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Survey or statistical program
- National Population Health Survey: Household Component, Cross-sectional (1)
- Canadian Survey on Disability (1)
- Youth Smoking Survey (1)
- Canada's Alcohol and Other Drugs Survey (1)
- Survey on Smoking in Canada (1)
- Canadian Tobacco, Alcohol and Drugs Survey (1)
- National Population Health Survey: Health Institutions Component, Longitudinal (1)
Results
All (18)
All (18) (0 to 10 of 18 results)
- Surveys and statistical programs – Documentation: 89-654-X2023004Description: The Canadian Survey on Disability (CSD) is a national survey of Canadians aged 15 and over whose everyday activities are limited because of a long-term condition or health-related problem. The 2022 CSD Concepts and Methods Guide is designed to assist CSD data users by providing relevant information on survey content and concepts, sampling design, collection methods, data processing, data quality and product availability.Release date: 2023-12-01
- Public use microdata: 82M0020XDescription: The Canadian Tobacco, Alcohol and Drugs Survey (CTADS) is a biennial general population survey of tobacco, alcohol and drug use among Canadians aged 15 years and older, with the primary focus on 15- to 24-year-olds. The CTADS is a telephone survey conducted by Statistics Canada on behalf of Health Canada.Release date: 2018-11-01
- 3. Hierarchical Bayes small area estimation under a spatial model with application to health survey data ArchivedArticles and reports: 12-001-X201100111445Description:
In this paper we study small area estimation using area level models. We first consider the Fay-Herriot model (Fay and Herriot 1979) for the case of smoothed known sampling variances and the You-Chapman model (You and Chapman 2006) for the case of sampling variance modeling. Then we consider hierarchical Bayes (HB) spatial models that extend the Fay-Herriot and You-Chapman models by capturing both the geographically unstructured heterogeneity and spatial correlation effects among areas for local smoothing. The proposed models are implemented using the Gibbs sampling method for fully Bayesian inference. We apply the proposed models to the analysis of health survey data and make comparisons among the HB model-based estimates and direct design-based estimates. Our results have shown that the HB model-based estimates perform much better than the direct estimates. In addition, the proposed area level spatial models achieve smaller CVs than the Fay-Herriot and You-Chapman models, particularly for the areas with three or more neighbouring areas. Bayesian model comparison and model fit analysis are also presented.
Release date: 2011-06-29 - 4. Interaction between data collection and sample design: A plus in a complex selection process? ArchivedArticles and reports: 11-522-X200800010990Description:
The purpose of the Quebec Health and Social Services User Satisfaction Survey was to provide estimates of user satisfaction for three types of health care institutions (hospitals, medical clinics and CLSCs). Since a user could have visited one, two or all three types, and since the questionnaire could cover only one type, a procedure was established to select the type of institution at random. The selection procedure, which required variable selection probabilities, was unusual in that it was adjusted during the collection process to adapt increasingly to regional disparities in the use of health and social services.
Release date: 2009-12-03 - 5. Correcting self-reported estimates of obesity: Can we more closely approximate measured values? ArchivedArticles and reports: 11-522-X200800011003Description:
This study examined 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 where respondents were asked to report their height and weight and were subsequently measured. Regression analyses were used to determine which socio-demographic and health characteristics were associated with the discrepancies between reported and measured values. The sample was then split into two groups. In the first, the self-reported BMI and the predictors of the discrepancies were regressed on the measured BMI. Correction equations were generated using all predictor variables that were significant at the p<0.05 level. These correction equations were then tested in the second group to derive estimates of sensitivity, specificity and of obesity prevalence. Logistic regression was used to examine the relationship between measured, reported and corrected BMI and obesity-related health conditions. Corrected estimates provided more accurate measures of obesity prevalence, mean BMI and sensitivity levels. Self-reported data exaggerated the relationship between BMI and health conditions, while in most cases the corrected estimates provided odds ratios that were more similar to those generated with the measured BMI.
Release date: 2009-12-03 - Articles and reports: 12-001-X200900110884Description:
The paper considers small domain estimation of the proportion of persons without health insurance for different minority groups. The small domains are cross-classified by age, sex and other demographic characteristics. Both hierarchical and empirical Bayes estimation methods are used. Also, second order accurate approximations of the mean squared errors of the empirical Bayes estimators and bias-corrected estimators of these mean squared errors are provided. The general methodology is illustrated with estimates of the proportion of uninsured persons for several cross-sections of the Asian subpopulation.
