Julie Bernier holds Bachelors and Masters degrees in Statistics from the University Laval. She has worked in population health for more than 25 years. She joined Statistics Canada in 1995 as a survey methodologist and moved to the area of health analysis in 2000. Mrs. Bernier is now the Director of the Health Analysis Division at Statistics Canada, a division specializing in population health research and data integration, that also produces the peer-reviewed journal ‘Health Reports’. Her main areas of expertise are measures of health status, ageing and longitudinal analysis.
Using surveys, linked data and microsimulation models to examine the health of older adults: Surveillance, planning and evaluation
Sharanjit Uppal is a senior research economist with Statistics Canada. He received his Ph.D. in Economics from the University of Manitoba. His areas of research include labour economics, economics of ageing, and population health. He has published articles in the Applied Economics, Social Science and Medicine, International Journal of Manpower, Journal of World Health and Population, Health Reports, Perspectives on Labour and Income, Insights on Canadian Society, Education Matters, and International Journal of Health Services.
The purpose of this workshop is to highlight the varying sources of data that can be used to examine the health and well-being of older Canadians, as well as to aid in planning and evaluation of programs and policies aimed at older adults.
Survey data is very useful for its depth of information on a target population. Cross-sectional data can be used to examine the associations between health and its determinants at all ages, including during older adulthood. An example using the General Social Survey will study how various domain of satisfaction contribute to overall life satisfaction, an outcome known as being related to mental and cognitive health among seniors will be given.
The strengths of survey data can be augmented by linking survey respondents to other information, such as administrative health data, tax information, or the Census. Linked data has the advantage of creating a larger set of outcomes than might be available on a survey, and can also add a longitudinal perspective to previously cross-sectional data. An example using the Canadian Community Health Survey linked to the 2011 Census will show the association between health, social and demographic factors on the transition to nursing and retirement homes.
Lastly, microsimulation models offers planners and policy makers the opportunity to project the future incidence and prevalence of risk factors and diseases, and future demands for healthcare resources. In addition, microsimulation modeling offers a method of evaluation that allows for the examination of the results, consequences, and benefits of a program in advance of implementation. An example using results of the POHEM-Dementia model will show projections of the future prevalence of Alzheimer’s and other dementias and its related costs under current conditions, as well as under conditions of decreasing or increasing incidence of the condition.