needs than one that scored very close to the high end for the same indicators.

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Comparing methods of categorizing and scoring neighbourhood rates

In addition to doing simple counts, there are various ways that indicators can be organized in an index. One way is to multiply rate ratios across indicators to create an index for the set of places to use in population needs-based resource allocation (Basrur et al., 1996; Fitzgerald et al., 2006). This only works with a small number of indicators. A more common practice is the summing and averaging of indicators that have been standardized to fall within a tight range (i.e. between 0 and 1) to create a composite score for each place.1Urban HEART could be used to create composite indices by using colour frequency rates transformed into standardized scores. In addition to a composite based on all the indicators, composites could be developed by domain. Different weighting options could be considered based on criteria such as the quality of the indicators or the correlations results when multiple indicators are strongly correlated and may be measuring the same concept.

The score-range transformation method provides a useful complement and alternative to counts based on the colour categories. The position of each neighbourhood can be determined relative to others in a way that also captures distance and clustering. This method also takes into account the range in the values of the scores for the indicators, meaning that a neighbourhood with a very poor score will be ranked as higher need than a neighbourhood with a higher score, even if both scores are officially within the red category. The rates are standardized to fall between 0 and 1 so that they can be summed and averaged to create a composite score overall as well as a score for each of the domains. The score-range transformation locates each value in relation to the overall range of the variable. If the variables were selected because they indicate greater need (a high rate indicates greater need) the variables are then transformed using (v - min) / (max - min), where ‘v’ is each of the area’s values and ‘min’ and ‘max’ are the overall minimum and maximum values.

Variables with an opposite relationship such as post-secondary education or access to green spaces, are included in the model by using the opposite formula (max - v) / (max - min). The standardization makes it possible to include variables that act in different directions, have narrow or large differences, assign weights for each variable, and sum their scores to produce a single index by dividing the total by the number of indicators included.

The disadvantage of this method is that it treats the differences in the actual rates as relevant, which may be of concern if the quality of the data is questionable. For example, for one variable, a rate could not be reported. If this was the case, we divided the neighbourhood index sum by one less to account for the number of variables. The technical users of the Urban HEART data, using the Excel document, will have these scores because they are included in the master indicator worksheet by indicator and by domain. See table in Appendix C for additional information.

1 Examples of using summed and averaged scores based on standardized variables transformed to fall into a range between 0–1 are the Toronto studies assessing spatial vulnerability to heat (Rinner et al., 2011) and studies of level of need for access to community-based primary health care in Ontario (Patychuk, 2012). March 28, 2014, ver.1, 21

Table 6 offers an example of how these two methods generate similar yet distinct indices. In Column A, Method One was used to calculate the 28 neighbourhoods with the highest need. Column B shows the same when Method Two is used. Twenty-three neighbourhoods are common to both columns, meaning that when either approach is used, these neighbourhoods emerge as those with highest need. Each column also includes five unique neighbourhoods which only make the list of those with the highest needs when that particular method is used to calculate a final score. It is important to note that not only do all 28 neighbourhoods have six or more red scores, but 28 also represents the top quintile (20%) of neighbourhoods that are faring the poorest. This should give users confidence that Urban HEART’s results have has consistency and stability in identifying the ranking of neighbourhoods, whether using a simple count of the number of variables in the three colour categories or when using a method which considers the differences in the actual range in values for each indicator.

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Table 6. Comparison between‘sum of colour categories’ method and ‘score range transformation’ method across all domains A. Method One: Rank and sum of ‘red’ (R), B. Method Two: Rank and composite ‘yellow’ (Y)and ‘green’ (G) categories (sum of score based on the average of the colour categories method) standardized rates (score range transformation method) ID Neighbourhood R Y G ID Neighbourhood Score 112 Beechborough-Greenbrook 10 5 0 24 0.76 72 10 2 3 112 Beechborough-Greenbrook 0.76 24 Black Creek 9 6 0 25 Glenfield-Jane Heights 0.73 25 Glenfield-Jane Heights 9 5 1 115 0.72 115 Mount Dennis 9 5 1 85 South Parkdale 0.71 121 Oakridge 9 6 0 121 Oakridge 0.71 85 South Parkdale 8 6 1 125 0.70 27 Heights 8 6 1 2 Mount Olive-Silverstone- 0.69 Jamestown 61 8 6 1 139 0.69 5 Elms-Old 8 5 2 72 Regent Park 0.69 125 Ionview 7 8 0 138 0.69 139 Scarborough Village 7 7 1 61 Crescent Town 0.68 111 Rockcliffe-Smythe 7 7 1 28 Rustic 0.67 124 Kennedy Park 7 7 1 5 Elms-Old Rexdale 0.67 6 -The 7 7 1 113 Weston 0.67 Westway 136 West Hill 7 7 1 136 West Hill 0.66 2 Mount Olive-Silverstone- 7 7 1 27 0.66 Jamestown 22 7 6 2 26 -Roding-CFB 0.66 91 Weston-Pellam Park 7 6 2 111 Rockcliffe-Smythe 0.66 43 6 9 0 43 Victoria Village 0.65 126 6 9 0 21 0.65 28 Rustic 6 8 1 110 Keelesdale-Eglinton West 0.64 113 Weston 6 8 1 137 Woburn 0.64 26 Downsview-Roding-CFB 6 8 1 6 Kingsview Village-The 0.64 Westway 135 Morningside 6 8 1 126 Dorset Park 0.63 55 6 7 2 91 Weston-Pellam Park 0.63 44 6 7 2 124 Kennedy Park 0.63 73 6 5 4 132 Malvern 0.63 Notes: Each of the 23 neighbourhoods that appear in both columns has been assigned a separate colour. The uncoloured neighbourhoods in each column are unique to that column.

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In addition to calculating an overall rank based on all of the indicators, we created indices ranking the neighbourhoods by each domain using both methods (See Tables 7, 20, and 43). Each domain summary includes the ranking of neighbourhoods by the sum of counts for highest red, lowest green, and then for highest yellow when the numbers of red and green are equal. This is a three-level sort order done in Excel. Where there are multiple neighbourhoods in each cluster of counts (RYG), then an additional sort is done using one or more indicators to ensure the highest rates are included in the top 28. The tables below also include the ranking of neighbourhoods based on the domain score using the score-range transformation method. As with the above comparison for total colour counts and total scores, most of the same neighbourhoods are included among the top quintile for both methods.

Table 7. Economic opportunity indices A. Rank and sum of ‘red’ (R), ‘yellow’ (Y) B. Rank and composite score based on and ‘green’ (G) categories theaverage of the standardized rates ID Neighbourhood R Y G ID Neighbourhood Score 72 Regent Park 3 0 0 72 Regent Park 0.92 121 Oakridge 3 0 0 121 Oakridge 0.81 61 Crescent Town 3 0 0 24 Black Creek 0.78 44 Flemingdon Park 3 0 0 139 Scarborough Village 0.74 85 South Parkdale 3 0 0 61 Crescent Town 0.73 24 Black Creek 3 0 0 55 Thorncliffe Park 0.73 139 Scarborough Village 3 0 0 44 Flemingdon Park 0.72 55 Thorncliffe Park 3 0 0 2 Mount Olive- 0.70 Silverstone-Jamestown 2 Mount Olive- 3 0 0 85 South Parkdale 0.68 Silverstone- Jamestown 27 York University 3 0 0 25 Glenfield-Jane Heights 0.67 Heights 28 Rustic 3 0 0 115 Mount Dennis 0.66 112 Beechborough- 3 0 0 28 Rustic 0.66 Greenbrook 25 Glenfield-Jane 3 0 0 138 Eglinton East 0.62 Heights 73 Moss Park 2 1 0 112 Beechborough- 0.62 Greenbrook 115 Mount Dennis 2 1 0 136 West Hill 0.61 136 West Hill 2 1 0 73 Moss Park 0.59 138 Eglinton East 2 1 0 74 North St. James Town 0.58 5 Elms-Old Rexdale 2 1 0 113 Weston 0.57 22 Humbermede 2 1 0 5 Elms-Old Rexdale 0.56 111 Rockcliffe-Smythe 2 1 0 22 Humbermede 0.56 124 Kennedy Park 2 1 0 78 Kensington-Chinatown 0.55 53 2 1 0 27 York University 0.55 Heights

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137 Woburn 2 1 0 53 Henry Farm 0.54 78 Kensington- 1 2 0 137 Woburn 0.54 Chinatown 74 North St.James Town 1 2 0 124 Kennedy Park 0.53 113 Weston 1 2 0 125 Ionview 0.52 6 Kingsview Village- 1 2 0 135 Morningside 0.52 The Westway 125 Ionview 1 2 0 6 Kingsview Village-The 0.51 Westway

The economic opportunity domain includes three indicators: unemployment rate, low income measure, and social assistance/Ontario Works. The shaded neighbourhoods are those that occur in the top 28 for the economic opportunity domain for both methods. There were some additional neighbourhoods that had one red, two yellow, and zero green. These neighbourhoods were sorted so that those with the highest percent on social assistance and highest percent low income ranked above others with lower rates and were included in the top 28. As shown above 27 of the 28 neighbourhoods emerge in the top 28 for both methods. The following section presents specific information on each indicator, by domain, and discusses all data sources used.

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5. Description of Indicators

5.1. Economic Opportunity

Indicator 1: Unemployment Rate This indicator was identified as a required indicator through the Urban HEART Delphi process.

Indicator Description and Data Sources

Unemployment rate is defined as the number of unemployed persons who are seeking work. This is expressed as a percentage of the labour force and includes individuals who are 15 years of age or older. In order to calculate this, we divided the total number of individuals who are 15 or older and in the labour force by the number of unemployed individuals. For the purpose of this study, unemployed persons were defined as those who were in the labour force, but who were without paid or self-employed work during the week of Sunday May 1st, 2011 to Saturday May 7th, 2011.

A person was considered to be part of the labour force if he or she was available for work and was actively looking for full or part-time work at any point within the four weeks preceding Sunday May 1st, 2011. Those who were on temporary lay-off and who were expecting to return to work, as well as those starting a job within four weeks were also considered to be part of the labour force. The data used to calculate unemployment rate were sourced from the 2011 National Household Survey (NHS).

The unemployment rate for Toronto in 2011 was 9.3%. Figure 2 provides the distribution of unemployment rates across Toronto’s neighbourhoods. Unemployment rates range between 5.0% and 17.1% with 70% of neighbourhood rates falling within 1 standard deviation of the neighbourhood mean. Six neighbourhoods had high unemployment rates (>14.5%).

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Figure 2. Histogram of neighbourhood unemployment rates (total population aged 15 and over), 2011

30 Mean = 9.37 Std. Dev. = 2.62 N = 140 Min = 5 Max = 17.1 20 10 Numberneighbourhoods of 0 4.13 6.75 9.37 11.99 14.61 17.23 -2SD -1SD Mean +1SD +2SD +3SD Unemployment rate (%)

Indicator Uses and Limitations

Unemployment rates are a useful indication of relative access to income and socioeconomic inclusion. Although the Urban HEART Delphi process recommended that unemployment rates among those aged 25–64 be used as a required indicator, the only unemployment rate data available were from the 2011 National Household Survey (NHS) which includes individuals aged 15 and older. This is slightly problematic, as it includes students.

Unemployment and labour force participation rates vary by age, sex, and population. The Urban HEART process used a summary indicator, which included the rates for both sexes and all population groups combined. These rates were calculated for each neighbourhood to identify relative differences among neighbourhoods. However, we did not describe differences within each neighbourhood’s population. Age 25–64 was suggested as the preferred indicator for unemployment rates because it includes only those who are primary working age and who are most likely to be looking for full time employment. Additionally, this excludes those in younger age groups who are more likely to be in school full-time. In the NHS, full-time students, looking for full-time work who are not employed and are available for work are considered unemployed (http://www12.statcan.gc.ca/nhs-enm/2011/ref/guides/99-012-x/99-012-x2011007-eng.cfm). Past census analysis shows that high numbers and high rates of unemployed youth increase neighbourhood unemployment rates. The majority of unemployed youth under the age of 25 are attending school (Census 2006 and City of Toronto analysis of NHS 2011, see: http://www.toronto.ca/demographics/pdf/nhs-backgrounder-labour-education-work- March 28, 2014, ver.1, 27

commuting.pdf). The NHS includes students in unemployment rates, whereas the labour force survey (SLID) does not.

The unemployment rate only captures those individuals who were actively looking for work and excludes those who have given up looking for work. Therefore, it is also useful to look at labour force participation (LFP) rates. The 2011 NHS unemployment rate is strongly correlated (>0.6) with the 2011 NHS LFP rate. The unemployment rate increased as the labour force participation rate decreased. Table 8 provides a correlation matrix which illustrates correlations between the NHS unemployment rate and other data sources. This is useful in assessing the similarities between data sources.

The 2011 NHS was a voluntary survey, which was not comparable to past censuses because of methodological differences, and thus it may not be representative of the local populations due to variations in response rates by geography and population groups. The NHS non-response rate for neighbourhoods ranged between 19.5% and 40.2%. Eighteen neighbourhoods have a non- response rate greater than 33%. The other available source of unemployment data was the mandatory Labour Force Survey. Its monthly sampling only produces a total of 100,000 respondents nationally. This is not enough for neighbourhood level reporting.

Given the limitations of the NHS, the neighbourhood unemployment rates were also tested for their correlation with other available indicators from the 2006 Census and 2012 social assistance/Ontario Works (OW) use rates. These are also presented in Table 8. Both the 2011 NHS Labour Force Participation rates and the unemployment rates were strongly correlated with the same variables in the 2006 Census (>0.9 for LFP rates and >0.75 for unemployment rates). Under the Ontario Works Act, OW has the primary objective of assisting people to become employed. OW provides income and other support to close to two-thirds (64%) of Toronto unemployed residents. The 2011 NHS unemployment rate was strongly correlated (>0.73) with OW rates in 2012. The NHS unemployment rate variable provided a picture of unemployment disparities across Toronto neighbourhoods, which was consistent with other measures. It is recommended that the unemployment rate indicator be used in conjunction with the other measures in this domain rather than as a single indicator.

Selecting Cut-off Values

The City of Toronto uses a target of 6%, the unemployment rate for Canada, in its benchmarking. In January 2013, the city’s Economic and Development Committee approved the target of eliminating the gap between the city’s unemployment rate and the national unemployment rate by 2018. The external target, based on the rates among the 20th percentile of comparison cities with the highest rates, was also 6%. Unemployment rates for comparison cities are listed in Table 8. There were only six neighbourhoods that were at or above this rate in 2011.

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Table 8. External comparators for unemployment rate A. Profile of census: CSDs Population, Population Percent Unemployment (Census subdivisions) that 2011 density per recent rate age 15+ are best matches with Census km2 Immigrants Toronto (%) Montréal 1,649,519 4,518 8.9 10 Brampton 523,911 1,967 8.4 9.5 Toronto 2,615,060 4,150 9.9 9.3 Mississauga 713,443 2,440 8.8 8.7 Markham 301,709 1,419 7.5 8.1 Surrey 468,251 1,480 7.8 7.9 Burnaby 223,218 2,464 7.2 7.4 Coquitlam 126,456 1,034 6.7 7.2 Vancouver 603,502 5,249 8.4 7.1 Richmond 190,473 1,474 6.9 7.1 Richmond Hill 185,541 1,838 6.1 6.9 Edmonton 812,201 1,187 5.8 6.1 Calgary 1,096,833 1,329 6.3 6 Winnipeg 663,617 1,430 6.9 5.9 Saskatoon 222,189 1,060 5.1 5.7

In our analysis, which was completed as of September 2013, the cut-off recommended for this indicator for red was Benchmark #1 (11.3%). That is the rate at the 20th percentile of the population with the highest unemployment rates. This includes 28 neighbourhoods most in need of improvement in unemployment rates. The cut-off recommended for green was Target #1 (7.4%), which is the unemployment rate for the wealthiest population quintile. This was achieved by 37 neighbourhoods, and it represents a potentially achievable target for all neighbourhoods and for Toronto as a whole. Potential cut-off measures are listed in Table 9.

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Table 9. Potential cut-off measures for unemployment to determine ‘red’ and ‘green’ neighbourhoods Potential cut-off measures above which neighbourhoods could be categorized as ‘red’ (highest unemployment) Benchmark #1: Population quintile 11.3 -20th percentile with the highest unemployment rate Benchmark #2: Standard deviations 11.92 -standard deviations greater than 1 represent the worst unemployment rates Benchmark #3: Rate ratios (RR) 11.16 Benchmark #4: External marker/benchmark 9.3 -20th percentile of 15 comparisons cities ranked by highest unemployment rates; selected cities had the highest percentage of recent immigrants (>5%) and high density (>1,000 pop/km2) Potential cut-off measures at or below which neighbourhoods could be categorized as ‘green’ (lowest unemployment) Target #1:Rate of wealthiest population quintile 7.4 Target #2.1: Population quintile 7.2 -20th percentile of the population with the best unemployment rates(See Benchmark #1 above) Target #2.2: Standard deviations 6.68 -standard deviations less than -1 SD representthe best unemployment rates Target #2.3: Rate ratios 7.4 -RRs greater than 80% of the city rate (city rate plus or minus 20% of the city rate) (See Benchmark #3 above) Target #3: External target 6.0 -20th percentile of 15 comparison cities Target #4: Policy target 6.0 -city uses 6% based on the Canada rate for its economic benchmarking and performance management Natural breaks/widest rate differences occur at Benchmark #2 (1 standard deviation above the mean neighbourhood rate) Selected Benchmark/Target:Based on analysis conducted in September 2013, the recommended cut-off for this indicator for ‘red’ was Benchmark #1(population quintile: 20th percentile with the highest unemployment rate) and the recommended cut-off for ‘green’ was Target #1 (wealthiest income quintile).

Income Quintiles

In our analysis, we also calculated the rates for indicators by income quintiles of the population. This was used as an indicator of health and social inequality, and provided a potentially achievable target for all population groups. It also points to the amount of potentially avoidable and unfair problems due to ‘unjust social arrangements’ stemming from economic inequality (WHO, 2010). Rates of unemployment by neighbourhood income quintile are provided in Table 10 and Figure 3.

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Figure 3. Unemployment rate by neighbourhood income quintile, 2011

Table 10. Unemployment rate by neighbourhood income quintile, 2011 Income quintile Persons age Persons age 15+ Persons age Unemployment 15+ in the unemployed 15+ in each rate (%) labour force quintile (%) Q1. Lowest income 250,720 29,980 17.9 12.0 Q2. 2nd lowest 245,135 26,925 17.5 11.0 income Q3. Middle income 261,180 25,185 18.7 9.6 Q4. Mid-high 321,175 24,900 23.0 7.8 income Q5. Highest 319,370 23,580 22.9 7.4 income City total/average 1,397,580 130,570 100 9.3

Table 11. Correlation analysis for unemployment rate UE LFP UE LFP UE UE UE SA 2011 2011 2006 2006 2006- 2006- 2006A 2012 24 64 LL UE2011 1 LFP2011 -0.60 1 UE2006 0.75 -0.52 1 LFP2006 -0.49 0.93 -0.45 1 UE2006-24 0.19 -0.28 0.45 -0.26 1 UE2006-64 0.74 -0.43 0.95 -0.37 0.19 1 UE2006ALL 0.74 -0.43 0.95 -0.36 0.19 0.99 1 SA2012 0.73 -0.40 0.73 -0.36 0.09 0.76 0.76 1 Notes: UE2011 = Unemployment rate age 15+ 2011 NHS; LFP2011 = Percent in the labour force 15+ 2011 NHS; UE2006 = Unemployment rate age 15+ 2006 Census; LFP2006 = Percent in the labour force 15+ 2011 NHS; UE2006-24 = 2006 age 15-24 unemployment rate; UE2006-64 = 2006 age 25-64 unemployment rate; UE2006ALL = 2006 age 25+ unemployment rate; SA2012 = percent of population on social assistance in 2012.

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Indicator 2: Low Income

This indicator was identified as a required indicator through the Urban HEART Delphi process.

Indicator Description and Data Sources

In Urban HEART, low income was operationalized as the percentage of the population living with incomes below the after-tax low income measures (LIM-AT) established in 2010 for the city of Toronto. Our data were sourced from Statistics Canada (Source: T1-Family File, Table F- 18, Statistics Canada, Income Statistics Division, 2010, Annual Estimates for Census Families and Individuals, 13C0016). We made modifications to census tracts 35 and 299. These modifications are discussed below.

In order to determine the proportion of households experiencing low income, we divided the number of families and non-family persons filing income tax forms for the 2012 by the total number of low income families and non-family persons. The low income rate for Toronto based on the after-tax LIM rate in 2010 was 22.3%. Figure 4 shows the distribution of low income rates across Toronto neighbourhoods. The LIM rate ranges between 5.58% and 49.85% with 72% of neighbourhood rates falling within 1 standard deviation of the neighbourhood mean (21.08%), which is between 13.13% and 29.05%.

Figure 4. Histogram of neighbourhood low income measures (all families and non-family persons by number of persons), 2010

30 Mean = 21.09 Std. Dev. = 7.96 N = 140 Min = 5.58 Max = 49.85 20 10 Number of neighbourhoods of Number 0 5.17 13.13 21.09 29.05 37.01 44.97 -2SD -1SD Mean +1SD +2SD +3SD Low income (%) March 28, 2014, ver.1, 32

Indicator Uses and Limitations

The after tax-low income measure (LIM-AT) is the poverty measure used by the Ontario Poverty Reduction Strategy. The low income indicator is important for Urban HEART both as a required indicator and as the basis for calculating population income quintiles. The source for the proportion of persons below the LIM-AT was income tax filing data compiled by the Canada Revenue Agency and provided in the Statistics Canada 2010 Small Area Administrative Data T1 Family File —T1FF. Given the problems with the NHS, many organizations are turning to the T1FF at the census tract level as an alternative source for income information. The T1FF is very different from the previous long form census and the 2011 NHS in several ways. It includes all tax filers including those not living in private households that are excluded from the census and NHS income data. As well, it is not a sample as it uses census families rather than economic families. Additionally, it includes commercial addresses. It also allocates records to a geographic location using the single link indicator (SLI) on the postal code conversion file (PCCF) rather than the actual address, which is used by the census and NHS. These differences result in different populations covered in the T1FF than the census and NHS income variables. This results in geographic misallocations.

After matching multiple 2010 T1FF and 2011 Census variables there were two census tracts in two different neighbourhoods that had more persons than the 2011 Census and that had LIM rates far outside all other socioeconomic status variables that were matched by census track and by neighbourhood. These other measures were social assistance (2012), unemployment (2011), and the Low Income Cut Off – LICO (2006). Rather than exclude these neighbourhoods, we replaced what appeared to be very over-inflated population counts from the T1FF with the 2011 Census population counts for the two census tracts in these neighbourhoods. Based on information from the other SES variables, the T1FF Toronto LIM-AT rate was assigned to these census tracts. This modification moved these neighbourhoods outside the lowest income quintile. The modified census tracts were CT 35 in neighbourhood 76 (Bay Street Corridor), and CT 299 in neighbourhood 38 (Lansing Westgate). The results of these modifications are shown in Table 12. Using these modifications resulted in the Toronto rate (22.2%), based on the aggregated data for the neighbourhoods, being slightly different than the sum of Forward Sortation Area data for Toronto (22.3%).

When the 2010 LIM rates from the 2011 NHS became available in September 2013, we calculated and compared the income quintiles for the NHS LIMs with the T1FF LIMs to identify if using the NHS LIMs would have resulted in different neighbourhoods falling into the lowest income quintile. The comparison confirmed that the cut-off was correct. Of the 24 neighbourhoods coded red using the T1FF LIMs, there were only three that would not have been coded red if the NHS LIMs were used instead. These neighbourhoods had rates close to the cut- off limit. Therefore, the LIM variable based on the T1FF used in Urban HEART was not replaced when the NHS LIMs became available.

The neighbourhood LIM rates were tested for their correlation with other neighbourhood SES variables from other sources: Social Assistance 2012 (Toronto Employment and Social Services — TESS), unemployment 2011 (NHS, 2011), LICO-AT (2006 Census) and LIM-AT 2010 March 28, 2014, ver.1, 33

(NHS, 2011). These correlations are displayed in Table 13. We found a very strong correlation (>0.9%) with our modified LIM-AT 2010 T1FF and the 2006 LICO-ATs from the 2006 Census, and strong correlations (>0.7%) with Social Assistance 2012; Unemployment 2011 NHS; and Marginalization 2006.

Table 12. Comparison between two neighbourhood census tracts before and after modifications # Before Persons LIPs LIM-AT # After Persons LIPS LIM-AT modification (%) modification (%) 38 Lansing- 25,650 12,5 48.8 38 Lansing- 14,680 2,942 20.0 Westgate 20 Westgate 76 Bay Street 29,570 11,9 40.3 76 Bay Street 20,012 5,336 26.7 Corridor 20 Corridor Notes: LIPs = low income persons; LIM-AT = low income-after tax.

Table 13. Correlation matrix for income LIM2010 MLIM2010 LICO2006 INCSA2010 UE2011 ONMARG LIM2011 LIM2010 1 MLIM2010 0.95 1 LICO2006 0.87 0.92 1 INCSA2012 0.67 0.75 0.75 1 UE2011 0.68 0.74 0.66 0.73 1 ONMARG 0.66 0.73 0.71 0.71 0.65 1 LIM2011 0.88 0.93 0.93 0.76 0.74 0.73 1 Notes: LIM2010 = Percent low income individual 2010 T1FF; MLIM2010 = Modified LIM T1FF 2010; LICO2006 = Percent of individuals-incidence of low income after-tax; INCSA2012 = Percent of population that are recipients of social assistance; UE2011 = NHS Unemployment rate 2011 ages 15+; ONMARG = Ontario Marginalization Index combined quintiles; LIM2011 = Low Income Measure NHS 2011.

Selecting Cut-off Values

Based on our analysis, completed as of September 2013, the cut-off recommended for this indicator for red was B1 — the rate at the 20th percentile of the population with the highest proportion of the population below the after tax LIM (AT-LIM). This included 24 neighbourhoods which were faring the worst in access to income. We used this internal measure rather than the external measures for comparison cities because the 20th percentile was the city rate and this would include almost half of the city’s neighbourhoods. While this was an important indicator of a high poverty rate in Toronto relative to other cities, a more focused measure was more useful for the intended purposes of the Urban HEART dashboard.

The cut-off recommended for the green category was calculated as 16.65%. This was based on the Ontario Poverty Reduction target. This was achieved by 42 neighbourhoods. It represented a potentially achievable target for all neighbourhoods and for Toronto as a whole. The Ontario Poverty Reduction Strategy target is a 25% reduction in the number of children in poverty. We applied this approach to calculate what a 25% reduction in the number of people (all ages) in Toronto below the after tax low income measure in 2010 would amount to. A 25% reduction in

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the number of low income persons was equal to a LIM-AT rate of 16.65%. Table 14 presents the potential cut-off measures for low income.

