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Presented in almanac style, the 2012 Canada Year Book contains more than 500 pages of tables, charts and succinct analytical articles on every major area of Statistics Canada's expertise. The Canada Year Book is the premier reference on the social and economic life of Canada and its citizens.

This publication contains monthly, quarterly, and annual estimates of gross domestic product for 326 industries, including aggregates and special industry groupings. Estimates are seasonally adjusted, in 1997 dollars, for the year 1997 to the most current period. A brief text, supplemented by charts selected of major industry groupings, provides analytical highlights.

Survey quality is a multi-faceted concept that originates from two different development paths. One path is the total survey error paradigm that rests on four pillars providing principles that guide survey design, survey implementation, survey evaluation, and survey data analysis. We should design surveys so that the mean squared error of an estimate is minimized given budget and other constraints. It is important to take all known error sources into account, to monitor major error sources during implementation, to periodically evaluate major error sources and combinations of these sources after the survey is completed, and to study the effects of errors on the survey analysis. In this context survey quality can be measured by the mean squared error and controlled by observations made during implementation and improved by evaluation studies. The paradigm has both strengths and weaknesses. One strength is that research can be defined by error sources and one weakness is that most total survey error assessments are incomplete in the sense that it is not possible to include the effects of all the error sources. The second path is influenced by ideas from the quality management sciences. These sciences concern business excellence in providing products and services with a focus on customers and competition from other providers. These ideas have had a great influence on many statistical organizations. One effect is the acceptance among data providers that product quality cannot be achieved without a sufficient underlying process quality and process quality cannot be achieved without a good organizational quality. These levels can be controlled and evaluated by service level agreements, customer surveys, paradata analysis using statistical process control, and organizational assessment using business excellence models or other sets of criteria. All levels can be improved by conducting improvement projects chosen by means of priority functions. The ultimate goal of improvement projects is that the processes involved should gradually approach a state where they are error-free. Of course, this might be an unattainable goal, albeit one to strive for. It is not realistic to hope for continuous measurements of the total survey error using the mean squared error. Instead one can hope that continuous quality improvement using management science ideas and statistical methods can minimize biases and other survey process problems so that the variance becomes an approximation of the mean squared error. If that can be achieved we have made the two development paths approximately coincide.

Coca is a native bush from the Amazon rainforest from which cocaine, an illegal alkaloid, is extracted. Asking farmers about the extent of their coca cultivation areas is considered a sensitive question in remote coca growing regions in Peru. As a consequence, farmers tend not to participate in surveys, do not respond to the sensitive question(s), or underreport their individual coca cultivation areas. There is a political and policy concern in accurately and reliably measuring coca growing areas, therefore survey methodologists need to determine how to encourage response and truthful reporting of sensitive questions related to coca growing. Specific survey strategies applied in our case study included establishment of trust with farmers, confidentiality assurance, matching interviewer-respondent characteristics, changing the format of the sensitive question(s), and non enforcement of absolute isolation of respondents during the survey. The survey results were validated using satellite data. They suggest that farmers tend to underreport their coca areas to 35 to 40% of their true extent.

Nonresponse in longitudinal studies often occurs in a nonmonotone pattern. In the Survey of Industrial Research and Development (SIRD), it is reasonable to assume that the nonresponse mechanism is past-value-dependent in the sense that the response propensity of a study variable at time point t depends on response status and observed or missing values of the same variable at time points prior to t. Since this nonresponse is nonignorable, the parametric likelihood approach is sensitive to the specification of parametric models on both the joint distribution of variables at different time points and the nonresponse mechanism. The nonmonotone nonresponse also limits the application of inverse propensity weighting methods. By discarding all observed data from a subject after its first missing value, one can create a dataset with a monotone ignorable nonresponse and then apply established methods for ignorable nonresponse. However, discarding observed data is not desirable and it may result in inefficient estimators when many observed data are discarded. We propose to impute nonrespondents through regression under imputation models carefully created under the past-value-dependent nonresponse mechanism. This method does not require any parametric model on the joint distribution of the variables across time points or the nonresponse mechanism. Performance of the estimated means based on the proposed imputation method is investigated through some simulation studies and empirical analysis of the SIRD data.

The propensity-scoring-adjustment approach is commonly used to handle selection bias in survey sampling applications, including unit nonresponse and undercoverage. The propensity score is computed using auxiliary variables observed throughout the sample. We discuss some asymptotic properties of propensity-score-adjusted estimators and derive optimal estimators based on a regression model for the finite population. An optimal propensity-score-adjusted estimator can be implemented using an augmented propensity model. Variance estimation is discussed and the results from two simulation studies are presented.

