Survey on Home Health Care and Related Services (SHHCRS)
Detailed information for 2017
This survey collects the financial and operating data needed to develop national and regional economic policies and programs.
Data release - July 31, 2019
The objective of this survey is to collect financial data for home health care and related services 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
- Financial statements and performance
- Health and disability among seniors
- Health care services
Data sources and methodology
The target population consists of all private sector establishments classified to the Home health care services (NAICS 621610) and to the Services for the elderly and persons with disabilities (NAICS 624120) North American Industry Classification System (NAICS 2017) during the reference year.
The observed population consists of all private sector establishments classified to the Home health care services (NAICS 621610) and to the Services for the elderly and persons with disabilities (NAICS 624120) according to the North American Industry Classification System (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 type does not apply to this statistical program.
This is a census of all sampling units in the survey population.
Data are collected for all units of the target population, therefore no sampling is done.
Data are extracted from administrative files supplied by Canada Revenue Agency (CRA) 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 included. Estimation of totals is done by simple aggregation of the values of all estimation units that are found in the domain of estimation.
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, 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.
Census surveys are not subject to sampling errors but still, are subject to non-sampling errors. Non-sampling error may occur for various reasons during the processing of data.
Non-sampling error is not related to sampling and may occur for various reasons during the processing of data. Under or over-coverage of the population, and mistakes in recording, coding and processing data are examples of non-sampling errors.
Coverage errors consist of omissions, erroneous inclusions, duplications and misclassification of units in the survey frame. The Business Register (BR) is the common frame for all surveys using the IBSP model. The BR is a data service center updated through a number of sources including administrative data files, feedback received from conducting Statistics Canada business surveys, and profiling activities including direct contact with companies to obtain information about their operations and Internet research findings. Using the BR will ensure quality, while avoiding overlap between surveys and minimizing response burden to the greatest extent possible.