Table 14. Potential cut-off measures for low income to determine ‘red’ and ‘green’ neighbourhoods Potential cut-off measures above which neighbourhoods could be categorized as ‘red’ (highest percentage of low income) Benchmark #1: Population quintile 28.1 -20th percentile with the highest proportion of the population below the LIM-AT. The rate 28.1 was selected as the cut-off for ‘red’. -There were two neighbourhoods very close to this cut-off so we compared this indicator with the same variable in the NHS (based on 2010 income data, and geocoded according to the address in the NHS). The neighbourhood above the cut-off with a rate of 28.1 was also in the first quintile of the NHS LIM-AT distribution but the neighbourhood with a LIM rate of 28.08 in the T1FF was in the 3rd quintile in the NHS AT-LIM. -Given this, we decided to maintain the cut-off at 28.1, which falls between these two neighbourhoods. Benchmark #2: Standard deviations 29.2 -standard deviations greater than 1 represent the worst rates Benchmark #3: Rate ratios (RR) 26.64 -neighbourhood rate/city rate always expressed as two decimal points, at 1.2 times higher than the city rate. Benchmark #4: External marker/benchmark 22.3 -20th percentile of 15 comparison cities ranked by highest rates; selected cities had the highest percentage of recent immigrants (>5%) and high density (>1,000 pop/km2). Potential cut-off measures at which neighbourhoods could be categorized as ‘green’ (lowest percent of low income) Target #1: Rate of wealthiest population quintile 11.7 Target #2.1: Population quintile 16.4 -20th percentile of the population with the best rates (see Benchmark #1 above) Target #2.2: Standard deviations 13.2 -standard deviations less than -1 represent the lowest rates Target #2.3: Rate ratios 17.76 -RRs greater than 0.80% of the city rate (see Benchmark #3 above) Target #3: External target 13.5 - 20th percentile of 15 comparison cities ranked by lowest rates (see external benchmark above) Target #4: Policy target 16.65 -The Ontario Poverty Reduction Strategy target is a 25% reduction in the number of children in poverty. We use the sum of low income persons in the Toronto Forward Sortation Areas, it is the most accurate description of Toronto residents as it excludes business postal codes. Natural breaks/widest rate differences occur at the 10th percentile and the 50th percentile (close to the external benchmark) when neighbourhoods are ranked according to highest rate of low income. Because the LIM rates are more spread out in higher income areas, there are several wide differences once the LIM rates are less than 80% of the city rate. Natural breaks were not used in

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selecting cut-offs. Selected Benchmark/Target:Based on analysis conducted in September 2013, the recommended cut-off for this indicator for ‘red’ was Benchmark #1(population quintile: 20th percentile with the highest LIM-AT rates)and the recommended cut-off for ‘green’ was Target #4(policy target: Ontario Poverty Reduction Strategy).

Income Quintiles

Differences in the rates of low income by neighbourhood income quintile are displayed in Table 15 and Figure 5. Additionally, the external comparison cities used for the low income indicator are displayed in Table 16.

Table 15. Low income by neighbourhood income quintile, 2010 Income quintile Total Total low Percent Households with Number of pop. income of pop. low income in neighbourhoods pop. (%) each quintile (%) Q1. Lowest income 495,420 157,940 19.6 31.9 24 Q2. 2nd lowest income 511,102 135,776 20.3 26.6 23 Q3. Middle income 503,410 114,260 20.0 22.7 25 Q4. Mid-high income 502,860 92,472 19.9 18.4 31 Q5. Highest income 508,810 59,440 20.2 11.7 37 City total/average 2,521,602 559,888 100 22.2 140

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Figure 5.Low income by neighbourhood income quintile, 2010

Table 16. External comparators for low income CSDs Population, Population Percent All families and non-family persons, (Census 2011 per sq. km recent number of persons subdivisions) Census immigrants All All low AT-LIM (%) persons income percent persons (%) Montréal 1,649,519 4,518 8.9 933,700 238,860 25.6 Richmond 190,473 1,474 6.9 194,430 48,210 24.8 Toronto 2,615,060 4,150 9.9 2,496,770 55,7650 22.3 EB = 22.3 Burnaby 223,218 2,464 7.2 212,940 46,650 21.9 Vancouver 603,502 5,249 8.4 561,420 121,830 21.7 Coquitlam 126,456 1,034 6.7 121,090 23,120 19.1 Surrey 468,251 1,480 7.8 448,250 84,490 18.8 Markham 301,709 1,419 7.5 225,760 42,050 18.6 Richmond 185,541 1,838 6.1 185,510 32,810 17.7 Hill Mississauga 713,443 2,440 8.8 709,560 124,370 17.5 Brampton 523,911 1,967 8.4 524,320 89,140 17.0 Winnipeg 663,617 1,430 6.9 635,720 99,200 15.6 Saskatoon 222,189 1,060 5.1 208,130 28,190 13.5 ET = 13.5 Edmonton 812,201 1,187 5.8 757,180 97,970 12.9 Calgary 1,096,833 1,329 6.3 1,047,880 121,840 11.6 Notes: AT-LIM = after tax low income; EB = external benchmark; ET = external target.

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Indicator 3: Social Assistance This indicator was identified as a strongly recommended indicator through the Urban HEART Delphi process.

Indicator Description and Data Sources

In order to calculate social insurance rates, we divided the total population, as reported for all ages in the 2011 census, by the number of persons and their dependents who received OW, or non-OW special assistance for medical items. Additionally, persons receiving ODSP who were participating in OW employment programs were included in this calculation.

The social assistance rate for Toronto in 2010 was 10% of the city’s total population in 2012. Figure 6 shows the distribution of social assistance rates across Toronto neighbourhoods. The social assistance rates range between less than 1% and 29.1% with 65% of neighbourhood rates falling within 1 standard deviation of the neighbourhood mean, which was 16.05% and 3.56%.

The total was based on applications for assistance and their eligibility. This was higher than the monthly snapshots, i.e. the number of SA cases in June 2012 was 104,326 and the number of persons covered was 174,662. Ontario Works is a legislated program so benchmarks and targets cannot be applied. This measure does not include all persons on ODSP, which totaled approximately 88,900 in 2011.

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Figure 6. Histogram of neighbourhood social assistance rates (Ontario Works recipients, and ODSP recipients participating in OW employment programs, and non-OW members receiving special assistance for medical items), 2012

30 Mean = 9.8 Std. Dev. = 6.24 N = 140 Min = .42 Max = 29.06 20 10 Numberneighbourhoods of 0 3.56 9.8 16.04 22.28 28.52 -1SD Mean +1SD +2SD +3SD Social assistance (%)

Indicator Uses and Limitations

Ontario Works provides income and other support to close to two-thirds (64%) of Toronto unemployed residents. The main limitation of this measure was that it did not include most people on the Ontario Disability Support Program. There were 88,900 ODSP recipients in Toronto in 2011. The number of people who are eligible but do not apply for social assistance is also unknown. Whether or not some groups are more or less likely to seek OW than other groups that are equally eligible is also unknown.

Selecting Cut-off Values for Urban HEART Categories

Based on analysis completed as of September 2013, the cut-off recommended for this indicator, for red, was the rate at the 20th percentile of the population with the highest proportion of the population on social assistance (SA)/Ontario Works (OW) (B1). As shown in the graph of the distribution of SA rates on the previous page, the number of neighbourhoods with high rates starts to be more spread out after at this point. This result includes 26 neighbourhoods with rates ranging between 15.4% and 29.1%. The cut-off recommended for the green category was a rate of 80% or lower than the city rate (T2.3 rate ratios) and it encompasses 61 neighbourhoods. This was more relevant than lower targets because low rates are not achievable in Toronto given that large number of people that will require this assistance that are more concentrated in low income March 28, 2014, ver.1, 39

neighbourhoods. Social assistance programs and eligibility requirements vary by province so comparisons could only be made using Ontario cities. Comparison of social assistance caseloads is part of the Ontario Municipal Benchmarking Initiative (OMBI). However, only one of the OMBI municipalities met our criteria (high recent immigration). Since SA is a response to a needed legislated program, targets are not set and strategies in other areas (employment, etc.) affect social assistance caseloads. Potential cut-off measures are displayed in Table 17.

Table 17. Potential cut-off measures for social assistance to determine ‘red’ and ‘green’ neighbourhoods Potential cut-off measures above which neighbourhoods could be categorized as ‘red’ (highest percent on social assistance) Benchmark #1: Population quintile 15.1 -20th percentile with the highest proportion of the population that are recipients of social assistance (OW) programs. This was selected as the cut-off for ‘red’ (worst rates) for Urban HEART (September 2013: This includes 26 neighbourhoods) Benchmark #2: Standard deviations 16.0 -standard deviations greater than 1 from the city mean represent the worst rates Benchmark #3: Rate ratios (RR) 12.0 -RRs greater than 1.2 (deemed a reasonable cut-off measure for ‘red’, including 42 neighbourhoods) Benchmark #4: External marker/benchmark NA -20th percentile of 15 comparison cities ranked by highest rates Potential cut-off measures at or below which neighbourhoods could be categorized as ‘green’ (lowest percent on social assistance) Target #1: Rate of wealthiest population quintile 3.1 Target #2.1: Population quintile 4.7 -20th percentile of the population with the best rates (see Benchmark #1 above) Target #2.2: Standard deviations 3.6 Target #2.3: Rate ratios 8.0 -RRs greater than 0.8 of the city rate (city rate minus 20% of the city rate) (see Benchmark #3 above) Target #3: External target NA -20th percentile of 15 comparison cities (see External Benchmark above) Target #4: Policy target NA -This program is delivered according to provincial regulations so targets for levels are not appropriate Natural breaks/Widest rate differences occur at 16.2% and 14.4% (above and below Benchmark #1) and at 11.7% (at Benchmark #3.). Social assistance rates are more spread out in higher income areas, so wide differences in rate differences occur that are too common to be relevant in deciding how to set cut-offs Selected Benchmark/Target:Based on analysis conducted in September 2013, the recommended cut- off for this indicator for ‘red’ was Benchmark #1(population quintile: 20th percentile with the highest proportion of social assistance recipients) and the recommended cut-off for ‘green’ was Target #3(external target: 20th percentile of 15 comparison cities).

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Income Quintiles

Social assistance rates by income quintile are displayed in Table 18 and Figure 7.

Table 18.Social assistance by neighbourhood income quintile, 2012 Income quintile Total pop. Total social Percent of Social assistance pop. (%) assistance recipients recipients (%) Q1. Lowest income 503,750 80,874 19.3 16.1 Q2. 2nd lowest income 493,375 60,068 18.9 12.2 Q3. Middle income 496,695 53,426 19.0 10.8 Q4. Mid-high income 552,670 41,211 21.1 7.5 Q5. Highest income 568,285 25,479 21.7 3.1 City total/average 2,614,775 261,058 100.0 10.0

Figure 7.Social assistance by neighbourhood income quintile, 2012

% Recipeints of Social Assistance (OW), Toronto 2012, Toronto Employment Services 18 16.1 16

14 12.2 12 10.8 10

8 7.5

6 3.1 4

2

0 Q1 Lowest Q2 2nd Lowest Q3 Middle Q4 Mid-High Q5 Highest Income income Income Income Income # Persosns who are recipients of OW, persons on ODSP accessing OW employment programs plus others receiving asssitance for medical items by Neighbourhood Income Quintile

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External Comparators

Social assistance programs and eligibility requirements vary by province. Indicators such as government transfer payments (NHS, 2011), past census data, or the percentage of social assistance in the T1FF (income tax data) for cities in other provinces are not comparable with Ontario cities because of these eligibility and program differences. Comparison of SA (OW) caseloads is part of the Ontario Municipal Benchmarking Initiative (OMBI), and Toronto has the highest rate for that indicator. Only one of our comparison cities was a part of the OMBI members, therefore we could not use an external cut-off.

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5.2. Social and Human Development Table 19. Social and human development indices A. Rank and sum of red (R), yellow (Y) and green B. Rank and composite score based on (G) categories theaverage of the standardized rates ID Neighbourhood R Y G ID Neighbourhood Score 25 Glenfield-Jane Heights 3 0 0 112 Beechborough-Greenbrook 0.98 24 Black Creek 3 0 0 25 Glenfield-Jane Heights 0.94 112 Beechborough-Greenbrook 3 0 0 24 Black Creek 0.92 111 Rockcliffe-Smythe 3 0 0 111 Rockcliffe-Smythe 0.88 26 Downsview-Roding-CFB 3 0 0 110 Keelesdale-Eglinton West 0.88 113 Weston 3 0 0 113 Weston 0.88 125 Ionview 3 0 0 26 Downsview-Roding-CFB 0.87 72 Regent Park 3 0 0 125 Ionview 0.84 121 Oakridge 3 0 0 91 Weston-Pellam Park 0.83 78 Kensington-Chinatown 2 1 0 78 Kensington-Chinatown 0.83 43 Victoria Village 2 1 0 121 Oakridge 0.82 85 South Parkdale 2 1 0 109 Caledonia-Fairbank 0.82 29 Maple Leaf 2 1 0 29 Maple Leaf 0.81 110 Keelesdale-Eglinton West 2 1 0 115 Mount Dennis 0.81 115 Mount Dennis 2 1 0 43 Victoria Village 0.81 5 Elms-Old Rexdale 2 1 0 85 South Parkdale 0.81 92 -Davenport 2 1 0 5 Elms-Old Rexdale 0.81 91 Weston-Pellam Park 2 1 0 72 Regent Park 0.80 109 Caledonia-Fairbank 2 1 0 92 Corso Italia-Davenport 0.76 93 Dovercourt-Wallace 2 1 0 28 Rustic 0.75 Emerson-Junction 28 Rustic 2 1 0 61 Crescent Town 0.73 22 Humbermede 2 1 0 81 Trinity-Bellwoods 0.72 139 Scarborough Village 2 1 0 93 Dovercourt-- 0.72 Junction 138 Eglinton East 2 1 0 22 Humbermede 0.70 44 Flemingdon Park 2 1 0 70 South Riverdale 0.70 31 Yorkdale-Glen Park 2 1 0 21 Humber Summit 0.69 124 Kennedy Park 2 1 0 139 Scarborough Village 0.69 27 York University Heights 2 1 0 57 Broadview North 0.69

The social and human development domain includes three indicators: high school graduation, marginalization, and post-secondary education. The shaded neighbourhoods are those that occur in the top 28 for the social and human development domain for both methods. There were some additional neighbourhoods that had two reds, one yellow, and zero greens (beyond the 28 listed here). These were sorted so that those with the lowest percent with post-secondary education, highest marginalization and low graduation were included in the top 28. As shown above, 23 of the 28 neighbourhoods emerge in the top 28 for both methods.

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Indicator 4: High School Graduation

This indicator was identified as a required indicator through the Urban HEART Delphi process.

Indicator Description and Data Sources

The desired indicator for high school graduation is the Grade 9 cohort measure. This is the measure of high school graduation used by the Ministry of Education, which has set a target of 85% of Grade 9 students completing high school by the end of five years from the year they start secondary school. For additional information, see the 2011 Annual Report of the Office of the Auditor General of Ontario (http://www.auditor.on.ca/en/reports_en/en11/313en11.pdf).

This measure is used by both the Toronto District School Board (TDSB) and the Toronto Catholic District School Board (TCDSB). Information is not available for the two French school boards or for private secondary schools. At the time the Urban HEART report was being prepared, the Grade 9 cohort graduation measure which combined data for the TCDSB and the TDSB was not yet available. Because the Delphi process identified this as a required indicator, it was decided to include a stand-in composite measure that could be used to predict the likelihood that neighbourhoods being researched could achieve or not achieve the target of 85% with the expectation that the stand-in measure would be replaced with the actual combined measures once it was available and validated. Legal agreements between the City of Toronto, TDSB and TCDSB, once completed, would result in postal code data for this indicator being provided to the city and rolled up into neighbourhood rates for the two data sets combined into one.

This stand-in indicator for high school graduation was used to predict the likelihood that secondary school students would achieve the Ministry of Education’s target for 85% high school completion rate within five years. This is the Grade 9 cohort measure. Students entering secondary school in 2006 were followed for five years and their graduation status was based on their status as of October 2011. Secondary School Completion means completing the credits for the Ontario Secondary School Diploma (OSSD) or accumulating at least 30 credits in high school courses. Excluded from the denominator were students who transferred out of the school board. Students who do not complete secondary school within five years were either returning to school for another year or years, or had dropped out, or were otherwise no longer in the system. This indicator was prepared from combined data sourced from the Toronto District School Board and the Toronto Catholic District School Boards, through data sharing agreements between the City of Toronto and the two school boards. When available, the data based on the postal codes of the residence of students will be used to produce Grade 9 cohort graduation rates for Toronto neighbourhoods. It is recommended that when the desired combined indicator is available, that this stand-in indicator prepared for Urban HEART reported herein be replaced by the new desired indicator.

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Indicator Uses and Limitations

Although the measures were based on data from the two largest school boards, we were unable to include students from French and private schools in our calculations. Additionally, the use of a single cohort for Grade 9 would result in small numbers for some neighbourhoods, even with both schools combined. This could mean some instability in the rates. This could be improved by using a cohort that combined more than one Grade 9 cohort (e.g. those entering Grade 9 in 2005, 2006 and 2007) rather than only those entering in 2006. A further limitation was that this measure did not include students who entered the system after Grade 9. These students were more likely to be immigrants to Canada. This means that this measure likely underrepresented the graduation rates of recent immigrant students.

Selecting Cut-off Values

The indicator shown is a composite of the four indicators listed in Table 20. Because one of the indicators was provided on the condition that it not be made public but be included in a composite index, only the result of the combination are provided rather than the specific rates for each of the component’s variables. The composite was based on the following indicator-specific cut-offs that were used to categorize neighbourhoods into three groups for each indicator. Neighbourhoods that scored in the category of lowest high school graduation rates on three of the four indicators were categorized as red. Neighbourhoods that scored in the category of highest high school graduation rates on three of the four indicators were categorized as green. As we used a composite variable for this indicator, we were unable to run the data by income quintiles or use external comparators in selecting targets and benchmarks.

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Table 20. List of indicators used for high school completion rates Indicator Data set Cut-off values Percentage of age 20–24 2006 Census Low: High: with secondary school < = 85% > = 92.5% completion. 92.5% Percentage of Grade 12 Toronto District School Board, Low: High: students entering Grade 12 prepared for Urban HEART, July < = 58.6% >75% in 2010–2011 who 2013. Note that this measure does graduated (completed OSSD not exclude transfers out so it is or accumulated 30 credits) lower than it would be if this by October 2011. group was excluded. Percentage of Grade 9 TCDSB, Wellbeing Toronto Low: High: cohort in 2003 (13–14 year website, rates only — no <80% > = 85% olds who completed the full numerator, denominator of school year 2003–2004) description for validation who completed secondary purposes. school (graduated or accumulated 30 credits) by October 31, 2009. Percentage of Grade 9 TDSB quintile map only. Low: High: cohort in 2006 who <74.68% > = 86.22% completed secondary school (Graduated or accumulated 30 credits by October 2011).

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Indicator 5: Marginalization

This indicator was identified as a required indicator through the Urban HEART Delphi process.

Indicator Description and Data Sources

The Ontario Marginalization Index (ON-MARG) consists of 18 census variables that capture residential instability, ethnic concentration, dependency, and material deprivation. Derived from the 2006 Census, these 18 variables are the result of a principal components factor analysis of 42 measures. For Urban HEART, we calculated ON-MARG indexes for all 140 Toronto neighbourhoods, and used this index as our indicator. Neighbourhoods were divided into quintiles based on ON-MARG indexes ranging from 1 (lowest marginalization) to 5 (highest marginalization). General information about the ON-MARG index can be found at: http://www.torontohealthprofiles.ca/onmarg.php. More information on how to use the ON- MARG index is available at: http://www.torontohealthprofiles.ca/onmarg/userguide_data/ON- Marg_user_guide_1.0_FINAL_MAY2012.pdf

The ON-MARG index has been validated by Matheson et al. (2012). The average ON-MARG index for Toronto in 2006 was 2.4. Figure 8 shows a histogram of ON-MARG indexes across Toronto neighbourhoods. ON-MARG indexes ranged between 1.0 and 3.4, with 58% of neighbourhood indexes falling within 1 standard deviation of the mean (between 1.84 and 2.96).

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Figure 8. Histogram of neighbourhood Ontario Marginalization Indexes, 2006

Mean = 2.4 Std. Dev. = .56 N = 140

20 Min = 1 Max = 3.4 15 10 Numberneighbourhoods of 5 0 1.28 1.84 2.4 2.96 3.52 -2SD -1SD Mean +1SD +2SD Ontario Marginalization Index

Indicator Uses and Limitations

The theoretical foundation of the ON-MARG index is informed by existing research on deprivation and marginalization. The index was empirically derived using principal components analysis, and has been shown to be stable across time periods and across rural and urban areas. Research finds that the ON-MARG is associated with various health outcomes such as hypertension, depression, youth smoking, alcohol consumption, injuries, body mass index, and infant birth weight. Moreover, the ON-MARG has been identified as a useful tool to examine health inequities in Ontario (see http://goo.gl/szvqlV).

The ON-MARG index is currently being used by several organizations and institutions. For example, Public Health Ontario is using the ON-MARG index as an online tool for public health units across the province to develop population health interventions. Peel Public Health is using the ON-MARG index to identify areas most in need of in-school dental screening. Toronto’s Hospital for Sick Children is also using the ON-MARG index to examine the links between area- level factors and health outcomes.

Because the ON-MARG index was created using 2006 Census data, one potential limitation is that it may not be possible to update the index using data collected from the 2011 voluntary National Household Survey (NHS). Given its breadth and stability, however, the ON-MARG index remains a good and comprehensive indicator of marginalization.

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Selecting Cut-off Values

Table 21 presents potential cut-off measures for the ON-MARG index to determine red and green neighbourhoods. Neighbourhoods with benchmarks greater than identified cut-offs could be coded red. Neighbourhoods with targets less than identified cut-offs could be coded green.

Table 21. Potential cut-off measures for the ON-MARG index to determine ‘red’ and ‘green’ neighbourhoods Potential cut-off measures above which neighbourhoods could be categorized as ‘red’ (most marginalized) Benchmark #1: Population quintile about 3.0 -20th percentile with the highest marginalization rate Benchmark #2: Standard deviations 2.957 -standard deviations greater than +1 represent the worst marginalization rates Benchmark #3: Rate ratios NA Benchmark #4: External marker/benchmark less than 2.9 -20th percentile of 15 comparison cities ranked by highest rates Potential cut-off measures above which neighbourhoods could be categorized as ‘green’ (least marginalized) Target #1: Rate of wealthiest population quintile 1.8 Target #2.1: Population quintile about 1.8 -20th percentile of the population with the best rates (see Benchmark #1 above) Target #2.2: Standard deviations 1.843 -standard deviations less than -1 represent the lowest marginalization rates Target #2.3: Rate ratios NA -rate ratios greater than 80% of the city rate (city rate ratio minus 20%) (See Benchmark #3 above) Target #3: External target Less than 2.2 -20th percentile of 15 comparison cities ranked by lowest rates Target #4: Policy target NA Target #5: Natural breaks/widest rate differences NA -not relevant for this ‘ordinal’ measure Selected Benchmark/Target: Based on analyses conducted in September 2013, the recommended cut-offs for the ON-MARG index used External Comparators. Neighbourhoods coded as ‘red’ have ON-MARG indexes greater than 2.9 (most marginalized), ‘green’ neighbourhoods have indexes less than 2.2 (lowest marginalization), and ‘yellows’ fall between these two cut-offs (2.2 to 2.9).

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Income Quintiles

Table 22 and figure 9 present the income quintiles of Toronto neighbourhoods based on total population and ranked by low-income measures-after tax (LIM-AT).

Table 22. ON-MARG index by neighbourhood income quintile, 2006 Income quintile Total pop. Number of Weighted share of neighbourhoods neighbourhood rates Q1. Lowest income 502,978 24 2.84 Q2. 2nd lowest income 493,158 23 2.77 Q3. Middle income 497,206 25 2.61 Q4. Mid-high income 552,777 31 2.25 Q5. Highest income 568,148 37 1.80 City total/average 2,614,267 140 2.4

Figure 9.ON-MARG index by neighbourhood income quintile, 2006

Marginalization Rates(2006) by Neighbourhood Income Quintile 3.0 2.8 2.8 2.6

2.5 2.2

2.0 1.8

1.5

1.0

0.5

0.0 Q1 Low est Q2 2nd Low est Q3 Middle Q4 Mid-High Q5 Highest Inc ome income Inc ome Inc ome Inc ome Neighbourhood Income Quintiles

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Indicator 6: Post-secondary Education

This indicator was identified as a strongly recommended indicator through the Urban HEART Delphi process.

Indicator Description and Data Sources

Post-secondary education is measured as the percent of persons aged 25–64 with a post- secondary certificate, diploma, or degree in 2011 at the neighbourhood level. This indicator is calculated by dividing the number of persons aged 25–64 with a post-secondary certificate, diploma or degree by the total population age 25–64 years by highest certificate, diploma or degree. Post-secondary data is derived from the 2011 National Household Survey (NHS), Statistics Canada, June 26, 2013. More information about this indicator can be found at: http://www12.statcan.gc.ca/nhs-enm/2011/dp-pd/prof/details/download- telecharger/comprehensive/comp-ivt-xml-nhs-enm.cfm?Lang=E.

The original Delphi process identified “percent of population aged 25–64 with university education” as the recommended indicator. However, the Urban HEART Steering Committee noted the importance of recognizing other types of educational training such as college diplomas and trade certificates. The key idea is that university credentials as well as other forms of post- secondary training facilitate greater access to employment opportunities. For Urban HEART, the operational definition of post-secondary education was measured as the “percent of population aged 25–64 with post-secondary certificate, diploma or degree”.

In 2011, the post-secondary education completion rate for the population aged 25–65 for Toronto was 68.9%. Figure 10 presents a histogram of post-secondary completion rates across Toronto neighbourhoods. Post-secondary completion rates ranged between 37.5% and 91.7%, with 64% of neighbourhood rates falling within 1 standard deviation of the mean (between 56.02% and 81.58%).

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Figure 10. Histogram of neighbourhood completion of post-secondary education rates, 2011

Mean = 68.8 Std. Dev. = 12.78 N = 140 20 Min = 118 Max = 573 15 10 Numberneighbourhoods of 5 0 43.24 56.02 68.8 81.58 94.36 -2SD -1SD Mean +1SD +2SD Completion of post-secondary education (%)

Indicator Uses and Limitations

The age group 25–64 is the preferred category for post-secondary education completion rates for two reasons: first, this group excludes younger cohorts who are generally in the process of earning post-secondary credentials, and second, this age group also excludes older cohorts who generally have lower levels of education compared to younger age groups. The Urban HEART sample of post-secondary completion rates reflects a high proportion of younger age groups, including for example, recent immigrants. Age-specific comparisons or age-adjustments are not possible because only two age groups are available in the 2011 National Household Survey (NHS): age 15+ and aged 25–64.

