Survey of Health Care Clinics in Canada (SHCCC)

Detailed information for 2023

By collecting information about medical and diagnostic clinics in Canada, this survey aims to better understand access to MRIs, CT scans and ultrasounds in Canada and to update information on the Canadian business registry. Responses to the Survey on Health Care Clinics in Canada will remain confidential.

Data release - Scheduled for January 29, 2025

• Questionnaire(s) and reporting guide(s)
• Description
• Data sources and methodology
• Data accuracy

Description

The Survey on Health Care Clinics in Canada aims at better understanding patients access to care in Canada, with Cycle 1 of the survey focusing on medical and diagnostic clinics.

The questions focus on the number of MRIs, CT scans and ultrasounds performed in the past fiscal year, and whether these scans were charged directly to patients insured under a provincial or territorial health care insurance plan. The survey also collects information on the total amount charged directly to patients for these services.

Data on operating revenue and expenses will be used to maintain the Canadian Business Register which lists all active businesses in Canada.

Responses to the Survey on Health Care Clinics in Canada will remain confidential. No data sharing agreements with Health Canada are planned for this survey.

Reference period: The calendar year, or the 12-month fiscal period for which the final day occurs on or between April 1st of the reference year and March 31st of the following year.

Collection period: October through December following the year after the reference period

Subjects

• Health
• Health care services

Data sources and methodology

Target population

The target population consists of all private sector establishments classified to the code 621510 - Medical and diagnostic laboratories according to the North American Industry Classification System (NAICS) 2022 during the reference year.

Instrument design

The Survey on Health Care Clinics in Canada is administered as part of the Integrated Business Statistics Program (IBSP). As such, it is conducted online using a secure electronic questionnaire platform.

Sampling

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

Sampling unit:

The sampling unit is the establishment, as defined on the Business Register.

Stratification method:

Prior to the selection of a random sample, establishments are classified into homogeneous groups (i.e., groups with the same NAICS codes, same geography, and same ownership structure). Then, each group is divided into sub-groups (i.e. small, medium or large), called strata, based on the annual revenue.

Sampling and sub-sampling:

Following stratification, a sample, of a predetermined size, is allocated into each stratum, with the objective of optimizing the overall quality of the survey while respecting the available resources. The sample allocation can result in two kinds of strata: take-all strata where all units are sampled with certainty, and take-some strata where a sample of units are randomly selected.

Data sources

Data collection for this reference period: 2024-10-03 to 2024-12-20

Responding to this survey is mandatory.

Data are collected directly from survey respondents and extracted from administrative files.

Data were collected using an electronic questionnaire. Respondents were contacted by telephone interviewers who also followed-up on questionnaires that failed specific edit checks. Respondents were offered both English and French questionnaires.

A strategy to replace survey data with tax data has been introduced to reduce the response burden and survey costs. The strategy involves using tax data instead of survey data for some simple units (for example, a single location and a single activity).

As part of the Integrated Business Statistics Program (IBSP), T1 tax data are used for unincorporated businesses and T2 tax data for incorporated businesses. Data replacement may be used to correct outliers or to replace partially or completely missing data. Tax data may also be used to reconcile survey data.

Data integration combines data from multiple data sources including survey data collected from respondents, administrative data from the Canadian Revenue Agency or other forms of auxiliary data when applicable. During the data integration process, data are imported, transformed, validated, aggregated and linked from the different data source providers into the formats, structures and levels required for IBSP processing. Administrative data are used in a data replacement strategy for a large number of financial variables for most small and medium enterprises and a select group of large enterprises to avoid collection of these variables. Administrative data are also used as an auxiliary source of data for editing and imputation when respondent data is not available.

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

Error detection

Error detection is an integral part of both collection and data processing activities. Automated edits are applied to data records during collection to identify reporting and capture errors. These edits identify potential errors based on year-over-year changes in key variables, totals, and ratios that exceed tolerance thresholds, as well as identify problems in the consistency of collected data (e.g. a total variable does not equal the sum of its parts). During data processing, other edits are used to automatically detect errors or inconsistencies that remain in the data following collection. These edits include value edits (e.g. Value > 0, Value > -500, Value = 0), linear equality edits (e.g. Value1 + Value2 = Total Value), linear inequality edits (e.g. Value1 >= Value2), and equivalency edits (e.g. Value1 = Value2). When errors are found, they can be corrected using the failed edit follow up process during collection or via imputation. Extreme values are also flagged as outliers, using automated methods based on the distribution of the collected information. Following their detection, these values are reviewed in order to assess their reliability. Manual review of other units may lead to additional outliers identified. These outliers are excluded from use in the calculation of ratios and trends used for imputation, and during donor imputation. In general, every effort is made to minimize the non-sampling errors of omission, duplication, misclassification, reporting and processing.

