work in progress simulating the local dynamics of cardiovascular health and related risk factors
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Work in Progress Simulating the Local Dynamics of Cardiovascular Health and Related Risk Factors. Jack Homer Homer Consulting [email protected] Bobby Milstein Centers for Disease Control and Prevention [email protected] University of Michigan Tobacco Modeling Meeting May 2008. - PowerPoint PPT PresentationTRANSCRIPT
Work in Progress
Simulating the Local Dynamics of Cardiovascular Health and
Related Risk FactorsJack Homer
Homer [email protected]
Bobby Milstein
Centers for Disease Control and [email protected]
University of Michigan Tobacco Modeling MeetingMay 2008
This work was funded by the CDC’s Division for Heart Disease and Stroke Prevention and by the National Institutes of Health’s 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.
ContributorsCore 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
Brief Background on System Dynamics Modeling
Compartmental models resting on a general theory of how systems change (or resist change) – often in ways we don’t 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 workshopsForrester 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 2006;96(3):452-458.
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?
Smoking
Obesity
Secondhandsmoke
Healthinessof diet
Extent ofphysical activity
Psychosocialstress
Diagnosisand control
First-time CVevents and
deaths
Access to and marketingof smoking quit products
and services
Access to andmarketing of mental
health services
Sources ofstress
Access to andmarketing of healthy
food options
Access to andmarketing of physical
activity options
Access to andmarketing of weight
loss services
Access to andmarketing ofprimary care
Particulate airpollution
Utilization ofquality primary
care
Tobacco taxes andsales/marketing
regulations
Smoking bans atwork and public
places
Junk food taxes andsales/marketing
regulations
Downwardtrend in CV
event fatality
Quality of primarycare provision
Chronic Disorders
Costs from CV and other riskfactor complications and
from utilization of services
Anti-smokingsocial marketing
High BP
Highcholesterol
Diabetes
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 CDC’s “STEPS” program since 2004.
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 CDC’s “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 2008;5(2). Available at http://www.cdc.gov/pcd/issues/2008/apr/07_0230.htm
Direct Risk Factors
Smoking
Secondhandsmoke
First-time CVevents and
deaths
Particulate airpollution
Downwardtrend in CV
event fatalityChronic Disorders
High BP
Highcholesterol
Diabetes
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 N=5573, 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
Indirect Risk Factors
Smoking
Obesity
Secondhandsmoke
Healthinessof diet
Extent ofphysical activity
Psychosocialstress
Diagnosisand control
First-time CVevents and
deaths
Particulate airpollution
Utilization ofquality primary
care
Downwardtrend in CV
event fatalityChronic Disorders
High BP
Highcholesterol
Diabetes
Tobacco and Air Quality Interventions
Smoking
Obesity
Secondhandsmoke
Healthinessof diet
Extent ofphysical activity
Psychosocialstress
Diagnosisand control
First-time CVevents and
deaths
Access to and marketingof smoking quit products
and services
Particulate airpollution
Utilization ofquality primary
care
Tobacco taxes andsales/marketing
regulations
Smoking bans atwork and public
places
Downwardtrend in CV
event fatalityChronic Disorders
Anti-smokingsocial marketing
High BP
Highcholesterol
Diabetes
Health Care Interventions
Smoking
Obesity
Secondhandsmoke
Healthinessof diet
Extent ofphysical activity
Psychosocialstress
Diagnosisand control
First-time CVevents and
deaths
Access to and marketingof smoking quit products
and services
Access to andmarketing ofprimary care
Particulate airpollution
Utilization ofquality primary
care
Tobacco taxes andsales/marketing
regulations
Smoking bans atwork and public
places
Downwardtrend in CV
event fatality
Quality of primarycare provision
Chronic Disorders
Anti-smokingsocial marketing
High BP
Highcholesterol
Diabetes
Interventions Affecting Stress
Smoking
Obesity
Secondhandsmoke
Healthinessof diet
Extent ofphysical activity
Psychosocialstress
Diagnosisand control
First-time CVevents and
deaths
Access to and marketingof smoking quit products
and services
Access to andmarketing of mental
health services
Sources ofstress
Access to andmarketing ofprimary care
Particulate airpollution
Utilization ofquality primary
care
Tobacco taxes andsales/marketing
regulations
Smoking bans atwork and public
places
Downwardtrend in CV
event fatality
Quality of primarycare provision
Chronic Disorders
Anti-smokingsocial marketing
High BP
Highcholesterol
Diabetes
Healthy Diet Interventions
Smoking
Obesity
Secondhandsmoke
Healthinessof diet
Extent ofphysical activity
Psychosocialstress
Diagnosisand control
First-time CVevents and
deaths
Access to and marketingof smoking quit products