Because post-secondary education data were collected from the 2011 voluntary NHS, it is important to note that these data may not be comparable to previous censuses due to methodological differences. They also may not be entirely representative of local populations due to different response rates across geographic groups. Non-response in the NHS at the neighbourhood level ranged between 19.5% and 40.2% (based on population weighted census tract non-response rates). Eighteen neighbourhoods had a non-response rate greater than 33%.

Given these limitations, post-secondary education completion rates were correlated with other available education variables from the Toronto District School Board (TDSB) and the 2006 Census (see Table 23). The post-secondary completion and university degree variables in 2011 were positively and significantly correlated with the same variables in the 2006 Census (correlation coefficients over 0.90). The correlation coefficients between these variables and Grade 12 graduation rates (TDSB, 2011) were over 0.6, suggesting a strong positive association, despite the fact that these data were collected across different age groups and from different data March 28, 2014, ver.1, 52

sources. In all, the 2011 NHS post-secondary completion variable produces similar findings when compared with similar education variables from the TSBS and census.

Table 23. Correlations between NHS, TSBS, and census education variables (N=140) Less than No Post- Post- Bachelor’s No Grade 12 Catholic Grade 9 no high certificate secondary secondary degree (%), certificate graduate school cohort who school (%), 2011 (%), aged (%), 2011, 2006, aged (%), 2006, Proportion, graduation graduated certificate NHS, aged 25–64 aged 25–64 25-64 aged 20–24 20111 (%), 2006- (%), 2011 25-64 20112 NHS, age 15+ Less than no high 1 school certificate (%), 2011 NHS, age 15+ No certificate (%), 0.97 1 2011 NHS, aged 25-64 Post-secondary (%), -0.95 -0.96 1 aged 25–64 Post-secondary (%), -0.88 -0.87 0.96 1 2011, aged 25–64 Bachelor’s degree (%), -0.89 -0.87 0.95 0.98 1 2006, aged 25-64 No certificate (%), 0.73 0.73 -0.73 -0.69 -0.75 1 2006, aged 20–24 Grade 12 graduate -0.55 -0.60 0.62 0.60 0.65 -0.64 1 Proportion, 20111 Catholic school -0.05 -0.09 0.02 -0.04 -0.01 -0.10 0.09 1 graduation Grade 9 cohort who -0.47 -0.49 0.51 0.48 0.52 -0.56 0.73 0.12 1 graduated (%), 2006- 112 Notes: Bolded values are significant at p < 0.05. 1 Grade 12 graduate Proportion, 2011 (63 based on denom. <100) 2 Percent in 2006–2011 Grade 9 cohort who graduated by Oct. 31, 2011 TDSB

Selecting Cut-off Values

Post-secondary education completion rates range widely across Toronto neighbourhoods, and it is one of the few indicators where external comparisons are a fair and relevant method to compare neighbourhoods at both ends of the spectrum. Based on analyses conducted in September 2013, recommended post-secondary completion cut-offs for red was 62.1% (external benchmark that includes 46 neighbourhoods with completions rates ranging between 37.5% and 62.0%). The recommended cut-off for green was 72.5% (external target that includes 58 neighbourhoods).

Alternative options are to use internal measures as an alternative to external comparisons. Using internal measures would adjust recommended cut-offs and affect which neighbourhoods are coded red, yellow, or green. For example, if benchmark #2 was used, the recommend cut-off would be set at 56%, meaning that 22 neighbourhoods would qualify as red. In another example, if a natural break of 78% was used as the recommended cut-off, 41 neighbourhoods would be counted as green.

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Table 24 presents potential cut-off measures for post-secondary completion rates to determine red and green neighbourhoods. Neighbourhoods with benchmarks greater than identified cut-offs could be coded red. Neighbourhoods with targets less than identified cut-offs could be coded green.

Table 24. Potential cut-off measures for post-secondary completion rates to determine ‘red’ and ‘green’ neighbourhoods Potential cut-off measures above which neighbourhoods could be categorized as ‘red’ (lowest post-secondary completion rate) Benchmark #1: Population quintile 57.9 -20th percentile with the lowest post-secondary completion rate Benchmark #2: Standard deviations 56.0 -Standard deviations less than -1 represent the worst (lowest) rates Benchmark #3: Rate ratios 55.1 -rate ratios that is 0.8 of the city rate Benchmark #4: External marker/benchmark 62.1 -20th percentile of 15 comparison cities ranked by lowest rates Potential cut-off measures above which neighbourhoods could be categorized as ‘green’ (highest post-secondary completion rate) Target #1: Rate of wealthiest population quintile 77.7 Target #2.1: Population quintile 81.9 -20th percentile of the population with the best rates (see Benchmark #1 above) Target #2.2: Standard deviations 81.6 -standard deviations greater than +1 represent the highest post-secondary rates Target #2.3: Rate ratios 82.7 -rate ratios greater than 1.2 times the city rate (city rate plus 20% of city rate) (See Benchmark #3 above) Target #3: External target 72.5 -20th percentile of 15 comparison cities ranked by highest rates Target #4: Policy target NA Target #5: Natural breaks/widest rate differences (>1% occur around benchmarks #2 and #3; external benchmark, and targets #1 and #2. Selected Benchmark/Target:Based on analyses conducted in September 2013, the recommended cut-offs for post-secondary completion rates used the Benchmark #4(external benchmark: 20th percentile of 15 comparison cities ranked by lowest rates) for ‘reds’ and the Target #3 (external target: 20th percentile of 15 comparison cities ranked by highest rates) for ‘greens’.

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Income Quintiles

Table 25 presents the income quintiles of Toronto neighbourhoods based on total population and ranked by low-income measures-after tax (LIM-AT). Neighbourhood differences in income quintiles is used as an indicator of social inequalities in health, which refers to potentially avoidable and unfair problems due to ‘unjust social arrangements’ (WHO, 2010).

Table 25. Post-secondary completion by neighbourhood income quintile Income Total population Post-secondary Percent with post- Percentage of quintiles aged 25–64 by certificate, secondary certificate, population highest certificate, diploma or diploma or degree (%) diploma or degree degree, age 25– (%) 64 Q1. Lowest 281,745 182,070 64.6 18.9 Income Q2. 2nd Lowest 276,690 174440 63.0 18.6 income Q3. Middle 281,970 180,645 64.1 18.9 Income Q4. Mid-High 328,130 239,190 72.9 22.0 Income Q5. Highest 320,980 249,340 77.7 21.5 Income Toronto Rate 1,489,515 1,025,685 68.9 100.0

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External Comparators for Post-Secondary Completion Rates

Table 26 compares 15 Canadian cities across census data and post-secondary completion rates.

Table 26. Comparison of external comparators of post-secondary completion rates, 15 comparison Canadian cities A. Profile of Census: CSDs Population, Population Percent Percent aged 25–64 (Census subdivisions) that 2011 density per recent with post-secondary are best matches with Census km2 immigrants certificate, diploma Toronto (%) or degree (%) Richmond Hill (3519038) 185,541 1,838 6.1 77.3 T 00000 Vancouver (5915022) 603,502 5,249 8.4 73 CY 00000 Burnaby (5915025) 223,218 2,464 7.2 72.5 CY 00000 Coquitlam (5915034) 126,456 1,034 6.7 70.9 CY 00000 Mississauga (3521005) 713,443 2,440 8.8 70.7 CY 00001 Calgary (4806016) 1,096,833 1,329 6.3 69.9 CY 00001 Markham (3519036) 301,709 1,419 7.5 69.5 T 00000 Montréal (2466023) 1,649,519 4,518 8.9 69.1 V 00000 Toronto (3520) or 2,615,060 4,150 9.9 68.9 (3520005) C 00001 Richmond (5915015) 190,473 1,474 6.9 68.3 CY 00000 Saskatoon (4711066) 222,189 1,060 5.1 67 CY 00001 Edmonton (4811061) 812,201 1,187 5.8 65.6 CY 00000 Winnipeg (4611040) 663,617 1,430 6.9 62.1 CY 00000 Brampton (3521010) 523,911 1,967 8.4 59.8 CY 00000 Surrey (5915004) 468,251 1,480 7.8 58.6 CY 00000

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Table 27 summarizes the domain ‘social and human development’.

Table 27. Social and human development summary table: High school graduation, marginalization, and post-secondary completion Toronto rate (Total) NA 2.40 68.9 101 179 140 Neighbourhood range Low/Ave./ 1.0–3.4 37.5–91.7 High ID Neighbourhoods High school Marginalization Post- R Y G graduation secondary completion 25 Glenfield-Jane Heights Low 3.0 37.5 3 0 0 24 Black Creek Low 3.0 40.9 3 0 0 112 Beechborough-Greenbrook Low 3.4 41.6 3 0 0 111 Rockcliffe-Smythe Low 3.0 48.3 3 0 0 26 Downsview-Roding-CFB Low 3.0 50.1 3 0 0 113 Weston Low 3.2 53.3 3 0 0 125 Ionview Low 3.0 54.2 3 0 0 72 Regent Park Low 3.0 61.1 3 0 0 121 Oakridge Low 3.2 61.7 3 0 0 78 Kensington-Chinatown Low 3.4 65.5 2 1 0 43 Victoria Village Low 3.4 68.2 2 1 0 85 South Parkdale Low 3.2 64.1 2 1 0 29 Maple Leaf Low 2.8 54.4 2 1 0 110 Keelesdale-Eglinton West Low 2.6 39.4 2 1 0 115 Mount Dennis Low 2.6 50.1 2 1 0 5 Elms-Old Rexdale Low 2.6 51.1 2 1 0 92 Corso Italia-Davenport Low 2.6 58.4 2 1 0 91 Weston-Pellam Park Low 2.4 42.2 2 1 0 109 Caledonia-Fairbank Low 2.4 44.3 2 1 0 Dovercourt-Wallace 93 Emerson-Junction Low 2.4 60.8 2 1 0 28 Rustic Ave. 3.4 50.4 2 1 0 22 Humbermede Ave. 3.2 54.0 2 1 0 139 Scarborough Village Ave. 3.2 56.4 2 1 0 138 Eglinton East Ave. 3.2 57.9 2 1 0 44 Flemingdon Park Ave. 3.2 62.0 2 1 0 31 Yorkdale-Glen Park Ave. 3.0 52.5 2 1 0 124 Kennedy Park Ave. 3.0 56.5 2 1 0 27 York University Heights Ave. 3.0 59.3 2 1 0 137 Woburn Ave. 3.0 59.7 2 1 0 127 Ave. 3.0 61.0 2 1 0 61 Crescent Town Low 2.8 67.4 1 2 0 Kingsview Village-The 6 Westway Ave. 2.8 57.5 1 2 0 136 West Hill Ave. 2.8 58.1 1 2 0 Mount Olive-Silverstone- 2 Jamestown Ave. 2.4 48.7 1 2 0 126 Dorset Park Ave. 2.8 58.9 1 2 0 135 Morningside Ave. 2.4 59.6 1 2 0 March 28, 2014, ver.1, 57

55 Thorncliffe Park Ave. 3.4 66.6 1 2 0 1 West Humber-Clairville Ave. 2.4 55.3 1 2 0 -Beaumond 3 Heights Ave. 2.8 56.5 1 2 0 74 North St. James Town Ave. 3.2 69.5 1 2 0 4 Rexdale-Kipling Ave. 2.8 57.6 1 2 0 119 Wexford-Maryvale Ave. 2.8 59.3 1 2 0 13 West Mall Ave. 3.0 62.3 1 2 0 30 Brookhaven-Amesbury Ave. 2.8 50.3 1 2 0 84 Little Portugal Ave. 2.8 61.3 1 2 0 70 South Riverdale Low 2.6 67.8 1 2 0 21 Humber Summit Ave. 2.8 47.3 1 2 0 65 Greenwood-Coxwell Low 2.4 66.6 1 2 0 116 Steeles Ave. 2.6 60.4 1 2 0 81 Trinity-Bellwoods Low 2.6 64.8 1 2 0 Agincourt South-Malvern 128 West Ave. 2.6 60.6 1 2 0 117 L’Amoreaux Ave. 2.8 60.0 1 2 0 57 Broadview North Low 2.6 70.2 1 2 0 118 Tam O’Shanter-Sullivan Ave 3.0 66.1 1 2 0 73 Moss Park Low 2.0 71.0 1 1 1 132 Malvern Ave. 2.2 57.8 1 1 1 35 Westminster-Branson Ave. 3.2 77.4 1 1 1 129 Agincourt North High 2.6 57.2 1 1 1 130 Milliken High 2.4 54.2 1 1 1 23 Pelmo Park-Humberlea Ave. 2.2 56.8 1 1 1 32 Englemount-Lawrence Ave. 3.2 73.5 1 1 1 75 Church-Yonge Corridor Low 1.8 82.1 1 0 2 66 Danforth Village - Toronto Ave. 2.4 71.8 0 3 0 69 Blake-Jones Ave. 2.6 67.2 0 3 0 120 -Birchmount Ave. 2.6 64.4 0 3 0 18 Ave. 2.6 66.3 0 3 0 Willowridge-Martingrove- 7 Richview Ave. 2.6 64.5 0 3 0 45 -Donalda Ave. 2.4 71.9 0 3 0 54 O’Connor-Parkview Ave. 2.6 65.0 0 3 0 Humber Heights- 8 Westmount Ave. 2.6 67.0 0 3 0 107 Oakwood- Ave. 2.8 62.9 0 3 0 108 Briar Hill-Belgravia Ave. 2.8 65.1 0 3 0 83 Ave. 2.6 68.1 0 3 0 53 Henry Farm Ave. 2.6 79.6 0 2 1 131 Rouge Ave. 1.6 67.0 0 2 1 90 Junction Area Ave. 2.2 69.7 0 2 1 123 Ave. 2.2 63.6 0 2 1 19 Long Branch Ave. 2.0 67.2 0 2 1 86 Roncesvalles Ave. 2.2 71.1 0 2 1 20 Alderwood Ave. 2.0 63.5 0 2 1 140 Ave. 1.8 69.8 0 2 1 March 28, 2014, ver.1, 58

Eringate-Centennial-West 11 Deane Ave. 2.0 71.6 0 2 1 36 West Ave. 2.6 76.1 0 2 1 94 Wychwood Ave. 2.8 73.3 0 2 1 47 Ave. 2.4 77.5 0 2 1 62 East End-Danforth Ave. 2.0 72.4 0 2 1 122 Birchcliffe-Cliffside Ave. 1.8 65.8 0 2 1 51 Willowdale East Ave. 2.4 85.7 0 2 1 59 Danforth Ave. 2.0 69.1 0 2 1 17 Ave. 2.4 73.0 0 2 1 60 Woodbine-Lumsden Ave. 2.2 64.1 0 2 1 14 Islington-City Centre West Ave. 2.6 74.6 0 2 1 37 Willowdale West Ave. 2.8 85.2 0 2 1 34 Ave. 2.6 72.9 0 2 1 134 Highland Creek Ave. 1.6 72.5 0 1 2 49 Bayview Woods-Steeles High 2.6 81.7 0 1 2 9 Edenbridge-Humber Valley Ave. 2.0 74.3 0 1 2 48 High 2.4 81.6 0 1 2 50 Newtonbrook East High 2.8 80.6 0 1 2 52 High 2.4 83.4 0 1 2 64 Woodbine Corridor Ave. 2.0 74.5 0 1 2 76 Bay Street Corridor Ave. 2.2 89.2 0 1 2 Cabbagetown-South St. 71 James Town Ave. 1.8 80.4 0 1 2 46 Pleasant View High 2.4 73.5 0 1 2 102 Forest Hill North High 2.4 84.1 0 1 2 80 Palmerston-Little Italy Ave. 2.2 76.3 0 1 2 95 Annex Ave. 1.8 85.0 0 1 2 114 Lambton Ave. 1.8 76.1 0 1 2 42 Banbury- High 2.4 80.7 0 1 2 39 Bedford Park-Nortown Ave. 1.6 80.1 0 1 2 Waterfront Communities- 77 The Island Ave. 1.8 85.4 0 1 2 58 Ave. 1.8 73.1 0 1 2 104 Mount Pleasant West Ave. 1.8 85.4 0 1 2 106 Humewood-Cedarvale Ave. 2.0 80.6 0 1 2 16 Stonegate-Queensway Ave. 1.8 75.9 0 1 2 67 -Danforth Ave. 1.8 81.9 0 1 2 82 Niagara Ave. 1.8 79.6 0 1 2 96 Casa Loma Ave. 2.0 87.1 0 1 2 97 Yonge-St. Clair Ave. 2.0 91.5 0 1 2 63 Ave. 1.4 82.3 0 1 2 98 Rosedale-Moore Park Ave. 2.0 90.0 0 1 2 68 North Riverdale Ave. 1.4 80.0 0 1 2 133 Centennial Scarborough High 1.6 76.6 0 0 3 79 University High 2.2 81.4 0 0 3 10 Princess-Rosethorn High 1.6 82.2 0 0 3 Bridle Path-Sunnybrook- 41 High 1.4 89.1 0 0 3 March 28, 2014, ver.1, 59

40 St. Andrew-Windfields High 1.4 84.4 0 0 3 12 High 1.8 79.0 0 0 3 56 -Bennington High 1.4 85.1 0 0 3 Runnymede-Bloor West 89 Village High 1.2 83.2 0 0 3 103 Lawrence Park South High 1.0 89.7 0 0 3 15 Kingsway South High 1.6 88.8 0 0 3 33 Clanton Park High 2.2 78.0 0 0 3 38 Lansing-Westgate High 1.8 83.7 0 0 3 88 High 1.8 81.2 0 0 3 101 Forest Hill South High 2.0 87.3 0 0 3 99 Mount Pleasant East High 1.6 86.3 0 0 3 100 Yonge-Eglinton High 1.6 88.0 0 0 3 105 Lawrence Park North High 1.0 91.7 0 0 3 87 High Park-Swansea High 2.0 82.8 0 0 3 Notes: (1)High school graduation: Composite measure of likelihood that Grade 9 cohort will have obtained their secondary school certificate or 30 credits within five years; (2) Marginalization ON-MARG combined score, 2006; (3) Percent aged 25–64 with post-secondary certificate, diploma or degree; (4) ** Where neighbourhoods have the same number of ‘red’, ‘yellow’ and ‘green’, the composite score is used to sort them so that worst scores (highest ranks) rank first.

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5.3. Governance and Civic Engagement There is only one indicator in the governance and civic engagement domain so there is no composite for this domain. Table 28 is restricted to demonstrating the relationship between the rates and the scores.

Table 28. Governance and civic engagement: Municipal voting A. Rank by ‘red’ for lowest voter participation B. Rank by score for lowest voter participation rates ID Neighbourhood Rate ID Neighbourhood Score 79 University 34.5 79 University 1.00 27 York University Heights 36.0 27 York University Heights 0.94 51 Willowdale East 36.7 51 Willowdale East 0.91 110 Keelesdale-Eglinton West 36.8 110 Keelesdale-Eglinton West 0.90 2 Mount Olive-Silverstone- 37.1 2 Mount Olive-Silverstone- 0.89 Jamestown Jamestown 78 Kensington-Chinatown 37.4 78 Kensington-Chinatown 0.88 109 Caledonia-Fairbank 37.6 109 Caledonia-Fairbank 0.87 130 Milliken 38.0 130 Milliken 0.85 115 Mount Dennis 38.1 115 Mount Dennis 0.85 116 Steeles 38.3 116 Steeles 0.84 36 Newtonbrook West 38.3 36 Newtonbrook West 0.84 129 Agincourt North 39.1 129 Agincourt North 0.81 48 Hillcrest Village 39.1 48 Hillcrest Village 0.81 50 Newtonbrook East 39.2 50 Newtonbrook East 0.80 112 Beechborough-Greenbrook 39.6 112 Beechborough-Greenbrook 0.79 76 Bay Street Corridor 39.7 76 Bay Street Corridor 0.78 132 Malvern 39.7 132 Malvern 0.78 91 Weston-Pellam Park 39.9 91 Weston-Pellam Park 0.77 22 Humbermede 40.4 22 Humbermede 0.75 126 Dorset Park 40.5 126 Dorset Park 0.75 35 Westminster-Branson 40.5 35 Westminster-Branson 0.75 18 New Toronto 40.8 18 New Toronto 0.74 47 Don Valley Village 41.0 47 Don Valley Village 0.73 13 Etobicoke West Mall 41.2 13 Etobicoke West Mall 0.72 134 Highland Creek 41.4 134 Highland Creek 0.71 92 Corso Italia-Davenport 41.4 92 Corso Italia-Davenport 0.71 5 Elms-Old Rexdale 41.6 5 Elms-Old Rexdale 0.70 6 Kingsview Village-The Westway 41.6 6 Kingsview Village-The 0.70 Westway

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Indicator 7: Voter Participation

This indicator was identified as a required indicator through the Urban HEART Delphi process.

Indicator Description and Data Source

Voter participation is measured as the number of eligible electors who voted in the 2010 Municipal Election. This indicator is calculated by dividing the number of eligible electors who voted in the 2010 Municipal Election by the denominator total number of eligible electors in the 2010 Municipal Election. Voter participation data were obtained from Toronto Open Data for the 2010 City of Toronto Municipal Elections. More information about this indicator can be found at: http://toronto.ca/elections/index.htm

Each polling station had a count for number of eligible voters, and the number of those who voted. These polling stations were geographically coded into ArcGIS mapping software, and then aggregated up to the sub-ward level. Each sub-ward level was then clipped by the neighbourhood boundaries, and geographically weighted by the percentage of that area that fell into the neighbourhood. To account for spaces where there was no population present, dissemination blocks with zero population were removed along with any green and park space. Figure 11 shows a histogram of municipal voting across Toronto neighbourhoods.

Figure 11. Histogram of neighbourhood municipal voting, 2010

25 Mean = 46.56 Std. Dev. = 5.4 N = 140 Min = 34.48 Max = 58.33 20 15 10 Numberneighbourhoods of 5 0 35.76 41.16 46.56 51.96 57.36 -2SD -1SD Mean +1SD +2SD Municipal voting (%)

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Indicator Uses and Limitations

Voter participation is the only indicator that represents the governance and civic engagement domain. Voting is an important indicator of social participation, however, it is important to note that no single indicator can fully capture governance and civic engagement. There were some poll results that could not be geographically linked to neighbourhoods (i.e. including advance polls), so the total voter participation rate for Urban HEART (46.6%) was lower than the rate published by the City Clerk’s Office (50.55%). Additionally, rates of advanced polling may vary by neighbourhood and polling stations and the undercount may affect individual neighbourhoods differently.

Selecting Cut-off Values

Table 29 presents potential cut-off measures for voting participation to determine red and green neighbourhoods. Neighbourhoods with benchmarks greater than identified cut-offs could be coded red. Neighbourhoods with targets less than identified cut-offs could be coded green.

Toronto had the highest voter participation (46.6%) among its comparator cities, which means an external comparator could not be used. Income quintiles were also not a possibility because voter participation did not have a normal distribution across income quintiles.

Table 29. Potential cut-off measures for voting participation to determine ‘red’ and ‘green’ neighbourhoods Potential cut-off measures above which neighbourhoods could be categorized as ‘red’ (lowest voter participation rate) Benchmark #1: Population quintile 41.4 -20th percentile with the lowest voter participation rate Benchmark #2: Standard deviations 41.2 -standard deviations less than -1 represent the lowest voter rates Benchmark #3: Rate ratios 36.56 -rate ratio that is 0.8 of the city rate Benchmark #4: External marker/benchmark 27.3 -20th percentile of 15 comparisons cities ranked by lowest rates (see below, cities were selected that had the highest percentage of recent immigrants (>5%) and high density (>1,000 pop/km2) Potential cut-off measures above which neighbourhoods could be categorized as ‘green’ (highest voter participation rate) Target #1: Rate of wealthiest population quintile 49.2 Target #2.1: Population quintile 51.3 -20th percentile of the population with the best rates (See Benchmark #1 above) Target #2.2: Standard deviations 52.0 -standard deviations greater than +1 represent the highest voter rates Target #2.3: Rate ratios 54.84 -rate ratios greater than 1.2 times the city rate (city rate +20% of the city rate) (See Benchmark #3 above) Target #3: External target 38.3 March 28, 2014, ver.1, 63

-20th percentile of 15 comparisons cities ranked by highest rates (See external benchmark above) Target #4: Policy target NA -None identified Target #5: Natural breaks/widest rate differences -There are no major (wide) rate differences near the benchmark and target measures that serve as important natural breaks in the rates Selected Benchmark/Target:Based on analyses conducted in September 2013, the recommended cut-offs for voter participation used Benchmark #1 (population quintile: 20th percentile with the lowest voter rates)for ‘reds’ and Target #2.1 (population quintile: 20th percentile of the population with the highest voter rates) for ‘greens’.

Income Quintiles

Table 30 and figure 12 present the income quintiles of Toronto neighbourhoods based on total population and ranked by low-income measures-after tax (LIM-AT). Neighbourhood differences in income quintiles is used as an indicator of social inequalities in health, which refers to potentially avoidable and unfair problems due to ‘unjust social arrangements’ (WHO, 2010).

Table 30. Voter participation by neighbourhood income quintile, 2010 Income quintile Numerator Denominator Cumulative Voter (% voter (area of neighbourhood) percent of city participation participation) in square kilometres population rate (%) Q1. Lowest income 33.69 79.89221 12.0 42.2 Q2. 2nd lowest income 44.70 107.1863 15.9 41.7 Q3. Middle income 51.46 114.0302 18.3 45.1 Q4. Mid-high income 66.02 141.0441 23.5 46.8 Q5. Highest income 84.67 172.169 30.2 49.2 City total/average 56.12 122.86 100.0 45.7

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Figure 12.Voter participation by neighbourhood income quintile, 2010

External Comparators

Table 31 compares 10 Canadian cities across census data and voter participation rates for municipal elections.

Table 31. Comparison of external comparators of voter participation rates, 10 comparison Canadian cities A. Profile of census: CSDs Population, Population Voter Year of (Census subdivisions) that are best 2011 density per participation for election matches with Toronto Census kkm2 municipal election (%) Surrey (5915004) CY 00000 468,251 1,480 24.12 2008 Saskatoon (4711066) CY 00001 222,189 1,060 27.32 2009 Richmond Hill (3519038) T 00000 185,541 1,838 28.80 2010 Vancouver (5915022) CY 00000 603,502 5,249 30.79 2008 Calgary (4806016) CY 00001 1,096,833 1,329 32.91 2007 Edmonton (4811061) CY 00000 812,201 1,187 33.43 2010 Mississauga (3521005) CY 00001 713,443 2,440 34.30 2010 Markham (3519036) T 00000 301,709 1,419 36.00 2010 Winnipeg (4611040) CY 00000 663,617 1,430 38.30 2010 Toronto (3520) or (3520005) C 2,615,060 4,150 46.56 2010 Note: Data were unavailable for the following comparator cities: Montréal, Brampton, Coquitlam, Richmond and Burnaby. March 28, 2014, ver.1, 65

Table 32 summarizes the municipal voting domain by colour codes.

