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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.

Since 2007/2008, Statistics Canada has centred analysis of data holdings related to health as well as our program of dissemination of health research within the new Health Information and Research Division (HIRD).

The new division has launched a comprehensive approach to analytical planning including environmental scanning and consultation; establishment of strategic multi-year priorities for health research at Statistics Canada; a process of project selection and review that ensures that analytical effort addresses our priorities; metrics to measure our adherence to priorities and the impact of our analytical effort; and communication and dissemination of analytical plans.

This multi-year analytical plan identifies the key high-level priority areas for Statistics Canada's investment in health research for 2008/2009 to 2010/2011, and serves as a blueprint for subsequent operational research planning.

This newsletter article describes the process and preliminary results of eliciting scores from small groups of lay Canadians about their relative preference for a health state compared with full health. Health preference measurement helps assess the relative impact of diseases on health-related quality of life. This is part of the Population Health Impact of Disease in Canada (PHI) research program.

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.

In 1994, Statistics Canada began data collection for the National Population Health Survey (NPHS), a household survey designed to mesure the health status of Canadians and to expand knowledge of health determinants. The survey is longitudinal, with data being collected on selected panel members every second year. This article focuses on the NPHS sample design ant its rationale. Topics include sample allocation, representativeness, and selection; modifications in Quebec and the territories; and integration of the NPHS with the National Longitudinal Survey of Children. The final section considers some methodological issues to be addresses in future waves of the survey.

Changes in Statistics Canada's annual population estimates, introduced in 1993, have an impact on a wide range of social, economic and demographic indicators. Any indicator that relies on population estimates will be affected by the new figures. This article describes the adjustment and examines its impact on health and vital statistics rates. With rare exceptions, all rates decrease as the denominators are adjusted upward. For example, accident rates, suicide rates, and age-specific fertility rates based on the adjustment population are lower than those previously calculated. The extent of the adjustment, however, depends on the geographic and demographic characteristics of the population at risk. Analysts whose work concentrates on special subgroups for whom the adjustment is particularly great (such as young adult men) may wish to pay closer attention to the new population figures. Although the new rates are lower than before, underlying trends and patterns over time or across subcategories are quite similar. The revised series incorporates estimates of net census undercoverage, and for the first time, includes non-permanent residents. In 1991, net census undercoverage and non-permanent residents together amounted to about one million persons, or 3.6% of the revised Canadian population of 28,120,100.

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.

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.

This newsletter article describes the process and preliminary results of eliciting scores from small groups of lay Canadians about their relative preference for a health state compared with full health. Health preference measurement helps assess the relative impact of diseases on health-related quality of life. This is part of the Population Health Impact of Disease in Canada (PHI) research program.

In 1994, Statistics Canada began data collection for the National Population Health Survey (NPHS), a household survey designed to mesure the health status of Canadians and to expand knowledge of health determinants. The survey is longitudinal, with data being collected on selected panel members every second year. This article focuses on the NPHS sample design ant its rationale. Topics include sample allocation, representativeness, and selection; modifications in Quebec and the territories; and integration of the NPHS with the National Longitudinal Survey of Children. The final section considers some methodological issues to be addresses in future waves of the survey.

Changes in Statistics Canada's annual population estimates, introduced in 1993, have an impact on a wide range of social, economic and demographic indicators. Any indicator that relies on population estimates will be affected by the new figures. This article describes the adjustment and examines its impact on health and vital statistics rates. With rare exceptions, all rates decrease as the denominators are adjusted upward. For example, accident rates, suicide rates, and age-specific fertility rates based on the adjustment population are lower than those previously calculated. The extent of the adjustment, however, depends on the geographic and demographic characteristics of the population at risk. Analysts whose work concentrates on special subgroups for whom the adjustment is particularly great (such as young adult men) may wish to pay closer attention to the new population figures. Although the new rates are lower than before, underlying trends and patterns over time or across subcategories are quite similar. The revised series incorporates estimates of net census undercoverage, and for the first time, includes non-permanent residents. In 1991, net census undercoverage and non-permanent residents together amounted to about one million persons, or 3.6% of the revised Canadian population of 28,120,100.

Since 2007/2008, Statistics Canada has centred analysis of data holdings related to health as well as our program of dissemination of health research within the new Health Information and Research Division (HIRD).

The new division has launched a comprehensive approach to analytical planning including environmental scanning and consultation; establishment of strategic multi-year priorities for health research at Statistics Canada; a process of project selection and review that ensures that analytical effort addresses our priorities; metrics to measure our adherence to priorities and the impact of our analytical effort; and communication and dissemination of analytical plans.

This multi-year analytical plan identifies the key high-level priority areas for Statistics Canada's investment in health research for 2008/2009 to 2010/2011, and serves as a blueprint for subsequent operational research planning.