Release date: 2009-06-22 - Articles and reports: 12-001-X200800110615Description:
We consider optimal sampling rates in element-sampling designs when the anticipated analysis is survey-weighted linear regression and the estimands of interest are linear combinations of regression coefficients from one or more models. Methods are first developed assuming that exact design information is available in the sampling frame and then generalized to situations in which some design variables are available only as aggregates for groups of potential subjects, or from inaccurate or old data. We also consider design for estimation of combinations of coefficients from more than one model. A further generalization allows for flexible combinations of coefficients chosen to improve estimation of one effect while controlling for another. Potential applications include estimation of means for several sets of overlapping domains, or improving estimates for subpopulations such as minority races by disproportionate sampling of geographic areas. In the motivating problem of designing a survey on care received by cancer patients (the CanCORS study), potential design information included block-level census data on race/ethnicity and poverty as well as individual-level data. In one study site, an unequal-probability sampling design using the subjectss residential addresses and census data would have reduced the variance of the estimator of an income effect by 25%, or by 38% if the subjects' races were also known. With flexible weighting of the income contrasts by race, the variance of the estimator would be reduced by 26% using residential addresses alone and by 52% using addresses and races. Our methods would be useful in studies in which geographic oversampling by race-ethnicity or socioeconomic characteristics is considered, or in any study in which characteristics available in sampling frames are measured with error.
Release date: 2008-06-26 - Articles and reports: 12-001-X200800110618Description:
The National Health and Nutrition Examination Survey (NHANES) is one of a series of health-related programs sponsored by the United States National Center for Health Statistics. A unique feature of NHANES is the administration of a complete medical examination for each respondent in the sample. To standardize administration, these examinations are carried out in mobile examination centers. The examination includes physical measurements, tests such as eye and dental examinations, and the collection of blood and urine specimens for laboratory testing. NHANES is an ongoing annual health survey of the noninstitutionalized civilian population of the United States. The major analytic goals of NHANES include estimating the number and percentage of persons in the U.S. population and in designated subgroups with selected diseases and risk factors. The sample design for NHANES must create a balance between the requirements for efficient annual and multiyear samples and the flexibility that allows changes in key design parameters to make the survey more responsive to the needs of the research and health policy communities. This paper discusses the challenges involved in designing and implementing a sample selection process that satisfies the goals of NHANES.
Release date: 2008-06-26 - Articles and reports: 11-522-X200600110427Description:
The National Health and Nutrition Examination Surveys (NHANES) is one of a series of health-related programs sponsored by the United States National Center for Health Statistics. A unique feature of NHANES is the administration of a complete medical examination for each respondent in the sample. To standardize administration, these examinations are carried out in mobile examination centers (MECs). The examination includes physical measurements, tests such as eye and dental examinations, and the collection of blood and urine specimens for laboratory testing. NHANES is an ongoing annual health survey of the noninstitutionalized civilian population of the United States. The major analytic goals of NHANES include estimating the number and percentage of persons in the U.S. population and in designated subgroups with selected diseases and risk factors. The sample design for NHANES needs to create a balance between the requirements for efficient annual and multiyear samples and the flexibility that allows changes in key design parameters to make the survey more responsive to the needs of the research and health policy communities. This paper discusses the challenges involved in designing and implementing a sample selection process that satisfies the goals of NHANES.
Release date: 2008-03-17 - 10. Comparison of design-based and model-based methods in analyzing complex health survey data: A case study ArchivedArticles and reports: 11-522-X20020016708Description:
In this paper, we discuss the analysis of complex health survey data by using multivariate modelling techniques. Main interests are in various design-based and model-based methods that aim at accounting for the design complexities, including clustering, stratification and weighting. Methods covered include generalized linear modelling based on pseudo-likelihood and generalized estimating equations, linear mixed models estimated by restricted maximum likelihood, and hierarchical Bayes techniques using Markov Chain Monte Carlo (MCMC) methods. The methods will be compared empirically, using data from an extensive health interview and examination survey conducted in Finland in 2000 (Health 2000 Study).
The data of the Health 2000 Study were collected using personal interviews, questionnaires and clinical examinations. A stratified two-stage cluster sampling design was used in the survey. The sampling design involved positive intra-cluster correlation for many study variables. For a closer investigation, we selected a small number of study variables from the health interview and health examination phases. In many cases, the different methods produced similar numerical results and supported similar statistical conclusions. Methods that failed to account for the design complexities sometimes led to conflicting conclusions. We also discuss the application of the methods in this paper by using standard statistical software products.
