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- Articles and reports: 82-003-X201901200003Description:
This article provides a description of the Canadian Census Health and Environment Cohorts (CanCHECs), a population-based linked datasets of the household population at the time of census collection. The CanCHEC datasets are rich national data resources that can be used to measure and examine health inequalities across socioeconomic and ethnocultural dimensions for different periods and locations. These datasets can also be used to examine the effects of exposure to environmental factors on human health.
Release date: 2019-12-18 - 2. Canadian Census Health and Environment Cohorts: Creation of a new health surveillance program ArchivedStats in brief: 11-629-X2019006Description:
This video describes a new health surveillance program at Statistics Canada: The Canadian Census Health and Environment Cohorts (CanCHECs). The video describes the attributes of and the datasets included in the CanCHECs, how the CanCHECs can be used, and their strengths and limitations. Recent examples of research projects based on the CanCHECs are presented along with information about how to apply for access to these data.
Release date: 2019-12-18 - Articles and reports: 11-633-X2016003Description:
Large national mortality cohorts are used to estimate mortality rates for different socioeconomic and population groups, and to conduct research on environmental health. In 2008, Statistics Canada created a cohort linking the 1991 Census to mortality. The present study describes a linkage of the 2001 Census long-form questionnaire respondents aged 19 years and older to the T1 Personal Master File and the Amalgamated Mortality Database. The linkage tracks all deaths over a 10.6-year period (until the end of 2011, to date).
Release date: 2016-10-26 - 4. Combining cohorts in longitudinal surveys ArchivedArticles and reports: 12-001-X201300111828Description:
A question that commonly arises in longitudinal surveys is the issue of how to combine differing cohorts of the survey. In this paper we present a novel method for combining different cohorts, and using all available data, in a longitudinal survey to estimate parameters of a semiparametric model, which relates the response variable to a set of covariates. The procedure builds upon the Weighted Generalized Estimation Equation method for handling missing waves in longitudinal studies. Our method is set up under a joint-randomization framework for estimation of model parameters, which takes into account the superpopulation model as well as the survey design randomization. We also propose a design-based, and a joint-randomization, variance estimation method. To illustrate the methodology we apply it to the Survey of Doctorate Recipients, conducted by the U.S. National Science Foundation.
Release date: 2013-06-28 - 5. Evaluation and treatment of non-response in the ELFE cohort: Results of the pilot studies ArchivedArticles and reports: 11-522-X200800010960Description:
Non-response is inevitable in any survey, despite all the effort put into reducing it at the various stages of the survey. In particular, non-response can cause bias in the estimates. In addition, non-response is an especially serious problem in longitudinal studies because the sample shrinks over time. France's ELFE (Étude Longitudinale Française depuis l'Enfance) is a project that aims to track 20,000 children from birth to adulthood using a multidisciplinary approach. This paper is based on the results of the initial pilot studies conducted in 2007 to test the survey's feasibility and acceptance. The participation rates are presented (response rate, non-response factors) along with a preliminary description of the non-response treatment methods being considered.
Release date: 2009-12-03 - Articles and reports: 12-001-X20050018083Description:
The advent of computerized record linkage methodology has facilitated the conduct of cohort mortality studies in which exposure data in one database are electronically linked with mortality data from another database. This, however, introduces linkage errors due to mismatching an individual from one database with a different individual from the other database. In this article, the impact of linkage errors on estimates of epidemiological indicators of risk such as standardized mortality ratios and relative risk regression model parameters is explored. It is shown that the observed and expected number of deaths are affected in opposite direction and, as a result, these indicators can be subject to bias and additional variability in the presence of linkage errors.
Release date: 2005-07-21 - 7. The effect of record linkage errors on statistical inference in cohort mortality studies ArchivedArticles and reports: 11-522-X20010016277Description:
This paper discusses in detail issues dealing with the technical aspects of designing and conducting surveys. It is intended for an audience of survey methodologists.
The advent of computerized record-linkage methodology has facilitated the conduct of cohort mortality studies in which exposure data in one database are electronically linked with mortality data from another database. In this article, the impact of linkage errors on estimates of epidemiological indicators of risk, such as standardized mortality ratios and relative risk regression model parameters, is explored. It is shown that these indicators can be subject to bias and additional variability in the presence of linkage errors, with false links and non-links leading to positive and negative bias, respectively, in estimates of the standardized mortality ratio. Although linkage errors always increase the uncertainty in the estimates, bias can be effectively eliminated in the special case in which the false positive rate equals the false negative rate within homogeneous states defined by cross-classification of the covariates of interest.
Release date: 2002-09-12 - 8. Creation of an occupational surveillance system in Canada: Combining data for a unique Canadian study ArchivedSurveys and statistical programs – Documentation: 11-522-X19990015652Description:
Objective: To create an occupational surveillance system by collecting, linking, evaluating and disseminating data relating to occupation and mortality with the ultimate aim of reducing or preventing excess risk among workers and the general population.
