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PA and Obesity update

PA and Obesity update. Physical Activity. This work is complete Claude presented last time report is available Benefits for POHEM-OA new features (income, education) potentially better model of HUI comparator model eg diabetes projections will show examples in software discussion later.

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PA and Obesity update

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  1. PA and Obesity update

  2. Physical Activity • This work is complete • Claude presented last time • report is available • Benefits for POHEM-OA • new features (income, education) • potentially better model of HUI • comparator model • eg diabetes projections • will show examples in software discussion later

  3. Obesity • Adult obesity (BMI) model in POHEM-OA • improved model available • New work with PHAC • cost of obesity (completed) • plus descriptive statistics • improved adjustment of SR-BMI • new costing methodology • GP/specialist visits, Hospitalizations, Drugs • attributing costs to current BMI category • documentation and workbook pivot tables available • childhood obesity (not started) • add children to starting population • derive equations for dynamics of change in BMI in children • Benefits for POHEM-OA • all of the above

  4. Show workbook pivot tables • (end)

  5. PA details below

  6. Quantifying Physical Activity We focused on four indicators of PA from the surveys: • Biking for errands (6 categories  2 categories): some or none. • Walking for errands (6 categories  4 categories): • None • no more than 5 hours/week • 6 to 10 hours/week • more than 10 hours/week • Overall level of physical activity (4 categories): • Sit • Stand or walk • Lift • Heavy work • Leisure time PA (LTPA) (continuous  4 categories): • None • no more than 30 minutes/day • 30 to 60 minutes/day • more than 1 hour/day

  7. Physical Activity Modeling • Derived equations using longitudinally followed individuals on the NPHS • Equations depend on many covariates and history of individual’s physical activity • Generalized logit regression • Physical activity updated every two years in model, because NPHS data collected and analysed on two year cycles

  8. PA modeling: covariates

  9. Chronic condition modeling • Derived equations for risk of developing • diabetes, • hypertension, • heart disease, • cancer • mortality • from longitudinally followed individuals on the NPHS • Equations depend on many covariates and history of individual • Logistic regression with “complementary log-log”

  10. Diabetes prevalence projections for Females (cross-sectional) • Age groups • 30-39 • 40-49 • 50-59 • 60-69 • 70-79 • 80+ * Simulation □ CCHS

  11. Simple What-if Scenario We consider a simple scenario where • everybody has at least one hour of active leisure time per day and • everybody walks more than 5 hours/week for errands.

  12. Impact of intervention on Diabetes prevalence Females Males Δ baseline + increased PA

  13. Impact of intervention on Life Expectancy and HALE Life expectancy gain of 1.9 and 2.2 years for females and males respectively. The gain in HALE is 2.9 for both females and males

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