Non-response in longitudinal studies is addressed by assessing the accuracy of response propensity models constructed to discriminate between and predict different types of non-response. Particular attention is paid to summary measures derived from receiver operating characteristic (ROC) curves and logit rank plots. The ideas are applied to data from the UK Millennium Cohort Study. The results suggest that the ability to discriminate between and predict non-respondents is not high. Weights generated from the response propensity models lead to only small adjustments in employment transitions. Conclusions are drawn in terms of the potential of interventions to prevent non-response.

We propose a new approach to small area estimation based on joint modelling of means and variances. The proposed model and methodology not only improve small area estimators but also yield "smoothed" estimators of the true sampling variances. Maximum likelihood estimation of model parameters is carried out using EM algorithm due to the non-standard form of the likelihood function. Confidence intervals of small area parameters are derived using a more general decision theory approach, unlike the traditional way based on minimizing the squared error loss. Numerical properties of the proposed method are investigated via simulation studies and compared with other competitive methods in the literature. Theoretical justification for the effective performance of the resulting estimators and confidence intervals is also provided.

Collinearities among explanatory variables in linear regression models affect estimates from survey data just as they do in non-survey data. Undesirable effects are unnecessarily inflated standard errors, spuriously low or high t-statistics, and parameter estimates with illogical signs. The available collinearity diagnostics are not generally appropriate for survey data because the variance estimators they incorporate do not properly account for stratification, clustering, and survey weights. In this article, we derive condition indexes and variance decompositions to diagnose collinearity problems in complex survey data. The adapted diagnostics are illustrated with data based on a survey of health characteristics.

This paper develops two Bayesian methods for inference about finite population quantiles of continuous survey variables from unequal probability sampling. The first method estimates cumulative distribution functions of the continuous survey variable by fitting a number of probit penalized spline regression models on the inclusion probabilities. The finite population quantiles are then obtained by inverting the estimated distribution function. This method is quite computationally demanding. The second method predicts non-sampled values by assuming a smoothly-varying relationship between the continuous survey variable and the probability of inclusion, by modeling both the mean function and the variance function using splines. The two Bayesian spline-model-based estimators yield a desirable balance between robustness and efficiency. Simulation studies show that both methods yield smaller root mean squared errors than the sample-weighted estimator and the ratio and difference estimators described by Rao, Kovar, and Mantel (RKM 1990), and are more robust to model misspecification than the regression through the origin model-based estimator described in Chambers and Dunstan (1986). When the sample size is small, the 95% credible intervals of the two new methods have closer to nominal confidence coverage than the sample-weighted estimator.

Using 2011 Census data, this profile provides a statistical overview of the age and sex as well as families, households, marital status, structural type of dwelling and collectives and language characteristics for census metropolitan areas, tracted census agglomerations and census tracts.

In the census product line, groups of related variables are referred to as 'release components of profiles.' These are made available with the major releases of variables of the census cycle, starting with age and sex. Together, they will form a complete Census Profile of all the variables for each level of geography, plus one cumulative profile for the dissolved census subdivisions.

Starting with the age and sex major day of release, and on major days of release thereafter, profile component data are available at the Canada, province and territory, economic region, census division and census subdivision levels, at the census metropolitan area, census agglomeration, population centre, and census tract levels, designated places, and at the federal electoral district (based on the 2003 Representation Order) level. Profile component data for all other standard geographic areas, including dissemination areas, dissolved census subdivisions, and forward sortation areas, will be available after the major days of release.

Using 2011 Census data, this profile provides a statistical overview of the age and sex variables as well as families, households, marital status, structural type of dwelling and collectives and language characteristics for census metropolitan areas, census agglomerations and census subdivisions.

In the census product line, groups of related variables are referred to as 'release components of profiles.' These are made available with the major releases of variables of the census cycle, starting with age and sex. Together, they will form a complete Census Profile of all the variables for each level of geography, plus one cumulative profile for the dissolved census subdivisions.

Starting with the age and sex major day of release, and on major days of release thereafter, profile component data are available at the Canada, province and territory, economic region, census division and census subdivision levels, at the census metropolitan area, census agglomeration, population centre, and census tract levels, designated places, and at the federal electoral district (based on the 2003 Representation Order) level. Profile component data for all other standard geographic areas, including dissemination areas, dissolved census subdivisions, and forward sortation areas, will be available after the major days of release.