and services
Access to andmarketing of mental
health services
Sources ofstress
Access to andmarketing of healthy
food options
Access to andmarketing ofprimary care
Particulate airpollution
Utilization ofquality primary
care
Tobacco taxes andsales/marketing
regulations
Smoking bans atwork and public
places
Junk food taxes andsales/marketing
regulations
Downwardtrend in CV
event fatality
Quality of primarycare provision
Chronic Disorders
Anti-smokingsocial marketing
High BP
Highcholesterol
Diabetes
Physical Activity & Weight Loss Interventions
Smoking
Obesity
Secondhandsmoke
Healthinessof diet
Extent ofphysical activity
Psychosocialstress
Diagnosisand control
First-time CVevents and
deaths
Access to and marketingof smoking quit products
and services
Access to andmarketing of mental
health services
Sources ofstress
Access to andmarketing of healthy
food options
Access to andmarketing of physical
activity options
Access to andmarketing of weight
loss services
Access to andmarketing ofprimary care
Particulate airpollution
Utilization ofquality primary
care
Tobacco taxes andsales/marketing
regulations
Smoking bans atwork and public
places
Junk food taxes andsales/marketing
regulations
Downwardtrend in CV
event fatality
Quality of primarycare provision
Chronic Disorders
Anti-smokingsocial marketing
High BP
Highcholesterol
Diabetes
Adding Up the Costs
Smoking
Obesity
Secondhandsmoke
Healthinessof diet
Extent ofphysical activity
Psychosocialstress
Diagnosisand control
First-time CVevents and
deaths
Access to and marketingof smoking quit products
and services
Access to andmarketing of mental
health services
Sources ofstress
Access to andmarketing of healthy
food options
Access to andmarketing of physical
activity options
Access to andmarketing of weight
loss services
Access to andmarketing ofprimary care
Particulate airpollution
Utilization ofquality primary
care
Tobacco taxes andsales/marketing
regulations
Smoking bans atwork and public
places
Junk food taxes andsales/marketing
regulations
Downwardtrend in CV
event fatality
Quality of primarycare provision
Chronic Disorders
Costs from CV and other riskfactor complications and
from utilization of services
Anti-smokingsocial marketing
High BP
Highcholesterol
Diabetes
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– Relative risks of stress for high BP, high cholesterol, smoking, and obesity
A “status quo” baseline scenario
• 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
Obesity prevalence
0.4
0.3
0.2
0.1
0
1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040Time (Year)
Uncontrolled high BP prevalence
0.3
0.2
0.1
0
1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040Time (Year)
Smoking prevalence
0.3
0.2
0.1
0
1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040Time (Year)
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.
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.
Obese % of non-CVD popn
Uncontrolled hypertension %of non-CVD popn
Smoking % of non-CVD popn
• 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
There are significant gains even at the least effective end
of the uncertainty range
There are significant gains even at the least effective end
of the uncertainty range
Testing All 19 Interventions Combined, with Uncertainty Ranges
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%
% Change from Base CaseDeaths from CVD per Capita*
4
2
01990 2000 2010 2020 2030 2040
All 19 Interventionswith uncertainty range
Base Case
Deaths from CVD if all risk factors = 0
20.2%
19.3%
Testing Selected Intervention Clusters
Primary Care interventions (N=3): • Quality of Primary care increased• Primary care services marketed• Access to primary care increased
Air Quality interventions (N=2)• Air pollution cut to half of recent value• Workplaces allowing smoking cut to zero
Tobacco interventions (N=4)• Tobacco tax and sales restrictions• Social marketing against smoking• Smoke quit services marketed• Access to smoking quit services increased
% Change vs. Base Run Intervention Cluster 2015 2040
Base case -- --
Primary care -11.5% -9.1%
Primary care + Air quality
-16.2% -12.3%
Primary care + Air quality +
Tobacco-18.6% -15.4%
All 19 interventions -20.2% -19.3%
CV Deaths
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.
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.
Intervention Effects on Smoking Inflows & Outflows
Age 18 smokers
Social marketing
Tax & sales restrict
0.5 [0.4-0.7]
0.5 [0.3-0.7]
Adult smoking initiation/relapse
Smoking quits
Adult Smokers
Use of quit services2.25 [1.5-3]
1.3 [1.2-1.5]1.85 [1.5-2.5]
0.7 [0.5-0.8]
0.6 [0.4-0.7]
Workplace ban(for those who work) 1.25 [1.2-1.4]
0.6 [0.5-0.7]
0.65 [0.55-0.75]
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)
10.0%
20.0%
34.3%
0%
10%
20%
30%
40%
Baseline If Full Access If Full Access,Max Mktg & Max
Quality Care
% o
f s
mo
ke
rs u
sin
g q
uit
sv
cs
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
Effects of Interventions on Smoking Prevalence
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%.