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Table 32. Municipal voting Corridor Amesbury 34 Bathurst 44.8 domain summary table 132 Malvern 39.7 107 Oakwood- 42.5 Manor Toronto rate 45.7 91 Weston-Pellam 39.9 Vaughan 81 Trinity- 44.9 Neighbourhood 34.5- Park 21 Humber 42.5 Bellwoods range 58.3 22 Humbermede 40.4 Summit 131 Rouge 45.0 79 University 34.5 126 Dorset Park 40.5 125 Ionview 42.6 24 Black Creek 45.0 27 York 36.0 35 Westminster- 40.5 40 St. Andrew- 42.9 53 Henry Farm 45.1 University Branson Windfields 124 Kennedy Park 45.6 Heights 18 New Toronto 40.8 136 West Hill 43.0 31 Yorkdale-Glen 45.7 51 Willowdale 36.7 47 Don Valley 41.0 111 Rockcliffe- 43.0 Park East Village Smythe 28 Rustic 45.8 110 Keelesdale- 36.8 13 Etobicoke 41.2 41 Bridle Path- 43.1 85 South Parkdale 45.9 Eglinton West West Mall Sunnybrook- 127 Bendale 45.9 2 Mount Olive- 37.1 134 Highland 41.4 York Mills 37 Willowdale 45.9 Silverstone- Creek 138 Eglinton East 43.2 West Jamestown 92 Corso Italia- 41.4 117 L’Amoreaux 43.2 118 Tam 46.0 78 Kensington- 37.4 Davenport 84 Little Portugal 43.3 O’Shanter- Chinatown 5 Elms-Old 41.6 46 Pleasant View 43.6 Sullivan 109 Caledonia- 37.6 Rexdale 135 Morningside 43.6 120 Clairlea- 46.0 Fairbank 6 Kingsview 41.6 32 Englemount- 43.6 Birchmount 130 Milliken 38.0 Village-The Lawrence 61 Crescent Town 46.1 115 Mount Dennis 38.1 Westway 137 Woburn 43.8 17 Mimico 46.3 116 Steeles 38.3 26 Downsview- 41.7 90 Junction Area 43.9 14 Islington-City 46.4 36 Newtonbrook 38.3 Roding-CFB 113 Weston 43.9 Centre West West 128 Agincourt 41.7 1 West Humber- 44.0 73 Moss Park 46.6 129 Agincourt 39.1 South-Malvern Clairville 83 Dufferin Grove 46.7 North West 139 Scarborough 44.3 86 Roncesvalles 46.7 48 Hillcrest 39.1 19 Long Branch 41.9 Village 133 Centennial 46.7 Village 33 Clanton Park 42.0 108 Briar Hill- 44.6 Scarborough 50 Newtonbrook 39.2 25 Glenfield-Jane 42.1 Belgravia 38 Lansing- 46.9 East Heights 93 Dovercourt- 44.8 Westgate 112 Beechborough- 39.6 49 Bayview 42.2 Wallace 39 Bedford Park- 47.0 Greenbrook Woods-Steeles Emerson- Nortown 76 Bay Street 39.7 30 Brookhaven- 42.4 Junction 80 Palmerston- 47.1 March 28, 2014, ver.1, 67

Little Italy 3 Thistletown- 49.9 70 South 52.5 52 Bayview 54.8 43 Victoria 47.3 Beaumond Riverdale Village Village Heights 8 Humber 52.5 105 Lawrence Park 54.9 29 Maple Leaf 47.3 100 Yonge- 50.2 Heights- North 75 Church-Yonge 47.8 Eglinton Westmount 98 Rosedale- 55.0 Corridor 16 Stonegate- 50.4 55 Thorncliffe 52.5 Moore Park 20 Alderwood 47.9 Queensway Park 89 Runnymede- 55.5 102 Forest Hill 47.9 42 Banbury-Don 50.4 10 Princess- 52.5 Bloor West North Mills Rosethorn Village 101 Forest Hill 48.0 74 North St. 50.6 62 East End- 52.7 59 Danforth East 55.8 South James Town Danforth York 119 Wexford- 48.1 123 Cliffcrest 50.7 99 Mount Pleasant 52.8 67 Playter Estates- 57.0 Maryvale 66 Danforth 50.7 121 Oakridge 48.3 Village - 44 Flemingdon 52.8 71 Cabbagetown- 58.0 12 Markland 48.3 Toronto Park South St. Wood 97 Yonge-St. 50.9 114 Lambton Baby 52.9 James Town 11 Eringate- 48.3 Clair Point 56 Leaside- 58.3 Centennial- 69 Blake-Jones 51.1 72 Regent Park 52.9 Bennington West Deane 103 Lawrence Park 51.3 122 Birchcliffe- 53.1 Notes: Since there was only 65 Greenwood- 48.3 South Cliffside one indicator for this Coxwell 106 Humewood- 51.3 96 Casa Loma 53.5 domain a count by red, 104 Mount Pleasant 48.5 Cedarvale 63 The Beaches 53.6 yellow and green for each West 77 Waterfront 51.5 82 Niagara 53.8 neighbourhood was not 45 Parkwoods- 48.7 Communities- 88 High Park 53.8 applicable. Donalda The Island North 60 Woodbine- 49.0 57 Broadview 51.7 140 Guildwood 53.9 Lumsden North 64 Woodbine 54.1 94 Wychwood 49.0 7 Willowridge- 52.1 Corridor 23 Pelmo Park- 49.1 Martingrove- 15 Kingsway 54.3 Humberlea Richview South 4 Rexdale- 49.5 9 Edenbridge- 52.1 68 North 54.3 Kipling Humber Valley Riverdale 58 Old East York 49.8 54 O’Connor- 52.2 87 High Park- 54.7 95 Annex 49.8 Parkview Swansea March 28, 2014, ver.1, 68

5.4. Physical Environment and Infrastructure

Indicator 8: Access to Meeting and Gathering Places

This indicator was identified as a required indicator through the Urban HEART Delphi process.

Indicator Description and Data Sources

For this indicator, we calculated the average number of meeting places within a 10 minute walking distance. This was weighted using population weights. The data used to calculate access to meeting and gathering places were sourced from Toronto Open Data addresses for places of worship, parks, and recreation sites.

The average number of community meeting places in Toronto in 2011 was 14.99. Figure 13 provides the distribution of rates of neighbourhood community places for meeting across Toronto’s neighbourhoods, and Figure 14 describes how this indicator was created.

Figure 13. Histogram of neighbourhood community places for meeting (places of worship and the parks and recreation centres), 2011

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Figure 14.Calculating access to meeting and gathering places

Using the addresses of places of worship and the parks and recreation centres, XLS data from Toronto Open Data was obtained and then geographically coded for all the meeting and gathering places. Starting from the centroid from each dissemination block (DB) from the 2011 Census, we used a road network buffer to calculate the number of meeting and gathering places within a 10 minute walking distance (720m) (See figure 13). These values were then weighted by their population in the DB, and aggregated up to the neighbourhood level.

Indicator Uses and Limitations

Meeting and gathering places promote social interaction, develop communities and create social networks. In turn, this creates a better quality of life for residents. However, a limitation is that this indicator includes all places of worship for various religious sects and does not consider the populations that do not access places of worship outside of their denominations or do not access places of worship at all. To take into consideration residents who live on neighbourhood boundaries and who have access to meeting and gathering places in other neighbourhoods, we used the method mentioned above. Also, this method helps to exclude inaccessible gathering places that may not be within a 10 minute walking distance of residences within the neighbourhood.

Selecting Cut-off Values

Table 33 presents potential cut-off measures for voting participation to determine red and green neighbourhoods. Neighbourhoods with benchmarks greater than identified cut-offs is coded red. Neighbourhoods with targets less than identified cut-offs is coded green.

Due to the unique method used in creating the indicator, no external comparators could be obtained. Since neighbourhoods have different geographic sizes and the number of people that live in them can also vary, this poses a challenge when we are comparing them. While the spatial method above help to adjust for geographical differences, population weighted quintiles were used to account for population differences. March 28, 2014, ver.1, 70

Table 33. Potential cut-off measures for voting participation to determine ‘red’ and ‘green’ neighbourhoods Potential cut-off measures below which neighbourhoods could be categorized as ‘red’ (lowest number of meeting and gathering places) per population (weighted) Benchmark #1: Population quintile 8.56 -20th percentile with the lowest access to meeting and gathering places Benchmark #2: Standard deviations 7.06 -standard deviations greater than 1 represent the worst (lowest) rates Benchmark #3: Rate ratios (RR) 8.62 Benchmark #4: External marker/benchmark NA -20th percentile of 15 comparisons cities ranked by lowest rates; selected cities had the highest percentage of recent immigrants (>5%) and high density (>1,000 pop/km2) Potential cut-off measures at which neighbourhoods categorized as ‘green’ (highest number of meeting and gathering places) Target #1: Rate of wealthiest population quintile 11.6 Target #2.1: Population quintile 20.1 -20th percentile of the population with the best rates (see Benchmark #1 above) Target #2.2: Standard deviations 22.9 -standard deviations greater than +1 SD represent the best rates Target #2.3: Rate ratios 18.2 -RRs greater than 80% of the city rate (city rate plus 20% of the city rate) (see Benchmark #3 above) Target #3: External target NA -20th percentile of 15 comparisons cities ranked by highest rates (see External Benchmark above) Target #4: Policy target NA -None identified Natural breaks/widest rate differences. -There are no major (wide) rate differences near the benchmark and target measures that serve as important natural breaks in the rates. Selected Benchmark/Target:Based on analysis conducted in September 2013, the recommended cut-off for this indicator for ‘red’ was Benchmark #1(population quintile: 20th percentile with the lowest access to meeting and gathering places) and the recommended cut- off for ‘green’ was Target #2.1 (population quintile: 20th percentile of the population with the best access to meeting and gathering places).

Income Quintiles

In our analysis, we calculated the average number of meeting and gathering places by neighbourhood income quintiles of the population. This was used as an indicator of health and

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social inequality, and provided a potentially achievable target for all population groups. The average number of meeting and gathering places by income quintile are provided in Table 34 and Figure 15.

Table 34.Access to meeting and gathering places by neighbourhood income quintile, 2011 Income quintile Denominator Number of Average rate in (area of neighbourhood) in neighbourhoods each quintile square kilometres (%) Q1. Lowest income 502,978 24 16.7 Q2. 2nd lowest income 493,158 23 11.5 Q3. Middle income 497,206 25 17.4 Q4. Mid-high income 552,777 31 14.9 Q5. Highest income 568,148 37 11.6 City total/average 2,614,267 140 14.4

Figure 15.Access to meeting and gathering places by neighbourhood income quintile, 2011

Access to Meeting and Gathering Places, Income Quintiles, Toronto Neighbourhoods 20 17.4 16.7 14.9

11.5 11.6

10

0 Q1 Lowest Q2 2nd Lowest Q3 Mid Income Q4 Mid-High Q5 Highest Income Income Income Income

External Comparisons

Due to the custom computational analysis required to create the data for Toronto, we were unable to create external comparators. Each of the 16,000 neighbourhood blocks were weighted by population and a 10 minute travel time was calculated by network buffers (the distance traveled on roads). The data used for this analysis are available from Toronto Open Data’s website and are not available for other cities.

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Indicator 9: Walkability This indicator was identified as a required indicator through the Urban HEART Delphi process.

Indicator Description and Data Sources

Walk Score® (See www.walkscore.com) was internally validated using the Toronto Utilitarian Walkability Index, 2012 (TUWI) from Toronto Public Health created by Urban Design 4 Health Ltd. Walk Score® analyzes walking paths and routes to nearby facilities and amenities from a location, then assigns a score which illustrates how walkable locations are. Walk Scores are reported as percentages. Higher percentages indicate higher degrees of walkability.

Walk Score® is an externally validated tool publically available for most cities across the world. Values for each neighbourhood were obtained from the walkscore.com website, which are aggregated scores across all points in a particular neighbourhood. To assist with internally validating the tool, we did a comparison between Walk Score® and the Toronto Walkability Utilitarian Index, by standardizing the values and looking at the differences between the two data sets. We found both to be quite comparable with minor differences. The reason the data team chose to use Walk Score® instead of the TUWI is because the data are publically available on the Walk Score® website. Figure 16 provides the distribution of neighbourhood Walk Scores for Toronto in 2012. The average Walk Score for Toronto was 72.27%.

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Figure 16. Histogram of neighbourhood Walk Scores (walkability index), 2012

30 Mean = 72.27 Std. Dev. = 12.79 N = 140 Min = 42 Max = 99 20 10 Numberneighbourhoods of 0 46.69 59.48 72.27 85.06 97.85 -2SD -1SD Mean +1SD +2SD Walk Score

Indicator Uses and Limitations

Living in a walkable neighbourhood with nearby amenities promotes healthier lifestyles and discourages the use of cars for ordinary tasks such as visiting a grocery store. Walk Score® has calculated international Walk Score® target rates. Rates of 90% and above represent extremely walkable areas.

Toronto is considered to be a more walkable city than other cities in Canada. Our comparator cities scored substantially lower than Toronto on the Walk Score®. See Table 35 for a listing of external comparison cities. Using Canadian comparisons, all of our neighbourhoods would be graded as green. Therefore, we used the Walk Score® system’s target rates rather than comparator cities to show variability in walkability across Toronto’s neighbourhoods.

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Table 35. External comparators for Walk Score A. Profile of census: CSDs (Census Population, Population City average subdivisions) that are best matches 2011 Census density per walk score with Toronto km2 Markham (3519036) T 00000 301,709 1,419 47 Brampton (3521010) CY 00000 523,911 1,967 48 Calgary (4806016) CY 00001 1,096,833 1,329 48 Richmond Hill (3519038) T 00000 185,541 1,838 50 Surrey (5915004) CY 00000 468,251 1,480 51 Edmonton (4811061) CY 00000 812,201 1,187 51 Saskatoon (4711066) CY 00001 222,189 1,060 52 Coquitlam (5915034) CY 00000 126,456 1,034 53 Winnipeg (4611040) CY 00000 663,617 1,430 53 Richmond (5915015) CY 00000 190,473 1,474 55 Mississauga (3521005) CY 00001 713,443 2,440 59 Burnaby (5915025) CY 00000 223,218 2,464 64 Montréal (2466023) V 00000 1,649,519 4,518 70 Toronto (3520) or (3520005) C 2,615,060 4,150 71 Vancouver (5915022) CY 00000 603,502 5,249 78

Selecting Cut-off Values

As Walk Score® comes with its own policy target rate of 90. We’ve selected that as our target. The selection of this target resulted in 17 neighbourhoods being coded as green. Most comparator cities’ average scores fared poorly compared to Toronto so it is unreasonable to use any external comparators in establishing the benchmark. Walk Score’s policy benchmark (PT) is a score of 50 or below. Areas with scores of 50 or below are considered ‘car dependent.’ However, this excludes nearly all of Toronto’s neighbourhoods. Since walk scores are given as a whole number, when looking at the population quintile (B1), there was no even break between the neighbourhood counts since the value of 61 is shared across a cluster of neighbourhoods. One standard deviation below the mean (B2) was used as the benchmark cut-off for this indicator as it encapsulated the minimum amount of categorized neighbourhoods required for Urban HEART while none of the other measures did. Table 36 present potential cut-off measures for red and green neighbourhoods based on Walk Scores.

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Table 36. Potential cut-off measures for Walk Scoresto determine ‘red’ and ‘green’ neighbourhoods Potential cut-off measures at which neighbourhoods are categorized as ‘red’ (lowest walk scores) Benchmark #1: Population quintile 61 - NA -20th percentile with the lowest walk score (score includes 18%–24% of the population as that cluster of neighbourhoods share the same Walk Score® value) Benchmark #2: Standard deviations 59.48 -standard deviations less-1 from the mean represent the worst rates Benchmark #3: Rate ratios (RR) 56.8 -neighbourhood walk scores less than 0.80 of the city rate Benchmark #4: External marker/benchmark 50 -neighbourhood walk scores less than 0.50based on Walk Score® Potential cut-off measures at which neighbourhoods are categorized as ‘green’ (highest walk scores) Target #1: Rate of wealthiest population quintile 69.7 Target #2.1: Population quintile 84 -20th percentile of the population with the best rates (see Benchmark #1 above) -includes 77.8%– 80.6% of the population Target #2.2: Standard deviations 85.06 - Walk Score® rates at least +1 standard deviations from mean Target #2.3: Rate ratios 85.2 -RRs at least 1.2 times the city rate (see Benchmark #3 above) Target #3: External target 68.8 -based on Walk Score® rates Target #4: Policy target 90 -based on Walk Score® values Natural breaks/widest rate differences. NA -not applicable because this measure is an ordinal variable Selected Benchmark/Target: Based on analysis completed as of September 2013, the recommended cut-off for this indicator for ‘red’ was Benchmark #2 (standard deviations less than 59.48) and the recommended cut-off for ‘green’ was Target #4 (policy target: Walk Score® value of 90).

Income Quintiles

Table 37 and Figure 17presentaverage Walk Scores by income quintiles. This was used as an indicator of health and social inequality, and provided a potentially achievable target for all population groups. It also points to the amount of potentially avoidable and unfair problems due to ‘unjust social arrangements’ stemming from economic inequality (WHO, 2010).

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Table 37. Walk Score by neighbourhood income quintile, 2012 Income quintile Total pop. Percent of Number of Average Walk Population (%) neighbourhoods Score in each quintile Q1. Lowest income 502,978 19.2 24 73.6 Q2. 2nd lowest income 493,158 18.9 23 66.8 Q3. Middle income 497,206 19.0 25 71.6 Q4. Mid-high income 552,777 21.1 31 76.4 Q5. Highest income 568,148 21.7 37 69.7 City total/average 2,614,267 100 140 71.7

Figure 17.Walk Score by neighbourhood income quintile, 2012

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Indicator 10: Access to Healthier Food Stores

This indicator was identified as a recommended indicator through the Urban HEART Delphi process.

Indicator Description and Data Sources

This indicator is based on the average number of healthier food stores within a 10 minute walking distance. We used population weights in our calculations. Toronto Dinesafe 2013 provides the distribution of healthier food stores for Toronto in 2013. The average (http://www.toronto.ca/health/dinesafe) was the data source used for this indicator. The average number of healthier food stores for Toronto’s neighbourhoods was 3.91 and the numbers range between 0.46 and 22.31 (see Figure 18). Figure 19 describes how this indicator was created.

Figure 18. Neighbourhood access to healthier food stores, 2013

80 Mean = 3.91 Std. Dev. = 3.45 N = 140 Min. = 0.46 Max = 22.31 60 40 Numberneighbourhoods of 20 0 .46 3.91 7.36 10.81 14.26 -1SD Mean +1SD +2SD +3SD Access to healthier food stores

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Figure 19.Calculating access to healthier food stores Using DineSafe Toronto 2013 data, we geographically coded all the grocery and food store addresses. As per a discussion with researcher Brian Cook of Toronto Public Health we used grocery stores, convenience stores, and fruit/farmers’ markets as indicators of healthier food stores, as these establishments sell fruits and vegetables. Starting from the centroid of each dissemination block (DB) from the 2011 Census, we used a road network buffer to calculate the number of ‘healthier food stores’ within a 10 minute walking distance (720m) (See figure 18). These values were then weighted by their population in the DB, and aggregated up to the neighbourhood level.

Indicator Uses and Limitations

The above method was used to enable the research team to consider residents who live on neighbourhood boundaries and who have access to healthier food stores in other neighbourhoods. Also, this method helps to exclude inaccessible food stores that may not be within a 10 minute walking distance of residences within a given neighbourhood.

Due to the custom computational analysis required to create the data for Toronto, we were unable to create the same data for the comparator cities. Each of the 16,000 neighbourhood blocks were weighted by population and a 10 minute travel time was calculated by network buffers (the distance traveled on roads). The data used for this analysis are only available from DineSafe Toronto’s website and data are not available for other cities.

Selecting Cut-off Values

Table 38 presents potential cut-off measures for access to healthier food stores to determine red and green neighbourhoods. Neighbourhoods with benchmarks greater than identified cut-offs could be coded red. Neighbourhoods with targets less than identified cut-offs could be coded green.Due to the unique method used in creating the indicator, no external comparators could be obtained. Since neighbourhoods have different geographic sizes and the number of people that live in them can also vary, this poses a challenge when we are comparing them. While the spatial March 28, 2014, ver.1, 79

method above help to adjust for geographical differences, population weighted quintiles were used to account for population differences.

Table 38. Potential cut-off measures for access to healthier food stores to determine ‘red’ and ‘green’ neighbourhoods Potential cut-off measures below which neighbourhoods could be categorized as ‘red’ (lowest number of healthier food stores) Benchmark #1: Population quintile 1.36 -20th percentile with the lowest access to healthier food stores Benchmark #2: Standard deviations 0.46 -standard deviations less than -1 SD from the mean representing the worst (lowest) rates Benchmark #3: Rate ratios (RR) 3.10 Benchmark #4: External marker/benchmark NA -20th percentile of 15 comparisons cities ranked by lowest rates (See below.); selected cities had the highest percent of recent immigrants (>5%) and high density (>1,000 pop/km2) Potential cut-off measures at which neighbourhoods could be categorized as ‘green’ (highest number of healthier food stores) Target #1: Rate of wealthiest population quintile 2.8 Target #2.1: Population quintile 5.974 -20th percentile of the population with the best rates (see Benchmark #1 above) Target #2.2: Standard deviations 7.5 -standard deviations greater than +1 from the mean Target #2.3: Rate ratios 8.672 -RRs at least 1.2 times the city rate (city rate +20% of the city rate) (see Benchmark #3 above) Target #3: External target NA -20th percentile of 15 comparisons cities ranked by highest rates (see External Benchmark above) Target #4: Policy target NA -none identified Natural breaks/widest rate differences. -There are no major (wide) rate differences near the benchmark and target measures that serve as important natural breaks in the rates Selected Benchmark/Target:Based on analysis conducted in September 2013, the recommended cut-off for this indicator for ‘red’ was Benchmark #1 (population quintile: 20th percentile with the lowest access to healthier food stores) and the recommended cut-off for ‘green’ was Target #2.1 (population quintile: 20th percentile of the population with the best access to healthier food stores).

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Income Quintiles

Table 39 and Figure 20 provide the average number of healthier food stores by income quintiles.

Table 39. Access to healthier food storesby neighbourhood income quintile, 2013 Income quintile Population Number of Average number of neighbourhoods healthier food stores in each quintile Q1. Lowest income 502,978 24 4.6 Q2. 2nd lowest income 493,158 23 3.2 Q3. Middle income 497,206 25 4.1 Q4. Mid-high income 552,777 31 4.4 Q5. Highest income 568,148 37 2.8 City total/average 2,614,267 140 3.8

Figure 20.Access to healthier food stores by neighbourhood income quintile, 2013

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Indicator 11: Green Space This indicator was identified as a recommended indicator through the Urban HEART Delphi process.

Indicator Description and Data Sources

Green spaced was measured using the average amount of green space per square kilometer. We used population weights in our analysis. The data used to calculate green space were obtained from the Shapefile of parks and green space, which was obtained from Toronto Open Data. Figures 21 and 22 present the distribution of neighbourhood green spaces in square meters for Toronto and describe how this indicator was created, respectively.

Figure 21. Histogram of neighbourhood green space (shapefile of parks and green space), 2011 

30 Mean = 45.47 Std. Dev. = 23.93 N = 140 Min. = 11.28 Max = 113.52 20 10 Numberneighbourhoods of 0 21.54 45.47 69.4 93.33 -1SD Mean +1SD +2SD Green space

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Figure 22. Calculating green space

Using the parks and green space shapefile, we mapped out all the public green spaces in Toronto. Using the centroids of dissemination blocks from the 2011 Census we created one kilometre buffers around each block, then calculated the amount of green space within these buffers (See figure below.). Using population weights, we then averaged the amount of green space per one square kilometre, which was then aggregated up to the neighbourhood level.

Indicator Uses and Limitations

The above method was used so that we could consider residents who live on neighbourhood boundaries and who have access to parks and green spaces in other neighbourhoods. Also, this method helps to exclude inaccessible green spaces in neighbourhoods that may not be near any residents. As this technique was used, we were unable to establish the city rates for the comparison cities as each of the 16,000 neighbourhood blocks were weighted by population and a one kilometre Euclidean buffer was created. The data used for this analysis are only available from Open Data Toronto and are not available for other cities.

Selecting Cut-off Values

Table 40 presents potential cut-off measures for access to green space to determine red and green neighbourhoods. Neighbourhoods with benchmarks greater than identified cut-offs could be coded red. Neighbourhoods with targets less than identified cut-offs could be coded green. Due to the unique method used in creating the indicator, no external comparators could be obtained. Since neighbourhoods have different geographic sizes and the number of people that live in them can also vary, this poses a challenge when we are comparing them. While the spatial method above help to adjust for geographical differences, population weighted quintiles were used to account for population differences.

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Table 40. Potential cut-off measures for green space to determine ‘red’ and ‘green’ neighbourhoods Potential cut-off measures for categorizing neighborhoods as ‘red’ (lowest amount of green space) Benchmark #1: Population quintile 23.03 -20th percentile with the lowest green space rate Benchmark #2: Standard deviations 21.52 -standard deviations less than -1 from the mean represent the worst (lowest) rates Benchmark #3: Rate ratios (RR) 35.99 Benchmark #4: External marker/benchmark NA -20th percentile of 15 comparisons cities ranked by highest unemployment rates; selected cities had the highest percent of recent immigrants (>5%) and high density (>1,000 pop/km2) Potential cut-off measures for categorizing neighbourhoods as ‘green’ (highest amount of green space) Target #1: Rate of wealthiest population quintile 51 Target #2.1: Population quintile 64.43 -20th percentile of the population with the best rates (see Benchmark #1 above) Target #2.2: Standard deviations 69.4 -standard deviations greater than +1 from the mean Target #2.3: Rate ratios 65.86 -RRs least 1.2 times the city rate (city rate +20% of the city rate) (see Benchmark #3 above) Target #3: External target NA -20th percentile of 15 comparisons cities ranked by highest rates (see External Benchmark above) Target #4: Policy target NA -none identified Natural breaks/widest rate differences. There are no major (wide) rate differences near the benchmark and target measures that serve as important natural breaks in the rates Selected Benchmark/Target:Based on analysis conducted in September 2013, the recommended cut-off for this indicator for ‘red’ was Benchmark #1(population quintile: 20th percentile with the lowest green space rate) and the recommended cut-off for ‘green wasTarget #2.1 (population quintile: 20th percentile with the highest green space rate).

