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Canadian Community Health Survey (CCHS) – Annual component

User guide 2010 and 2009-2010 Microdata files

June 2011

What’s new in the 2010 Canadian Community Health Survey?
1.0 Introduction
2.0 Background
3.0 CCHS redesign in 2007
4.0 Content structure of the CCHS
5.0 Sample Design
6.0 Data Collection
7.0 Data Processing
8.0 Weighting
9.0 Data Quality
10.0 Guidelines for tabulation, analysis and release
11.0 Approximate sampling variability tables
12.0 Microdata files: description, access and use
Appendix A – Canadian community health survey content (2009–2010)
Appendix B – Selection of optional content by province and territory (2010 and 2009–2010
Appendix C – Available geography in the master and share files and their corresponding codes: Canada, provincesS/territories, health reagions and peer groups
Appendix D(2010) – Sample allocation by health region and frame and by local health integrated network (LHIN) and frames in the CCHS in Ontario
Appendix E(2010) – Response rates by health region and frame and response rates by local health integrated network (LHIN) and frame in the CCHS in Ontario
Appendix F (2009–2010) – Sample allocation by health region and frame by local health integrated network (LHIN) and frame in the CCHS in Ontario
Appendix G (2009–2010) – Response rates by health region and frame by local health integrated network (LHIN) and frame in the CCHS in Ontario

What’s new in the 2010 Canadian Community Health Survey?

Content

There have been a few changes to existing modules in the Canadian Community Health Survey (CCHS) content in 2010. Also, new modules were introduced for one year as part of the 2010 common content.

Changes

  • Contact with health care processional (CHP). This module was moved from common annual content to common 1-year content in 2010.
  • Unmet health care needs (UCN) This module was reintroduced in the survey in 2010 in the 1-year common content after having been suspended since 2007. Although the module name is new, the questions included in this module used to be part of the Health care utilisation (HCU) module.
  • The sub-module on Chronic fatigue syndrome, multiple chemical sensitivities and fibromyalgia was included in the Chronic conditions (CCC) module. The last time these three chronic conditions had been asked was in the 2005 CCHS.

New modules

  • Loss of productivity due to health issues (LOP): This new module was developed in replacement of the two week disability (TWD) module.
  • Neurological conditions (NEU): This new module was introduced in 2010 as a 1-year common content to be repeated in 2011. Respondents or persons in their household identified with a neurological condition will be contacted for a follow-up survey on neurological conditions in Canada.
  • H1N1 flu shot (H1N1): This new module collected in the 2010 survey only provides information on whether or not respondents have received the H1N1 flu shot in the past 12 months.

Methodology

  • The 2010 CCHS used three sampling frames to select the sample of households: 49.5% of the sample of households came from an area frame, 49.5% came from a list frame of telephone numbers and the remaining 1% came from a Random Digit Dialling (RDD) sampling frame. However, for the last two collection periods of 2010, 40.5% of the sample came from the area frame, 58.5% from the list frame of telephone numbers and 1% from the RDD frame. The transfer of sample from the area frame to the list frame was done to reduce collection costs.
  • Starting with the 2010 and 2009–2010 datasets, the 2006 Census population counts have been used to produce the population projection counts. These counts are used to ensure that the CCHS survey weights and resulting estimates are consistent with known population totals. Prior to 2010, 2001 Census population counts were used. Evaluation studies have confirmed that the impact of this change on CCHS estimates should be minimal.

Collection

  • In 2009, interviewers were asked to obtain verbal permission from parents/guardians to interview youths between the ages of 12 to 15 who were selected for interviews. In 2010, the Parental Consent block (PGC) was added into the collection applications. The addition of this block formalizes the process of requesting permission from the parent or guardian (given one exists in the household) of a 12 to15 year old to complete the survey.
  • Prior to 2010, interviewers were instructed to ask modules including household level questions to the person most knowledgeable (PMK) about the household. In 2010, a formal block was included in the application to do the transition between respondents aged 12 to 15 to the PMK. Household level information asked at the end of the survey (Home Safety, Insurance coverage, Food Security, Neurology conditions, Education, Income and Administration) are now answered by the most knowledgeable person in the household.

Geography

  • In 2010, the definition of health regions (HR) in Alberta was modified between the time of sampling and the creation of the data files. There are now five HRs in Alberta, which are simple aggregations of the nine HRs that were defined at the time of sampling. As a result of this, the total of health regions went from 121 in 2009 to 117 in 2010.

1.0 Introduction

The Canadian Community Health Survey (CCHS) is a cross–sectional survey that collects information related to health status, health care utilization and health determinants for the Canadian population. It surveys a large sample of respondents and is designed to provide reliable estimates at the health region level. In 2007, major changes were made to the CCHS design. Data is now collected on an ongoing basis with annual releases, rather than every two years as was the case prior to 2007. The survey’s objectives were also revised and are as follows:

  • support health surveillance programs by providing health data at the national, provincial and intra–provincial levels;
  • provide a single data source for health research on small populations and rare characteristics;
  • timely release of information easily accessible to a diverse community of users; and
  • create a flexible survey instrument that includes a rapid response option to address emerging issues related to the health of the population.

Details of the other redesign changes are provided in section 3.

The CCHS data is always collected from persons aged 12 and over living in private dwellings in the 117 health regions covering all provinces and territories. Excluded from the sampling frame are individuals living on Indian Reserves and on Crown Lands, institutional residents, full-time members of the Canadian Forces, and residents of certain remote regions. The CCHS covers approximately 98% of the Canadian population aged 12 and over.

The purpose of this document is to facilitate the manipulation of the CCHS microdata files and to describe the methodology used. The CCHS produces three types of microdata files: master files, share files and public use microdata files (PUMF). The characteristics of each of these files are presented in this guide. The PUMF is released every two years and contains two years of data. The next PUMF file will be released in September 2011 and will include the data collected for the years 2009 and 2010.

Any questions about the data sets or their use should be directed to:

Electronic Products Help Line: 1–800–949–9491

For custom tabulations or general data support:
Client Custom Services, Health Statistics Division: 613–951–1746
E–mail: hd–ds@statcan.gc.ca

For remote access support: 613–951–1746
E–mail: cchs–escc@statcan.gc.ca
Fax: 613–951–0792

2.0 Background

In 1991, the National Task Force on Health Information cited a number of issues and problems with the health information system. The members felt that data was fragmented; incomplete, could not be easily shared, was not being analysed to the fullest extent, and the results of research were not consistently reaching Canadians.1

In responding to these issues, the Canadian Institute for Health Information (CIHI), Statistics Canada and Health Canada joined forces to create a Health Information Roadmap. From this mandate, the Canadian Community Health Survey (CCHS) was conceived. The format, content and objectives of the CCHS evolved through extensive consultation with key experts and federal, provincial and community health region stakeholders to determine their data requirements.2

To meet many data requirements, the CCHS had a two–year data collection cycle. Until the redesign in 2007, the first year of the survey cycle, designated by ".1", was a general population health survey, designed to provide reliable estimates at the health region level. The second year of the survey cycle, designated by ".2", had a smaller sample and was designed to provide provincial level results on specific health topics.

New designations for Cycles .1 and .2

As of 2007, the regional component of the CCHS program began being collected on an ongoing basis. To avoid confusion with the health focused surveys, the two components stopped using the “.1” and “.2” designations to distinguish them. Henceforth, the x.1 cycles of the CCHS are designated as "the annual component" of the CCHS. The full title is "The Canadian Community Health Survey – Annual component, 2009" and the short title is simply "CCHS – 2009". The focused content component of the survey remains unchanged. It will continue to examine in greater detail more specific topics or populations. It will be designated by the name of the survey followed by the topic of the themes covered by each survey (example, “Canadian Community Health Survey on Healthy Aging” or “CCHS – Healthy Aging”).

3.0 CCHS Redesign in 2007

Until 2005, the CCHS data were collected every two years over a one year period and released every two years, about six months after the end of the collection period. There were two main objectives for the 2007 CCHS redesign: to address the needs of partners to increase the survey’s content and the frequency of data releases, and to ensure better use of operational resources. For these reasons, the proposed changes to the CCHS design focused on improving the survey’s efficiency and flexibility through ongoing data collection.

Extensive consultations were held across Canada with key experts and federal, provincial and health region stakeholders to gather input on the proposed changes and detailed information on the data requirements and products of the various partners.

Below are the main changes arising from the CCHS redesign:

  • In the past, the CCHS data were collected from 130,000 respondents over a 12–month period. Now, data collection takes place on an ongoing basis. The sample, which retains the same size, is divided into 12 two–month collection periods. Each collection period is representative of the population living in the ten Canadian provinces during the two months. For operational reasons, the sample in the territories is representative of their population after 12 months.
  • The common content component is divided into three: the annual common content (previously referred to as core content), the one year and two-year common content (previously referred to as theme content). The one year common content is asked for one year and re-introduced every two or four years. The two year common content is asked for two years and re-introduced every four years. The two year and one year common content was created to take advantage of the continuous collection approach. The data collection time for this component can be adjusted based on the prevalence of the desired estimates and their geographic level. The annual common content will remain relatively stable over time. At the discretion of the provinces and regions, the optional content can also be adjusted on an annual basis, rather than every two years.
  • Content and collection changes inevitably impact the dissemination strategy. Previously, data were released every two years. Since 2008, CCHS data are released annually. Every two years, a file combining the two years’ sample (130,000 respondents) is also be released. In addition to these regular files, other special files will be made available when additional content has been collected during collection periods that do not correspond to the standard annual periods, which is January to December.
  • The annual data collection is divided into six two–month periods. Unlike the previous collection strategy, these periods no longer overlap, which provides more efficient oversight of collection and offers the possibility of changing the collection interface every two months, if necessary.

4.0 Content Structure of the CCHS

In addition to socio–demographic and administrative data, the content of the CCHS includes three components, each of which addresses a different need: the common content component comprising the annyal common content, the two year and one year common content, the optional content component, and the rapid response component. AppendixA lists the modules included in the 2009 and 2100 questionnaire by component.

The average length of a CCHS interview is estimated at 40 to 45minutes.

Table 4.1 Length of survey by component
CCHS component Average interview time
Common content
  • Annual
  • One and two-year
30 minutes
(20 minutes)
(5 minutes)
Optional content 10 minutes
Rapid response content (optional) 2 minutes

4.1 Common content

The CCHS common content component includes questions asked of respondents in all provinces and territories (unless otherwise specified). It is divided into three components: the annual common content, one-year and two year common content.

The annual common content consists of questions asked of all survey respondents. These questions will remain relatively stable in the questionnaire for a period of about six years, unless a major concern is raised about quality.

The one year and two-year common content (previously called theme content) comprises questions related to a specific topic. Combined, the two year and one year common content take about 10 minutes of the interview time. Modules comprising this content type could be reintroduced in the survey every two, four or six years, if required. This component enables CCHS to better plan its content in the medium term.

Some of the modules in the one year common content may be asked of a sub sample of respondents if the objective of these questions is to provide reliable data at the national or provincial level, rather than at the health region level. This approach is used to minimize the related response burden and costs.

4.2 Optional content

The optional content component gives health regions the opportunity to select content that addresses their provincial or regional public health priorities. The optional content is selected from a long list of modules available for inclusion in the CCHS. The content modules selected by a region are asked only of residents in the regions that selected these modules. In reality, since 2005 (cycle 3.1), the regions and provinces have opted to coordinate the optional content selected in order to ensure a uniform selection of optional modules provincially. The optional content may vary annually depending on needs and must be reviewed every two years.

It should be noted that, unlike the modules included in the common content, the resulting data from the optional content modules is not easily generalized across Canada3.

Appendix B presents the selection results of the optional content for the current year by province of residence.

4.3 Rapid response content

The rapid response component is offered on a cost–recovery basis to organizations interested in obtaining national estimates on an emerging or specific topic related to the health of the population. The rapid response content takes a maximum of two minutes of interview time. The questions appear in the questionnaire for a single collection period (two months) and are asked of all CCHS respondents during that period.

4.4 Content included in data files

The survey produces different data files:

  • one year reference period
  • combined two years reference periods and
  • one year sub-sample data files.

Table 4.2 provides clarification about the data files available for the 2009 and 2010 CCHS.

One year data files

The survey produces data files every year. In June 2010, an annual file based on the 2009 reference period has been released. It includes respondents from the 2009 data collection and variables from the common annual content, common one year content, common two year content as well as optional content.

Two year data files

Every two years, a file combining the most recent two years is released. A combined file also to be released in June 2011 contains data from 2009 and 2010. The following two year file is scheduled to be released in 2013, and will include both the 2011 and 2012 reference years.

The two-year data file includes all respondents and the questions that were in the survey over the two year reference period. Unless otherwise specified, it is the question component from the common annual and two-year content and selected optional content over the two year period. The one-year common content and optional content selected for one year only are not available in the two-year data file.

Sub-sample data files

Any modules collected from a sub-sample of the population will continue to be disseminated in separate files. These files include the annual and one year common content collected from a sub-sample of respondents. Sub-sample files have been released as follow:

Year Modules
2000 Waiting times and Access to health care services
2003 Dental visits, Driving and safety, Health utility index, Medication use, Oral health 2
2005 Waiting times, Access to health care services, Patient satisfaction, Health Utility Index, Measured height and weight, Fruit and vegetable consumption, Labour force – long form
2007 Waiting times, Access to health care services and Patient satisfaction
2008 Measured height and weight
2009 Waiting times, Access to health care services
Table 4.2 Content components included in 2009 and 2010 data files
Files Annual common content 2009 one year common content1 2010 one year common content2 2009-2010 two-year common content Optional content3
2009 Main
Sub-sample
(2 modules)
Yes
Yes
No
Yes
N/A
N/A
Yes
No
Yes
No
2010 Main Yes N/A Yes Yes Yes
2009-2010 Main Yes No No Yes Yes
  1. The 2009 annual common content was comprised of two modules (Access to health care services and Waiting times) which were all asked to a sub-sample of respondents.
  2. The 2010 annual common content will include a group of modules related to chronic disease screening.
  3. Optional content will be included in the 2009–2010 data file (to be released in 2011) if it is asked of respondents in a province during the two year period. Otherwise, it will only be included in the file of the year in which it was collected. Note that if an annual common content module from one year is selected for the optional content of a jurisdiction during the second year, the module will be included in the two-year data file and will be processed as optional content.

5.0 Sample design

5.1 Target population

The CCHS targets persons aged 12 years and older who are living in private dwellings in the ten provinces and three territories. Persons living on Indian Reserves or Crown lands, those residing in institutions, full–time members of the Canadian Forces and residents of certain remote regions are excluded from this survey. The CCHS covers approximately 98% of the Canadian population aged 12 and older.

5.2 Health regions

For administrative purposes, each province is divided into health regions (HR) and each territory is designated as a single HR. Statistics Canada is sometimes asked to make minor changes to the boundaries of some of the HRs to correspond to the geography of the Census, or to better account for the health data needs determined by the new geographic boundaries. For CCHS 2010, data was collected in 114 HRs in the ten provinces, as well as to one HR per territory, totalling 117 HRs (Appendix C).

In 2010, the definition of HRs in Alberta was modified between the time of sampling and the creation of the data files. There are now 5 HRs in Alberta, which are simple aggregations of the 9 HRs that were defined at the time of sampling4. The current chapter on sample design, as well as the figures on sample sizes provided in Appendix D and Appendix F, refer to the 9 HRs as they were defined at the time of sampling.

5.3 Sample size and allocation

To provide reliable estimates for each HR given the budget allocated to the CCHS component, it was determined that the survey should consist of a sample of nearly 130,000 respondents over a period of 2 years. Although producing reliable estimates for each HR was a primary objective, the quality of the estimates for certain key characteristics at the provincial level was also deemed important. Therefore, the sample allocation strategy, consisting of three steps, gave relatively equal importance to the HRs and the provinces. In the first step, a minimum size of 500 respondents per HR was imposed. This is considered the minimum for obtaining a reasonable level of data quality. However, due to response burden, a maximum sampling fraction of 1 out of 20 dwellings was imposed to avoid sampling too many dwellings in smaller regions also targeted by other surveys. Note that very few HRs have a size lower than 500 due to limit of the sampling fraction. In this first step, 60,350 units were allocated in total. The second step involves allocating the rest of the available sample by using an allocation proportional to the population size by province. The total sample size by province is therefore the sum of the sizes established by the two first steps. This sample allocation strategy was used for CCHS 2005 and the sample sizes have remained mainly the same since then. The sample was then divided evenly between the 2 collection years. Table 5.1 gives the annual sample size for 2010 and the total sample size for 2009-2010.

Table 5.1 Number of health regions and targeted sample sizes by province/territory, 2010 and 2009–2010
Province Number of HRs Targeted sample size 2010 Targeted sample size 2009–2010
Newfoundland and Labrador 4 2,005 4,010
Prince Edward Island 3 1,001 2,002
Nova Scotia 6 2,520 5,041
New Brunswick 7 2,575 5,150
Quebec 16 12,144 24,289
Ontario1 36 22,207 44,379
Manitoba 10 3,750 7,500
Saskatchewan 11 3,860 7,720
Alberta 9 6,100 12,200
British Columbia 16 8,050 16,095
Yukon 1 600 1,200
Northwest Territories 1 600 1,200
Nunavut 1 350 700
Canada 121 65,762 131,486
  1. The sample size for Ontario includes the buy–in extra sample by LHIN. The initial sample size for Ontario before the buy–in was 20,880 units (refer to section 5.7 for further details).

In the third step, the provincial sample was allocated among its HRs proportionally to the square root of the estimated population in each HR. This three–step approach gives sufficient sample for each HR with minimal disturbance to the proportionality of the allocation by province.

Note that the three territories were not part of the above allocation strategy as they were dealt with separately. Each year, 600 sample units were allocated to the Yukon, 600 to the Northwest Territories and 350 to Nunavut. These sizes are determined according to the available budget. The sample allocation for the territories is done proportionally to the population sizes of the strata. The strata used were the same as those defined by the Labour Force Survey (LFS), which group together communities (for more details, see section 5.4.1).

The sample was then divided between the area frame and the list frame5, as described in the next section. We should finally mention that the size of the samples taken from each frame was increased before data collection in order to account for the anticipated out-of-scope and non-response rates based on the rates obtained in previous CCHS cycles. The sample sizes by HR and frame are provided in Appendix D for 2010 and in Appendix F for 2009–2010.

5.4 Frames, household sampling strategies

The CCHS used three sampling frames to select the sample of households: 49.5% of the sample of households came from an area frame, 49.5% came from a list frame of telephone numbers and the remaining 1% came from a Random Digit Dialling (RDD) sampling frame. This describes the usual strategy for the CCHS. For the last two collection periods of 2010, 40.5% came from the area frame, 58.5% from the list frame of telephone numbers and 1% from the RDD frame. The transfer of sample from the area frame to the list frame was done to reduce collection costs.

5.4.1 Sampling of households from the area frame

The CCHS used the area frame designed for the Canadian Labour Force Survey (LFS) as a sampling frame. The sampling plan of the LFS is a multistage stratified cluster design in which the dwelling is the final sampling unit6. In the first stage, homogeneous strata are formed and independent samples of clusters are drawn from each stratum. In the second stage, dwelling lists are prepared for each cluster and dwellings, or households, are selected from these lists.

For the purpose of the LFS plan, each province is divided into three types of regions: major urban centres, cities, and rural regions. Geographic or socio–economic strata are created within each major urban centre. Within the strata, between 150 and 250 dwellings are grouped together to create clusters. Some urban centres have separate strata for apartments or for census Dissemination Areas (DA) to pinpoint households with high income, immigrants and aboriginals. In each stratum, six clusters or residential buildings (sometimes 12 or 18 apartments) are chosen by a random sampling method with a probability proportional to size (PPS), the size of which corresponds to the number of households. The number six is used throughout the sample design to allow for one sixth of the LFS sample to be rotated each month.

The other cities and rural regions of each province are stratified first on a geographical basis, then according to socio–economic characteristics. In the majority of strata, six clusters (usually census DAs) are selected using the PPS method. Some geographically isolated urban centres are covered by a three–stage sampling design. This type of sampling plan is used for Quebec, Ontario, Alberta and British Columbia.

Once the new clusters are listed, the sample is obtained using a systematic sampling of dwellings. The sample size for each systematic sample is called the “yield”. Table 5.2 gives an overview of the types of PSUs used in the LFS sample and the yield predicted by systematic sample. As the sampling rates are determined in advance, there is frequently a difference between the expected sample size and the numbers that are obtained. The yield of the sample, for example, is sometimes excessive. This can particularly happen in sectors where there is an increase in the number of dwellings due to new construction. To reduce the cost of collection, an excessive output is corrected by eliminating, from the beginning, a part of the units selected and by modifying the weight of the sample design. This change is dealt with during weighting.

