David T. Levy, Ph.D. Lombardi Comprehensive Cancer Center Georgetown University - Columbia University ...
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
David
T.
Levy,
Ph.D.
Lombardi
Comprehensive
Cancer
Center
Georgetown
University
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. philosophyComputational 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 partCharacteristics 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 loopsTypes 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 behaviorsTobacco 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 policiesSimSmoke: Basic Approach
Smoking-
Policy Cigarette Attributable
Changes Use Deaths
Taxes Norms, Former and
Total Mortality and
Clean air laws Attitudes, current
smokers, by type:
Media Camp. Opportu-
nities relative risks Lung cancer
Marketing Other cancers
Bans
Heart disease
Warning labels
Stroke
Cessation Tx
COPD
Youth Access
MCH OutcomesModel 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 ratesPopulation Model: Evolution
of Population
• Start with the Population in the base year, first year of
the model, based on data availability and policies
• Evolves through time:
Birth Death
rates rates
Births Population Deaths
Don’t explicitly account for immigrants due to data difficulties,
but make population correctionsSmoking Model:
Evolution of Smokers
Initiation
Population Ever Smoker* Not quit Current
Smoker**
Not initiate Cessation
(quit) Relapse
Never Smoker Ex-Smoker
* Usually as smoked 100 cigarettes lifetime ** usually as smoked some or all daysSmoking-Attributable
Deaths
%
smokers
and ex- Death Deaths
Total Deaths smokers rates by
Attributable to
smoking Smoking
Relative status
risks
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 genderRelationship 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 panelPolicy 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 issuesWe 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 rolePolicies 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 bansPast 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
Policymakers have used models for:
• 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.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
30.0%
Smoking prevalence > 25% less as a result of
policies by 2010 and grows over time!
25.0%
20.0%
15.0%
10.0%
Policies actually implemented Policies at 1993 level
5.0%
Price only
0.0%
1993 1997 2001 2004 2007 2011 2021 2031 2041Advocacy: Minnesota Deaths
Averted Due to Policies
MALE AND 1993-‐20 1993-‐204
FEMALE SADs
1993
2003
2011
2021
2031
2041
11
1
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
¨ NetherlandsPlanning: Male Smoking Prevalence:
SimSmoke Predictions vs. Surveys, Minnesota
28.0%
26.0%
24.0%
22.0%
20.0%
18.0%
16.0%
14.0%
12.0%
10.0% 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
SimSmoke CPS-TUS MATSIreland Male Smoking Prevalence,1998-2010
data, data, dataPlanning: 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 healthPlanning: 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 tobaccoFDA 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) 30Planning:
Modeling
the
effects
of
a
ban
on
menthol
cigare=es
Scenarios
inves@gated:
Possible
effects
of
a
ban:
n Menthol
smokers
switch
to
1. 10%
of
the
former
menthol
non-‐menthol
brand.
smokers
quit
and
10%
of
those
who
would
have
n Menthol
smokers
quit
at
iniRated
as
menthol
differenRal
rate
than
if
smokers
never
smoke;
non-‐menthol
smoker.
2. 20%
quit
and
20%
do
not
n Some
individuals
who
iniRate,
and;
would
have
iniRated
smoking
with
menthol
3. 30%
quit
and
30%
do
not
cigareXes
never
start.
iniRate
Planning Modeling a Menthol Ban Using SimSmoke
32Heuristic: Youth Access Policy
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 policiesHeuristic: Policy Components
Affecting Retail Compliance
Compliance Publicity Penalties
Checks Per
Year
Multiplicative relationship
Retail
Compliance
S-shaped curve, subject to
substitution into other sources
Reduction in Smoking Rates
Reduced Smoking A
Advertising Expenditures per CapitaHeuristic: The Decision to Quit
Success
Self Quit
Fail
Success
Rx Pharm.
Attempts Fail
to Quit
Success
NRT OTC
Fail
Current Behavioral Success
Smoker Treatment
Fail
Behavioral Success
No quit & Rx
Fail
attempt Pharm
Continues Behavioral Success
Smoking & NRT OTC
Fail
Framework used to show effects for specific policiesHeuristic: 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)Heuristic: Smokeless as Harm Reduction
} 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
cigarettesHeuristic: Health effects and polytobacco use:
simple example with only cigarettes and smokeless
Initiation Initiation
cigarette smokeless
use use
Sole Dual Sole
cigarette cigarette & smokeless
use smokeless us
(habit) habit (habit)
Cigarette Smokeless
Dual use
only only
attributable
attributable attributable
death
death death
Need to know relative risks for those who continue to use and for former usersTobacco Use in Sweden, Males,
2004-2020
18.0%
16.0%
14.0%
12.0%
10.0%
8.0%
6.0% Declines in cigarette use
accompanied by constant rates of
4.0%
sole and dual use of snus,
suggesting that users are shifting
2.0% from single to dual use
0.0%
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
39Heuristic: 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 issuesHeuristic: Tobacco control is complex:
Modeling provides a framework
Industry behavior Tobacco Control Policy
Tobacco, retail Taxes, laws, regulations
Environment
Attitudes, norms, Physiology
opportunities (economic, Genetics, diet, other
other)
Risky behaviors: Using
cigarettes, cigars, and
smokeless and other Health Outcomes
non-combustibles Death, disease, dollars
Limited evidence for many of these linkages,
models provide guidance on areas for future researchNeed 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)You can also read