Imputation

When non-response occurs, when respondents do not completely answer the questionnaire, or when reported data are considered incorrect during the error detection steps, imputation is used to fill in the missing information and modify the incorrect information. Many methods of imputation may be used to complete a questionnaire, including manual changes made by an analyst. The automated, statistical techniques used to impute the missing data include deterministic imputation, replacement using historical data (with a trend calculated, when appropriate), replacement using auxiliary information available from other sources, replacement based on known data relationships for the sample unit, and replacement using data from a similar unit in the sample (known as donor imputation). Usually, key variables are imputed first and are used as anchors in subsequent steps to impute other, related variables.

Imputation generates a complete and coherent microdata file that covers all survey variables.

Estimation

The sample used for estimation comes from a single-phase sampling process. An initial sampling weight (the design weight) is calculated for each unit of the survey and is simply the inverse of the probability of selection that is conditional on the realized sample size. The weight calculated for each sampling unit indicates how many other units it represents. The final weights are usually either one or greater than one. Sampling units which are "Take-all" (also called "must-take") have sampling weights of one and only represent themselves.

Estimation of totals is done by simple aggregation of the weighted values of all estimation units that are found in the domain of estimation. Estimates are computed for several domains of estimation such as industrial groups and provinces/territories, based on the most recent classification information available for the estimation unit and the survey reference period. It should be noted that this classification information may differ from the original sampling classification since records may have changed in size, industry or location. Changes in classification are reflected immediately in the estimates.

When some enterprises have reported data combining many units located in more than one province or territory, or in more than one industrial classification, data allocation is required. Factors based on information from sources such as tax files and Business Register profiles are used to allocate the data reported on the combined report among the various estimation units where this enterprise is in operation. The characteristics of the estimation units are used to derive the domains of estimation, including the industrial classification and the geography.

Units with larger than expected size are seen as misclassified and their weight is adjusted so that they only represent themselves (large units found in a stratum of small units for example).

Quality evaluation

Prior to the data release, combined survey results are analyzed for comparability; in general, this includes a detailed review of individual responses (especially for the largest companies), general economic conditions and coherence with results from related economic indicators, historical trends, and information from other external sources (e.g. associations, trade publications or newspaper articles).

Disclosure control

Statistics Canada is prohibited by law from releasing any information it collects that 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

There is no seasonal adjustment. Data may be revised based on updated information.

Data accuracy

The methodology of this survey has been designed to control errors and to reduce their potential effects on estimates. However, the survey results remain subject to errors, of which sampling error is only one component of the total survey error. Sampling error results when observations are made only on a sample and not on the entire population. All other errors arising from the various phases of a survey are referred to as non-sampling errors. For example, these types of errors can occur when a respondent provides incorrect information or does not answer certain questions; when a unit in the target population is omitted or covered more than once; when GST data for records being modeled for a particular month are not representative of the actual record for various reasons; when a unit that is out of scope for the survey is included by mistake or when errors occur in data processing, such as coding or capture errors.

Prior to publication, combined survey results are analyzed for comparability; in general, this includes a detailed review of individual responses (especially for large businesses), general economic conditions and historical trends.

A common measure of data quality for surveys is the coefficient of variation (CV). The CV, defined as the standard error divided by the sample estimate, is a measure of precision in relative terms. Since the CV is calculated from responses of individual units, it also measures some non-sampling errors.

The formula used to calculate CV as percentages is:

CV (X) = S(X) * 100% / X
where X denotes the estimate and S(X) denotes the standard error of X.

Confidence intervals can be constructed around the estimates using the estimate and the CV. Thus, for our sample, it is possible to state with a given level of confidence that the expected value will fall within the confidence interval constructed around the estimate. For example, if an estimate of $12,000,000 has a CV of 2%, the standard error will be $240,000 (the estimate multiplied by the CV). It can be stated with 68% confidence that the expected values will fall within the interval whose length equals the standard deviation about the estimate, i.e. between $11,760,000 and $12,240,000.

Alternatively, it can be stated with 95% confidence that the expected value will fall within the interval whose length equals two standard deviations about the estimate, i.e. between $11,520,000 and $12,480,000.

Finally, due to the small contribution of the non-survey portion to the total estimates, bias in the non-survey portion has a negligible impact on the CVs. Therefore, the CV from the survey portion is used for the total estimate that is the summation of estimates from the surveyed and non-surveyed portions.