Keyword search

Sort Help
entries

Results

All (11)

All (11) (0 to 10 of 11 results)

  • Articles and reports: 82-003-X201500514169
    Description:

    The Cancer Risk Management Model incorporates the risk of developing cancer, disease screening and clinical management with cost and labour data to assess health outcomes and economic impact. A screening module added to the lung cancer module enables a variety of scenarios to be evaluated for different target populations with varying rates of participation, compliance, and frequency of low-dose computed tomography screening.

    Release date: 2015-05-20

  • Articles and reports: 82-003-X201300611796
    Geography: Canada
    Description:

    The study assesses the feasibility of using statistical modelling techniques to fill information gaps related to risk factors, specifically, smoking status, in linked long-form census data.

    Release date: 2013-06-19

  • Articles and reports: 82-003-X201200111625
    Geography: Canada
    Description:

    This study compares estimates of the prevalence of cigarette smoking based on self-report with estimates based on urinary cotinine concentrations. The data are from the 2007 to 2009 Canadian Health Measures Survey, which included self-reported smoking status and the first nationally representative measures of urinary cotinine.

    Release date: 2012-02-15

  • Articles and reports: 82-003-X200800310680
    Geography: Canada
    Description:

    This study examines the feasibility of developing correction factors to adjust self-reported measures of body mass index to more closely approximate measured values. Data are from the 2005 Canadian Community Health Survey, in which respondents were asked to report their height and weight, and were subsequently measured.

    Release date: 2008-09-17

  • Articles and reports: 11-522-X200600110444
    Geography: Province or territory
    Description:

    General population health surveys often include small samples of smokers. Few longitudinal studies specific to smoking have been carried out. We discuss development of the Ontario Tobacco Survey (OTS) which combines a rolling longitudinal, and repeated cross-sectional components. The OTS began in July 2005 using random selection and data-collection by telephones. Every 6 months, new samples of smokers and non-smokers provide data on smoking behaviours and attitudes. Smokers enter a panel study and are followed for changes in smoking influences and behaviour. The design is proving to be cost effective in meeting sample requirements for multiple research objectives.

    Release date: 2008-03-17

  • Articles and reports: 11-522-X20040018747
    Description:

    This document describes the development and pilot of the first American Indian and Alaska Native Adult Tobacco Survey. Meetings with expert panels and tribal representatives helped to adapt methods.

    Release date: 2005-10-27

  • Articles and reports: 11-522-X20020016718
    Description:

    Cancer surveillance research requires accurate estimates of risk factors at the small area level. These risk factors are often obtained from surveys such as the National Health Interview Survey (NHIS) or the Behavioral Risk Factors Surveillance Survey (BRFSS). Unfortunately, no one population-based survey provides ideal prevalence estimates of such risk factors. One strategy is to combine information from multiple surveys, using the complementary strengths of one survey to compensate for the weakness of the other. The NHIS is a nationally representative, face-to-face survey with a high response rate; however, it cannot produce state or substate estimates of risk factor prevalence because sample sizes are too small. The BRFSS is a state-level telephone survey that excludes non-telephone households and has a lower response rate, but does provide reasonable sample sizes in all states and many counties. Several methods are available for constructing small-area estimators that combine information from both the NHIS and the BRFSS, including direct estimators, estimators under hierarchical Bayes models and model-assisted estimators. In this paper, we focus on the latter, constructing generalized regression (GREG) and 'minimum-distance' estimators and using existing and newly developed small-area smoothing techniques to smooth the resulting estimators.

    Release date: 2004-09-13

  • Articles and reports: 12-001-X20010026090
    Geography: Census metropolitan area
    Description:

    The number of calls in a telephone survey is used as an indicator of how difficult an intended respondent is to reach. This permits a probabilistic division of the non-respondents into non-susceptibles (those who will always refuse to respond), and the susceptible non-respondents (those who were not available to respond) in a model of the non-response. Further, it permits stochastic estimation of the views of the latter group and an evaluation of whether the non-response is ignorable for inference about the dependent variable. These ideas are implemented on the data from a survey in Metropolitan Toronto of attitudes toward smoking in the workplace. Using a Bayesian model, the posterior distribution of the model parameters is sampled by Markov Chain Monte Carlo methods. The results reveal that the non-response is not ignorable and those who do not respond are twice as likely to favor unrestricted smoking in the workplace as are those who do.

