Canadian Health Survey on Children and Youth (CHSCY)

Detailed information for 2019





Record number:


The Canadian Health Survey on Children and Youth explores issues that have an impact on the physical and mental health of children and youth, such as physical activity, the use of electronic devices, time spent in school and extracurricular activities. Information from the survey will be used to develop programs and policies to help improve the lives of Canadian children and youth.

Data release - July 23, 2020


The main objectives of the Canadian Health Survey on Children and Youth are

- to provide current, detailed and ongoing health-related information on children and youth at the national, provincial and territorial levels
- to provide information to support evidenced-based policy and program development and evaluation
- to support research initiatives on children's health and well-being
- to support children's health surveillance programs by providing information on a regular and timely basis.

The data collected will be used by Statistics Canada, Health Canada and the Public Health Agency of Canada, provincial and territorial ministries of Health, as well as by other federal and provincial departments. The information collected from respondents will be used to monitor, plan, implement and evaluate programs to improve the health of Canadian children and youth. Researchers from various fields are also interested in the survey data and will use the information to conduct research into the various factors that affect the health and well-being of children and youth in Canada.


  • Children and youth
  • Health
  • Health and well-being (youth)
  • Health care services
  • Risk behaviours

Data sources and methodology

Target population

The 2019 Canadian Health Survey on Children and Youth (CHSCY) covers the population aged 1 to 17 as of January 31, 2019, living in the ten provinces and the three territories. Excluded from the survey's coverage are children and youth living on First Nation reserves and other Aboriginal settlements in the provinces, children and youth living in foster homes and the institutionalized population.

Based on a study comparing the Canadian Child Benefit (CCB) file with the 2018 population estimates, the CCB covers 98% of the Canadian population aged 1 to 17 in all provinces and 96% in all territories.

Instrument design

The survey content was developed based on consultation across Canada with key experts and federal and provincial stakeholders. The goal of the consultation was to provide advice to Statistics Canada on what survey content would be relevant for programs and policies and to fill data gaps related to children and youth. The questionnaire was developed by Statistics Canada, in collaboration with the Public Health Agency of Canada and Health Canada.

Qualitative testing by Statistics Canada's Questionnaire Design Resource Centre, using face-to-face interviews and focus groups, was conducted in November 2014, March 2015, November 2015, June 2016 and April to May 2018.


This is a sample survey with a cross-sectional design.

The sampling frame for the Canadian Health Survey on Children and Youth is the Canada Child Benefit file.

The sampling units are children and youth aged 1 to 17 years of age as of January 31, 2019.

In terms of geography, the sample is primarily stratified by province. The two exceptions are in the North (the three Territories are grouped together to form a single stratum) and in Ontario (sub-regions of the province's Local Health Integration Networks make up the geographic strata). The sample is further stratified into three age groups: children aged 1 to 4 years old, children aged 5 to 11 years old and youth aged 12 to 17 years old.

The sample size for the survey is 92,170 raw units.

Data sources

Data collection for this reference period: 2019-02-11 to 2019-06-28

Responding to this survey is voluntary.

Data are collected directly from survey respondents.

Respondents are given an opportunity to complete the questionnaire online using an e-questionnaire. If an e-questionnaire is not completed by March 31st, 2019, a Statistics Canada interviewer will call and ask the respondent to complete the questionnaire over the telephone.

View the Questionnaire(s) and reporting guide(s) .

Error detection

Some editing of the data is performed at the time of the interview within the electronic questionnaire. The questionnaire has built-in checks for some out-of-range or extreme values that prompt respondents and interviewers to verify the recorded answer. Flow errors are controlled in the application through programmed skip patterns. For example, questions that do not apply to the respondent are not asked.


The household income and the household income category were asked to the person most knowledgeable. If the respondent didn't respond to one or both questions, these variables were imputed. Donor imputation was used. The imputation was done by household size, province, subLHIN (for Ontario) and income category when it was provided by the respondent. Income was imputed for around 10% of respondents of the master file.


In order for estimates produced from survey data to be applicable to 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 respondent included in the final sample. This weight corresponds to the number of persons in the entire population that are represented by the respondent.

As described in the sampling section, the Canadian Health Survey on Children and Youth (CHSCY) uses the Canada Child Benefit as a frame for its sample selection. From this frame, initial weights are calculated for each sampled children. These weights then undergo several adjustments, including for non-response and calibration to known population totals, to create the final weights.

The steps for weighting are described in chapter 7 of the CHSCY User Guide.

Bootstrap weights are created through resampling the original sample and applying similar adjustments to the bootstrap weights as to the sample weights.

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.

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

Proportions and ratios are obtained by summing the final weights of records having the characteristic of the numerator and the denominator, and then dividing the first estimate by the second.

Disclosure control

Statistics Canada is prohibited by law from releasing any information it collects which could identify any person, business, or organization, unless consent has been given by the respondent or as permitted by the Statistics Act. Various confidentiality rules are applied to all data that are released or published to prevent the publication or disclosure of any information deemed confidential. If necessary, data are suppressed to prevent direct or residual disclosure of identifiable data.

Revisions and seasonal adjustment

This methodology type does not apply to this statistical program.

Data accuracy

Throughout the collection process, control and monitoring measures were put in place and corrective action was taken to minimize non-sampling errors. These measures included response rate evaluation, reported and non-reported data evaluation, on-site observation of interviews, improved collection tools for interviewers and others.

Once processing steps are completed, data validation steps are undertaken. A validation program is run in order to compare estimates for the health indicators taken from the common content with the 2017-2018 Canadian Community Health Survey estimations. This validation is performed at various geographical levels, as well as by age and sex. Significant differences are examined further to find any anomalies in data.

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