Using 2011 Census data, this profile provides a statistical overview of the age and sex variables as well as families, households, marital status, structural type of dwelling and collectives and language characteristics for Canada, provinces, territories and federal electoral districts (2003 Representation Order).

In the census product line, groups of related variables are referred to as 'release components of profiles.' These are made available with the major releases of variables of the census cycle, starting with age and sex. Together, they will form a complete Census Profile of all the variables for each level of geography, plus one cumulative profile for the dissolved census subdivisions.

Starting with the age and sex major day of release, and on major days of release thereafter, profile component data are available at the Canada, province and territory, economic region, census division and census subdivision levels, at the census metropolitan area, census agglomeration, population centre, and census tract levels, designated places, and at the federal electoral district (based on the 2003 Representation Order) level. Profile component data for all other standard geographic areas, including dissemination areas, dissolved census subdivisions, and forward sortation areas, will be available after the major days of release.

Using 2011 Census data, this profile provides a statistical overview of the age and sex as well as families, households, marital status, structural type of dwelling and collectives and language characteristics for designated places.

In the census product line, groups of related variables are referred to as 'release components of profiles.' These are made available with the major releases of variables of the census cycle, starting with age and sex. Together, they will form a complete Census Profile of all the variables for each level of geography, plus one cumulative profile for the dissolved census subdivisions.

Starting with the age and sex major day of release, and on major days of release thereafter, profile component data are available at the Canada, province and territory, economic region, census division and census subdivision levels, at the census metropolitan area, census agglomeration, population centre, and census tract levels, designated places, and at the federal electoral district (based on the 2003 Representation Order) level. Profile component data for all other standard geographic areas, including dissemination areas, dissolved census subdivisions, and forward sortation areas, will be available after the major days of release.

Using 2011 Census data, this profile provides a statistical overview of the age and sex as well as families, households, marital status, structural type of dwelling and collectives and language characteristics for population centres.

In the census product line, groups of related variables are referred to as 'release components of profiles.' These are made available with the major releases of variables of the census cycle, starting with age and sex. Together, they will form a complete Census Profile of all the variables for each level of geography, plus one cumulative profile for the dissolved census subdivisions.

Starting with the age and sex major day of release, and on major days of release thereafter, profile component data are available at the Canada, province and territory, economic region, census division and census subdivision levels, at the census metropolitan area, census agglomeration, population centre, and census tract levels, designated places, and at the federal electoral district (based on the 2003 Representation Order) level. Profile component data for all other standard geographic areas, including dissemination areas, dissolved census subdivisions, and forward sortation areas, will be available after the major days of release.

Using 2011 Census data, this profile provides a statistical overview of the age and sex variables as well as families, households, marital status, structural type of dwelling and collectives and language characteristics for economic regions.

In the census product line, groups of related variables are referred to as 'release components of profiles.' These are made available with the major releases of variables of the census cycle, starting with age and sex. Together, they will form a complete Census Profile of all the variables for each level of geography, plus one cumulative profile for the dissolved census subdivisions.

Starting with the age and sex major day of release, and on major days of release thereafter, profile component data are available at the Canada, province and territory, economic region, census division and census subdivision levels, at the census metropolitan area, census agglomeration, population centre, and census tract levels, designated places, and at the federal electoral district (based on the 2003 Representation Order) level. Profile component data for all other standard geographic areas, including dissemination areas, dissolved census subdivisions, and forward sortation areas, will be available after the major days of release.

This topic presents data on the language composition of Canada and illustrates the linguistic characteristics of the Canadian population, including mother tongue, knowledge of official languages, first official language spoken and languages spoken at home.

This topic also presents data on language retention and language transmission by the parents to the child by other demographic characteristics. Data on languages are presented at the person level.

This topic presents data on the language composition of Canada and illustrates the linguistic characteristics of the Canadian population, including mother tongue, knowledge of official languages, first official language spoken and languages spoken at home.

This topic also presents data on language retention and language transmission by the parents to the child by other demographic characteristics. Data on languages are presented at the person level.

This topic presents data on the language composition of Canada and illustrates the linguistic characteristics of the Canadian population, including mother tongue, knowledge of official languages, first official language spoken and languages spoken at home.

This topic also presents data on language retention and language transmission by the parents to the child by other demographic characteristics. Data on languages are presented at the person level.

This topic presents data on the language composition of Canada and illustrates the linguistic characteristics of the Canadian population, including mother tongue, knowledge of official languages, first official language spoken and languages spoken at home.

