Proposed Statistical Methodology for the Canadian Heart Health Surveys Follow-up Study www.chhsnet.ca.

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Presentation transcript:

Proposed Statistical Methodology for the Canadian Heart Health Surveys Follow-up Study

Design of Canadian Heart Health Survey Population-based cross-sectional surveys Nine Canadian provinces during the period 1986 to 1990/92 Target population: - people aged 18 to 74 years - not living in institutions Sampling design and frame: - stratified, two-stage, - geographical areas: metropolitan, urban or rural using 1986 census

Type of outcome variableStatistical Model Presence or absence of hypertension (Dichotomous) Logistic Time to mortality (as a result of linkage of CHHS database with CMDB) (Event data/follow-up data) Survival Number of visits to physician’s office (Count) Poisson Blood pressure/ triglyceride (Continuous) Multiple

Aim2. Examine the influence of obesity and other chronic disease risk factors on mortality. Aim3. Determine the influence of the social and environmental determinants of health on the risk of mortality due to obesity and chronic disease risk factors.

Type of outcome variableStatistical Model Presence or absence of hypertension / mortality (Dichotomous) Logistic Time to mortality (as a result of linkage of CHHS database with CMDB) (Event data/follow-up data) Survival Aim2. Examine the influence of obesity and other chronic disease risk factors on mortality.

Survival Analysis: Time of originTime to an eventCensored ( Date of study ) Time to mortalityLost to follow-up Alive Date of Death-Stats Canada - Independent variables: individual-level variables (CHHS dataset)

Aim3. Determine the influence of the social and environmental determinants of health on the risk of mortality due to obesity and chronic disease risk factors. Social and environmental variables – community-level variables (Stats Canada 1991 census) Two levels Community (Level-2) Individuals (Level-1) OR Three levels: Province (Level-3) Community (Level-2) Individuals (Level-1)

Ignoring the hierarchical nature of the data will lead to biased estimates of standard errors of regression coefficients. Random Effects Model Hierarchical Models Mixed Models Multilevel Models

Softwares Different softwares are available to perform multilevel analysis SAS MLwiN HLM STATA

The Canadian Heart Health Surveys Follow-up Study is a New Emerging Team, funded by the Canadian Institutes for Health Research and the Heart and Stroke Foundation of Canada