Equipos de alto desempeño, como respuesta efectiva a la crisis (coaching):

The data for this thesis came from 3 separate sources:

(1) 2009-2010 Canadian Community Health Survey (CCHS) Master file.

(2) 2011 CFM Leads Business Dataset containing the location of various food outlets across Canada.

(3) 2011 Census and National Household Survey.

4.1.1

2009-2010 Canadian Community Health Survey (CCHS)

Individual level data were taken from the Canadian Community Health Survey (CCHS) conducted by Statistics Canada. The CCHS is a cross-sectional survey, collecting data from the Canadian population with regards to health status, health care utilization, and health determinants. The target population of the survey was individuals over the age of 12 who lived in private dwellings in 117 health regions across the provinces and territories in Canada. Statistics Canada adopted a multi-stage, stratified cluster sampling design. Criteria for exclusion from the survey included those living on Indian Reserves and Crown Lands, residing in an institution, being a full-time member of the Canadian Forces, or residing in certain remote regions in Canada (420).

The CCHS used three sampling techniques to select households: 49% of the sampled households were gathered using a Labour Force Survey (LFS) area fame

sampling method, which used a combination of stratified and cluster geographic sampling method. The remaining 50% used a combination of a telephone list frame (49%) and random digit dialing (1%). A total sample of 172,671 was initially selected for this cycle. Out of this total sample, 131,486 individuals responded to the survey, resulting in an overall response rate of 76.1% for the 2009-2010 survey. Greater details describing the methodology used for data collection by Statistics Canada can be found elsewhere (420,421).

The CCHS provided survey sampling weights for use in the data analyses. Weights are assigned values given to each survey participant that denotes the number of individuals in the Canadian population he/she was representative of. In case of the CCHS, these weighted values correspond to the number of persons in the Canadian general population that are represented by the survey respondents. As the CCHS used two overlapping sampling frames with separate sampling techniques, when calculating the weights for the study population, household level weights were calculated independently for the area and telephone sampling frames. These household weights were then

combined into a single set of values through an “integration” step, implemented using a dual-frame technique, which was used as the final person-level weight after a few final adjustments by Statistics Canada (421).

For this thesis, the CCHS confidential master file was the primary source of individual-level data. The master file provides un-suppressed and continuous data that were not available in the public use micro data files. Many variables, such as age, BMI, ethnicity and income, were either recategorized into categorical variables or suppressed in the public CCHS files due to small cells to maintain confidentiality of respondents, resulting in the need for the raw data the master file provided. Furthermore, the master file provided 6-digit postal codes for each of the survey respondents, allowing for the creation of the neighbourhood measure used to construct and link to the food

environment data. The 2009-2010 CCHS master file was accessed and analyzed in the Statistics Canada’s Research Data Centre (RDC) at the University of Western Ontario.

4.1.2

2011 Census and National Household Survey

The 2011 Canadian National Household Survey (similar to Census data collected in previous years but voluntary in nature) was used to compile neighbourhood level variables at the Forward Sortation Area (FSA) level. FSAs are the first 3-digits in the standard 6 digit Canadian postal codes and considered as proxy for neighbourhoods in this thesis. The rationale for this is provided in section 4.2.1.1. A total of 1,621 FSAs were found in Canada’s 2011 Census data. Neighbourhood variables at the FSA level were merged to the corresponding FSAs of the respondents in the CCHS 2009-2010 Master file.

Two different types of measures were gathered from the 2011 Canada Census and 2011 NHS. The first was the total area per FSA, measured as km2, calculated using the 2011 FSA Boundary File available through Statistics Canada website (422). The boundary file was inserted into ArcGIS 10.1, and using a combination of the North American 1983 Corrections and Conditional Release Statistical Statistics Canada projection and the calculate geometry function, individual area counts per FSA were obtained. The second was the 2011 Census population counts per FSA, available through the CHASS Data Centre, which contains a collection of on-line databases and custom built search and retrieval programs that are maintained by Computing in the Humanities and Social Sciences (CHASS) at the University of Toronto (423).

Data with regards to the 2011 Census data on neighbourhood covariates were obtained through the CHASS Data Centre. Previous iterations of the Census collected data on neighbourhood level socioeconomic status, ethnicity, and transportation through the long form census questionnaire, more widely known as Census Form 2B. However, in the 2011 Canada Census, Statistics Canada replaced this long form with the National Household Survey (NHS), a new voluntary, self-administered survey designed to collect social and economic data about the Canadian population (424). The use of the survey was not without weaknesses. The NHS had significantly higher non-response rates compared

to the previous long form census. At the national level, the total NHS Global Non-

response Rate was 26.1% compared to 6.5% from the 2006 long form census, resulting in reduced data quality (425). While the response rates for the 2011 NHS may be of lower quality, using a more recent source of data can better represent the socioeconomic status in the population. The final responses are weighted so that the data from the sample more accurately represent the NHS's target population. The weighting process involved

calculating initial sampling weights of roughly 3, and then adjusting the weights for the survey's total non-response and calibrating them against census population totals at a geographic level (424).

4.1.3

2011 CFM Leads Business Dataset

Food outlets data in Canada for the year of 2010 was obtained through the CFM Leads Canada 2011 Business Data. CFM Leads is a business data holding company that specializes in compiling lists of business outlets by collecting data from multiple sources, such as public directories like the yellow pages, relevant association directories, and telephone directories. While all food outlet information is not guaranteed to be perfectly accurate, CFM Leads claims that their data lists are up to 85%-95% accurate (426), with all entries being frequently run through the National Change of Address (NCOA) database and cross checked against new movers list. For all intents and purposes within this thesis, the data set is assumed to be sufficiently accurate. This data set contained individual data holdings on the name, address, postal code, Standard Industrial

Classification (SIC) name, 4 digit main code, and 2 digit sub codes, as well as categorical measures on employees count and annual sales volume.

The CFM dataset was first entered into the ArcGIS program and joined using their postal codes to the DMTI CANMAP Postal Code and DMTI CANMAP Retired Postal Code layers in order to link to the food outlets to their corresponding longitudinal and latitudinal coordinates. Any remaining observations that were missing postal codes but had available civic addresses were then geocoded in ArcGIS 10.1 using the NA_10 North American Locator. Geocoding is the process of matching raw address data to a digital spatial data set and corresponding information, which provided latitude and

longitude coordinates (80). All locations were scored at less than 100% match were then reverse gecoded using the STATA 12, Geocode3 coord function and address function in order to obtain full postal codes. Any remaining locations that were left unmatched after the automation processes were then manual internet searched using Google Maps. From these processes, 136823 food outlets were obtained for subsequent analysis this study.