Survey of Health Care Clinics in Canada (SHCCC)

Detailed information for 2022

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 - January 23, 2025

• 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 Reference Year 2022 focusing on medical and diagnostic clinics.

Estimates for this reference period are modeled using responses from the Reference Year 2023 collection and aggregated with administrative data from Reference Year 2022.

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.

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.

The target population consists of all private sector establishments classified to the codes 621110 - Offices of physicians, 621494 - Community Health Centres and 621510 - Medical and diagnostic laboratories according to the North American Industry Classification System (NAICS) 2022 during the reference year.

Instrument design

This methodology type does not apply to this statistical program.

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 are extracted from administrative files and used with Reference Year 2023 collection data which was collected from 2024-10-03 to 2024-12-20.

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

Donor and historical imputation methods were used when records were missing or had erroneous figures.

Estimation

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.

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 estimation 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.