Release date: 2000-03-02
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- Articles and reports: 82-003-X201901200003Description:
This article provides a description of the Canadian Census Health and Environment Cohorts (CanCHECs), a population-based linked datasets of the household population at the time of census collection. The CanCHEC datasets are rich national data resources that can be used to measure and examine health inequalities across socioeconomic and ethnocultural dimensions for different periods and locations. These datasets can also be used to examine the effects of exposure to environmental factors on human health.
Release date: 2019-12-18 - 2. Canadian Census Health and Environment Cohorts: Creation of a new health surveillance program ArchivedStats in brief: 11-629-X2019006Description:
This video describes a new health surveillance program at Statistics Canada: The Canadian Census Health and Environment Cohorts (CanCHECs). The video describes the attributes of and the datasets included in the CanCHECs, how the CanCHECs can be used, and their strengths and limitations. Recent examples of research projects based on the CanCHECs are presented along with information about how to apply for access to these data.
Release date: 2019-12-18 - Articles and reports: 11-633-X2016003Description:
Large national mortality cohorts are used to estimate mortality rates for different socioeconomic and population groups, and to conduct research on environmental health. In 2008, Statistics Canada created a cohort linking the 1991 Census to mortality. The present study describes a linkage of the 2001 Census long-form questionnaire respondents aged 19 years and older to the T1 Personal Master File and the Amalgamated Mortality Database. The linkage tracks all deaths over a 10.6-year period (until the end of 2011, to date).
Release date: 2016-10-26 - 4. Combining cohorts in longitudinal surveys ArchivedArticles and reports: 12-001-X201300111828Description:
A question that commonly arises in longitudinal surveys is the issue of how to combine differing cohorts of the survey. In this paper we present a novel method for combining different cohorts, and using all available data, in a longitudinal survey to estimate parameters of a semiparametric model, which relates the response variable to a set of covariates. The procedure builds upon the Weighted Generalized Estimation Equation method for handling missing waves in longitudinal studies. Our method is set up under a joint-randomization framework for estimation of model parameters, which takes into account the superpopulation model as well as the survey design randomization. We also propose a design-based, and a joint-randomization, variance estimation method. To illustrate the methodology we apply it to the Survey of Doctorate Recipients, conducted by the U.S. National Science Foundation.
Release date: 2013-06-28 - 5. Evaluation and treatment of non-response in the ELFE cohort: Results of the pilot studies ArchivedArticles and reports: 11-522-X200800010960Description:
Non-response is inevitable in any survey, despite all the effort put into reducing it at the various stages of the survey. In particular, non-response can cause bias in the estimates. In addition, non-response is an especially serious problem in longitudinal studies because the sample shrinks over time. France's ELFE (Étude Longitudinale Française depuis l'Enfance) is a project that aims to track 20,000 children from birth to adulthood using a multidisciplinary approach. This paper is based on the results of the initial pilot studies conducted in 2007 to test the survey's feasibility and acceptance. The participation rates are presented (response rate, non-response factors) along with a preliminary description of the non-response treatment methods being considered.
Release date: 2009-12-03 - Articles and reports: 12-001-X20050018083Description:
The advent of computerized record linkage methodology has facilitated the conduct of cohort mortality studies in which exposure data in one database are electronically linked with mortality data from another database. This, however, introduces linkage errors due to mismatching an individual from one database with a different individual from the other database. In this article, the impact of linkage errors on estimates of epidemiological indicators of risk such as standardized mortality ratios and relative risk regression model parameters is explored. It is shown that the observed and expected number of deaths are affected in opposite direction and, as a result, these indicators can be subject to bias and additional variability in the presence of linkage errors.
Release date: 2005-07-21 - 7. The effect of record linkage errors on statistical inference in cohort mortality studies ArchivedArticles and reports: 11-522-X20010016277Description:
This paper discusses in detail issues dealing with the technical aspects of designing and conducting surveys. It is intended for an audience of survey methodologists.
The advent of computerized record-linkage methodology has facilitated the conduct of cohort mortality studies in which exposure data in one database are electronically linked with mortality data from another database. In this article, the impact of linkage errors on estimates of epidemiological indicators of risk, such as standardized mortality ratios and relative risk regression model parameters, is explored. It is shown that these indicators can be subject to bias and additional variability in the presence of linkage errors, with false links and non-links leading to positive and negative bias, respectively, in estimates of the standardized mortality ratio. Although linkage errors always increase the uncertainty in the estimates, bias can be effectively eliminated in the special case in which the false positive rate equals the false negative rate within homogeneous states defined by cross-classification of the covariates of interest.
Release date: 2002-09-12
Reference (1)
Reference (1) ((1 result))
- 1. Creation of an occupational surveillance system in Canada: Combining data for a unique Canadian study ArchivedSurveys and statistical programs – Documentation: 11-522-X19990015652Description:
Objective: To create an occupational surveillance system by collecting, linking, evaluating and disseminating data relating to occupation and mortality with the ultimate aim of reducing or preventing excess risk among workers and the general population.
Release date: 2000-03-02
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