Private nursing and residential care facilities (NRCF)
Detailed information for 2018
This survey collects the financial and operating data needed to develop national and regional economic policies and programs.
Data release - February 19, 2020
The objective of this survey is to collect financial data for nursing and residential care facilities in Canada in order to produce statistics about this industry.
Data collected from businesses are aggregated with information from other sources to produce official estimates of national and provincial economic production for this industry.
Survey estimates are made available to businesses, governments, investors, associations, and the public. The data are used to monitor industry growth, measure performance, and make comparisons to other data sources to better understand this industry.
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.
- Health and disability among seniors
- Health care services
Data sources and methodology
The target population consists of all private sector establishments classified to the code 623 - Nursing and residential care facilities according to the North American Industry Classification System (NAICS) 2017 during the reference year.
The observed population consists of all private sector establishments classified to the code 623 - Nursing and residential care facilities according to the NAICS 2017 found on Statistics Canada Business Register as of the last day of the reference year (including establishments active for a part of the reference year).
This methodology does not apply.
This survey is a census with a longitudinal design.
Data are collected for all units of the target population, therefore no sampling is done.
Data are extracted from administrative files.
Data extracted from administrative files are supplied by Canada Revenue Agency and aggregated to the estimated domains. Data are analyzed for erroneous figures, and data confidentiality analysis is conducted to ensure confidentiality of each enterprise.
Error detection is an integral part of data processing activities. Edits are applied to data records during integration 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 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.
Donor and historical imputation methods were used when records were missing or had erroneous figures.
All units in the observed population are being surveyed. Estimation of totals is done by simple aggregation of the 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.
Factors based on information from sources such as tax files and Business Register profiles are used to allocate the data 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.
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, coherence with results from related economic indicators, historical trends, and information from other external sources (e.g. associations, trade publications or newspaper articles).
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.
In order to prevent any data disclosure, confidentiality analysis is done using primary suppression (direct disclosure) as well as for secondary suppression (residual disclosure). Direct disclosure occurs when the value in a tabulation cell is composed of or dominated by few enterprises while residual disclosure occurs when confidential information can be derived indirectly by piecing together information from different sources or data series.
Revisions and seasonal adjustment
There is no seasonal adjustment. Data from previous years may be revised based on updated information.
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. Errors can arise from the various phases of a survey. For example, these types of errors can occur 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.