Using Built Environment Indicators in Public Health: Proximity to a Community Focal Point Indicator

Transcription

1 Using Built Environment Indicators in Public Health: Proximity to a Community Focal Point Indicator APHEO Built Environment Sub-group September 15, 2014

2 Built Environment Sub-Group The built environment influences human behaviour in a complex manner Important elements of the built environment: 3 D s Design, Density, Diversity Certain community designs have strong potential to influence risk factors such as: Physical activity Access to food (healthy eating) alcohol and tobacco Shade (sun exposure) Indicators developed were supported by literature reviews at a number of points

3 Current Built Environment Sub- Group Members Aimee Powell (PH Planner, Peel) Brian Mosely (GIS analyst, KFL&A) Brenda Guarda (Epidemiologist, SMDHU) Fabio Cabarcas (Senior Policy Analyst, Halton Region) Deb Moore (Epidemiologist, Niagara Region) Debeka Navaranjan (Epidemiologist, York Region) Ryan Waterhouse (GIS analyst, Niagara Region) Steve Johnson (GIS analyst, PHO) Sean Nix (Professor, Engineering Technology, Mohawk College) Ahalya Mahendra (Epidemiologist, PHAC- Ontario Regional Office)

4 Proximity to grocery stores indicator Debeka Navaranjan Epidemiologist September 15, 2014

5 Building Healthy Communities (BHC) Workgroup Promote and support built environments that improve the health and quality of life of York Region residents In 2009, BHC undertook review of indicators on the built environment and health Feasibility study undertaken to map select built environment indicators

6 Indicator: Proximity to grocery stores Rationale: Access to dependable and affordable supply of nutritious food contributes to attainment of full physical and mental potential, and lowers risk for chronic disease Goal: Use spatial analysis to explore access to grocery stores Worked with GIS and Nutrition Services to develop methodology

7 Data Sources and Methodology Indicator Percentage of population within 1 km of grocery store by census tract Data Sources 2006 Census for population and census tract data Health Protection inspection database, Hedgehog, was used to identify food facilities Supermarket captured large retail food stores Methodology 1 km buffer around supermarket calculated based on road network Combined grocery buffer areas with census tract information Determined percentage of population within 1km of supermarket for each census tract

8 Proportional Approach + 1km Buffer Census tracts Combined buffer & census tracts Limitation Using area ratio, calculated population proportion for each census tract Assumes homogenous distribution of population within census tract

9 A case of built environment indicator development and use in public health Fabio Cabarcas, Senior Policy Analyst Chronic Disease Prevention September 15, 2014

10 Developing Built Environment Indicators Collaborative process Population density 1st phase (Three months, 2011) Working with Planning Services % population GIS in proximity to 17 Section diverse uses. E.g. Including a full time student/intern Transit stops 400 m. Consulting with Epidemiology Grocery stores 800 m. Convenience stores 400 m. Elementary schools 1,500 m. # of intersections/km2 2nd phase (Two months?) Proximity to grocery stores/ transit By epidemiology team Km of trails/# of residents X10n by income Consulting with nutrition team (alt: /km2) Calculated Km of bike by factor paths analysis /# of residents Land use diversity 3rd phase (ongoing) X10n (alt: /km2) Walkability (loadings: residential Consulting with Epidemiology density= 0.81; Land use diversity = 0.64; intersection density= 0.69) Area level analysis with Census 2006 data

11

12 Developing Built Environment Indicators Working with Planning Services GIS Section Rattan, A., Campese, A., & Eden, A. (2012). Modeling Walkability. Automating analysis so it is easily repeated. ArcUser, (Winter). Retrieved from Multiple data sources, e.g. Person per unit estimates and parcel layers available (Planning) 2010 Employment Survey (Planning) Census Dissemination Area Distance to focal point travelled network Partially informed by APHEO indicators

13 Proximity to grocery stores/ transit by income Working with Epidemiology and Nutrition teams Combining with Census 2006 data >30% of owners/tenants that spend 30% or more of household income on Rent/Owner's major payments (2006 Census) <67% of residents live within 400m of a transit stop (2006 Census) <67% of residents live within 800m of a grocery store (Planning Services/Health mapping data) Defining meaningful cut-offs (relative, depending on distribution) Consultation with nutrition team to select variables and approach to measurement Intentionally avoided use of terms from literature Credit: Emma Tucker, Jason Letchford, Epidemiology team

14 Discussion Data Availability Limited to data available through the region Not all required categories specific to mapping purpose are equally available Need to re-classify categories identified in original datasets to meet the needs of mapping/research study

15 Discussion: Data Availability Hedgehog: primary function is as an inspection database Thus. Classification and categories of food premises in Hedgehog from a food safety/risk perspective Details on types of foods sold were not available Re-classifiedfood establishments identified in Hedgehog to meet the needs of spatial analysis/research study

16 Example Nutrition Services using Hedgehog data for Healthy Food Zones mapping project Mapping food outlets (4 categories: convenience stores, supermarkets, fast food, beverages & snacks) within 1km of secondary schools No standalone fast food category in Hedgehog, fast food facilities would get classified under multiple categories Food Take Out and Restaurant categories were used to reclassify facilities into a Fast Food category for the project

17 Discussion: Data Availability Already available data Unable to assess job densities open spaces (inconsistent definitions) directness and frequency of transit service pedestrian infrastructure off-road pedestrian connections direct healthy and affordable food availability Needed to gather additional information from other sources (e.g. School Boards) Needed to standardize definitions (North American Industry Classification System (NAICS) code field) Unable to compare with other jurisdictions Better provincial level data sources needed

18 Discussion: Data Availability Some advantages found: Person per unit estimates and parcel layers NO homogeneous distribution assumption Reliable data for most focal points Easier to reproduce in 4-5 year cycles

19 Discussion: Use Misinterpretation; Ethics Complexity of health data (risk factors, causality) Mapping causality Very cautious to not use maps alone to inform program planning and policy analysis

20 Discussion: Use Key to assess peer-reviewed evidence Emerging/new research evidence Difficulties to validate locally some associations Population Densities Smaller than larger health units Few health and behavioral outcome data sources for small geographies Census tracts to large to capture land use

21 Discussion: Moving Forward Many questions about some of the potential uses keep learning about it Nutrition team consulting with key stakeholders validating access to grocery stores maps very constructive approach to identify opportunities for action Found area level associations with census 2006 transportation to work data (report in progress)

22 Discussion: Moving Forward Developed methodology for selected built environment indicators to be mapped Need to explore potential use of these indicators, given limitations What can be done with results? How can mapping such indicators be used for program planning/long-term monitoring/evaluation?

23 Questions? APHEO Built Environment and Health Core Indicators