Table 5.2 Major first–stage units, sizes and yields
Area Primary Sampling Unit (PSU) Size (households per PSU) Yield (sampled households)
Toronto, Montreal, Vancouver Cluster 150–250 6
Other cities Cluster 150–250 8
Most rural areas / small urban centres Cluster 100–250 10

Due to the specific of the CCHS, some modifications had to be incorporated in this sampling strategy. To obtain an annual sample of about 32,000 respondents for a given year of CCHS, close to 48,000 dwellings had to be selected from the area frame to account for vacant dwellings and non-responding households. Each month, the LFS design provides approximately 60,000 dwellings distributed across the various economic regions in the ten provinces, whereas the CCHS required 48,000 dwellings distributed across the HRs, which have different geographic boundaries from those of the LFS economic regions. Overall, the CCHS required a lower number of dwellings than those generated by the LFS selection mechanism, which corresponds to an adjustment factor of 0.80 (48,000/60,000). However, since the adjustment factors varied from 0.3 to 3.0 at the HR level, certain adjustments were required.

The changes made to the selection mechanism in the regions varied depending on the size of the adjustment factors. For HRs that had a factor smaller than or equal to 1, the number of PSUs selected was reduced if necessary. For example, if the factor was 0.5 then only 3 PSUs were selected in each stratum instead of the usual number of 6 PSUs. For those HRs with a factor greater than 1 but smaller than or equal to 2, the sampling process of dwellings within a PSU was repeated for a subset of the selected PSUs that were part of the same HR. For example, if the factor was 1.6 then the selection of dwellings within a PSU was repeated for 4 of the 6 PSUs in all strata of that HR. When it was necessary to have a repeated selection of dwellings within a PSU and there were no more dwellings available in that PSU, then another PSU was selected. When the factor was greater than 2, the sampling process of dwellings was repeated among other PSUs that were part of the same HR7.

Finally, when the number of dwellings available in the selected PSUs was greater than the requested number of dwellings for a given HR, a sub–sample of dwellings was selected. This process is called ‘stabilization’.

Sampling of households from the area frame in the three territories

For operational reasons, the LFS area frame sample design for the three territories was different. For each territory, the larger communities each have their own stratum while smaller communities are grouped into strata based on various characteristics (population, geographical information, proportion of Inuit and/or Aboriginal persons, and median household income). The LFS defined five design strata in the Yukon, ten in the Northwest Territories and seven in Nunavut. For strata consisting of a group of communities, the first stage of selection consisted of randomly selecting one community with a probability proportional to population size within each design stratum. Then, within the selected community, the second stage consisted of selecting households using the same sampling strategy as the one described above. The CCHS selected its sample from the same communities sampled by the LFS, while ensuring that different dwellings were selected. If too many or too few dwellings were available for a community within a stratum, another community was selected for the CCHS. For larger communities with their own stratum, only one stage design was necessary where households were selected directly using the same sampling strategy described above.

It is worth mentioning that the frame for the CCHS covered 90% of the private households in the Yukon, 97% in the Northwest Territories and 71% in Nunavut8.

5.4.2 Sampling of households from the list frame of telephone numbers

With the exception of 5 HRs (the two RDD-only HRs and the three territories), the list frame of telephone numbers was used in all HRs to complement the area frame. The list frame consists of the Canada Phone directory which is an external administrative database of names, addresses and telephone numbers from telephone directories in Canada updated every six months. It was linked to administrative postal code conversion files to map each telephone number to a stratum. Within each stratum, the required number of telephone numbers was selected using a simple random sampling process from the list. As for the RDD frame, additional telephone numbers were selected to account for the numbers not in service or out-of-scope.

It is important to mention that the undercoverage of the list frame is higher than the one for the RDD as unlisted numbers do not have a chance of being selected. Nevertheless, as the list frame is always used as a complement to the area frame, the impact of the undercoverage of the list frame is minimal and is dealt with during weighting.

5.4.3 Sampling of households from the Random Digit Dialing frame of telephone numbers

In four HRs, a Random Digit Dialing (RDD) sampling frame of telephone numbers was used to select a sample of households. The sampling of households from the RDD frame used the Elimination of Non-Working Banks (ENWB) method, a procedure adopted by the General Social Survey9. A bank of one hundred telephone numbers (the first eight digits of a ten-digit telephone number) is considered to be non-working if it does not contain any residential telephone numbers. At first, the frame consists of a list of all possible banks and, as non-working banks are identified, they are eliminated from the frame. It should be noted that these banks are eliminated only when there is evidence from various sources that they are non-working. When there is no information about a bank it is left on the frame. The Canada Phone Directory and telephone companies’ billing address files were used in conjunction with various internal administrative files to eliminate non-working banks.

Using available geographic information (postal codes), the banks on the frame were regrouped to create RDD strata to encompass, as closely as possible, the HR areas. Within each RDD stratum, a bank was randomly chosen and a number between 00 and 99 was generated at random to create a complete, ten-digit telephone number. This procedure was repeated until the required number of telephone numbers within the RDD stratum was reached. Frequently, the number generated is not in service or is out-of-scope, and therefore, many additional numbers must be generated to reach the targeted sample size. This success rate varies from region to region. Within the CCHS, the success rates ranged from 25% to 50% among the four HRs which required the use of the RDD frame.

5.5 Sample allocation over the collection period

In order to balance interviewer workload and to minimize possible seasonal effects on estimates of certain key characteristics such as physical activity, the initial sample of dwellings / telephone numbers was allocated at random, within each HR, over a two-month data collection period.

In the area frame, each start selected within each HR was randomly assigned to a collection period accounting for a number of constraints related to field operations or weighting, while maintaining a uniform size for each period. For example, a sample that is representative of the Canadian population is ensured every six months by ensuring that the dwelling sample covers all LFS strata during this period.

For the lists of telephone numbers, independent samples were selected in each collection period. This strategy ensures that each sample is representative of the Canadian population that is within the scope of the survey in each two months.

5.6 Sampling of interviewees

As was done for the previous cycles, the selection of individual respondents was designed to ensure over-representation of youths (12 to 19). The selection strategy that was adopted accounted for user needs, cost, design efficiency, response burden and operational constraints. One person is selected per household using varying probabilities taking into account the age and the household composition. The selection probabilities resulted from simulations using various parameters in order to determine the optimal approach without causing extreme sampling weights.

The selection weight multiplicative factors were modified between 2009 and 2010 to increase the probability of selecting respondents in the 12-19 and the 20-29 age groups. Table 5.3 gives the selection weight multiplicative factors used to determine the probabilities of selection of individuals in sampled households by age group, for 2009 and for 2010. For example, in 2010, for a three-person household formed of two adults of age 45 to 64 and one 15-year-old, the teenager would have a 7/9 chance of being selected (i.e., 70/(70+10+10)) while each of the adults would have a 1/9 chance of being selected. To avoid extreme sampling weights, there is one exception to this rule: if the size of the household is greater than or equal to 5 or if the number of 12-19 year olds is greater than or equal to 3 then the selection weight multiplicative factor equals 1 for each individual in the household. Consequently, all people in that household have the same probability of being selected.

Table 5.3 Selection weight multiplicative factors for the person–level sampling strategy by age
Selection Weight Multiplicative Factors
Age 12 to 19 20 to 29 30 to 44 45 to 64 65+
Factor (2009) 65 25 20 10 10
Factor (2010) 70 50 20 10 10

5.7 Supplementary buy-in sample in Ontario

The province of Ontario requested a sample increase in order to produce estimates at the Local Health Integrated Network (LHIN) geography level. Ontario contains 14 LHIN. The CCHS sample was increased in order to obtain a minimum size of 2,000 per LHIN over a period of 2 years. As the HR and LHIN boundaries intersect each other, the stratification level used was the HRLHIN overlap. The preliminary sample sizes allotted by HR are therefore preserved. In cases where the HR allocation prevented the sample from reaching sizes of 2,000 per LHIN, the sample was then increased, and was allocated proportionally to the size of the population within the HRLHIN overlap. Table 5.4 provides the sample sizes of targeted respondents by LHIN for 2010 and 2009–2010.

Table 5.4 Targeted respondents by Local Health Integrated Network (LHIN), 2010 and 2009-2010.
LHIN Targeted respondents 2010 Targeted respondents 2009–2010
01–Erie St. Clair 1,550 3,100
02–South West 2,561 5,122
03–Waterloo Wellington 1,242 2,484
04–Hamilton Niagara Haldimand Brant 2,597 5,194
05–Central West 1,056 2,125
06–Mississauga Halton 1,115 2,230
07–Toronto Central 1, 084 2,165
08–Central 1,411 2,822
09–Central East 2,108 4,216
10–South East 1,313 1,626
11–Champlain 2,057 4,114
12–North Simcoe Muskoka 1,047 2,097
13–North East

1,990 3,980
14–North West 1,041 2,104
Ontario 22,172 44,379

The total sample size of the HRLHIN overlapping areas was then allocated equally between the list frame and the area frame. The usual sample selection procedures within each frame were then applied to the total sample. The additional sample was included as part of the full CCHS sample. Sample sizes by Local Health Integrated Network and frame are given in Appendix D for 2010 and in Appendix F for 2009-2010.

6.0 Data collection

6.1 Computer–assisted interviewing

Between January and December 2010, over 60,000 valid interviews were conducted using computer assisted interviewing (CAI). Approximately half the interviews were conducted in person using computer assisted personal interviewing (CAPI) and the other half were conducted over the phone using computer assisted telephone interviewing (CATI). Between January 2009 and December 2010, over 120,000 valid interviews were conducted.

CAI offers two main advantages over other collection methods. First, CAI offers a case management system and data transmission functionality. This case management system automatically records important management information for each attempt on a case and provides reports for the management of the collection process.CAI also provides an automated call scheduler, i.e. a central system to optimise the timing of call–backs and the scheduling of appointments used to support CATI collection.

The case management system routes the questionnaire applications and sample files from Statistics Canada’s main office to regional collection offices (in the case of CATI) and from the regional offices to the interviewers laptops (for CAPI). Data returning to the main office takes the reverse route. To ensure confidentiality, the data is encrypted before transmission. The data are then unencrypted when they are on a separate secure computer with no remote access.

Second, CAI allows for custom interviews for every respondent based on their individual characteristics and survey responses. This includes:

  • questions that are not applicable to the respondent are skipped automatically
  • edits to check for inconsistent answers or out–of–range responses are applied automatically and on–screen prompts are shown when an invalid entry is recorded. Immediate feedback is given to the respondent and the interviewer is able to correct any inconsistencies.
  • question text, including reference periods and pronouns, is customised automatically based on factors such as the age and sex of the respondent, the date of the interview and answers to previous questions.

6.2 CCHS application development

The CCHS uses two separate CAI applications to collect data, one for telephone interviews (CATI) and one for personal interviews (CAPI). This was done in order to customise each applications’ functionality to the type of interview being conducted. Each application consisted of entry, health content , and exit components.

Entry and exit components contain standard sets of questions designed to guide the interviewer through contact initiation, collection of important sample information, respondent selection and determination of cases status. The health content consists of the health modules themselves and made up the bulk of the applications. This includes common modules asked of all respondents and optional modules which differed by health region. Each application underwent three stages of testing: block, integrated and end to end.

Block level testing consists of independently testing each content module or "block" to ensure skip patterns, logic flows and text, in both official languages, are specified correctly. Skip patterns or logic flows across modules are not tested at this stage as each module is treated as a stand alone questionnaire. Once all blocks are verified by several testers they are added together along with entry and exit components into integrated applications. These newly integrated applications are then ready for the next stage of testing.

Integrated testing occurs when all of the tested modules are added together, along with the entry and exit components, into an integrated application. This second stage of testing ensures that key information such as age and gender are passed from the entry to the health content and exit components of the applications. It also ensures that variables affecting skip patterns and logic flows are correctly passed between modules within the health content. Since, at this stage the applications essentially function as they will in the field, all possible scenarios faced by interviewers are simulated to ensure proper functionality. These scenarios test various aspects of the entry and exit components including, establishing contact, collecting contact information, determining whether a case is in scope, rostering households, creating appointments and selecting respondents. The applications are also tested to ensure that during an interview, correct modules are triggered reflecting health region optional content selections.

End to end testing occurs when the fully integrated applications are placed in simulated collection environment. The applications are loaded onto computers that are connected to a test server. Data is then collected, transmitted and extracted in real time, exactly as it would be done in the field. This last stage of testing allows for the testing of all technical aspects of data input, transmission and extraction for each of the CCHS applications. It also provided a final chance of finding errors within the entry, health content and exit components.

6.3 Interviewer training

Project managers, senior interviewers and interviewers from regional collection offices were sent self study training packages before the start of collection. These packages were prepared by the CCHS project team and were used by existing experienced CCHS interviewers to reinforce their previous training. Project managers and senior interviewers also conducted customised training sessions for new CCHS interviewing staff as needed. There were also specific training sessions to deal with various topics related to CCHS collection on a monthly basis. The focus of the training sessions were to get interviewers comfortable using the CCHS 2010 applications, and familiarise interviewers with survey content and to introduce interviewers to interviewing procedures specific to the CCHS. The training focused on:

  • goals and objectives of the survey including a focus on the survey redesign
  • survey methodology
  • application functionality
  • review of the questionnaire content and exercises with an emphasis on significant content changes
  • interviewer techniques for maintaining response – complete exercises to minimise non–response
  • use of mock interviews to simulate difficult situations and practise potential non–response situations
  • survey management
  • transmission procedures

One of the key aspects of the training was a focus on minimizing non–response. Exercises to minimise non–response were prepared for interviewers. The purpose of these exercises was to have the interviewers practice convincing reluctant respondents to participate in the survey. There was also a series of refusal avoidance workshops given to the senior interviewers responsible for refusal conversion in each regional collection office.CAT selecte call centre.

6.4 The interview

Sample units selected from the telephone list and RDD (Random Digit Dialling) frames were interviewed from centralised call centres using CATI. The CATI interviewers were supervised by a senior interviewer located in the same call centre. Units selected from the area frame were interviewed by decentralised field interviewers using CAPI. While in some situations field interviewers were permitted to complete some or part of an interview by telephone, roughly three-quarters of these interviews were conducted exclusively in person.CAPI interviewers worked independently from their homes using laptop computers and were supervised from a distance by senior interviewers. The variable SAM_TYP on the microdata files indicates whether a case was selected from the area frame (CAPI) or from the telephone or RDD frame (CATI).

In all selected dwellings, a knowledgeable household member was asked to supply basic demographic information on all residents of the dwelling. One member of the household was then selected for a more in-depth interview, which is referred to as the health content Interview.

CAPI interviewers were trained to make an initial personal contact with each sampled dwelling. In cases where this initial visit resulted in non-response, telephone follow-ups were permitted. The variable ADM_N09 on the microdata files indicates whether the interview was completed face-to-face, by telephone or using a combination of the two techniques.

To ensure the quality of the data collected, interviewers were instructed to make every effort to conduct the interview with the selected respondent in privacy. In situations where this was unavoidable, the respondent was interviewed with another person present. Flags on the microdata files indicate whether somebody other than the respondent was present during the interview (ADM_N10) and whether the interviewer felt that the respondent’s answers were influenced by the presence of the other person (ADM_N11).

To ensure the best possible response rate attainable, many practices were used to minimise non-response, including:

a) Introductory letters
Before the start of each collection period introductory letters explaining the purpose of the survey were sent to the sampled households. These explained the importance of the survey and provided examples of how CCHS data would be used.

b) Initiating contact
Interviewers were instructed to make all reasonable attempts to obtain interviews. When the timing of the interviewer's call (or visit) was inconvenient, an appointment was made to call back at a more convenient time. If requests for appointments were unsuccessful over the telephone, interviewers were instructed to follow-up with a personal visit. If no one was home on first visit, a brochure with information about the survey and intention to make contact was left at the door. Numerous call-backs were made at different times on different days.

c) Refusal conversion
For individuals who at first refused to participate in the survey, a letter was sent from the nearest Statistics Canada Regional Office to the respondent, stressing the importance of the survey and the household's collaboration. This was followed by a second call (or visit) from a senior interviewer, a project supervisor or another interviewer to try to convince respondent of the importance of participating in the survey.

d) Language barriers
To remove language as a barrier to conducting interviews, each of the Statistics Canada Regional Offices recruited interviewers with a wide range of language competencies. When necessary, cases were transferred to an interviewer with the language competency needed to complete an interview.

e) Youth interviews
In 2009, interviewers were obliged to obtain verbal permission from parents/guardians to interview youths between the ages of 12 to 15 who were selected for interviews. In 2010, the Parental Consent block (PGC) was added into the applications. The addition of this block formalizes the process of requesting permission from the parent or guardian (given one exists in the household) of a 12-15 year old to complete the survey. Several procedures were followed by interviewers to alleviate potential parental concerns and to ensure a completed interview. Interviewers carried with them a card entitled "Note to parents / guardians about interviewing youths for the Canadian Community Health Survey". This card explained the purpose of collecting information from youth, lists the subjects to be covered in the survey, asks for permission to share and link the obtained information and explains the need to respect a child's right to privacy and confidentiality.

If a parent/guardian asked to see the actual questions; interviewers were instructed to either show the survey questions, or if the interviewer was being conducted by phone, to immediately have the regional office send a copy of the questionnaire.

If privacy could not be obtained to interview the selected youth either in person or over the phone (another person listening in) the interview was coded a refusal. However, for CAPI interviews, if privacy could not be obtained to interview the selected youth, the interviewer was able to propose to the parent/guardian that the interviewer read the questions out loud and the youth enter their answers directly on the computer.

The Person Most Knowledgeable (PMK) block was added to the 2010 application to collect household level information found at the end of the survey (Home Safety, Insurance coverage, Food Security, Neurology conditions, Education, Income and Administration) from the most knowledgeable person in the household. This block is initiated when the selected respondent is between the ages of 12 to 15. The block again formalizes the process of identifying a person in the household who is likely better able to answer these household level questions than the young selected respondent. If a PMK is found then the interview moves from the younger selected respondent between the ages of 12 and 15, to a parent, guardian who finishes the rest of the interview after the PMK block.

Since the PMK block was not collected in 2009, PMK variables are not included in the 2009-2010 data file.

f) Proxy interviews
In cases where the selected respondent was, for reasons of physical or mental health, incapable of completing an interview, another knowledgeable member of the household supplied information about the selected respondent. This is known as a proxy interview. While proxy interviewees were able to provide accurate answers to most of the survey questions, the more sensitive or personal questions were beyond the scope of knowledge of a proxy respondent. This resulted in some questions from the proxy interview being unanswered. Every effort was taken to keep proxy interviews to a minimum.

6.5 Field operations

The majority of the 2009 and 2010 sample was divided on a yearly basis into six non-overlapping two-month collection periods. Regional collection offices were instructed to use the first 4 weeks of each collection period to resolve the majority of the sample, with next 4 weeks being used finalise the remaining sample and to follow up on outstanding non-response cases. All cases were to have been attempted by the second week of each collection period. Sample files were sent approximately two weeks before the start of each collection period to centralised collection offices. A series of dummy cases were included with each CAPI sample. These cases were completed by senior interviewers for the purposes of ensuring that all data transmission procedures were working through the collection cycle. Once, the samples were received, project supervisors were responsible for planning CAPI interviewer assignments. Wherever possible, assignments were generally no larger than 15 cases per interviewer.

Transmission of cases from each of the CATI offices to head office was the responsibility of the regional office project supervisor, senior interviewer and the technical support team. These transmissions were performed nightly and sent all completed cases to Statistics Canada’s head office. Completed CAPI interviews were transmitted daily from the interviewer’s home directly to Statistics Canada’s head office using a secure telephone transmission.

Transmission of cases from each of the CATI offices to head office was the responsibility of the regional office project supervisor, senior interviewer and the technical support team. These transmissions were performed nightly and sent all completed cases to Statistics Canada’s head office. Completed CAPI interviews were transmitted daily from the interviewer’s home directly to Statistics Canada’s head office using a secure telephone transmission.

For final response rates, refer to Appendix E for 2010 and to Appendix G for 2009-2010.

6.6 Quality control and collection management

During collection year, several methods are used to ensure data quality and to optimize collection. These included using internal measures to verify interviewer performance and the use of a series of ongoing reports to monitor various collection targets and data quality.

A system of validation was used for CAPI cases whereby interviewers had their work validated on a regular basis by the Regional Office. Each collection period, randomly selected cases were flagged in the sample. Regional office managers and supervisors created lists of cases to be validated. These cases were handed to the validation team who then contacted households to verify that a legitimate interview took place. Validation procedures generally occurred during the first few weeks of a collection period to ensure that any issues were detected promptly. Interviewers were provided feedback by their supervisors on a regular basis.

CATI interviewers were also randomly chosen for validation. Validation in the CATI collection offices consisted of senior interviewers monitoring interviews to ensure proper techniques and procedures (reading the questions as worded in the applications, not prompting respondents for answers, etc.) were followed by the interviewer.

A series of reports were produced to effectively track and manage collection targets and to assist in identifying other collection issues.

Cumulative reports were generated at the end of each collection period, showing response, link, share and proxy rates for both the CATI and CAPI samples by individual health region. The reports were useful in identifying health regions that were below collection target levels, allowing the regional offices to focus efforts in these regions.