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Table 41 and Figure 23 show the average amount of green space per square kilometer by income quintiles. This was used as an indicator of health and social inequality, and provided a potentially achievable target for all population groups.

Table 41. Green space by neighbourhood income quintiles, 2011 Income quintile Total pop. Number of neighbourhoods Average amount of green space in each quintile Q1. Lowest income 502,978 24 51.4 Q2. 2nd lowest income 493,158 23 47.8 Q3. Middle income 497,206 25 43.4 Q4. Mid-high income 552,777 31 35.3 Q5. Highest income 568,148 37 51.0 City total/average 2,614,267 140 45.8

Figure 23.Green space by neighbourhood income quintiles, 2011

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Table 42 summarizes the indicators used for the physical environment and infrastructure domain.

Table 42. Summary of indicators for physical environment and infrastructure domain Toronto rate (Total) 15 71 3.91 45.5 107.0 347 106 Neighbourhood range 3.4–39.9 42–99 0.47– 11.3– 22.3 113.5 ID Neighbourhoods Community Walk Healthier Green Red Yellow Green places for Score food space meeting stores 134 Highland Creek 4.2 54 0.46 36.9 3 1 0 10 Princess-Rosethorn 4.1 48 0.47 53.5 3 1 0 Willowridge-Martingrove- 7 Richview 6.5 51 0.93 42.1 3 1 0 123 Cliffcrest 3.7 54 0.92 44.6 3 1 0 133 Centennial Scarborough 6.1 54 1.13 36.8 3 1 0 140 Guildwood 3.4 59 0.56 55.1 3 1 0 Kingsview Village-The 6 Westway 6.4 56 0.83 54.2 3 1 0 131 Rouge 3.4 42 0.56 90.7 3 0 1 Bridle Path-Sunnybrook- 41 York Mills 4.5 58 0.90 59.8 3 1 0 Eringate-Centennial-West 11 Deane 7.3 57 0.54 70.1 3 0 1 9 Edenbridge-Humber Valley 5.0 49 0.77 91.3 3 0 1 49 Bayview Woods-Steeles 5.0 57 0.79 84.1 3 0 1 135 Morningside 5.4 53 0.83 103.8 3 0 1 40 St. Andrew-Windfields 5.6 60 0.93 35.0 2 2 0 20 Alderwood 7.5 70 0.62 30.6 2 2 0 53 Henry Farm 3.8 76 0.71 41.0 2 2 0 1 West Humber-Clairville 7.9 57 1.43 58.2 2 2 0 52 Bayview Village 6.4 71 1.07 52.5 2 2 0 136 West Hill 7.6 66 1.34 57.5 2 2 0 43 Victoria Village 7.6 71 1.19 55.5 2 2 0 5 Elms-Old Rexdale 10.2 48 0.96 113.5 2 1 1 115 Mount Dennis 8.4 59 1.57 95.2 2 1 1 46 Pleasant View 12.2 66 1.51 19.6 1 3 0 45 Parkwoods-Donalda 8.7 63 1.09 41.7 1 3 0 116 Steeles 9.1 61 0.77 48.1 1 3 0 129 Agincourt North 5.6 66 3.55 27.6 1 3 0 Agincourt South-Malvern 128 West 9.6 66 3.22 22.2 1 3 0 119 Wexford-Maryvale 8.0 67 3.20 26.8 1 3 0 130 Milliken 7.4 65 2.43 44.2 1 3 0 4 Rexdale-Kipling 12.5 58 1.71 51.1 1 3 0 42 Banbury-Don Mills 6.2 67 1.37 56.8 1 3 0 126 Dorset Park 10.0 68 4.48 23.0 1 3 0 19 Long Branch 9.0 72 0.59 50.6 1 3 0 103 Lawrence Park South 14.9 72 2.23 22.9 1 3 0 31 Yorkdale-Glen Park 13.6 72 3.63 15.9 1 3 0 March 28, 2014, ver.1, 86

102 Forest Hill North 11.9 77 3.41 13.9 1 3 0 48 Hillcrest Village 8.62 68 0.94 60.4 1 3 0 12 Markland Wood 8.56 69 1.64 56.3 1 3 0 39 Bedford Park-Nortown 10.4 73 5.32 17.5 1 3 0 47 Don Valley Village 14.9 79 1.36 37.8 1 3 0 56 Leaside-Bennington 8.5 77 2.01 62.4 1 3 0 Runnymede-Bloor West 89 Village 14.7 81 4.97 22.4 1 3 0 107 Oakwood-Vaughan 19.1 82 5.46 17.7 1 3 0 8 Humber Heights-Westmount 9.4 58 2.08 72.3 1 2 1 26 Downsview-Roding-CFB 12.2 59 2.21 64.5 1 2 1 23 Pelmo Park-Humberlea 10.8 57 2.30 74.1 1 2 1 15 Kingsway South 10.0 68 1.23 67.6 1 2 1 13 Etobicoke West Mall 24.7 74 0.75 26.5 1 2 1 32 Englemount-Lawrence 17.1 70 5.97 13.4 1 2 1 35 Westminster-Branson 12.6 61 0.97 87.4 1 2 1 108 Briar Hill-Belgravia 13.5 81 6.03 15.8 1 2 1 114 Lambton Baby Point 8.5 70 1.86 87.5 1 2 1 Thistletown-Beaumond 3 Heights 12.4 54 2.63 105.1 1 2 1 59 Danforth East York 18.5 77 6.75 18.5 1 2 1 44 Flemingdon Park 18.5 63 0.85 103.2 1 2 1 90 Junction Area 25.9 83 5.11 15.2 1 2 1 94 Wychwood 19.1 86 6.37 19.2 1 2 1 22 Humbermede 19.4 58 4.21 96.0 1 2 1 69 Blake-Jones 30.1 89 5.70 17.6 1 2 1 Waterfront Communities-The 77 Island 10.8 92 8.67 20.3 1 1 2 66 Danforth Village – Toronto 23.7 86 7.46 13.3 1 1 2 84 Little Portugal 25.1 88 10.38 17.3 1 1 2 Dovercourt-Wallace Emerson- 93 Junction 34.6 88 10.06 19.1 1 1 2 95 Annex 25.7 94 7.62 21.5 1 0 3 86 Roncesvalles 29.8 91 7.60 21.4 1 0 3 70 South Riverdale 29.3 91 8.32 22.9 1 0 3 83 Dufferin Grove 26.1 90 11.43 14.0 1 0 3 80 Palmerston-Little Italy 31.0 95 9.87 20.2 1 0 3 79 University 29.3 97 12.27 11.3 1 0 3 75 Church-Yonge Corridor 26.8 98 12.38 22.3 1 0 3 78 Kensington-Chinatown 29.3 97 22.31 13.7 1 0 3 73 Moss Park 39.9 95 10.96 20.5 1 0 3 50 Newtonbrook East 8.8 64 1.51 34.9 0 4 0 138 Eglinton East 10.7 62 2.15 33.6 0 4 0 118 Tam O’Shanter-Sullivan 9.5 64 2.25 34.2 0 4 0 17 Mimico 9.3 71 2.26 31.9 0 4 0 132 Malvern 16.7 61 2.04 32.2 0 4 0 36 Newtonbrook West 11.6 69 1.93 34.2 0 4 0 122 Birchcliffe-Cliffside 10.6 71 1.76 35.4 0 4 0

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28 Rustic 13.7 60 3.34 34.0 0 4 0 14 Islington-City Centre West 9.1 72 1.61 40.3 0 4 0 124 Kennedy Park 14.8 62 2.41 37.5 0 4 0 33 Clanton Park 13.6 63 3.89 27.1 0 4 0 139 Scarborough Village 10.7 70 2.16 38.6 0 4 0 16 Stonegate-Queensway 8.7 67 2.04 51.6 0 4 0 29 Maple Leaf 16.1 66 2.35 32.5 0 4 0 127 Bendale 10.3 64 3.23 46.7 0 4 0 120 Clairlea-Birchmount 8.8 69 2.33 53.6 0 4 0 101 Forest Hill South 13.8 76 2.70 24.8 0 4 0 117 L’Amoreaux 14.5 65 3.20 38.8 0 4 0 18 New Toronto 13.6 79 2.10 27.7 0 4 0 125 Ionview 13.5 70 3.05 36.4 0 4 0 137 Woburn 9.2 66 2.89 57.3 0 4 0 30 Brookhaven-Amesbury 15.2 62 4.22 38.2 0 4 0 112 Beechborough-Greenbrook 19.1 62 3.38 39.2 0 4 0 24 Black Creek 16.8 62 1.70 64.1 0 4 0 96 Casa Loma 16.4 80 2.48 31.0 0 4 0 109 Caledonia-Fairbank 15.6 69 4.61 36.0 0 4 0 105 Lawrence Park North 13.4 78 4.75 25.0 0 4 0 121 Oakridge 11.5 71 2.86 62.4 0 4 0 106 Humewood-Cedarvale 12.9 80 4.25 31.6 0 4 0 97 Yonge-St.Clair 13.2 84 1.88 47.0 0 4 0 51 Willowdale East 17.7 84 2.84 33.1 0 4 0 63 The Beaches 13.8 88 2.66 41.3 0 4 0 58 Old East York 14.4 69 4.64 62.7 0 4 0 38 Lansing-Westgate 14.8 77 3.63 56.4 0 4 0 99 Mount Pleasant East 10.1 88 4.50 49.6 0 4 0 27 York University Heights 13.7 60 1.57 64.4 0 3 1 Mount Olive-Silverstone- 2 Jamestown 10.5 61 2.06 82.6 0 3 1 54 O’Connor-Parkview 13.8 67 1.56 69.2 0 3 1 111 Rockcliffe-Smythe 11.3 61 2.15 88.7 0 3 1 34 Bathurst Manor 13.2 61 1.56 91.6 0 3 1 60 Woodbine-Lumsden 22.0 73 1.69 45.5 0 3 1 21 Humber Summit 12.1 61 2.24 90.8 0 3 1 25 Glenfield-Jane Heights 17.8 61 3.49 65.9 0 3 1 110 Keelesdale-Eglinton West 24.0 69 5.39 30.6 0 3 1 37 Willowdale West 20.8 78 3.10 48.4 0 3 1 113 Weston 15.3 73 6.19 48.7 0 3 1 98 Rosedale-Moore Park 13.9 84 2.89 66.5 0 3 1 62 East End-Danforth 20.8 85 5.40 25.4 0 3 1 64 Woodbine Corridor 23.3 85 3.48 35.0 0 3 1 100 Yonge-Eglinton 23.2 89 4.04 26.5 0 3 1 65 Greenwood-Coxwell 26.7 88 2.33 33.0 0 3 1 88 High Park North 22.8 84 3.68 41.0 0 3 1 55 Thorncliffe Park 10.3 73 2.06 110.2 0 3 1 61 Crescent Town 12.6 77 6.53 62.0 0 3 1 March 28, 2014, ver.1, 88

85 South Parkdale 21.2 83 5.64 38.7 0 3 1 57 Broadview North 18.5 74 7.78 48.8 0 3 1 87 High Park-Swansea 14.6 79 4.58 81.4 0 3 1 82 Niagara 25.0 84 3.25 61.3 0 3 1 104 Mount Pleasant West 14.9 95 6.32 39.7 0 2 2 91 Weston-Pellam Park 26.9 75 9.36 32.2 0 2 2 67 Playter Estates-Danforth 18.2 90 8.33 48.7 0 2 2 92 Corso Italia-Davenport 27.3 79 11.65 32.1 0 2 2 72 Regent Park 36.6 88 9.66 34.4 0 2 2 74 North St. James Town 21.1 93 8.78 51.4 0 1 3 68 North Riverdale 31.7 90 7.27 48.5 0 1 3 76 Bay Street Corridor 20.1 99 12.98 23.5 0 1 3 81 Trinity-Bellwoods 34.0 94 10.07 25.3 0 1 3 Cabbagetown-South St. James 30.1 91 11.66 50.8 0 1 3 71 Town Notes: (1) Community Places for Meeting: Average number of libraries, recreation facilities, places of worship within walking distance (population weighted): (2) Walk Score®; (3) Access to health food stores within walking distance (population weighted); (4) Average amount of green space (including parks and public areas) per square kilometre in a one kilometre circular buffer from each residential block in the neighbourhood (population weighted); (5) ** Where neighbourhoods have the same number of ‘red’, ‘yellow’ and ‘green’, the composite score is used to sort them so that worst scores (highest ranks) are ranked first.

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5.5. Population Health

The population health domain includes four indicators: premature mortality, self-reported mental health, preventable hospitalizations, and diabetes prevalence. The shaded neighbourhoods are those that occur in the top 28 for the population health domain for both methods. There were some additional neighbourhoods, beyond the 28 listed here, which had two reds, two yellows and zero greens. These were sorted so that those with the highest rate of avoidable hospitalizations and diabetes prevalence were included in the top 28. As shown in Table 43, 21 neighbourhoods emerge in the top 28 for both methods.

Table 43. Population health indices A. Rank and sum of ‘red’ (R), ‘yellow’ (Y) and B. Rank and composite score based on the ‘green’ (G) categories average of the standardized rates ID Neighbourhoods R Y G ID Neighbourhoods Score 72 Regent Park 4 0 0 72 Regent Park 0.74 61 Crescent Town 4 0 0 85 South Parkdale 0.73 85 South Parkdale 3 1 0 73 Moss Park 0.70 74 North St. James Town 3 1 0 61 Crescent Town 0.63 125 Ionview 3 1 0 21 Humber Summit 0.62 120 Clairlea-Birchmount 3 1 0 112 Beechborough-Greenbrook 0.59 112 Beechborough-Greenbrook 3 1 0 24 Black Creek 0.58 91 Weston-Pellam Park 3 1 0 125 Ionview 0.57 25 Glenfield-Jane Heights 3 1 0 25 Glenfield-Jane Heights 0.56 24 Black Creek 3 1 0 2 Mount Olive-Silverstone-Jamestown 0.55 3 Thistletown-Beaumond Heights 3 1 0 74 North St. James Town 0.55 121 Oakridge 3 1 0 84 Little Portugal 0.54 124 Kennedy Park 3 0 1 91 Weston-Pellam Park 0.53 73 Moss Park 2 2 0 4 Rexdale-Kipling 0.53 84 Little Portugal 2 2 0 115 Mount Dennis 0.53 4 Rexdale-Kipling 2 2 0 3 Thistletown-Beaumond Heights 0.53 86 Roncesvalles 2 2 0 113 Weston 0.52 55 Thorncliffe Park 2 2 0 121 Oakridge 0.52 62 East End-Danforth 2 2 0 137 Woburn 0.51 115 Mount Dennis 2 2 0 138 Eglinton East 0.50 70 South Riverdale 2 2 0 136 West Hill 0.50 126 Dorset Park 2 2 0 135 Morningside 0.49 65 Greenwood-Coxwell 2 2 0 126 Dorset Park 0.49 1 West Humber-Clairville 2 2 0 55 Thorncliffe Park 0.49 111 Rockcliffe-Smythe 2 2 0 65 Greenwood-Coxwell 0.49 21 Humber Summit 2 2 0 27 York University Heights 0.49 113 Weston 2 2 0 119 Wexford-Maryvale 0.49 2 Mount Olive-Silverstone- 2 2 0 132 Malvern 0.49 Jamestown

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Indicator 12: Premature Mortality Rate

This indicator was identified as a required indicator through the Urban HEART Delphi process.

Indicator Description and Data Sources

The premature mortality rate is the number of deaths among individuals aged 74 and younger divided by the number of all individuals 74 and younger within a given population. Because death rates are generally higher for older individuals, we need to standardize our findings so that changes in the age of the population are taken into account. Using the age distribution of the 1991 Canadian population as our standard, we used the direct method of standardization (for 5 year age groups) to calculate this rate. In other words, when we show the premature mortality rate for 2005 and 2009, both rates are calculated as if the age distribution for both years was the same as it was in 1991. This ensures that any changes in the rate are not due to an increase or decrease in the number of older people between those years. To determine the number of deaths of individuals aged 0-74, we used data from the Ministry of Health and Long-Term Care (IntelliHEALTH ONTARIO). To determine the overall population, we used data from the 2006 Census (Statistics Canada).

The average premature mortality rate (PMR) for Toronto between 2005 and 2009 was 211.1 per 100,000 persons. Figure 24 shows the distribution of the PMRs across Toronto neighbourhoods. The rates range between 118.0 and 573.0 with 71% of neighbourhood rates falling within 1 standard deviation of the neighbourhood mean (between 152.90 and 285.80). Some neighbourhoods have rates that are far higher than the others. For example, the PMR for Moss Park is more than 2.7 times higher than the overall Toronto rate. In the majority of cases, neighbourhood rates that appear at least 1.2 times greater than that of the city average are significantly different. Likewise, rates that are 20% lower than the city average are also significantly different (p <.05).

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Figure 24. Histogram of neighbourhood premature mortality rates (number of deaths age 0–74 (Both sexes) age 0–74), 2005-09

40 Mean = 219.35 Std. Dev. = 65.45 N = 140 Min = 118 Max = 573 30 20 Numberneighbourhoods of 10 0 86.45 152.9 219.35 285.8 352.25 418.7 -2SD -1SD Mean +1SD +2SD +3SD Premature mortality rate

Indicator Uses and Limitations

Premature mortality gives more weight to the death of younger people than to older people because younger people are more likely to die from preventable causes. However, not all premature deaths are avoidable and preventable. Using estimates based on national data, we assume that approximately 72% of deaths for individuals under age 75 are avoidable, and among these, approximately 65% are preventable. Further information on overall mortality rates is available from Toronto Public Health (TPH) in the TPH’s 2013 report ‘All-cause mortality and life expectancy.’

Premature mortality rates are the most common summary health indicator used to measure health inequalities across and between populations and over time. Using after-tax LIM rates for Toronto, we found the PMR was 1.3 times higher among the lowest income quintile compared to the highest income quintile. We can conclude that if all of Toronto had premature mortality rates equal to those in the highest income quintile, there would be 3,533 fewer deaths over five years.

Selecting Cut-off Values

We based the cut-off values for red, yellow, and green on two sources. To determine the cut-off for reds, we used the premature mortality rate at the 20th percentile among 12 health regions March 28, 2014, ver.1, 92

selected for comparison. As the neighbourhoods with rates above this marker were significantly higher than the city rate, this benchmark was affirmed as relevant. Potential cut-off measures are displayed in Table 44.

The target PMR (green) was chosen to be equal to (or less then) the PMR found in neighbourhood in the highest income quintile. A good alternate internal cut-off could be the cut- off at the rate ratio, where rates were 1.2 times higher than the city rate (B3) or at the 20th percentile (B1) as rates above these markers were also significantly higher than the city rate. Cut- off measures, neighbourhoods with rates >B cut-offs would be coloured red. Those at or below T measures would be green.

Table 44. Potential cut-off measures for premature mortality to determine ‘red’ and ‘green’ neighbourhoods Potential cut-off measures above which neighbourhoods could be categorized as ‘red’ (highest PMR) Benchmark #1: Population quintile 254.5 -20th percentile with the highest PMR/ASMR Benchmark #2: Standard deviations 285.8 -standard deviations greater than +1 from the mean represent the highest (worst) rates Benchmark #3: Rate ratios (RR) 253.32 -RR is 1.2 times greater the city rate Benchmark #4: External marker/benchmark 271.4 -20th percentile of 12 comparison health regions ranked by highest rates -these were the 12 Statistics Canada health regions that included the 15 comparison cities used for the other indicators) Potential cut-off measures at which neighbourhoods could be categorized as ‘green’ (lowest PMR) Target #1: Rate of wealthiest population quintile 187.4 Target #2.1: Population quintile 163.9 -20th percentile of the population with the best rates (see Benchmark #1 above) Target #2.2: Standard deviations 152.9 -standard deviations less than -1 from the mean Target #2.3: Rate ratios 168.88 -RRs less than 80% of the city rate(see Benchmark #3 above) Target #3: External target 169.8 -20th percentile of 12 health regions ranked by best rates (see External Benchmark above) Target #4: Policy target NA -none identified Natural breaks/widest rate differences occur at +1 SD, 12th percentile, and just above the external marker for the ‘red’ cut-off. They also occur near the external target which is close to Target #2.3 (ratio ratios).

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Selected Benchmark/Target:Based on analysis conducted in September 2013, the recommended cut-off for this indicator for ‘red’ was Benchmark #4 (external benchmark: 20th percentile of 12 comparison health regions ranked by highest rates) and the recommended cut-off for ‘green’ was Target #1 (wealthiest income quintile rate).

Income Quintiles

The differences in the rates between income quintiles of the population were used as an indicator of health and social inequalities. These are displayed in Table 45 and Figure 25.

Table 45. Premature mortality rates by neighbourhood income quintile, 2005-09 Income quintile Total pop. Number of Percent of pop. Age adj. PMR per deaths (%) 100,000 persons in each quintile Q1. Lowest income 456,045 5,950 19.6 248.1 Q2. 2nd lowest income 440,525 5,170 18.9 203.3 Q3. Middle income 453,355 5,899 19.5 218.4 Q4. Mid-high income 473,700 6,251 20.4 223.8 Q5. Highest income 503,415 5,763 21.6 187.4 City total/average 2,327,040 29,033 100.0 211.1

Figure 25.Premature mortality rates by neighbourhood income quintile, 2005-09

Age Adj. Premature Mortality Rates, Both Sexes, 2005-09, Toronto 300.0 248.1 250.0 218.4 223.8 203.3 200.0 187.4

150.0

100.0

50.0

0.0 Q1 Low est Income Q2 2nd Low est Q3 Middle Income Q4 Mid-High Income Q5 Highest Income income Neighbourhood Income Quintile

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External Comparators

The external comparators used for this indicator are presented in Table 46.

Table 46. External comparator health regions and cities for premature mortality rate Health Regions/Cities PMR rate Percentile rankings Winnipeg Regional Health Authority, MA [4610-A] 291.8 Saskatoon Regional Health Authority, SK [4706-A] 277.8 Edmonton Zone, AB [4834-B] 261.9 271.1 = 20th percentile for highest ranked rates Région de Montréal, QC [2406-G] * 250.5 Fraser South Health Service Delivery Area, BC 223.3 [5923-J] Calgary Zone, AB [4832-B] 222.1 City of Toronto Health Unit, ON [3595-G] 217.7 Vancouver Health Service Delivery Area, BC 215.1 [5932-G] Fraser North Health Service Delivery Area, BC 211.8 [5922-J] Peel Regional Health Unit, ON [3553-J] 190.8 York Regional Health Unit, ON [3570-J] 161.4 169.8 = 20th percentile for lowest ranked rates Richmond Health Service Delivery Area, BC 152.1 [5931-J] Notes: PMR = Premature mortality rate (number of deaths of age <75/100,000, 2006– 2009); * 2006–2007 and 2007–2009 averaged; Twelve health regions, /5 = 2.4, so the lines above is the 20th percentile for the highest rate = 271.4, and the 20th percentile for the lowest rates = 169.8; Source: Statistics Canada. Table 102-4311: Premature and potentially avoidable mortality, three-year average 2006-2008 and 2007–2009; Rates for these two time periods have been averaged for the Urban HEART comparisons, data accessed: September4, 2013.

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Indicator 13: Mental Health

This indicator was identified as a required indicator through the Urban HEART Delphi process.

Indicator Description and Data Sources

Strong mental Health was measured using the proportion of residents in the neighbourhood who were over the age of 12 who said they have very good or excellent mental health between 2005 and 2011. We calculated this using data from four waves of the Canadian Community Health Survey (CCHS) between 2005 and 2011. We divided the number of participants age 12 and over who reported excellent or very good mental health by the number of participants age 12 and over who responded to this question.

The CCHS data were prepared for Urban HEART by the Institute for Clinical Services Evaluative Sciences (ICES) in July 2013. The 2012 data were not available at the time these data were prepared. Due to the small number of respondents, data for one neighbourhood were not available for analysis, so these calculations are for 139 neighbourhoods rather than 140.

On the whole, the percentage of individuals aged 12 and over who reported that their mental health was very good or excellent was 73.4%. Figure 26 shows the distribution of the perceived mental health ratings across Toronto neighbourhoods. The rates range between 47.4% and 96.8%, and 68% of neighbourhood rates fall within 1 standard deviation of the neighbourhood mean (between 64.29% and 82.26%).

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Figure 26. Histogram of neighbourhood self-rated mental health (proportion of residents in the neighbourhood (over age 12) who said they have very good or excellent mental health), 2005–11

40 Mean = 73.28 Std. Dev. = 8.98 N = 139 Min = 47.4 Max = 96.8 30 20 Number of neighbourhoods of Number 10 0 46.34 55.32 64.3 73.28 82.26 91.24 -3SD -2SD -1SD Mean +1SD +2SD Mental health

Indicator Uses and Limitations

The CCHS data are susceptible to several limitations. Self-reported data can be subject to over- or under-reporting, depending on what participants think is a desirable response or on how they conceptualize mental illness. The CCHS has been shown to under-represent people with low income, low education and new immigrants. Finally, it is also likely that the CCHS under- represents people with poor mental health who are homeless, or are living in institutions. This is problematic because these are groups that often have poorer mental health than the population participating in general surveys (Toronto Public Health, 2013).

The CCHS is conducted by Statistics Canada and is subject to the reporting requirements set by Statistics Canada. The CCHS requirements state that a bootstrapping program must be used to generate the standard error (SE) from adjusted weights from combined files. This enabled the use of rates based on small counts. Rates based on less than 10 observations and rates with a coefficient of variation less than 33.3% must be suppressed. This is why only 139 neighbourhoods were included in the calculation. Additionally, rates with a coefficient of variation between 16.6 and 33.3 must be reported with caution. In our calculations, the sample size prior to weighting was 12,000 residents across Toronto neighbourhoods, and although the

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sample met the reporting criteria set by Statistics Canada for all but one neighbourhood, it is not known how well the sample represented the population in each neighbourhood.

Further analysis is planned at ICES to assess the representativeness of the combined sample at the neighbourhood level, but this was not available at the time of analysis. One neighbourhood rate could not be reported and 18 (13%) required reporting with caution because of the wide variation in the responses in the neighbourhoods (variability is often greater when the sample size is small). When an additional cycle of data from the CCHS was added to the first analysis, which included 2007–2011 data, the addition narrowed the confidence levels and most neighbourhoods had rates within a very similar ranking before and after the addition. So while the sample size was small, this suggested that the addition of more responses may not change these results substantially for most neighbourhoods. The range of response rates among the 140 neighbourhoods was very wide (from 47% to 92%). Results were compared with other local data. For additional information see Toronto Public Health (March, 2012) and Toronto’s health status indicator series ‘Self-reported mental health’. For details about the sampling method and imputation visit: http://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurveyandSDDS=3226andlang=enandd b=imdbandadm=8anddis=2.