    Release date: 2002-02-28

  • Articles and reports: 12-001-X20010015857
    Description:

    This article describes and evaluates a procedure for imputing missing values for a relatively complex data structure when the data are missing at random. The imputations are obtained by fitting a sequence of regression models and drawing values from the corresponding predictive distributions. The types of regression models used are linear, logistic, Poisson, generalized logit or a mixture of these depending on the type of variable being imputed. Two additional common features in the imputation process are incorporated: restriction to a relevant subpopulation for some variables and logical bounds or constraints for the imputed values. The restrictions involve subsetting the sample individuals that satisfy certain criteria while fitting the regression models. The bounds involve drawing values from a truncated predictive distribution. The development of this method was partly motivated by the analysis of two data sets which are used as illustrations. The sequential regression procedure is applied to perform multiple imputation analysis for the two applied problems. The sampling properties of inferences from multiply imputed data sets created using the sequential regression method are evaluated through simulated data sets.

    Release date: 2001-08-22

  • Articles and reports: 82-003-X19980044511
    Geography: Canada
    Description:

    This article discusses some of the benefits and challenges of data from a longitudinal panel as exemplified by the National Population Health Survey (NPHS). An overview of content and collection methods, sample design, response rates, and some of the special methodological and operational approaches for this longitudinal survey.

    Release date: 1999-04-29
Data (1)

Data (1) ((1 result))

  • Table: 82-567-X
    Description:

    The National Population Health Survey (NPHS) is designed to enhance the understanding of the processes affecting health. The survey collects cross-sectional as well as longitudinal data. In 1994/95 the survey interviewed a panel of 17,276 individuals, then returned to interview them a second time in 1996/97. The response rate for these individuals was 96% in 1996/97. Data collection from the panel will continue for up to two decades. For cross-sectional purposes, data were collected for a total of 81,000 household residents in all provinces (except people on Indian reserves or on Canadian Forces bases) in 1996/97.

    This overview illustrates the variety of information available by presenting data on perceived health, chronic conditions, injuries, repetitive strains, depression, smoking, alcohol consumption, physical activity, consultations with medical professionals, use of medications and use of alternative medicine.

    Release date: 1998-07-29
Analysis (10)

Analysis (10) ((10 results))

  • Articles and reports: 82-003-X201500514169
    Description:

    The Cancer Risk Management Model incorporates the risk of developing cancer, disease screening and clinical management with cost and labour data to assess health outcomes and economic impact. A screening module added to the lung cancer module enables a variety of scenarios to be evaluated for different target populations with varying rates of participation, compliance, and frequency of low-dose computed tomography screening.

    Release date: 2015-05-20

  • Articles and reports: 82-003-X201300611796
    Geography: Canada
    Description:

    The study assesses the feasibility of using statistical modelling techniques to fill information gaps related to risk factors, specifically, smoking status, in linked long-form census data.

    Release date: 2013-06-19

  • Articles and reports: 82-003-X201200111625
    Geography: Canada
    Description:

    This study compares estimates of the prevalence of cigarette smoking based on self-report with estimates based on urinary cotinine concentrations. The data are from the 2007 to 2009 Canadian Health Measures Survey, which included self-reported smoking status and the first nationally representative measures of urinary cotinine.

    Release date: 2012-02-15

  • Articles and reports: 82-003-X200800310680
    Geography: Canada
    Description:

    This study examines the feasibility of developing correction factors to adjust self-reported measures of body mass index to more closely approximate measured values. Data are from the 2005 Canadian Community Health Survey, in which respondents were asked to report their height and weight, and were subsequently measured.