Using information obtained from the CAI applications, further analysis was done in head office in order to identify interviews that were completed below acceptable time frames. These short interviews were flagged, removed from the microdata and treated as non-response.

7.0 Data processing

7.1 Editing

Most editing of the data was performed at the time of the interview by the computer-assisted interviewing (CAI) application. It was not possible for interviewers to enter out-of-range values and flow errors were controlled through programmed skip patterns. For example, CAI ensured that questions that did not apply to the respondent were not asked.

In response to some types of inconsistent or unusual reporting, warning messages were invoked but no corrective action was taken at the time of the interview. Where appropriate, edits were instead developed to be performed after data collection at Head Office. Inconsistencies were usually corrected by setting one or both of the variables in question to "not stated".

7.2 Coding

Pre-coded answer categories were supplied for all suitable variables. Interviewers were trained to assign the respondent’s answers to the appropriate category.

In the event that a respondent’s answer could not be easily assigned to an existing category, several questions also allowed the interviewer to enter a long-answer text in the "Other-specify" category. All such questions were closely examined in head office processing. For some of these questions, write-in responses were coded into one of the existing listed categories if the write-in information duplicated a listed category. For all questions, the "Other-specify" responses are taken into account when refining the answer categories for future cycles.

7.3 Creation of derived variables

To facilitate data analysis and to minimize the risk of error, a number of variables on the file have been derived using items found on the CCHS questionnaire. Derived variables generally have a "D", "G" or "F" in the fourth character of the variable name. In some cases, the derived variables are straightforward, involving collapsing of response categories. In other cases, several variables have been combined to create a new variable. The Derived Variables Documentation (DV) provides details on how these more complex variables were derived. For more information on the naming convention, please go to Section 12.6.

7.4 Weighting

The principle behind estimation in a probability sample such as CCHS is that each person in the sample "represents", besides himself or herself, several other persons not in the sample. For example, in a simple random 2% sample of the population, each person in the sample represents 50 persons in the population. In the terminology used here, it can be said that each person has a weight of 50.

The weighting phase is a step that calculates, for each person, his or her associated sampling weight. This weight appears on the PUMF, and must be used to derive meaningful estimates from the survey. For example, if the number of individuals who smoke daily is to be estimated, it is done by selecting the records referring to those individuals in the sample having that characteristic and summing the weights entered on those records.

8.0 Weighting

In order for estimates produced from survey data to be representative of the covered population, and not just the sample itself, users must incorporate the survey weights in their calculations. A survey weight is given to each person included in the final sample, that is, the sample of persons having responded to the survey. This weight corresponds to the number of persons in the entire population that are represented by the respondent.

As described in Section 5, the CCHS has recourse to three sampling frames for its sample selection: an area frame acting as the primary frame and two frames made up of telephone numbers used to complement the area frame. Since only minor differences differentiate the two telephone frames in terms of weighting, they are treated together as one and referred to as being part of the telephone frame.

Depending on the need, one or two frames are used for the selection of the sample within a given health region (HR). When two frames are used, the weighting strategy treats both the area and telephone frames independently to come up with separate household-level weights for each of the frames used. These household-level weights are then combined into a single set of household weights through a step called "integration". After applying person-level selection weights and some further adjustments, this integrated weight becomes the final person-level weight.

8.1 Overview

As mentioned earlier, units from both the area and telephone frames are treated separately up to the integration step. The following sections describe the weighting process for the provinces. Sub-section 8.2 provides details on the weighting strategy for the area frame, while sub-section 8.3 deals with the strategy for the telephone frame. The integration of the two frames is discussed in 8.4. This is followed by the last weighting steps including calibration, where the weights are adjusted to control for seasonality and to match known population totals. These steps are explained in sub-section 8.5.

Although the two frames are used to cover the three territories, the sampling methods used are slightly different from those used in the provinces. These modifications affect the weighting of these three regions substantially, and they are reported in sub-section 8.6.

Diagram A presents an overview of the different adjustments that are part of the weighting strategy. A numbering system is used to identify each adjustment and will be used throughout the section. Letters A and T are used as prefixes to refer to adjustments applied to the units on the Area and Telephone frames respectively, while prefix I identifies adjustments applied from the Integration step onwards.

Diagram A Weighting strategy overview

Diagram A

8.2 Weighting of the area frame sample

A0 – Initial weight

The weighting on the area frame sample begins with a weight provided by the Labour Force Survey (LFS). This weight is based on the LFS design since the CCHS area frame sample design is based on the LFS. The LFS design consists of a sample of dwellings within clusters selected from LFS strata. In the initial adjustment A0, the LFS weight is adjusted to take into consideration the fact that the CCHS selects a sample to be representative of the Health Region. To do so, the CCHS selects a different number of clusters than the LFS and can repeat the sampling of dwellings within the selected clusters. The resulting weight is called weight A0. For more details about the selection mechanism, as well as a more complete definition of LFS strata and clusters, refer to Statistics Canada (1998)10.

A1 – Sub–cluster adjustment

In clusters that experience significant growth, a sub-sampling methodology is used to ensure that the workload of the interviewers is kept at a reasonable level. This can consist of sub-sampling from the selected dwellings, dividing the cluster into sub-clusters, or reclassifying the cluster as a stratum and creating new clusters within the stratum. In all these cases, a sub-sample adjustment is calculated and applied to the CCHS weight. This adjustment is applied to weight A0 to produce weight A1. Again, more information can be found in the LFS documentation (Statistics Canada (1998)).

A2 – Stabilization

In some HRs, the increase of the sample size as described in section 5, results in a larger sample than necessary. Stabilization is used to bring the sample size back down to the desired level. The stabilization process consists of randomly sub-sampling dwellings at the HR level from the dwellings originally selected within each cluster. An adjustment factor representing the effect of this stabilization is calculated in order to adjust the probability of selection appropriately. This factor, multiplied by weight A1, produces weight A2.

A3 – Removal of out–of–scope units

Among all dwellings sampled, a certain proportion is identified during collection as being out-of-scope. Dwellings that are demolished or under construction, vacant, seasonal or secondary, and institutions are examples of out-of-scope cases for the CCHS. These dwellings and their associated weight are simply removed from the sample. This leaves a sample that consists of, and is representative of, in-scope dwellings or households. These remaining in-scope dwellings maintain the same weight as in the previous step, which is now called weight A3.

A4 – Household nonresponse

During collection, a certain proportion of sampled households inevitably result in nonresponse. This usually occurs when a household refuses to participate in the survey, provides unusable data, or cannot be reached for an interview. Weights of the nonresponding households are redistributed to responding households within response homogeneity groups (RHGs). In order to create the response homogeneity groups, a scoring method based on logistic regression models is used to determine the propensity to respond and these response probabilities are used to divide the sample into groups with similar response properties. The information available for nonrespondents is limited so the regression model uses characteristics such as the collection period and geographic information, as well as paradata or process data, which includes the number of contact attempts, the time/day of attempt, and whether the household was called on a weekend or weekday. Starting in 2008, RHGs were formed within province to better control for provincial totals. An adjustment factor is calculated within each response group as follows:

Formula 1

Weight A3 is multiplied by this factor to produce weight A4 for the responding households. Non-responding households are dropped from the process at this point.

8.3 Weighting of the telephone frame sample

As mentioned earlier, the telephone frame is composed of two frames: a Random Digit Dialling (RDD) frame and a list frame. Only one of the frames can be used within an HR. When the list frame is used, it is always used as a complement to the area frame within the HR. When the RDD frame is used, it is always used as the only frame within the HR. For the purposes of weighting, units coming from the two telephone frames are treated together and therefore are subject to the same adjustments.

The geographical boundaries used to select the sample from the telephone frame do not always conform to the HR geography. Consequently, some units may have been sampled from one HR but the information collected at the time of the interview places them in a neighbouring HR. This is handled in the weighting by applying the first 3 telephone adjustments (T0, T1 and T2) relative to the HR assigned at the time of sample selection. The remaining 2 adjustments (T3 and T4) are applied to the HR based on information collected from the respondent to ensure that all units belong to their correct HR.

T0 –Initial weight

The initial design weight is defined as the inverse of the probability of selection and is computed separately for the RDD and list frame samples since the method of selection differs between these two frames. For the RDD frame, the selection of telephone numbers is done within each RDD stratum. An RDD stratum is an aggregation of area code prefixes (ACP: the first six digits of a 10-digit telephone number), with each ACP containing valid banks of one hundred numbers (see Norris and Paton11 for more details). Therefore, the probability of selection is the ratio between the number of sampled units and one hundred times the number of banks within the RDD stratum.

For the list frame, telephone numbers are randomly selected among those assigned to the specific HR. The probability of selection corresponds to the ratio of the number of sampled units to the number of telephone numbers on the list within the HR. The ratio is based on the frame available and the number of units selected for the particular two-month collection period. The probability of selection can therefore change depending on sample allocation and frame updates. The inverse of these probabilities represents the initial weight T0.

T1 – Number of collection periods

On the area frame, the entire sample is selected at the beginning of the year. This is in contrast to the telephone frame, where samples are drawn every two months. Each of these samples comes with an initial weight that allows each sample to be representative of the population at the HR level. To ensure that the total sample represents the population only once, an adjustment factor is applied to reduce the weights of each two-month sample. The adjustment factor applied to each two-month sample is equal to the the inverse of the number of samples being combined (i.e. the number of collection periods). Following this adjustment, the entire list frame sample corresponds to the average over the entire combined collection period. The initial weights are multiplied by this adjustment factor to produce weight T1.

T2 – Removal of out–of–scope numbers

Telephone numbers associated with businesses, institutions or other out-of-scope dwellings, as well as numbers not in service or any other non-working numbers are all examples of out-of-scope cases for the telephone frame. Similar to the methods used on the area frame, these cases are simply removed from the process, leaving only in-scope dwellings in the sample. These in-scope dwellings keep the same weight as in the previous step, now called weight T2.

T3 – Household nonresponse

The adjustment applied here to compensate for the effect of household nonresponse is identical to the one applied for the area frame (adjustment A4) although the paradata used does differ because of the differences in collection applications for personal and telephone interviews. The adjustment factor calculated within each response homogeneity group is obtained as follows:

Formula 2

The weight T2 of responding households is multiplied by this factor to produce the weight T3. Nonresponding households are removed from the process at this point.

T4 – Multiple phone lines

Some households can possess more than one residential telephone line. This has an impact on the weighting because these households have a higher probability of being selected. The weights for these households need to be adjusted for the number of residential telephone lines within the household. The adjustment factor represents the inverse of the number of lines in the household. The weight T4 is obtained by multiplying this factor by the weight T3.

8.4 Integration of the telephone and area frames (I1)

This step consists of integrating the weights for households common to the area and telephone frames into a single weight by applying a method of integration12. Those units on the area frame that are not on the telephone frame do not have their weights adjusted. For all others units, an adjustment factor α between 0 and 1 is applied to the weights. The weight of the area frame units is multiplied by this factor a, while the weight of the telephone frame units is multiplied by 1– α. Note that in the case where an HR is covered by only one frame, the adjustment factor is equal to 1. Starting in 2008, a fixed α of 0.4 has been used for those units on both frames to ensure greater comparability of estimates across years. The product between the factor derived here and the final household weight calculated earlier (A4 or T4, depending on which frame the unit belongs to), gives the integrated household weight I1.

8.5 Post–integration weighting steps

I2 – Creation of person level weight

Since persons are the desired sampling units, the household-level weights computed to this point need to be converted to the person level. This weight is obtained by multiplying the weight I1 by the inverse of the probability of selection of the person selected in the household. This gives the weight I2. As mentioned earlier, the probability of selection for an individual changes depending on the number of people in the household and the ages of those individuals (see Section 5.6 for more details).

I3 – Person nonresponse

A CCHS interview can be seen as a two-part process. First, the interviewer gets the complete roster of the people within the household. Second, the selected person is interviewed. In some cases, interviewers can only get through the first part, either because they cannot get in touch with the selected person, or because that selected person refuses to be interviewed. Such individuals are defined as person nonrespondents and an adjustment factor must be applied to the weights of person respondents to account for this nonresponse. Using the same methodology that is used in the treatment of household nonresponse, the adjustment is applied within response homogeneity groups. In this process, the scoring method is used to define a response probability based on characteristics available for both respondents and non-respondents. All characteristics collected when creating the roster of household members are available for the estimation of the response probabilities as well as geographic information and some paradata. The probabilities are grouped into response homogeneity groups and the following adjustment factor is calculated within each group:

Formula 3

Weight I2 for responding persons is multiplied by the above adjustment factor to produce weight I3. Nonresponding persons are dropped from the weighting process from this point onward.

I4 – Winsorization

Following the series of adjustments applied to the respondents, some units may come out with extreme weights compared to other units of the same domain of interest. These units could represent a large proportion of their HR or have a large impact on the variance. In order to prevent this, the weight of these outlier units is adjusted downward using a "winsorization" trimming approach.

I5 – Calibration

The last step necessary to obtain the final CCHS weight is calibration (I5). Calibration is done using CALMAR13 to ensure that the sum of the final weights corresponds to the population estimates defined at the HR level, for all 10 age-sex groups of interest. The five age groups are 12-19, 20-29, 30-44, 45-64, 65+, for both males and females. Starting in 2009, additional controls at sub-HR levels were introduced for the applicable HRs. These controls included grouped CLSCs in health regions 2403 (National Capital Region, Quebec) and 2415 (Laurentides, Quebec) as well as DHAs across Nova Scotia. A minimum domain size of 20 respondents is required to calibrate at the HR by age by sex level. For domains that have less 20 respondents, some collapsing is done within province and / or within gender. At the same time, weights are adjusted to ensure that each collection period (two-month period) is equally represented within the sample. Note that the calibration is done using the most up to date geography and may not match the geography used in sampling.

The population estimates are based on the 2006 Census counts and counts of birth, death, immigration and emigration since that time. The average of these monthly estimates for each of the HR-age-sex post-strata by collection period is used to calibrate. The weight I4 is adjusted using CALMAR to obtain the final weight I5. Weight I5 corresponds to the final CCHS person-level weight and can be found on the data file with the variable name WTS_M for master or PUMF users. Prior to the 2010 and 2009-2010 reference period, 2001 Census population counts were used. Evaluation studies have confirmed that the impact of this change on CCHS estimates should be minimal.

8.6 Particular aspects of the weighting in the three territories

As described in Section 5, the sampling frame used in the three territories is somewhat different from the one used in the provinces. Therefore, the weighting strategy is adapted to comply with these differences. This section summarises the changes applied to the steps described in sub-sections 8.1 to 8.5

For the area frame, as mentioned in sub-section 5.4.1, an additional stage of selection is added in the territories where each territory is stratified into groupings of communities and one community is selected within each group. The capital of each territory forms a stratum on its own and is selected automatically at the first stage. This has an effect in the computation of the probability of selection, and therefore in the value of the initial weight (A0). Once the initial weight is calculated, the same series of adjustments (A1 to A4) is applied to the area frame units. Household-level and person-level nonresponse adjustment classes are built in the same way as for the provinces, using the same set of variables.

For the weighting of the telephone frame units, it should be noted that only the RDD frame is used and its use is exclusive to the capitals of the Yukon and the Northwest Territories. All of the telephone frame adjustments are applied to derive a final weight for the telephone units. The two sets of weights (area and telephone) are subsequently integrated and post-stratified in a similar way to what is done for the provinces, with three exceptions. First, the integration is applied only to units located in the Yukon and Northwest Territories capitals since the other communities are covered only by the area frame. Second, the population counts used for calibration for Nunavut represent 70% of the entire population because of the under-coverage of the area frame that was described in section 5.4.1.

Finally, starting with the 2008 and 2007-2008 reference year products, controls have been put in place to ensure that the proportion of aboriginals and the proportion of individuals in the capital regions are controlled in the Northwest Territories and Yukon. A similar control based on Inuit status was introduced for Nunavut. Starting in 2009, the proportion of individuals in the capital regions is controlled in Nunavut. These controls ensure that the proportion of the estimates represented by these different groups is consistent with proportions indicated by the 2006 Census.

8.7 Creation of a share weight

Along with the master file and PUMF which contain all CCHS respondents, a share file is created which contains only a portion (>90%) of the original CCHS respondents. The individuals on this share file have agreed to share their data with certain partners. To compensate for the loss of some respondents from the file, the weights of these "sharers" must be adjusted by the factor:

Formula 14

Similar to the nonresponse adjustments, this factor is calculated within homogeneity groups, where in this case, individuals with similar estimated propensity to share will be grouped together. The final weight after this adjustment is called WTS_S.

8.8 Weighting for a two-year file

When two years of data are combined to create a two-year file, new weights are calculated straightforwardly by halving the annual weights. This ensures that the sum of the final weights is equal to the average population size over the two years. For more information on combining multiple years, please refer to the article "Combining cycles of the Canadian Community Health Survey" published in the Statistics Canada Health Reports publication (82-003) at the following link: 82-003-x

9.0 Data quality

9.1 Response rates for 2010

In total, 88,410 of the selected units in the CCHS 2010 were in-scope for the survey14. Out of these, 71,315 households accepted to participate in the survey resulting in an overall household-level response rate of 80.7%. Among these responding households, 71,315 individuals (one per household) were selected to participate to the survey, out of which a response was obtained for 63,191 individuals, resulting in an overall person-level response rate of 88.6%. At the Canada level, this yields a combined response rate of 71.5% for the CCHS 2010. Table 9.1 provides combined response rates as well as relevant information for their calculation by health region or group of health regions. Table 9.2 provides the same data by Local Health Integrated Network (LHIN) level.

Table 9.1 : 2010 Response rate by health region and frames

(see Appendix E)

Table 9.2 : 2010 Reponse rate by Local Health Integrated Network (LHIN) and frames in Ontario

(see Appendix E)

9.2 Response rates for 2009-2010

In total, 172,671 of the selected units in the CCHS 2009-2010 were in-scope for the survey. Out of these, 139,841 households accepted to participate in the survey resulting in an overall household-level response rate of 81.0%. Among these responding households, 139,841 individuals (one per household) were selected to participate to the survey, out of which a response was obtained for 124,870 individuals, resulting in an overall person-level response rate of 89.3%. At the Canada level, this yields a combined response rate of 72.3% for the CCHS 2009–2010. Table 9.3 provides combined response rates as well as relevant information for their calculation by health region or group of health regions. Table 9.4 provides the same data by Local Health Integrated Network (LHIN) level.

Table 9.3 : 2009-2010 response rate by health regions and frames

(see Appendix G)

Table 9.3 : 2009-2010 response rate by Local Health Integrated Network (LHIN) and frames in Ontario

(see Appendix G)

Next, we describe how the various components of the equation should be handled to correctly compute combined response rates.

Household–level response rate
HHRR = Number of responding households in both frames / All in–scope households in both frames

Person–level response rate
PPRR = Number of responding persons in both frames / All selected persons in both frames

Combined response rate = HHRR x PPRR

Below is an example on how to calculate the combined response rate for Canada using the information found in Table 9.1. The same method applies to rates computed for smaller regions such as province or health region, or to rates computed for the CCHS 2009–2010 using the information found in Table 9.3.

HHRR =
33,387 + 37,928 = 71,315 = 0.807
40,070 + 48,340 = 88,410

PPRR =
30,449 + 32,742 = 63,191 = 0.886
33,387 + 37,928 = 71,315

Combined response rate = 0.807 x 0.866

= 0.715

= 71.5%

9.3 Survey Errors

The estimates derived from this survey are based on a sample of individuals. Somewhat different figures might have been obtained if a complete census had been taken using the same questionnaire, interviewers, supervisors, processing methods, etc. than those actually used. The difference between the estimates obtained from the sample and the results from a complete count under similar conditions is called the sampling error of the estimate.

Errors which are not related to sampling may occur at almost every phase of a survey operation. Interviewers may misunderstand instructions, respondents may make errors in answering questions, the answers may be incorrectly entered on the computer and errors may be introduced in the processing and tabulation of the data. These are all examples of non–sampling errors.

9.3.1 Non–sampling Errors

Over a large number of observations, randomly occurring errors will have little effect on estimates derived from the survey. However, errors occurring systematically will contribute to biases in the survey estimates. Considerable time and effort was made to reduce non-sampling errors in the CCHS 2010. Quality assurance measures were implemented at each step of data collection and processing to monitor the quality of the data. These measures included the use of highly skilled interviewers, extensive training with respect to the survey procedures and questionnaire, and the observation of interviewers to detect problems. Testing of the CAI application and field tests were also essential procedures to ensure that data collection errors were minimized.

A major source of non-sampling errors in surveys is the effect of non-response on the survey results. The extent of non-response varies from partial non-response (failure to answer just one or some questions) to total non-response. Partial non-response to the CCHS was minimal; once the questionnaire was started, it tended to be completed with very little non-response. Total non-response occurred either because a person refused to participate in the survey or because the interviewer was unable to contact the selected person. Total non-response was handled by adjusting the weight of persons who responded to the survey to compensate for those who did not respond. See section 8 for details on the weight adjustment for non-response.