Release date: 2004-09-13
Data (6)
Data (6) ((6 results))
- Public use microdata: 82M0020XDescription: The Canadian Tobacco, Alcohol and Drugs Survey (CTADS) is a biennial general population survey of tobacco, alcohol and drug use among Canadians aged 15 years and older, with the primary focus on 15- to 24-year-olds. The CTADS is a telephone survey conducted by Statistics Canada on behalf of Health Canada.Release date: 2018-11-01
- 2. Youth Smoking Survey ArchivedPublic use microdata: 82M0011XDescription:
The main objective of the 2002 Youth Smoking Survey (YSS) is to provide current information on the smoking behaviour of students in grades 5 to 9 (in Quebec primary school grades 5 and 6 and secondary school grades 1 to 3), and to measure changes that occurred since the last time the survey was conducted in 1994. Additionally, the 2002 survey collected basic data on alcohol and drug use by students in grades 7 to 9 (in Quebec secondary 1 to 3). Results of the Youth Smoking Survey will help with the evaluation of anti-smoking and anti-drug use programs, as well as with the development of new programs.
Release date: 2004-07-14 - Public use microdata: 82M0010XDescription:
The National Population Health Survey (NPHS) program is designed to collect information related to the health of the Canadian population. The first cycle of data collection began in 1994. The institutional component includes long-term residents (expected to stay longer than six months) in health care facilities with four or more beds in Canada with the principal exclusion of the Yukon and the Northwest Teritories. The document has been produced to facilitate the manipulation of the 1996-1997 microdata file containing survey results. The main variables include: demography, health status, chronic conditions, restriction of activity, socio-demographic, and others.
Release date: 2000-08-02 - Public use microdata: 89M0007XDescription:
Information in this microdata file refers to survey data collected in September - November, 1994 for persons 15 years of age and older in Canada's ten provinces. The survey's main data objectives were to measure the prevalence and patterns of alcohol and other drug use, to assess harm and other consequences of drug use and to evaluate trends in recent patterns of use. Canada's Alcohol and Other Drugs Survey (CADS) also updates and expands upon data collected in the first survey, the National Alcohol and Other Drugs Survey (NADS), conducted in 1989.
Release date: 2000-07-07 - 5. National Population Health Survey 1994-1995 ArchivedPublic use microdata: 82F0001XDescription:
The National Population Health Survey (NPHS) uses the Labour Force Survey sampling frame to draw a sample of approximately 22,000 households. The sample is distributed over four quarterly collection periods. In each household, some limited information is collected from all household members and one person, aged 12 years and over, in each household is randomly selected for a more in-depth interview.
The questionnaire includes content related to health status, use of health services, determinants of health and a range of demographic and economic information. For example, the health status information includes self-perception of health, a health status index, chronic conditions, and activity restrictions. The use of health services is probed through visits to health care providers, both traditional and non-traditional, and the use of drugs and other medications. Health determinants include smoking, alcohol use, physical activity and in the first survey, emphasis has been placed on the collection of selected psycho-social factors that may influence health, such as stress, self-esteem and social support. The demographic and economic information includes age, sex, education, ethnicity, household income and labour force status.
Release date: 1995-11-21 - 6. Survey on Smoking in Canada (1994) ArchivedPublic use microdata: 82M0008XDescription:
The survey, begun in February 1994, monitors the smoking patterns of Canadians over a 12 month period and to measure any changes in smoking resulting from the decrease in taxes in cigarettes which took place in February 1994 in some provinces. It is related to MDF 82M0006. Updates are included in the microdata file price. A guide for this microdata file is available.
Release date: 1995-06-08
Analysis (10)
Analysis (10) ((10 results))
- 1. Hierarchical Bayes small area estimation under a spatial model with application to health survey data ArchivedArticles and reports: 12-001-X201100111445Description:
In this paper we study small area estimation using area level models. We first consider the Fay-Herriot model (Fay and Herriot 1979) for the case of smoothed known sampling variances and the You-Chapman model (You and Chapman 2006) for the case of sampling variance modeling. Then we consider hierarchical Bayes (HB) spatial models that extend the Fay-Herriot and You-Chapman models by capturing both the geographically unstructured heterogeneity and spatial correlation effects among areas for local smoothing. The proposed models are implemented using the Gibbs sampling method for fully Bayesian inference. We apply the proposed models to the analysis of health survey data and make comparisons among the HB model-based estimates and direct design-based estimates. Our results have shown that the HB model-based estimates perform much better than the direct estimates. In addition, the proposed area level spatial models achieve smaller CVs than the Fay-Herriot and You-Chapman models, particularly for the areas with three or more neighbouring areas. Bayesian model comparison and model fit analysis are also presented.