This topic also presents data on language retention and language transmission by the parents to the child by other demographic characteristics. Data on languages are presented at the person level.

Presented in almanac style, the 2012 Canada Year Book contains more than 500 pages of tables, charts and succinct analytical articles on every major area of Statistics Canada's expertise. The Canada Year Book is the premier reference on the social and economic life of Canada and its citizens.

Survey quality is a multi-faceted concept that originates from two different development paths. One path is the total survey error paradigm that rests on four pillars providing principles that guide survey design, survey implementation, survey evaluation, and survey data analysis. We should design surveys so that the mean squared error of an estimate is minimized given budget and other constraints. It is important to take all known error sources into account, to monitor major error sources during implementation, to periodically evaluate major error sources and combinations of these sources after the survey is completed, and to study the effects of errors on the survey analysis. In this context survey quality can be measured by the mean squared error and controlled by observations made during implementation and improved by evaluation studies. The paradigm has both strengths and weaknesses. One strength is that research can be defined by error sources and one weakness is that most total survey error assessments are incomplete in the sense that it is not possible to include the effects of all the error sources. The second path is influenced by ideas from the quality management sciences. These sciences concern business excellence in providing products and services with a focus on customers and competition from other providers. These ideas have had a great influence on many statistical organizations. One effect is the acceptance among data providers that product quality cannot be achieved without a sufficient underlying process quality and process quality cannot be achieved without a good organizational quality. These levels can be controlled and evaluated by service level agreements, customer surveys, paradata analysis using statistical process control, and organizational assessment using business excellence models or other sets of criteria. All levels can be improved by conducting improvement projects chosen by means of priority functions. The ultimate goal of improvement projects is that the processes involved should gradually approach a state where they are error-free. Of course, this might be an unattainable goal, albeit one to strive for. It is not realistic to hope for continuous measurements of the total survey error using the mean squared error. Instead one can hope that continuous quality improvement using management science ideas and statistical methods can minimize biases and other survey process problems so that the variance becomes an approximation of the mean squared error. If that can be achieved we have made the two development paths approximately coincide.

Coca is a native bush from the Amazon rainforest from which cocaine, an illegal alkaloid, is extracted. Asking farmers about the extent of their coca cultivation areas is considered a sensitive question in remote coca growing regions in Peru. As a consequence, farmers tend not to participate in surveys, do not respond to the sensitive question(s), or underreport their individual coca cultivation areas. There is a political and policy concern in accurately and reliably measuring coca growing areas, therefore survey methodologists need to determine how to encourage response and truthful reporting of sensitive questions related to coca growing. Specific survey strategies applied in our case study included establishment of trust with farmers, confidentiality assurance, matching interviewer-respondent characteristics, changing the format of the sensitive question(s), and non enforcement of absolute isolation of respondents during the survey. The survey results were validated using satellite data. They suggest that farmers tend to underreport their coca areas to 35 to 40% of their true extent.

Nonresponse in longitudinal studies often occurs in a nonmonotone pattern. In the Survey of Industrial Research and Development (SIRD), it is reasonable to assume that the nonresponse mechanism is past-value-dependent in the sense that the response propensity of a study variable at time point t depends on response status and observed or missing values of the same variable at time points prior to t. Since this nonresponse is nonignorable, the parametric likelihood approach is sensitive to the specification of parametric models on both the joint distribution of variables at different time points and the nonresponse mechanism. The nonmonotone nonresponse also limits the application of inverse propensity weighting methods. By discarding all observed data from a subject after its first missing value, one can create a dataset with a monotone ignorable nonresponse and then apply established methods for ignorable nonresponse. However, discarding observed data is not desirable and it may result in inefficient estimators when many observed data are discarded. We propose to impute nonrespondents through regression under imputation models carefully created under the past-value-dependent nonresponse mechanism. This method does not require any parametric model on the joint distribution of the variables across time points or the nonresponse mechanism. Performance of the estimated means based on the proposed imputation method is investigated through some simulation studies and empirical analysis of the SIRD data.

The propensity-scoring-adjustment approach is commonly used to handle selection bias in survey sampling applications, including unit nonresponse and undercoverage. The propensity score is computed using auxiliary variables observed throughout the sample. We discuss some asymptotic properties of propensity-score-adjusted estimators and derive optimal estimators based on a regression model for the finite population. An optimal propensity-score-adjusted estimator can be implemented using an augmented propensity model. Variance estimation is discussed and the results from two simulation studies are presented.

