David  T.  Levy,  Ph.D.  Lombardi  Comprehensive  Cancer  Center    Georgetown  University  

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My Background n PhD in Economics

n Economics Research (Competition Policy) ¨  Empirical studies (usually large data sets)

¨  Mathematical modeling

¨  Some cost-effectiveness

n Public Health ¨  Previously dabbled in alcohol and traffic

safety policy

¨  Simulation modeling using a multidisciplinary approach, BA in pol. philosophy

Computational Models n  Simulation models/computational models are used

in other fields, but are increasingly common in public health, especially in the fields of tobacco control and obesity

n  Models are especially useful where there are dynamic systems with many stages (e.g., policy -> environment -> behaviors -> health outcomes) and where the effects unfold over time.

n  Models attempt to make the connections between stages across stages and over time explicit, focusing on the movement of whole system rather than an isolated part

Characteristics of Modeling n  Generally combine data and parameters from

different sources

n  Provides structure by developing a framework and making assumptions explicit

n  Incorporates the effects that are difficult to distinguish empirically in statistical studies ¨  Non-linear relationships*

¨  Interdependencies*

¨  Dynamic processes*

¨  Feedback loops

Types of Model

n Macro-simulations: groups of individuals (e.g., current, former and never smokers)

¨ Uni-directional causality

¨ Systems dynamic (feedback loops)

n Micro-simulations: individuals in proportion to their composition in the population ¨  Monte-Carlo

¨  Agent-based and network models; make explicit assumptions about behaviors

Tobacco Control and Smoking n Tobacco control policies provide an

example of one the greatest public health success stories – important to study what type of policies work in tobacco control and lessons for other public health risks

n Smoking is a behavioral risk factor with clearest link to cancer- can study the role of dose, duration, and age; and the interaction with other non-cancer chronic diseases

What is SimSmoke? •  SimSmoke simulates the dynamics of smoking rates

and smoking-attributed deaths in a State or Nation, and the effects of policies on those outcomes.

•  Compartmental (macro) model with smokers, ex-smokers and never smokers evolving through time by age and gender.

•  Focus on tobacco control policies ¨  Effects vary by:

n  depending on the way the policy is implemented, n  by age and gender n  the length of time that the policy is in effect

¨  Nonlinear and interactive effects of policies

SimSmoke: Basic Approach

Policy Changes

Taxes

Clean air laws

Media Camp.

Marketing Bans

Warning labels Cessation Tx

Youth Access

Cigarette Use

Smoking-Attributable

Deaths Total Mortality and

by type:

Lung cancer

Other cancers

Heart disease Stroke

COPD

MCH Outcomes

Norms, Attitudes, Opportu-nities

Former and current smokers, relative risks

Model Setup

n Excel model: Easily modifiable and transferable. Based on previously developed C++ model.

n Transparent and easily adaptable by user

n Easily Downloaded

Basic Structure of Model

n  Population model begins with initial year population and moves through time with births and deaths (Markov model)

n  Smoking model distinguishes population in never smokers, smokers, and ex-smokers and moves through time with initiation, cessation and relapse (Markov model)

n  Smoking-attributable deaths depend on smoking rates and RRs

n  Policy modules- one for each policy with independent effects on smoking rates

Population Model: Evolution of Population

Population Deaths

Death rates

Births

Birth rates

•  Start with the Population in the base year, first year of the model, based on data availability and policies

•  Evolves through time:

Don’t explicitly account for immigrants due to data difficulties, but make population corrections

Smoking Model: Evolution of Smokers

Population

Never Smoker

Ever Smoker* Current Smoker**

Ex-Smoker

Initiation Not quit

Relapse

Cessation (quit)

Not initiate

* Usually as smoked 100 cigarettes lifetime ** usually as smoked some or all days

Smoking-Attributable Deaths

Smoking attributable deaths = (Smoker death rate –never

smoker death rate) * # Smokers + Σ years quit (Ex -smoker death rate –never smoker death rate) * # Ex-smokers

Summed over ages and by gender

Total Deaths Deaths Attributable to Smoking

Death rates by smoking status Relative

risks

% smokers and ex-smokers

Relationship between policies and

smoking rates based on:

n  Evidence from tobacco and other risky behavior literature,

n  Theories (Economics, Sociology, Psychology, Epidemiology, etc), and

n  Advice by a multidisciplinary expert panel

Policy Effect Sizes

n  In percentage terms relative to smoking rate (1+PR), PR = percent reduction Based on studies

n  Initial impact on cessation through prevalence (1+PR). Maintained

through initiation rates (1+PR) and increased through cessation rates (1-PR) Less known about these effects

n  Effects may differ by age or gender

n  Effects depend on the way in which policy is implemented: level, coverage, degree of enforcement, publicity, etc.- newly incorporated enforcement and information issues

We use MPOWER Policies

n  Taxes –as a percent of retail prices, effects depend on size of tax increase and initial price. through elasticities (uses constant elasticities, vary by age, but not gender), no effect yet on smuggling. Goal= specific ad valorem and excise tax at 70% of price

n  Smoke-Free Air Laws depend on: ¨ Where applied:

n  Worksites (3 levels) n  Restaurants and bars n  Other public places

¨ Enforcement now has a stronger role

Policies based on FCTC/MPOWER

n  Tobacco control/media campaigns

n  Marketing/Advertising Bans

n  Health Warnings

n  Cessation Treatment: Availability of pharmacotherapy, cessation treatment (financial access, quitlines and web-based treatment

n  Youth access (minimum purchase age): enforcement and vending and self-service bans

Past vs. Future n  Tracking Period- starts from year where requisite

data available, e.g., 1993 for most US models, and continues to the current recent year. The tracking period is used to: ¨  Calibrate the model- adjust the parameters

¨  Validate the model- test how well it predicts

¨  Examine the role of past policies

n  Future Projection- examine the effect of policies from current year forward, e.g., the effect of a ciga- rette tax increase or the ability to reach the Healthy People 2020 smoking prevalence goal of 12%

Models developed for: 33 Countries:

Albania*, Argentina*, Bangladesh, Brazil,* Canada, China, Czech Rep,* Egypt, Finland,* France,* Germany,* Great Britain,* India, Indonesia, Ireland,* Italy,* Japan,* Korea*, Malaysia, Mexico, Netherlands*, Pakistan, Poland, Philippines, Taiwan*, Russia, Spain, Sweden, Thailand,* Turkey, Ukraine, US,* Vietnam*

6 States: Arizona*, California*, Kentucky*, Massachusetts, Minnesota,* NY

* Paper published

•  ADVOCACY: Justification by forecasting future tobacco use and health outcomes and showing the effect of past policies

•  PLANNING:

•  Estimate the likely impact of alternative interventions in specific situations and on specific populations

•  Assess and rank strategies for reaching goals prior to commitment of resources

•  Develop more systematic surveillance and evaluation networks

•  HEURISTIC: Understanding the complex network of policies

surrounding tobacco use and health outcomes at research and policy-making levels.

Policymakers have used models for:

Counterfactuals: If no policies n  To consider the effect of all policies implemented since

1993 (baseline year), we first set policies through 2010 to their 1993 levels to obtain the counterfactual smoking rates (the absence of post-1993 policies).

n  The difference between the smoking prevalence with polices at 1993 levels and the smoking rate with actual policies implemented yields the net effect of policies implemented since 1989.

n  For the role of single policies, we compared the scenario with only that policy implemented to the counterfactual policy scenario.

n  The impact of policies on deaths was estimated by subtracting the number of SADs with policies implemented from their number with policies kept at 1993 levels.

Advocacy: Impact of Past Policies in Minnesota

Smoking prevalence > 25% less as a result of policies by 2010 and grows over time!

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

1993 1997 2001 2004 2007 2011 2021 2031 2041

Policies actually implemented Policies at 1993 level

Price only

Advocacy: Minnesota Deaths Averted Due to Policies

MALE AND FEMALE SADs   1993   2003   2011   2021   2031   2041  

1993-­‐2011  

1993-­‐2041  

Policies actually implemented   5,575   5,640   5,932   6,261   5,918   4,920  

108,253  

285,365  

Policies at 1993 level   5,575   5,759   6,515   7,586   7,697   6,844  

111,150  

333,053  

LIVES SAVED  

All policies   119   583   1,325   1,779   1,924   2,897  

47,687  

Price only   52   268   623   858   967   1,329  

22,829  

Smoke free air only   41   234   552   765   834  

1,098  

20,228  

Mass media only   61   275   583   761   824  

1,396  

20,833  

Youth access only   41   209   485   688   811  

1,027  

18,321  

Cessation treatment only   44   235   548   747   819  

1,152  

19,901  

Advocacy: Other successes due to tobacco policies

Percent reduction in smoking prevalence (18 and above):

n  > 30% reduction ¨  Brazil (almost 50% reduction due to policies)

¨  California

n  At least 25% Reduction ¨  United Kingdom

¨  Minnesota

¨  Thailand

n  20% Reduction ¨  Arizona

¨  Korea

¨  Ireland ¨  NYS

¨  Netherlands

Planning: Male Smoking Prevalence: SimSmoke Predictions vs. Surveys, Minnesota

10.0%

12.0%

14.0%

16.0%

18.0%

20.0%

22.0%

24.0%

26.0%

28.0%

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

SimSmoke CPS-TUS MATS

Ireland Male Smoking Prevalence,1998-2010 data, data, data

Planning: Ranking the effect of future policies Ireland SimSmoke male prevalence

Policies/Year 2010 2011 2020 2030 2040

Status Quo Policies 26.1% 25.9% 24.1% 21.8% 20.0% Independent Policy Effects                          Tax  70%  of  Retail  Price 26.1% 25.1% 23.1% 20.6% 18.7%                Complete  Smoke  Free  &  Enforcement 26.1% 25.9% 24.1% 21.7% 20.0%                Comprehensive  Ad  Ban  &  Enforcement 26.1% 25.8% 24.0% 21.6% 19.9%                High  Intensity  Tobacco  Control  Campaign 26.1% 24.3% 22.4% 20.0% 18.2%                Strong  Health  Warnings 26.1% 25.6% 23.8% 21.4% 19.7%                Strong  Youth  Access  Enforcement   26.0% 25.8% 23.5% 20.8% 18.8%                CessaRon  Treatment  Policies 26.1% 25.4% 23.1% 20.6% 18.8% Combined Policy Effects                          All  of  the  above 26.0% 22.4% 19.3% 16.4% 14.4% % Change in Smoking Prevalence from Status Quo           Independent Policy Effects                        Tax  70%  of  Retail  Price -­‐3.3% -­‐4.3% -­‐5.5% -­‐6.6%                Complete  Smoke  Free  &  Enforcement -­‐0.2% -­‐0.3% -­‐0.3% -­‐0.3%                Comprehensive  Ad  Ban  &  Enforcement -­‐0.5% -­‐0.6% -­‐0.7% -­‐0.7%                High  Intensity  Tobacco  Control  Campaign -­‐6.3% -­‐7.3% -­‐8.2% -­‐8.9%                Strong  Health  Warnings -­‐1.2% -­‐1.5% -­‐1.6% -­‐1.6%                Strong  Youth  Access  Enforcement   -­‐0.4% -­‐2.5% -­‐4.2% -­‐6.0%                CessaRon  Treatment  Policies -­‐2.2% -­‐4.3% -­‐5.5% -­‐5.9% Combined Policy Effects                        All  of  the  above -­‐13.5% -­‐19.9% -­‐24.6% -­‐28.1%

Planning: Health Effects Delayed SimSmoke Projections Smoking-Attributable Deaths Status Quo vs. All FCTC Policies for Finland

More immediate impact on heart disease and maternal and child health

Planning: There may be limits to current policies: We may need more than traditional policies to reduce smoking by more than 50%

n  Those with the weakest current policies (e.g., Russia and China) show the potential for largest reductions in smoking prevalence, with forecasts of about a 50% reduction in smoking prevalence in going from very limited policies to fully FCTC-consistent policies

n  How can we surpass a 50% reduction? ¨  Improved cessation treatments, e.g. better and more tailored

interventions with follow-up and integrated services

¨  May need to alter the tobacco products available, e.g., reduce nicotine and other addictive constituents or disallow current cigarettes in favor of safer forms of tobacco

30

FDA Public health standard “Public health standard” calls for the review of the scientific

evidence regarding

1.  Risks and benefits of the tobacco product standard to the population as a whole, including both users and non-users of tobacco products;

2.  Whether there is an increased or decreased likelihood that existing users of tobacco products will stop using such products; and

3.  Whether there is an increased or decreased likelihood that those who do not currently use tobacco products, most notably youth, will start to use tobacco products

Example: Mandatory “product standards” that would limit the allowable levels of ingredients in tobacco products (menthol, nicotine, etc)

Planning:  Modeling  the  effects  of  a  ban  on  menthol  cigare=es  Possible  effects  of  a  ban:  n  Menthol  smokers  switch  to  

non-­‐menthol  brand.  

n  Menthol  smokers  quit  at  differenRal  rate  than  if  non-­‐menthol    smoker.  

n  Some  individuals  who  would  have  iniRated  smoking  with  menthol  cigareXes  never  start.    

Scenarios  inves@gated:    

1.  10%  of  the  former  menthol  smokers    quit  and  10%  of  those  who  would  have  iniRated  as  menthol  smokers  never  smoke;    

2.  20%  quit  and  20%  do  not  iniRate,  and;    

3.  30%  quit  and  30%  do  not  iniRate  

32

P l a n n i n g M o d e l i n g a M e n t h o l B a n U s i n g S i m S m o k e

n  Past literature suggests youth access policies lead to increased retail compliance.

n  Effects on actual smoking rates are unclear. Two potential reasons ¨  Role of non-retail sources of cigarettes (parents older

friends theft)

¨  Level and extent of policies

Heuristic: Youth Access Policy

Redu

ctio

n in S

mok

ing R

ates

Advertising Expenditures per Capita

A

Heuristic: Policy Components Affecting Retail Compliance

Compliance Checks Per

Year

Penalties Publicity

Retail Compliance

Multiplicative relationship

S-shaped curve, subject to substitution into other sources

Reduced Smoking

Current Smoker

Attempts to Quit

No quit attempt

Continues Smoking

Self Quit

Rx Pharm.

NRT OTC

Behavioral Treatment

Behavioral & Rx Pharm

Behavioral & NRT OTC

Success

Fail

Success

Fail

Success

Fail

Success

Fail

Success

Fail

Success

Fail

Heuristic: The Decision to Quit

Framework used to show effects for specific policies

Heuristic: Cessation Treatment Policies n  AVAILABILITY: Ability to obtain NRT, Buproprion and

Varenecline by Rx or over-the counter n  FINANCIAL ACCESS: payment or mandatory coverage

for cessation treatments ¨  Prescription or OTC pharmacotherapies alone ¨  Behavioral treatment alone ¨  Pharmacotherapies and behavioral

n  QUITLINES: delivered by government and coordinated through health care system

n  BRIEF INTERVENTIONS: delivered by health care providers

n  Web-based treatment: supervised and used by health care agencies of provider

n  Follow-up of Care: health care providers, quitlines, web Each of the above affects quit attempts and treatment use with potential

interactions (synergies among policies)

}  Harm reduction: As a substitute for cigarettes (provides the nicotine fix), it has been suggested that use of at least some smokeless can reduce overall harm, because of lower health risk, similar to methadone for heroine addicts. Smokeless risks less than cigarettes (which are not inhaled into lung), but depends on contents, also no second hand smoke.

}  Potentially harm increasing, if:

}  If smokeless leads to increased youth initiation and acts as a gateway to cigarettes

}  Encourages dual use with cigarettes instead of cessation from cigarettes

Heuristic: Smokeless as Harm Reduction

Heuristic: Health effects and polytobacco use: simple example with only cigarettes and smokeless

Sole cigarette

use (habit)

Sole smokeless

us (habit)

Initiation cigarette

use

Initiation smokeless

use

Dual cigarette & smokeless

habit

Cigarette only

attributable death

Dual use attributable

death

Smokeless only

attributable death

Need to know relative risks for those who continue to use and for former users

39

Tobacco Use in Sweden, Males,

2004-2020

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

18.0%

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Male Cigarette Use (alone) Male Snus Use (alone) Male Combined Snus and Cigarette Use

Declines in cigarette use accompanied by constant rates of sole and dual use of snus, suggesting that users are shifting from single to dual use

Heuristic: Future challenges for Sim-Smoke and tobacco control modeling

n  Constantly changing market with new products and dual uses for cigarettes, smokeless, cigars, and pipes; transitions in the use of the different products is unlikely to be stable

n  Relative health risks are often unknown, especially for new products and for dual use

n  Difficult to anticipate industry reactions to policies both in consumer markets and in the political arena

n  Need to consider the heterogeneity of individuals; tobacco users are increasingly low SES and with mental health issues

Heuristic: Tobacco control is complex: Modeling provides a framework

Industry behavior Tobacco, retail

Tobacco Control Policy Taxes, laws, regulations

Environment Attitudes, norms,

opportunities (economic, other)

Physiology Genetics, diet, other

Risky behaviors: Using cigarettes, cigars, and smokeless and other

non-combustibles Health Outcomes

Death, disease, dollars

Limited evidence for many of these linkages, models provide guidance on areas for future research

Need for Collaborative Modeling

Since different models will highlight different aspects of the problem, information from the different models will need to be combined in a systematic manner

An example is NCI’s CISNET program:

n  The models consider common research questions using a natural history of disease framework

n  The models use a common data sources to help identify reasons for any differences results

n  The results are compared to provide a reasonable range of outcomes for decision-makers

n  Models are well documented using publicly available model profiler

Georgetown University is home for smoking/lung group (Levy) and coordinating center for the breast cancer group (Mandelblatt)