Release date: 2011-06-29 - 2. Interaction between data collection and sample design: A plus in a complex selection process? ArchivedArticles and reports: 11-522-X200800010990Description:
The purpose of the Quebec Health and Social Services User Satisfaction Survey was to provide estimates of user satisfaction for three types of health care institutions (hospitals, medical clinics and CLSCs). Since a user could have visited one, two or all three types, and since the questionnaire could cover only one type, a procedure was established to select the type of institution at random. The selection procedure, which required variable selection probabilities, was unusual in that it was adjusted during the collection process to adapt increasingly to regional disparities in the use of health and social services.
Release date: 2009-12-03 - 3. Correcting self-reported estimates of obesity: Can we more closely approximate measured values? ArchivedArticles and reports: 11-522-X200800011003Description:
This study examined 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 where respondents were asked to report their height and weight and were subsequently measured. Regression analyses were used to determine which socio-demographic and health characteristics were associated with the discrepancies between reported and measured values. The sample was then split into two groups. In the first, the self-reported BMI and the predictors of the discrepancies were regressed on the measured BMI. Correction equations were generated using all predictor variables that were significant at the p<0.05 level. These correction equations were then tested in the second group to derive estimates of sensitivity, specificity and of obesity prevalence. Logistic regression was used to examine the relationship between measured, reported and corrected BMI and obesity-related health conditions. Corrected estimates provided more accurate measures of obesity prevalence, mean BMI and sensitivity levels. Self-reported data exaggerated the relationship between BMI and health conditions, while in most cases the corrected estimates provided odds ratios that were more similar to those generated with the measured BMI.
Release date: 2009-12-03 - Articles and reports: 12-001-X200900110884Description:
The paper considers small domain estimation of the proportion of persons without health insurance for different minority groups. The small domains are cross-classified by age, sex and other demographic characteristics. Both hierarchical and empirical Bayes estimation methods are used. Also, second order accurate approximations of the mean squared errors of the empirical Bayes estimators and bias-corrected estimators of these mean squared errors are provided. The general methodology is illustrated with estimates of the proportion of uninsured persons for several cross-sections of the Asian subpopulation.
Release date: 2009-06-22 - Articles and reports: 12-001-X200800110615Description:
We consider optimal sampling rates in element-sampling designs when the anticipated analysis is survey-weighted linear regression and the estimands of interest are linear combinations of regression coefficients from one or more models. Methods are first developed assuming that exact design information is available in the sampling frame and then generalized to situations in which some design variables are available only as aggregates for groups of potential subjects, or from inaccurate or old data. We also consider design for estimation of combinations of coefficients from more than one model. A further generalization allows for flexible combinations of coefficients chosen to improve estimation of one effect while controlling for another. Potential applications include estimation of means for several sets of overlapping domains, or improving estimates for subpopulations such as minority races by disproportionate sampling of geographic areas. In the motivating problem of designing a survey on care received by cancer patients (the CanCORS study), potential design information included block-level census data on race/ethnicity and poverty as well as individual-level data. In one study site, an unequal-probability sampling design using the subjectss residential addresses and census data would have reduced the variance of the estimator of an income effect by 25%, or by 38% if the subjects' races were also known. With flexible weighting of the income contrasts by race, the variance of the estimator would be reduced by 26% using residential addresses alone and by 52% using addresses and races. Our methods would be useful in studies in which geographic oversampling by race-ethnicity or socioeconomic characteristics is considered, or in any study in which characteristics available in sampling frames are measured with error.
