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Work in Progress Simulating the Local Dynamics of Cardiovascular Health and Related Risk Factors

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Title: Work in Progress Simulating the Local Dynamics of Cardiovascular Health and Related Risk Factors


1
Work in ProgressSimulating the Local Dynamics
of Cardiovascular Health and Related Risk
Factors
  • Jack Homer
  • Homer Consulting
  • jhomer_at_comcast.net
  • Bobby Milstein
  • Centers for Disease Control and Prevention
  • bmilstein_at_cdc.gov
  • University of Michigan Tobacco Modeling Meeting
  • May 2008

This work was funded by the CDCs Division for
Heart Disease and Stroke Prevention and by the
National Institutes of Healths Office of
Behavioral and Social Science Research. The work
was done in collaboration with the Health and
Human Services Department of Austin/Travis
County, Texas, and with Indigent Care
Collaboration of Central Texas. The external
contractors are Sustainability Institute and RTI
International.
2
Contributors
  • Core Design Team
  • CDC Darwin Labarthe, Diane Orenstein, Bobby
    Milstein, Marilyn Metzler, Rosanne Farris
  • Austin Adolfo Valadez, Phil Huang, Karina Loyo,
    Rick Schwertfeger, Cindy Batcher, Ella Pugo, Josh
    Vest
  • NIH Patty Mabry
  • Consultants Kristina Wile, Jack Homer, Justin
    Trogdon
  • Organizational Sponsors
  • Austin/Travis County Health and Human Services
    Department
  • CDC Division for Heart Disease and Stroke
    Prevention
  • CDC Division of Adult and Community Health
  • CDC Division of Nutrition, Physical Activity, and
    Obesity
  • CDC Division of Diabetes Translation
  • CDC Office on Smoking and Health
  • CDC NCCDPHP Office of the Director
  • Indigent Care Collaborative (Austin, TX)
  • NIH Office of Behavioral and Social Science
    Research
  • RTI International
  • Sustainability Institute
  • Texas Department of Health

3
Brief Background on System Dynamics Modeling
  • Compartmental models resting on a general theory
    of how systems change (or resist change) often
    in ways we dont expect
  • Developed for corporate policies in the 1950s,
    and applied to health policies since the 1970s
  • Concerned with understanding dynamic complexity
  • Accumulation (stocks and flows)
  • Feedback (balancing and reinforcing loops)
  • Used primarily to craft far-sighted, but
    empirically based, strategies
  • Anticipate real-world delays and resistance
  • Identify high leverage interventions
  • Modelers engage stakeholders through interactive
    workshops

Forrester JW. Industrial Dynamics. Cambridge,
MA MIT Press 1961. Sterman JD. Business
Dynamics Systems Thinking and Modeling for a
Complex World. Boston, MA Irwin/McGraw-Hill
2000.
Homer J, Hirsch G. System dynamics modeling for
public health Background and opportunities.
American Journal of Public Health
200696(3)452-458.
4
Purpose of the Cardiovascular Risk Model
  • How do local conditions affect multiple risk
    factors for CVD, and how do those risks, in turn,
    affect population health status and costs over
    time?
  • How do different local interventions affect
    cardiovascular health and related expenditures in
    the short- and long-term?
  • How might local health leaders better balance
    their policy efforts given limited resources?