Because mental health is a complex concept that is not well captured by any single variable, it would be better if we had a range of measures that could take a variety of issues into account, including discrimination, violence, disability, isolation, poverty, and exclusion, as well as issues faced specifically by youth, women, newcomers, people with serious mental health problems, and people lacking access to services and support. A mental health care indicator was considered (people hospitalized for mental health problems who were readmitted to hospital within 30 days) but it was not possible to produce this for Urban HEART by September, 2013.

Selecting Cut-off Measures

Because this was the only health indicator based on survey data where a small sample size could reduce the stability of rates, to minimize this instability at the lower rate end, we used the measure that best identifies the outliers (using standard deviations). Potential cut-off measures are displayed in Table 47. Based on our analysis, the cut-off recommended for this indicator for red was 1 standard deviation below the neighbourhood mean (64.3%), which included 23 neighbourhoods. The cut-off recommended for green was the rate of the highest income quintile (78.2%). There were 42 neighbourhoods at or above this rate, T1. When we looked at self- reported mental health data in our comparison cities, the rates of self-reported mental health that was very good or excellent fell within a narrow range. The 20th and 80th percentile rates covered too narrow a range to be used with Toronto neighbourhoods, which included a much wider range. The internal measure we used did a better job of distinguishing neighbourhoods that fell at either extreme.

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Table 47. Potential cut-off measures for self-rated mental health to determine ‘red’ and ‘green’ neighbourhoods Potential cut-off measures below which neighbourhoods could be categorized as red (highest mental health) Benchmark #1: Population quintile 65.7 -20th percentile with the highest rates of self-reported mental health Benchmark #2: Standard deviations 64.3 -standard deviations less than -1 from the mean represent the worst rates Benchmark #3: Rate ratios (RR) 58.72 -RRs less than 0.80 of the mean represent the lowest self-reported mental health rates Benchmark #4: External marker/benchmark 71.2 -20th percentile of 12 comparisons health regions ranked by lowest rates -these were the 12 Statistics Canada health regions that included the 15 comparison cities used for the other indicators) Potential cut-off measures at which neighbourhoods could be categorized as green (lowest mental health) Target #1: Rate of wealthiest population quintile 78.2 Target #2.1: Population quintile 80.0 -20th percentile of the population with the best rates (see Benchmark #1 above) Target #2.2: Standard deviations 82.3 -standard deviations greater than 1 from the mean represent best rates Target #2.3: Rate ratios 88.08 -RRs greater than 1.2 times the city rate (see Benchmark #3 above) Target #3: External target 75.0 -20th percentile of 12 health regions ranked by best rates (see External Benchmark) Target #4: Policy target NA -none identified Natural breaks/widest differences: Minor breaks occur around the 20th, 25th and 30th percentile as well as between the 76th to 78th percentile when the population is ranked from lowest to highest rate of self-reported mental health Selected Benchmark/Target:Based on analysis conducted in September 2013, the recommended cut-off for this indicator for ‘red’ was Benchmark #2(standard deviations less than -1) and the cut-off recommended for ‘green’ was Target #1 (wealthiest population quintile).

Income Quintiles

The rate of good or excellent mental health among those in the highest income quintile was set as a target and used for comparison as a way of indicating health inequities and the amount of potentially avoidable and unfair health problems due to ‘unjust social arrangements’ (WHO, 2008). Mental health by neighbourhood income quintiles are displayed in Table 48 and Figure 27.

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Table 48. Self-rated mental health rate by neighbourhood income quintile, 2005-11 Income quintile Weighted Percent of pop. Mental health rate in denominator for rate (%) each quintile Q1. Lowest income 364,656 17.5 69.8 Q2. 2nd lowest income 409,212 19.7 72.2 Q3. Middle income 365,450 17.6 70.6 Q4. Mid-high income 448,063 21.5 74.4 Q5. Highest income 494,660 23.8 78.2 City total/average 2,082,041 100.0 73.4

Figure 27.Self-rated mental health rate by neighbourhood income quintile, 2005-11

External Comparisons

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Table 49 presents the external comparison health regions used for the mental health indicator.

Table 49. External comparator health regions and cities for self-rated mental health Health regions/Cities SRMH (%) Vancouver Health Service Delivery Area, BC 69.5 Fraser South Health Service Delivery Area, BC 71.0 71.2 Winnipeg Regional Health Authority, MA 71.4 Saskatoon Regional Health Authority, SK 72.1 Richmond Health Service Delivery Area, BC 72.9 Fraser North Health Service Delivery Area, BC 73.3 City of Toronto Health Unit, ON 73.5 Edmonton Zone, AB 73.8 Région de Montréal, QC 74.4 Peel Regional Health Unit, ON 74.5 75.0 York Regional Health Unit, ON 75.3 Calgary Zone, AB 76.8 Notes: SRMH = self-rated mental health (very good or excellent, CCHS combined cycles, 2005–2011); Twelve Health regions /5 = 2.4, thus the line above is in between second and third (20th percentile highest rate = 75.0) and 10th and 11th lowest rate (20th percentile lowest rate = 71.2 Statistics Canada, Health Indicators. Source: Canadian Community Health Survey).

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Indicator 14: Potentially Preventable Hospitalizations/Ambulatory Case Sensitive Conditions Hospitalization Rate

This indicator was identified as a required indicator through the Urban HEART Delphi process.

Indicator Description and Data Sources

Potentially preventable hospitalizations were measured using the age and sex standardized rate of hospitalizations for specific chronic conditions for individuals 74 and younger. To calculate the rate, we divided the number of hospital discharges related to these conditions among people aged 0-74 by the age-standardized population of individuals aged 0-74 in 2011. Because this is based on the number of discharges rather than the number of people who were discharged at least once during this period of time, individuals may be represented more than once if they had multiple hospitalizations.

Hospital discharge (or ACSC) data came from the Discharge Abstract Database at the Canadian Institute for Health Information (CIHI). Population statistics were examined by age group using the 2011 Statistics Canada Census which were prepared for Urban HEART by ICES. The ACSC hospitalization rate for Toronto was 243.8 per 100,000 persons under 75 years old (2009–2011). The graph below shows the distribution of the ACSC hospitalization rates across Toronto neighbourhoods. The rates range between 79.28 and 608.66 with 70% of neighbourhood rates falling within 1 standard deviation of the neighbourhood mean (between 156.18 and 336.09). These rates are displayed in Figure 28.

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Figure 28. Histogram of neighbourhood potentially avoidable hospitalizations (age and sex standardized rate of hospitalizations age 0–74, 2009-11

30 Mean = 246.13 Std. Dev. - 89.95 N = 140 Min = 79.28 Max = 608.66 20 10 Numberneighbourhoods of 0 66.23 156.18 246.13 336.08 426.03 515.98 -2SD -1SD Mean +1SD +2SD +3SD Preventable hospitalizations

Indicator Uses and Limitations

The ACSC hospitalization rate is an indirect measure of timely, appropriate access to primary care and self-management of chronic disease. It is strongly related to income level, and in Toronto, it may also be an indication of access to primary care. Although these hospitalizations are often called preventable or avoidable, it is not known what percentage of these hospitalizations were actually avoidable or preventable.

Preventable hospitalizations are frequently used as a standard indicator relating to chronic diseases. This indicator is also relevant to national and provincial health care planning and service accountability. For additional information, please see www.torontohealthprofiles.ca.

Different health conditions may explain higher rates in different neighbourhoods. For example, the location and concentration of housing for people with disabilities or the distribution of lower income populations with poorer health may explain why some communities may have higher rates than others. Factors such as higher rates of heart disease and diabetes in some population groups, differential access to resources for self-care, and access to timely and appropriate primary care are also factors that should be considered.

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Selecting Cut-off Measures for Urban HEART

Although we could have used external comparators to set the benchmark for red, because this process would have resulted in 64 neighbourhoods being coded red (a number too large for the intended uses of Urban HEART), we instead recommended using the rate ratio of more than 20% (1.2 times) higher than the city rate (B3) for the cut-off. This included the highest rates as well as almost all of the significantly higher rates. Potential cut-off rates are displayed in Table 50.

The ACSC hospitalization rate of the highest income quintile was thought to be a measure of a potentially achievable rate for the whole population if social conditions were more equitable, so this was recommended as the target for this indicator.

Table 50. Potential cut-off measures for potentially avoidable hospitalizations to determine ‘red’ and ‘green’ neighbourhoods Potential cut-off measures for categorizing neighbourhoods as ‘red’ (highest ACSCH rate) Benchmark #1: Population quintile 315.6 -20th percentile with the highest ACSC hospitalizations Benchmark #2: Standard deviations 336.1 -standard deviations greater than +1from the mean represent the worst rates Benchmark #3: Rate ratios (RR) 292.6 -RRs greater than 1.2 times the city rate Benchmark #4: External marker/benchmark 250.7 -20th percentile of 12 comparison health regions ranked by highest rates Potential cut-off measures for categorizing neighbourhoods as ‘green’ (lowest ACSCH rate) Target #1: Rate of wealthiest population quintile 188.1 Target #2.1: Population quintile 163.9 -20th percentile of the population with the best rates (see Benchmark #1 above) Target #2.2: Standard deviations 156.2 -standard deviations less than -1 from mean Target #2.3: Rate ratios 195.1 -RRs less than 80% of the city rate (see Benchmark #3 above) Target #3: External target 173.8 -20th percentile of 12 health regions ranked by best rates (see External Benchmark above) Target #4: Policy target NA -none identified Natural breaks/widest rate differences occur at Benchmark #2, in between Benchmark #2 and External Benchmark, and at Target #1, Selected Benchmark/Target: Based on analysis conducted in September 2013, the recommended cut-off for this indicator for ‘red’ was Benchmark #3 (rate ratios 1.2 times greater than the city rate) and the recommended cut-off for ‘green’ was Target #1 (wealthiest income quintile rate). March 28, 2014, ver.1, 104

Income Quintiles

Differences in the rates for indicators between income quintiles of the population are indicative of health and social inequalities. ACSC hospitalizations by income quintiles are displayed in Table 51 and Figure 29.

Table 51. ACSC hospitalizations by neighbourhood income quintile, 2009-11 Income quintile Total pop. Number of Percent of Adjusted ACSC hospitals pop. (%) hospitalization rate in each quintile Q1. Lowest income 1,416,270 4,156 19.5 293.2 Q2. 2nd lowest income 1,364,595 3,623 18.8 244.4 Q3. Middle income 1,381,035 3,785 19.0 251.4 Q4. Mid-high income 1,531,890 4,114 21.1 248.0 Q5. Highest income 1,569,240 3,204 21.6 188.1 City total/average 7,263,030 18,882 100.0 243.8

Figure 29.ACSC hospitalizations by neighbourhood income quintile, 2009-11

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External Comparators

The health regions used as external comparators for this indicator are displayed in Table 52.

Table 52. External comparator health regions and cities for ACSC hospitalizations Health Regions/Cities ACSCH (Ave.) Saskatoon Health Region, SK 298.3 Edmonton Zone, AB 259.3 250.7 ASSS de Montréal, QC 237.7 Calgary Zone, AB 235.3 City of Toronto Health Unit, ON 228 Fraser South Health Service Delivery Area, BC 227.7 Peel Regional Health Unit, ON 218 Fraser North Health Service Delivery Area, BC 213 Winnipeg Regional Health Authority, MA 204.7 Vancouver Health Service Delivery Area, BC 192 173.8 Richmond Health Service Delivery Area, BC 161.7 York Regional Health Unit, ON 154.3 Notes: ACSCH = Ambulatory case sensitive conditions hospitalization rate; Source: CIHI Health Indicators.

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Indicator 15: Diabetes Prevalence

This indicator was identified as a strongly recommended indicator through the Urban HEART Delphi process.

Indicator Description and Data Sources

Diabetes prevalence was measured as the number of persons aged 20 or older with diabetes per 100 persons. These data were age and sex standardized and accessed on April 1st, 2011. As the rate is provided per 100 persons, it is often expressed as a percent.

The numerator used for performing calculations was derived using a validated algorithm to identify those with at least one hospitalization or two physician visits in two years following a diagnosis of diabetes. The data source for this is the Ontario Diabetes Database (ODD). The ODD is a validated electronic database which identifies persons with diagnosed diabetes. This algorithm was found to be highly sensitive (86%) and specific (97%) for identifying patients for whom diabetes was recorded in primary care charts (Hux et al., 2002).

The denominator used for performing our calculations was the total of individuals aged 20 or older with a valid OHIP card, who were alive on April 1st, 2011, and who were living in the City of Toronto. This was derived from the Registered Persons Database (RPDB) from Ministry of Health and Long-term Care (MOHLTC). The 1991 Census was used for standardization. This indicator was prepared by ICES.

The Diabetes Prevalence rate for Toronto was 11.3% in April 2011. Figure 30 shows the distribution of diabetes prevalence rates across Toronto neighbourhoods. The rates range between 4.1% and 14.0% with 61% of neighbourhood rates falling within 1 standard deviation of the neighbourhood mean (between 6.33 and 11.21).

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Figure 30. Histogram of neighbourhood diabetes prevalence(number of persons age 20+ with at least one hospitalization or two physician visits in two years with a diagnosis of diabetes), 2011

25 Mean = 8.77 Std. Dev. = 2.44 N = 140 Min = 4.1 Max = 14 20 15 10 Number of nieghbourhoods of Number 5 0 3.89 6.33 8.77 11.21 13.65 -2SD -1SD Mean +1SD +2SD Diabetes

Indicator Uses and Limitations

One important limitation of this indicator was that persons receiving diabetes care from Community Health Centres (CHC) may be underrepresented because CHC visits are not included in the OHIP billings used to produce the physicians’ claims in the algorithm. However, those CHC clients who had a hospitalization for diabetes were included. Another possible limitation with this indicator is that the Ontario Diabetes Database (ODD) excludes those individuals with invalid health card numbers.

Ontario launched a provincial Diabetes Management Strategy in 2008 that aims “to prevent, manage and treat diabetes across the province.” There are no specific targets for diabetes prevalence or rate reductions in the strategy. There were, however, targets in the strategy for reducing rates of physical inactivity, achieving a rate of 80% of Ontarians with diabetes receiving key tests (blood sugar, cholesterol and retinal eye exams) within the recommended times, and maintaining or reducing specific treatment and hospitalization rates for diabetes complications. For more information see the 2012 Annual Report of the Office of the Auditor General of Ontario.

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The prevalence rates that emerged from the measure used here (algorithm of one hospital visit or two physician visits for diabetes over two years), captured a larger number of persons than rates emerging from people self-reporting that they had a diagnosis of diabetes in the Canadian Community Health Survey. It is important to note that persons with Type 2 diabetes can remain asymptomatic for up to 10 years and this often goes undiagnosed. It is estimated that at least 284,000 Ontarians may not know they have diabetes. The data from the ODD does not capture population characteristics associated with higher rates of diabetes that may be important to more effective prevention. The under representation of clients of CHC was a potential limitation of the data used here, especially in neighbourhoods where a high proportion of residents use CHC services for primary health care. Local researchers have used more sophisticated methods such as data linkage across multiple data sets (Creatore et al. 2010) to more effectively identify prevalence differences among population groups (place of birth). The experiences of specific immigrant communities (Hyman & Guruge, 2011) and Aboriginal communities (Lavallée & Howard, 2011) need to inform the development of more appropriate and relevant prevention and management strategies.

Selecting Cut-off Measures

Based on analysis completed as of September 2013, neighbourhoods that have the rate that are 1.2 times higher than city rate (or more) were recommended to be categorized as red. This included 44 neighbourhoods with rates ranging between 10.3 and 14.0%. All the rates more than 20% (1.2 times) higher than the city rate are also statistically significantly higher than the city rate. The cut-off recommended for green was the diabetes rate for the T1 (highest income quintile), which was 7.1%. Forty neighbourhoods showed prevalence rates at or below this rate. Potential cut-off measures are listed in Table 53.

Table 53. Potential cut-off measures for diabetes prevalence to determine ‘red’ and ‘green’ neighbourhoods Potential cut-off measures for categorizing neighbourhoods above them as ‘red’ (highest diabetes rate) Benchmark #1: Population quintile 11.3 -20th percentile with the highest diabetes prevalence rate Benchmark #2: Standard deviations 11.2 -standard deviations greater than +1 SD from the mean represent the worst rates Benchmark #3: Rate ratios (RR) 10.2 -RRs 1.2 times greater than the city rate Benchmark #4: External marker/benchmark NA -external comparators are not available because these rates are based on the Ontario Diabetes Database. The new national chronic diseases surveillance system (CDSS) is not yet public Potential cut-off measures at or above which neighbourhoods could be ‘green’ (lowest diabetes rate) Target #1: Rate of wealthiest population quintile 7.1 Target #2.1: Population quintile 6.5 -20th percentile of the population with the best rates (see Benchmark #1 above)

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Target #2.2: Standard deviations 6.3 -standard deviations less than -1 from the mean (lowest rates) Target #2.3: Rate ratios 6.8 -RRs less than 80% of the city rate (see Benchmark #3 above) Target #3: External target NA -based on 12 comparison health regions; not applicable (see External Benchmark above) Target #4: Policy target NA -None were found specific to diabetes prevalence but there are others for physical activity and tests and care for those with diabetes in the provincial diabetes strategy Natural breaks/widest rate differences occur at Benchmark #2 and before and after Benchmark #3 and at several of the target measures. However, the rate differences are in the order of 0.1 or less so not useful as cut-off measures Selected Benchmark/Target: Based on analysis conducted in September 2013, the recommended cut- off for this indicator for ‘red’ was Benchmark #3 (rate ratios 1.2 times greater than the city rate) and the recommended cut-off for ‘green’ was Target #1 (wealthiest income quintile rate).

Income Quintiles

Differences in the rates for indicators between income quintiles of the population is used as an indicator of health and social inequalities. Diabetes prevalence by neighbourhood income quintile is displayed in Table 54 and Figure 31.

Table 54. Diabetes rates by neighbourhood income quintile, 2011 Income quintile Total pop. Number with Percent of Adjusted diabetes population (%) diabetes rate in each quintile Q1. Lowest income 529,421 53,123 19.9 9.9 Q2. 2nd lowest income 513,581 57,583 19.3 10.0 Q3. Middle income 506,342 53,926 19.0 9.6 Q4. Mid-high income 563,948 49,114 21.2 8.0 Q5. Highest income 550,120 45,914 20.7 7.1 City total/average 2,663,412 259,660 100.0 8.5

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Figure 31.Diabetes rates by neighbourhood income quintile, 2011

External Comparators

External comparators for diabetes prevalence were not available. The rates generated were derived from the ODD. The new national Chronic Diseases Surveillance System (CDSS) is not yet public.

Table 55 summarizes the indicators used for the population health domain.

Table 55. Population health domain summary Toronto rate (Total) 211.1 73.4 243.8 8.5 13 253 174 3 Neeighbourhood range 118.0– 47.4– 79.3–608.7 4.1–14.0 573.0 96.8 ID Neeighbourhoods Premature Mental Preventable Diabetes R Y G mortality health hospitalizations ACSCH 72 Reegent Park 403.9H 61.3E 496.5H 11.4H 4 0 0 61 Crescent Town 284.4H 47.4EL 324.8H 10.9H 4 0 0 85 South Parkdale 421.3H 57.3L 537.0H 9.8H 3 1 0 Beechborough- 112 Greenbrook 306.7H 65.7E 324.5 12.5H 3 1 0 24 Black Creek 228.3 58.4L 315.6H 12.7H 3 1 0 125 Ionview 243.5 63.4 372.8H 11.6H 3 1 0 25 Glenfield-Jane Heights 214.9 61.5 318.6H 12.7H 3 1 0 74 North St. James Town 323.1H 69.8 391.9H 10.3H 3 1 0 91 Weston-Pellam Park 257.4 63.2E 322.3H 10.9H 3 1 0 Thistletown-Beaumond 3 Heights 244.6 63.7E 314.7H 11.2H 3 1 0 121 Oakridge 315.9H 75.8 307.9H 11.8H 3 1 0 March 28, 2014, ver.1, 111

120 Clairlea-Birchmount 271.7H 74.7 327.8H 10.9H 3 1 0 124 Kennedy Park 273.8H 87.1H 293.1H 12.2H 3 0 1 73 Moss Park 573.0H 76.1 608.7H 7.8L 2 2 0 21 Humber Summit 231.9 49.0E 279.7 12.7H 2 2 0 Mount Olive- 2 Silverstone-Jamestown 220.8 61.7 285.1H 12.9H 2 2 0 84 Little Portugal 271.4H 62.5E 395.2H 9.3H 2 2 0 4 Rexdale-Kipling 261.4H 68.7 391.3H 10.6H 2 2 0 115 Mount Dennis 249.3 65.8E 341.3H 11.2H 2 2 0 113 Weston 311.3H 71.3 291.4H 11.4H 2 2 0 126 Dorset Park 197.5 72.5 327.4H 12.4H 2 2 0 55 Thorncliffe Park 214.1 73.5 361.4H 11.6H 2 2 0 65 Greenwood-Coxwell 297.6H 65.1E 326.6H 8.6 2 2 0 York University 27 Heights 211.3 60.6 280.3 10.4H 2 2 0 119 Wexford-Maryvale 205.7 61.1 289.5H 10.4H 2 2 0 1 West Humber-Clairville 208.0 72.9 310.5H 12.1H 2 2 0 43 Victoria Village 253.5H 68.7 308.0H 10.3H 2 2 0 69 Blake-Jones 301.1H 58.0E 248.9 8.1 2 2 0 111 Rockcliffe-Smythe 264.6H 72.9 305.8H 10.7H 2 2 0 70 South Riverdale 318.2H 72.9 333.5H 8.2 2 2 0 62 East End-Danforth 335.3H 77.0 343.9H 8.0L 2 2 0 86 Roncesvalles 293.8H 75.5 364.8H 7.5L 2 2 0 90 Junction Area 274.0H 74.8 301.4H 8.4 2 2 0 136 West Hill 268.3H 82.2 373.4H 12.0H 2 1 1 139 Scarborough Village 236.1 78.2 313.9H 12.4H 2 1 1 5 Elms-Old Rexdale 173.9 62.8 239.6 11.3H 2 1 1 18 New Toronto 334.0H 80.5 327.3H 8.9 2 1 1 122 Birchcliffe-Cliffside 317.6H 78.4 345.8H 8.2 2 1 1 19 Long Branch 304.9H 81.0 390.2H 8.0 2 1 1 30 Brookhaven-Amesbury 225.4 82.9 299.1H 11.5H 2 1 1 64 Woodbine Corridor 339.3H 80.9 349.5H 7.6L 2 1 1 Kingsview Village-The 6 Westway 199.9 82.3 305.5H 10.7H 2 1 1 Cabbagetown-South 71 St. James Town 367.4H 85.9H 327.5H 6.6L 2 0 2 137 Woburn 189.6L 64.1 281.1H 12.5H 1 3 0 138 Eglinton East 228.3 72.3 277.0 12.8H 1 3 0 135 Morningside 239.9 72.1 238.2 13.1H 1 3 0 132 Malvern 196.8 74.7 250.6 14.0H 1 3 0 131 Rouge 201.1 72.2 235.5 13.5H 1 3 0 54 O’Connor-Parkview 246.9H 71.5 353.4H 9.8H 1 3 0 Danforth Village – 66 Toronto 258.2H 64.6E 332.3H 8.3 1 3 0 Dovercourt-Wallace 93 Emerson-Junction 225.2 62.1 276.8 9.5H 1 3 0 59 Danforth East York 225.7 58.1L 272.9 8.5 1 3 0 31 Yorkdale-Glen Park 201.3 65.0 251.7 10.8H 1 3 0 March 28, 2014, ver.1, 112

13 Etobicoke West Mall 216.1 66.7E 327.9H 9.2H 1 3 0 60 Woodbine-Lumsden 253.5 71.9 394.6H 8.1 1 3 0 Downsview-Roding- 26 CFB 225.6 67.4 230.8 10.8H 1 3 0 35 Westminster-Branson 200.5 54.5E 211.4L 8.9H 1 3 0 Keelesdale-Eglinton 110 West 232.0 74.8 225.9 11.8H 1 3 0 109 Caledonia-Fairbank 222.9 67.3 214.0 10.6H 1 3 0 127 Bendale 215.3 76.1 249.5 11.5H 1 3 0 45 Parkwoods-Donalda 188.1L 60.3 230.7 8.4 1 3 0 81 Trinity-Bellwoods 206.5 63.5 202.3 8.7 1 3 0 94 Wychwood 227.5 74.5 316.7H 8.1L 1 3 0 28 Rustic 184.4 73.8 236.6 12.4H 1 2 1 22 Humbermede 177.0L 75.2 259.3 11.7H 1 2 1 29 Maple Leaf 205.6 69.0 179.0L 10.9H 1 2 1 20 Alderwood 256.9H 80.0 294.0 8.5 1 2 1 53 Henry Farm 154.1L 63.8E 250.2 8.3 1 2 1 Humber Heights- 8 Westmount 211.0 63.5E 156.8L 8.7 1 2 1 17 Mimico 248.8H 81.4 316.5H 7.5L 1 2 1 129 Agincourt North 139.2L 61.1 163.3L 9.5H 1 1 2 75 Church-Yonge Corridor 352.0H 81.7 281.7 6.5L 1 1 2 23 Pelmo Park-Humberlea 175.7 93.5H 254.5 10.9H 1 1 2 134 Highland Creek 154.2L 78.2 141.4L 12.4H 1 0 3 76 Bay Street Corridor 294.2H 85.4H 163.7L 5.1L 1 0 3 108 Briar Hill-Belgravia 205.0 65.2E 236.1 9.9H 0 4 0 32 Englemount-Lawrence 231.6 66.4 254.2 8.8 0 4 0 57 Broadview North 229.5 64.7E 252.4 7.9L 0 4 0 44 Flemingdon Park 196.3 73.9 263.3 10.2H 0 4 0 107 Oakwood-Vaughan 222.8 69.5 238.8 9.1H 0 4 0 58 Old East York 229.2 67.5 266.8 8.0 0 4 0 78 Kensington-Chinatown 227.8 65.2 209.1 8.3 0 4 0 140 Guildwood 208.2 69.5 214.3 8.1 0 4 0 Islington-City Centre 14 West 216.2 75.5 214.6L 8.3 0 4 0 92 Corso Italia-Davenport 181.4 65.4 263.5 9.2H 0 3 1 Willowridge- 7 Martingrove-Richview 167.6L 71.3 262.0 9.7H 0 3 1 123 Cliffcrest 253.2H 81.1 245.2 9.5H 0 3 1 82 Niagara 257.5L 74.0 286.7 7.0L 0 3 1 117 L’Amoreaux 172.9L 74.6 215.0L 10.1H 0 3 1 80 Palmerston-Little Italy 206.3 67.9 262.3 7.0L 0 3 1 67 Playter Estates-Danforth 233.6 69.7 229.1 6.7L 0 3 1 36 Newtonbrook West 173.6L 68.8 203.1L 8.0L 0 3 1 83 Dufferin Grove 183.6 77.2 240.0 8.5 0 3 1 95 Annex 234.1 73.3 235.7 5.5L 0 3 1 104 Mount Pleasant West 195.4 74.4 193.9L 6.0L 0 3 1 37 Willowdale West 186.1 NA 202.4 7.3L 0 2 1 March 28, 2014, ver.1, 113