9.3.2 Sampling Errors

Since it is an unavoidable fact that estimates from a sample survey are subject to sampling error, sound statistical practice calls for researchers to provide users with some indication of the magnitude of this sampling error. The basis for measuring the potential size of sampling errors is the standard deviation of the estimates derived from survey results. However, because of the large variety of estimates that can be produced from a survey, the standard deviation of an estimate is usually expressed relative to the estimate to which it pertains. This resulting measure, known as the coefficient of variation (CV) of an estimate, is obtained by dividing the standard deviation of the estimate by the estimate itself and is expressed as a percentage of the estimate.

For example, suppose hypothetically that it is estimated that 25% of Canadians aged 12 and over are regular smokers and that this estimate is found to have a standard deviation of 0.003. Then the CV of the estimate is calculated as:

(0.003/0.25) x 100% = 1.20%

Statistics Canada commonly uses CV results when analyzing data and urges users producing estimates from the CCHS data files to also do so. For details on how to determine CVs, see Section 11. For guidelines on how to interpret CV results, see the table at the end of Sub–section 10.4.

10.0 Guidelines for tabulation, analysis and release

This section of the documentation outlines the guidelines to be used by users in tabulating, analyzing, publishing or otherwise releasing any data derived from the survey files. With the aid of these guidelines, users of microdata should be able to produce figures that are in close agreement with those produced by Statistics Canada and, at the same time, will be able to develop currently unpublished figures in a manner consistent with these established guidelines.

10.1 Rounding guidelines

In order that estimates for publication or other release derived from the data files (Master, Share or PUMF) correspond to those produced by Statistics Canada, users are urged to adhere to the following guidelines regarding the rounding of such estimates:

a) Estimates in the main body of a statistical table are to be rounded to the nearest hundred units using the normal rounding technique. In normal rounding, if the first or only digit to be dropped is 0 to 4, the last digit to be retained is not changed. If the first or only digit to be dropped is 5 to 9, the last digit to be retained is raised by one. For example, in normal rounding to the nearest 100, if the last two digits are between 00 and 49, they are changed to 00 and the preceding digit (the hundreds digit) is left unchanged. If the last digits are between 50 and 99 they are changed to 00 and the proceeding digit is incremented by 1;

b) Marginal sub–totals and totals in statistical tables are to be derived from their corresponding unrounded components and then are to be rounded themselves to the nearest 100 units using normal rounding;

c) Averages, proportions, rates and percentages are to be computed from unrounded components (i.e., numerators and/or denominators) and then are to be rounded themselves to one decimal using normal rounding. In normal rounding to a single digit, if the final or only digit to be dropped is 0 to 4, the last digit to be retained is not changed. If the first or only digit to be dropped is 5 to 9, the last digit to be retained is increased by 1;

d) Sums and differences of aggregates (or ratios) are to be derived from their corresponding unrounded components and then are to be rounded themselves to the nearest 100 units (or the nearest one decimal) using normal rounding;

e) In instances where, due to technical or other limitations, a rounding technique other than normal rounding is used resulting in estimates to be published or otherwise released that differ from corresponding estimates published by Statistics Canada, users are urged to note the reason for such differences in the publication or release document(s);

f) Under no circumstances are unrounded estimates to be published or otherwise released by users. Unrounded estimates imply greater precision than actually exists.

10.2 Sample weighting guidelines for tabulation

The sample design used for this survey was not self–weighting. That is to say, the sampling weights are not identical for all individuals in the sample. When producing simple estimates, including the production of ordinary statistical tables, users must apply the proper sampling weight. If proper weights are not used, the estimates derived from the data file cannot be considered to be representative of the survey population, and will not correspond to those produced by Statistics Canada.

Users should also note that some software packages might not allow the generation of estimates that exactly match those available from Statistics Canada, because of their treatment of the weight field.

10.2.1 Definitions: categorical estimates, quantitative estimates

Before discussing how the survey data can be tabulated and analyzed, it is useful to describe the two main types of point estimates of population characteristics that can be generated from the data files.

Categorical estimates:
Categorical estimates are estimates of the number or percentage of the surveyed population possessing certain characteristics or falling into some defined category. The number of individuals who smoke daily is an example of such an estimate. An estimate of the number of persons possessing a certain characteristic may also be referred to as an estimate of an aggregate.

Example of categorical question:

At the present do/does …smoke cigarettes daily, occasionally or not at all? (SMK_202)
Daily
Occasionally
Not at all

Quantitative estimates:
Quantitative estimates are estimates of totals or of means, medians and other measures of central tendency of quantities based upon some or all of the members of the surveyed population.

An example of a quantitative estimate is the average number of cigarettes smoked per day by individuals who smoke daily. The numerator is an estimate of the total number of cigarettes smoked per day by individuals who smoke daily, and its denominator is an estimate of the number of individuals who smoke daily.

Example of quantitative question:

How many cigarettes do/does you/he/she smoke each day now? (SMK_204)
Number of cigarettes

10.2.2 Tabulation of categorical estimates

Estimates of the number of people with a certain characteristic can be obtained from the data file by summing the final weights of all records possessing the characteristic of interest.

Proportions and ratios of the form x/yare obtained by:

  1. summing the final weights of records having the characteristic of interest for the numerator (x);
  2. summing the final weights of records having the characteristic of interest for the denominator (y); then
  3. dividing the numerator estimate by the denominator estimate.

10.2.3 Tabulation of quantitative estimates

Estimates of sums or averages for quantitative variables can be obtained using the following three steps (only step a) is necessary to obtain the estimate of a sum):

  1. multiplying the value of the variable of interest by the final weight and summing this quantity over all records of interest to obtain the numerator(x);
  2. summing the final weights of records having the characteristic of interest for the denominator (y); then
  3. dividing the numerator estimate by the denominator estimate.

For example, to obtain the estimate of the average number of cigarettes smoked each day by individuals who smoke daily, first compute the numerator (x) by summing the product between the value of variable SMK_204 and the weight WTS_M.Next, sum this value over those records with a value of "daily" to the variable SMK_202. The denominator (y) is obtained by summing the final weight of those records with a value of "daily" to the variable SMK_202. Divide (x) by (y) to obtain the average number of cigarettes smoked each day by daily smokers.

10.3 Guidelines for statistical analysis

The CCHS is based upon a complex design, with stratification and multiple stages of selection, and unequal probabilities of selection of respondents. Using data from such complex surveys presents problems to analysts because the survey design and the selection probabilities affect the estimation and variance calculation procedures that should be used.

While many analysis procedures found in statistical packages allow weights to be used, the meaning or definition of the weight in these procedures can differ from what is appropriate in a sample survey framework, with the result that while in many cases the estimates produced by the packages are correct, the variances that are calculated are almost meaningless.

For many analysis techniques (for example linear regression, logistic regression, analysis of variance), a method exists that can make the application of standard packages more meaningful. If the weights on the records are rescaled so that the average weight is one (1), then the results produced by the standard packages will be more reasonable; they still will not take into account the stratification and clustering of the sample's design, but they will take into account the unequal probabilities of selection. The rescaling can be accomplished by using in the analysis a weight equal to the original weight divided by the average of the original weights for the sampled units (people) contributing to the estimator in question.

10.4 Release guidelines

Before releasing and/or publishing any estimate from the data files, users must first determine the number of sampled respondents having the characteristic of interest (for example, the number of respondents who smoke when interested in the proportion of smokers for a given population) in order to ensure that enough observations are available to calculate a quality estimate. For users of the PUMF, if this number is less than 30, the unweighted estimate should not be released regardless of the value of the coefficient of variation for this estimate. For users of the master or share files, it is recommended to have at least 10 observations in the numerator and 20 in the denominator. For weighted estimates, based on sample sizes of 10 or more (30 for the PUMF), users should determine the coefficient of variation of the estimate and follow the guidelines below.

Table 10.1 Sampling variability guidelines


Type of Estimate
CV(in%) Guidelines
Acceptable 0.0 ≤ CV ≤ 16.5 Estimates can be considered for general unrestricted release. Requires no special notation.
Marginal 16.6 < CV ≤ 33.3 Estimates can be considered for general unrestricted release but should be accompanied by a warning cautioning subsequent users of the high sampling variability associated with the estimates. Such estimates should be identified by the letter E (or in some other similar fashion).
Unacceptable CV > 33.3 Statistics Canada recommends not to release estimates of unacceptable quality. However, if the user chooses to do so then estimates should be flagged with the letter F (or in some other fashion) and the following warning should accompany the estimates:
“The user is advised that…(specify the data)…do not meet Statistics Canada’s quality standards for this statistical program. Conclusions based on these data will be unreliable and most likely invalid. These data and any consequent findings should not be published. If the user chooses to publish these data or findings, then this disclaimer must be published with the data.”

11.0 Approximate sampling variability tables

In order to supply coefficients of variation that will be applicable to a wide variety of categorical estimates produced from a PUMF and that could be readily accessed by the user, a set of Approximate Sampling Variability Tables will be produced with each PUMF. These "look–up" tables allow the user to obtain an approximate coefficient of variation based on the size of the estimate calculated from the survey data.

The coefficients of variation (CV) are derived using the variance formula for simple random sampling and incorporating a factor which reflects the multi–stage, clustered nature of the sample design. This factor, known as the design effect, was determined by first calculating design effects for a wide range of characteristics and then choosing, for each table produced, a conservative value among all design effects relative to that table. The value chosen was then used to generate a table that applies to the entire set of characteristics.

The Approximate Sampling Variability Tables, along with the design effects, the sample sizes and the population counts that were used to produce them, are provided in the document Approximate Sampling Variability Tables, which is available to the share file and PUMF users. All coefficients of variation in the Approximate Sampling Variability Tables are approximate and, therefore, unofficial. Options concerning the computation of exact coefficients of variation are discussed in sub-section 11.7.

Remember: As indicated in Sampling Variability Guidelines in Section 10.4, if the number of observations on which an estimate is based is less than 30, the weighted estimate should not be released regardless of the value of the coefficient of variation. Coefficients of variation based on small sample sizes are too unpredictable to be adequately represented in the tables.

11.1 How to use the CV tables for categorical estimates

The following rules should enable the user to determine the approximate coefficients of variation from the Sampling Variability Tables for estimates of the number, proportion or percentage of the surveyed population possessing a certain characteristic and for ratios and differences between such estimates.

Rule 1: Estimates of numbers possessing a characteristic (aggregates)

The coefficient of variation depends only on the size of the estimate itself. On the appropriate Approximate Coefficients of Variations Table, locate the estimated number in the left–most column of the table (headed "Numerator of Percentage") and follow the asterisks (if any) across to the first figure encountered. Since not all the possible values for the estimate are available, the smallest value which is the closest must be taken (as an example, if the estimate is equal to 1,700 and the two closest available values are 1,000 and 2,000, the first has to be chosen). This figure is the approximate coefficient of variation.

Rule 2: Estimates of proportions or percentages of people possessing a characteristic

The coefficient of variation of an estimated proportion (or percentage) depends on both the size of the proportion and the size of the numerator upon which the proportion is based. Estimated proportions are relatively more reliable than the corresponding estimates of the numerator of the proportion when the proportion is based upon a sub–group of the population. This is due to the fact that the coefficients of variation of the latter type of estimates are based on the largest entry in a row of a particular table, whereas the coefficients of variation of the former type of estimators are based on some entry (not necessarily the largest) in that same row. (Note that in the tables the CVs decline in value reading across a row from left to right). For example, the estimated proportion of individuals who smoke daily out of those who smoke at all is more reliable than the estimated number who smoke daily.

When the proportion (or percentage) is based upon the total population covered by each specific table, the CV of the proportion is the same as the CV of the numerator of the proportion. In this case, this is equivalent to applying Rule 1.

When the proportion (or percentage) is based upon a subset of the total population (e.g., those who smoke at all), reference should be made to the proportion (across the top of the table) and to the numerator of the proportion (down the left side of the table). Since not all the possible values for the proportion are available, the smallest value which is the closest must be taken (for example, if the proportion is 23% and the two closest values available in the column are 20% and 25%, 20% must be chosen). The intersection of the appropriate row and column gives the coefficient of variation.

Rule 3: Estimates of differences between aggregates or percentages

The standard error of a difference between two estimates is approximately equal to the square root of the sum of squares of each standard error considered separately. That is, the standard error of a difference (Formula 4) is:

Formula 5

where X1 is estimate 1, X2 is estimate 2, and a1 and a2 are the coefficients of variation of X1 and X2 respectively. The coefficient of variation of d is given by Formula 3. This formula is accurate for the difference between independent populations or subgroups, but is only approximate otherwise. It will tend to overstate the error, if X1 and X2 are positively correlated and understate the error if X1 and X2 are negatively correlated.

Rule 4: Estimates of ratios

In the case where the numerator is a subset of the denominator, the ratio should be converted to a percentage and Rule 2 applied. This would apply, for example, to the case where the denominator is the number of individuals who smoke at all and the numerator is the number of individuals who smoke daily out of those who smoke at all.

Consider the case where the numerator is not a subset of the denominator, as for example, the ratio of the number of individuals who smoke daily or occasionally as compared to the number of individuals who do not smoke at all. The standard deviation of the ratio of the estimates is approximately equal to the square root of the sum of squares of each coefficient of variation considered separately multiplied by R, where R is the ratio of the estimates (Formula 6). That is, the standard error of a ratio is:

Formula 7

Where α1 and α2 are the coefficients of variation of X1 and X2 respectively.

The coefficient of variation of R is given by Formula 7. The formula will tend to overstate the error, if X1 and X2 are positively correlated and understate the error if X1 and X2 are negatively correlated.

Rule 5: Estimates of differences of ratios

In this case, Rules 3 and 4 are combined. The CVs for the two ratios are first determined using Rule 4, and then the CV of their difference is found using Rule 3.

11.2 Examples of using the CV tables for categorical estimates

The following "real life" examples are included to assist users in applying the foregoing rules.

Example 1: Estimates of numbers possessing a characteristic (aggregates)

Suppose that a user estimates that 4,722,617 individuals smoke daily in Canada. How does the user determine the coefficient of variation of this estimate?

1) Refer to the CANADA level CV table.

2) The estimated aggregate (4,722,617) does not appear in the left–hand column (the "Numerator of Percentage" column), so it is necessary to use the smallest figure closest to it, namely 4,000,000.

3) The coefficient of variation for an estimated aggregate (expressed as a percentage) is found by referring to the first non–asterisk entry on that row, namely, 1.70%.

4) So the approximate coefficient of variation of the estimate is 1.70%. According to the Sampling Variability Guidelines presented in Section 10.4, the finding that there were 4,722,617 individuals who smoke daily is publishable with no qualifications.

Example 2 : Estimates of proportions or percentages possessing a characteristic

Suppose that the user estimates that 4,722,617/6,081,453=77.7% of individuals in Canada who smoke at all smoke daily. How does the user determine the coefficient of variation of this estimate?

1) Refer to the CANADA level CV table.

2) Because the estimate is a percentage which is based on a subset of the total population (i.e., individuals who smoke at all, that is to say, daily or occasionally), it is necessary to use both the percentage (77.7%) and the numerator portion of the percentage (4,722,617) in determining the coefficient of variation.

3) The numerator (4,722,617) does not appear in the left–hand column (the "Numerator of Percentage" column) so it is necessary to use the smallest figure closest to it, namely 4,000,000. Similarly, the percentage estimate does not appear as any of the column headings, so it is necessary to use the figure closest to it, 70.0%.

4) The figure at the intersection of the row and column used, namely 1.0% is the coefficient of variation (expressed as a percentage) to be used.

5) So the approximate coefficient of variation of the estimate is 1.0%. According to the Sampling Variability Guidelines presented in Section 10.4, the finding that 77.7% of individuals who smoke at all smoke daily can be published with no qualifications.

Example 3 : Estimates of differences between aggregates or percentages

Suppose that a user estimates that, among men, 2,535,367/13,078,499 = 19.4% smoke daily (estimate 1), while for women, this percentage is estimated at 2,187,250 / 13,476,931 = 16.2% (estimate 2). How does the user determine the coefficient of variation of the difference between these two estimates?

1) Using the CANADA level CV table in the same manner as described in example 2 gives the CV for estimate 1 as 2.41.5% (expressed as a percentage), and the CV for estimate 2 as 2.41.5% (expressed as a percentage).

2) Using rule 3, the standard error of a difference (d= X2X1) is :

Formula 5

Where X1 is estimate 1, X2 is estimate 2, and α1 and α2 are the coefficients of variation of X1 and X2 respectively. The standard error of the difference d= (0.194 – 0.162) = 0.032 is :

Formula 8

3) The coefficient of variation of d is given by oa/d= 0.0061/0.032 = 0.190.

4) So the approximate coefficient of variation of the difference between the estimates is 12.59.0% (expressed as a percentage). According to the Sampling Variability Guidelines presented in Section 10.4, this estimate can be published but a warning has to be issued with no qualifications.

Example 4 : Estimates of ratios

Suppose that the user estimates that 4,722,617 individuals smoke daily, while 1,358,836 individuals smoke occasionally. The user is interested in comparing the estimate of daily to occasional smokers in the form of a ratio. How does the user determine the coefficient of variation of this estimate?

1) First of all, this estimate is a ratio estimate, where the numerator of the estimate (= X1) is the number of individuals who smoke occasionally. The denominator of the estimate (= X2) is the number of individuals who smoke daily.

2) Refer to the CANADA level CV table.

3) The numerator of this ratio estimate is 1,358,836. The smallest figure closest to it is 1,000,000. The coefficient of variation for this estimate (expressed as a percentage) is found by referring to the first non–asterisk entry on that row, namely, 3.72.3%.

4) The denominator of this ratio estimate is 4,722,617. The figure closest to it is 4,000,000. The coefficient of variation for this estimate (expressed as a percentage) is found by referring to the first non–asterisk entry on that row, namely, 1.07%.

5) So the approximate coefficient of variation of the ratio estimate is given by rule 4, which is,

Formula 9,

That is,

Formula 10

where α1 and α2 are the coefficients of variation of X1 and X2 respectively. The obtained ratio of occasional to daily smokers is 1,358,836/4,722,617 which is 0.29:1. The coefficient of variation of this estimate is 4.12.5% (expressed as a percentage), which is releasable with no qualifications, according to the Sampling Variability Guidelines presented in Section 10.4.

11.3 How to use the CV tables to obtain confidence limits

Although coefficients of variation are widely used, a more intuitively meaningful measure of sampling error is the confidence interval of an estimate. A confidence interval constitutes a statement on the level of confidence that the true value for the population lies within a specified range of values. For example a 95% confidence interval can be described as follows: if sampling of the population is repeated indefinitely, each sample leading to a new confidence interval for an estimate, then in 95% of the samples the interval will cover the true population value.

Using the standard error of an estimate, confidence intervals for estimates may be obtained under the assumption that under repeated sampling of the population, the various estimates obtained for a population characteristic are normally distributed about the true population value. Under this assumption, the chances are about 68 out of 100 that the difference between a sample estimate and the true population value would be less than one standard error, about 95 out of 100 that the difference would be less than two standard errors, and about 99 out of 100 that the differences would be less than three standard errors. These different degrees of confidence are referred to as the confidence levels.

Confidence intervals for an estimate, x, are generally expressed as two numbers, one below the estimate and one above the estimate, as Formula 11, where k is determined depending upon the level of confidence desired and the sampling error of the estimate.

Confidence intervals for an estimate can be calculated directly from the Approximate Sampling Variability Tables by first determining from the appropriate table the coefficient of variation of the estimate x, and then using the following formula to convert to a confidence interval (CI):

Formula 12

Where ax is determined coefficient of variation for x, and

z=1 if a 68% confidence interval is desired
z=1.6 if a 90% confidence interval is desired
z=2 if a 95% confidence interval is desired
z=3 if a 99% confidence interval is desired.

Note: Release guidelines presented in section 10.4 which apply to the estimate also apply to the confidence interval. For example, if the estimate is not releasable, then the confidence interval is not releasable either.

11.4 Example of using the CV tables to obtain confidence limits

A 95% confidence interval for the estimated proportion of individuals who smoke daily from those who smoke at all (from example 2, sub–section 11.2) would be calculated as follows:

x = 0.777

z = 2

ax = 0.016 is the coefficient of variation of this estimate as determined from the tables.

C1r = {0.777 – (2) (0.777) (0.0061) , 0.777 + (2) (0.777) (0.0061)}

C1r = {0.7618 , 0.79386}

11.5 How to use the CV tables to do a Z–test

Standard errors may also be used to perform hypothesis testing, a procedure for distinguishing between population parameters using sample estimates. The sample estimates can be numbers, averages, percentages, ratios, etc. Tests may be performed at various levels of significance, where a level of significance is the probability of concluding that the characteristics are different when, in fact, they are identical.

Let X1 and X2 be sample estimates for 2 characteristics of interest. Let the standard error on the difference X1-X2 be oa. If the ratio of X1-X2 over ao is between –2 and 2, then no conclusion about the difference between the characteristics is justified at the 5% level of significance. If however, this ratio is smaller than –2 or larger than +2, the observed difference is significant at the 0.05 level.