The CDC is partnering on this project with the
Austin (Travis County), Texas, Dept. of Health
and Human Services. The model is calibrated to
represent the overall US, but is informed by the
experience and data of the Austin team, which
has been supported by the CDCs STEPS program
since 2004.
Homer J, Milstein B, Wile K, Pratibhu P, Farris
R, Orenstein D. Modeling the local dynamics of
cardiovascular health risk factors, context, and
capacity. Preventing Chronic Disease 20085(2).
Available at http//www.cdc.gov/pcd/issues/2008/ap
r/07_0230.htm
5
Direct Risk Factors
6
Calculating First-Time CV Events Deaths
  • Based on well-established Framingham approach for
    calculating probability of first-time events
    deaths in individuals
  • CVD CHD (MI, angina, cardiac arrest)
    Stroke/TIA CHF PAD
  • Modifies individual-level risk calculator for use
    with populations
  • Uses prevalences of uncontrolled chronic
    disorders by sex/age group
  • Introduces secondhand smoke and pollution as
    additional risk factors
  • Combines risks multiplicatively to account for
    overlapping conditions
  • Adjustment exponents reproduce synergies seen in
    individual-level calculator
  • Adjustment multipliers reproduce AHA event and
    death frequencies for 2003
  • - Anderson et al, Am Heart J 1991 (based on
    Framingham MA population N5573, 1968-1987)
  • - Homer Risk calculation in the CVD model
    project document, June 19, 2007
  • - NHANES 1988-94 1999-04
  • - AHA Heart Disease and Stroke Statistics 2006
    Update

7
Indirect Risk Factors
8
Tobacco and Air Quality Interventions
9
Health Care Interventions
10
Interventions Affecting Stress
11
Healthy Diet Interventions
12
Physical Activity Weight Loss Interventions
13
Adding Up the Costs
14
Data Sources for Modeling CVD Risk
  • Census
  • Population, deaths, births, net immigration,
    health coverage
  • AHA NIH statistical reports
  • Cardiovascular events, deaths, and prevalence
    (CHD, stroke, CHF, PAD)
  • National Health and Nutrition Examination Survey
    (NHANES)
  • Risk factor prevalences by age (18-29, 30-64,
    65) and sex (M, F)
  • Chronic disorder diagnosis and control
    (hypertension, high cholesterol, diabetes)
  • Behavioral Risk Factor Surveillance System
    (BRFSS)
  • Diet physical activity
  • Primary care utilization
  • Lack of needed emotional/social support ?
    Psychosocial stress
  • Medical Examination Panel (MEPS) / National
    Health Interview (NHIS)
  • Medical and productivity costs attributable to
    smoking, obesity, and chronic disorders
  • Research literature
  • CVD risk calculator, and relative risks from SHS,
    air pollution, obesity, and inactivity
  • Medical and productivity costs of cardiovascular
    events
  • Questionnaires for CDC and Austin teams (expert
    judgment)
  • Potential effects of social services marketing
    on utilization behavior
  • Effects of behavioral services on smoking, weight
    loss, stress reduction

15
A status quo baseline scenario
  • A straightforward starting point for what if
    analysis
  • Assume no changes after 2000 in contextual
    factors or in risk factor inflow and outflow
    rates
  • Any changes in risk prevalences after 2000 are
    due to bathtub adjustment and population aging

Obese of non-CVD popn
Uncontrolled hypertension of non-CVD popn
  • Result Past trends continue after 2000, but
    decelerate and level off
  • Increasing obesity, high BP, and diabetes
  • Decreasing smoking
  • High cholesterol mixed bag by age and sex, flat
    overall

Smoking of non-CVD popn
The model is calibrated to reproduce data from
NHANES 1988-94 and 1999-2004 on risk factor
prevalences in the non-CVD population by age and
sex.
16
Testing All 19 Interventions Combined, with
Uncertainty Ranges
Change from Base Case
Deaths from CVD per Capita
Deaths 2015 Deaths 2040
All uncertain parameters at least impact -15.0 -14.2
All uncertain parameters at best-guess impact -20.2 -19.3
All uncertain parameters at most impact -28.2 -26.3
4
Base Case
19.3

20.2
All 19 Interventions with uncertainty range
2
Deaths from CVD if all risk factors 0
0
1990
2000
2010
2020
2030
2040
There are significant gains even at the least
effective end of the uncertainty range
17
Testing Selected Intervention Clusters
Change vs. Base Run
  • Primary Care interventions (N3)
  • Quality of Primary care increased
  • Primary care services marketed
  • Access to primary care increased
  • Air Quality interventions (N2)
  • Air pollution cut to half of recent value
  • Workplaces allowing smoking cut to zero
  • Tobacco interventions (N4)
  • Tobacco tax and sales restrictions
  • Social marketing against smoking
  • Smoke quit services marketed
  • Access to smoking quit services increased
  • The 3 (or even just the first 2) clusters
    together provide a large fraction of the CV
    deaths reduction of all 19 interventions,
    especially in the shorter term
  • 92 (80) by 2015,
  • 80 (64) by 2040.