Smoking Prevalence (non-CVD population)
0.3
0.2
0.1
01990 2000 2010 2020 2030 2040
All19
PC 3
PC 3 + AirQ 2 + Tob 4
PC 3 + AirQ 2
Base
Effects of Interventions on Secondhand Smoke Exposure
Fraction Nonsmokers SHS Exposure (non-CVD population)
0.6
0.4
0.2
01990 2000 2010 2020 2030 2040
All19
PC 3
PC 3 + AirQ 2 PC 3 + AirQ 2 + Tob 4
Base
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%.
Deaths from CVD per 1000 (non-CVD population)4
3
2
1
01990 2000 2010 2020 2030 2040
All19
Deaths from CVD if all risk factors = 0
PC 3
PC 3 + AirQ 2 + Tob 4
PC 3 + AirQ 2
Base
Effects of Interventions on CVD deaths
Note the increasing impacts over time for Tobacco4 as well as the other “gradual impact” interventions included in All19:
Physical activity, Nutrition, Weight loss, Stress
Effects of Interventions on Smoking-related Cancer & COPD Deaths
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.
NonCVD deaths from smoking complications
400,000
300,000
200,000
100,000
0
1990 2000 2010 2020 2030 2040
All19
PC 3
PC 3 + AirQ 2
PC 3 + AirQ 2 + Tob 4
Base
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 CombinedBase 19.6 10.8 30.4PC3 17.7 10.4 28.2PC3AirQ2 17.1 10.3 27.4PC3AirQ2Tob4 16.6 6.8 23.5All19 16.1 6.5 22.6
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.
4.7 million lives saved
due to air quality & tobacco
interventions
Effects of Interventions on Costs of CV Events and Related Risk Factor Complications
An average of $321 per capita could be saved—and justified for intervention spending—due
to air quality and tobacco interventions
Complication and Risk Factor Management Costs per Capita
3,000
2,000
1,000
0
1990 2000 2010 2020 2030 2040
PC 3
All risk factors = 0
All19
PC 3 + AirQ 2 PC 3 + AirQ2 + Tob 4
Base
EXTRAS
Comparing Air Pollution vs. Tobacco-5 Interventions on Deaths and Costs
CVD and NonCVD deaths from RF complications per 10008
6
4
2
01990 2000 2010 2020 2030 2040
Base
Air Pollution
Tobacco-5
Complication and Risk Factor Management Costs per Capita3,000
2,000
1,000
01990 2000 2010 2020 2030 2040
Base
Air Pollution
Tobacco-5
All Risk Factors = 0
Smoking
Obesity
Secondhandsmoke
Healthinessof diet
Extent ofphysical activity
Psychosocialstress
Diagnosisand control
First-time CVevents and
deaths
Access to and marketingof smoking quit products
and services
Access to andmarketing of mental
health services
Sources ofstress
Access to andmarketing of healthy
food options
Access to andmarketing of physical
activity options
Access to andmarketing of weight
loss services
Access to andmarketing ofprimary care
Particulate airpollution
Utilization ofquality primary
care
Tobacco taxes andsales/marketing
regulations
Smoking bans atwork and public
places
Junk food taxes andsales/marketing
regulations
Downwardtrend in CV
event fatality
Quality of primarycare provision
Chronic Disorders
Anti-smokingsocial marketing
High BP
Highcholesterol
Diabetes
How Smoking is Modeled
SmokingAdults
Newly smokingadults
Quitting or dying
2040
0
0.3% Smokers
1990
• 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.
• 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.
Effects of Interventions on Youth Smoking
Smoking fraction of age 18
0.4
0.3
0.2
0.1
01990 2000 2010 2020 2030 2040
All19
PC 3
PC 3 + AirQ 2
PC 3 + Air` 2 + Tob 4
Base
Effects of Interventions on Smoking Quits
Smoking quit rate (combining all sex/age groups)
0.08
0.06
0.04
0.02
01990 2000 2010 2020 2030 2040
All19
PC 3
PC 3 + AirQ 2
PC 3 + Air` 2 + Tob 4
Base
Effects of Interventions on Use of Quit Services & Products by Smokers
Use of Quit Services & Products by Smokers
0.4
0.3
0.2
0.1
01990 2000 2010 2020 2030 2040
All19
PC 3
PC 3 + AirQ 2
PC 3 + Air` 2 + Tob 4
Base
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 2008;5(2). Available at http://www.cdc.gov/pcd/issues/2008/apr/07_0230.htm