Predicting the population health effects of changing tobacco exposures: Statistical models for regulatory compliance

Transcription

1 Predicting the population health effects of changing tobacco exposures: Statistical models for regulatory compliance NASEM ENDS Workshop February 2017 Annette M. Bachand, Ph.D.

2 Goal of Statistical Models: Predict population health effects of alternative tobacco products (ATPs) in the absence of sufficient empirical data Relevant to FSPTCA: Must demonstrate that a reduction in morbidity or mortality is reasonably likely

3 WEIGHING RISKS AND BENEFITS Base case Cigarettes only Counterfactual Cigarettes and ATP Baseline population health effect Harmful changes in exposure patterns Beneficial changes in exposure patterns

4 INTRODUCTION OF AN ATP TO NEVER TOBACCO USERS No tobacco use Smoking Expected beneficial transition ATP use No tobacco use No tobacco use Potential harmful transition ATP use Smoking

5 INTRODUCTION OF AN ATP TO CURRENT SMOKERS Smoking Smoking Expected beneficial transition ATP use Smoking Smoking cessation Potential harmful transition ATP use

6 Following a hypothetical birth cohort over time: Data requirements

7 Age-specific probability of Initiation, cessation, and relapse (smoking & ATP use) Switching between products Adding a product (dual use) Age range Age interval width Study population, population size DYNAMIC POPULATION MODEL Mortality rates (by age, years of smoking, years since quitting) ERR (ATP vs. smoking) Entered as fixed values or with a degree of uncertainty

8 Illustrative Example

9 ILLUSTRATIVE EXAMPLE 3% of base case never tobacco users initiate ATP Of them, 50% switch to smoking Some base case continuing smokers switch to ATP How many are needed to offset harm? Is this realistic?

10 ILLUSTRATIVE EXAMPLE: RESULTS FOR AGE Tipping point Benefit due to switching to ATP Deficit due to ATP initiation and gateway effect ATP

11 Challenges

12 Age-specific probability of Current Initiation, data for cessation, future predictions and relapse (smoking & ATP use) Lack Switching of data for between a new products Adding a product (dual use) Age range Age interval width Birth Study cohort vs. cross-section population, population size DYNAMIC POPULATION MODEL Mortality rates (by Hard age, to years find of smoking, years since quitting) ERR (ATP vs. Lack of data for smoking) a new product

13 BIRTH COHORT VS. CROSS-SECTION Same age, followed until all cohort members have died Relatively few input values Assumptions clearly defined Complete follow-up Exposure histories known All deaths accounted for Mixed ages, followed over a predetermined time interval Many input values o Many birth cohorts, new members o Two time variables, age and year Assumptions difficult to assess Shorter, incomplete follow-up o Partial exposure histories o Tobacco-related mortality missed?

14 BIRTH COHORT VS. CROSS-SECTION Flexible o Tipping point analyses o Easy expansion to >2 products Less flexible o No direct estimates of tipping points o More difficult to expand Limitations o No direct predictions for crosssectional populations o However, does provide evidence of the effects of introducing a MRTP to a cross-sectional population Other limitations o In theory, direct predictions are possible for the modeled crosssectional population; but o Validity and generalizability? o Tipping points? See: Bachand, A.M. and Sulsky, S.I., Predicting the population health effects of changing tobacco exposures: statistical models for regulatory compliance. Recent Advances in Tobacco Science, :p. 9-22

15 THANK YOU

16 CONTACT INFORMATION Annette Bachand, Ph.D. Major support: RAI Services Company (Winston-Salem, NC) Additional support: Swedish Match (Stockholm, Sweden) The dynamic population modeler described here was developed independently of the sponsors.