18
Intervention Effects on Smoking Inflows
Outflows
Age 18 smokers
0.5 0.4-0.7
Social marketing
0.65 0.55-0.75
0.5 0.3-0.7
0.6 0.4-0.7
Adult smoking initiation/relapse
Adult Smokers
0.7 0.5-0.8
Tax sales restrict
0.6 0.5-0.7
1.85 1.5-2.5
1.3 1.2-1.5
Workplace ban (for those who work)
1.25 1.2-1.4
Smoking quits
2.25 1.5-3
Use of quit services
  • Sources
  • Terry Pechacek CDC, personal correspondence,
    citing CPSTF
  • (re taxes and sales restrictions and re
    social marketing).
  • Moskowitz et al, AJPH 2000 (re workplace bans)
  • Glasgow et al, Tobacco Control 1997 (re
    workplace bans)
  • Terry Pechacek, citing multiple studies and CPSG
    (re quit services)
  • Abby Rosenthal CDC, personal correspondence (re
    quit services)

19
Use of Quit Services by Smokers
  • Sources
  • - MEPS spending analysis, re baseline use of
    quit services and products
  • Terry Pechacek CDC, personal correspondence,
    citing Group Health
  • Cooperative study, re effects of marketing
    and quality primary care

20
Effects of Interventions on Smoking Prevalence
Smoking Prevalence (non-CVD population)
0.3
Base
0.2
PC 3
PC 3 AirQ 2
0.1
PC 3 AirQ 2 Tob 4
All19
0
1990
2000
2010
2020
2030
2040
In the base run, smoking prevalence among the
non-CVD population declines from 17.7 in 2010
to 13.5 in 2040. The AirQ2 intervention cluster
reduces the 2040 value to 12.9 (due to the
effect of indoor smoking laws), and then adding
the Tob4 cluster reduces it to 4.5.
21
Effects of Interventions on Secondhand Smoke
Exposure
Fraction Nonsmokers SHS Exposure (non-CVD
population)
0.6
Base
0.4
0.2
PC 3
PC 3 AirQ 2 Tob 4
PC 3 AirQ 2
All19
0
1990
2000
2010
2020
2030
2040
In the base run, the fraction of non-smokers with
significant secondhand smoke exposure declines
from 19.1 in 2010 to 15.4 in 2040, tracking the
decline in smoking. The AirQ2 intervention
cluster reduces the 2040 value to 4.2 (due to
the effect of indoor smoking laws), and then
adding the Tob4 cluster reduces it to 1.5.
22
Effects of Interventions on CVD deaths
Deaths from CVD per 1000 (non-CVD population)
4
PC 3
Base
PC 3 AirQ 2
3
All19
PC 3 AirQ 2 Tob 4
2