Agincourt South- 128 Malvern West 161.9L 70.4 183.6L 9.5H 0 2 2 Eringate-Centennial- 11 West Deane 181.4L 71.8 186.7L 8.2 0 2 2 118 Tam O’Shanter-Sullivan 163.9L 80.0 197.8L 9.4H 0 2 2 33 Clanton Park 176.8L 81.0 196.4L 8.7 0 2 2 116 Steeles 137.3L 74.1 139.0L 9.0H 0 2 2 46 Pleasant View 143.5L 78.0 185.1L 8.5 0 2 2 114 Lambton Baby Point 187.8 72.2E 187.4 6.2L 0 2 2 47 Don Valley Village 146.1L 71.9 156.1L 7.6L 0 2 2 79 University 246.4 83.8 231.0 6.0L 0 2 2 106 Humewood-Cedarvale 149.3L 77.3 194.6L 7.0L 0 2 2 Runnymede-Bloor West 89 Village 204.3 77.5 138.6L 6.8L 0 2 2 48 Hillcrest Village 140.7L 76.0 135.0L 7.5L 0 2 2 12 Markland Wood 198.2 90.3H 187.5L 7.6L 0 2 2 88 High Park North 214.0 86.1H 195.0L 5.5L 0 2 2 102 Forest Hill North 138.2L 71.2 150.1L 6.6L 0 2 3 133 Centennial Scarborough 170.2L 79.3 170.3L 9.5H 0 1 3 34 Bathurst Manor 148.3L 78.7 173.7L 8.5 0 1 3 68 North Riverdale 254.5H 78.2 154.2L 6.2L 0 1 3 52 Bayview Village 127.5L 64.5 163.9L 6.0L 0 1 3 130 Milliken 118.0L 78.2 121.3L 9.0H 0 1 3 Waterfront Communities- 77 The Island 217.3 81.4 164.6L 6.1L 0 1 3 87 High Park-Swansea 184.6L 74.9 154.6L 5.4L 0 1 3 38 Lansing-Westgate 139.0L 76.3 149.0L 6.4L 0 1 3 10 Princess-Rosethorn 174.6 75.4 88.9L 6.3L 0 1 3 51 Willowdale East 144.5L 72.3 103.8L 6.0L 0 1 3 99 Mount Pleasant East 176.6L 76.6 140.0L 5.0L 0 1 3 100 Yonge-Eglinton 163.8L 72.8 122.6L 4.8L 0 1 3 105 Lawrence Park North 162.0L 76.0 147.5L 4.7L 0 1 3 63 The Beaches 223.8 84.0H 147.6L 4.7L 0 1 3 96 Casa Loma 179.7 90.0H 196.9 4.6L 0 1 3 56 Leaside-Bennington 145.4L 77.7 79.3L 4.9L 0 1 3 Edenbridge-Humber 9 Valley 187.2 78.6 184.8L 6.8L 0 0 4 16 Stonegate-Queensway 177.2L 80.2 186.8L 6.7L 0 0 4 50 Newtonbrook East 180.5L 79.4 127.5L 6.8L 0 0 4 42 Banbury-Don Mills 163.5L 78.7 137.3L 6.5L 0 0 4 49 Bayview Woods-Steeles 158.7L 84.3 161.1L 7.1L 0 0 4 97 Yonge-St. Clair 148.9L 83.7H 186.4L 4.2L 0 0 4 40 St. Andrew-Windfields 146.7L 89.9H 148.1L 5.7L 0 0 4 Bridle Path-Sunnybrook- 41 York Mills 147.1L 88.9H 153.7L 4.5L 0 0 4 39 Bedford Park-Nortown 119.1L 87.0H 99.5L 5.6L 0 0 4 103 Lawrence Park South 133.4L 83.7 79.4L 4.2L 0 0 4

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15 Kingsway South 132.7L 96.8H 170.3L 5.0L 0 0 4 98 Rosedale-Moore Park 143.L 86.6H 86.0L 4.1L 0 0 4 101 Forest Hill South 135.1L 92.5H 122.5L 4.7L 0 0 4 Notes: Indicator Definitions:(1)Premature mortality: Age adjusted number of deaths age <75/100,000 population age under 75, both sexes, 2005–2009, Ontario Mortality Data, 2005–2009; (2) Mental health: Percent aged 20+ reporting very good or excellent mental health, 2005–2011, Canadian Community Health Survey; (3) Preventable hospitalizations: Age and sex adjusted number of ambulatory care sensitive condition hospitalizations/100,000 population, 2009–2011, discharge abstracts data base, CIHI; (4) Diabetes prevalence: Age and sex adjusted number of persons age 20+ with diabetes, Ontario Diabetes Database (ODD) and the Ontario Registered Persons Data Base, Ontario Ministry of Health and Long-term Care; (5) E Indicates use with caution as these rates may be unstable due to small sample size or wide variation among CCHS variables. For Health indicators, ‘H’ (higher) and ‘L’ (lower) indicates rates are significantly higher or lower than the city rate (19 times out of 20) 95% confidence intervals. ** Where neighbourhoods have the same number of ‘red’, ‘yellow’ and ‘green’, the composite score is used to sort them so that the worst scores (highest ranks) rank first.

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6. Accountability and Acknowledgements

The following people contributed to the production, testing or comparability of indicators for Urban HEART @Torontofrom June 2013 to October 2013 (see Table 56). The support and contribution of others including Kelly Murphy (Coordinator) and members of the Urban HEART Steering Committee was also very useful to overall decision making for the indicator set and this contribution is gratefully acknowledged.

Table 56. Urban HEART @Toronto Contributors Domain Indicators Testing & QA Production Access/Advice/Comparative data Unemployment Ashitava Dianne Peter Viducis, Toronto, rate (Required) Halder, Patychuk Strategic Growth and Sector Dianne Development; Lorne Turner, Patychuk Toronto, Executive Management Division City Manager’s Office;Diane Dyson, Woodgreen Low income Dianne Dianne Brendan Rahman, Community measure Patychuk Patychuk, Data Program; Staff at (Required) Eddie Farrell Statistics Canada, Income Statistics Division Social Assistance Dianne Ashitava Jocelyn Hollmann, Toronto Economic opportunity (Strongly Patychuk, Halder Employment and Social recommended) Ashitava Services; Lorne Turner, Halder Executive Management Division, Toronto City Manager’s Office

High school Dianne Rob Brown, Harvey Low and Mat Krepicz, graduation Patychuk, TDSB & City of Toronto, SPAR; Rob (Required) (Four Ashitava Dianne Brown, TDSB Indicators) Halder Patychuk Marginalization Eddie Farrell, Flora (Required) Antony Matheson, Chum, CRICH Dianne Patychuk Post-secondary Ashitava Ashitava education Halder, Halder, (Recommended) Dianne Dianne Social and human development Patychuk Patychuk

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Municipal voter Eddie Farrell, Eddie Farrell Leanne Holt, Federation of participation Antony Chum Canadian Municipalities (Required) Gov./Civic engagement Community Eddie Farrell, Eddie Farrell Pierre-Nick Durette places for meeting Antony Chum (Required) Walkability Eddie Farrell, Eddie Farrell, Rick Glazier, ICES; Marco (Required) Antony Chum Antony Belmont, City of Toronto Chum Access to healthy Eddie Eddie Farrell Brian Cook, Toronto Public food choices Farrell,Antony Health infrastructure infrastructure (Recommended) Chum Access to green Eddie Farrell, Eddie Farrell Physical environment and space Antony Chum (Recommended) Premature Ashitava Catalina mortality Halder, Yokingco, (Required) Dianne Toronto Patychuk Public Health Self-rated mental Ashitava Marisa Pat O’Campo, CRICH health (Required) Halder, Creatore, Dianne ICES Patychuk Potentially Dianne Mohammed Health Indicators Team at avoidable Patychuk Agha, ICES Canadian Institute for Health hospitalizations and Marisa Information

Population health (Required) Creator, ICES Diabetes Ashitava Marisa Peter Gozdyra, CRICH prevalence Halder, Creator, (Recommended) Dianne ICES Patychuk Notes: Shirley Bryant Toronto Central LHIN also contributed information for an additional indicator but it was not possible to prepare the indicator for inclusion in this version of Urban HEART; Harvey Low provided updated neighbourhood names; Wayne Chu provided access to the Community Data Program data; Peter Gozdyra provided access to diabetes and mapping information.

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References

Auditor General of Ontario.(2011). Report of the Office of the Auditor General of Ontario.Retrieved from:http://www.auditor.on.ca/en/reports_en/en11/313en11.pdf

Basrur, S., Deeks, S., Demeter, S., Gray, P., Havey, B., Heimann, A., Johnson, I., Mulder, C., Patychuk, D., Sider, D., and Williams, D. (1996). Towards equitable funding for public health.Final report.Equitable funding for public health working group. Ontario.

Canadian Institute for Health Information (CIHI).(2012). Health indicators report.Retrieved from:https://secure.cihi.ca/free_products/health_indicators_2012_en.pdf

City of Toronto.(2012). Working as one. A workforce development strategy for Toronto.Retrieved from: http://www.toronto.ca/legdocs/mmis/2012/ed/bgrd/backgroundfile- 45082.pdf

Creatore, M. I., Moineddin, R., Booth, G., Manuel, D. H., DesMeules, M., McDermott, S., & Glazier, R. H. (2010).Age-and sex-related prevalence of diabetes mellitus among immigrants to Ontario, Canada. Canadian Medical Association Journal, 182(8), 781-789.

Duncan, D. T., Aldstadt, J., Whalen, J., Melly, S. J., & Gortmaker, S. L. (2011). Validation of Walk Score® for estimating neighborhood walkability: An analysis of four US metropolitan areas. International journal of environmental research and public health, 8(11), 4160-4179.

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Appendix A: The National Household Survey (NHS)

The 2011 National Household Survey (NHS) is a voluntary survey and is not comparable to past censuses because of methodological differences. It may not be representative of local populations or individual neighbourhoods due to variations in the response rates by geography and population groups. We calculated that the NHS non-response rate for neighbourhoods (based on population-weighted census tract non-response rates). Non-response rates are displayed in Table A1. Non-response ranged between 19.5% and 40.2%. Eighteen neighbourhoods had a non- response rate greater than 33%. Urban HEART includes two NHS variables: ‘Unemployment Rates (age 15+) 2011’ and ‘% Age 25–64 with a Post-Secondary Education Certificate, Diploma or Degree, 2011’.

Table A1: Neighbourhoods with the highest non-response rate in the 2011 NHS Neighbourhood Neighbourhood name Percent non-response ID 79 University 40.2% 80 Palmerston-Little Italy 39.3% 84 Little Portugal 38.2% 90 Junction Area 37.8% 81 Trinity-Bellwoods 37.0% 113 Weston 37.0% 139 Scarborough Village 35.7% 136 West Hill 35.5% 73 Moss Park 34.7% 86 Roncesvalles 34.6% 111 Rockcliffe-Smythe 34.6% 28 Rustic 34.6% 50 Newtonbrook East 34.5% 5 Elms-Old Rexdale 34.3% Bridle Path-Sunnybrook-York 41 33.9% Mills 72 Regent Park 33.7% 49 Bayview Woods-Steeles 33.6% 92 Corso Italia-Davenport 33.4% 85 South Parkdale 33.2% 4 Rexdale-Kipling 33.0%

NHS neighbourhood unemployment rates and labour force participation rates were found to be strongly correlated with each other. They were also strongly correlated with 2006 unemployment and labour force participation rates and with social assistance rates (correlation coefficients

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ranged from >0.6 to >0.9). Unemployment rates went up as labour force participation rates went down. The social assistance indicator in the NHS is comprised of Ontario Works (OW) recipients and people in the Ontario Disability Support Program participating in OW employment programs, and non-OW members receiving special assistance for medical items. The correlation between social assistance and unemployment rates is relevant because the primary objective of the OW program is assisting people to become employed. OW provides income and other support to 64% of Toronto’s unemployed residents and OW caseloads are very sensitive to unemployment rates and other economic trends. Therefore, given these strong correlations and despite the limitations of the NHS, the NHS unemployment rate variable provides a picture of unemployment disparities across Toronto neighbourhoods that is consistent with other measures.

When looking at educational completion rates, age 25–64 is the preferred age category because it excludes young adults who are not yet old enough to have graduated from a post-secondary institution or program. It also leaves out individuals age 65 and over who generally have lower education levels than younger age groups. At this point, only two categories are available for this variable in the NHS (age 15+ and age 25–64), so any further age-specific comparisons or age adjusting is not possible. The NHS post-secondary education rates were correlated with other available education variables from TDSB and the 2006 Census. The post-secondary completion and university degree indicators in 2011 were very strongly correlated (>0.9) with the same variables in the 2006 Census. The correlation between these variables and Grade 12 graduation rates, as sourced from the 2011 Toronto District School Board data was >0.6, which is a strong positive relationship especially given that these data are for different age groups and different data sources. The NHS education variable provides a picture of education disparities across Toronto neighbourhoods, which is consistent with other measures of education.

The Low Income Variable and T1FF (Income tax data)

The low income indicator is important for Urban HEART both as a required indicator and as the basis for calculating population income quintiles. The source for the proportion of persons below the After-tax Low Income Measures was income tax filing data compiled by the Canada Revenue Agency and provided in the Statistics Canada 2010 Small Area Administrative Data T1 Family File (T1FF). Because of the problems with the NHS (highlighted above) and the elimination of the long-form census, many organizations are turning to the T1FF at the census tract level as an alternative source for income information. The T1FF is very different from the previous long-form census and the 2011 NHS in several ways. For one, it encompasses all taxfilers, including those not living in private households. Additionally, it is not a sample and it includes all households who have filed for taxes. Finally, it allocates records to a geographic location using the single link indicator (SLI) on the postal code conversion file (PCCF) rather than the actual address which is used by the census and NHS. These variations mean that different populations are included in the T1FF than are included census and NHS income variables and it also results in geographic misallocations.

After matching multiple 2010 T1FF and 2011 Census variables, there were two neighbourhoods that had a significantly higher population in the 2010 T1FF than the population counts for the March 28, 2014, ver.1, 121

2011Census. Rather than exclude these neighbourhoods, we replaced the clearly over-inflated population counts from the T1FF with the 2011 census population counts for these neighbourhoods. Based on information from the other variables related to socioeconomic status (SES), the city LIM rate was assigned to these neighbourhoods. This moved these neighbourhoods outside the lowest income quintile. When the 2010 LIM rates from the 2011 NHS became available in September 2013, we calculated and compared the income quintiles for the NHS LIMs with the T1FF LIMs to identify if using the NHS LIMs would have resulted in different neighbourhoods falling into the lowest income quintile since this was the cut-off category for ‘red’. The comparison confirmed that the cut-off was correct as the neighbourhoods on either side of the cut off in the T1FF were in different quintiles in the NHS. Of the 24 neighbourhoods coded ‘red’ using the T1FF LIMs, there were only three that would not have been coded ‘red’ if the NHS LIMs were used instead and vice versa. These neighbourhoods had rates close to the cut-off. Therefore, the LIM variable based on the T1FF used in Urban HEART was not replaced when the NHS LIMs became available.

Some Options for Using the TIFF Data for Urban Heart

There are many options available for using the T1FF data. The first would be to use it as is, but with an acknowledgement of the limitations. Second, would be to use it with caution and to minimize the weight given to the variable so that it is not given the same gravity in decision making as better quality indicators. A third option would be to delete those census tracks that have the largest excess counts and unreliable numbers of low income persons. Other options include modifying the census tracts with the largest excess by removing the estimated excess (i.e. apply the median rate of excess to the census comparator and remove the count that exceed that) or replacing the census track with the 2011 actual census population count and apply the city rate.

An additional option is to replace the census track rates in those areas that have obviously over- inflated population counts with the city rate and use the census population in calculations. Additionally, requests can be made to the Statistics Canada Income Division for custom queries with specific modifications, such as the exclusion of commercial and non-residential addresses, post office boxes, and postal codes that include public centres (such as Toronto City Hall or the Civic Centre). They are also able to provide data by Dissemination Areas (DA) for these CTs so that DAs can be used as an alternate source for these CTs only (excluding the DAs that are problematic).

A final option would be to use an alternate source of data for information about income and social economic status (SES)/wealth quintiles (other than LIMs) or an alternate indicator (calculated income per person).

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Appendix B: Correlation Analyses

Correlation analysis demonstrates the strength of the relationships between indicators in a dataset. If the correlation is positive, it means that when one variable increases, the other tends to increase. If the correlation is negative, it means that when one variable increases, the other tends to decrease. The strongest relationships are represented by the numbers (correlation coefficients) that are closest to +1 or -1. Deciding which cut-offs to use to distinguish between strong and weak relationships depends on the types of data used. For data with precise measurements and many points of data, a higher value (i.e.+ or -0.6) can be considered strong, but for ordinal, rank, Likert scales or less precise measurements, +0.4 or -0.4 may be considered a strong relationship. For example, in the Urban HEART indicator set, the High School Graduation variable is only three numbers (1: Low, 2: Ave and 3: High), therefore a correlation of +0.4 or -0.4 could be considered a strong correlation. The strongest correlations (>0.6 or less than -0.6) are highlighted in the table below and discussed on the following pages, along with more details about correlation analysis.

Correlation analysis is useful for indicator testing and selection as well as for deciding weights to assign in composite indices. For example, new variables or indicators using new data sources can be tested for the strength of their correlation with indicators of known quality from trusted data sources. This can be used to decide inclusion or exclusion of the indicators. If highly correlated indicators are included in a domain composite or index, they may be weighted so as to minimize over counting. The results shown in the table below includes the Urban HEART indicators as of October 5th, 2013, and could vary if these indicators are revised or replaced. The data were analyzed and the correlations produced using the ‘Tools’, ’Analysis’, ‘Correlation’ function in Excel. For some Urban HEART variables, an increasing rate means faring worse while for others an increasing rate means faring better. The discussion below describes the relationships between several of the key indicators in the Urban HEART dataset and puts words around these complex relationships reflected by the many positive and negative coefficients in the table below.

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Table B1: Urban HEART @Toronto correlation matrix UE Rt LIM SA High ON- PS Voting Mtg. Walk Hlthy Green PMR MH ACSCH Diabetes School MARG Educ. Places Score Food Space Grad. UE Rt 1 LIM 0.74 1 SA 0.73 0.75 1 High School -0.38 -0.48 -0.65 1 Grad. ON-MARG 0.65 0.73 0.71 -0.48 1 PS Educ. -0.64 -0.49 -0.75 0.60 -0.65 1 Voting -0.43 -0.48 -0.31 0.19 -0.45 0.47 1 Mtg. Places -0.09 0.26 0.15 -0.38 0.06 -0.02 0.06 1 Walk Score -0.33 0.06 -0.17 -0.10 -0.18 0.41 0.25 0.73 1 Hlthy Food -0.11 0.25 0.05 -0.31 0.04 0.06 -0.004 0.75 0.73 1 Green Spaces 0.30 0.05 0.24 -0.04 0.12 -0.18 0.06 -0.42 -0.56 -0.47 1 PMR 0.19 0.44 0.51 -0.53 0.20 -0.26 0.09 0.52 0.36 0.40 -0.13 1 MH -0.39 -0.45 -0.44 0.47 -0.48 0.47 0.19 -0.18 0.02 -0.14 -0.07 -0.18 1 ACSCH 0.42 0.53 0.68 -0.57 0.45 -0.50 -0.06 0.38 0.13 0.21 -0.01 0.82 -0.34 1 Diabetes 0.71 0.55 0.74 -0.49 0.67 -0.88 -0.45 -0.11 -0.50 -0.15 0.25 0.23 -0.45 0.51 1

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Discussion of Urban HEART Correlation Analysis

For this discussion, a strong positive correlation (meaning that an increase in ‘x’ is associated with an increase in ‘y’) is considered to be rates at or above +0.6 (rounded) including correlations >0.55. A strong negative correlation (when an increase in ‘x’ is associated with a decrease in ‘y’) is rates at or below -0.6 (rounded) including correlations <-0.55. Correlations between -0.2 and +0.2 are considered the weakest. This quick summary below demonstrates how the information from correlations analysis can be used.

The strongest positive correlation (0.82) is between Preventable Hospitalizations (age and sex adjusted Ambulatory Care Sensitive Condition (ACSC) hospitalizations prior to age 75) and age adjusted Premature Mortality prior to age 75 (PMR). Higher rates of these hospitalizations are associated with higher rates of PMR. ACSC hospitalization rates is an indirect measure of timely appropriate access to primary care and self-management of chronic diseases, i.e. diabetes, COPD, asthma, heart conditions, hypertension, epilepsy. Diabetes prevalence (age 20+) is included in the Urban HEART indicator set. Although the relationship between ACSC and diabetes is moderately strong (0.51), this is not strong enough to suggest excluding diabetes prevalence or to make weighting adjustments when using both these variables in an index.

The strongest negative correlation (-0.88) is between diabetes and post-secondary education certificate, diploma or degree. Higher rates of diabetes are associated with lower rates of post- secondary education completion. This is very important for making decisions about prevention, community care management and access to care. It also reflects the diversity among the population groups which have high diabetes rates. While strongly correlated with low income, marginalization, unemployment and social assistance, what are not reflected here are the potential associations between diabetes and other population indicators that can affect access to prevention, health promotion and medical care (limited English language fluency, recent immigration, experience of racism, etc.). These would also be important in understanding and responding to high diabetes rates. These specific indicators are not part of the Urban HEART dataset. High diabetes is moderately correlated with low Walk Score©/walkability (-0.50), weakly correlated with access to green space (0.25).

Several physical environment and infrastructure variables are strongly correlated with others in the same domain (access to community meeting places; walkability/Walk Score©; access to stores with health food choices; access to green space). The first two were identified as required indicators and the second two were added as recommended indicators. All four of these indicators have different correlations with other variables in other domains. This suggests they measure different constructs and that they capture different aspects of environment and infrastructure, supporting the decision to include all of these indicators in this domain because together they provide a more comprehensive picture of environment and infrastructure than one or two could alone.

Strong correlations (>0.6 or <-0.6) are seen between higher unemployment, higher low income, higher social assistance and higher marginalization and lower post-secondary certificate, diploma or degree. The marginalization indicator is a composite variable that includes similar versions of March 28, 2014, ver.1, 125

several of these variables from 2006, while the other variables here are more current and from other data sources. The income is from the 2010 tax filer data (T1 Family File — T1FF), the unemployment and education variables are from the 2011 National Household Survey (NHS), and social assistance (SA) is from 2012 the Toronto Employment and Social Services Ontario Works database.

These variables cross two domains (economic opportunity, human and social development). The strong correlations among these variables could be used to make a case for not including all of them unless they clearly measured different constructs, or for adjusting the weight of all or some of them if used in an index. However, given the concerns raised about the quality of current data from NHS and T1FF, these strong correlations are helpful because they increase our confidence in the Urban HEART indicator set. By using all of them, we may better capture the high importance of income/socioeconomic status/socioeconomic position than if we just relied on a few of them. Triangulation, using multiple different indicators and different datasets, is one way to improve the quality of evidence.

Variables from the 2006 census are understood to be of higher quality than variables from the 2011 NHS or the 2010 T1FF for a variety of reasons, including low response rates, the under- and overrepresentation of groups in the NHS, and the misallocation of data and inclusion of commercial addresses in the T1FF (see Appendix A for a more complete description of these limitations). When these variables from these data sources were prepared for Urban HEART, correlation analysis was one of the tools used to decide if the quality of these variables was sufficient to include them. The results of these analyses are included in section 5 of this report. The results of the comparative analysis did result in a modification of the income variable prior to use, but the other NHS variables are included as is with limitations noted in the indicator reference materials. The strength of the relationships between these variables identified in the correlation analysis described herein increases our confidence in using these data from different sources as a good relative indicator of income disparities across Toronto neighbourhoods.

Two variables (self-rated mental health is ‘very good’ or ‘excellent’) and voter participation rates are not strongly correlated with any of the other Urban HEART indicators. This suggests they may capture other things important to urban health and they should be included in the indicator set.

About Correlation Analysis

Correlation provides evidence of an association between variables but is not evidence of a causal relation between variables. Correlation measures how well a straight line fits through a scatter of points when plotted on an X– Y axis. In a strong correlation, the points are scattered along a straight line. In a week correlation the points are scattered like a cloud. Assumptions for correlation analysis are: (1) There is a linear relationship between the x and y variables; (2) The variables are continuous (i.e. rates or ranks); (3) Both variables must be normally distributed; and (4) The x and y variables must be independent of each other.

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When these assumptions are not met, the correlation results can be misinterpreted. Scatter plots can identify if some of the x–y pairs are non-linear (i.e. some may be U shaped) which can affect what the result means. If the above assumptions are met, the square of the correlation coefficient is equal to the proportion of variation in the dependent variable that is accounted for, or explained, by variation in the independent variable. A correlation of 0.6, for example, indicates that 36% of the variation in the dependent variable is explained by variation in the independent variable. Some of the Urban HEART variables are not totally independent of each other. An example is the marginalization variable, which is a composite that includes variables representing income, employment and education (albeit from a different data source, time period or age group).