    Release date: 2008-09-17

  • Articles and reports: 11-522-X200600110444
    Geography: Province or territory
    Description:

    General population health surveys often include small samples of smokers. Few longitudinal studies specific to smoking have been carried out. We discuss development of the Ontario Tobacco Survey (OTS) which combines a rolling longitudinal, and repeated cross-sectional components. The OTS began in July 2005 using random selection and data-collection by telephones. Every 6 months, new samples of smokers and non-smokers provide data on smoking behaviours and attitudes. Smokers enter a panel study and are followed for changes in smoking influences and behaviour. The design is proving to be cost effective in meeting sample requirements for multiple research objectives.

    Release date: 2008-03-17

  • Articles and reports: 11-522-X20040018747
    Description:

    This document describes the development and pilot of the first American Indian and Alaska Native Adult Tobacco Survey. Meetings with expert panels and tribal representatives helped to adapt methods.

    Release date: 2005-10-27

  • Articles and reports: 11-522-X20020016718
    Description:

    Cancer surveillance research requires accurate estimates of risk factors at the small area level. These risk factors are often obtained from surveys such as the National Health Interview Survey (NHIS) or the Behavioral Risk Factors Surveillance Survey (BRFSS). Unfortunately, no one population-based survey provides ideal prevalence estimates of such risk factors. One strategy is to combine information from multiple surveys, using the complementary strengths of one survey to compensate for the weakness of the other. The NHIS is a nationally representative, face-to-face survey with a high response rate; however, it cannot produce state or substate estimates of risk factor prevalence because sample sizes are too small. The BRFSS is a state-level telephone survey that excludes non-telephone households and has a lower response rate, but does provide reasonable sample sizes in all states and many counties. Several methods are available for constructing small-area estimators that combine information from both the NHIS and the BRFSS, including direct estimators, estimators under hierarchical Bayes models and model-assisted estimators. In this paper, we focus on the latter, constructing generalized regression (GREG) and 'minimum-distance' estimators and using existing and newly developed small-area smoothing techniques to smooth the resulting estimators.

    Release date: 2004-09-13

  • Articles and reports: 12-001-X20010026090
    Geography: Census metropolitan area
    Description:

    The number of calls in a telephone survey is used as an indicator of how difficult an intended respondent is to reach. This permits a probabilistic division of the non-respondents into non-susceptibles (those who will always refuse to respond), and the susceptible non-respondents (those who were not available to respond) in a model of the non-response. Further, it permits stochastic estimation of the views of the latter group and an evaluation of whether the non-response is ignorable for inference about the dependent variable. These ideas are implemented on the data from a survey in Metropolitan Toronto of attitudes toward smoking in the workplace. Using a Bayesian model, the posterior distribution of the model parameters is sampled by Markov Chain Monte Carlo methods. The results reveal that the non-response is not ignorable and those who do not respond are twice as likely to favor unrestricted smoking in the workplace as are those who do.

    Release date: 2002-02-28

  • Articles and reports: 12-001-X20010015857
    Description:

    This article describes and evaluates a procedure for imputing missing values for a relatively complex data structure when the data are missing at random. The imputations are obtained by fitting a sequence of regression models and drawing values from the corresponding predictive distributions. The types of regression models used are linear, logistic, Poisson, generalized logit or a mixture of these depending on the type of variable being imputed. Two additional common features in the imputation process are incorporated: restriction to a relevant subpopulation for some variables and logical bounds or constraints for the imputed values. The restrictions involve subsetting the sample individuals that satisfy certain criteria while fitting the regression models. The bounds involve drawing values from a truncated predictive distribution. The development of this method was partly motivated by the analysis of two data sets which are used as illustrations. The sequential regression procedure is applied to perform multiple imputation analysis for the two applied problems. The sampling properties of inferences from multiply imputed data sets created using the sequential regression method are evaluated through simulated data sets.

    Release date: 2001-08-22

  • Articles and reports: 82-003-X19980044511
    Geography: Canada
    Description:

    This article discusses some of the benefits and challenges of data from a longitudinal panel as exemplified by the National Population Health Survey (NPHS). An overview of content and collection methods, sample design, response rates, and some of the special methodological and operational approaches for this longitudinal survey.

    Release date: 1999-04-29
Reference (0)

Reference (0) (0 results)

No content available at this time.

Date modified: