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

Computational Models

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

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.

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

Generally combine data and parameters from

different sources

Provides structure by developing a framework

and making assumptions explicit

Incorporates the effects that are difficult to

distinguish empirically in statistical studies

Non-linear relationships

Interdependencies

Dynamic processes

Feedback loops

Types of Model

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

Uni-directional causality

Systems dynamic (feedback loops)

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

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

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:

depending on the way the policy is implemented,

by age and gender

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

Relationship between policies and

smoking rates based on:

Evidence from tobacco and other risky behavior

literature,

Theories (Economics, Sociology, Psychology,

Epidemiology, etc), and

Advice by a multidisciplinary expert panel

Policies based on FCTC/MPOWER

Cigarette excise taxes: Through prices

Smoke-Free Air Laws: Worksites, restaurant and bars, other public places

Tobacco control/media campaigns

Marketing/Advertising Bans

Health Warnings

Cessation Treatment: Availability of

pharmacotherapy, cessation treatment (financial

access, quitlines and web-based treatment

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

Past vs. Future

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

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 built for:

32 Countries:

Albania*, Argentina*, Bangladesh, Brazil,* China,

Czech Republic,* 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: Brazil

Past Policies 1989-2010 To consider the effect of all policies implemented since 1989,

we first set policies through 2010 to their 1989 levels to obtain

the counterfactual smoking rates in the absence of post-1989

policies.

The difference between the smoking prevalence with polices

at 1989 levels and the smoking rate with actual policies

implemented yields the net effect of policies implemented

since 1989.

For the role of single policies, we compared the scenario with

only that policy implemented to the counterfactual policy

scenario.

The impact of policies on deaths was estimated by subtracting

the number of SADs with policies implemented from their

number with policies kept at 1989 levels.

Brazil Counterfactuals:

Smoking prevalence 1989-2010

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Status quo Counterfactual price only

Percent of the Reduction in 2010 Smoking Prevalence*

Due to Individual Policies Implemented Since 1989

48.4%

13.6%

6.3%

13.7%

7.8%

9.8% 0.3%

Price only Smoke-Free Air only Media onlyAdvertising only Health warnings only Cessation tx onlyYouth Access only

Effect of Policies Implemented:

1989-2010 Policy Implementation Year

1989 2000 2010 2010 Lower

Bounda

2010 Upper

Bounda

2050

SMOKING PREVALENCE

Counterfactual: all policies

at 1989 level 35.4% 32.6% 31.0% 24.9%

All policies implemented 35.4% 23.7% 16.8% 22.2% 10.5% 10.3%

Percent reduction in smoking prevalence from policy changea

All policies −27.4% −45.9% −27.8% −66.4% −59.1%

SMOKING ATTRIBUTABLE DEATHS

1989 2010 Cumulative 2010 2010 Lower

Bounda

2010 Upper

Bounda

Cumulative 2050

Counterfactual: all policies at

1989 level

181,957 283,048 4,998,024 20,401,516

All policies implemented

181,957 225,048 4,578,810 4,739,196 4,282,963 13,471,388

Deaths averted from policy change

All policies — 58,000 419,214 258,828 715,061 6,930,128

Low birth weight babies avoided from policy changes 1989-2010

With ;policies implemented 14,827 704,976

Advocacy: Other successes due to tobacco

policies

Percent reduction in smoking prevalence (18 and above):

> 30% reduction

Brazil (almost 50% reduction due to policies)

California

At least 25% Reduction

United Kingdom

Minnesota

Thailand

20% Reduction

Arizona

Korea

Ireland

NYS

Netherlands

Advocacy: There may be limits to current policies:

We may need more than traditional policies to

reduce smoking by more than 50%

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

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

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

Mexico: Many surveys

ENA, ENAULT, GATS

Ask different questions, may have

important implications for some day

smokers vs every day smokers

Planning: Ranking the effect of future policies

Brazil SimSmoke smoking prevalence

Policies/Year

2010

2015

2050

Lower

Bound

Upper

Bound

Cumu-

lative

2011–

2050

Lower

Bound

2011–

2050

Upper

Bound

2011–

2050

Smoking Prevalence

Smoking Attributable

Deaths

Status quo 16.8% 15.5% 10.3%

All FCTC policies implemented 16.8% 11.9% 6.3% 7.3% 4.7%

8,892,578 9,513,874 8,749,842

Reduction in Smoking

Prevalence 7,563,664 8,657,395 6,783,055

Independent policy effects

Tax at 75% of retail price −10.2% −16.7% −13.0% −21.5%

469,463 365,730 565,492

Well-enforced smoke-free air

laws

−4.5% −6.4% −3.1% −9.5%

268,042 135,972 396,336

Well-enforced marketing ban −3.0% −4.8% −2.4% −7.2%

171,180 86,231 254,867

High-intensity media campaign −4.8% −7.4% −3.6% −10.9%

305,436 157,126 459,018

Cessation treatment programs −2.3% −4.6% −6.9% −9.3%

198,382 100,530 489,257

Well-enforced youth access restrictions −0.8% −5.1% 0.0% −10.1% 28,491 0 42,734

With all policies implemented −23.5% −38.5% −29.0% −54.0% 1,328,914 856,474 1,966,787

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%

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

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

25

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 cigarettes

Possible effects of a ban:

Menthol smokers switch to non-menthol brand.

Menthol smokers quit at differential rate than if non-menthol smoker.

Some individuals who would have initiated smoking with menthol cigarettes never start.

Scenarios investigated:

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

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

3. 30% quit and 30% do not initiate

27

Pl 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 Sm o k e

Past literature suggests youth access policies

lead to increased retail compliance.

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

Heuristic: Policy Components Affecting

Enforcement Compliance

Checks Per

Year

Penalties Publicity/

Education

Compliance

Multiplicative relationship

S-shaped curve, subject to

substitution into other sources

Reduced Smoking

Originally applied to youth access, but applies to marketing restrictions and smoke-free air laws

Anti-tobacco

Norms

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

AVAILABILITY: Ability to obtain NRT, Buproprion and Varenecline by Rx or over-the counter

FINANCIAL ACCESS: payment or mandatory coverage for cessation treatments Prescription or OTC pharmacotherapies alone

Behavioral treatment alone

Pharmacotherapies and behavioral

QUITLINES: delivered by government and coordinated through health care system

BRIEF INTERVENTIONS: delivered by health care providers

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

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

34

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

35

New Tobacco Products

Will be important to consider whether

smokers become new users or dual users

Whether youth use these products instead

of cigarettes and whether they eventually

use cigarettes

Whether former smokers use these

products and then become smokers

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

Heuristic: Future challenges for Sim-

Smoke and tobacco control modeling

Better understanding of the initiation and cessation process

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

Difficult to anticipate industry reactions to policies both in

consumer markets and in the political arena

Need to consider the heterogeneity of nations and

individuals; tobacco users are increasingly low SES in MICs

and general population in LMICs with economic growth

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:

The models consider common research questions using a natural

history of disease framework

The models use a common data sources to help identify reasons for

any differences results

The results are compared to provide a reasonable range of

outcomes for decision-makers

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

Para la elaboración de constancias,

favor de enviar lista de

participantes presenciales con:

Profra. Berta Luz Téllez

btellez@insp.mx

Videos y presentaciones anteriores en:

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