Non-response in longitudinal studies is addressed by assessing the accuracy of response propensity models constructed to discriminate between and predict different types of non-response. Particular attention is paid to summary measures derived from receiver operating characteristic (ROC) curves and logit rank plots. The ideas are applied to data from the UK Millennium Cohort Study. The results suggest that the ability to discriminate between and predict non-respondents is not high. Weights generated from the response propensity models lead to only small adjustments in employment transitions. Conclusions are drawn in terms of the potential of interventions to prevent non-response.

We propose a new approach to small area estimation based on joint modelling of means and variances. The proposed model and methodology not only improve small area estimators but also yield "smoothed" estimators of the true sampling variances. Maximum likelihood estimation of model parameters is carried out using EM algorithm due to the non-standard form of the likelihood function. Confidence intervals of small area parameters are derived using a more general decision theory approach, unlike the traditional way based on minimizing the squared error loss. Numerical properties of the proposed method are investigated via simulation studies and compared with other competitive methods in the literature. Theoretical justification for the effective performance of the resulting estimators and confidence intervals is also provided.

Collinearities among explanatory variables in linear regression models affect estimates from survey data just as they do in non-survey data. Undesirable effects are unnecessarily inflated standard errors, spuriously low or high t-statistics, and parameter estimates with illogical signs. The available collinearity diagnostics are not generally appropriate for survey data because the variance estimators they incorporate do not properly account for stratification, clustering, and survey weights. In this article, we derive condition indexes and variance decompositions to diagnose collinearity problems in complex survey data. The adapted diagnostics are illustrated with data based on a survey of health characteristics.

This paper develops two Bayesian methods for inference about finite population quantiles of continuous survey variables from unequal probability sampling. The first method estimates cumulative distribution functions of the continuous survey variable by fitting a number of probit penalized spline regression models on the inclusion probabilities. The finite population quantiles are then obtained by inverting the estimated distribution function. This method is quite computationally demanding. The second method predicts non-sampled values by assuming a smoothly-varying relationship between the continuous survey variable and the probability of inclusion, by modeling both the mean function and the variance function using splines. The two Bayesian spline-model-based estimators yield a desirable balance between robustness and efficiency. Simulation studies show that both methods yield smaller root mean squared errors than the sample-weighted estimator and the ratio and difference estimators described by Rao, Kovar, and Mantel (RKM 1990), and are more robust to model misspecification than the regression through the origin model-based estimator described in Chambers and Dunstan (1986). When the sample size is small, the 95% credible intervals of the two new methods have closer to nominal confidence coverage than the sample-weighted estimator.

A benefit of multiple imputation is that it allows users to make valid inferences using standard methods with simple combining rules. Existing combining rules for multivariate hypothesis tests fail when the sampling error is zero. This paper proposes modified tests for use with finite population analyses of multiply imputed census data for the applications of disclosure limitation and missing data and evaluates their frequentist properties through simulation.

This report describes the quality indicators produced for the 2010 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.

Starting with the 2010 survey year, the Survey of Household Spending (SHS) has used a different collection methodology from previous surveys. The new methodology combines a questionnaire and a diary of expenses. Also, data collection is now continuous throughout the year. This note provides information to users and prospective users of data from the SHS about the methodological differences between the redesigned SHS and the former SHS.

The Guide to Culture Statistics links users to information on culture surveys, data and analysis at Statistics Canada. Topics include film and video production and distribution, movie theatres, TV viewing and radio listening, the performing arts, book and periodical publishing, heritage institutions, government and private sector funding of culture, culture trade and investment, the culture labour force, Canadians consumption of culture, and more.

This user's guide has been developed by the Culture Statistics Program to facilitate access to culture information throughout Statistics Canada. This guide is continually being updated to maintain its currency and usefulness.

The Dissemination Area Reference Maps, by Census Subdivisions, for areas outside Census Metropolitan Areas and Census Agglomerations cover areas outside census metropolitan areas and census agglomerations. Each map in this series covers one census subdivision and displays the boundaries and codes of dissemination areas, designated places and their names, as well as urban and rural areas within that census subdivision. There are 4,470 maps in this series. The maps also display census subdivision boundaries with detailed street network and other visible features such as railroads, rivers and lakes. The maps vary in scale and size; the maximum dimensions are 86 cm by 61 cm (34 inches by 24 inches). Dissemination areas reference maps are also available by census tracts for census metropolitan areas and census agglomerations (92-147-XIB), and by non-tracted census agglomerations (92-148-UIB). Together, the three sets of dissemination area maps cover all of Canada. A reference guide is available (92-145-GIE). Reference maps are available free on the Internet (www.statcan.gc.ca). To purchase this product in electronic format (PDF on CD-ROM) or paper format, please contact us.