Release date: 2008-06-26 - Articles and reports: 12-001-X200800110618Description:
The National Health and Nutrition Examination Survey (NHANES) is one of a series of health-related programs sponsored by the United States National Center for Health Statistics. A unique feature of NHANES is the administration of a complete medical examination for each respondent in the sample. To standardize administration, these examinations are carried out in mobile examination centers. The examination includes physical measurements, tests such as eye and dental examinations, and the collection of blood and urine specimens for laboratory testing. NHANES is an ongoing annual health survey of the noninstitutionalized civilian population of the United States. The major analytic goals of NHANES include estimating the number and percentage of persons in the U.S. population and in designated subgroups with selected diseases and risk factors. The sample design for NHANES must create a balance between the requirements for efficient annual and multiyear samples and the flexibility that allows changes in key design parameters to make the survey more responsive to the needs of the research and health policy communities. This paper discusses the challenges involved in designing and implementing a sample selection process that satisfies the goals of NHANES.
Release date: 2008-06-26 - Articles and reports: 11-522-X200600110427Description:
The National Health and Nutrition Examination Surveys (NHANES) is one of a series of health-related programs sponsored by the United States National Center for Health Statistics. A unique feature of NHANES is the administration of a complete medical examination for each respondent in the sample. To standardize administration, these examinations are carried out in mobile examination centers (MECs). The examination includes physical measurements, tests such as eye and dental examinations, and the collection of blood and urine specimens for laboratory testing. NHANES is an ongoing annual health survey of the noninstitutionalized civilian population of the United States. The major analytic goals of NHANES include estimating the number and percentage of persons in the U.S. population and in designated subgroups with selected diseases and risk factors. The sample design for NHANES needs to create a balance between the requirements for efficient annual and multiyear samples and the flexibility that allows changes in key design parameters to make the survey more responsive to the needs of the research and health policy communities. This paper discusses the challenges involved in designing and implementing a sample selection process that satisfies the goals of NHANES.
Release date: 2008-03-17 - 8. Comparison of design-based and model-based methods in analyzing complex health survey data: A case study ArchivedArticles and reports: 11-522-X20020016708Description:
In this paper, we discuss the analysis of complex health survey data by using multivariate modelling techniques. Main interests are in various design-based and model-based methods that aim at accounting for the design complexities, including clustering, stratification and weighting. Methods covered include generalized linear modelling based on pseudo-likelihood and generalized estimating equations, linear mixed models estimated by restricted maximum likelihood, and hierarchical Bayes techniques using Markov Chain Monte Carlo (MCMC) methods. The methods will be compared empirically, using data from an extensive health interview and examination survey conducted in Finland in 2000 (Health 2000 Study).
The data of the Health 2000 Study were collected using personal interviews, questionnaires and clinical examinations. A stratified two-stage cluster sampling design was used in the survey. The sampling design involved positive intra-cluster correlation for many study variables. For a closer investigation, we selected a small number of study variables from the health interview and health examination phases. In many cases, the different methods produced similar numerical results and supported similar statistical conclusions. Methods that failed to account for the design complexities sometimes led to conflicting conclusions. We also discuss the application of the methods in this paper by using standard statistical software products.
Release date: 2004-09-13 - Articles and reports: 91-209-X20000005746Geography: CanadaDescription: Since the 1950s, life expectancy at age 60 has grown considerably. A woman reaching age 60 in 1951 could expect to live an additional 19 years on average, whereas in 1996, a woman of that age could expect to live an average of 24 years. For men, however, the increase has been less pronounced; their life expectancy increased by just over three years during the same period.Release date: 2001-06-22
- Articles and reports: 82-003-X20000015300Geography: CanadaDescription:
This article examines the extent of proxy reporting in the Natiional Population Health (NPHS). It also explores associations between proxy reporting status and the prevalence of selected health problems, and investigates the relationship between changes in proxy reporting status and two-year incidence of health problems.
Release date: 2000-10-20
Reference (2)
Reference (2) ((2 results))
- Surveys and statistical programs – Documentation: 89-654-X2023004Description: The Canadian Survey on Disability (CSD) is a national survey of Canadians aged 15 and over whose everyday activities are limited because of a long-term condition or health-related problem. The 2022 CSD Concepts and Methods Guide is designed to assist CSD data users by providing relevant information on survey content and concepts, sampling design, collection methods, data processing, data quality and product availability.Release date: 2023-12-01
- Surveys and statistical programs – Documentation: 82-003-X20010036099Description:
Cycle 1.1 of the Canadian Community Health Survey (CCHS) will provide information for 136 health regions. A brief overview of the CCHS design, sampling strategy, interviewing procedures, data collection and processing is presented.
Release date: 2002-03-13
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