11.6 Example of using the CV tables to do a Z–test

Let us suppose we wish to test, at 5% level of significance, the hypothesis that there is no difference between the proportion of men who smoke daily AND the proportion of women who smoke daily. From example 3, sub–section 11.2, the standard error of the difference between these two estimates was found to be = 0.00461. Hence,

Formula 13

Since z= 85.25 is greater than 2, it must be concluded that there is a significant difference between the two estimates at the 0.05 level of significance. Note that the two sub–groups compared are considered as being independent, so the test is correct.

11.7 Exact variances/coefficients of variation

All coefficients of variation in the Approximate Sampling Variability Tables (CV Tables) are indeed approximate and, therefore, unofficial.

The computation of exact coefficients of variation is not a straightforward task since there is no simple mathematical formula that would account for all CCHS sampling frame and weighting aspects. Therefore, other methods such as resampling methods must be used in order to estimate measures of precision. Among these methods, the bootstrap method is the one recommended for analysis of CCHS data.

The computation of coefficients of variation (or any other measure of precision) with the use of the bootstrap method requires access to information that is considered confidential and not available on the PUMF. This computation must be done using the Master file. Access to the Master file is discussed in section 12.3.

For the computation of coefficients of variation, the bootstrap method is advised. A macro program, called “Bootvar”, was developed in order to give users easy access to the bootstrap method. The Bootvar program is available in SAS and SPSS formats, and is made up of macros that calculate the variances of totals, ratios, differences between ratios, and linear and logistic regressions.

There are a number of reasons why a user may require an exact variance. A few are given below.

Firstly, if a user desires estimates at a geographic level other than those available in the tables (for example, at the rural/urban level), then the CV tables provided are not adequate. Coefficients of variation of these estimates may be obtained using "domain" estimation techniques through the exact variance program.

Secondly, should a user require more sophisticated analyses such as estimates of parameters from linear regressions or logistic regressions, the CV tables will not provide correct associated coefficients of variation. Although some standard statistical packages allow sampling weights to be incorporated in the analyses, the variances that are produced often do not take into account the stratified and clustered nature of the design properly, whereas the exact variance program would do so.

Thirdly, for estimates of quantitative variables, separate tables are required to determine their sampling error. Since most of the variables for the CCHS are primarily categorical in nature, this has not been done. Thus, users wishing to obtain coefficients of variation for quantitative variables can do so through the exact variance program. As a general rule, however, the coefficient of variation of a quantitative total will be larger than the coefficient of variation of the corresponding category estimate (i.e., the estimate of the number of persons contributing to the quantitative estimate). If the corresponding category estimate is not releasable, the quantitative estimate will not be either. For example, the coefficient of variation of the estimate of the total number of cigarettes smoked each day by individuals who smoke daily would be greater than the coefficient of variation of the corresponding estimate of the number of individuals who smoke daily. Hence if the coefficient of variation of the latter is not releasable, then the coefficient of variation of the corresponding quantitative estimate will also not be releasable.

Lastly, should users find themselves in a position where they can use the CV tables, but this renders a coefficient of variation in the "marginal" range (16.6% – 33.3%), the user should release the associated estimate with a warning cautioning users of the high sampling variability associated with the estimate. This would be a good opportunity to recalculate the coefficient of variation through the exact variance program to find out if it is releasable without a qualifying note. The reason for this is that the coefficients of variation produced by the tables are based on a wide range of variables and are therefore considered crude, whereas the exact variance program would give an exact coefficient of variation associated with the variable in question.

11.8 Release cut–offs for the CCHS

The document Approximate Sampling Variability Table, which is available to the share file and PUMF users, presents tables giving the minimum cut–offs for estimates of totals at the Canada, provincial, health region and CLSC levels and those for various age groups at the Canada level. Estimates smaller than the value given in the "Marginal" column may not be released under any circumstances.

12.0 Microdata Files: Description, Access and Use

The CCHS produces three types of microdata files: master files, share files and public use microdata files (PUMF). Table 12.1 includes the list of all available 2010 and 2009-2010 data files.

12.1 Master files

The master files contain all variables and all records from the survey collected during a collection period. These files are accessible at Statistics Canada for internal use and in Statistics Canada’s Research Data Centres (RDC), and are also subject to custom tabulation requests.

12.1.1 Research Data Centre

The RDC Program enables researchers to use the survey data in the master files in a secure environment in several universities across Canada. Researchers must submit research proposals that, once approved, give them access to the RDC. For more information, please consult the following web page: RDC

12.1.2 Custom tabulations

Another way to access the master files is to offer all users the option of having staff in Client Services of the Health Statistics Division prepare custom tabulations. This service is offered on a cost–recovery basis. It allows users who do not possess knowledge of tabulation software products to get custom results. The results are screened for confidentiality and reliability concerns before release. For more information, please contact Client Services at 613–951–1746 or by e–mail at: hd–ds@statcan.gc.ca.

12.1.3 Remote access

Finally, the remote access service to the survey master files is another way to have access to these data if, for some reason, the user cannot access a Research Data Centre. Each purchaser of the microdata product can be supplied with a synthetic or ‘dummy’ master file and a corresponding record layout. With these tools, the researcher can develop his own set of analytical computer programs. The code for the custom tabulations is then sent via e–mail to cchs–escc@statcan.gc.ca. The code will then be transferred into Statistics Canada’s internal secured network and processed using the appropriate master file of CCHS data. Estimates generated will be released to the user, subject to meeting the guidelines for analysis and release outlined in Section 10 of this document. Results are screened for confidentiality and reliability concerns and then the output is returned to the client. There is no charge for this service.

12.2 Share files

The share files contain all variables and all records of CCHS respondents who agreed to share their data with Statistic Canada’s partners, which are the provincial and territorial health departments, Health Canada and the Public Health Agency of Canada. Statistics Canada also asks respondents living in Quebec for their permission to share their data with the Institut de la statistique du Québec. The share file is released only to these organizations. Personal identifiers are removed from the share files to respect respondent confidentiality. Users of these files must first certify that they will not disclose, at any time, any information that might identify a survey respondent.

12.3 Public use microdata files

The public use microdata files (PUMF) are developed from the master files using a technique that balances the need to ensure respondent confidentiality with the need to produce the most useful data possible at the health region level. The PUMF must meet stringent security and confidentiality standards required by the Statistics Act before they are released for public access. To ensure that these standards have been achieved, each PUMF goes through a formal review and approval process by an executive committee of Statistics Canada.

Variables most likely to lead to identification of an individual are deleted from the data file or are collapsed to broader categories.

The PUMF contains the data collected over two years. It includes questions that were asked over two years. Unless otherwise specified, these questions are usually those included in the annual common content and in the two-year common content as well as the optional content selected for two years by the provinces and territories.

There is no charge to access the PUMF in a post–secondary educational institution that is part of the Data Liberation Initiative. They are also free of charge from Client Services on request at 613-951-1746 or by e–mail at hd-ds@statcan.gc.ca.

Table 12.1 2009 CCHS data files
Reference period Files File name Sampling weight Bootstrap weights file Variables included Records included
2010 Main master file HS.txt WTS_M b5.txt All common and all optional modules. All respondent records
Share file HS.txt WTS_S b5.txt All common and all optional modules. Records of all respondents who agreed to share their data
2009–2010 Main master file HS.txt WTS_S b5.txt All common annual and 2-yr and optional modules that were selected for 2 years All respondent records
Share file HS.txt WTS_S b5.txt All common annual and 2-yr and optional modules that were selected for 2 years Records of all respondents who agreed to share their data

12.4 How to use the CCHS data files: annual data file or two–year data file?

Since the 2008 and 2007–2008 data were released, users that have access to share files or master files have had the choice of using one–year or two–year data files. Decisions about which period to use in a given data analysis should be guided by the level of detail and the quality required. With a one–year file, estimates will not always available because of the quality associated with limited sample sizes.

Before interpreting and using a CCHS estimate, it is recommended to make sure that the estimates meets the following rules:

  • Coefficient of Variation 33.3% or less
  • a minimum of 10 respondents in the domain with the characteristic and
  • total domain of interest includes at least 20 respondents.

This will not be possible for rare characteristics and detailed domains with one-year files. Instead, users will have to rely on two-year files or multi-year files.

Where the use of either a one–year or two–year file is viable, the user should consider the trade–off between accuracy and currency. If it is important to reflect the current characteristics of a population as closely as possible, the one–year file would be preferable. However, with the increased sample size, more detailed estimates and analyses can be carried out with a two–year file.

12.5 Use of weight variable

The weight variable WTS_M represents the sampling weight for key survey files. For a given respondent, the sampling weight can be interpreted as the number of people the respondent represents in the Canadian population.This weight must always be used when computing statistical estimates in order to make inference at the population level possible.The production of unweighted estimates is not recommended. The sample allocation, as well as the survey design specifics can cause such results to not correctly represent the population. Refer to section 8 on weighting for a more detailed explanation on the creation of this weight. The weight variable WTS_M must be used for regional analyses.

The Food Security module, included in certain reference period data files, measures concepts that apply not only to the respondent’s situation, but also to that of the respondent’s entire household. Depending on the level of analysis, the analysis of the variables may require use of a weight calculated to represent the number of Canadian households, rather than the number of persons. This weight variable WTS_HH is found in a separate file (HS_HHWT.txt). It can be used in place of the variable WTS_M for household analyses at the national and provincial levels.

12.6 Variable naming convention beginning in 2007

The variable naming convention adopted allows data users to easily use and identify the data based on the module and variable type. The CCHS variable naming convention fulfils two requirements: to restrict variable names to a maximum of eight characters for ease of use by analytical software products and to identify easily conceptually identical variables from one survey collection period to the next. Questions to which changes are made between two collection periods, and where the changes alter the concept measured by the question, are entirely renamed to avoid any confusion in the analysis.

The CCHS variable naming convention was changed beginning with the data from the 2007 collection period. The letter corresponding to the survey version (for example, A =2000 ( cycle 1.1), C =2003 cycle 2.1) and E =2005 (3.1) is no longer used in the variable names. A new variable (REFPER, format = YYYYMM–YYYYMM) was added to the microdata files in order to identify the beginning and the end of the reference during which data included in the file were collected. This variable will be useful, notably for users wanting to use data from several collection periods at a time. Therefore, variable names for identical modules or questions from one collection year to the next (example, 2007 and 2008) will be the same.

The naming convention used for variables beginning with the 2007 CCHS use up to eight characters. The variable names are structured as follows:

Positions 1 to 3: Module/questionnaire section name
Position 4: Variable type (underscore, C, D, F or G)
Positions 5 to 8: Question number and answer option for multiple response questions

Example1 shows that the structure of the variable name for question 202, Smoking Module, is SMK_202 :

Positions 1 to 3: SMK Smoking module
Position 4 : _ ( underscore = collected data)
Position 5 to 8: 202 Question number

Example 2 shows the structure of the variable name for question2 of the Health Care Utilization Module (HCU_02A), which is a multi–response question:

Positions 1 to 3: HCU Health care utilization module
Position 4 : _ ( underscore = collected data)
Position 5 to 8: 02AA Corresponding question number and answer option

Positions1 to 3 contain the acronyms for each of the modules. These acronyms appear beside the module names given in the table in AppendixA.

Position 4 designates the variable type based on whether it is a variable collected directly from a questionnaire question (“_”), from a coded (“C”), derived (“D”), grouped (“G”), or flag (“F”) variable.

In general, the last four positions (5 to 8) follow the variable numbering used on the questionnaire. The letter "Q" used to represent the word "question" is removed, and all question numbers are presented in a two or three digit format. For example, question Q01A in the questionnaire becomes simply 01A, and question Q15 becomes simply 15.

Table 12.2 Designation of codes used in the 4th position of the CCHS variable names
_ Collected variable A variable that appears directly on the questionnaire
C Coded variable A variable coded from one or more collected variables (e.g., SIC, Standard Industrial Classification code)
D Derived variable A variable calculated from one or more collected or coded variables, usually calculated during head office processing (e.g., Health Utility Index)
F Flag variable A variable calculated from one or more collected variables (like a derived variable), but usually calculated by the data collection computer application for later use during the interview (e.g., work flag)
G Grouped variable Collected, coded, suppressed or derived variables collapsed into groups (e.g., age groups)

For questions that have more than one response option, the final position in the variable naming sequence is represented by a letter. For this type of question, new variables were created to differentiate between a "yes" or "no" answer for each response option. For example, if Q2 had 4 response options, the new questions would be named Q2A for option 1, Q2B for option 2, Q2C for option 3, etc. If only options 2 and 3 were selected, then Q2A = No, Q2B = Yes, Q2C = Yes and Q2D = No.

12.7 Variable naming convention before 2007

As mentioned earlier, the variable naming convention was changed in 2007. The flag for the cycle in which the variables were collected was removed. This flag was found in the 4th position for 2000 to 2005 data (cycles 1.1 to 3.1).

Here is the list of letters used in the CCHS microdata files between cycles 1.1 and 3.1 and their corresponding cycle.

Letter Cycle and cycle name

A 2000 Cycle 1.1: Canadian Community Health Survey

B 2002 Cycle 1.2: Canadian Community Health Survey – Mental Health and Well–Being

C 2003 Cycle 2.1: Canadian Community Health Survey

D 2004 Cycle 2.2: Canadian Community Health Survey – Nutrition

E 2005 Cycle 3.1: Canadian Community Health Survey

12.8 Guidelines for the use of sub–sample variables – Not applicable to 2010 and 2009–2010 data files

12.9 Data dictionaries

Separate data dictionary reports, including universe statements and frequencies, are provided for the main master file and each of the sub–sample files.

In the master file data dictionary reports, optional content modules are treated in the same way as previous CCHS cycles. For each module, a flag indicates whether a given respondent lives in a health region where the module was selected as optional content. When the flag is equal to 2 (No), all variables in the module have “not applicable” values. For example, the DOWST variable indicates if the Work stress module applies to a given respondent.

12.10 Differences in calculation of common content variables using different files

Variables from common content modules can be estimated using either of the two data files provided, when a one year and a two-year data file is available. Depending on which file is used, very small differences will be observed.

All official Statistics Canada estimates of variables from common modules are based on the main master file sampling weight.

Appendix A

Appendix A – Canadian community health survey content (2009–2010)
Annual common content (allregions)
  • Age of respondent (ANC)
  • Alcohol use (ALC)
  • Chronic conditions (CCC)
  • Exposure to second-hand smoke (ETS)
  • Flu shots (FLU)
  • Fruit and vegetable consumption (FVC)
  • General health (GEN)
  • Health care utilization (HCU)
  • Pain and discomfort (HUP)
  • Height and weight – Self-reported (HWT)
  • Maternal experiences - Breastfeeding (MEX)
  • Fruit and vegetable consumption (FVC)
  • Physical activities (PAC)
  • Restriction of activities (RAC)
  • Smoking (SMK)
Administration and socio–demographic information
  • Administrative information (ADM)
  • Dwelling characteristics (DWL)
  • Education (EDU)
  • Income (INC)
  • Labour force (LBS)
  • Socio–demographic characteristics (SDC)
  • Person most knowledgeable about the household (PMK–2010 only, not in 2009–2010 data file)
Two year / One year common content (allregions)heme content (all regions)
2009–2010:Injuries and Functional Health 2009 Only: Health Service and Access (sub–sample)i 2010 Only: Health Care Utilization and Economic Burden
  • Health Utilities Index (HUI)
  • Activities of daily living (ADL)
  • of protective equipment (UPE)
  • Sexual behaviours (SXB)
  • Injuries (INJ)
  • Access to health care services (ACC)
  • Wait times (WTM)
  • Contacts with health professionals (CHP)
  • Unmet health care needs (UCN)
  • H1N1 Immunization
  • Neurological conditions (NEU)
  • Loss of Productivity (LOP)
  • Fibromyalgia (CC3)
  • Chronic fatigue syndrome and multiple chemical sensitivities (CC4)
Optional content (certain regions)
  • Alcohol use – Dependence (ALD)
  • Alcohol use during the past week (ALW)
  • Blood pressure check (BPC)
  • Breast examination (BRX)
  • Breast self–examination (BSX)
  • Changes made to improve health (CIH)
  • Colorectal cancer screening (CCS)
  • Consultations about mental health (CMH)
  • Dental visits (DEN)
  • Depression (DEP)
  • Diabetes care (DIA)
  • Dietary supplement use – Vitamins and minerals (DSU)
  • Distress (DIS)
  • Driving and safety (DRV)
  • Eye examinations (EYX)
  • Food choices (FDC)
  • Food security (FSC)
  • Health care system satisfaction (HCS)
  • Health status (SF-36) (SFR)
  • Home care services (HMC)
  • Home safety (HMS)
  • Illicit drugs use (IDU)
  • Insurance coverage (INS)
  • Mammography (MAM)
  • Mastery (MAS)
  • Maternal experiences – Alcohol use during pregnancy (MXA)
  • Maternal experiences – Smoking during pregnancy (MXS)
  • Oral health 2 (OH2)
  • Pap smear test (PAP)
  • Patient satisfaction – Community-based care (PSC)
  • Patient satisfaction – Health care services (PAS)
  • Physical activities – Facilities at work (PAF)
  • Problem gambling (CPG)
  • Prostate cancer screening (PSA)
  • Psychological well-being (PWB)
  • Satisfaction with life (SWL)
  • Sedentary activities (SAC)
  • Self-esteem (SFE)
  • Smoking – Other tobacco products (TAL)
  • Smoking – Physician counselling (SPC)
  • Smoking – Stages of change (SCH)
  • Smoking cessation methods (SCA)
  • Social support – Availability (SSA)
  • Social support – Utilization (SSU)
  • Stress – Coping with stress (STC)
  • Stress – Sources (STS)
  • Suicidal thoughts and attempts (SUI)
  • Sun safety behaviours (SSB)
  • Voluntary organizations - Participation (ORG)
Rapid Response
2009
  • Sleep Apnea (SLA) (JanFeb 2009)
  • Osteoporosis (OST) (MarApr 2009)
  • Infertility (IFT) (SepDec 2009)
2010
  • Stigma towards depression (STG) (May – June 2010)
  • Mental Health Experience (MHE) (May – June 2010)
iAsked of a sub–sample of respondents.These theme modules were not asked of respondents in the territories.

Appendix B – Selection of optional content by province and territory (2010 and 2009–2010)

Standard table symbols

Appendix B (2010) – Selection of optional content by province or territory
Optional Modules Newfoundland Prince–Edward–Island Nova–Scotia New
Brunswick
Quebec Ontario Manitoba Saskatchewan Alberta British
Columbia
Yukon Northwest
Territories
Nunavut
Access to health care services (ACC)
Alcohol use – Dependence (ALD)
Alcohol use during the past week (ALW)
Blood pressure check (BPC)
Breast examinations (BRX)
Breast self examinations (BSX)
Changes made to improve health (CIH)
Colorectal cancer screening (CCS)
Consultations about mental health (CMH)
Dental visits (DEN)
Depression (DEP)
Diabetes care (DIA)
Dietary supplement use – Vitamins and minerals (DSU)
Distress (DIS)
Driving and safety (DRV)
Eye examinations (EYX)
Food choices (FDC)
Food security (FSC)
Health care system satisfaction (HCS)
Health status (SF–36) (SFR)
Home care services (HMC)
Home safety (HMS)
Illicit drugs use (IDG)
Insurance coverage (INS)
Mammography (MAM)
Mastery (MAS)
Maternal experiences – Alcohol use during pregnancy (MXA)
Maternal experiences – Smoking during pregnancy (MXS)
Oral health 2 (OH2)
PAP smear test (PAP)
Patient satisfaction – Health care services (PAS)
Patient satisfaction – Community–based care (PSC)
Physical activities – Facilities at work (PAF)
Problem gambling (CPG)
Prostate cancer screening (PSA)
Psychological well-being (PWB)
Satisfaction with life (SWL)
Sedentary activities (SAC)
Self-esteem (SFE)
Smoking – Physician counselling (SPC)
Smoking – Stages of change (SCH)
Smoking cessation methods (SCA)
Social support – Availability (SSA)
Social support – Utilization (SSU)
Stress – Coping with stress (STC)
Stress – Sources (STS)
Suicidal thoughts and attempts (SUI)
Sun safety behaviours (SSB)
Smoking – Other tobacco products (TAL)
Voluntary organizations – Participation (ORG)
Waiting times (WTM)

Note: • denotes selected

Standard table symbols

Appendix B (2009–2010) – Selection of optional content by province or territory
Optional Modules Newfoundland Prince–Edward–Island Nova–Scotia New
Brunswick
Quebec Ontario Manitoba Saskatchewan Alberta British
Columbia
Yukon Northwest
Territories
Nunavut
Access to health care services (ACC)
Alcohol use – Dependence (ALD)
Alcohol use during the past week (ALW)
Blood pressure check (BPC)
Breast examinations (BRX)
Breast self examinations (BSX)
Changes made to improve health (CIH)
Colorectal cancer screening (CCS)
Consultations about mental health (CMH)
Dental visits (DEN)
Depression (DEP)
Diabetes care (DIA)
Dietary supplement use – Vitamins and minerals (DSU)
Distress (DIS)
Driving and safety (DRV)
Eye examinations (EYX)
Food choices (FDC)
Food security (FSC)
Health care system satisfaction (HCS)
Health status (SF–36) (SFR)
Home care services (HMC)
Home safety (HMS)
Illicit drugs use (IDG)
Insurance coverage (INS)
Mammography (MAM)
Mastery (MAS)
Maternal experiences – Alcohol use during pregnancy (MXA)
Maternal experiences – Smoking during pregnancy (MXS)
Oral health 2 (OH2)
PAP smear test (PAP)
Patient satisfaction – Health care services (PAS)
Patient satisfaction – Community–based care (PSC)
Physical activities – Facilities at work (PAF)
Problem gambling (CPG)
Prostate cancer screening (PSA)
Psychological well-being (PWB)
Satisfaction with life (SWL)
Sedentary activities (SAC)
Self-esteem (SFE)
Smoking – Physician counselling (SPC)
Smoking – Stages of change (SCH)
Smoking cessation methods (SCA)
Social support – Availability (SSA)
Social support – Utilization (SSU)
Stress – Coping with stress (STC)
Stress – Sources (STS)
Suicidal thoughts and attempts (SUI)
Sun safety behaviours (SSB)
Smoking – Other tobacco products (TAL)
Voluntary organizations – Participation (ORG)
Waiting times (WTM)

Note: • denotes selected

Appendix C

Appendix C – Available geography in the master and share files and their corresponding codes: Canada, provinces/territories, health regions and peer groups
0 Canada
10 Newfoundland and Labrador
1011–C Eastern Regional Integrated Health Authority
1012–I Central Regional Integrated Health Authority
1013–I Western Regional Integrated Health Authority
1014–H Labrador–Grenfell Regional Integrated Health Authority
11 Prince Edward Island
1101–D Kings County
1102–A Queens County
1103–C Prince County
12 Nova Scotia
1201–C Zone 1
1202–C Zone 2
1203–C Zone 3
1204–C Zone 4
1205–I Zone 5
1206–A Zone 6
13 New Brunswick
1301–C Zone 1
1302–C Zone 2
1303–C Zone 3
1304–C Zone 4
1305–I Zone 5
1306–I Zone 6
1307–I Zone 7
24 Quebec
2401–C Région du Bas–Saint–Laurent
2402–C Région du Saguenay – Lac–Saint–Jean
2403–A Région de la Capitale–Nationale
2404–C Région de la Mauricie et du Centre–du–Québec
2405–C Région de l'Estrie
2406–G Région de Montréal
2407–A Région de l'Outaouais
2408–C Région de l'Abitibi–Témiscamingue
2409–H Région de la Côte–Nord
2410–H Région du Nord–du–Québec
2411–I Région de la Gaspésie – Îles–de–la–Madeleine
2412–E Région de la Chaudière–Appalaches
2413–A Région de Laval
2414–E Région de Lanaudière
2415–E Région des Laurentides
2416–A Région de la Montérégie
35 Ontario by Local Health Integration Network
3501 Erie St. Clair Health Integration Network
3502 South West Health Integration Network
3503 Waterloo Wellington Health Integration Network
3504 Hamilton Niagara Haldimand Brant Health Integration Network
3505 Central West Health Integration Network
3506 Mississauga Halton Health Integration Network
3507 Toronto Central Health Integration Network
3508 Central Health Integration Network
3509 Central East Health Integration Network
3510 South East Health Integration Network
3511 Champlain Health Integration Network
3512 North Simcoe Muskoka Health Integration Network
3513 North East Health Integration Network
3514 North West Health Integration Network
35 Ontario by Health Unit
3526–C District of Algoma Health Unit
3527–A Brant County Health Unit
3530–B Durham Regional Health Unit
3531–E Elgin–St. Thomas Health Unit
3533–E Grey Bruce Health Unit
3534–E Haldimand–Norfolk Health Unit
3535–E Haliburton, Kawartha, Pine Ridge District Health Unit
3536–B Halton Regional Health Unit
3537–A City of Hamilton Health Unit
3538–A Hastings and Prince Edward Counties Health Unit
3539–E Huron County Health Unit
3540–A Chatham–Kent Health Unit
3541–A Kingston, Frontenac and Lennox and Addington Health Unit
3542–A Lambton Health Unit
3543–E Leeds, Grenville and Lanark District Health Unit
3544–A Middlesex–London Health Unit
3546–A Niagara Regional Area Health Unit
3547–C North Bay Parry Sound District Health Unit
3549–H Northwestern Health Unit
3551–B City of Ottawa Health Unit
3552–E Oxford County Health Unit
3553–B Peel Regional Health Unit
3554–E Perth District Health Unit
3555–A Peterborough County–City Health Unit
3556–H Porcupine Health Unit
3557–E Renfrew County and District Health Unit
3558–E Eastern Ontario Health Unit
3560–E Simcoe Muskoka District Health Unit
3561–C Sudbury and District Health Unit
3562–C Thunder Bay District Health Unit
3563–C Timiskaming Health Unit
3565–B Waterloo Health Unit
3566–B Wellington–Dufferin–Guelph Health Unit
3568–B Windsor–Essex County Health Unit
3570–B York Regional Health Unit
3595–G City of Toronto Health Unit
46 Manitoba
4610–A Winnipeg Regional Health Authority
4615–A Brandon Regional Health Authority
4620–E North Eastman Regional Health Authority
4625–E South Eastman Regional Health Authority
4630–E Interlake Regional Health Authority
4640–D Central Regional Health Authority
4645–D Assiniboine Regional Health Authority
4660–D Parkland Regional Health Authority
4670–H NOR-MAN Regional Health Authority
4685–F Burntwood/Churchill
47 Saskatchewan
4701–D Sun Country Regional Health Authority
4702–D Five Hills Regional Health Authority
4703–D Cypress Regional Health Authority
4704–A Regina Qu'Appelle Regional Health Authority
4705–D Sunrise Regional Health Authority
4706–A Saskatoon Regional Health Authority
4707–D Heartland Regional Health Authority
4708–D Kelsey Trail Regional Health Authority
4709–C Prince Albert Parkland Regional Health Authority
4710–H Prairie North Regional Health Authority
4714–F Mamawetan/Keewatin/Athabasca
48 Alberta
4831–A South Zone
4832–B Calgary Zone
4833–E Central Zone
4834–B Edmonton Zone
4835–E North Zone
59 British Columbia
5911–E East Kootenay Health Service Delivery Area
5912–C Kootenay–Boundary Health Service Delivery Area
5913–A Okanagan Health Service Delivery Area
5914–C Thompson/Cariboo Health Service Delivery Area
5921–A Fraser East Health Service Delivery Area
5922–B Fraser North Health Service Delivery Area
5923–B Fraser South Health Service Delivery Area
5931–B Richmond Health Service Delivery Area
5932–G Vancouver Health Service Delivery Area
5933–B North Shore/Coast Garibaldi Health Service Delivery Area
5941–A South Vancouver Island Health Service Delivery Area
5942–A Central Vancouver Island Health Service Delivery Area
5943–C North Vancouver Island Health Service Delivery Area
5951–H Northwest Health Service Delivery Area
5952–H Northern Interior Health Service Delivery Area
5953–H Northeast Health Service Delivery Area
60 Yukon
6001–H Yukon
61 Northwest Territories
6101–H Northwest Territories
62 Nunavut – 10 largest communities
6201–F Nunavut – 10 largest communities
A Peer group A
B Peer group B
C Peer group C
D Peer group D
E Peer group E
F Peer group F
G Peer group G
H Peer group H
I Peer group I
J Peer group J

Appendix D (2010) – Sample allocation by health region and frame and by Local Health Integrated Network (LHIN) and frames in the CCHS in Ontario

Standard table symbols

Appendix D (2010) – Sample allocation by health region and frame
Geography Area Frame Phone frames Combined
Province/Territory
Health Region
expected No. of respondents raw sample size expected No. of respondents raw sample size expected No. of respondents raw sample size
Canada
Total 31,092 47,287 34,632 60,424 65,724 107,711
Newfoundland
Total 943 1,384 1,062 1,578 2,005 2,962
1011 381 554 429 642 810 1,196
1012 221 340 249 374 470 714
1013 200 277 225 326 425 603
1014 141 214 159 236 300 450
Prince Edward Island
Total 471 828 530 986 1,001 1,814
1101 84 151 94 184 178 335
1102 216 380 244 446 460 826
1103 171 297 192 356 363 653
Nova Scotia
Total 1,184 1,839 1,337 2,004 2,521 3,843
1201 186 280 210 320 396 600
1202 150 229 170 254 320 483
1203 169 263 191 306 360 569
1204 165 297 185 268 350 565
1205 197 257 223 336 420 593
1206 317 513 358 520 675 1,033
New Brunswick
Total 1,211 1,890 1,364 2,070 2,575 3,960
1301 235 357 265 402 500 759
1302 228 388 257 402 485 790
1303 221 369 249 382 470 751
1304 127 187 143 216 270 403
1305 118 186 132 202 250 388
1306 162 212 183 274 345 486
1307 120 191 135 192 255 383
Quebec
Total 5,520 7,935 6,625 11,778 12,145 19,713
2401 282 372 318 496 600 868
2402 295 426 333 528 628 954
2403 436 659 490 848 926 1,507
2404 377 517 426 630 803 1,147
2405 290 467 328 536 618 1,003
2406 730 1,092 823 1,526 1,553 2,618
2407 303 475 342 612 645 1,087
2408 282 364 318 470 600 834
2409 282 390 318 622 600 1,012
2410 0 0 400 1,176 400 1,176
2411 282 404 318 554 600 958
2412 340 454 383 694 723 1,148
2413 315 462 355 636 670 1,098
2414 337 481 381 636 718 1,117
2415 358 532 403 708 761 1,240
2416 611 840 689 1,106 1,300 1,946
Ontario
Total 10,317 15,867 11,855 20,898 22,172 36,765
3526 200 280 225 406 425 686
3527 190 295 215 364 405 659
3530 383 610 432 706 815 1,316
3531 160 240 180 310 340 550
3533 236 361 266 490 502 851
3534 182 287 204 370 386 657
3535 223 384 252 470 475 854
3536 331 483 374 634 705 1,117
3537 388 626 437 794 825 1,420
3538 221 363 249 406 470 769
3539 139 191 156 286 295 477
3540 188 241 212 368 400 609
3541 237 440 268 472 505 912
3542 204 289 231 440 435 729
3543 223 333 252 420 475 753
3544 353 581 397 644 750 1,225
3546 360 538 405 674 765 1,212
3547 188 301 212 406 400 707
3549 169 302 223 437 392 739
3551 482 797 543 896 1,025 1,693
3552 176 243 199 312 375 555
3553 626 903 706 1,286 1,332 2,189
3554 153 203 172 268 325 471
3555 200 311 225 408 425 719
3556 176 265 199 338 375 603
3557 176 272 199 362 375 634
3558 244 335 276 466 520 801
3560 476 729 631 1,157 1,107 1,886
3561 254 419 286 476 540 895
3562 260 431 389 700 649 1,131
3563 118 214 132 242 250 456
3565 360 551 405 674 765 1,225
3566 273 365 310 488 583 853
3568 336 498 379 668 715 1,166
3570 444 659 500 916 944 1,575
3595 988 1,527 1,114 2,144 2,102 3,671
Manitoba
Total 1,765 2,502 1,985 3,258 3,750 5,760
4610 496 698 559 858 1,055 1,556
4615 132 188 148 216 280 404
4620 118 161 132 242 250 403
4625 141 177 159 256 300 433
4630 162 268 183 330 345 598
4640 188 248 212 318 400 566
4645 167 231 188 286 355 517
4660 125 180 140 228 265 408
4670 118 170 132 230 250 400
4685 118 181 132 294 250 475
Saskatchewan
Total 1,697 2,465 2,163 4,192 3,860 6,657
4701 141 189 159 262 300 451
4702 141 222 159 248 300 470
4703 125 184 140 222 265 406
4704 291 414 329 528 620 942
4705 146 215 164 260 310 475
4706 310 453 350 534 660 987
4707 127 210 143 210 270 420
4708 122 168 138 210 260 378
4709 153 222 172 298 325 520
4710 141 189 159 274 300 463
4714 0 0 250 1,146 250 1,146
Alberta1
Total 2,868 4,538 3,232 5,484 6,100 10,022
4821 240 313 270 434 510 747
4822 195 269 220 356 415 625
4823 656 1,043 739 1,228 1,395 2,271
4824 329 541 371 616 700 1,157
4825 209 297 236 400 445 697
4826 616 1,032 694 1,152 1,310 2,184
4827 254 417 286 528 540 945
4828 219 334 246 420 465 754
4829 150 293 170 350 320 643
British Columbia
Total 3,781 5,921 4,264 7,372 8,045 13,293
5911 143 219 162 286 305 505
5912 146 194 164 268 310 462
5913 277 353 313 528 590 881
5914 235 357 265 418 500 775
5921 244 399 276 456 520 855
5922 357 564 403 706 760 1,270
5923 376 540 424 718 800 1,258
5931 200 293 225 394 425 687
5932 376 644 424 846 800 1,490
5933 256 441 289 558 545 999
5941 317 463 358 610 675 1,073
5942 247 353 278 440 525 793
5943 125 249 140 198 265 447
5951 153 273 172 318 325 591
5952 200 367 225 374 425 741
5903 129 212 146 254 275 466
Yukon
6001 475 779 125 432 600 1,211
Northwest Territories
6101 510 826 90 372 600 1,198
Nunavut
6201 350 512 0 0 350 512
  1. As mentioned in section 5.2, the figures for Alberta are based on the definition of HRs that was used at the time of sampling.
Appendix D (2010) – Sample allocation by Local Health Integrated Network (LHIN) and frames in the CCHS in Ontario
Georaphy Area Frame Phone frames Combined
Province/
LHIN
expected No. of respondents raw sample size expected No. of respondents raw sample size expected No. of respondents raw sample size
Ontario
Total 10,317 15,867 11,856 20,898 22,172 36,765
3501 728 1,028 822 1,476 1,550 2,504
3502 1,205 1,801 1,356 2,304 2,561 4,105
3503 584 843 658 1,068 1,242 1,911
3504 1,221 1,888 1,376 2,372 2,597 4,260
3505 496 712 560 1,012 1,056 1,724
3506 524 776 591 1,062 1,115 1,838
3507 509 820 575 1,120 1,084 1,940
3508 663 989 748 1,386 1,411 2,375
3509 991 1,570 1,117 1,978 2,108 3,548
3510 617 1,043 696 1,196 1,313 2,239
3511 966 1,498 1,091 1,826 2,057 3,324
3512 448 687 599 1,093 1,047 1,780
3513 936 1,479 1,054 1,868 1,990 3,347
3514 429 733 612 1,137 1,041 1,870

Appendix E (2010) - Response rates by health region and frame and Response rates by Local Health Integrated Network (LHIN) and frame in the CCHS in Ontario

Standard table symbols

Appendix E (2010) – Table 9.1 response rates by health region and frame
Geography Area frame Phone frames Combined
Province/Territory/
Health Region
No. in scope HH No. resp. HH HH resp. rates No. pers. select. No. resp. Pers. resp. rates Resp. rates No. in scope HH No. resp. HH HH resp. rates No. pers. select. No. resp. Pers. resp. rates Resp. rates Combined resp. rates
Canada
Total 40,070 33,387 83.3 33,387 30,449 91.2 76.0 48,340 37,928 78.5 37,928 32,742 86.3 67.7 71.5
Newfoundland
Total 1,128 999 88.6 999 934 93.5 82.8 1,327 1,108 83.5 1,108 936 84.5 70.5 76.2
1011 473 414 87.5 414 384 92.8 81.2 556 466 83.8 466 397 85.2 71.4 75.9
1012 255 228 89.4 228 210 92.1 82.4 295 241 81.7 241 204 84.6 69.2 75.3
1013 225 200 88.9 200 192 96.0 85.3 285 247 86.7 247 216 87.4 75.8 80.0
1014 175 157 89.7 157 148 94.3 84.6 191 154 80.6 154 119 77.3 62.3 73.0
Prince Edward Island
Total 641 541 84.4 541 491 90.8 76.6 693 558 80.5 558 482 86.4 69.6 72.9
1101 104 92 88.5 92 82 89.1 78.8 60 39 65.0 39 32 82.1 53.3 69.5
1102 293 233 79.5 233 207 88.8 70.6 383 309 80.7 309 269 87.1 70.2 70.4
1103 244 216 88.5 216 202 93.5 82.8 250 210 84.0 210 181 86.2 72.4 77.5
Nova Scotia
Total 1,496 1,264 84.5 1,264 1,144 90.5 76.5 1,644 1,368 83.2 1,368 1,198 87.6 72.9 74.6
1201 215 198 92.1 198 184 92.9 85.6 261 213 81.6 213 188 88.3 72.0 78.2
1202 199 169 84.9 169 158 93.5 79.4 204 174 85.3 174 156 89.7 76.5 77.9
1203 194 162 83.5 162 154 95.1 79.4 248 208 83.9 208 189 90.9 76.2 77.6
1204 221 188 85.1 188 173 92.0 78.3 215 177 82.3 177 153 86.4 71.2 74.8
1205 216 188 87.0 188 168 89.4 77.8 271 221 81.5 221 191 86.4 70.5 73.7
1206 451 359 79.6 359 307 85.5 68.1 445 375 84.3 375 321 85.6 72.1 70.1
New Brunswick
Total 1,564 1,322 84.5 1,322 1,197 90.5 76.5 1,680 1,416 84.3 1,416 1,232 87.0 73.3 74.9
1301 301 233 77.4 233 209 89.7 69.4 317 263 83.0 263 234 89.0 73.8 71.7
1302 295 246 83.4 246 228 92.7 77.3 332 285 85.8 285 257 90.2 77.4 77.4
1303 304 259 85.2 259 241 93.1 79.3 305 275 90.2 275 238 86.5 78.0 78.7
1304 159 133 83.6 133 117 88.0 73.6 172 137 79.7 137 122 89.1 70.9 72.2
1305 158 146 92.4 146 133 91.1 84.2 160 138 86.3 138 115 83.3 71.9 78.0
1306 185 173 93.5 173 156 90.2 84.3 232 189 81.5 189 157 83.1 67.7 75.1
1307 162 132 81.5 132 113 85.6 69.8 162 129 79.6 129 109 84.5 67.3 68.5
Quebec
Total 6,915 5,740 83.0 5,740 5,340 93.0 77.2 9,324 7,201 77.2 7,201 6,213 86.3 66.6 71.1
2401 285 265 93.0 265 252 95.1 88.4 392 314 80.1 314 274 87.3 69.9 77.7
2402 351 308 87.7 308 299 97.1 85.2 423 336 79.4 336 296 88.1 70.0 76.9
2403 629 513 81.6 513 480 93.6 76.3 748 591 79.0 591 510 86.3 68.2 71.9
2404 429 373 86.9 373 350 93.8 81.6 548 438 79.9 438 391 89.3 71.4 75.8
2405 381 301 79.0 301 270 89.7 70.9 437 361 82.6 361 332 92.0 76.0 73.6
2406 950 714 75.2 714 655 91.7 68.9 1,346 921 68.4 921 763 82.8 56.7 61.8
2407 412 337 81.8 337 310 92.0 75.2 503 401 79.7 401 357 89.0 71.0 72.9
2408 299 266 89.0 266 247 92.9 82.6 402 326 81.1 326 280 85.9 69.7 75.2
2409 329 291 88.4 291 276 94.8 83.9 426 324 76.1 324 273 84.3 64.1 72.7
2410 . . . . . . . 402 325 80.8 325 282 86.8 70.1 70.1
2411 344 316 91.9 316 291 92.1 84.6 432 329 76.2 329 271 82.4 62.7 72.4
2412 394 345 87.6 345 320 92.8 81.2 561 451 80.4 451 394 87.4 70.2 74.8
2413 432 341 78.9 341 317 93.0 73.4 557 415 74.5 415 344 82.9 61.8 66.8
2414 417 331 79.4 331 310 93.7 74.3 543 417 76.8 417 363 87.1 66.9 70.1
2415 483 384 79.5 384 357 93.0 73.9 594 457 76.9 457 392 85.8 66.0 69.5
2416 780 655 84.0 655 606 92.5 77.7 1,010 795 78.7 795 691 86.9 68.4 72.5
Ontario
Total 13,545 11,061 81.7 11,061 9,952 90.0 73.5 17,800 13,590 76.3 13,590 11,574 85.2 65.0 68.7
3526 250 216 86.4 216 199 92.1 79.6 326 264 81.0 264 223 84.5 68.4 73.3
3527 264 219 83.0 219 194 88.6 73.5 297 238 80.1 238 201 84.5 67.7 70.4
3530 557 445 79.9 445 403 90.6 72.4 613 473 77.2 473 399 84.4 65.1 68.5
3531 224 201 89.7 201 192 95.5 85.7 254 199 78.3 199 172 86.4 67.7 76.2
3533 302 263 87.1 263 242 92.0 80.1 377 290 76.9 290 258 89.0 68.4 73.6
3534 241 202 83.8 202 175 86.6 72.6 311 254 81.7 254 218 85.8 70.1 71.2
3535 256 208 81.3 208 187 89.9 73.0 332 267 80.4 267 234 87.6 70.5 71.6
3536 441 357 81.0 357 312 87.4 70.7 553 433 78.3 433 362 83.6 65.5 67.8
3537 547 415 75.9 415 369 88.9 67.5 668 485 72.6 485 404 83.3 60.5 63.6
3538 318 277 87.1 277 233 84.1 73.3 335 261 77.9 261 223 85.4 66.6 69.8
3539 162 143 88.3 143 132 92.3 81.5 212 171 80.7 171 149 87.1 70.3 75.1
3540 205 191 93.2 191 183 95.8 89.3 291 222 76.3 222 197 88.7 67.7 76.6
3541 366 267 73.0 267 231 86.5 63.1 355 284 80.0 284 254 89.4 71.5 67.3
3542 235 199 84.7 199 187 94.0 79.6 352 270 76.7 270 230 85.2 65.3 71.0
3543 292 247 84.6 247 230 93.1 78.8 378 298 78.8 298 268 89.9 70.9 74.3
3544 493 407 82.6 407 375 92.1 76.1 580 449 77.4 449 373 83.1 64.3 69.7
3546 472 396 83.9 396 372 93.9 78.8 576 458 79.5 458 390 85.2 67.7 72.7
3547 227 196 86.3 196 171 87.2 75.3 281 215 76.5 215 184 85.6 65.5 69.9
3549 279 204 73.1 204 176 86.3 63.1 414 325 78.5 325 277 85.2 66.9 65.4
3551 738 561 76.0 561 487 86.8 66.0 788 606 76.9 606 524 86.5 66.5 66.3
3552 221 180 81.4 180 173 96.1 78.3 278 220 79.1 220 197 89.5 70.9 74.1
3553 775 651 84.0 651 559 85.9 72.1 1,097 811 73.9 811 643 79.3 58.6 64.2
3554 178 155 87.1 155 150 96.8 84.3 225 185 82.2 185 164 88.6 72.9 77.9
3555 256 218 85.2 218 201 92.2 78.5 303 230 75.9 230 196 85.2 64.7 71.0
3556 235 195 83.0 195 176 90.3 74.9 290 227 78.3 227 196 86.3 67.6 70.9
3557 209 192 91.9 192 172 89.6 82.3 287 231 80.5 231 206 89.2 71.8 76.2
3558 305 255 83.6 255 229 89.8 75.1 401 317 79.1 317 279 88.0 69.6 72.0
3560 538 430 79.9 430 381 88.6 70.8 1,086 846 77.9 846 721 85.2 66.4 67.9
3561 341 296 86.8 296 262 88.5 76.8 368 292 79.3 292 261 89.4 70.9 73.8
3562 388 283 72.9 283 246 86.9 63.4 699 541 77.4 541 461 85.2 66.0 65.0
3563 134 114 85.1 114 107 93.9 79.9 211 163 77.3 163 141 86.5 66.8 71.9
3565 486 400 82.3 400 370 92.5 76.1 605 471 77.9 471 417 88.5 68.9 72.1
3566 316 275 87.0 275 246 89.5 77.8 410 329 80.2 329 297 90.3 72.4 74.8
3568 422 335 79.4 335 307 91.6 72.7 595 447 75.1 447 366 81.9 61.5 66.2
3570 603 485 80.4 485 428 88.2 71.0 815 571 70.1 571 467 81.8 57.3 63.1
3595 1,269 983 77.5 983 895 91.0 70.5 1,837 1,247 67.9 1,247 1,022 82.0 55.6 61.7
Manitoba
Total 2,168 1,840 84.9 1,840 1,682 91.4 77.6 2,412 2,024 83.9 2,024 1,774 87.6 73.5 75.5
4610 640 496 77.5 496 438 88.3 68.4 734 627 85.4 627 553 88.2 75.3 72.1
4615 169 143 84.6 143 139 97.2 82.2 172 152 88.4 152 134 88.2 77.9 80.1
4620 131 115 87.8 115 106 92.2 80.9 153 130 85.0 130 111 85.4 72.5 76.4
4625 177 158 89.3 158 147 93.0 83.1 202 173 85.6 173 155 89.6 76.7 79.7
4630 187 161 86.1 161 148 91.9 79.1 217 176 81.1 176 158 89.8 72.8 75.7
4640 226 198 87.6 198 183 92.4 81.0 261 224 85.8 224 190 84.8 72.8 76.6
4645 197 182 92.4 182 167 91.8 84.8 217 179 82.5 179 163 91.1 75.1 79.7
4660 140 125 89.3 125 115 92.0 82.1 172 146 84.9 146 127 87.0 73.8 77.6
4670 138 118 85.5 118 107 90.7 77.5 147 112 76.2 112 93 83.0 63.3 70.2
4685 163 144 88.3 144 132 91.7 81.0 137 105 76.6 105 90 85.7 65.7 74.0
Saskatchewan
Total 2,034 1,771 87.1 1,771 1,664 94.0 81.8 2,742 2,244 81.8 2,244 1,985 88.5 72.4 76.4
4701 160 153 95.6 153 151 98.7 94.4 203 171 84.2 171 157 91.8 77.3 84.8
4702 197 178 90.4 178 171 96.1 86.8 207 167 80.7 167 142 85.0 68.6 77.5
4703 140 134 95.7 134 130 97.0 92.9 176 134 76.1 134 120 89.6 68.2 79.1
4704 351 310 88.3 310 284 91.6 80.9 426 347 81.5 347 307 88.5 72.1 76.1
4705 155 142 91.6 142 139 97.9 89.7 209 177 84.7 177 161 91.0 77.0 82.4
4706 390 306 78.5 306 273 89.2 70.0 479 385 80.4 385 334 86.8 69.7 69.9
4707 154 135 87.7 135 131 97.0 85.1 167 143 85.6 143 126 88.1 75.4 80.1
4708 134 120 89.6 120 113 94.2 84.3 148 126 85.1 126 114 90.5 77.0 80.5
4709 192 152 79.2 152 141 92.8 73.4 224 175 78.1 175 158 90.3 70.5 71.9
4710 161 141 87.6 141 131 92.9 81.4 186 160 86.0 160 135 84.4 72.6 76.7
4714 . . . . . . . 317 259 81.7 259 231 89.2 72.9 72.9
Alberta
Total 3,898 3,244 83.2 3,244 2,925 90.2 75.0 4,500 3,571 79.4 3,571 3,084 86.4 68.5 71.6
4831 497 426 85.7 426 400 93.9 80.5 656 533 81.3 533 478 89.7 72.9 76.1
4832 917 796 86.8 796 728 91.5 79.4 1,026 802 78.2 802 682 85.0 66.5 72.6
4833 699 569 81.4 569 517 90.9 74.0 812 644 79.3 644 552 85.7 68.0 70.7
4834 917 727 79.3 727 639 87.9 69.7 988 783 79.3 783 685 87.5 69.3 69.5
4835 868 726 83.6 726 641 88.3 73.8 1,018 809 79.5 809 687 84.9 67.5 70.4
British Columbia
Total 4,977 4,143 83.2 4,143 3,760 90.8 75.5 5,869 4,552 77.6 4,552 3,991 87.7 68.0 71.5
5911 188 167 88.8 167 156 93.4 83.0 226 172 76.1 172 156 90.7 69.0 75.4
5912 162 145 89.5 145 134 92.4 82.7 185 152 82.2 152 132 86.8 71.4 76.7
5913 314 290 92.4 290 275 94.8 87.6 462 374 81.0 374 330 88.2 71.4 78.0
5914 279 245 87.8 245 221 90.2 79.2 320 251 78.4 251 222 88.4 69.4 74.0
5921 347 284 81.8 284 252 88.7 72.6 352 277 78.7 277 247 89.2 70.2 71.4
5922 517 436 84.3 436 402 92.2 77.8 591 451 76.3 451 382 84.7 64.6 70.8
5923 480 396 82.5 396 372 93.9 77.5 587 446 76.0 446 380 85.2 64.7 70.5
5931 247 197 79.8 197 190 96.4 76.9 307 225 73.3 225 183 81.3 59.6 67.3
5932 533 400 75.0 400 376 94.0 70.5 639 457 71.5 457 405 88.6 63.4 66.6
5933 285 222 77.9 222 168 75.7 58.9 442 334 75.6 334 289 86.5 65.4 62.9
5941 417 355 85.1 355 321 90.4 77.0 509 403 79.2 403 360 89.3 70.7 73.5
5942 300 264 88.0 264 242 91.7 80.7 359 293 81.6 293 263 89.8 73.3 76.6
5943 211 174 82.5 174 161 92.5 76.3 165 130 78.8 130 120 92.3 72.7 74.7
5951 205 165 80.5 165 140 84.8 68.3 227 186 81.9 186 167 89.8 73.6 71.1
5952 315 253 80.3 253 216 85.4 68.6 300 242 80.7 242 214 88.4 71.3 69.9
5953 177 150 84.7 150 134 89.3 75.7 198 159 80.3 159 141 88.7 71.2 73.3
Yukon
6001 659 573 86.9 573 541 94.4 82.1 201 167 83.1 167 158 94.6 78.6 81.3
Northwest Territories
6101 641 556 86.7 556 518 93.2 80.8 148 129 87.2 129 115 89.1 77.7 80.2
Nunavut
6201 404 333 82.4 333 301 90.4 74.5 . . . . . . . 74.5
Appendix E (2010) – Table 9.2 Response rate by Local Health Integrated Network (LHIN) and frame in the CCHS in Ontario
Geography Area frame Phone frames Combined
Province/
LHIN
No. in scope HH No. resp. HH HH resp. rates No. pers. select. No. resp. Pers. resp. rates Resp. rates No. in scope HH No. resp. HH HH resp. rates No. pers. select. No. resp. Pers. resp. rates Resp. rates Combined resp. rates
Ontario
Total 13,545 11,061 81.7 11,061 9,952 90.0 73.5 17,800 13,590 76.3 13,590 11,574 85.2 65.0 68.7
3501 862 725 84.1 725 677 93.4 78.5 1,238 939 75.8 939 793 84.5 64.1 70.0
3502 1,561 1,332 85.3 1,332 1,244 93.4 79.7 1,918 1,510 78.7 1,510 1,308 86.6 68.2 73.4
3503 757 641 84.7 641 587 91.6 77.5 929 731 78.7 731 653 89.3 70.3 73.5
3504 1,666 1,342 80.6 1,342 1,210 90.2 72.6 2,008 1,554 77.4 1,554 1,319 84.9 65.7 68.8
3505 574 474 82.6 474 395 83.3 68.8 870 634 72.9 634 509 80.3 58.5 62.6
3506 727 605 83.2 605 538 88.9 74.0 918 695 75.7 695 558 80.3 60.8 66.6
3507 600 468 78.0 468 432 92.3 72.0 926 634 68.5 634 529 83.4 57.1 63.0
3508 930 735 79.0 735 664 90.3 71.4 1,243 872 70.2 872 715 82.0 57.5 63.5
3509 1,320 1,069 81.0 1,069 961 89.9 72.8 1,601 1,212 75.7 1,212 1,022 84.3 63.8 67.9
3510 893 723 81.0 723 631 87.3 70.7 995 784 78.8 784 693 88.4 69.6 70.1
3511 1,335 1,076 80.6 1,076 951 88.4 71.2 1,546 1,210 78.3 1,210 1,058 87.4 68.4 69.7
3512 466 367 78.8 367 325 88.6 69.7 1,016 785 77.3 785 671 85.5 66.0 67.2
3513 1,187 1,017 85.7 1,017 915 90.0 77.1 1,479 1,164 78.7 1,164 1,008 86.6 68.2 72.1
3514 667 487 73.0 487 422 86.7 63.3 1,113 866 77.8 866 738 85.2 66.3 65.2

Appendix F (2009–2010) – Sample allocation by health region and frame and by Local Health Integrated Network (LHIN) and frames in the CCHS in Ontario

Appendix F (2009–2010) – Sample allocation by health region and frame
Geography Area Frame Phone frames Combined
Province/Territory/
Health Region
expected No. of respondents raw sample size expected No. of respondents raw sample size expected No. of respondents raw sample size
Canada
Total 64,228 95,175 67,258 116,344 131,486 211,519
Newfoundland
Total 1,946 2,788 2,064 3,174 4,010 5,962
1011 786 1,129 834 1,290 1,620 2,419
1012 456 673 484 734 940 1,407
1013 413 559 437 668 850 1,227
1014 291 428 309 482 600 910
Prince Edward Island
Total 972 1,613 1,030 1,934 2,002 3,547
1101 173 310 183 352 356 662
1102 446 746 474 866 920 1,612
1103 353 557 373 716 726 1,273
Nova Scotia
Total 2,445 3,742 2,596 3,924 5,041 7,666
1201 384 586 407 632 791 1,218
1202 310 465 330 506 640 971
1203 349 502 371 576 720 1,078
1204 340 618 360 538 700 1,156
1205 407 541 433 660 840 1,201
1206 655 1,030 695 1,012 1,350 2,042
New Brunswick
Total 2,500 3,830 2,650 4,008 5,150 7,838
1301 485 744 515 786 1,000 1,530
1302 471 791 499 780 970 1,571
1303 456 742 484 748 940 1,490
1304 262 389 278 414 540 803
1305 243 356 257 382 500 738
1306 335 451 355 514 690 965
1307 248 357 262 384 510 741
Quebec
Total 11,394 16,039 12,895 22,776 24,289 38,815
2401 582 755 618 970 1,200 1,725
2402 609 849 647 1,074 1,256 1,923
2403 899 1,306 953 1,574 1,852 2,880
2404 779 1,038 827 1,242 1,606 2,280
2405 599 922 637 1,010 1,236 1,932
2406 1,507 2,206 1,599 2,984 3,106 5,190
2407 626 983 664 1,134 1,290 2,117
2408 582 747 618 926 1,200 1,673
2409 582 795 618 1,180 1,200 1,975
2410 0 0 800 2,424 800 2,424
2411 582 815 618 1,070 1,200 1,885
2412 702 938 744 1,330 1,446 2,268
2413 650 942 690 1,200 1,340 2,142
2414 696 961 740 1,224 1,436 2,185
2415 738 1,068 783 1,332 1,521 2,400
2416 1,261 1,715 1,339 2,102 2,600 3,817
Ontario
Total 21,428 31,763 22,951 40,056 44,379 71,819
3526 413 598 437 742 850 1,340
3527 393 577 417 676 810 1,253
3530 791 1,184 839 1,354 1,630 2,538
3531 330 479 350 586 680 1,065
3533 476 720 518 940 994 1,660
3534 375 577 397 718 772 1,295
3535 461 700 489 884 950 1,584
3536 684 982 726 1,186 1,410 2,168
3537 801 1,248 849 1,490 1,650 2,738
3538 456 680 484 844 940 1,524
3539 287 419 303 562 590 981
3540 388 491 412 698 800 1,189
3541 490 820 520 922 1,010 1,742
3542 422 575 448 806 870 1,381
3543 461 668 489 798 950 1,466
3544 728 1,145 772 1,274 1,500 2,419
3546 743 1,043 787 1,292 1,530 2,335
3547 388 611 412 790 800 1,401
3549 369 637 423 875 792 1,512
3551 995 1,547 1,055 1,706 2,050 3,253
3552 364 488 386 594 750 1,082
3553 1,297 1,852 1,376 2,492 2,673 4,344
3554 316 426 334 514 650 940
3555 413 627 437 792 850 1,419
3556 364 551 386 602 750 1,153
3557 364 550 386 686 750 1,236
3558 504 690 536 862 1,040 1,552
3560 1,036 1,534 1,191 2,231 2,227 3,765
3561 524 812 556 956 1,080 1,768
3562 592 914 720 1,288 1,312 2,202
3563 243 397 257 458 500 855
3565 743 1,111 787 1,274 1,530 2,385
3566 566 747 602 944 1,168 1,691
3568 694 995 736 1,268 1,430 2,263
3570 917 1,286 972 1,786 1,889 3,072
3595 2,040 3,081 2,162 4,166 4,202 7,247
Manitoba
Total 3,642 5,086 3,858 6,246 7,500 11,332
4610 1,024 1,412 1,086 1,644 2,110 3,056
4615 272 388 288 444 560 832
4620 243 329 257 470 500 799
4625 291 382 309 496 600 878
4630 335 540 355 612 690 1,152
4640 388 501 412 594 800 1,095
4645 345 470 365 556 710 1,026
4660 258 370 272 438 530 808
4670 243 352 257 458 500 810
4685 243 341 257 534 500 875
Saskatchewan
Total 3,503 5,020 4,217 8,290 7,720 13,310
4701 291 376 309 490 600 866
4702 291 423 309 512 600 935
4703 258 369 272 444 530 813
4704 601 848 639 1,032 1,240 1,880
4705 301 431 319 488 620 919
4706 640 908 680 1,062 1,320 1,970
4707 262 423 278 426 540 849
4708 252 357 268 414 520 771
4709 316 508 334 604 650 1,112
4710 291 378 309 532 600 910
4714 0 0 500 2,286 500 2,286
Alberta1
Total 5,920 9,028 6,280 10,560 12,200 19,588
4821 495 686 525 842 1,020 1,528
4822 403 528 427 674 830 1,202
4823 1,354 2,039 1,436 2,350 2,790 4,389
4824 679 1,054 721 1,204 1,400 2,258
4825 432 587 458 736 890 1,323
4826 1,271 2,020 1,349 2,298 2,620 4,318
4827 524 821 556 990 1,080 1,811
4828 452 710 478 816 930 1,526
4829 310 585 330 650 640 1,235
British Columbia
Total 7,808 12,013 8,287 13,984 16,095 25,997
5911 296 451 314 550 610 1,001
5912 301 430 319 502 620 932
5913 572 747 608 1,014 1,180 1,761
5914 485 689 515 820 1,000 1,509
5921 504 752 536 858 1,040 1,610
5922 737 1,107 783 1,294 1,520 2,401
5923 776 1,145 824 1,408 1,600 2,553
5931 413 580 438 730 851 1,310
5932 776 1,283 824 1,560 1,600 2,843
5933 529 937 562 1,032 1,091 1,969
5941 655 958 696 1,138 1,351 2,096
5942 510 726 541 824 1,051 1,550
5943 258 430 272 408 530 838
5951 316 584 335 636 651 1,220
5952 413 674 438 734 851 1,408
5903 267 518 284 476 551 994
Yukon
6001 950 1,517 250 738 1,200 2,255
Northwest Territories
6101 1,020 1,642 180 654 1,200 2,296
Nunavut
6201 700 1,092 0 0 700 1,092
  1. As mentioned in section 5.2, the figures for Alberta are based on the definition of HRs that was used at the time of sampling.
Appendix F (2009–2010) – Sample allocation by Local Health Integrated Network (LHIN) and frames in the CCHS in Ontario
Geography Area Frame Phone frames Combined
Province/
LHIN
expected No. of respondents raw sample size expected No. of respondents raw sample size expected No. of respondents raw sample size
Ontario
Total 21,428 31,763 22,952 40,056 44,379 71,819
3501 1,504 2,061 1,596 2,772 3,100 4,833
3502 2,487 3,654 2,635 4,434 5,122 8,088
3503 1,206 1,710 1,278 2,046 2,484 3,756
3504 2,521 3,738 2,673 4,508 5,194 8,246
3505 1,032 1,484 1,093 1,966 2,125 3,450
3506 1,082 1,548 1,148 2,004 2,230 3,552
3507 1,050 1,675 1,115 2,164 2,165 3,839
3508 1,369 1,929 1,453 2,706 2,822 4,635
3509 2,047 3,039 2,169 3,808 4,216 6,847
3510 1,274 1,984 1,352 2,360 2,626 4,344
3511 1,996 2,972 2,118 3,458 4,114 6,430
3512 967 1,446 1,130 2,119 2,097 3,565
3513 1,932 2,970 2,048 3,548 3,980 6,518
3514 961 1,552 1,143 2,163 2,104 3,715

Appendix G (2009–2010) – Response rates by health region and frame and by Local Health Integrated Network (LHIN) and frame in the CCHS in Ontario

Standard table symbols

Appendix G (2009–2010) – Table 9.3 response rates by health region and frame
Geography Area frame Phone frames Combined
Province/Territory/
Health Region
No. in scope HH No. resp. HH HH resp. rates No. pers. select No. resp. Pers. resp. rates Resp. rates No. in scope HH No. resp. HH HH resp. rates No. pers. select No. resp. Pers. resp. rates Resp. rates Combined resp. rates
Canada
Total 80,206 66,694 83.2 66,694 60,924 91.3 76.0 92,465 73,147 79.1 73,147 63,946 87.4 69.2 72.3
Newfoundland
Total 2,269 1,990 87.7 1,990 1,840 92.5 81.1 2,663 2,245 84.3 2,245 1,928 85.9 72.4 76.4
1011 942 800 84.9 800 726 90.8 77.1 1,104 933 84.5 933 799 85.6 72.4 74.5
1012 506 457 90.3 457 420 91.9 83.0 601 500 83.2 500 431 86.2 71.7 76.9
1013 460 417 90.7 417 396 95.0 86.1 563 479 85.1 479 425 88.7 75.5 80.3
1014 361 316 87.5 316 298 94.3 82.5 395 333 84.3 333 273 82.0 69.1 75.5
Prince Edward Island
Total 1,261 1,065 84.5 1,065 961 90.2 76.2 1,338 1,103 82.4 1,103 952 86.3 71.2 73.6
1101 207 182 87.9 182 166 91.2 80.2 115 86 74.8 86 72 83.7 62.6 73.9
1102 601 494 82.2 494 439 88.9 73.0 721 595 82.5 595 514 86.4 71.3 72.1
1103 453 389 85.9 389 356 91.5 78.6 502 422 84.1 422 366 86.7 72.9 75.6
Nova Scotia
Total 3,027 2,572 85.0 2,572 2,325 90.4 76.8 3,204 2,706 84.5 2,706 2,387 88.2 74.5 75.6
1201 438 412 94.1 412 383 93.0 87.4 501 419 83.6 419 376 89.7 75.0 80.8
1202 388 330 85.1 330 306 92.7 78.9 409 350 85.6 350 314 89.7 76.8 77.8
1203 380 318 83.7 318 302 95.0 79.5 445 373 83.8 373 338 90.6 76.0 77.6
1204 468 408 87.2 408 376 92.2 80.3 435 369 84.8 369 322 87.3 74.0 77.3
1205 456 385 84.4 385 347 90.1 76.1 529 434 82.0 434 376 86.6 71.1 73.4
1206 897 719 80.2 719 611 85.0 68.1 885 761 86.0 761 661 86.9 74.7 71.4
New Brunswisk
Total 3,057 2,605 85.2 2,605 2,351 90.2 76.9 3,285 2,803 85.3 2,803 2,484 88.6 75.6 76.2
1301 622 497 79.9 497 448 90.1 72.0 644 547 84.9 547 489 89.4 75.9 74.0
1302 585 488 83.4 488 454 93.0 77.6 644 557 86.5 557 494 88.7 76.7 77.1
1303 558 481 86.2 481 440 91.5 78.9 615 543 88.3 543 489 90.1 79.5 79.2
1304 326 275 84.4 275 245 89.1 75.2 329 277 84.2 277 249 89.9 75.7 75.4
1305 287 262 91.3 262 228 87.0 79.4 309 271 87.7 271 237 87.5 76.7 78.0
1306 388 362 93.3 362 330 91.2 85.1 429 357 83.2 357 306 85.7 71.3 77.8
1307 291 240 82.5 240 206 85.8 70.8 315 251 79.7 251 220 87.6 69.8 70.3
Quebec
Total 14,002 11,529 82.3 11,529 10,752 93.3 76.8 18,091 14,204 78.5 14,204 12,383 87.2 68.4 72.1
2401 599 555 92.7 555 529 95.3 88.3 767 630 82.1 630 553 87.8 72.1 79.2
2402 716 613 85.6 613 585 95.4 81.7 880 730 83.0 730 655 89.7 74.4 77.7
2403 1,237 986 79.7 986 931 94.4 75.3 1,375 1,094 79.6 1,094 965 88.2 70.2 72.6
2404 886 762 86.0 762 714 93.7 80.6 1,074 870 81.0 870 784 90.1 73.0 76.4
2405 735 566 77.0 566 524 92.6 71.3 828 692 83.6 692 631 91.2 76.2 73.9
2406 1,951 1,449 74.3 1,449 1,339 92.4 68.6 2,604 1,843 70.8 1,843 1,533 83.2 58.9 63.1
2407 829 675 81.4 675 620 91.9 74.8 941 759 80.7 759 680 89.6 72.3 73.4
2408 618 547 88.5 547 509 93.1 82.4 800 655 81.9 655 573 87.5 71.6 76.3
2409 675 591 87.6 591 566 95.8 83.9 817 624 76.4 624 530 84.9 64.9 73.5
2410 . . . . . . . 964 775 80.4 775 682 88.0 70.7 70.7
2411 685 631 92.1 631 589 93.3 86.0 825 638 77.3 638 543 85.1 65.8 75.0
2412 829 734 88.5 734 687 93.6 82.9 1,102 873 79.2 873 769 88.1 69.8 75.4
2413 860 675 78.5 675 625 92.6 72.7 1,057 809 76.5 809 686 84.8 64.9 68.4
2414 850 690 81.2 690 639 92.6 75.2 1,054 827 78.5 827 719 86.9 68.2 71.3
2415 958 764 79.7 764 696 91.1 72.7 1,091 854 78.3 854 745 87.2 68.3 70.3
2416 1,574 1,291 82.0 1,291 1,199 92.9 76.2 1,912 1,531 80.1 1,531 1,335 87.2 69.8 72.7
Ontario
Total 27,207 22,290 81.9 22,290 20,163 90.5 74.1 33,503 25,846 77.1 25,846 22,332 86.4 66.7 70.0
3526 537 469 87.3 469 432 92.1 80.4 590 475 80.5 475 416 87.6 70.5 75.2
3527 530 438 82.6 438 377 86.1 71.1 549 437 79.6 437 380 87.0 69.2 70.2
3530 1,080 872 80.7 872 791 90.7 73.2 1,179 920 78.0 920 775 84.2 65.7 69.3
3531 427 367 85.9 367 337 91.8 78.9 488 388 79.5 388 337 86.9 69.1 73.7
3533 608 544 89.5 544 506 93.0 83.2 711 551 77.5 551 492 89.3 69.2 75.7
3534 488 397 81.4 397 354 89.2 72.5 592 470 79.4 470 406 86.4 68.6 70.4
3535 472 383 81.1 383 339 88.5 71.8 611 504 82.5 504 442 87.7 72.3 72.1
3536 917 747 81.5 747 671 89.8 73.2 1,041 815 78.3 815 696 85.4 66.9 69.8
3537 1,094 835 76.3 835 742 88.9 67.8 1,258 944 75.0 944 807 85.5 64.1 65.9
3538 594 511 86.0 511 445 87.1 74.9 682 539 79.0 539 465 86.3 68.2 71.3
3539 355 318 89.6 318 299 94.0 84.2 438 359 82.0 359 316 88.0 72.1 77.6
3540 414 389 94.0 389 374 96.1 90.3 538 421 78.3 421 374 88.8 69.5 78.6
3541 700 535 76.4 535 466 87.1 66.6 694 554 79.8 554 497 89.7 71.6 69.1
3542 480 402 83.8 402 374 93.0 77.9 648 519 80.1 519 452 87.1 69.8 73.2
3543 556 467 84.0 467 425 91.0 76.4 697 553 79.3 553 491 88.8 70.4 73.1
3544 974 787 80.8 787 734 93.3 75.4 1,107 857 77.4 857 740 86.3 66.8 70.8
3546 920 769 83.6 769 710 92.3 77.2 1,096 849 77.5 849 742 87.4 67.7 72.0
3547 478 411 86.0 411 359 87.3 75.1 551 431 78.2 431 367 85.2 66.6 70.6
3549 535 390 72.9 390 344 88.2 64.3 712 566 79.5 566 492 86.9 69.1 67.0
3551 1,425 1,050 73.7 1,050 929 88.5 65.2 1,493 1,170 78.4 1,170 1,022 87.4 68.5 66.9
3552 449 380 84.6 380 367 96.6 81.7 535 428 80.0 428 381 89.0 71.2 76.0
3553 1,666 1,409 84.6 1,409 1,236 87.7 74.2 2,157 1,636 75.8 1,636 1,348 82.4 62.5 67.6
3554 389 345 88.7 345 333 96.5 85.6 437 359 82.2 359 319 88.9 73.0 78.9
3555 499 424 85.0 424 387 91.3 77.6 587 455 77.5 455 405 89.0 69.0 72.9
3556 493 405 82.2 405 360 88.9 73.0 515 395 76.7 395 344 87.1 66.8 69.8
3557 420 395 94.0 395 356 90.1 84.8 544 428 78.7 428 380 88.8 69.9 76.3
3558 611 503 82.3 503 461 91.7 75.5 737 576 78.2 576 511 88.7 69.3 72.1
3560 1,188 939 79.0 939 848 90.3 71.4 1,872 1,462 78.1 1,462 1,274 87.1 68.1 69.3
3561 643 563 87.6 563 492 87.4 76.5 749 589 78.6 589 540 91.7 72.1 74.1
3562 782 595 76.1 595 535 89.9 68.4 1,129 883 78.2 883 769 87.1 68.1 68.2
3563 147 125 85.0 125 117 93.6 79.6 392 304 77.6 304 260 85.5 66.3 69.9
3565 979 808 82.5 808 742 91.8 75.8 1,126 880 78.2 880 781 88.8 69.4 72.4
3566 660 583 88.3 583 536 91.9 81.2 780 632 81.0 632 564 89.2 72.3 76.4
3568 853 693 81.2 693 640 92.4 75.0 1,128 853 75.6 853 714 83.7 63.3 68.3
3570 1,175 955 81.3 955 858 89.8 73.0 1,573 1,160 73.7 1,160 973 83.9 61.9 66.6
3595 2,669 2,087 78.2 2,087 1,887 90.4 70.7 3,567 2,484 69.6 2,484 2,060 82.9 57.8 63.3
Manitoba
Total 4,363 3,689 84.6 3,689 3,358 91.0 77.0 4,624 3,883 84.0 3,883 3,467 89.3 75.0 75.9
4610 1,297 1,016 78.3 1,016 901 88.7 69.5 1,420 1,202 84.6 1,202 1,075 89.4 75.7 72.7
4615 358 291 81.3 291 270 92.8 75.4 357 305 85.4 305 273 89.5 76.5 75.9
4620 269 240 89.2 240 223 92.9 82.9 275 235 85.5 235 208 88.5 75.6 79.2
4625 354 305 86.2 305 278 91.1 78.5 384 334 87.0 334 298 89.2 77.6 78.0
4630 376 325 86.4 325 297 91.4 79.0 406 341 84.0 341 312 91.5 76.8 77.9
4640 463 410 88.6 410 379 92.4 81.9 477 401 84.1 401 346 86.3 72.5 77.1
4645 409 373 91.2 373 335 89.8 81.9 437 356 81.5 356 326 91.6 74.6 78.1
4660 269 235 87.4 235 220 93.6 81.8 337 277 82.2 277 247 89.2 73.3 77.1
4670 283 245 86.6 245 228 93.1 80.6 295 239 81.0 239 210 87.9 71.2 75.8
4685 285 249 87.4 249 227 91.2 79.6 236 193 81.8 193 172 89.1 72.9 76.6
Saskatchewan
Total 4,108 3,616 88.0 3,616 3,413 94.4 83.1 5,484 4,491 81.9 4,491 4,036 89.9 73.6 77.7
4701 316 305 96.5 305 299 98.0 94.6 385 322 83.6 322 292 90.7 75.8 84.3
4702 368 328 89.1 328 313 95.4 85.1 429 346 80.7 346 307 88.7 71.6 77.8
4703 282 256 90.8 256 250 97.7 88.7 358 288 80.4 288 258 89.6 72.1 79.4
4704 736 664 90.2 664 607 91.4 82.5 853 687 80.5 687 619 90.1 72.6 77.2
4705 310 282 91.0 282 272 96.5 87.7 388 322 83.0 322 293 91.0 75.5 80.9
4706 780 634 81.3 634 586 92.4 75.1 945 772 81.7 772 682 88.3 72.2 73.5
4707 292 256 87.7 256 249 97.3 85.3 334 278 83.2 278 254 91.4 76.0 80.4
4708 279 251 90.0 251 238 94.8 85.3 309 267 86.4 267 244 91.4 79.0 82.0
4709 435 360 82.8 360 339 94.2 77.9 443 359 81.0 359 331 92.2 74.7 76.3
4710 310 280 90.3 280 260 92.9 83.9 357 301 84.3 301 265 88.0 74.2 78.7
4714 . . . . . . . 683 549 80.4 549 491 89.4 71.9 71.9
Alberta
Total 7,641 6,281 82.2 6,281 5,634 89.7 73.7 8,568 6,809 79.5 6,809 5,984 87.9 69.8 71.7
4831 1,024 862 84.2 862 812 94.2 79.3 1,204 989 82.1 989 889 89.9 73.8 76.3
4832 1,769 1,482 83.8 1,482 1,362 91.9 77.0 1,968 1,549 78.7 1,549 1,345 86.8 68.3 72.4
4833 1,373 1,111 80.9 1,111 1,005 90.5 73.2 1,529 1,229 80.4 1,229 1,079 87.8 70.6 71.8
4834 1,754 1,372 78.2 1,372 1,170 85.3 66.7 1,940 1,526 78.7 1,526 1,347 88.3 69.4 68.1
4835 1,721 1,454 84.5 1,454 1,285 88.4 74.7 1,927 1,516 78.7 1,516 1,324 87.3 68.7 71.5
British Columbia
Total 10,000 8,215 82.2 8,215 7,485 91.1 74.9 11,138 8,581 77.0 8,581 7,553 88.0 67.8 71.1
5911 382 331 86.6 331 307 92.7 80.4 432 329 76.2 329 304 92.4 70.4 75.1
5912 328 297 90.5 297 276 92.9 84.1 371 297 80.1 297 264 88.9 71.2 77.3
5913 659 593 90.0 593 562 94.8 85.3 869 688 79.2 688 615 89.4 70.8 77.0
5914 557 488 87.6 488 447 91.6 80.3 613 486 79.3 486 434 89.3 70.8 75.3
5921 651 539 82.8 539 483 89.6 74.2 656 520 79.3 520 454 87.3 69.2 71.7
5922 990 819 82.7 819 764 93.3 77.2 1,096 822 75.0 822 712 86.6 65.0 70.8
5923 1,015 844 83.2 844 780 92.4 76.8 1,162 886 76.2 886 766 86.5 65.9 71.0
5931 479 395 82.5 395 370 93.7 77.2 589 430 73.0 430 353 82.1 59.9 67.7
5932 1,060 782 73.8 782 741 94.8 69.9 1,209 842 69.6 842 726 86.2 60.0 64.7
5933 617 492 79.7 492 377 76.6 61.1 822 609 74.1 609 535 87.8 65.1 63.4
5941 846 691 81.7 691 633 91.6 74.8 923 728 78.9 728 647 88.9 70.1 72.4
5942 616 522 84.7 522 490 93.9 79.5 668 548 82.0 548 494 90.1 74.0 76.6
5943 355 287 80.8 287 272 94.8 76.6 330 268 81.2 268 246 91.8 74.5 75.6
5951 457 370 81.0 370 325 87.8 71.1 460 371 80.7 371 335 90.3 72.8 72.0
5952 577 436 75.6 436 383 87.8 66.4 580 469 80.9 469 414 88.3 71.4 68.9
5953 411 329 80.0 329 275 83.6 66.9 358 288 80.4 288 254 88.2 70.9 68.8
Yukon
6001 1,236 1,095 88.6 1,095 1,026 93.7 83.0 329 277 84.2 277 256 92.4 77.8 81.9
Northwest Territories
6101 1,245 1,065 85.5 1,065 988 92.8 79.4 238 199 83.6 199 184 92.5 77.3 79.0
Nunavut
6201 790 682 86.3 682 628 92.1 79.5 . . . . . . . 79.5
Appendix G (2009–2010) – Table 9.4 Response rate by Local Health Integrated Network (LHIN) and frame in the CCHS in Ontario
Geography Area frame Phone frames Combined
Province/
Health Region
No. in scope HH No. resp. HH HH resp. rates No. pers. select No. resp. Pers. resp. rates Resp. rates No. in scope HH No. resp. HH HH resp. rates No. pers. select No. resp. Pers. resp. rates Resp. rates Combined resp. rates
Ontario
Total 27,207 22,290 81.9 22,290 20,163 90.5 74.1 33,503 25,846 77.1 25,846 22,332 86.4 66.7 70.0
3501 1,747 1,484 84.9 1,484 1,388 93.5 79.5 2,314 1,793 77.5 1,793 1,540 85.9 66.6 72.1
3502 3,172 2,713 85.5 2,713 2,553 94.1 80.5 3,679 2,915 79.2 2,915 2,561 87.9 69.6 74.6
3503 1,536 1,307 85.1 1,307 1,196 91.5 77.9 1,755 1,391 79.3 1,391 1,246 89.6 71.0 74.2
3504 3,317 2,664 80.3 2,664 2,390 89.7 72.1 3,806 2,943 77.3 2,943 2,543 86.4 66.8 69.3
3505 1,300 1,076 82.8 1,076 926 86.1 71.2 1,693 1,263 74.6 1,263 1,037 82.1 61.3 65.6
3506 1,476 1,241 84.1 1,241 1,121 90.3 75.9 1,761 1,348 76.5 1,348 1,128 83.7 64.1 69.5
3507 1,352 1,042 77.1 1,042 965 92.6 71.4 1,799 1,270 70.6 1,270 1,075 84.6 59.8 64.7
3508 1,789 1,443 80.7 1,443 1,298 90.0 72.6 2,379 1,732 72.8 1,732 1,447 83.5 60.8 65.9
3509 2,537 2,074 81.8 2,074 1,860 89.7 73.3 3,084 2,372 76.9 2,372 2,018 85.1 65.4 69.0
3510 1,682 1,374 81.7 1,374 1,210 88.1 71.9 1,930 1,527 79.1 1,527 1,345 88.1 69.7 70.7
3511 2,624 2,087 79.5 2,087 1,872 89.7 71.3 2,910 2,286 78.6 2,286 2,014 88.1 69.2 70.2
3512 1,060 827 78.0 827 745 90.1 70.3 1,748 1,356 77.6 1,356 1,183 87.2 67.7 68.7
3513 2,298 1,973 85.9 1,973 1,760 89.2 76.6 2,804 2,201 78.5 2,201 1,934 87.9 69.0 72.4
3514 1,317 985 74.8 985 879 89.2 66.7 1,841 1,449 78.7 1,449 1,261 87.0 68.5 67.8

Notes

  1. 1999.Health Information Roadmap: Responding to Needs, Health Canada, Statistics Canada. page 3.
  2. 1999.Health Information Roadmap: Beginning the Journey. Canadian Institute for Health Information/Statistics Canada. ISBN 1–895581–70–2. p.19.
  3. Unless all health regions in Canada select an optional module in the same collection period, which has never happened to date.
  4. The correspondence between the 5 new HRs and the 9 HRs defined at the time of sampling is as follows: HR 4831 (4821 and 4822), HR 4832 (4823), HR 4833 (4824 and 4825), HR 4834 (4826) and HR 4835 (4827, 4828 and 4829).
  5. Except for 2 regions which use a random digit dialing frame (RDD) only (section 5.4.3) and the three territories which use only area frame and random digit dialing frame (RDD) (sections 5.4.1 and 5.4.3).
  6. Statistics Canada (2008).Methodology of the Canadian Labour Force Survey. Statistics Canada.Cat. No. 71–526–XIE.
  7. To reduce listing costs, the sampling process of dwellings was repeated up to 3 times within PSUs already selected in urban areas only. These cases were exceptions, however.
  8. In Nunavut, because of operational difficulties inherent to remote locales, only the 10 largest communities are covered by the survey: Iqaluit, Cambridge Bay, Baker Lake, Arviat, Rankin Inlet, Kugluktuk, Pond Inlet, Cape Dorset, Pangnirtung and Igloolik.
  9. Norris, D.A. and Paton, D.G. (1991). Canada’s General Social Survey: Five Years of Experience, Survey Methodology, 17, 227–240.
  10. Statistics Canada. 1998.Methodology of the Canadian Labour Force Survey. Statistics Canada.Cat. No. 71–526–XPB.
  11. Norris, D.A. and Paton, D.G. 1991. Canada’s General Social Survey: Five Years of Experience.Survey Methodology. 17, 227–240.
  12. Skinner, C.J. and Rao, J.N.K. 1996. Estimation in Dual Frame Surveys with Complex Designs.Journal of the American Statistical Association. 91, 433, 349–356.
  13. Sautory O. Calmar 2: A New Version of the Calmar Calibration Adjustment Program.Proceedings of Statistics Canada Symposium (Statistics Canada, Catalogue no. 11–522–XCB), 2003.
  14. Among the units selected, some are not in–scope for the survey. They are, for examples, vacant, demolished or non–residential dwellings or invalid phone numbers such as phone numbers without service or non–residential lines. These units are identified during the data collection, otherwise, they would have been excluded before the sample selection. These units are not considered in the calculation of response rates.