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Appendix C: Urban HEART @Toronto master matrix

Table C1: Urban HEART @Toronto master matrix Domains Economic opportunity Human and social Gov./ Physical environment and Population health development Civic infrastructure ‘Reds’ 28 24 26 27 28 46 24 28 22 29 28 23 24 43 44 ‘Yellows’ 75 74 53 87 57 36 82 79 101 81 86 68 66 54 56 ‘Greens’ 37 42 61 26 55 58 34 33 17 30 26 49 42 43 40 Toronto rate 9.3 22.2 10.0 NA 2.40 68.9 45.7 15 71 3.91 45.5 211.1 73.4 243.8 8.5 Neighbourhood range 5.0-17.1 5.6- 0.4- Low/Ave/H 1.0-3.4 37.5- 34.5- 3.4- 42-99 0.47- 11.3- 118.0- 47.4- 79.3- 4.1- igh 49.8 29.1 91.7 58.3 39.9 22.3 113.5 573.0 96.8 608.7 14.0 Cut-off for ‘reds’ 11.3 28.1 15.10 <85% >2.9 62.1 41.4 8.56 59.48 1.36 23.03 271.4 64.3 292.6 10.2 Number of ‘reds’ 28 24 26 27 28 46 24 28 22 29 28 23 23 43 44 Cut-off for ‘greens’ 7.4 16.65 7.99 >85% <=2.2 72.5 51.3 20.1 90 5.974 64.43 187.4 78.2 188.1 7.1 Number of ‘greens’ 37 42 61 27 55 58 34 33 17 30 26 49 42 43 40 Neighbourhood names Unemploy- Low Social High school Marginal- Post- sec. Munic. Comm. Walk Healthier Green Prema- Mental Prevent- Diabe-tes R Y G ment income assis- Grad. ization educ. voting places for Score food space ture health able (some are abbreviated) tance meeting stores mortality hosps. ASSCH

112 Beechborough-Greenbrook 11.6 28.6 22.8 Low 3.4 41.6 39.6 19.1 62 3.38 39.2 306.7H 65.7E 324.5 12.5H 10 5 0 72 Regent Park 15.8 49.8 24.9 Low 3.0 61.1 52.9 36.6 88 9.66 34.4 403.9H 61.3E 496.5H 11.4H 10 2 3 24 Black Creek 13.6 33.5 29.1 Low 3.0 40.9 45.0 16.8 62 1.70 64.1 228.3 58.4L 315.6H 12.7H 9 6 0 121 Oakridge 17.1 36.2 22.0 Low 3.2 61.7 48.3 11.5 71 2.86 62.4 315.9H 75.8 307.9H 11.8H 9 6 0 25 Glenfield-Jane Heights 13.5 28.1 23.1 Low 3.0 37.5 42.1 17.8 61 3.49 65.9 214.9 61.5 318.6H 12.7H 9 5 1 115 Mount Dennis 13.6 28.0 22.3 Low 2.6 50.1 38.1 8.4 59 1.57 95.2 249.3 65.8E 341.3H 11.2H 9 5 1 85 South Parkdale 13.0 34.1 21.0 Low 3.2 64.1 45.9 21.2 83 5.64 38.7 421.3H 57.3L 537.0H 9.8H 8 6 1 61 Crescent Town 16.2 35.5 16.9 Low 2.8 67.4 46.1 12.6 77 6.53 62.0 284.4H 47.4EL 324.8H 10.9H 8 6 1 27 York University Heights 11.4 29.0 17.0 Ave 3.0 59.3 36.0 13.7 60 1.57 64.4 211.3 60.6 280.3 10.4H 8 6 1 5 Elms-Old Rexdale 12.3 27.1 17.6 Low 2.6 51.1 41.6 10.2 48 0.96 113.5 173.9 62.8 239.6 11.3H 8 5 2 125 Ionview 13.3 25.4 12.8 Low 3.0 54.2 42.6 13.5 70 3.05 36.4 243.5 63.4 372.8H 11.6H 7 8 0 2 Mount Olive-Silverstone-Jmtn 14.8 32.3 20.0 Ave 2.4 48.7 37.1 10.5 61 2.06 82.6 220.8 61.7 285.1H 12.9H 7 7 1 139 Scarborough Village 14.0 32.7 24.9 Ave 3.2 56.4 44.3 10.7 70 2.16 38.6 236.1 78.2 313.9H 12.4H 7 7 1 136 West Hill 13.5 27.1 18.7 Ave 2.8 58.1 43.0 7.6 66 1.34 57.5 268.3H 82.2 373.4H 12.0H 7 7 1 111 Rockcliffe-Smythe 11.5 23.1 15.4 Low 3.0 48.3 43.0 11.3 61 2.15 88.7 264.6H 72.9 305.8H 10.7H 7 7 1 6 Kingsview Village-The 9.4 27.5 20.0 Ave 2.8 57.5 41.6 6.4 56 0.83 54.2 199.9 82.3 305.5H 10.7H 7 7 1 Westway March 28, 2014, ver.1, 128

124 Kennedy Park 11.8 28.4 15.1 Ave 3.0 56.5 45.6 14.8 62 2.41 37.5 273.8H 87.1H 293.1H 12.2H 7 7 1 91 Weston-Pellam Park 12.3 24.8 14.6 Low 2.4 42.2 39.9 26.9 75 9.36 32.2 257.4 63.2E 322.3H 10.9H 7 6 2 22 Humbermede 13.1 25.3 16.3 Ave 3.2 54.0 40.4 19.4 58 4.21 96.0 177.0L 75.2 259.3 11.7H 7 6 2 43 Victoria Village 10.8 24.9 15.1 Low 3.4 68.2 47.3 7.6 71 1.19 55.5 253.5H 68.7 308.0H 10.3H 6 9 0 126 Dorset Park 11.4 24.8 11.5 Ave 2.8 58.9 40.5 10.0 68 4.48 23.0 197.5 72.5 327.4H 12.4H 6 9 0 28 Rustic 14.4 28.9 19.3 Ave 3.4 50.4 45.8 13.7 60 3.34 34.0 184.4 73.8 236.6 12.4H 6 8 1 113 Weston 10.0 27.8 22.8 Low 3.2 53.3 43.9 15.3 73 6.19 48.7 311.3H 71.3 291.4H 11.4H 6 8 1 26 Downsview-Roding-CFB 10.0 21.7 16.2 Low 3.0 50.1 41.7 12.2 59 2.21 64.5 225.6 67.4 230.8 10.8H 6 8 1 135 Morningside 12.7 24.9 14.4 Ave 2.4 59.6 43.6 5.4 53 0.83 103.8 239.9 72.1 238.2 13.1H 6 8 1 55 Thorncliffe Park 15.7 32.7 19.9 Ave 3.4 66.6 52.5 10.3 73 2.06 110.2 214.1 73.5 361.4H 11.6H 6 7 2 44 Flemingdon Park 15.3 34.3 19.6 Ave 3.2 62.0 52.8 18.5 63 0.85 103.2 196.3 73.9 263.3 10.2H 6 7 2 73 Moss Park 8.6 38.6 21.5 Low 2.0 71.0 46.6 39.9 95 10.96 20.5 573.0H 76.1 608.7H 7.8L 6 5 4 138 Eglinton East 14.1 27.1 18.3 Ave 3.2 57.9 43.2 10.7 62 2.15 33.6 228.3 72.3 277.0 12.8H 5 10 0 137 Woburn 13.3 28.2 12.6 Ave 3.0 59.7 43.8 9.2 66 2.89 57.3 189.6L 64.1 281.1H 12.5H 5 10 0 1 West Humber-Clairville 10.0 20.8 8.5 Ave 2.4 55.3 44.0 7.9 57 1.43 58.2 208.0 72.9 310.5H 12.1H 5 10 0 110 Keelesdale-Eglinton West 10.8 21.1 15.5 Low 2.6 39.4 36.8 24.0 69 5.39 30.6 232.0 74.8 225.9 11.8H 5 9 1 3 Thistletown-Beaumond Hts. 11.0 22.1 12.6 Ave 2.8 56.5 49.9 12.4 54 2.63 105.1 244.6 63.7E 314.7H 11.2H 5 9 1 53 Henry Farm 12.8 29.5 13.3 Ave 2.6 79.6 45.1 3.8 76 0.71 41.0 154.1L 63.8E 250.2 8.3 5 8 2 66 Danforth Village – Toronto 6.5 14.2 6.6 Ave 2.4 71.8 50.7 23.7 86 7.46 13.3 258.2H 64.6E 332.3H 8.3 5 8 2 78 Kensington-Chinatown 10.1 39.3 13.6 Low 3.4 65.5 37.4 29.3 97 22.31 13.7 227.8 65.2 209.1 8.3 5 7 3 74 North St. James Town 11.1 38.1 15.0 Ave 3.2 69.5 50.6 21.1 93 8.78 51.4 323.1H 69.8 391.9H 10.3H 5 7 3 109 Caledonia-Fairbank 9.8 18.1 11.7 Low 2.4 44.3 37.6 15.6 69 4.61 36.0 222.9 67.3 214.0 10.6H 4 11 0 4 Rexdale-Kipling 11.2 20.3 12.6 Ave 2.8 57.6 49.5 12.5 58 1.71 51.1 261.4H 68.7 391.3H 10.6H 4 11 0 119 Wexford-Maryvale 10.0 20.4 11.2 Ave 2.8 59.3 48.1 8.0 67 3.20 26.8 205.7 61.1 289.5H 10.4H 4 11 0 31 Yorkdale-Glen Park 10.2 19.7 10.2 Ave 3.0 52.5 45.7 13.6 72 3.63 15.9 201.3 65.0 251.7 10.8H 4 11 0 132 Malvern 13.0 23.1 11.3 Ave 2.2 57.8 39.7 16.7 61 2.04 32.2 196.8 74.7 250.6 14.0H 4 10 1 30 Brookhaven-Amesbury 9.7 26.2 19.2 Ave 2.8 50.3 42.4 15.2 62 4.22 38.2 225.4 82.9 299.1H 11.5H 4 10 1 13 Etobicoke West Mall 9.9 20.2 10.0 Ave 3.0 62.3 41.2 24.7 74 0.75 26.5 216.1 66.7E 327.9H 9.2H 4 10 1 69 Blake-Jones 9.6 28.6 13.1 Ave 2.6 67.2 51.1 30.1 89 5.70 17.6 301.1H 58.0E 248.9 8.1 4 10 1 35 Westminster-Branson 9.1 27.3 11.4 Ave 3.2 77.4 40.5 12.6 61 0.97 87.4 200.5 54.5E 211.4L 8.9H 4 9 2 93 Dovercourt-Wallace Emerson- 7.9 21.4 10.3 Low 2.4 60.8 44.8 34.6 88 10.06 19.1 225.2 62.1 276.8 9.5H 4 9 2 Junction 84 Little Portugal 5.7 18.7 8.2 Ave 2.8 61.3 43.3 25.1 88 10.38 17.3 271.4H 62.5E 395.2H 9.3H 4 8 3 129 Agincourt North 11.3 25.8 6.4 High 2.6 57.2 39.1 5.6 66 3.55 27.6 139.2L 61.1 163.3L 9.5H 4 7 4 131 Rouge 9.9 13.3 6.5 Ave 1.6 67.0 45.0 3.4 42 0.56 90.7 201.1 72.2 235.5 13.5H 4 7 4 70 South Riverdale 7.2 23.7 10.5 Low 2.6 67.8 52.5 29.3 91 8.32 22.9 318.2H 72.9 333.5H 8.2 4 6 5 March 28, 2014, ver.1, 129

134 Highland Creek 8.8 16.1 5.5 Ave 1.6 72.5 41.4 4.2 54 0.46 36.9 154.2L 78.2 141.4L 12.4H 4 4 7 75 Church-Yonge Corridor 8.4 31.8 10.9 Low 1.8 82.1 47.8 26.8 98 12.38 22.3 352.0H 81.7 281.7 6.5L 4 4 7 120 Clairlea-Birchmount 9.7 27.2 10.9 Ave 2.6 64.4 46.0 8.8 69 2.33 53.6 271.7H 74.7 327.8H 10.9H 3 12 0 127 Bendale 10.5 23.6 9.8 Ave 3.0 61.0 45.9 10.3 64 3.23 46.7 215.3 76.1 249.5 11.5H 3 12 0 21 Humber Summit 11.0 24.2 13.3 Ave 2.8 47.3 42.5 12.1 61 2.24 90.8 231.9 49.0E 279.7 12.7H 3 11 1 18 New Toronto 8.8 24.1 14.7 Ave 2.6 66.3 40.8 13.6 79 2.10 27.7 334.0H 80.5 327.3H 8.9 3 11 1 65 Greenwood-Coxwell 10.1 23.6 12.5 Low 2.4 66.6 48.3 26.7 88 2.33 33.0 297.6H 65.1E 326.6H 8.6 3 11 1 19 Long Branch 8.7 18.1 10.6 Ave 2.0 67.2 41.9 9.0 72 0.59 50.6 304.9H 81.0 390.2H 8.0 3 10 2 123 Cliffcrest 9.1 16.7 8.5 Ave 2.2 63.6 50.7 3.7 54 0.92 44.6 253.2H 81.1 245.2 9.5H 3 10 2 90 Junction Area 7.9 18.6 8.2 Ave 2.2 69.7 43.9 25.9 83 5.11 15.2 274.0H 74.8 301.4H 8.4 3 10 2 29 Maple Leaf 6.9 16.6 9.0 Low 2.8 54.4 47.3 16.1 66 2.35 32.5 205.6 69.0 179.0L 10.9H 3 9 3 116 Steeles 11.1 26.7 6.2 Ave 2.6 60.4 38.3 9.1 61 0.77 48.1 137.3L 74.1 139.0L 9.0H 3 9 3 7 Willowridge-Martingrove- 8.1 15.9 9.5 Ave 2.6 64.5 52.1 6.5 51 0.93 42.1 167.6L 71.3 262.0 9.7H 3 9 3 Richview. 130 Milliken 10.5 28.08 7.1 High 2.4 54.2 38.0 7.4 65 2.43 44.2 118.0L 78.2 121.3L 9.0H 3 7 5 20 Alderwood 7.4 10.1 4.3 Ave 2.0 63.5 47.9 7.5 70 0.62 30.6 256.9H 80.0 294.0 8.5 3 7 5 23 Pelmo Park-Humberlea 9.8 14.0 9.2 Ave 2.2 56.8 49.1 10.8 57 2.30 74.1 175.7 93.5H 254.5 10.9H 3 7 5 140 Guildwood 6.4 9.5 3.1 Ave 1.8 69.8 53.9 3.4 59 0.56 55.1 208.2 69.5 214.3 8.1 3 7 5 86 Roncesvalles 6.4 21.1 15.1 Ave 2.2 71.1 46.7 29.8 91 7.60 21.4 293.8H 75.5 364.8H 7.5L 3 7 5 11 Eringate-Centennial-W. Deane 7.6 12.1 4.1 Ave 2.0 71.6 48.3 7.3 57 0.54 70.1 181.4L 71.8 186.7L 8.2 3 6 6 49 Bayview Woods-Steeles 10.4 23.0 4.9 High 2.6 81.7 42.2 5.0 57 0.79 84.1 158.7L 84.3 161.1L 7.1L 3 4 8 133 Centennial Scarborough 7.8 10.0 3.0 High 1.6 76.6 46.7 6.1 54 1.13 36.8 170.2L 79.3 170.3L 9.5H 3 4 8 79 University 11.5 27.9 3.3 High 2.2 81.4 34.5 29.3 97 12.27 11.3 246.4 83.8 231.0 6.0L 3 3 9 9 Edenbridge-Humber Valley 8.0 11.7 5.0 Ave 2.0 74.3 52.1 5.0 49 0.77 91.3 187.2 78.6 184.8L 6.8L 3 2 10 10 Princess-Rosethorn 7.0 8.3 2.7 Hi 1.6 82.2 52.5 4.1 48 0.47 53.5 174.6 75.4 88.9L 6.3L 3 2 10 41 Bridle Path-Sunnybrk-Yk Mills 5.8 8.0 0.4 Hi 1.4 89.1 43.1 4.5 58 0.90 59.8 147.1L 88.9H 153.7L 4.5L 3 2 10 45 Parkwoods-Donalda 11.2 22.5 10.6 Ave 2.4 71.9 48.7 8.7 63 1.09 41.7 188.1L 60.3 230.7 8.4 2 13 0 36 Newtonbrook West 9.8 28.7 9.2 Ave 2.6 76.1 38.3 11.6 69 1.93 34.2 173.6L 68.8 203.1L 8.0L 2 11 2 32 Englemount-Lawrence 7.6 25.5 12.4 Ave 3.2 73.5 43.6 17.1 70 5.97 13.4 231.6 66.4 254.2 8.8 2 11 2 54 O’Connor-Parkview 9.2 21.8 16.3 Ave 2.6 65.0 52.2 13.8 67 1.56 69.2 246.9H 71.5 353.4H 9.8H 2 11 2 128 Agincourt South-Malvern W. 10.7 27.1 7.0 Ave 2.6 60.6 41.7 9.6 66 3.22 22.2 161.9L 70.4 183.6L 9.5H 2 10 3 47 Don Valley Village 11.1 25.4 8.0 Ave 2.4 77.5 41.0 14.9 79 1.36 37.8 146.1L 71.9 156.1L 7.6L 2 10 3 8 Humber Heights-Westmount 8.3 16.7 8.1 Ave 2.6 67.0 52.5 9.4 58 2.08 72.3 211.0 63.5E 156.8L 8.7 2 10 3 81 Trinity-Bellwoods 8.1 23.0 8.4 Low 2.6 64.8 44.9 34.0 94 10.07 25.3 206.5 63.5 202.3 8.7 2 10 3 94 Wychwood 7.6 17.1 6.5 Ave 2.8 73.3 49.0 19.1 86 6.37 19.2 227.5 74.5 316.7H 8.1L 2 10 3 62 East End-Danforth 7.8 20.2 9.1 Ave 2.0 72.4 52.7 20.8 85 5.40 25.4 335.3H 77.0 343.9H 8.0L 2 10 3 March 28, 2014, ver.1, 130

92 Corso Italia-Davenport 10.0 16.3 9.4 Low 2.6 58.4 41.4 27.3 79 11.65 32.1 181.4 65.4 263.5 9.2H 2 9 4 122 Birchcliffe-Cliffside 10.6 14.5 8.2 Ave 1.8 65.8 53.1 10.6 71 1.76 35.4 317.6H 78.4 345.8H 8.2 2 9 4 59 Danforth East York 7.0 16.7 6.3 Ave 2.0 69.1 55.8 18.5 77 6.75 18.5 225.7 58.1L 272.9 8.5 2 8 5 48 Hillcrest Village 9.1 24.7 3.9 High 2.4 81.6 39.1 8.62 68 0.94 60.4 140.7L 76.0 135.0L 7.5L 2 8 5 51 Willowdale East 7.9 29.9 2.6 Ave 2.4 85.7 36.7 17.7 84 2.84 33.1 144.5L 72.3 103.8L 6.0L 2 8 5 50 Newtonbrook East 9.4 29.1 4.2 High 2.8 80.6 39.2 8.8 64 1.51 34.9 180.5L 79.4 127.5L 6.8L 2 6 7 64 Woodbine Corridor 7.0 17.6 7.7 Ave 2.0 74.5 54.1 23.3 85 3.48 35.0 339.3H 80.9 349.5H 7.6L 2 6 7 52 Bayview Village 8.4 21.1 3.5 Hi 2.4 83.4 54.8 6.4 71 1.07 52.5 127.5L 64.5 163.9L 6.0L 2 6 7 40 St. Andrew-Windfields 8.5 17.2 3.4 High 1.4 84.4 42.9 5.6 60 0.93 35.0 146.7L 89.9H 148.1L 5.7L 2 5 8 76 Bay Street Corridor 9.3 26.7 3.5 Ave 2.2 89.2 39.7 20.1 99 12.98 23.5 294.2H 85.4H 163.7L 5.1L 2 4 9 71 Cabbagetown-S. St. James Twn 7.4 20.4 8.2 Ave 1.8 80.4 58.0 30.1 91 11.66 50.8 367.4H 85.9H 327.5H 6.6L 2 4 9 107 Oakwood-Vaughan 8.3 21.1 11.4 Ave 2.8 62.9 42.5 19.1 82 5.46 17.7 222.8 69.5 238.8 9.1H 1 14 0 117 L’Amoreaux 10.6 26.8 8.6 Ave 2.8 60.0 43.2 14.5 65 3.20 38.8 172.9L 74.6 215.0L 10.1H 1 13 1 108 Briar Hill-Belgravia 7.8 20.6 10.0 Ave 2.8 65.1 44.6 13.5 81 6.03 15.8 205.0 65.2E 236.1 9.9H 1 13 1 57 Broadview North 10.9 22.0 11.3 Low 2.6 70.2 51.7 18.5 74 7.78 48.8 229.5 64.7E 252.4 7.9L 1 12 2 118 Tam O’Shanter-Sullivan 9.8 23.1 7.2 Ave 3.0 66.1 46.0 9.5 64 2.25 34.2 163.9L 80.0 197.8L 9.4H 1 11 3 17 Mimico 7.3 16.5 9.9 Ave 2.4 73.0 46.3 9.3 71 2.26 31.9 248.8H 81.4 316.5H 7.5L 1 10 4 60 Woodbine-Lumsden 8.4 15.2 7.4 Ave 2.2 64.1 49.0 22.0 73 1.69 45.5 253.5 71.9 394.6H 8.1 1 10 4 46 Pleasant View 9.9 21.3 4.9 High 2.4 73.5 43.6 12.2 66 1.51 19.6 143.5L 78.0 185.1L 8.5 1 9 5 83 Dufferin Grove 6.9 21.4 11.4 Ave 2.6 68.1 46.7 26.1 90 11.43 14.0 183.6 77.2 240.0 8.5 1 9 5 102 Forest Hill North 8.6 16.6 5.1 High 2.4 84.1 47.9 11.9 77 3.41 13.9 138.2L 71.2 150.1L 6.6L 1 7 7 80 Palmerston-Little Italy 8.2 18.9 4.8 Ave 2.2 76.3 47.1 31.0 95 9.87 20.2 206.3 67.9 262.3 7.0L 1 7 7 12 Markland Wood 8.3 9.4 2.3 High 1.8 79.0 48.3 8.56 69 1.64 56.3 198.2 90.3H 187.5L 7.6L 1 7 7 114 Lambton Baby Point 5.1 16.4 13.3 Ave 1.8 76.1 52.9 8.5 70 1.86 87.5 187.8 72.2E 187.4 6.2L 1 6 8 95 Annex 7.3 19.1 3.9 Ave 1.8 85.0 49.8 25.7 94 7.62 21.5 234.1 73.3 235.7 5.5L 1 6 8 42 Banbury-Don Mills 7.1 14.0 3.8 Hig 2.4 80.7 50.4 6.2 67 1.37 56.8 163.5L 78.7 137.3L 6.5L 1 5 9 39 Bedford Park-Nortown 6.3 11.2 2.1 Ave 1.6 80.1 47.0 10.4 73 5.32 17.5 119.1L 87.0H 99.5L 5.6L 1 5 9 56 Leaside-Bennington 7.6 5.6 1.4 High 1.4 85.1 58.3 8.5 77 2.01 62.4 145.4L 77.7 79.3L 4.9L 1 5 9 89 Runnymede-Bloor W. Village 6.9 8.6 2.6 High 1.2 83.2 55.5 14.7 81 4.97 22.4 204.3 77.5 138.6L 6.8L 1 5 9 77 Waterfront Communities-The 5.1 17.1 6.6 Ave 1.8 85.4 51.5 10.8 92 8.67 20.3 217.3 81.4 164.6L 6.1L 1 4 10 Island 103 Lawrence Park South 8.2 6.7 1.6 High 1.0 89.7 51.3 14.9 72 2.23 22.9 133.4L 83.7 79.4L 4.2L 1 4 10 15 Kingsway South 5.9 5.8 0.8 High 1.6 88.8 54.3 10.0 68 1.23 67.6 132.7L 96.8H 170.3L 5.0L 1 2 12 14 Islington-City Centre West 7.3 17.1 6.7 Ave 2.6 74.6 46.4 9.1 72 1.61 40.3 216.2 75.5 214.6L 8.3 0 12 3 58 Old East York 6.5 13.4 6.2 Ave 1.8 73.1 49.8 14.4 69 4.64 62.7 229.2 67.5 266.8 8.0 0 10 5 37 Willowdale West 6.4 24.5 3.4 Ave 2.8 85.2 45.9 20.8 78 3.10 48.4 186.1 NA 202.4 7.3L 0 9 5 March 28, 2014, ver.1, 131

34 Bathurst Manor 7.8 17.5 6.6 Ave 2.6 72.9 44.8 13.2 61 1.56 91.6 148.3L 78.7 173.7L 8.5 0 9 6 33 Clanton Park 7.5 17.5 5.0 High 2.2 78.0 42.0 13.6 63 3.89 27.1 176.8L 81.0 196.4L 8.7 0 9 6 104 Mount Pleasant West 7.4 19.4 4.7 Ave 1.8 85.4 48.5 14.9 95 6.32 39.7 195.4 74.4 193.9L 6.0L 0 8 7 38 Lansing-Westgate 8.0 20.0 3.2 High 1.8 83.7 46.9 14.8 77 3.63 56.4 139.0L 76.3 149.0L 6.4L 0 8 7 16 Stonegate-Queensway 8.0 11.8 6.5 Ave 1.8 75.9 50.4 8.7 67 2.04 51.6 177.2L 80.2 186.8L 6.7L 0 7 8 106 Humewood-Cedarvale 6.7 15.4 4.9 Ave 2.0 80.6 51.3 12.9 80 4.25 31.6 149.3L 77.3 194.6L 7.0L 0 7 8 82 Niagara 5.0 15.3 5.9 Ave 1.8 79.6 53.8 25.0 84 3.25 61.3 257.5L 74.0 286.7 7.0L 0 7 8 67 Playter Estates-Danforth 7.6 12.9 3.8 Ave 1.8 81.9 57.0 18.2 90 8.33 48.7 233.6 69.7 229.1 6.7L 0 7 8 97 Yonge-St. Clair 7.0 9.6 2.4 Ave 2.0 91.5 50.9 13.2 84 1.88 47.0 148.9L 83.7H 186.4L 4.2L 0 6 9 96 Casa Loma 6.0 11.0 1.8 Ave 2.0 87.1 53.5 16.4 80 2.48 31.0 179.7 90.0H 196.9 4.6L 0 6 9 63 The Beaches 6.5 9.8 3.2 Ave 1.4 82.3 53.6 13.8 88 2.66 41.3 223.8 84.0H 147.6L 4.7L 0 6 9 88 High Park North 7.8 16.5 4.9 High 1.8 81.2 53.8 22.8 84 3.68 41.0 214.0 86.1H 195.0L 5.5L 0 6 9 101 Forest Hill South 7.0 8.2 1.2 High 2.0 87.3 48.0 13.8 76 2.70 24.8 135.1L 92.5H 122.5L 4.7L 0 5 10 99 Mount Pleasant East 6.4 8.8 1.8 High 1.6 86.3 52.8 10.1 88 4.50 49.6 176.6L 76.6 140.0L 5.0L 0 5 10 100 Yonge-Eglinton 5.7 12.7 1.8 High 1.6 88.0 50.2 23.2 89 4.04 26.5 163.8L 72.8 122.6L 4.8L 0 5 10 105 Lawrence Park North 5.7 7.1 1.1 High 1.0 91.7 54.9 13.4 78 4.75 25.0 162.0L 76.0 147.5L 4.7L 0 5 10 87 High Park-Swansea 6.7 11.9 7.7 High 2.0 82.8 54.7 14.6 79 4.58 81.4 184.6L 74.9 154.6L 5.4L 0 4 11 98 Rosedale-Moore Park 5.9 8.8 2.4 Ave 2.0 90.0 55.0 13.9 84 2.89 66.5 143.L 86.6H 86.0L 4.1L 0 4 11 68 North Riverdale 5.9 13.5 4.1 Ave 1.4 80.0 54.3 31.7 90 7.27 48.5 254.5H 78.2 154.2L 6.2L 0 3 12

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