1
Deaths from CVD if all risk factors 0
0
1990
2000
2010
2020
2030
2040
23
Effects of Interventions on Smoking-related
Cancer COPD Deaths
NonCVD deaths from smoking complications
400,000
Base
300,000
PC 3
PC 3 AirQ 2
200,000
PC 3 AirQ 2 Tob 4
100,000
All19
0
1990
2000
2010
2020
2030
2040
Includes smoking-related deaths from cancers and
respiratory diseases, based on 2001 data from
SAMMEC (http//apps.nccd.cdc.gov/sammec/).
SAMMEC Smoking Attributable Mortality,
Morbidity and Economic Costs. Male 273,665
cancer and respir deaths due to smoking Female
135,296.
24
Effects of Interventions on Preventable Deaths
(2010-2040 cumulative)
Cumulative deaths 2010-2040 (in non-CVD
population) from CV and other risk factor
complications, in millions
From From Other
CV Complications
Combined Base 19.6
10.8 30.4 PC3 17.7
10.4 28.2 PC3AirQ2
17.1 10.3
27.4 PC3AirQ2Tob4 16.6
6.8 23.5 All19
16.1 6.5 22.6
4.7 million lives saved due to air quality
tobacco interventions
Over 30 years, the Tob4 intervention cluster
reduces CV deaths by 0.5m, and reduces other
deaths (cancers respiratory) by 3.4m, for a
total reduction of 3.9m. Note that the CV deaths
are based on the Framingham methodology, whereas
the smoking-related deaths from other
complications are based on the SAMMEC
methodology.
25
Effects of Interventions on Costs of CV Events
and Related Risk Factor Complications
Complication and Risk Factor Management Costs per
Capita
3,000
An average of 321 per capita could be savedand
justified for intervention spendingdue to air
quality and tobacco interventions
Base
PC 3
PC 3 AirQ 2
2,000
PC 3 AirQ2 Tob 4
All19
1,000
All risk factors 0
0
1990
2000
2010
2020
2030
2040
26
EXTRAS
27
Comparing Air Pollution vs. Tobacco-5
Interventions on Deaths and Costs
CVD and NonCVD deaths from RF complications per
1000
8
6
Base
Air Pollution
4
Tobacco-5
2
0
1990
2000
2010
2020
2030
2040
Complication and Risk Factor Management Costs per
Capita
3,000
Base
Air Pollution
2,000
Tobacco-5
1,000
All Risk Factors 0
0
1990
2000
2010
2020
2030
2040
28
How Smoking is Modeled
  • Historical estimates of current smoking
    prevalence among non-CVD popn from NHANES 1988-94
    and 1999-2004 by sex and age group.
  • Smoking prevalence in adults is modeled as a
    stock affected by flows of initiation and
    quitting, by the inflow of teen smokers turning
    age 18, and by deaths (related to CVD and
    otherwise).
  • Historical estimates of Age 18 smoking fraction
    by sex from YRBSS.
  • Baseline rates of adults quitting smoking based
    on Mendez Warner AJPH 2007 and Sloan et al MIT
    Press 2004 (Fig. 2.1)
  • Baseline rates of adult initiation/relapse
    adjusted to reproduce NHANES adult smoking trends
    by sex and age.

Smoking Adults
Newly smoking adults
Quitting or dying
Smokers
0.3
0
2040
1990
29
Effects of Interventions on Youth Smoking
Smoking fraction of age 18
0.4
0.3
PC 3
Base
0.2
PC 3 AirQ 2
0.1
PC 3 Air 2 Tob 4
All19
0
1990
2000
2010
2020
2030
2040
30
Effects of Interventions on Smoking Quits
Smoking quit rate (combining all sex/age groups)
0.08
All19
0.06
PC 3 Air 2 Tob 4
PC 3 AirQ 2
0.04
PC 3
Base
0.02
0
1990
2000
2010
2020
2030
2040
31
Effects of Interventions on Use of Quit Services
Products by Smokers
Use of Quit Services Products by Smokers
0.4
All19
0.3
PC 3 Air 2 Tob 4
0.2
PC 3
0.1
PC 3 AirQ 2
Base
0
1990
2000
2010
2020
2030
2040
32
Simulating the Local Dynamics of Cardiovascular
Health and Related Risk Factors
Homer J, Milstein B, Wile K, Pratibhu P, Farris
R, Orenstein D. Modeling the local dynamics of
cardiovascular health risk factors, context, and
capacity. Preventing Chronic Disease 20085(2).
Available at http//www.cdc.gov/pcd/issues/2008/ap
r/07_0230.htm
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