The Dissemination Area Reference Maps, by Non-tracted Census Agglomerations cover smaller census agglomerations that are not part of the census tract program. Each map in the series covers one census agglomeration and displays the boundaries and codes of dissemination areas, designated places and their names, urban core, urban fringe and rural fringe, within that census agglomeration. There are 271 maps in this series and inset maps were created to show detail for the more concentrated areas. The maps also display census subdivision boundaries with detailed street network and other visible features such as railroads, rivers and lakes. The maps vary in scale and size, the maximum dimensions being 86 cm by 61 cm (34 inches by 24 inches). Dissemination area reference maps are also available by census tracts for census metropolitan areas and census agglomerations (92-147-XIB) and by census subdivisions for areas outside census metropolitan areas and census agglomerations (92-145-UIB). Together, the three sets of dissemination area maps cover all of Canada. A reference guide is available (Catalogue No. 92-145-GIE). Reference maps are available free on the Internet (www.statcan.gc.ca). To purchase this product in electronic format (PDF on CD-ROM) or paper format, please contact us.

The Dissemination Area Reference Maps, by Census Tract, for Census Metropolitan Areas and Census Agglomerations cover all census metropolitan areas and census agglomerations that are part of the census tract program. Each map in the series covers one census tract and displays the boundaries and unique identifiers of dissemination areas within a census tract. Inset maps are available to show detail for the more concentrated areas. The maps display census tract, census subdivision, and census metropolitan area or census agglomeration boundaries along with street network and other visible features such as railroads, rivers and lakes.

Dissemination area reference maps are also available for non-tracted census agglomerations (92-148-X), and by census subdivisions for areas outside census metropolitan areas and census agglomerations (92-145-X). Together, the three sets of dissemination area maps cover all of Canada.

A reference guide is available (92-143-G).

The Overview of the Census is a reference document covering each phase of the Census of Population and Census of Agriculture. It provides an overview of the 2011 Census from legislation governing the census to content determination, collection, processing, data quality assessment and data dissemination. It also traces the history of the census from the early days of New France to the present.

In addition, the Overview of the Census informs users about the steps taken to protect confidential information, along with steps taken to verify the data and minimize errors. It also provides information on the possible uses of census data and covers the different levels of geography and the range of products and services available.

The Overview of the Census may be useful to both new and experienced users who wish to familiarize themselves with and find specific information about the 2011 Census. The first part covers the Census of Population, while the second is about the Census of Agriculture.

The Dissemination Area Reference Maps, by Census Subdivisions, for areas outside Census Metropolitan Areas and Census Agglomerations cover areas outside census metropolitan areas and census agglomerations. Each map in this series covers one census subdivision and displays the boundaries and unique identifiers of dissemination areas, designated places and their names, as well as population centres and rural areas within a census subdivision. The maps also display census subdivision boundaries with street network and other visible features such as railroads, rivers and lakes.

Dissemination areas reference maps are also available by census tracts for census metropolitan areas and census agglomerations (92-147-X), and by non-tracted census agglomerations (92-148-X). Together, the three sets of dissemination area maps cover all of Canada.

A reference guide is available (92-143-G).

The Dissemination Area Reference Maps, by Non-tracted Census Agglomerations cover census agglomerations that are not part of the census tract program. Each map in the series covers one census agglomeration and displays the boundaries and unique identifiers of dissemination areas, designated places and their names, core, fringe and rural areas, within a census agglomeration. Inset maps are available to show detail for the more concentrated areas. The maps also display census subdivision boundaries with street network and other visible features such as railroads, rivers and lakes.

Dissemination area reference maps are also available by census tracts for census metropolitan areas and census agglomerations (92-147-X) and by census subdivisions for areas outside census metropolitan areas and census agglomerations (92-145-X). Together, the three sets of dissemination area maps cover all of Canada.

A reference guide is available (Catalogue No. 92-143-G).

Mental illnesses largely involve alterations in mood, thinking, and behaviour, as well as other domains of mental functioning, and affect almost all Canadians in some way, either directly or indirectly. They routinely cause significant impairments in emotional functioning, which may lead to social or physical limitations. In some cases, such as in agoraphobia, individuals cannot even leave their homes due to intense anxiety; depression can cause an individual to lose all interest in life. This document describes the mental illnesses that have the greatest impact on Canadians in terms of prevalence or severity of disability, and how they affect the health status of Canadians.